From 203d3342e0ccbf0230d3a10c012b2e9a56f585d3 Mon Sep 17 00:00:00 2001 From: Doresic Date: Tue, 12 Dec 2023 15:49:19 +0100 Subject: [PATCH 01/31] Clear output of all notebooks --- doc/example/amici.ipynb | 1443 ++- doc/example/censored.ipynb | 441 +- doc/example/conversion_reaction.ipynb | 190 +- doc/example/custom_objective_function.ipynb | 746 +- doc/example/fixed_parameters.ipynb | 321 +- doc/example/getting_started.ipynb | 8708 +------------------ doc/example/hierarchical.ipynb | 594 +- doc/example/julia.ipynb | 361 +- doc/example/model_selection.ipynb | 359 +- doc/example/nonlinear_monotone.ipynb | 972 +-- doc/example/ordinal.ipynb | 516 +- doc/example/petab_import.ipynb | 213 +- doc/example/prior_definition.ipynb | 51 +- doc/example/sampler_study.ipynb | 793 +- doc/example/sampling_diagnostics.ipynb | 328 +- doc/example/store.ipynb | 683 +- doc/example/synthetic_data.ipynb | 121 +- doc/example/workflow_comparison.ipynb | 1476 +--- 18 files changed, 1328 insertions(+), 16988 deletions(-) diff --git a/doc/example/amici.ipynb b/doc/example/amici.ipynb index 2c9a7bb42..97257cc49 100644 --- a/doc/example/amici.ipynb +++ b/doc/example/amici.ipynb @@ -2,6 +2,12 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# AMICI in pyPESTO\n", "\n", @@ -20,17 +26,17 @@ "In order to run optimizations and/or uncertainty analysis, we turn to pyPESTO (**P**arameter **ES**timation **TO**olbox for python).\n", "\n", "pyPESTO is python tool for parameter estimation. It provides an interface to the model simulation tool [AMICI](https://github.com/AMICI-dev/AMICI) for the simulation of Ordinary Differential Equation (ODE) models specified in the SBML format. With it we can optimize our model parameters given measurement data, we can do uncertainty analysis via profile likelihoods and/or through sampling methods. pyPESTO provides an interface to many optimizers, global and local, such as e.g. SciPy optimizers, Fides and Pyswarm. Additionally it interfaces samplers such as pymc, emcee and some of its own samplers." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 1, + }, "outputs": [], "source": [ "# import\n", @@ -64,138 +70,116 @@ "\n", "# output directory\n", "model_output_dir = \"tmp/\" + model_name" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } + }, + "source": [ + "## 1. Create a pyPESTO problem" + ] }, { "cell_type": "markdown", - "source": [ - "## 1. Create a pyPESTO problem" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Create a pyPESTO objective from AMICI\n", "\n", "Before we can use AMICI to simulate our model, the SBML model needs to be translated to C++ code. This is done by `amici.SbmlImporter`." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 2, + }, "outputs": [], "source": [ "sbml_file = f\"./{model_name}/{model_name}.xml\"\n", "# Create an SbmlImporter instance for our SBML model\n", "sbml_importer = amici.SbmlImporter(sbml_file)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "In this example, we want to specify fixed parameters, observables and a $\\sigma$ parameter. Unfortunately, the latter two are not part of the [SBML standard](http://sbml.org/). However, they can be provided to `amici.SbmlImporter.sbml2amici` as demonstrated in the following." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "In this example, we want to specify fixed parameters, observables and a $\\sigma$ parameter. Unfortunately, the latter two are not part of the [SBML standard](http://sbml.org/). However, they can be provided to `amici.SbmlImporter.sbml2amici` as demonstrated in the following." + ] }, { "cell_type": "markdown", - "source": [ - "#### Constant parameters\n", - "\n", - "Constant parameters, i.e. parameters with respect to which no sensitivities are to be computed (these are often parameters specifying a certain experimental condition) are provided as a list of parameter names." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Constant parameters\n", + "\n", + "Constant parameters, i.e. parameters with respect to which no sensitivities are to be computed (these are often parameters specifying a certain experimental condition) are provided as a list of parameter names." + ] }, { "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "constant_parameters = [\"ratio\", \"specC17\"]" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "constant_parameters = [\"ratio\", \"specC17\"]" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "#### Observables\n", "\n", "We used SBML's [`AssignmentRule`](http://sbml.org/Software/libSBML/5.13.0/docs//python-api/classlibsbml_1_1_rule.html) as a non-standard way to specify *Model outputs* within the SBML file. These rules need to be removed prior to the model import (AMICI does at this time not support these rules). This can be easily done using `amici.assignmentRules2observables()`.\n", "\n", "In this example, we introduced parameters named `observable_*` as targets of the observable AssignmentRules. Where applicable we have `observable_*_sigma` parameters for $\\sigma$ parameters (see below)." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Observables:\n", - "{'observable_pSTAT5A_rel': {'formula': '(100 * pApB + 200 * pApA * specC17) / '\n", - " '(pApB + STAT5A * specC17 + 2 * pApA * '\n", - " 'specC17)',\n", - " 'name': 'observable_pSTAT5A_rel'},\n", - " 'observable_pSTAT5B_rel': {'formula': '-(100 * pApB - 200 * pBpB * (specC17 - '\n", - " '1)) / (STAT5B * (specC17 - 1) - pApB + '\n", - " '2 * pBpB * (specC17 - 1))',\n", - " 'name': 'observable_pSTAT5B_rel'},\n", - " 'observable_rSTAT5A_rel': {'formula': '(100 * pApB + 100 * STAT5A * specC17 + '\n", - " '200 * pApA * specC17) / (2 * pApB + '\n", - " 'STAT5A * specC17 + 2 * pApA * specC17 '\n", - " '- STAT5B * (specC17 - 1) - 2 * pBpB * '\n", - " '(specC17 - 1))',\n", - " 'name': 'observable_rSTAT5A_rel'}}\n" - ] - } - ], + }, + "outputs": [], "source": [ "# Retrieve model output names and formulae from AssignmentRules and remove the respective rules\n", "observables = amici.assignmentRules2observables(\n", @@ -205,82 +189,63 @@ ")\n", "print(\"Observables:\")\n", "pprint(observables)" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "#### $\\sigma$ parameters\n", "\n", "To specify measurement noise as a parameter, we simply provide a dictionary with observable names as keys and a list of (preexisting) parameter names as values to indicate which sigma parameter is to be used for which observable." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'observable_pSTAT5A_rel': 'sd_pSTAT5A_rel',\n", - " 'observable_pSTAT5B_rel': 'sd_pSTAT5B_rel',\n", - " 'observable_rSTAT5A_rel': 'sd_rSTAT5A_rel'}\n" - ] - } - ], + }, + "outputs": [], "source": [ "sigma_vals = [\"sd_pSTAT5A_rel\", \"sd_pSTAT5B_rel\", \"sd_rSTAT5A_rel\"]\n", "observable_names = observables.keys()\n", "sigmas = dict(zip(list(observable_names), sigma_vals))\n", "pprint(sigmas)" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "#### Generating the module\n", "\n", "Now we can generate the python module for our model. `amici.SbmlImporter.sbml2amici` will symbolically derive the sensitivity equations, generate C++ code for model simulation, and assemble the python module." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.72 s, sys: 212 ms, total: 1.93 s\n", - "Wall time: 53.7 s\n" - ] - } - ], + }, + "outputs": [], "source": [ "%%time\n", "\n", @@ -292,99 +257,89 @@ " constant_parameters=constant_parameters,\n", " sigmas=sigmas,\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "#### Importing the module and loading the model\n", - "\n", - "If everything went well, we are ready to load the newly generated model:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Importing the module and loading the model\n", + "\n", + "If everything went well, we are ready to load the newly generated model:" + ] }, { "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "model_module = amici.import_model_module(model_name, model_output_dir)" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "model_module = amici.import_model_module(model_name, model_output_dir)" + ] }, { "cell_type": "markdown", - "source": [ - "Afterwards, we can get an instance of our model from which we can retrieve information such as parameter names:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Afterwards, we can get an instance of our model from which we can retrieve information such as parameter names:" + ] }, { "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model parameters: ['Epo_degradation_BaF3', 'k_exp_hetero', 'k_exp_homo', 'k_imp_hetero', 'k_imp_homo', 'k_phos', 'sd_pSTAT5A_rel', 'sd_pSTAT5B_rel', 'sd_rSTAT5A_rel']\n", - "Model outputs: ['observable_pSTAT5A_rel', 'observable_pSTAT5B_rel', 'observable_rSTAT5A_rel']\n", - "Model states: ['STAT5A', 'STAT5B', 'pApB', 'pApA', 'pBpB', 'nucpApA', 'nucpApB', 'nucpBpB']\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], "source": [ "model = model_module.getModel()\n", "\n", "print(\"Model parameters:\", list(model.getParameterIds()))\n", "print(\"Model outputs: \", list(model.getObservableIds()))\n", "print(\"Model states: \", list(model.getStateIds()))" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "#### Running simulations and analyzing results\n", "\n", "After importing the model, we can run simulations using `amici.runAmiciSimulation`. This requires a `Model` instance and a `Solver` instance. But, in order go gain a value of fit, we also need to provide some data." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 9, + }, "outputs": [], "source": [ "# we prepare our data as it is reported in the benchmark collection\n", @@ -472,17 +427,17 @@ " 32.211077,\n", " ]\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 10, + }, "outputs": [], "source": [ "benchmark_parameters = np.array(\n", @@ -513,17 +468,17 @@ "\n", "# Run simulation using model parameters from the benchmark collection and default solver options\n", "rdata = amici.runAmiciSimulation(model, solver)" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 11, + }, "outputs": [], "source": [ "# Create edata instance with dimensions and timepoints\n", @@ -542,59 +497,50 @@ "edata.setObservedDataStdDev(np.array(16 * [10**0.585755271]), 0)\n", "edata.setObservedDataStdDev(np.array(16 * [10**0.818982819]), 1)\n", "edata.setObservedDataStdDev(np.array(16 * [10**0.498684404]), 2)" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Chi2 value reported in benchmark collection: 47.9765479\n", - "chi2 value using AMICI: 47.97654319310132\n" - ] - } - ], + }, + "outputs": [], "source": [ "rdata = amici.runAmiciSimulation(model, solver, edata)\n", "\n", "print(\"Chi2 value reported in benchmark collection: 47.9765479\")\n", "print(f\"chi2 value using AMICI: {rdata['chi2']}\")" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "#### Creating pyPESTO objective\n", "\n", "We are now set up to create our pyPESTO objective. This objective is a vital part of the pyPESTO infrastructure as it provides a blackbox interface to call any predefined objective function with some parameters and evaluate it. We can easily create an AmiciObjective by supplying the model, an amici solver and the data.\n", "\n", "Keep in mind, however, that you can use ANY function you would like for this." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 13, + }, "outputs": [], "source": [ "# we make some more adjustments to our model and the solver\n", @@ -607,39 +553,30 @@ "objective = pypesto.AmiciObjective(\n", " amici_model=model, amici_solver=solver, edatas=[edata], max_sensi_order=1\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "We can now call the objective function directly for any parameter. The value that is put out is the likelihood function. If we want to interact more with the AMICI returns, we can also return this by call and e.g. retrieve the chi2 value." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "We can now call the objective function directly for any parameter. The value that is put out is the likelihood function. If we want to interact more with the AMICI returns, we can also return this by call and e.g. retrieve the chi2 value." + ] }, { "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Objective value: 138.22199735603812\n", - "Chi^2 value of the same parameters: 47.97654319310132\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], "source": [ "# the generic objective call ## Add what we print\n", "print(f\"Objective value: {objective(benchmark_parameters)}\")\n", @@ -648,40 +585,40 @@ "print(\n", " f'Chi^2 value of the same parameters: {obj_call_with_dict[\"rdatas\"][0][\"chi2\"]}'\n", ")" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } + }, + "source": [ + "Now this makes the whole process already somewhat easier, but still, getting here took us quite some coding and effort. This will only get more complicated, the more complex the model is. Therefore in the next part we will show you how to bypass the tedious lines of code by using PEtab ." + ] }, { "cell_type": "markdown", - "source": [ - "Now this makes the whole process already somewhat easier, but still, getting here took us quite some coding and effort. This will only get more complicated, the more complex the model is. Therefore in the next part we will show you how to bypass the tedious lines of code by using PEtab ." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Create a pyPESTO problem + objective from Petab" + ] }, { "cell_type": "markdown", - "source": [ - "### Create a pyPESTO problem + objective from Petab" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "#### Background on PEtab\n", "\n", @@ -692,17 +629,17 @@ "A PEtab problem consist of an [SBML](https://sbml.org) file, defining the model topology and a set of `.tsv` files, defining experimental conditions, observables, measurements and parameters (and their optimization bounds, scale, priors...). All files, that make up a PEtab problem, can be structured in a `.yaml` file. The `pypesto.Objective` comming from a PEtab problem corresponds to the negative-log-likelihood/negative-log-posterior disrtibution of the parameters.\n", "\n", "For more details on PEtab, the interested reader is refered to [PEtab's format definition](https://petab.readthedocs.io/en/latest/documentation_data_format.html), for examples the reader is refered to the [PEtab benchmark collection](https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab). The Model from _[Böhm et al. JProteomRes 2014](https://pubs.acs.org/doi/abs/10.1021/pr5006923)_ is part of the benchmark collection and will be used as the running example throughout this notebook.\n" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 15, + }, "outputs": [], "source": [ "%%capture\n", @@ -711,140 +648,90 @@ "petab_problem = petab.Problem.from_yaml(petab_yaml)\n", "importer = pypesto.petab.PetabImporter(petab_problem)\n", "problem = importer.create_problem()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "code", - "execution_count": 16, - "outputs": [ - { - "data": { - "text/plain": " parameterName parameterScale lowerBound \\\nparameterId \nEpo_degradation_BaF3 EPO_{degradation,BaF3} log10 0.00001 \nk_exp_hetero k_{exp,hetero} log10 0.00001 \nk_exp_homo k_{exp,homo} log10 0.00001 \nk_imp_hetero k_{imp,hetero} log10 0.00001 \nk_imp_homo k_{imp,homo} log10 0.00001 \n\n upperBound nominalValue estimate \nparameterId \nEpo_degradation_BaF3 100000 0.026983 1 \nk_exp_hetero 100000 0.000010 1 \nk_exp_homo 100000 0.006170 1 \nk_imp_hetero 100000 0.016368 1 \nk_imp_homo 100000 97749.379402 1 ", - "text/html": "
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" - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check the dataframes. First the parameter dataframe\n", - "petab_problem.parameter_df.head()" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# Check the dataframes. First the parameter dataframe\n", + "petab_problem.parameter_df.head()" + ] }, { "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "data": { - "text/plain": " observableName \\\nobservableId \npSTAT5A_rel NaN \npSTAT5B_rel NaN \nrSTAT5A_rel NaN \n\n observableFormula \\\nobservableId \npSTAT5A_rel (100 * pApB + 200 * pApA * specC17) / (pApB + ... \npSTAT5B_rel -(100 * pApB - 200 * pBpB * (specC17 - 1)) / (... \nrSTAT5A_rel (100 * pApB + 100 * STAT5A * specC17 + 200 * p... \n\n noiseFormula observableTransformation \\\nobservableId \npSTAT5A_rel noiseParameter1_pSTAT5A_rel lin \npSTAT5B_rel noiseParameter1_pSTAT5B_rel lin \nrSTAT5A_rel noiseParameter1_rSTAT5A_rel lin \n\n noiseDistribution \nobservableId \npSTAT5A_rel normal \npSTAT5B_rel normal \nrSTAT5A_rel normal ", - "text/html": "
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" - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check the observable dataframe\n", - "petab_problem.observable_df.head()" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# Check the observable dataframe\n", + "petab_problem.observable_df.head()" + ] }, { "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "data": { - "text/plain": " observableId preequilibrationConditionId simulationConditionId \\\n0 pSTAT5A_rel NaN model1_data1 \n1 pSTAT5A_rel NaN model1_data1 \n2 pSTAT5A_rel NaN model1_data1 \n3 pSTAT5A_rel NaN model1_data1 \n4 pSTAT5A_rel NaN model1_data1 \n\n measurement time observableParameters noiseParameters \\\n0 7.901073 0.0 NaN sd_pSTAT5A_rel \n1 66.363494 2.5 NaN sd_pSTAT5A_rel \n2 81.171324 5.0 NaN sd_pSTAT5A_rel \n3 94.730308 10.0 NaN sd_pSTAT5A_rel \n4 95.116483 15.0 NaN sd_pSTAT5A_rel \n\n datasetId \n0 model1_data1_pSTAT5A_rel \n1 model1_data1_pSTAT5A_rel \n2 model1_data1_pSTAT5A_rel \n3 model1_data1_pSTAT5A_rel \n4 model1_data1_pSTAT5A_rel ", - "text/html": "
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" - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check the measurement dataframe\n", - "petab_problem.measurement_df.head()" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# Check the measurement dataframe\n", + "petab_problem.measurement_df.head()" + ] }, { "cell_type": "code", - "execution_count": 19, - "outputs": [ - { - "data": { - "text/plain": " conditionName\nconditionId \nmodel1_data1 condition1", - "text/html": "
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" - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the condition dataframe\n", - "petab_problem.condition_df.head()" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# check the condition dataframe\n", + "petab_problem.condition_df.head()" + ] }, { "cell_type": "markdown", - "source": [ - "This was really straightforward. With this we are still able to do all the same things we did before and also adjust solver setting, change the model etc." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "This was really straightforward. With this we are still able to do all the same things we did before and also adjust solver setting, change the model etc." + ] }, { "cell_type": "code", - "execution_count": 20, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Objective value: 138.22199736863757\n", - "Absolute tolerance before change: 1e-16\n", - "Absolute tolerance after change: 1e-15\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], "source": [ "# call the objective function ## fval=\n", "print(f\"Objective value: {problem.objective(benchmark_parameters)}\")\n", @@ -858,59 +745,59 @@ "print(\n", " f\"Absolute tolerance after change: {problem.objective.amici_solver.getAbsoluteTolerance()}\"\n", ")" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } + }, + "source": [ + "Now we are good to go and start the first optimization." + ] }, { "cell_type": "markdown", - "source": [ - "Now we are good to go and start the first optimization." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "## 2. Optimization\n", "\n", "Once setup, the optimization can be done very quickly with default settings. If needed these settings can be highly individualized and change according to the needs of our model. In this section we shall go over some of these settings." - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Optimizer\n", "\n", "The optimizer determines the algorithm with which we optimize our model. The main disjunction is between global and local optimizers.\n", "\n", "pyPESTO provides an interface to many optimizers, such as Fides, ScipyOptimizers, Pyswarm and many more. For a whole list of supported optimizers with settings for each optimizer you can [have a look here](https://pypesto.readthedocs.io/en/latest/api.html#optimize)." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 21, + }, "outputs": [], "source": [ "optimizer_options = {'maxiter': 1e4, 'fatol': 1e-12, 'frtol': 1e-12}\n", @@ -918,225 +805,133 @@ "optimizer = optimize.FidesOptimizer(\n", " options=optimizer_options, verbose=logging.WARN\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "### Startpoint method\n", - "\n", - "The startpoint methods describes how you want to choose your starpoints, in case you do a multistart opimization. The default here is `uniform` meaning that each startpoint is a uniform sample from the allowed parameter space. The other two notable options are either `latin_hypercube` or a self defined function." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Startpoint method\n", + "\n", + "The startpoint methods describes how you want to choose your starpoints, in case you do a multistart opimization. The default here is `uniform` meaning that each startpoint is a uniform sample from the allowed parameter space. The other two notable options are either `latin_hypercube` or a self defined function." + ] }, { "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "startpoint_method = pypesto.startpoint.uniform" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "startpoint_method = pypesto.startpoint.uniform" + ] }, { "cell_type": "markdown", - "source": [ - "### History options\n", - "\n", - "In some cases, it is good to trace what the optimizer did in each step, i.e. the history. There is a multitude of options on what to report here, but the most important one is `trace_record` which turns the history function on and off." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### History options\n", + "\n", + "In some cases, it is good to trace what the optimizer did in each step, i.e. the history. There is a multitude of options on what to report here, but the most important one is `trace_record` which turns the history function on and off." + ] }, { "cell_type": "code", - "execution_count": 23, - "outputs": [], - "source": [ - "# save optimizer trace\n", - "history_options = pypesto.HistoryOptions(trace_record=True)" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# save optimizer trace\n", + "history_options = pypesto.HistoryOptions(trace_record=True)" + ] }, { "cell_type": "markdown", - "source": [ - "### Optimization options\n", - "\n", - "Some further possible options for the optimization. Notably `allow_failed_starts`, which in case of a very complicated objective function, can help get to the desired number of optimizations when turned of. As we do not need this here, we create the default options." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Optimization options\n", + "\n", + "Some further possible options for the optimization. Notably `allow_failed_starts`, which in case of a very complicated objective function, can help get to the desired number of optimizations when turned of. As we do not need this here, we create the default options." + ] }, { "cell_type": "code", - "execution_count": 24, - "outputs": [ - { - "data": { - "text/plain": "{'allow_failed_starts': True,\n 'report_sres': True,\n 'report_hess': True,\n 'history_beats_optimizer': True}" - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "opt_options = optimize.OptimizeOptions()\n", - "opt_options" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "opt_options = optimize.OptimizeOptions()\n", + "opt_options" + ] }, { "cell_type": "markdown", - "source": [ - "### Running the optimization\n", - "\n", - "We now only need to decide on the number of starts as well as the engine we want to use for the optimization." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Running the optimization\n", + "\n", + "We now only need to decide on the number of starts as well as the engine we want to use for the optimization." + ] }, { "cell_type": "code", - "execution_count": 25, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine will use up to 8 processes (= CPU count).\n" - ] + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "n_starts = 20 # usually a value >= 100 should be used\n", "engine = pypesto.engine.MultiProcessEngine()" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 43, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/20 [00:00", - "text/markdown": "## Optimization Result \n\n* number of starts: 20 \n* best value: 145.7594473944086, id=10\n* worst value: 249.74599744335123, id=0\n* number of non-finite values: 0\n\n* execution time summary:\n\t* Mean execution time: 3.773s\n\t* Maximum execution time: 8.952s,\tid=4\n\t* Minimum execution time: 0.690s,\tid=1\n* summary of optimizer messages:\n\n | Count | Message |\n |--------:|:-----------------------------------------|\n | 18 | Trust Region Radius too small to proceed |\n | 2 | Converged according to fval difference |\n\n* best value found (approximately) 2 time(s)\n* number of plateaus found: 6\n\nA summary of the best run:\n\n### Optimizer Result\n\n* optimizer used: \n* message: Trust Region Radius too small to proceed \n* number of evaluations: 128\n* time taken to optimize: 3.092s\n* startpoint: [-0.15890311 -2.669684 -3.07785865 -2.33924103 2.20218441 2.69202827\n -4.60815317 4.61526113 4.65892721]\n* endpoint: [-1.52341922 -4.99872444 -2.03436045 -1.83082223 -1.67391186 3.94118859\n 0.50779203 0.80380654 0.79609736]\n* final objective value: 145.7594473944086\n* final gradient value: [ 1.25643019e-02 2.81170156e-02 4.13210117e-03 1.72722532e-02\n 4.46876982e-03 -9.07197649e-03 7.34889144e-04 -7.21740127e-04\n -6.05508217e-05]\n* final hessian value: [[ 2.37372423e+03 3.60035009e-01 2.64671284e+02 2.61587473e+03\n 8.75419211e+02 -8.64972412e+02 6.90582554e+01 -4.51763973e+01\n -2.39107885e+01]\n [ 3.60035009e-01 2.96735578e-04 2.94533113e-02 3.65701398e-01\n 1.60931779e-01 -1.70965758e-01 -1.76438574e-04 -2.71229932e-02\n -3.74423892e-02]\n [ 2.64671284e+02 2.94533113e-02 7.03217478e+01 2.78164570e+02\n 1.49629446e+02 -1.35646123e+02 -3.40041260e+00 -3.44539004e-01\n 3.73543709e+00]\n [ 2.61587473e+03 3.65701398e-01 2.78164570e+02 2.91408845e+03\n 9.22097568e+02 -8.64604114e+02 8.15865701e+01 -4.77283835e+01\n -3.38979574e+01]\n [ 8.75419211e+02 1.60931779e-01 1.49629446e+02 9.22097568e+02\n 4.25560637e+02 -4.03115866e+02 9.18790468e+00 -1.99750037e+01\n 1.07768093e+01]\n [-8.64972412e+02 -1.70965758e-01 -1.35646123e+02 -8.64604114e+02\n -4.03115866e+02 1.14623637e+03 -2.12431899e+01 -1.23869791e+01\n 3.36510580e+01]\n [ 6.90582554e+01 -1.76438574e-04 -3.40041260e+00 8.15865701e+01\n 9.18790468e+00 -2.12431899e+01 8.48286776e+01 0.00000000e+00\n 0.00000000e+00]\n [-4.51763973e+01 -2.71229932e-02 -3.44539004e-01 -4.77283835e+01\n -1.99750037e+01 -1.23869791e+01 0.00000000e+00 8.48320316e+01\n 0.00000000e+00]\n [-2.39107885e+01 -3.74423892e-02 3.73543709e+00 -3.38979574e+01\n 1.07768093e+01 3.36510580e+01 0.00000000e+00 0.00000000e+00\n 8.48305092e+01]]\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(Markdown(result.summary()))" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "display(Markdown(result.summary()))" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We can see some informative statistics, such as the mean execution time, best and worst values, a small table on the exit messages of the optimizer as well as detailed info on the best optimizer.\n", "\n", "As our best start is just as good as the reported benchmark value, we shall now further inspect the result thorough some useful visualisations." - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "## 3. Optimization visualization" + ] }, { "cell_type": "markdown", - "source": [ - "## 3. Optimization visualization" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Model fit\n", "\n", "Probably the most useful visualization there is, is one, where we visualize the found parameter dynamics against the measurements. This way we can see whether the fit is qualitatively and/or quantitatively good." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 45, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], + }, + "outputs": [], "source": [ "ax = model_fit.visualize_optimized_model_fit(\n", " petab_problem=petab_problem, result=result, pypesto_problem=problem\n", ")" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Waterfall plot\n", "\n", "The waterfall plot is a visualization of the final objective function values of each start. They are sorted from small to high and then plotted. Similar values will get clustered and get the same color.\n", "\n", "This helps determining whether the result is reproducible and whether we reliably found a local minimum that we hope to be the golbal one." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", - "execution_count": 46, - "outputs": [ - { - "data": { - "text/plain": "
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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.waterfall(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.waterfall(result);" + ] }, { "cell_type": "markdown", - "source": [ - "### Parameter plots\n", - "\n", - "To visualize the parameters, there is a multitude of options:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Parameter plots\n", + "\n", + "To visualize the parameters, there is a multitude of options:" + ] }, { "cell_type": "markdown", - "source": [ - "#### Parameter overview\n", - "\n", - "Here we plot the parameters of all starts within their bounds. This can tell us whether some bounds are always hit and might need to be questioned and if the best starts are similar or differ amongst themselves, hinting already for some unidentifiabilities." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Parameter overview\n", + "\n", + "Here we plot the parameters of all starts within their bounds. This can tell us whether some bounds are always hit and might need to be questioned and if the best starts are similar or differ amongst themselves, hinting already for some unidentifiabilities." + ] }, { "cell_type": "code", - "execution_count": 47, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.parameters(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.parameters(result);" + ] }, { "cell_type": "markdown", - "source": [ - "#### Parameter correlation plot\n", - "\n", - "To further look into possible uncertainties, we can plot the correlation of the final points. Sometimes, pairs of parameters are dependent on each other and fixing one might solve some unidentifiability." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Parameter correlation plot\n", + "\n", + "To further look into possible uncertainties, we can plot the correlation of the final points. Sometimes, pairs of parameters are dependent on each other and fixing one might solve some unidentifiability." + ] }, { "cell_type": "code", - "execution_count": 48, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.parameters_correlation_matrix(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.parameters_correlation_matrix(result);" + ] }, { "cell_type": "markdown", - "source": [ - "#### Parameter histogram + scatter\n", - "\n", - "In case we found some dependencies and for further investigation, can also specifically look at the histograms of certain parameters and the pairwise parameter scatter plot." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Parameter histogram + scatter\n", + "\n", + "In case we found some dependencies and for further investigation, can also specifically look at the histograms of certain parameters and the pairwise parameter scatter plot." + ] }, { "cell_type": "code", - "execution_count": 49, - "outputs": [ - { - 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ceeSRSZLDDz88/fr1yw9/+MOMGDFim97PySefnNGjRydJevbsmblz5+bJJ5+sf51p06bl85//fMaMGZMkOfbYY7PXXntlzJgxGTJkSA466KD6qN69e/ckyauvvpof//jHGT58eM4777z6+6qqqjJ9+vScfvrpadOmTZKkadOmueKKK9KsWbMkyaxZs/LSSy/ljjvuSI8ePZIkxx13XNasWZNp06blC1/4Qvbaa69teo8AANDQbBEDAADb6E9/+lMuvvjiVFdX5+KLL96me5944om0a9cuhx56aNasWZM1a9Zk7dq1OeGEE/LHP/4xy5YtS5Lst99+GTFiRH7yk5/krrvuypgxY7Lffvtt8KxOnTrVx/Ukqa6url8lvq3+/Tnrn718+fIkybx587Jy5cr06dOnfuY1a9akT58+SZK5c+du9JlPPvlk6urqNnrfqlWr8rvf/a7+2gMOOKA+rifJU089lY4dO9bH9fUGDhyYVatW5dlnn93m9wgAAA3NCnYAANhGL730Unr37p1f/vKXuf322zN48OCtvnfp0qVZsmRJDj300I2eX7JkSVq3bp0k6d+/fyZOnJgk6dWr17uubd++/buOtW3bNn/605+2ep71mjdvvsHnjRo1Sl1dXf3MSepXof//Fi9evNHj6+875ZRTNnq+tra2/uMWLVpscG7ZsmVp167du+7ZZ599kqQ+/gMAQCUJ7AAAsI2OO+64TJ8+PcOGDcu1116bfv36pUOHDlt1b6tWrbL//vunpqZmo+c7depU//G3vvWttGjRIs2aNcu4ceMyffr0Da79xz/+8a77X3/99bRt23Yb3s2W7bnnnkmSmpqa7L///u86vz56b+q+22677V0BPUk+8IEPbPI1W7dunddee+1dx5csWZIk9VvLAABAJdkiBgAAttH6oDx69Og0btw448eP3+p7jz766CxcuDBt27ZN165d6/+ZO3duZsyYUb/H+kMPPZT7778/o0ePzrhx4/LLX/4yc+bM2eBZf/3rX/Pyyy/Xf15bW5t58+alZ8+e2/8m/023bt3StGnT1NbWbjBzkyZNcu2112b+/PlJ/rXq/d+t33bmH//4xwb3vfHGG5kyZUr9CveNOeqoo7JgwYLMmzdvg+M//elP07Rp0xx++OEN+h4BAKCEFewAAFCouro6w4YNyze/+c3cf//9GTBgwBbv+cxnPpMf/ehHGTJkSM4///x06NAhv/nNb/L9738/gwYNStOmTfPGG29k/PjxOfbYY/PJT34ySdKvX79MmDAhvXr1qv9lonV1dTn//PMzbNiwNG7cONdff31at269TVvWbI02bdrk3HPPzZQpU/Lmm2/mox/9aGprazNlypRUVVWlS5cuSf7fivX7778/3bp1S+fOnTNw4MCMHTs2CxYsyGGHHZZXX301kydPTqdOnTa6Gv7fv06zZs3KhRdemKFDh6ZTp0557LHHMmfOnFx00UX1rwUAAJUksAMAwHY47bTTcs899+Sqq65Kr169trh1yR577JHbb78911xzTSZNmpQVK1akY8eO+drXvpazzz47SXLFFVfk7bffzhVXXFF/37hx49K/f/9cdtllufnmm5P8a4uVs88+O1dffXXefvvtHHPMMbnxxhuz1157Nfj7vOSSS9KuXbvMmjUrM2bMSOvWrdOzZ88MHz48rVq1SpKcdNJJuffeezNq1KiceuqpGT9+fCZMmJDp06fnjjvuyKJFi9K2bdv0798/l1xySf1q/Y1p3rx5Zs6cmWuuuaY+7B9wwAG56qqrcuqppzb4+wMAgBJVdet/cxEAALDTGDVqVJ566qk89thjlR4FAAD+Y1nBDgAADWDt2rXZ0tqVqqqqza7a3pXnAQCAXZHADgAADeDEE0/MggULNnvN0UcfnZkzZ74v85x11ll56qmnNntNx44drYAHAIDtYIsYAABoAC+++GJWr1692WtatGiRAw444H2Z55VXXslbb7212WuaNWuWzp07vy/zAADArkhgBwAAAACAAo0qPQAAAAAAAOyMBHYAAAAAACggsAMAAAAAQAGBHQAAAAAACgjsAAAAAABQQGAHAAAAAIACAjsAAAAAABT4v5epOT/vhbblAAAAAElFTkSuQmCC\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.parameter_hist(result=result, parameter_name=\"k_exp_hetero\")\n", - "visualize.parameter_hist(result=result, parameter_name=\"k_imp_homo\");" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.parameter_hist(result=result, parameter_name=\"k_exp_hetero\")\n", + "visualize.parameter_hist(result=result, parameter_name=\"k_imp_homo\");" + ] }, { "cell_type": "code", - "execution_count": 50, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.optimization_scatter(result, parameter_indices=[1, 4]);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.optimization_scatter(result, parameter_indices=[1, 4]);" + ] }, { "cell_type": "markdown", - "source": [ - "We definitely need to look further into it, and thus we turn to uncertainty quantification in next next section." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "We definitely need to look further into it, and thus we turn to uncertainty quantification in next next section." + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## 4. Uncertainty quantification\n", "\n", "This mainly consists of two parts:\n", "* Profile Liklihoods\n", "* MCMC sampling" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Profile likelihood\n", "\n", "The profile likelihood uses an optimization scheme to calculate the confidence intervals for each parameter. We start with the best found parameterset of the optimization. Then in each step, we increase/decrease the parameter of interest, fix it and then run one local optimization. We do this until we either hit the bounds or reach a sufficiently bad fit.\n", "\n", "To run the profiling we do not need a lot of setup, as we did this already for the optimization." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 51, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:00", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot profiles\n", - "pypesto.visualize.profiles(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# plot profiles\n", + "pypesto.visualize.profiles(result);" + ] }, { "cell_type": "markdown", - "source": [ - "### Sampling\n", - "\n", - "We can use MCMC sampling to get a distribution on the posterior of the parameters. Here again, we do not need a lot of setup. We only need to define a sampler, of which pyPESTO offers a multitude." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Sampling\n", + "\n", + "We can use MCMC sampling to get a distribution on the posterior of the parameters. Here again, we do not need a lot of setup. We only need to define a sampler, of which pyPESTO offers a multitude." + ] }, { "cell_type": "code", - "execution_count": 53, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 5000/5000 [00:18<00:00, 267.95it/s]\n", - "Elapsed time: 22.839196\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], "source": [ "# Sampling\n", "sampler = sample.AdaptiveMetropolisSampler()\n", @@ -1633,102 +1296,84 @@ " n_samples=5000,\n", " result=result,\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "For visualization purposes, we can visualize the trace of the objective function value, as well as a scatter plot of the parameters, just like in the optimization. We do omit the scatter plot here, as it has a very large size." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "For visualization purposes, we can visualize the trace of the objective function value, as well as a scatter plot of the parameters, just like in the optimization. We do omit the scatter plot here, as it has a very large size." + ] }, { "cell_type": "code", - "execution_count": 54, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot objective function trace\n", - "visualize.sampling_fval_traces(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# plot objective function trace\n", + "visualize.sampling_fval_traces(result);" + ] }, { "cell_type": "code", - "execution_count": 55, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.sampling_1d_marginals(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.sampling_1d_marginals(result);" + ] }, { "cell_type": "markdown", - "source": [ - "## 5. Saving results\n", - "\n", - "Lastly, the whole process took quite some time, but is not necessarily finished. It is therefore very useful, to be able to save the result as is. pyPESTO uses the hdf5 Format and with two very short commands we are able to read and write a result from and to an hdf5 file." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "## 5. Saving results\n", + "\n", + "Lastly, the whole process took quite some time, but is not necessarily finished. It is therefore very useful, to be able to save the result as is. pyPESTO uses the hdf5 Format and with two very short commands we are able to read and write a result from and to an hdf5 file." + ] }, { "cell_type": "markdown", - "source": [ - "### Save result object in HDF5 File" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Save result object in HDF5 File" + ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# create temporary file\n", @@ -1745,123 +1390,99 @@ " sample=True,\n", " profile=True,\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "### Reload results" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Reload results" + ] }, { "cell_type": "code", - "execution_count": 57, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: You are loading a problem.\n", - "This problem is not to be used without a separately created objective.\n" - ] - } - ], - "source": [ - "# Read result\n", - "result2 = store.read_result(fn, problem=True)" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# Read result\n", + "result2 = store.read_result(fn, problem=True)" + ] }, { "cell_type": "markdown", - "source": [ - "As the warning already suggests, we need to assign the problem again correctly." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "As the warning already suggests, we need to assign the problem again correctly." + ] }, { "cell_type": "code", - "execution_count": 58, - "outputs": [], - "source": [ - "result2.problem = problem" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "result2.problem = problem" + ] }, { "cell_type": "markdown", - "source": [ - "Now we are able to quickly load the result and visualize them." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Now we are able to quickly load the result and visualize them." + ] }, { "cell_type": "markdown", - "source": [ - "### Plot (reloaded) results" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Plot (reloaded) results" + ] }, { "cell_type": "code", - "execution_count": 59, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot profiles\n", - "pypesto.visualize.profiles(result2);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# plot profiles\n", + "pypesto.visualize.profiles(result2);" + ] } ], "metadata": { diff --git a/doc/example/censored.ipynb b/doc/example/censored.ipynb index 28ef1540a..54485d385 100644 --- a/doc/example/censored.ipynb +++ b/doc/example/censored.ipynb @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -100,386 +100,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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observableIdpreequilibrationConditionIdsimulationConditionIdmeasurementtimeobservableParametersnoiseParametersobservableTransformationnoiseDistributionmeasurementTypecensoringBounds
0ActivityNaNInhibitor_00.0000005NaN1linnormalinterval-censored10.0;16.0
1ActivityNaNInhibitor_30.0000005NaN1linnormalinterval-censored10.0;16.0
2ActivityNaNInhibitor_1017.8926545NaN1linnormalNaNNaN
3ActivityNaNInhibitor_250.0000005NaN1linnormalright-censored20.0
4ActivityNaNInhibitor_3516.8121045NaN1linnormalNaNNaN
5ActivityNaNInhibitor_509.1731295NaN1linnormalNaNNaN
6ActivityNaNInhibitor_754.1509285NaN1linnormalNaNNaN
7ActivityNaNInhibitor_1000.0000005NaN1linnormalleft-censored3.0
8ActivityNaNInhibitor_3000.0000005NaN1linnormalleft-censored3.0
9YbarNaNInhibitor_00.0000005NaN1linnormalNaNNaN
10YbarNaNInhibitor_30.0599995NaN1linnormalNaNNaN
11YbarNaNInhibitor_100.1999945NaN1linnormalNaNNaN
12YbarNaNInhibitor_250.4990435NaN1linnormalNaNNaN
13YbarNaNInhibitor_350.6591695NaN1linnormalNaNNaN
14YbarNaNInhibitor_500.8149545NaN1linnormalNaNNaN
15YbarNaNInhibitor_750.9163835NaN1linnormalNaNNaN
16YbarNaNInhibitor_1000.9489815NaN1linnormalNaNNaN
17YbarNaNInhibitor_3000.9881305NaN1linnormalNaNNaN
\n", - "
" - ], - "text/plain": [ - " observableId preequilibrationConditionId simulationConditionId \\\n", - "0 Activity NaN Inhibitor_0 \n", - "1 Activity NaN Inhibitor_3 \n", - "2 Activity NaN Inhibitor_10 \n", - "3 Activity NaN Inhibitor_25 \n", - "4 Activity NaN Inhibitor_35 \n", - "5 Activity NaN Inhibitor_50 \n", - "6 Activity NaN Inhibitor_75 \n", - "7 Activity NaN Inhibitor_100 \n", - "8 Activity NaN Inhibitor_300 \n", - "9 Ybar NaN Inhibitor_0 \n", - "10 Ybar NaN Inhibitor_3 \n", - "11 Ybar NaN Inhibitor_10 \n", - "12 Ybar NaN Inhibitor_25 \n", - "13 Ybar NaN Inhibitor_35 \n", - "14 Ybar NaN Inhibitor_50 \n", - "15 Ybar NaN Inhibitor_75 \n", - "16 Ybar NaN Inhibitor_100 \n", - "17 Ybar NaN Inhibitor_300 \n", - "\n", - " measurement time observableParameters noiseParameters \\\n", - "0 0.000000 5 NaN 1 \n", - "1 0.000000 5 NaN 1 \n", - "2 17.892654 5 NaN 1 \n", - "3 0.000000 5 NaN 1 \n", - "4 16.812104 5 NaN 1 \n", - "5 9.173129 5 NaN 1 \n", - "6 4.150928 5 NaN 1 \n", - "7 0.000000 5 NaN 1 \n", - "8 0.000000 5 NaN 1 \n", - "9 0.000000 5 NaN 1 \n", - "10 0.059999 5 NaN 1 \n", - "11 0.199994 5 NaN 1 \n", - "12 0.499043 5 NaN 1 \n", - "13 0.659169 5 NaN 1 \n", - "14 0.814954 5 NaN 1 \n", - "15 0.916383 5 NaN 1 \n", - "16 0.948981 5 NaN 1 \n", - "17 0.988130 5 NaN 1 \n", - "\n", - " observableTransformation noiseDistribution measurementType \\\n", - "0 lin normal interval-censored \n", - "1 lin normal interval-censored \n", - "2 lin normal NaN \n", - "3 lin normal right-censored \n", - "4 lin normal NaN \n", - "5 lin normal NaN \n", - "6 lin normal NaN \n", - "7 lin normal left-censored \n", - "8 lin normal left-censored \n", - "9 lin normal NaN \n", - "10 lin normal NaN \n", - "11 lin normal NaN \n", - "12 lin normal NaN \n", - "13 lin normal NaN \n", - "14 lin normal NaN \n", - "15 lin normal NaN \n", - "16 lin normal NaN \n", - "17 lin normal NaN \n", - "\n", - " censoringBounds \n", - "0 10.0;16.0 \n", - "1 10.0;16.0 \n", - "2 NaN \n", - "3 20.0 \n", - "4 NaN \n", - "5 NaN \n", - "6 NaN \n", - "7 3.0 \n", - "8 3.0 \n", - "9 NaN \n", - "10 NaN \n", - "11 NaN \n", - "12 NaN \n", - "13 NaN \n", - "14 NaN \n", - "15 NaN \n", - "16 NaN \n", - "17 NaN " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from pandas import option_context\n", "\n", @@ -522,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -539,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -565,19 +188,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 10/10 [00:18<00:00, 1.83s/it]\n" - ] - } - ], + "outputs": [], "source": [ "np.random.seed(n_starts)\n", "\n", @@ -596,30 +211,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from pypesto.visualize import waterfall\n", "\n", @@ -636,20 +230,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_categories_from_pypesto_result(res, figsize=(15, 10))\n", "plt.show()" diff --git a/doc/example/conversion_reaction.ipynb b/doc/example/conversion_reaction.ipynb index 57ff1f51c..e31217c77 100644 --- a/doc/example/conversion_reaction.ipynb +++ b/doc/example/conversion_reaction.ipynb @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -97,24 +97,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# load amici module (the usual starting point later for the analysis)\n", "sys.path.insert(0, os.path.abspath(model_output_dir))\n", @@ -147,21 +136,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 100/100 [00:07<00:00, 13.61it/s]\n" - ] - } - ], + "outputs": [], "source": [ "# create objective function from amici model\n", "# pesto.AmiciObjective is derived from pesto.Objective,\n", @@ -193,50 +174,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": "" - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - 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\n" 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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "visualize.waterfall(result)\n", "visualize.parameters(result)\n", @@ -256,21 +200,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 11/11 [00:01<00:00, 5.75it/s]\n" - ] - } - ], + "outputs": [], "source": [ "import pypesto.profile as profile\n", "\n", @@ -294,22 +230,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# specify the parameters, for which profiles should be computed\n", "ax = visualize.profiles(result)" @@ -328,22 +255,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 10000/10000 [01:07<00:00, 148.81it/s]\n", - "Elapsed time: 76.66041799999999\n" - ] - } - ], + "outputs": [], "source": [ "import pypesto.sample as sample\n", "\n", @@ -358,22 +276,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = visualize.sampling_scatter(result, size=[13, 6])" ] @@ -391,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -424,22 +333,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": "{'conditions': [{'timepoints': array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]),\n 'output_ids': ['ratio'],\n 'x_names': ['k1', 'k2'],\n 'output': array([0. , 1.95196396, 2.00246152, 2.00290412, 2.00290796,\n 2.00290801, 2.00290801, 2.002908 , 2.002908 , 2.002908 ,\n 2.002908 ]),\n 'output_sensi': None,\n 'output_weight': None,\n 'output_sigmay': None}],\n 'condition_ids': ['condition_0'],\n 'comment': None,\n 'parameter_ids': ['k1', 'k2']}" - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "dict(prediction)" ] @@ -457,37 +357,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "parameterId k1 k2\n", - "parameterId k1 k2\n", - "lowerBound -2 -2\n", - "upperBound 2 2\n", - "ensemble_mean -0.575923 -0.349279\n", - "ensemble_std 0.125729 0.111493\n", - "ensemble_median -0.575923 -0.349279\n", - "within lb: 1 std True True\n", - "within ub: 1 std True True\n", - "within lb: 2 std True True\n", - "within ub: 2 std True True\n", - "within lb: 3 std True True\n", - "within ub: 3 std True True\n", - "within lb: perc 5 True True\n", - "within lb: perc 20 True True\n", - "within ub: perc 80 True True\n", - "within ub: perc 95 True True\n" - ] - } - ], + "outputs": [], "source": [ "# We want to use the sample result to create a prediction\n", "from pypesto.ensemble import ensemble\n", @@ -512,31 +388,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:00,\n 'std': ,\n 'median': ,\n 'percentile 1': ,\n 'percentile 5': ,\n 'percentile 10': ,\n 'percentile 25': ,\n 'percentile 75': ,\n 'percentile 90': ,\n 'percentile 95': ,\n 'percentile 99': }" - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# run a prediction\n", "ensemble_prediction = my_ensemble.predict(\n", diff --git a/doc/example/custom_objective_function.ipynb b/doc/example/custom_objective_function.ipynb index b85000630..36f9938a3 100644 --- a/doc/example/custom_objective_function.ipynb +++ b/doc/example/custom_objective_function.ipynb @@ -2,64 +2,70 @@ "cells": [ { "cell_type": "markdown", - "source": [ - "# Custom Objective Function" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "# Custom Objective Function" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "pyPESTO can not only do parameter estimation for PEtab and AMICI models, but is able to do so on any provided function.\n", "This is done by providing the objective with the function as well as possibly gradient and hessian.\n", "In this notebook, we will show a few different ways on how to do this. As sometimes manually providing the gradient and hessian is tedious, we will try to emphasize on the importance of those two." - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "After this notebook, you should ...\n", "* ... be able to create an objective from a given function.\n", "* ... be able to potentially add a gradient and hessian to the objective.\n", "* ... be able to run parameter estimation on the objective.\n", "* ... know the importance of gradient and hessian in terms of optimization speed and accuracy." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 1, + }, "outputs": [], "source": [ "# install if not done yet\n", "# %pip install pypesto --quiet" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 2, + }, "outputs": [], "source": [ "import logging\n", @@ -79,54 +85,54 @@ "np.random.seed(1912)\n", "\n", "%matplotlib inline" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "## 1. Objective + Problem Definition" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "## 1. Objective + Problem Definition" + ] }, { "cell_type": "markdown", - "source": [ - "In the following we will use the Rosenbrock Banana function, which we can directly get from scipy." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "In the following we will use the Rosenbrock Banana function, which we can directly get from scipy." + ] }, { "cell_type": "markdown", - "source": [ - "The first creation of the objective function is rather straightforward:\n", - "We create it by providing a function, as well as gradient and hessian." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "The first creation of the objective function is rather straightforward:\n", + "We create it by providing a function, as well as gradient and hessian." + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# first type of objective defined through callables\n", @@ -135,30 +141,30 @@ " grad=sp.optimize.rosen_der,\n", " hess=sp.optimize.rosen_hess,\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "The second option is to provide a function that returns objective value, gradient and hessian (last two optional) all as a tuple.\n", - "In this case, we just need to notify the pyPESTO objective of this through boolean values." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "The second option is to provide a function that returns objective value, gradient and hessian (last two optional) all as a tuple.\n", + "In this case, we just need to notify the pyPESTO objective of this through boolean values." + ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# second type of objective\n", @@ -171,29 +177,29 @@ "\n", "\n", "objective2 = pypesto.Objective(fun=rosen2, grad=True, hess=True)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "For later comparisons, we create two other objectives. One that is only provided with function and gradient, and one that only has the functional value, forcing it to rely on finite differences in optimization." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "For later comparisons, we create two other objectives. One that is only provided with function and gradient, and one that only has the functional value, forcing it to rely on finite differences in optimization." + ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# no hessian objective\n", @@ -206,41 +212,30 @@ "objective4 = pypesto.Objective(\n", " fun=sp.optimize.rosen,\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "To get from objective to a usable parameter estimation problem, we need to additionally provide the bounds of our parameters." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "To get from objective to a usable parameter estimation problem, we need to additionally provide the bounds of our parameters." + ] }, { "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Some initial guesses supplied violate the bounds set for this problem.\n", - "Some initial guesses supplied violate the bounds set for this problem.\n", - "Some initial guesses supplied violate the bounds set for this problem.\n", - "Some initial guesses supplied violate the bounds set for this problem.\n" - ] + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "dim_full = 15\n", "lb = -5 * np.ones((dim_full, 1))\n", @@ -261,16 +256,16 @@ "problem4 = pypesto.Problem(\n", " objective=objective4, lb=lb, ub=ub, x_guesses=x_guesses\n", ")" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Illustration\n", "\n", @@ -281,17 +276,17 @@ "This function has a global minimum at $x^* = (1, \\dots, 1)$ with $f(x^*) = 0$. If $n\\geq 4$, the function has an additional local minimum at $x^* = (-1, 1, \\dots, 1)$ with $f(x^*) = 4$.\n", "\n", "We will illustrate the function for $n=2$." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 7, + }, "outputs": [], "source": [ "x = np.arange(-2, 2, 0.1)\n", @@ -301,27 +296,18 @@ "for j in range(0, x.shape[0]):\n", " for k in range(0, x.shape[1]):\n", " z[j, k] = objective1([x[j, k], y[j, k]], (0,))" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "data": { - "text/plain": "
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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + }, + "outputs": [], "source": [ "fig = plt.figure()\n", "fig.set_size_inches(*(14, 10))\n", @@ -330,76 +316,59 @@ "plt.xlabel(\"x axis\")\n", "plt.ylabel(\"y axis\")\n", "ax.set_title(\"cost function values\");" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "And a contour plot:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "And a contour plot:" + ] }, { "cell_type": "code", - "execution_count": 23, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "fig = plt.figure()\n", "fig.set_size_inches(*(14, 10))\n", "ax = plt.axes()\n", "ax.contourf(x, y, z, 100, norm=\"log\");" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "## 2. Optimization" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "## 2. Optimization" + ] }, { "cell_type": "code", - "execution_count": 24, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n" - ] + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "# optimizer\n", "optimizer = optimize.ScipyOptimizer()\n", @@ -407,45 +376,30 @@ "engine = pypesto.engine.MultiProcessEngine()\n", "# starts\n", "n_starts = 20" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "As a first comparison, we time each optimization. We also use the same optimizer and engine for all optimizations." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "As a first comparison, we time each optimization. We also use the same optimizer and engine for all optimizations." + ] }, { "cell_type": "code", - "execution_count": 26, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Performing parallel task execution on 8 processes.\n", - "100%|██████████| 20/20 [00:00<00:00, 127.02it/s]\n", - "Performing parallel task execution on 8 processes.\n", - "100%|██████████| 20/20 [00:04<00:00, 4.30it/s] \n", - "Performing parallel task execution on 8 processes.\n", - "100%|██████████| 20/20 [00:00<00:00, 170.82it/s]\n", - "Performing parallel task execution on 8 processes.\n", - "100%|██████████| 20/20 [00:00<00:00, 202.26it/s]\n" - ] + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "# run optimization of problem 1\n", "result1 = optimize.minimize(\n", @@ -463,63 +417,30 @@ "result4 = optimize.minimize(\n", " problem=problem4, optimizer=optimizer, n_starts=n_starts, engine=engine\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "As a first step, let us take a look at the different result summaries:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "As a first step, let us take a look at the different result summaries:" + ] }, { "cell_type": "code", - "execution_count": 27, - "outputs": [ - { - "data": { - "text/plain": "", - "text/markdown": "# Result 1\n## Optimization Result \n\n* number of starts: 20 \n* best value: 7.453730008169132e-11, id=3\n* worst value: 3.986623815839845, id=4\n* number of non-finite values: 0\n\n* execution time summary:\n\t* Mean execution time: 0.039s\n\t* Maximum execution time: 0.049s,\tid=11\n\t* Minimum execution time: 0.025s,\tid=19\n* summary of optimizer messages:\n\n | Count | Message |\n |--------:|:------------------------------------------------|\n | 20 | CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH |\n\n* best value found (approximately) 15 time(s)\n* number of plateaus found: 2\n" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "", - "text/markdown": "# Result 2\n## Optimization Result \n\n* number of starts: 20 \n* best value: 7.453730008169132e-11, id=3\n* worst value: 3.986623815839845, id=4\n* number of non-finite values: 0\n\n* execution time summary:\n\t* Mean execution time: 0.048s\n\t* Maximum execution time: 0.068s,\tid=7\n\t* Minimum execution time: 0.036s,\tid=19\n* summary of optimizer messages:\n\n | Count | Message |\n |--------:|:------------------------------------------------|\n | 20 | CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH |\n\n* best value found (approximately) 15 time(s)\n* number of plateaus found: 2\n" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "", - "text/markdown": "# Result 3\n## Optimization Result \n\n* number of starts: 20 \n* best value: 7.453730008169132e-11, id=3\n* worst value: 3.986623815839845, id=4\n* number of non-finite values: 0\n\n* execution time summary:\n\t* Mean execution time: 0.039s\n\t* Maximum execution time: 0.050s,\tid=7\n\t* Minimum execution time: 0.022s,\tid=5\n* summary of optimizer messages:\n\n | Count | Message |\n |--------:|:------------------------------------------------|\n | 20 | CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH |\n\n* best value found (approximately) 15 time(s)\n* number of plateaus found: 2\n" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": "", - "text/markdown": "# Result 4\n## Optimization Result \n\n* number of starts: 20 \n* best value: 0.10824257960966652, id=9\n* worst value: 9.838560189421912, id=11\n* number of non-finite values: 0\n\n* execution time summary:\n\t* Mean execution time: 0.162s\n\t* Maximum execution time: 0.183s,\tid=0\n\t* Minimum execution time: 0.100s,\tid=19\n* summary of optimizer messages:\n\n | Count | Message |\n |--------:|:-----------------------------------------------------|\n | 20 | STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT |\n\n* best value found (approximately) 1 time(s)\n* number of plateaus found: 4\n" - }, - "metadata": {}, - "output_type": "display_data" + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "display(\n", " Markdown(\"# Result 1\\n\" + result1.optimize_result.summary(disp_best=False))\n", @@ -533,165 +454,106 @@ "display(\n", " Markdown(\"# Result 4\\n\" + result4.optimize_result.summary(disp_best=False))\n", ")" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "Here we can see already a big difference between the first three and the fourth. The one without gradients took approximately five times as long to finish the optimization as the other three. The best value found ist also not the same as for the others. But the biggest difference is probably the fact that while the first three all converged in all their starts, the one without gradient reach the maximum number of evaluations in most to all cases. Keep in mind: **All starts were the same for all problems**.\n", "\n", "A small detail here:\n", "When comparing the first three results, one may notice two things. For one, leaving out the hessian seems not make any difference. And additionally, while they are rather close in speed compared to the fourth one, the second one sticks out to be somewhat slower.\n", "This is mainly due to the fact that we chose the scipy optimizer \"l-bfgs-b\", which does not support the usage of a hessian, for the sake of comparing all four of them. This also explains why the second one is slower, as (by construction), whether needed or not, problem2 will evaluate the hessian." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "### Visualization" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Visualization" + ] }, { "cell_type": "code", - "execution_count": 28, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "# waterfalls\n", "visualize.waterfall(result1)\n", "visualize.waterfall(result2)\n", "visualize.waterfall(result3)\n", "visualize.waterfall(result4);" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "We can see here already the stark difference between the problem definitions. In order to compare things easier, we can also plot all results in one waterfall plot:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "We can see here already the stark difference between the problem definitions. In order to compare things easier, we can also plot all results in one waterfall plot:" + ] }, { "cell_type": "code", - "execution_count": 29, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "# plot one list of waterfalls\n", "visualize.waterfall(\n", " [result1, result2, result3, result4],\n", " legends=[\"Problem 1\", \"Problem 2\", \"Problem 3\", \"Problem 4\"],\n", ");" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "We can also take a look at the parameters, that each optimizer found:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "We can also take a look at the parameters, that each optimizer found:" + ] }, { "cell_type": "code", - "execution_count": 30, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "# plot parameters\n", "ax = visualize.parameters(\n", @@ -703,43 +565,43 @@ " legends=[\"Problem 4\", \"Problem 1\", \"Problem 2\", \"Problem 3\"],\n", ")\n", "ax2.set_title(\"Estimated parameters all problems\");" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } + }, + "source": [ + "## 3. Profiling" + ] }, { "cell_type": "markdown", - "source": [ - "## 3. Profiling" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "We want to create profiles for our parameters to know more about their uncertainties. We shall create profiles for problem 1 and 4. For problem 1 specifically, we create two profiles, as we have seen, that there are two distinct optima, so we want to start our profile likelihood from both optima.\n", "\n", "Note that when running profiles, the cost function is interpreted as a negative log likelihood function. This has some implications on the stopping criteria of the algorithm and on the confidence intervals. Therefore a meaningful statistical interpretation of the results is only possible if the cost function is a negative log likelihood function or a negative log posterior function. The same holds for sampling." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 31, + }, "outputs": [], "source": [ "# we create references for each \"best point\":\n", @@ -766,28 +628,18 @@ " \"legend\": \"First optimum problem 4\",\n", "}\n", "ref4 = visualize.create_references(ref4)[0];" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 32, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:02<00:00, 1.38it/s]\n", - "100%|██████████| 4/4 [00:02<00:00, 1.79it/s]\n", - "100%|██████████| 4/4 [00:10<00:00, 2.65s/it]\n" - ] - } - ], + }, + "outputs": [], "source": [ "# compute profiles\n", "profile_options = profile.ProfileOptions(whole_path=True)\n", @@ -821,27 +673,18 @@ " profile_options=profile_options,\n", " filename=None,\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 33, - "outputs": [ - { - "data": { - "text/plain": "
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- }, - "metadata": {}, - "output_type": "display_data" - } - ], + }, + "outputs": [], "source": [ "# specify the parameters, for which profiles should be computed\n", "visualize.profiles(\n", @@ -850,89 +693,72 @@ " reference=[ref, ref2],\n", " profile_list_ids=[0, 1],\n", ");" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 34, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], + }, + "outputs": [], "source": [ "visualize.profiles(\n", " result4,\n", " profile_indices=[0, 1, 3, 5],\n", " reference=[ref4],\n", ");" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "As we can see here, the second optimum disagrees only in the very first value. Here it becomes apparent that it is only a local optimum, as the profile for the second optimum is very flat. Comparing the profiles of proplem 1 and 4, we can see, that while the convergence of problem4 was quite bad, the profiles look really similar." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "As we can see here, the second optimum disagrees only in the very first value. Here it becomes apparent that it is only a local optimum, as the profile for the second optimum is very flat. Comparing the profiles of proplem 1 and 4, we can see, that while the convergence of problem4 was quite bad, the profiles look really similar." + ] }, { "cell_type": "markdown", - "source": [ - "### Profile approximation" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Profile approximation" + ] }, { "cell_type": "markdown", - "source": [ - "When computing the profiles is computationally too demanding, it is possible to employ to at least consider a normal approximation with covariance matrix given by the Hessian or FIM at the optimal parameters." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "When computing the profiles is computationally too demanding, it is possible to employ to at least consider a normal approximation with covariance matrix given by the Hessian or FIM at the optimal parameters." + ] }, { "cell_type": "code", - "execution_count": 35, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Computing Hessian/FIM as not available in result.\n" - ] + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "result1 = profile.approximate_parameter_profile(\n", " problem=problem1,\n", @@ -941,39 +767,30 @@ " result_index=0,\n", " n_steps=1000,\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "These approximate profiles require at most one additional function evaluation, can however yield substantial approximation errors:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "These approximate profiles require at most one additional function evaluation, can however yield substantial approximation errors:" + ] }, { "cell_type": "code", - "execution_count": 36, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "axes = visualize.profiles(\n", " result1,\n", @@ -986,38 +803,23 @@ " \"Local profile approximation\",\n", " ],\n", ");" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 37, - "outputs": [ - { - "data": { - "text/plain": "
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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + }, + "outputs": [], "source": [ "visualize.profile_cis(\n", " result1, confidence_level=0.95, profile_list=2, show_bounds=True\n", ");" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] } ], "metadata": { diff --git a/doc/example/fixed_parameters.ipynb b/doc/example/fixed_parameters.ipynb index e2b9fb269..a88f945d1 100644 --- a/doc/example/fixed_parameters.ipynb +++ b/doc/example/fixed_parameters.ipynb @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +89,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -113,68 +113,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", 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", 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93.989975e+00[-0.994974648260114, 1.0, 0.9999999725753793, ...[7.78676721049365e-05, nan, -2.747946901432181...
\n", - "
" - ], - "text/plain": [ - " fval x \\\n", - "0 4.679771e-17 [0.9999999998641961, 1.0, 1.0000000002266116, ... \n", - "1 4.825331e-16 [0.9999999995848748, 1.0, 0.9999999991941183, ... \n", - "2 1.394704e-14 [1.0000000026325193, 1.0, 0.9999999987812758, ... \n", - "3 3.989975e+00 [-0.9949747468838975, 1.0, 0.9999999999585671,... \n", - "4 3.989975e+00 [-0.9949747461383964, 1.0, 0.9999999963588824,... \n", - "5 3.989975e+00 [-0.9949747436177196, 1.0, 0.9999999894437084,... \n", - "6 3.989975e+00 [-0.9949747458936441, 1.0, 0.99999997533737, 1... \n", - "7 3.989975e+00 [-0.9949747793023977, 1.0, 1.000000023888003, ... \n", - "8 3.989975e+00 [-0.9949748033666262, 1.0, 1.0000000080319777,... \n", - "9 3.989975e+00 [-0.994974648260114, 1.0, 0.9999999725753793, ... \n", - "\n", - " grad \n", - "0 [-1.0891474676223493e-07, nan, 2.2706484163692... \n", - "1 [-3.329303753527845e-07, nan, -8.0749345757971... \n", - "2 [2.1112804950914665e-06, nan, -1.2211616799204... \n", - "3 [-4.2116658605095836e-08, nan, -4.151572285811... \n", - "4 [5.468066182068299e-07, nan, -3.64839985427999... \n", - "5 [2.5380648831507813e-06, nan, -1.0577404068293... \n", - "6 [7.40153570877311e-07, nan, -2.471195460075688... \n", - "7 [-2.5651750697797127e-05, nan, 2.3935779637870... \n", - "8 [-4.466176453288284e-05, nan, 8.0480417566767e... \n", - "9 [7.78676721049365e-05, nan, -2.747946901432181... " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result2.optimize_result.as_dataframe([\"fval\", \"x\", \"grad\"])" ] diff --git a/doc/example/getting_started.ipynb b/doc/example/getting_started.ipynb index dd17ce3b6..9d1dcd6c9 100644 --- a/doc/example/getting_started.ipynb +++ b/doc/example/getting_started.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -64,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -128,21 +128,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████| 10/10 [00:00<00:00, 496.47it/s]\n" - ] - } - ], + "outputs": [], "source": [ "# choose optimizer\n", "optimizer = optimize.ScipyOptimizer()\n", @@ -166,42 +158,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': '0',\n", - " 'x': array([0., 0.]),\n", - " 'fval': 0.0,\n", - " 'grad': array([0., 0.]),\n", - " 'hess': None,\n", - " 'res': None,\n", - " 'sres': None,\n", - " 'n_fval': 4,\n", - " 'n_grad': 4,\n", - " 'n_hess': 0,\n", - " 'n_res': 0,\n", - " 'n_sres': 0,\n", - " 'x0': array([0.6901338 , 7.17776654]),\n", - " 'fval0': 51.996617134691896,\n", - " 'history': ,\n", - " 'exitflag': 0,\n", - " 'time': 0.007859945297241211,\n", - " 'message': 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL',\n", - " 'optimizer': \"\"}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# E.g. The best model fit was obtained by the following optimization run:\n", "result_custom_problem.optimize_result.list[0]" @@ -209,33 +172,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 7.523163845262637e-37,\n", - " 1.504632769052528e-36,\n", - " 1.5046327690525307e-36,\n", - " 3.0562853121379475e-36,\n", - " 3.1973446342366133e-36]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Objective function values of the different optimizer runs:\n", "result_custom_problem.optimize_result.get_for_key('fval')" @@ -279,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -308,34 +251,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-17 16:01:22.984 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 158.494 and h = 1.34133e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:22.986 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 158.494: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:23.022 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 158.494 and h = 1.34133e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:23.023 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 158.494: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:23.045 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 158.494 and h = 1.34133e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:23.046 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 158.494: AMICI failed to integrate the forward problem\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15 s, sys: 476 ms, total: 15.5 s\n", - "Wall time: 15.4 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "%%capture\n", @@ -362,48 +284,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': '3',\n", - " 'x': array([-1.56904695, -5. , -2.20985861, -1.78591646, 5. ,\n", - " 4.19769784, 0.693 , 0.58567634, 0.81886181, 0.49859024,\n", - " 0.107 ]),\n", - " 'fval': 138.2219738778035,\n", - " 'grad': array([ 1.02808139e-02, 5.53588489e-02, 2.03115656e-04, 1.00993749e-02,\n", - " -4.41596715e-05, -9.26213990e-03, nan, 2.11437217e-03,\n", - " 2.50708689e-03, -3.97887714e-04, nan]),\n", - " 'hess': None,\n", - " 'res': None,\n", - " 'sres': None,\n", - " 'n_fval': 191,\n", - " 'n_grad': 191,\n", - " 'n_hess': 0,\n", - " 'n_res': 0,\n", - " 'n_sres': 0,\n", - " 'x0': array([ 2.1608791 , -2.88363288, -4.61132422, 1.82016484, -1.49529658,\n", - " -1.08670354, 0.693 , -3.8208997 , 2.23962013, 0.32443793,\n", - " 0.107 ]),\n", - " 'fval0': 1950794339021.3562,\n", - " 'history': ,\n", - " 'exitflag': 0,\n", - " 'time': 2.9797539710998535,\n", - " 'message': 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH',\n", - " 'optimizer': \"\"}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# E.g. best model fit was obtained by the following optimization run:\n", "result.optimize_result.list[0]" @@ -411,33 +298,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "[138.2219738778035,\n", - " 149.58788363739558,\n", - " 149.58968254433313,\n", - " 150.06701061933796,\n", - " 158.809924925096,\n", - " 171.13435267913164,\n", - " 233.09982603500364,\n", - " 249.74596437461332,\n", - " 249.74599739701642,\n", - " 249.74599747333608]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Objective function values of the different optimizer runs:\n", "result.optimize_result.get_for_key('fval')" @@ -475,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -505,8287 +372,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", - "Performing parallel task execution on 8 processes.\n", - "2023-02-17 16:01:41.379 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 98.8572 and h = 1.56563e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:41.379 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 98.8572: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:41.408 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 98.8572 and h = 1.56563e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:41.408 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 98.8572: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:42.689 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 191.945 and h = 2.71313e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:42.690 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 191.945: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:42.903 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 191.945 and h = 2.71313e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:42.904 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 191.945: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:43.010 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 191.945 and h = 2.71313e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:43.010 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 191.945: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:43.595 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 51.0716 and h = 3.63082e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:43.595 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 51.0716: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:43.628 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 51.0716 and h = 3.63082e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:43.628 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 51.0716: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:50.278 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:TOO_MUCH_WORK] AMICI ERROR: in module CVODES in function CVode : At t = 76.964, mxstep steps taken before reaching tout. \n", - "2023-02-17 16:01:50.278 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 76.964: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:50.564 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 140.591 and h = 1.97937e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:50.565 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 140.591: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:51.675 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 167.379 and h = 7.66589e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:51.676 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 167.379: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:51.735 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 167.379 and h = 7.66589e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:51.736 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 167.379: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:55.008 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 188.834 and h = 3.15716e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:55.009 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 188.834: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:00.240 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.6972 and h = 1.45985e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:00.240 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.6972: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:00.294 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.6972 and h = 1.45985e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:00.294 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.6972: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:07.377 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 169.229 and h = 1.90001e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:07.378 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 169.229: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:07.511 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 169.229 and h = 1.90001e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:07.512 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 169.229: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:08.616 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 92.2863 and h = 1.84593e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:08.616 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 92.2863: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:08.718 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 118.037 and h = 2.126e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:08.718 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 118.037: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:08.830 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 118.037 and h = 2.126e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:08.831 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 118.037: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:08.900 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 118.037 and h = 2.126e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:08.900 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 118.037: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:12.500 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 138.745 and h = 1.31331e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:12.500 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 138.745: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:12.732 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 138.745 and h = 1.31331e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:12.732 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 138.745: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:12.830 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 138.745 and h = 1.31331e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:12.830 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 138.745: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:13.480 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.825 and h = 1.87415e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:13.480 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.825: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:13.674 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.825 and h = 1.87415e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:13.674 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.825: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:13.761 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.825 and h = 1.87415e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:13.761 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.825: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:15.776 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 168.782 and h = 3.21943e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:15.776 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 168.782: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:16.578 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 168.806 and h = 2.98398e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:16.578 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 168.806: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:18.407 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 78.788 and h = 1.37128e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:18.407 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 78.788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:18.474 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 78.788 and h = 1.37128e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:18.474 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 78.788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:18.529 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 78.788 and h = 1.37128e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:18.530 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 78.788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:20.019 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 150.727 and h = 1.41441e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:20.020 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 150.727: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:20.112 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 150.727 and h = 1.41441e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:20.113 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 150.727: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:22.871 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 66.7204 and h = 1.90309e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:22.871 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 66.7204: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:27.514 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 13.8788 and h = 2.73008e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:27.514 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 13.8788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:27.582 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 13.8788 and h = 2.73008e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:27.583 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 13.8788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:27.608 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 13.8788 and h = 2.73008e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:27.608 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 13.8788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:27.889 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 221.347 and h = 4.88446e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:27.889 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 221.347: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:28.730 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 97.978 and h = 2.09287e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:28.730 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 97.978: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:28.807 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 97.978 and h = 2.09287e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:28.807 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 97.978: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:28.886 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 97.978 and h = 2.09287e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:28.886 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 97.978: AMICI failed to integrate the forward problem\n", - "Performing parallel task execution on 8 processes.\n", - "Performing parallel task execution on 8 processes.\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +2.27E+04 | NaN | NaN | 1.0E+00 | 9.5E+04 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +9.53E+12 | NaN | NaN | 1.0E+00 | 4.4E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +7.39E+13 | NaN | NaN | 1.0E+00 | 3.4E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +3.54E+08 | NaN | NaN | 1.0E+00 | 1.6E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +4.33E+02 | NaN | NaN | 1.0E+00 | 6.4E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +3.21E+11 | NaN | NaN | 1.0E+00 | 1.4E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +4.32E+02 | NaN | NaN | 1.0E+00 | 6.4E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +8.98E+11 | NaN | NaN | 1.0E+00 | 4.1E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +9.31E+02 | -2.2E+04 | +1.3E-01 | 1.0E+00 | 9.5E+04 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +9.41E+06 | -9.5E+12 | +8.5E-02 | 1.0E+00 | 4.4E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +4.70E+04 | -3.5E+08 | +1.0E-01 | 1.0E+00 | 1.6E+09 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +3.88E+02 | -5.4E+02 | +9.0E-01 | 2.5E-01 | 3.0E+03 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +3.05E+02 | -1.3E+02 | +5.0E-01 | 1.0E+00 | 6.4E+01 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +1.79E+06 | -7.6E+06 | +8.9E-01 | 2.5E-01 | 3.6E+07 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +3.04E+02 | -8.4E+01 | +8.9E-01 | 5.0E-01 | 4.8E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +2.34E+11 | -7.4E+13 | +8.4E-02 | 1.0E+00 | 3.4E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +8.13E+03 | -3.9E+04 | +9.0E-01 | 2.5E-01 | 2.1E+05 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +7.96E+09 | -3.1E+11 | +9.3E-02 | 1.0E+00 | 1.4E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +3.05E+02 | -1.3E+02 | +5.4E-01 | 1.0E+00 | 6.4E+01 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +2.78E+05 | -1.5E+06 | +9.0E-01 | 5.0E-01 | 6.2E+06 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +2.73E+02 | -3.2E+01 | +5.6E-01 | 1.0E+00 | 7.5E+01 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +3.48E+10 | -2.0E+11 | +8.9E-01 | 2.5E-01 | 1.1E+12 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +1.52E+03 | -6.6E+03 | +9.0E-01 | 5.0E-01 | 3.3E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +4.37E+04 | -2.3E+05 | +9.0E-01 | 1.0E+00 | 9.6E+05 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +2.58E+02 | -4.7E+01 | +8.8E-01 | 1.0E+00 | 2.3E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 5| +2.51E+02 | -2.2E+01 | +7.0E-01 | 1.0E+00 | 9.4E+01 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +1.20E+09 | -6.8E+09 | +8.9E-01 | 2.5E-01 | 3.7E+10 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +2.58E+08 | -9.0E+11 | +8.9E-02 | 1.0E+00 | 4.1E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +2.68E+02 | -3.7E+01 | +8.8E-01 | 1.0E+00 | 1.9E+02 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 6| +2.51E+02 | +3.1E+00 | -1.3E+00 | 1.0E+00 | 1.7E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 3| +5.19E+09 | -3.0E+10 | +8.9E-01 | 5.0E-01 | 1.6E+11 | 7.6E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +4.18E+02 | -1.1E+03 | +9.0E-01 | 1.0E+00 | 5.3E+03 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 5| +7.03E+03 | -3.7E+04 | +9.0E-01 | 1.0E+00 | 1.5E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 7| +2.51E+02 | +3.2E+00 | -1.3E+00 | 2.5E-01 | 1.7E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 3| +2.58E+02 | +1.6E+02 | -1.0E+01 | 2.0E+00 | 3.8E+01 | 4.1E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 6| +1.28E+03 | -5.8E+03 | +9.0E-01 | 1.0E+00 | 2.4E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 8| +2.50E+02 | -1.1E+00 | +5.2E-01 | 6.2E-02 | 1.7E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 5| +2.63E+02 | -1.5E+02 | +8.8E-01 | 1.0E+00 | 8.2E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +7.78E+08 | -4.4E+09 | +8.9E-01 | 5.0E-01 | 2.4E+10 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +1.81E+08 | -1.0E+09 | +8.9E-01 | 5.0E-01 | 5.5E+09 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +2.68E+02 | +2.9E+06 | -2.2E+05 | 2.0E+00 | 3.7E+01 | 5.2E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 5| +1.17E+08 | -6.6E+08 | +8.9E-01 | 5.0E-01 | 3.6E+09 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 9| +2.50E+02 | -4.6E-02 | +2.0E-01 | 6.2E-02 | 6.5E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 7| +3.80E+02 | -9.0E+02 | +9.0E-01 | 1.0E+00 | 3.8E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +3.93E+07 | -2.2E+08 | +8.9E-01 | 2.5E-01 | 1.2E+09 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 6| +2.32E+02 | -3.1E+01 | +1.4E+00 | 2.0E+00 | 1.1E+02 | 4.0E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +2.58E+02 | +1.6E+02 | -9.3E+00 | 5.0E-01 | 3.8E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 6| +1.77E+07 | -9.9E+07 | +8.9E-01 | 5.0E-01 | 5.4E+08 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 10| +2.50E+02 | -5.6E-02 | +4.5E-01 | 1.6E-02 | 4.6E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +2.76E+07 | -1.5E+08 | +8.9E-01 | 1.0E+00 | 8.3E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 8| +2.56E+02 | -1.2E+02 | +8.7E-01 | 1.0E+00 | 5.9E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 11| +2.50E+02 | -2.1E-03 | +8.3E-02 | 1.6E-02 | 2.1E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +2.68E+02 | +8.2E+01 | -3.8E+00 | 5.0E-01 | 3.7E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 5| +2.50E+02 | -8.2E+00 | +7.7E-01 | 1.2E-01 | 3.8E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 7| +2.32E+02 | +2.8E+03 | -8.1E+01 | 4.0E+00 | 4.5E+01 | 1.1E+01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 7| +2.70E+06 | -1.5E+07 | +8.9E-01 | 5.0E-01 | 8.2E+07 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 12| +2.50E+02 | -9.9E-03 | +7.0E-01 | 3.9E-03 | 1.8E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 9| +2.32E+02 | -2.4E+01 | +1.6E+00 | 2.0E+00 | 7.0E+01 | 3.8E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 5| +4.23E+06 | -2.3E+07 | +8.9E-01 | 1.0E+00 | 1.3E+08 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 8| +4.12E+05 | -2.3E+06 | +8.9E-01 | 5.0E-01 | 1.2E+07 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 13| +2.50E+02 | -3.6E-05 | +2.6E-01 | 3.9E-03 | 1.3E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 8| +2.32E+02 | +2.5E+02 | -9.6E+00 | 1.0E+00 | 4.5E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 5| +2.57E+02 | -1.1E+01 | +9.5E-01 | 1.2E-01 | 3.7E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 14| +2.50E+02 | -3.8E-05 | +2.8E-01 | 3.9E-03 | 1.3E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 6| +2.50E+02 | +3.5E-01 | -5.0E-01 | 2.5E-01 | 1.0E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 9| +6.25E+04 | -3.5E+05 | +9.0E-01 | 5.0E-01 | 1.9E+06 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +5.97E+06 | -3.3E+07 | +8.9E-01 | 5.0E-01 | 1.8E+08 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 15| +2.50E+02 | -3.7E-05 | +2.8E-01 | 3.9E-03 | 1.3E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 10| +2.32E+02 | +1.0E+03 | -3.9E+01 | 4.0E+00 | 4.2E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 9| +2.22E+02 | -9.9E+00 | +4.0E-01 | 2.5E-01 | 4.5E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 6| +6.50E+05 | -3.6E+06 | +9.0E-01 | 1.0E+00 | 1.9E+07 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 6| +2.57E+02 | +3.5E+00 | -4.3E-01 | 2.5E-01 | 3.0E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 7| +2.50E+02 | +3.5E-01 | -5.0E-01 | 6.2E-02 | 1.0E+01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 10| +9.71E+03 | -5.3E+04 | +9.0E-01 | 5.0E-01 | 2.9E+05 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 16| +2.50E+02 | -3.9E-05 | +2.9E-01 | 3.9E-03 | 1.3E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 7| +9.99E+04 | -5.5E+05 | +9.0E-01 | 1.0E+00 | 3.0E+06 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +2.50E+02 | -3.8E-05 | +2.9E-01 | 3.9E-03 | 1.3E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 10| +2.18E+02 | -4.7E+00 | +6.3E-01 | 2.5E-01 | 4.8E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.32E+02 | +1.1E+02 | -5.5E+00 | 1.0E+00 | 4.2E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 11| +1.61E+03 | -8.1E+03 | +9.1E-01 | 1.0E+00 | 4.4E+04 | 5.1E-01 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) 8| +2.50E+02 | -2.9E-01 | +8.4E-01 | 1.6E-02 | 1.0E+01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 7| +2.53E+02 | -4.3E+00 | +9.3E-01 | 6.2E-02 | 3.0E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +2.50E+02 | -4.0E-05 | +3.0E-01 | 3.9E-03 | 1.3E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +2.28E+02 | -4.3E+00 | +1.7E-01 | 2.5E-01 | 4.2E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +2.50E+02 | -3.9E-05 | +3.0E-01 | 3.9E-03 | 1.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +4.30E+02 | -1.2E+03 | +9.0E-01 | 1.0E+00 | 6.6E+03 | 2.7E+00 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.17E+02 | -6.0E-01 | +6.6E-02 | 2.5E-01 | 2.1E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.23E+02 | -5.2E+00 | +8.6E-01 | 6.2E-02 | 6.5E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 8| +2.51E+02 | -1.4E+00 | +2.2E-01 | 1.2E-01 | 2.3E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 9| +2.50E+02 | +2.2E-02 | -1.7E-01 | 3.1E-02 | 4.7E+00 | 7.6E-02 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +2.50E+02 | -4.1E-05 | +3.1E-01 | 3.9E-03 | 1.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 4| +9.12E+05 | -5.1E+06 | +8.9E-01 | 5.0E-01 | 2.7E+07 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +2.50E+02 | -4.0E-05 | +3.1E-01 | 3.9E-03 | 1.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.35E+02 | -1.9E+02 | +9.3E-01 | 1.0E+00 | 1.0E+03 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 14| +2.20E+02 | -2.3E+00 | +5.9E-01 | 1.2E-01 | 3.3E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 8| +1.53E+04 | -8.5E+04 | +9.0E-01 | 1.0E+00 | 4.6E+05 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +2.14E+02 | -3.4E+00 | +8.3E-01 | 6.2E-02 | 4.6E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +2.50E+02 | -4.2E-05 | +3.3E-01 | 3.9E-03 | 1.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 10| +2.50E+02 | -6.0E-02 | +8.0E-01 | 7.8E-03 | 4.7E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 9| +2.50E+02 | -1.0E+00 | +8.3E-01 | 3.1E-02 | 2.5E+01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +2.20E+02 | +3.1E-01 | -1.7E-01 | 1.2E-01 | 1.9E+01 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 23| +2.50E+02 | -4.1E-05 | +3.3E-01 | 3.9E-03 | 1.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.12E+02 | -2.0E+00 | +7.7E-01 | 1.2E-01 | 2.1E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.50E+02 | +4.3E-04 | -2.9E-02 | 1.6E-02 | 1.7E+00 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 24| +2.50E+02 | -4.3E-05 | +3.4E-01 | 3.9E-03 | 1.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 10| +2.50E+02 | -1.5E-01 | +3.2E-01 | 6.2E-02 | 9.9E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 14| +2.14E+02 | -2.1E+01 | +7.7E-01 | 2.0E+00 | 1.4E+02 | 5.9E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 16| +2.19E+02 | -9.2E-01 | +8.5E-01 | 3.1E-02 | 1.9E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +2.50E+02 | -4.2E-05 | +3.4E-01 | 3.9E-03 | 1.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 14| +2.09E+02 | -2.5E+00 | +6.5E-01 | 2.5E-01 | 1.6E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 5| +1.41E+05 | -7.7E+05 | +9.0E-01 | 5.0E-01 | 4.2E+06 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +2.50E+02 | -8.0E-03 | +6.8E-01 | 3.9E-03 | 1.7E+00 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +2.50E+02 | -4.4E-05 | +3.5E-01 | 3.9E-03 | 1.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 9| +2.47E+03 | -1.3E+04 | +9.1E-01 | 1.0E+00 | 7.0E+04 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +2.18E+02 | -1.1E+00 | +9.1E-01 | 6.2E-02 | 1.5E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.50E+02 | +2.9E-02 | -1.4E-01 | 6.2E-02 | 5.7E+00 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 27| +2.50E+02 | -4.3E-05 | +3.5E-01 | 3.9E-03 | 1.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +2.14E+02 | +1.2E+04 | -3.9E+02 | 4.0E+00 | 5.8E+01 | 9.7E+00 | trr |0\n", - "2023-02-17 16:05:06 fides(INFO) 15| +2.02E+02 | -7.4E+00 | +1.0E+00 | 2.5E-01 | 2.9E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 28| +2.50E+02 | -4.5E-05 | +3.7E-01 | 3.9E-03 | 1.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +2.17E+02 | -1.3E+00 | +7.6E-01 | 1.2E-01 | 1.7E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 29| +2.50E+02 | -4.5E-05 | +3.7E-01 | 3.9E-03 | 1.1E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.50E+02 | -1.0E-04 | +1.0E-01 | 3.9E-03 | 4.0E-01 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +2.50E+02 | -1.1E-01 | +6.3E-01 | 1.6E-02 | 5.7E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 30| +2.50E+02 | -4.6E-05 | +3.8E-01 | 3.9E-03 | 1.1E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +2.17E+02 | +3.8E-01 | -2.1E-01 | 2.5E-01 | 1.7E+01 | 7.0E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 6| +2.19E+04 | -1.2E+05 | +9.0E-01 | 5.0E-01 | 6.4E+05 | 9.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 16| +1.88E+02 | -1.3E+01 | +1.8E-01 | 5.0E-01 | 2.3E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 16| +2.14E+02 | +3.6E+01 | -2.8E+00 | 9.1E-01 | 5.8E+01 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 14| +2.50E+02 | -4.0E-04 | +5.9E-01 | 9.8E-04 | 3.7E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 31| +2.50E+02 | -4.6E-05 | +3.8E-01 | 3.9E-03 | 1.1E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.50E+02 | -5.0E-03 | +7.3E-01 | 1.6E-02 | 5.3E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +2.16E+02 | -9.1E-01 | +5.2E-01 | 6.2E-02 | 1.7E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 10| +5.65E+02 | -1.9E+03 | +9.0E-01 | 1.0E+00 | 1.1E+04 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 32| +2.50E+02 | -4.8E-05 | +3.9E-01 | 3.9E-03 | 1.1E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +1.62E+02 | -2.6E+01 | +9.5E-01 | 1.2E-01 | 2.4E+02 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +2.15E+02 | -7.3E-01 | +7.1E-01 | 6.2E-02 | 1.6E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +2.50E+02 | -1.8E-05 | +4.9E-01 | 9.8E-04 | 6.0E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 33| +2.50E+02 | -8.5E-05 | +1.0E+00 | 3.9E-03 | 1.1E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 14| +2.50E+02 | -4.5E-03 | +6.8E-01 | 1.6E-02 | 5.8E-01 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 34| +2.50E+02 | +3.0E-03 | -1.3E+01 | 7.8E-03 | 3.4E-02 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 7| +3.57E+03 | -1.8E+04 | +9.0E-01 | 5.0E-01 | 9.9E+04 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +2.15E+02 | -2.4E-01 | +2.6E-01 | 6.2E-02 | 1.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 35| +2.50E+02 | +1.1E-03 | -1.0E+01 | 2.0E-03 | 3.4E-02 | 5.0E-03 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 16| +2.50E+02 | -1.7E-05 | +5.1E-01 | 9.8E-04 | 5.5E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +1.51E+02 | -1.2E+01 | +7.1E-01 | 2.5E-01 | 5.4E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 36| +2.50E+02 | +7.4E-05 | -2.0E+00 | 4.9E-04 | 3.4E-02 | 1.2E-03 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.58E+02 | -3.1E+02 | +9.3E-01 | 2.0E+00 | 1.7E+03 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +2.02E+02 | -1.2E+01 | +9.9E-01 | 2.3E-01 | 5.8E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +2.50E+02 | -4.3E-03 | +6.5E-01 | 1.6E-02 | 6.2E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 8| +7.19E+02 | -2.9E+03 | +8.9E-01 | 1.0E+00 | 1.5E+04 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 23| +2.14E+02 | -7.2E-01 | +6.0E-01 | 6.2E-02 | 1.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 37| +2.50E+02 | -3.1E-06 | +3.0E-01 | 1.2E-04 | 3.4E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +2.50E+02 | -1.8E-05 | +5.4E-01 | 9.8E-04 | 5.4E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +2.04E+02 | -5.3E+01 | +9.2E-01 | 2.0E+00 | 2.4E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +1.49E+02 | -1.5E+00 | +8.9E-01 | 2.5E-01 | 2.9E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 38| +2.50E+02 | -2.7E-06 | +1.0E+00 | 1.2E-04 | 1.9E-02 | 3.0E-04 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 24| +2.14E+02 | -1.0E-01 | +1.3E-01 | 6.2E-02 | 1.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 39| +2.50E+02 | -5.2E-06 | +1.0E+00 | 2.4E-04 | 8.2E-03 | 6.3E-04 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 16| +2.50E+02 | -3.8E-03 | +6.2E-01 | 1.6E-02 | 6.2E-01 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +2.50E+02 | -1.7E-05 | +5.6E-01 | 9.8E-04 | 5.0E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +2.14E+02 | -3.8E-01 | +9.0E-01 | 1.6E-02 | 1.8E+01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 9| +3.47E+02 | -3.7E+02 | +6.6E-01 | 2.0E+00 | 2.4E+03 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 40| +2.50E+02 | -9.1E-06 | +1.0E+00 | 4.9E-04 | 8.8E-03 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +1.48E+02 | -1.0E+00 | +6.6E-01 | 5.0E-01 | 3.8E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 41| +2.50E+02 | -1.9E-05 | +1.0E+00 | 9.8E-04 | 8.3E-03 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +1.89E+02 | -1.2E+01 | +7.6E-02 | 4.6E-01 | 3.2E+01 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +2.50E+02 | -3.6E-03 | +6.1E-01 | 1.6E-02 | 6.4E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.04E+02 | +7.8E+03 | -4.7E+02 | 2.0E+00 | 5.1E+01 | 4.2E+00 | trr |0\n", - "2023-02-17 16:05:06 fides(INFO) 19| +2.50E+02 | -1.8E-05 | +5.8E-01 | 9.8E-04 | 4.9E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 42| +2.50E+02 | -3.8E-05 | +1.0E+00 | 2.0E-03 | 1.1E-02 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +2.14E+02 | -3.5E-01 | +7.6E-01 | 3.1E-02 | 1.3E+01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 43| +2.50E+02 | -7.6E-05 | +1.0E+00 | 3.9E-03 | 1.0E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +1.48E+02 | +3.6E+01 | -1.4E+01 | 5.0E-01 | 2.5E+01 | 7.9E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 14| +1.72E+02 | -3.2E+01 | +8.5E-01 | 5.0E-01 | 5.1E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 10| +2.01E+02 | -1.5E+02 | +9.0E-01 | 2.0E+00 | 5.2E+02 | 1.8E+00 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +2.50E+02 | -3.2E-03 | +5.8E-01 | 1.6E-02 | 6.2E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +2.50E+02 | -1.7E-05 | +5.9E-01 | 9.8E-04 | 4.7E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 44| +2.50E+02 | -1.6E-04 | +1.0E+00 | 7.8E-03 | 1.4E-02 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 27| +2.13E+02 | -3.5E-01 | +6.8E-01 | 6.2E-02 | 8.4E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 45| +2.50E+02 | -3.3E-04 | +1.0E+00 | 1.6E-02 | 2.7E-02 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +1.72E+02 | +4.5E+03 | -2.0E+02 | 1.0E+00 | 1.3E+02 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 28| +2.13E+02 | -2.6E-01 | +4.4E-01 | 6.2E-02 | 1.3E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +1.48E+02 | +4.0E-01 | -4.6E-01 | 1.2E-01 | 2.5E+01 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 46| +2.50E+02 | -7.3E-04 | +1.1E+00 | 3.1E-02 | 5.6E-02 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +2.50E+02 | -1.8E-05 | +6.2E-01 | 9.8E-04 | 4.6E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +2.50E+02 | -3.1E-03 | +5.7E-01 | 1.6E-02 | 6.4E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +1.68E+02 | -2.1E+01 | +9.8E-01 | 1.1E-01 | 2.5E+02 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 47| +2.50E+02 | -1.8E-03 | +1.2E+00 | 6.2E-02 | 1.1E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 16| +1.60E+02 | -1.3E+01 | +5.5E-01 | 2.4E-01 | 1.3E+02 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 29| +2.13E+02 | +1.7E-02 | -5.8E-02 | 6.2E-02 | 8.9E+00 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 23| +1.48E+02 | -2.2E-01 | +7.3E-01 | 3.1E-02 | 2.5E+01 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 48| +2.50E+02 | -6.0E-03 | +1.5E+00 | 1.2E-01 | 2.2E-01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.01E+02 | +7.6E+02 | -3.3E+01 | 2.0E+00 | 1.3E+02 | 2.0E+00 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +2.50E+02 | -2.7E-03 | +5.5E-01 | 1.6E-02 | 6.1E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +2.50E+02 | -1.8E-05 | +6.2E-01 | 9.8E-04 | 4.4E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 49| +2.50E+02 | -3.3E-02 | +2.1E+00 | 2.5E-01 | 4.4E-01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 30| +2.13E+02 | -2.1E-01 | +8.2E-01 | 1.6E-02 | 8.9E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +1.53E+02 | -6.8E+00 | +9.6E-01 | 2.4E-01 | 1.9E+02 | 8.8E-02 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06.633 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 142.235 and h = 2.73909e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:06.633 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 142.235: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:06 fides(INFO) 50| +2.49E+02 | -5.0E-01 | +3.7E+00 | 5.0E-01 | 8.0E-01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 24| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.1E-02 | 1.1E+01 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 23| +2.50E+02 | -1.8E-05 | +6.4E-01 | 9.8E-04 | 4.4E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 31| +2.13E+02 | -1.5E-01 | +7.0E-01 | 3.1E-02 | 6.6E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +2.50E+02 | -2.6E-03 | +5.4E-01 | 1.6E-02 | 6.2E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +1.50E+02 | -3.2E+00 | +7.8E-01 | 2.4E-01 | 5.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +1.89E+02 | -1.2E+01 | +5.5E-01 | 3.2E-01 | 1.3E+02 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 51| +2.37E+02 | -1.2E+01 | +4.4E+00 | 1.0E+00 | 1.3E+00 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 32| +2.13E+02 | -1.4E-01 | +5.8E-01 | 3.1E-02 | 8.4E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +1.48E+02 | -5.5E-02 | +9.5E-01 | 7.8E-03 | 1.1E+01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 24| +2.50E+02 | -1.8E-05 | +6.4E-01 | 9.8E-04 | 4.3E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +1.50E+02 | +5.4E+01 | -1.2E+01 | 4.7E-01 | 6.5E+01 | 7.7E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +1.59E+02 | -8.8E+00 | +8.0E-01 | 2.3E-01 | 4.5E+01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +2.50E+02 | -2.3E-03 | +5.2E-01 | 1.6E-02 | 5.9E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +1.82E+02 | -6.7E+00 | +9.3E-01 | 3.2E-01 | 1.2E+02 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 33| +2.12E+02 | -6.5E-02 | +3.0E-01 | 3.1E-02 | 6.3E+00 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 52| +2.28E+02 | -9.6E+00 | +5.7E-01 | 2.0E+00 | 3.1E+01 | 2.9E+00 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +2.50E+02 | -1.8E-05 | +6.5E-01 | 9.8E-04 | 4.3E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +1.48E+02 | -4.0E-02 | +9.3E-01 | 1.6E-02 | 1.3E+01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +1.49E+02 | -3.2E-01 | +1.4E-01 | 1.2E-01 | 6.5E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 53| +2.28E+02 | +4.7E+03 | -1.9E+02 | 2.0E+00 | 4.1E+01 | 3.2E+00 | trr |0\n", - "2023-02-17 16:05:06 fides(INFO) 34| +2.12E+02 | -1.4E-01 | +5.3E-01 | 3.1E-02 | 8.8E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 14| +1.82E+02 | -8.6E-01 | -1.3E-01 | 6.4E-01 | 3.2E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 23| +2.50E+02 | -2.2E-03 | +5.2E-01 | 1.6E-02 | 6.0E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +2.50E+02 | -1.8E-05 | +6.5E-01 | 9.8E-04 | 4.2E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 27| +1.48E+02 | -4.3E-02 | +9.2E-01 | 3.1E-02 | 7.4E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 35| +2.12E+02 | -4.9E-02 | +2.7E-01 | 3.1E-02 | 5.7E+00 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +1.49E+02 | -5.4E-01 | +9.3E-01 | 2.9E-02 | 3.9E+01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 27| +2.50E+02 | -1.8E-05 | +6.6E-01 | 9.8E-04 | 4.2E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 24| +2.50E+02 | -2.0E-03 | +4.9E-01 | 1.6E-02 | 5.7E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 54| +2.21E+02 | -6.9E+00 | +2.5E-01 | 3.2E-01 | 4.1E+01 | 8.2E-01 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +1.79E+02 | -2.9E+00 | +9.5E-01 | 1.6E-01 | 3.2E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 36| +2.12E+02 | -1.2E-01 | +5.0E-01 | 3.1E-02 | 8.1E+00 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 28| +1.48E+02 | -5.0E-02 | +9.3E-01 | 6.2E-02 | 8.9E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +1.48E+02 | -4.9E-01 | +6.7E-01 | 5.9E-02 | 3.5E+01 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 28| +2.50E+02 | -1.8E-05 | +6.6E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +2.50E+02 | -1.9E-03 | +5.0E-01 | 1.6E-02 | 5.8E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 55| +2.14E+02 | -6.7E+00 | +8.1E-01 | 8.0E-02 | 7.1E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:06.818 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.0543 and h = 1.00881e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:06.818 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.0543: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:06 fides(INFO) 37| +2.12E+02 | -4.5E-02 | +2.6E-01 | 3.1E-02 | 5.3E+00 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 23| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.9E-02 | 2.9E+01 | 5.4E-02 | 2d |0\n", - "2023-02-17 16:05:06.820 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 142.802 and h = 3.01856e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:06.820 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 142.802: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:06 fides(INFO) 29| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 5.5E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 16| +1.76E+02 | -2.8E+00 | +1.0E+00 | 3.2E-01 | 5.7E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +1.55E+02 | -4.4E+00 | +1.1E+00 | 4.6E-01 | 1.3E+02 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 29| +2.50E+02 | -1.9E-05 | +6.7E-01 | 9.8E-04 | 4.2E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 38| +2.12E+02 | -1.0E-01 | +4.8E-01 | 3.1E-02 | 7.4E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 56| +2.13E+02 | -1.4E+00 | +8.9E-01 | 1.6E-01 | 2.1E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +2.50E+02 | -1.7E-03 | +4.7E-01 | 1.6E-02 | 5.5E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 30| +1.48E+02 | -3.0E-02 | +9.2E-01 | 3.1E-02 | 5.5E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 24| +1.48E+02 | -1.3E-01 | +9.5E-01 | 1.5E-02 | 2.9E+01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +1.76E+02 | +5.8E+00 | -1.0E+00 | 6.4E-01 | 3.4E+01 | 9.5E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 39| +2.12E+02 | -4.6E-02 | +2.9E-01 | 3.1E-02 | 4.9E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 30| +2.50E+02 | -1.8E-05 | +6.7E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +1.48E+02 | -1.4E-01 | +9.7E-01 | 2.9E-02 | 2.0E+01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 57| +2.13E+02 | -1.6E-01 | +8.3E-02 | 3.2E-01 | 9.5E+00 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 27| +2.50E+02 | -1.6E-03 | +4.7E-01 | 1.6E-02 | 5.6E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 31| +1.48E+02 | -4.1E-02 | +9.4E-01 | 6.2E-02 | 8.6E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +1.72E+02 | -4.7E+00 | +1.0E+00 | 1.6E-01 | 3.4E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 40| +2.12E+02 | -9.2E-02 | +4.9E-01 | 3.1E-02 | 6.6E+00 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 31| +2.50E+02 | -1.9E-05 | +6.7E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +1.48E+02 | -1.3E-01 | +9.2E-01 | 5.9E-02 | 2.0E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 58| +2.12E+02 | -1.1E+00 | +9.4E-01 | 8.0E-02 | 1.6E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 32| +1.48E+02 | -6.2E-02 | +9.3E-01 | 1.2E-01 | 3.4E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 28| +2.50E+02 | -1.4E-03 | +4.4E-01 | 1.6E-02 | 5.3E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +1.69E+02 | -3.0E+00 | +9.6E-01 | 3.2E-01 | 7.8E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 41| +2.12E+02 | -5.1E-02 | +3.6E-01 | 3.1E-02 | 4.5E+00 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 32| +2.50E+02 | -1.8E-05 | +6.7E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +1.55E+02 | -6.0E-02 | +2.0E-01 | 4.6E-01 | 1.6E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 59| +2.11E+02 | -8.6E-02 | +4.5E-01 | 1.6E-01 | 3.0E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 33| +1.48E+02 | -5.6E-02 | +5.5E-01 | 2.5E-01 | 7.0E+00 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 27| +1.48E+02 | -1.5E-01 | +8.3E-01 | 1.2E-01 | 1.2E+01 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 42| +2.12E+02 | -8.7E-02 | +5.3E-01 | 3.1E-02 | 5.9E+00 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:06.973 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 191.433 and h = 1.57281e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:06.973 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 191.433: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:06 fides(INFO) 29| +2.50E+02 | -1.3E-03 | +4.5E-01 | 1.6E-02 | 5.4E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +1.69E+02 | +0.0E+00 | +0.0E+00 | 6.4E-01 | 3.8E+01 | 9.4E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 33| +2.50E+02 | -1.9E-05 | +6.8E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 34| +1.48E+02 | +1.4E-01 | -7.5E-01 | 2.5E-01 | 3.6E+00 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 43| +2.12E+02 | -6.0E-02 | +4.7E-01 | 3.1E-02 | 4.0E+00 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 60| +2.11E+02 | -4.1E-01 | +1.6E+00 | 1.6E-01 | 4.8E+00 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 28| +1.48E+02 | +1.2E+00 | -3.6E+00 | 2.4E-01 | 1.1E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 21| +1.64E+02 | -4.6E+00 | +8.6E-01 | 1.6E-01 | 3.8E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 30| +2.50E+02 | -1.2E-03 | +4.2E-01 | 1.6E-02 | 5.1E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 34| +2.50E+02 | -1.9E-05 | +6.7E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +2.12E+02 | -8.8E-02 | +6.1E-01 | 3.1E-02 | 5.1E+00 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 35| +1.48E+02 | -5.7E-03 | +4.7E-02 | 6.2E-02 | 3.6E+00 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 22| +1.62E+02 | -2.0E+00 | +9.1E-01 | 3.2E-01 | 9.0E+01 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 29| +1.48E+02 | -2.4E-02 | +2.0E-01 | 5.9E-02 | 1.1E+01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 61| +2.10E+02 | -1.3E+00 | +1.6E+00 | 3.2E-01 | 4.7E+00 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 31| +2.50E+02 | -1.1E-03 | +4.2E-01 | 1.6E-02 | 5.2E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 35| +2.50E+02 | -2.6E-05 | +1.0E+00 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 45| +2.12E+02 | -7.4E-02 | +6.0E-01 | 3.1E-02 | 3.6E+00 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 36| +1.48E+02 | -3.6E-02 | +9.2E-01 | 1.6E-02 | 1.2E+01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 30| +1.48E+02 | -5.9E-02 | +6.2E-01 | 1.5E-02 | 7.1E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 23| +1.62E+02 | +6.4E+01 | -1.0E+01 | 6.4E-01 | 2.8E+01 | 7.8E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 62| +2.10E+02 | +3.8E+01 | -2.7E+01 | 6.4E-01 | 8.3E+00 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 46| +2.11E+02 | -9.7E-02 | +7.2E-01 | 3.1E-02 | 4.4E+00 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 36| +2.50E+02 | -4.4E-05 | +1.0E+00 | 2.0E-03 | 1.3E-02 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 32| +2.50E+02 | -9.9E-04 | +3.9E-01 | 1.6E-02 | 5.0E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 23| +1.55E+02 | -1.6E-01 | +9.6E-01 | 1.1E-01 | 3.5E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 37| +1.48E+02 | -1.9E-02 | +6.6E-01 | 3.1E-02 | 2.4E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 31| +1.48E+02 | -2.2E-02 | +9.5E-01 | 1.5E-02 | 1.2E+01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 47| +2.11E+02 | -9.3E-02 | +7.4E-01 | 3.1E-02 | 3.2E+00 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 24| +1.60E+02 | -1.6E+00 | +4.6E-01 | 1.6E-01 | 2.8E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 37| +2.50E+02 | -5.0E-05 | +5.4E-01 | 3.9E-03 | 3.3E-02 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 38| +1.48E+02 | -6.6E-03 | +9.5E-01 | 3.1E-02 | 2.7E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 63| +2.10E+02 | -1.7E-02 | -1.9E-02 | 1.6E-01 | 8.3E+00 | 4.2E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 48| +2.11E+02 | -1.2E-01 | +8.2E-01 | 3.1E-02 | 3.8E+00 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 33| +2.50E+02 | -9.9E-05 | +5.5E-02 | 1.6E-02 | 5.1E-01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 32| +1.48E+02 | -1.0E-02 | +8.3E-01 | 2.9E-02 | 1.9E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 25| +1.59E+02 | -8.2E-01 | +9.2E-01 | 1.6E-01 | 7.7E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 49| +2.11E+02 | -2.5E-01 | +9.9E-01 | 6.2E-02 | 2.9E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 38| +2.50E+02 | -1.4E-04 | +1.0E+00 | 3.9E-03 | 1.3E-01 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 39| +1.48E+02 | -9.5E-03 | +9.5E-01 | 6.2E-02 | 9.8E-01 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 64| +2.09E+02 | -2.9E-01 | +1.1E+00 | 4.0E-02 | 8.3E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 33| +1.48E+02 | -1.6E-02 | +9.3E-01 | 5.9E-02 | 3.7E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 26| +1.59E+02 | +1.4E+00 | -9.1E-01 | 3.2E-01 | 1.4E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 34| +2.50E+02 | -1.6E-03 | +5.5E-01 | 3.9E-03 | 7.6E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 39| +2.50E+02 | -1.9E-04 | +1.0E+00 | 7.8E-03 | 3.9E-02 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 50| +2.10E+02 | -8.2E-01 | +1.3E+00 | 1.2E-01 | 4.2E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 40| +1.48E+02 | -1.5E-02 | +9.6E-01 | 1.2E-01 | 1.4E+00 | 9.6E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 65| +2.09E+02 | -2.2E-03 | -4.0E-03 | 8.0E-02 | 2.7E+00 | 2.1E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 34| +1.48E+02 | -8.3E-03 | +3.8E-01 | 1.2E-01 | 1.5E+00 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 40| +2.50E+02 | -4.0E-04 | +1.0E+00 | 1.6E-02 | 3.2E-02 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 51| +2.06E+02 | -4.2E+00 | +1.9E+00 | 2.5E-01 | 5.5E+00 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 27| +1.59E+02 | -2.9E-01 | +3.6E-01 | 8.0E-02 | 1.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 35| +2.50E+02 | -5.5E-04 | +1.0E+00 | 3.9E-03 | 3.4E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 24| +1.55E+02 | -5.3E-02 | +8.6E-01 | 2.3E-01 | 9.3E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 41| +1.48E+02 | -1.7E-02 | +8.2E-01 | 2.5E-01 | 7.1E-01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 66| +2.09E+02 | -6.0E-02 | +9.2E-01 | 2.0E-02 | 2.7E+00 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 35| +1.48E+02 | +1.5E-01 | -2.0E+00 | 1.2E-01 | 2.4E+00 | 8.4E-02 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 28| +1.59E+02 | -2.9E-01 | +9.1E-01 | 8.0E-02 | 4.7E+01 | 1.3E-01 | trr |1\n", - "2023-02-17 16:05:07 fides(INFO) 41| +2.50E+02 | -9.0E-04 | +1.1E+00 | 3.1E-02 | 7.0E-02 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 42| +1.48E+02 | +1.8E-01 | -6.0E+00 | 5.0E-01 | 1.3E+00 | 2.7E-01 | trr |0\n", - "2023-02-17 16:05:07 fides(INFO) 52| +1.82E+02 | -2.4E+01 | +1.6E+00 | 5.0E-01 | 1.7E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 36| +2.50E+02 | -4.2E-04 | +9.5E-01 | 7.8E-03 | 3.5E-02 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 36| +1.48E+02 | -7.1E-03 | +2.8E-01 | 2.9E-02 | 2.4E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 42| +2.50E+02 | -2.3E-03 | +1.2E+00 | 6.2E-02 | 1.4E-01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 29| +1.59E+02 | -5.4E-02 | +6.6E-01 | 8.0E-02 | 7.8E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:07.368 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.2556 and h = 1.10609e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:07.368 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.2556: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:07 fides(INFO) 43| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 1.3E+00 | 6.7E-02 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 67| +2.09E+02 | +1.3E-02 | -3.3E-01 | 4.0E-02 | 8.9E-01 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 53| +1.82E+02 | +4.1E+01 | -3.7E+00 | 1.0E+00 | 4.0E+01 | 2.1E+00 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 37| +2.50E+02 | -7.0E-04 | +7.1E-01 | 1.6E-02 | 1.1E-01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 37| +1.48E+02 | -5.7E-03 | +5.0E-01 | 2.9E-02 | 4.8E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 30| +1.59E+02 | -1.4E-02 | +6.0E-01 | 8.0E-02 | 6.5E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 43| +2.50E+02 | -8.0E-03 | +1.6E+00 | 1.2E-01 | 2.9E-01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 25| +1.55E+02 | -5.3E-02 | +1.1E+00 | 4.6E-01 | 1.1E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +1.48E+02 | -2.9E-03 | +7.2E-01 | 3.1E-02 | 1.3E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 54| +1.75E+02 | -7.0E+00 | +3.9E-01 | 2.5E-01 | 4.0E+01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 68| +2.09E+02 | -1.2E-02 | +8.8E-01 | 1.0E-02 | 8.9E-01 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 38| +1.48E+02 | -1.7E-03 | +3.0E-01 | 2.9E-02 | 1.1E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 38| +2.50E+02 | -9.7E-04 | +1.0E+00 | 1.6E-02 | 2.7E-01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 31| +1.59E+02 | -2.1E-03 | +1.1E-01 | 8.0E-02 | 4.2E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +2.50E+02 | -5.1E-02 | +2.4E+00 | 2.5E-01 | 5.9E-01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 45| +1.48E+02 | -1.6E-03 | +9.8E-01 | 3.1E-02 | 1.4E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 55| +1.65E+02 | -9.7E+00 | +1.1E+00 | 2.5E-01 | 9.5E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 39| +1.48E+02 | -2.7E-03 | +3.8E-01 | 2.9E-02 | 3.2E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 26| +1.55E+02 | +2.1E+01 | -4.1E+02 | 9.1E-01 | 5.1E+00 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 45| +2.49E+02 | -1.0E+00 | +4.7E+00 | 5.0E-01 | 1.3E+00 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 32| +1.59E+02 | -7.8E-03 | +7.2E-01 | 2.0E-02 | 4.0E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 39| +2.50E+02 | -1.3E-03 | +1.0E+00 | 3.1E-02 | 4.4E-02 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 69| +2.09E+02 | -7.3E-03 | +9.6E-01 | 2.0E-02 | 6.9E-01 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 46| +1.48E+02 | -2.4E-03 | +9.7E-01 | 6.2E-02 | 4.0E-01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 40| +1.48E+02 | +4.8E-04 | -1.0E-01 | 2.9E-02 | 8.6E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 56| +1.51E+02 | -1.4E+01 | +1.1E+00 | 5.0E-01 | 5.4E+01 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 33| +1.59E+02 | -2.3E-03 | +8.5E-01 | 2.0E-02 | 2.5E+00 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 70| +2.09E+02 | +9.4E-03 | -7.8E-01 | 4.0E-02 | 4.6E-01 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 47| +1.48E+02 | -3.0E-03 | +8.2E-01 | 1.2E-01 | 7.2E-01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 40| +2.50E+02 | -1.9E-03 | +9.5E-01 | 6.2E-02 | 4.8E-02 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 41| +1.48E+02 | -8.9E-04 | +4.3E-01 | 7.4E-03 | 8.6E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 57| +1.51E+02 | +6.8E+00 | -1.5E+00 | 1.0E+00 | 6.2E+01 | 1.0E+00 | trr |0\n", - "2023-02-17 16:05:07 fides(INFO) 27| +1.55E+02 | -5.3E-02 | +1.4E+00 | 2.3E-01 | 5.1E+00 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 34| +1.59E+02 | -1.0E-03 | +7.6E-01 | 4.0E-02 | 1.4E+00 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 48| +1.48E+02 | +1.4E-02 | -2.2E+00 | 2.5E-01 | 5.3E-01 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 71| +2.09E+02 | -1.4E-03 | +3.6E-01 | 1.0E-02 | 4.6E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 41| +2.50E+02 | -2.2E-03 | +1.1E+00 | 1.2E-01 | 1.9E-01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 42| +1.48E+02 | -1.1E-03 | +9.4E-01 | 7.4E-03 | 2.7E+00 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 58| +1.49E+02 | -2.4E+00 | +9.7E-01 | 1.5E-01 | 6.2E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 35| +1.59E+02 | -7.6E-04 | +5.0E-01 | 8.0E-02 | 1.5E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 59| +1.49E+02 | +1.9E+00 | -1.2E+00 | 3.1E-01 | 1.5E+01 | 4.2E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 46| +2.30E+02 | -1.9E+01 | +3.6E+00 | 1.0E+00 | 6.0E+00 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 28| +1.55E+02 | +9.0E-01 | -9.7E+00 | 4.6E-01 | 8.2E+00 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 49| +1.48E+02 | -5.8E-04 | +2.4E-01 | 6.2E-02 | 5.3E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 43| +1.48E+02 | -5.3E-04 | +9.8E-01 | 1.5E-02 | 3.1E-01 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 36| +1.59E+02 | +3.0E-03 | -1.1E+00 | 8.0E-02 | 9.1E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 72| +2.09E+02 | -3.3E-03 | +1.0E+00 | 1.0E-02 | 6.9E-01 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 42| +2.50E+02 | -8.4E-04 | +1.4E+00 | 2.5E-01 | 1.8E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 50| +1.48E+02 | -1.0E-03 | +7.4E-01 | 1.6E-02 | 1.3E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +1.48E+02 | -7.2E-04 | +9.3E-01 | 2.9E-02 | 7.2E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 60| +1.48E+02 | -4.6E-01 | +4.5E-01 | 7.7E-02 | 1.5E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 43| +2.50E+02 | -3.8E-04 | +1.3E+00 | 2.5E-01 | 4.7E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 37| +1.59E+02 | -5.2E-04 | +5.3E-01 | 2.0E-02 | 9.1E-01 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 73| +2.09E+02 | -3.0E-03 | +6.9E-01 | 2.0E-02 | 2.2E-01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 51| +1.48E+02 | -2.7E-04 | +8.9E-01 | 1.6E-02 | 2.4E-01 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 45| +1.48E+02 | -7.6E-04 | +7.2E-01 | 5.9E-02 | 2.6E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 29| +1.55E+02 | -6.0E-02 | +1.2E+00 | 1.1E-01 | 8.2E+00 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 61| +1.48E+02 | -2.5E-01 | +4.8E-01 | 7.7E-02 | 4.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +2.50E+02 | -2.1E-04 | +1.2E+00 | 2.5E-01 | 8.0E-02 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 38| +1.59E+02 | -1.2E-04 | +8.9E-01 | 2.0E-02 | 7.5E-01 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 46| +1.48E+02 | +3.8E-03 | -1.7E+00 | 5.9E-02 | 5.2E-01 | 4.3E-02 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 74| +2.09E+02 | -6.2E-03 | +1.2E+00 | 2.0E-02 | 6.9E-01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 52| +1.48E+02 | -3.5E-04 | +9.2E-01 | 3.1E-02 | 1.1E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 47| +2.24E+02 | -6.0E+00 | +5.9E-01 | 2.0E+00 | 5.6E+01 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 62| +1.48E+02 | +6.5E-02 | -1.4E-01 | 7.7E-02 | 1.1E+01 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 39| +1.59E+02 | -8.4E-05 | +7.5E-01 | 4.0E-02 | 3.7E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 45| +2.50E+02 | -8.4E-05 | +1.4E+00 | 2.5E-01 | 5.6E-02 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 47| +1.48E+02 | -2.8E-04 | +4.1E-01 | 1.5E-02 | 5.2E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 53| +1.48E+02 | -5.6E-04 | +9.9E-01 | 6.2E-02 | 3.4E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 63| +1.48E+02 | -1.3E-01 | +7.3E-01 | 1.9E-02 | 1.1E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 75| +2.09E+02 | -1.1E-02 | +1.0E+00 | 4.0E-02 | 2.8E-01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 30| +1.54E+02 | -9.4E-02 | +5.4E-01 | 2.3E-01 | 4.2E+00 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 46| +2.50E+02 | -4.5E-05 | +1.4E+00 | 2.5E-01 | 1.1E-02 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 48| +1.48E+02 | -1.2E-04 | +4.9E-01 | 1.5E-02 | 2.5E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 40| +1.59E+02 | +1.3E-04 | -9.4E-01 | 8.0E-02 | 3.1E-01 | 6.6E-03 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 54| +1.48E+02 | -6.1E-04 | +9.0E-01 | 1.2E-01 | 1.0E-01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 64| +1.48E+02 | -4.6E-02 | +9.2E-01 | 1.9E-02 | 1.4E+01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 76| +2.09E+02 | -2.8E-02 | +1.2E+00 | 8.0E-02 | 4.1E-01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 41| +1.59E+02 | -3.6E-05 | +6.4E-01 | 2.0E-02 | 3.1E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 31| +1.54E+02 | -8.5E-02 | +1.3E-01 | 2.3E-01 | 2.8E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 49| +1.48E+02 | -9.6E-05 | +5.4E-01 | 1.5E-02 | 3.0E-01 | 7.1E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 47| +2.50E+02 | -2.1E-05 | +1.4E+00 | 2.5E-01 | 5.0E-03 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 55| +1.48E+02 | +2.3E-03 | -4.7E+00 | 2.5E-01 | 2.3E-01 | 5.3E-02 | trr |0\n", - "2023-02-17 16:05:07 fides(INFO) 65| +1.48E+02 | -3.8E-02 | +9.3E-01 | 3.8E-02 | 5.5E+00 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 77| +2.09E+02 | -9.8E-02 | +1.6E+00 | 1.6E-01 | 8.6E-01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 42| +1.59E+02 | -2.2E-05 | +6.9E-01 | 2.0E-02 | 2.2E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 50| +1.48E+02 | -4.4E-05 | +3.1E-01 | 1.5E-02 | 1.5E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 48| +2.50E+02 | -1.0E-05 | +1.4E+00 | 2.5E-01 | 2.5E-03 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 56| +1.48E+02 | -3.8E-05 | +1.7E-01 | 5.7E-02 | 2.3E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 78| +2.08E+02 | -7.3E-01 | +2.7E+00 | 3.2E-01 | 1.7E+00 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 66| +1.48E+02 | -5.2E-02 | +8.7E-01 | 7.7E-02 | 5.7E+00 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 43| +1.59E+02 | -9.7E-06 | +4.7E-01 | 2.0E-02 | 1.7E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:07.915 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.426 and h = 1.34871e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:07.915 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.426: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:07 fides(INFO) 32| +1.54E+02 | -1.2E-01 | +9.6E-01 | 5.7E-02 | 2.8E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 51| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.5E-02 | 4.5E-01 | 7.8E-03 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 49| +2.50E+02 | -4.6E-06 | +1.4E+00 | 2.5E-01 | 2.9E-04 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 79| +2.05E+02 | -3.5E+00 | +9.6E-01 | 6.4E-01 | 3.7E+00 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 67| +1.48E+02 | +1.3E-01 | -2.0E+00 | 1.5E-01 | 2.4E+00 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 57| +1.48E+02 | -1.2E-04 | +4.9E-01 | 1.4E-02 | 1.8E-01 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +1.59E+02 | -6.2E-06 | +3.7E-01 | 2.0E-02 | 1.3E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 52| +1.48E+02 | -6.5E-05 | +8.6E-01 | 3.7E-03 | 4.5E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 50| +2.50E+02 | -2.0E-06 | +1.4E+00 | 2.5E-01 | 2.6E-04 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(WARNING) Stopping as function difference 2.03E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 80| +2.02E+02 | -2.6E+00 | +6.2E-01 | 1.3E+00 | 4.4E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 68| +1.48E+02 | -8.2E-03 | +3.9E-01 | 3.8E-02 | 2.4E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 58| +1.48E+02 | -2.3E-05 | +9.1E-01 | 1.4E-02 | 1.8E-01 | 2.9E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 45| +1.59E+02 | +7.7E-08 | -4.1E-03 | 2.0E-02 | 1.1E-01 | 1.1E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 33| +1.54E+02 | -4.6E-02 | +8.8E-01 | 1.1E-01 | 1.0E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 48| +2.24E+02 | +2.3E+02 | -8.9E+00 | 2.0E+00 | 5.0E+01 | 1.9E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 53| +1.48E+02 | -3.0E-05 | +7.3E-01 | 7.4E-03 | 6.1E-02 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 69| +1.48E+02 | +2.3E-02 | -5.4E-01 | 3.8E-02 | 2.7E+00 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 59| +1.48E+02 | -2.8E-05 | +8.9E-01 | 2.8E-02 | 5.1E-02 | 6.1E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 81| +2.02E+02 | +6.9E+01 | -1.0E+01 | 1.3E+00 | 2.2E+01 | 3.0E+00 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) 46| +1.59E+02 | -3.8E-06 | +5.6E-01 | 5.0E-03 | 1.1E-01 | 2.9E-04 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 54| +1.48E+02 | -2.2E-05 | +1.0E+00 | 7.4E-03 | 5.7E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 70| +1.48E+02 | -6.9E-03 | +3.8E-01 | 9.6E-03 | 2.7E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 60| +1.48E+02 | -3.5E-05 | +8.4E-01 | 5.7E-02 | 6.1E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 55| +1.48E+02 | -3.6E-05 | +1.0E+00 | 1.5E-02 | 7.9E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 47| +1.59E+02 | -4.3E-07 | +2.7E-01 | 5.0E-03 | 7.0E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:08 fides(WARNING) Stopping as function difference 4.32E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:08 fides(INFO) 82| +1.96E+02 | -6.6E+00 | +1.8E+00 | 2.7E-01 | 2.2E+01 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 34| +1.54E+02 | -4.9E-02 | +8.7E-01 | 2.3E-01 | 1.1E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 61| +1.48E+02 | +5.0E-04 | -5.9E+00 | 1.1E-01 | 3.8E-02 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 0| +2.03E+13 | NaN | NaN | 1.0E+00 | 6.9E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:08 fides(INFO) 71| +1.48E+02 | -6.8E-03 | +8.7E-01 | 9.6E-03 | 6.0E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 56| +1.48E+02 | -4.2E-05 | +8.9E-01 | 2.9E-02 | 4.2E-02 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 1| +1.32E+10 | -2.0E+13 | +1.1E-01 | 1.0E+00 | 6.9E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 83| +1.96E+02 | +5.1E+00 | -6.2E-01 | 5.3E-01 | 2.9E+01 | 1.5E+00 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) 62| +1.48E+02 | +3.7E-06 | -1.1E-01 | 2.8E-02 | 3.8E-02 | 5.8E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 72| +1.48E+02 | -3.8E-03 | +8.6E-01 | 1.9E-02 | 6.6E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 57| +1.48E+02 | -2.8E-05 | +8.4E-01 | 5.9E-02 | 5.8E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 84| +1.89E+02 | -6.6E+00 | +1.5E+00 | 1.3E-01 | 2.9E+01 | 3.6E-01 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 49| +2.01E+02 | -2.3E+01 | +1.1E+00 | 2.3E-01 | 5.0E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 63| +1.48E+02 | -8.8E-06 | +9.8E-01 | 7.1E-03 | 3.8E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 2| +2.14E+09 | -1.1E+10 | +8.8E-01 | 2.5E-01 | 6.1E+10 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 35| +1.54E+02 | -2.3E-02 | +7.8E-01 | 4.6E-01 | 4.4E+00 | 7.8E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 73| +1.48E+02 | -6.8E-03 | +8.8E-01 | 3.8E-02 | 6.9E-01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 58| +1.48E+02 | +4.4E-04 | -1.3E+01 | 1.2E-01 | 3.7E-02 | 7.8E-03 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) 64| +1.48E+02 | -3.3E-06 | +6.2E-01 | 1.4E-02 | 3.8E-02 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 85| +1.82E+02 | -7.5E+00 | +7.6E-01 | 2.7E-01 | 3.2E+01 | 7.2E-01 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 3| +3.18E+08 | -1.8E+09 | +8.9E-01 | 5.0E-01 | 9.3E+09 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 74| +1.48E+02 | +3.8E-03 | -4.3E-01 | 7.7E-02 | 7.3E-01 | 8.7E-02 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 36| +1.54E+02 | -1.3E-02 | +9.2E-01 | 9.1E-01 | 4.6E+00 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 0| +3.89E+02 | NaN | NaN | 1.0E+00 | 6.4E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:08 fides(INFO) 59| +1.48E+02 | +1.6E-05 | -1.0E+00 | 9.9E-03 | 3.7E-02 | 2.0E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 65| +1.48E+02 | -4.5E-06 | +1.1E+00 | 1.4E-02 | 2.3E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 4| +4.57E+07 | -2.7E+08 | +9.0E-01 | 5.0E-01 | 1.4E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 75| +1.48E+02 | -2.0E-03 | +7.1E-01 | 1.9E-02 | 7.3E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 37| +1.54E+02 | +6.1E-02 | -6.0E+00 | 1.8E+00 | 5.8E-01 | 7.5E-01 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 60| +1.48E+02 | -1.4E-06 | +3.2E-01 | 2.5E-03 | 3.7E-02 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:08 fides(WARNING) Stopping as function difference 1.44E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:08 fides(INFO) 86| +1.82E+02 | +1.8E+02 | -8.8E+00 | 5.3E-01 | 4.6E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 1| +2.62E+02 | -1.3E+02 | +6.5E-01 | 1.0E+00 | 6.4E+01 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 5| +6.32E+06 | -3.9E+07 | +9.0E-01 | 5.0E-01 | 2.0E+08 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 76| +1.48E+02 | +7.0E-04 | -1.9E-01 | 1.9E-02 | 4.9E-01 | 1.7E-02 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 66| +1.48E+02 | -7.6E-06 | +1.3E+00 | 2.8E-02 | 3.3E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 2| +2.38E+02 | -2.4E+01 | +6.8E-01 | 1.0E+00 | 8.7E+01 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 38| +1.54E+02 | -2.1E-03 | +5.7E-01 | 2.0E-01 | 5.8E-01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 6| +9.04E+05 | -5.4E+06 | +9.0E-01 | 5.0E-01 | 2.9E+07 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 67| +1.48E+02 | +3.6E-06 | -4.7E-01 | 5.7E-02 | 1.7E-02 | 6.1E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 77| +1.48E+02 | +4.7E-04 | -2.3E-01 | 4.8E-03 | 4.9E-01 | 7.5E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 87| +1.73E+02 | -8.4E+00 | +7.9E-01 | 1.3E-01 | 4.6E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 3| +2.38E+02 | -1.7E+01 | -3.4E-01 | 1.0E+00 | 5.5E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 7| +1.32E+05 | -7.7E+05 | +8.9E-01 | 5.0E-01 | 4.4E+06 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 78| +1.48E+02 | -5.8E-04 | +5.9E-01 | 1.2E-03 | 4.9E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 68| +1.48E+02 | -2.4E-08 | +8.8E-03 | 1.4E-02 | 1.7E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 39| +1.54E+02 | -5.8E-04 | +8.9E-01 | 2.0E-01 | 5.2E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 88| +1.66E+02 | -7.4E+00 | +7.2E-01 | 2.7E-01 | 2.7E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 69| +1.48E+02 | +4.0E-07 | -4.9E-01 | 3.5E-03 | 3.4E-02 | 4.3E-04 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 4| +2.19E+02 | -1.9E+01 | +9.9E-01 | 2.5E-01 | 5.5E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 8| +1.97E+04 | -1.1E+05 | +8.9E-01 | 5.0E-01 | 6.9E+05 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 50| +2.01E+02 | +1.3E+02 | -4.8E+00 | 4.5E-01 | 5.6E+01 | 9.4E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 40| +1.54E+02 | -8.3E-04 | +1.0E+00 | 3.9E-01 | 2.3E-01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 79| +1.48E+02 | -2.7E-04 | +9.7E-01 | 1.2E-03 | 1.1E+00 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 0| +1.94E+12 | NaN | NaN | 1.0E+00 | 8.9E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:08 fides(INFO) 89| +1.66E+02 | +8.5E+01 | -5.9E+00 | 2.7E-01 | 5.0E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 5| +2.19E+02 | +1.6E+02 | -7.7E+00 | 5.0E-01 | 4.4E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 70| +1.48E+02 | -4.4E-06 | +1.4E+01 | 8.9E-04 | 3.4E-02 | 1.1E-04 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 80| +1.48E+02 | -2.5E-04 | +9.9E-01 | 2.4E-03 | 2.0E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 9| +3.04E+03 | -1.7E+04 | +8.9E-01 | 5.0E-01 | 1.1E+05 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 41| +1.54E+02 | +1.7E-03 | -1.6E+00 | 7.9E-01 | 3.7E-01 | 2.5E-01 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 90| +1.66E+02 | +5.9E+00 | -1.0E+00 | 6.7E-02 | 5.0E+01 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 6| +2.09E+02 | -1.0E+01 | +9.3E-01 | 1.2E-01 | 4.4E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 81| +1.48E+02 | -4.5E-04 | +9.7E-01 | 4.8E-03 | 2.9E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 1| +2.04E+06 | -1.9E+12 | +8.7E-02 | 1.0E+00 | 8.9E+12 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 71| +1.48E+02 | +4.6E-06 | -1.4E+01 | 1.8E-03 | 8.1E-03 | 2.1E-04 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 10| +5.75E+02 | -2.5E+03 | +8.8E-01 | 5.0E-01 | 1.7E+04 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 7| +2.06E+02 | -2.8E+00 | +2.6E-01 | 2.5E-01 | 3.2E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 82| +1.48E+02 | -8.1E-04 | +9.6E-01 | 9.6E-03 | 2.3E-01 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 2| +3.17E+05 | -1.7E+06 | +9.0E-01 | 2.5E-01 | 9.4E+06 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 42| +1.54E+02 | -3.0E-04 | +6.2E-01 | 1.2E-01 | 3.7E-01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 72| +1.48E+02 | +5.3E-06 | -4.8E+01 | 4.4E-04 | 8.1E-03 | 5.4E-05 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 91| +1.65E+02 | -1.1E+00 | +5.7E-01 | 1.7E-02 | 5.0E+01 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 11| +2.11E+02 | -3.6E+02 | +8.6E-01 | 5.0E-01 | 2.9E+03 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 8| +1.96E+02 | -1.0E+01 | +9.7E-01 | 2.5E-01 | 7.1E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 3| +4.97E+04 | -2.7E+05 | +9.0E-01 | 5.0E-01 | 1.5E+06 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 43| +1.54E+02 | -2.9E-04 | +8.4E-01 | 1.2E-01 | 1.3E-01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 83| +1.48E+02 | -1.4E-03 | +9.8E-01 | 1.9E-02 | 3.5E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 73| +1.48E+02 | +2.9E-06 | -5.4E+01 | 1.1E-04 | 8.1E-03 | 1.6E-05 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 12| +1.67E+02 | -4.4E+01 | +6.4E-01 | 1.0E+00 | 5.1E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 4| +8.02E+03 | -4.2E+04 | +9.0E-01 | 1.0E+00 | 2.3E+05 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 51| +1.95E+02 | -6.1E+00 | +6.2E-01 | 1.1E-01 | 5.6E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 9| +1.84E+02 | -1.1E+01 | +1.1E+00 | 5.0E-01 | 4.1E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 92| +1.64E+02 | -7.7E-01 | +9.9E-01 | 1.7E-02 | 3.9E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 74| +1.48E+02 | +3.8E-06 | -9.6E+01 | 2.8E-05 | 8.1E-03 | 9.6E-06 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 84| +1.48E+02 | -1.8E-03 | +9.3E-01 | 3.8E-02 | 2.1E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 44| +1.54E+02 | -1.1E-04 | +2.8E-01 | 2.4E-01 | 5.9E-01 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 5| +1.46E+03 | -6.6E+03 | +9.0E-01 | 1.0E+00 | 3.6E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 10| +1.76E+02 | -8.0E+00 | +3.6E-01 | 1.0E+00 | 2.6E+01 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 13| +1.50E+02 | -1.6E+01 | +9.1E-01 | 1.0E+00 | 2.6E+02 | 2.7E+00 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 75| +1.48E+02 | +5.2E-06 | -1.4E+02 | 6.9E-06 | 8.1E-03 | 8.9E-06 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 85| +1.48E+02 | +6.0E-04 | -4.5E-01 | 7.7E-02 | 3.3E-01 | 4.5E-02 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) 93| +1.63E+02 | -1.2E+00 | +9.0E-01 | 3.3E-02 | 3.3E+01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 6| +4.22E+02 | -1.0E+03 | +9.0E-01 | 1.0E+00 | 5.7E+03 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 45| +1.54E+02 | +7.4E-03 | -4.7E+00 | 2.4E-01 | 1.0E-01 | 7.6E-02 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) 14| +1.49E+02 | -1.1E+00 | +2.7E-01 | 1.0E+00 | 9.6E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 86| +1.48E+02 | -4.6E-04 | +7.7E-01 | 1.8E-02 | 3.3E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 7| +2.66E+02 | -1.6E+02 | +8.9E-01 | 2.0E+00 | 9.1E+02 | 4.8E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 11| +1.72E+02 | -4.6E+00 | +7.7E-01 | 1.0E+00 | 6.3E+01 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 46| +1.54E+02 | -2.6E-04 | +4.0E-01 | 5.1E-02 | 1.0E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 76| +1.48E+02 | +5.5E-06 | -2.1E+02 | 1.7E-06 | 8.1E-03 | 4.5E-06 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 15| +1.49E+02 | +1.4E+02 | -2.4E+01 | 1.0E+00 | 6.2E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 8| +2.37E+02 | -2.8E+01 | +1.0E+00 | 4.0E+00 | 1.3E+02 | 4.8E+00 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 94| +1.62E+02 | -1.2E+00 | +8.0E-01 | 6.7E-02 | 3.7E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 87| +1.48E+02 | +1.7E-03 | -1.5E+00 | 3.6E-02 | 1.5E-01 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 52| +1.93E+02 | -1.5E+00 | +8.0E-01 | 1.1E-01 | 2.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 16| +1.49E+02 | -4.9E-01 | +1.1E-01 | 2.5E-01 | 6.2E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 12| +1.71E+02 | -4.6E-01 | +7.5E-01 | 2.0E+00 | 1.8E+01 | 2.8E+00 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 77| +1.48E+02 | +1.5E-06 | -1.9E+02 | 4.3E-07 | 8.1E-03 | 1.1E-06 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 9| +2.37E+02 | +2.0E+02 | -1.2E+01 | 4.0E+00 | 3.2E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 47| +1.54E+02 | -9.5E-05 | +4.3E-01 | 5.1E-02 | 4.2E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 88| +1.48E+02 | -1.6E-04 | +4.8E-01 | 9.0E-03 | 1.5E-01 | 9.1E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 95| +1.62E+02 | +4.1E-01 | -4.3E-01 | 1.3E-01 | 2.2E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 10| +2.37E+02 | +3.3E+01 | -3.9E+00 | 1.0E+00 | 3.2E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 78| +1.48E+02 | +4.2E-06 | -2.0E+03 | 1.1E-07 | 8.1E-03 | 2.8E-07 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 89| +1.48E+02 | -1.0E-04 | +3.4E-01 | 9.0E-03 | 2.2E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 17| +1.48E+02 | -5.8E-01 | +9.5E-01 | 6.2E-02 | 6.4E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 13| +1.71E+02 | -1.2E-02 | +9.5E-02 | 2.0E+00 | 7.1E+00 | 3.1E-01 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 11| +2.37E+02 | +9.4E+00 | -9.6E-01 | 2.5E-01 | 3.2E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 48| +1.54E+02 | -4.6E-05 | +3.6E-01 | 5.1E-02 | 7.2E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 96| +1.61E+02 | -5.5E-01 | +1.0E+00 | 3.3E-02 | 2.2E+01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 12| +2.34E+02 | -3.9E+00 | +8.6E-01 | 6.2E-02 | 3.2E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 79| +1.48E+02 | +2.5E-06 | -4.6E+03 | 2.7E-08 | 8.1E-03 | 7.0E-08 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 90| +1.48E+02 | -9.1E-05 | +2.6E-01 | 9.0E-03 | 2.6E-01 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 18| +1.48E+02 | -3.9E-01 | +6.1E-01 | 1.2E-01 | 3.1E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 13| +2.33E+02 | -5.2E-01 | +1.1E-01 | 1.2E-01 | 2.0E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 49| +1.54E+02 | -1.1E-05 | +7.9E-02 | 5.1E-02 | 1.5E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 14| +1.71E+02 | -4.8E-02 | +5.8E-01 | 1.4E-01 | 4.7E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 97| +1.60E+02 | -1.0E+00 | +7.6E-01 | 6.7E-02 | 1.9E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 80| +1.48E+02 | +4.0E-07 | -3.0E+03 | 6.8E-09 | 8.1E-03 | 1.7E-08 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 19| +1.48E+02 | -2.0E-01 | +9.2E-01 | 1.2E-01 | 3.9E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 91| +1.48E+02 | +1.0E-04 | -2.6E-01 | 9.0E-03 | 2.1E-01 | 3.6E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 14| +2.31E+02 | -1.8E+00 | +9.2E-01 | 3.1E-02 | 4.1E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 53| +1.91E+02 | -2.0E+00 | +1.2E+00 | 2.3E-01 | 1.0E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 50| +1.54E+02 | -6.4E-05 | +7.1E-01 | 1.3E-02 | 5.9E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 81| +1.48E+02 | -4.6E-07 | +1.4E+04 | 1.7E-09 | 8.1E-03 | 4.4E-09 | 2d |1\n", - "2023-02-17 16:05:08 fides(WARNING) Stopping as function difference 4.61E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:08 fides(INFO) 92| +1.48E+02 | -7.7E-05 | +6.3E-01 | 2.3E-03 | 2.1E-01 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 15| +2.29E+02 | -2.0E+00 | +8.6E-01 | 6.2E-02 | 2.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 15| +1.71E+02 | -8.8E-03 | +8.3E-01 | 1.4E-01 | 5.9E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 20| +1.48E+02 | -6.9E-02 | +8.0E-01 | 2.5E-01 | 1.2E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 98| +1.60E+02 | +2.9E+00 | -9.3E-01 | 1.3E-01 | 4.1E+01 | 3.4E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 16| +2.26E+02 | -3.5E+00 | +1.2E+00 | 1.2E-01 | 1.8E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 51| +1.54E+02 | -1.5E-05 | +9.4E-01 | 1.3E-02 | 1.8E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 93| +1.48E+02 | -3.0E-05 | +8.5E-01 | 2.3E-03 | 3.7E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 21| +1.48E+02 | +2.4E-01 | -2.3E+00 | 4.9E-01 | 1.1E+01 | 5.1E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 17| +2.21E+02 | -4.4E+00 | +1.1E+00 | 2.5E-01 | 1.8E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 99| +1.60E+02 | -4.3E-01 | +3.8E-01 | 3.3E-02 | 4.1E+01 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 16| +1.71E+02 | -2.1E-03 | +9.9E-01 | 2.8E-01 | 1.2E+00 | 7.2E-02 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) 94| +1.48E+02 | -3.9E-05 | +9.9E-01 | 4.5E-03 | 6.3E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 18| +2.18E+02 | -3.2E+00 | +5.1E-01 | 5.0E-01 | 2.6E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 52| +1.54E+02 | -1.8E-05 | +1.1E+00 | 2.5E-02 | 3.4E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 22| +1.48E+02 | -1.8E-02 | +4.4E-01 | 1.2E-01 | 1.1E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 100| +1.59E+02 | -4.9E-01 | +1.0E+00 | 3.3E-02 | 2.8E+01 | 6.9E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 95| +1.48E+02 | -4.2E-05 | +6.7E-01 | 9.0E-03 | 1.9E-01 | 5.7E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 19| +1.98E+02 | -2.0E+01 | +1.5E+00 | 5.0E-01 | 4.0E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 17| +1.71E+02 | -4.8E-05 | +3.4E-01 | 5.6E-01 | 4.9E-01 | 5.5E-03 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) 53| +1.54E+02 | -2.5E-05 | +1.2E+00 | 5.1E-02 | 9.4E-02 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 0| +5.85E+12 | NaN | NaN | 1.0E+00 | 2.7E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:09 fides(INFO) 54| +1.91E+02 | -4.2E+00 | -3.0E-01 | 4.5E-01 | 2.5E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 23| +1.48E+02 | -7.8E-03 | +1.9E-01 | 1.2E-01 | 5.9E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 101| +1.59E+02 | -2.4E-01 | +4.7E-01 | 6.7E-02 | 8.2E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 96| +1.48E+02 | +5.6E-05 | -5.8E-01 | 9.0E-03 | 8.7E-02 | 1.7E-03 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 18| +1.71E+02 | -3.1E-06 | +3.2E-02 | 5.6E-01 | 1.8E-01 | 6.7E-03 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 20| +1.98E+02 | +1.9E+03 | -4.7E+01 | 1.0E+00 | 3.7E+01 | 2.1E+00 | trr |0\n", - "2023-02-17 16:05:09 fides(INFO) 1| +1.05E+07 | -5.9E+12 | +8.3E-02 | 1.0E+00 | 2.7E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 24| +1.48E+02 | -1.7E-02 | +8.1E-01 | 3.1E-02 | 9.3E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 54| +1.54E+02 | -9.9E-07 | +5.9E-02 | 1.0E-01 | 2.7E-02 | 4.2E-03 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) 97| +1.48E+02 | -1.2E-05 | +3.4E-01 | 2.3E-03 | 8.7E-02 | 6.5E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 2| +2.00E+06 | -8.5E+06 | +9.1E-01 | 2.5E-01 | 3.6E+07 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 21| +1.79E+02 | -1.9E+01 | +9.5E-01 | 2.5E-01 | 3.7E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 25| +1.48E+02 | -3.3E-03 | +5.9E-01 | 6.2E-02 | 2.4E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 102| +1.59E+02 | +7.1E-01 | -1.5E+00 | 6.7E-02 | 6.6E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 19| +1.71E+02 | -3.2E-05 | +3.8E-01 | 1.9E-03 | 1.9E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 55| +1.54E+02 | +5.1E-04 | -4.1E+00 | 1.2E-02 | 3.0E-02 | 1.8E-02 | trr |0\n", - "2023-02-17 16:05:09 fides(INFO) 22| +1.79E+02 | -1.4E+01 | -3.3E-02 | 5.0E-01 | 8.4E+01 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 98| +1.48E+02 | -2.4E-05 | +9.6E-01 | 2.3E-03 | 3.0E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:09.201 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 117.386 and h = 3.00881e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.202 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 117.386: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 103| +1.59E+02 | +0.0E+00 | +0.0E+00 | 1.7E-02 | 6.6E+00 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 26| +1.48E+02 | -1.6E-03 | +9.3E-01 | 6.2E-02 | 2.6E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 20| +1.71E+02 | -8.8E-06 | +3.9E-01 | 1.9E-03 | 1.4E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 3| +3.31E+05 | -1.7E+06 | +9.0E-01 | 5.0E-01 | 6.6E+06 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 99| +1.48E+02 | -7.5E-06 | +5.8E-01 | 4.5E-03 | 3.6E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 23| +1.68E+02 | -1.2E+01 | +1.0E+00 | 1.2E-01 | 8.4E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 56| +1.54E+02 | -1.4E-05 | +2.6E-01 | 2.1E-03 | 3.0E-02 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 4| +5.18E+04 | -2.8E+05 | +9.0E-01 | 1.0E+00 | 1.1E+06 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 27| +1.48E+02 | -1.6E-03 | +9.7E-01 | 1.2E-01 | 1.6E+00 | 9.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 104| +1.59E+02 | -3.3E-02 | +6.8E-01 | 4.2E-03 | 6.6E+00 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 21| +1.71E+02 | -2.4E-07 | +2.8E-02 | 1.9E-03 | 7.6E-02 | 1.8E-03 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) 5| +8.31E+03 | -4.4E+04 | +9.0E-01 | 1.0E+00 | 1.7E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 24| +1.61E+02 | -7.0E+00 | +6.1E-01 | 2.5E-01 | 6.1E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 100| +1.48E+02 | -1.2E-05 | +1.2E+00 | 4.5E-03 | 3.0E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 55| +1.89E+02 | -2.8E+00 | +9.3E-01 | 1.1E-01 | 2.5E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 57| +1.54E+02 | +6.2E-06 | -2.1E-01 | 2.1E-03 | 2.9E-02 | 4.2E-03 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 6| +1.47E+03 | -6.8E+03 | +9.0E-01 | 1.0E+00 | 2.7E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 25| +1.53E+02 | -7.2E+00 | +4.6E-01 | 2.5E-01 | 1.7E+02 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 28| +1.48E+02 | -1.2E-03 | +8.4E-01 | 2.5E-01 | 1.8E+00 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 105| +1.59E+02 | -3.4E-02 | +8.9E-01 | 4.2E-03 | 9.4E+00 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 101| +1.48E+02 | -1.4E-05 | +9.8E-01 | 9.0E-03 | 3.3E-02 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 22| +1.71E+02 | -3.9E-07 | +1.5E-01 | 1.7E-04 | 5.9E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 7| +4.09E+02 | -1.1E+03 | +9.0E-01 | 1.0E+00 | 4.3E+03 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 58| +1.54E+02 | -6.8E-06 | +7.5E-01 | 5.4E-04 | 2.9E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 29| +1.48E+02 | +1.7E-02 | -6.6E+00 | 4.9E-01 | 7.7E-01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 26| +1.49E+02 | -4.5E+00 | +9.6E-01 | 2.5E-01 | 2.1E+02 | 7.9E-01 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) 102| +1.48E+02 | +7.9E-06 | -5.8E-01 | 1.8E-02 | 2.0E-02 | 3.0E-03 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 8| +2.59E+02 | -1.5E+02 | +8.7E-01 | 1.0E+00 | 6.6E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 23| +1.71E+02 | -1.8E-06 | +3.0E+00 | 4.2E-05 | 2.8E-02 | 6.1E-05 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 106| +1.59E+02 | -4.6E-02 | +8.4E-01 | 8.3E-03 | 4.3E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 9| +2.39E+02 | -2.0E+01 | +1.1E+00 | 2.0E+00 | 8.1E+01 | 4.0E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 59| +1.54E+02 | -2.1E-06 | +6.9E-01 | 5.4E-04 | 1.7E-02 | 7.1E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 30| +1.48E+02 | +3.6E-04 | -3.1E-01 | 1.2E-01 | 7.7E-01 | 7.3E-02 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 27| +1.48E+02 | -9.4E-01 | +6.7E-01 | 5.0E-01 | 3.8E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 103| +1.48E+02 | -7.8E-06 | +1.5E+00 | 4.5E-03 | 2.0E-02 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 107| +1.59E+02 | -1.9E-02 | +7.9E-01 | 1.7E-02 | 5.2E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 31| +1.48E+02 | -3.0E-04 | +6.1E-01 | 3.1E-02 | 7.7E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 24| +1.71E+02 | -6.3E-07 | +1.2E+00 | 8.3E-05 | 1.9E-02 | 1.1E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(WARNING) Stopping as function difference 6.33E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 60| +1.54E+02 | -2.3E-06 | +1.2E+00 | 5.4E-04 | 1.2E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 10| +2.39E+02 | +2.5E+02 | -1.2E+01 | 4.0E+00 | 3.4E+01 | 1.1E+01 | trr |0\n", - "2023-02-17 16:05:09 fides(INFO) 104| +1.48E+02 | +1.9E-05 | -1.6E+00 | 9.0E-03 | 4.3E-02 | 3.8E-03 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 28| +1.48E+02 | -1.4E-02 | -3.5E-01 | 5.0E-01 | 2.5E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 11| +2.39E+02 | +6.1E+01 | -5.4E+00 | 1.0E+00 | 3.4E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 32| +1.48E+02 | -2.0E-04 | +9.4E-01 | 3.1E-02 | 1.1E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 56| +1.77E+02 | -1.2E+01 | +1.8E+00 | 2.3E-01 | 3.2E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 108| +1.59E+02 | +3.5E-02 | -1.2E+00 | 3.3E-02 | 2.7E+00 | 3.5E-02 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 105| +1.48E+02 | +7.4E-06 | -2.0E+00 | 2.3E-03 | 4.3E-02 | 9.6E-04 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 29| +1.48E+02 | -1.7E-01 | +9.4E-01 | 1.2E-01 | 2.5E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 12| +2.39E+02 | +6.7E+00 | -6.1E-01 | 2.5E-01 | 3.4E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 61| +1.54E+02 | -2.1E-06 | +1.1E+00 | 1.1E-03 | 4.2E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 33| +1.48E+02 | -1.4E-04 | +9.7E-01 | 6.2E-02 | 4.4E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 13| +2.35E+02 | -4.3E+00 | +8.8E-01 | 6.2E-02 | 3.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 106| +1.48E+02 | +4.3E-06 | -3.8E+00 | 5.6E-04 | 4.3E-02 | 2.4E-04 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 30| +1.48E+02 | -9.7E-02 | +3.7E-01 | 2.5E-01 | 9.0E+00 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 62| +1.54E+02 | -7.0E-07 | +4.7E-01 | 2.1E-03 | 6.9E-03 | 9.2E-04 | trr |1\n", - "2023-02-17 16:05:09 fides(WARNING) Stopping as function difference 6.97E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:09 fides(INFO) 109| +1.59E+02 | -9.5E-03 | +7.2E-01 | 8.3E-03 | 2.7E+00 | 8.9E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 34| +1.48E+02 | -1.1E-04 | +8.6E-01 | 1.2E-01 | 6.1E-01 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 14| +2.34E+02 | -8.2E-01 | +1.7E-01 | 1.2E-01 | 2.2E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 107| +1.48E+02 | +4.4E-06 | -9.6E+00 | 1.4E-04 | 4.3E-02 | 6.1E-05 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 31| +1.48E+02 | +7.1E-02 | -5.0E-01 | 2.5E-01 | 1.9E+01 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 0| +9.19E+02 | NaN | NaN | 1.0E+00 | 3.1E+03 | NaN | NaN |1\n", - "2023-02-17 16:05:09 fides(INFO) 15| +2.32E+02 | -1.8E+00 | +9.2E-01 | 3.1E-02 | 3.8E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 110| +1.59E+02 | -2.8E-03 | +6.2E-01 | 8.3E-03 | 1.0E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 35| +1.48E+02 | +3.6E-04 | -2.0E+00 | 2.5E-01 | 2.4E-01 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 32| +1.48E+02 | -5.4E-02 | +7.6E-01 | 6.2E-02 | 1.9E+01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 108| +1.48E+02 | +4.9E-06 | -1.7E+01 | 3.5E-05 | 4.3E-02 | 1.9E-05 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 1| +2.87E+02 | -6.3E+02 | +1.3E-01 | 1.0E+00 | 3.1E+03 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 16| +2.30E+02 | -2.0E+00 | +8.7E-01 | 6.2E-02 | 2.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 36| +1.48E+02 | -2.7E-05 | +3.7E-01 | 6.2E-02 | 2.4E-01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 111| +1.59E+02 | -1.7E-03 | +8.1E-01 | 8.3E-03 | 2.8E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 2| +2.61E+02 | -2.7E+01 | +8.9E-01 | 2.5E-01 | 7.8E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 109| +1.48E+02 | +6.0E-06 | -2.5E+01 | 8.8E-06 | 4.3E-02 | 1.1E-05 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 17| +2.27E+02 | -3.5E+00 | +1.2E+00 | 1.2E-01 | 1.8E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 33| +1.48E+02 | -2.7E-02 | +7.8E-01 | 1.2E-01 | 4.4E+00 | 9.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 37| +1.48E+02 | -1.3E-05 | +1.2E-01 | 6.2E-02 | 3.6E-01 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 18| +2.22E+02 | -4.9E+00 | +1.2E+00 | 2.5E-01 | 1.9E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 3| +2.61E+02 | +3.7E+02 | -1.9E+01 | 5.0E-01 | 3.3E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 0| +6.60E+12 | NaN | NaN | 1.0E+00 | 2.2E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 110| +1.48E+02 | +6.7E-06 | -4.9E+01 | 2.2E-06 | 4.3E-02 | 4.0E-06 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 112| +1.59E+02 | -8.3E-05 | +4.7E-01 | 8.3E-03 | 6.4E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 34| +1.48E+02 | +2.1E-02 | -6.9E-01 | 2.5E-01 | 9.4E+00 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 4| +2.51E+02 | -9.4E+00 | +8.8E-01 | 1.2E-01 | 3.3E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 38| +1.48E+02 | -3.1E-05 | +5.7E-01 | 1.5E-02 | 1.8E-01 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 19| +2.21E+02 | -1.2E+00 | +4.9E-02 | 5.0E-01 | 2.1E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:09.703 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0069 and h = 1.38872e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.703 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0069: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 111| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.5E-07 | 4.3E-02 | 1.0E-06 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 57| +1.77E+02 | +1.4E+02 | -6.5E+00 | 4.5E-01 | 4.1E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 35| +1.48E+02 | -1.2E-02 | +7.4E-01 | 6.2E-02 | 9.4E+00 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 113| +1.59E+02 | -4.1E-05 | +8.5E-01 | 8.3E-03 | 1.7E-01 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 5| +2.51E+02 | +8.2E+00 | -2.3E+00 | 2.5E-01 | 1.9E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 20| +2.13E+02 | -7.3E+00 | +7.8E-01 | 1.2E-01 | 6.6E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 1| +2.56E+09 | -6.6E+12 | +1.1E-01 | 1.0E+00 | 2.2E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 39| +1.48E+02 | -1.4E-05 | +9.5E-01 | 1.5E-02 | 3.3E-01 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 112| +1.48E+02 | +7.4E-06 | -7.0E+02 | 1.4E-07 | 4.3E-02 | 2.5E-07 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 36| +1.48E+02 | -2.9E-03 | +6.1E-01 | 6.2E-02 | 1.9E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 6| +2.50E+02 | -1.6E+00 | +6.5E-01 | 6.2E-02 | 1.9E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 21| +2.05E+02 | -8.7E+00 | +1.3E+00 | 2.5E-01 | 1.8E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 2| +4.81E+08 | -2.1E+09 | +8.8E-01 | 2.5E-01 | 9.3E+09 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 114| +1.59E+02 | -2.4E-06 | +5.2E-01 | 8.3E-03 | 4.1E-02 | 6.8E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 40| +1.48E+02 | -7.4E-06 | +5.9E-01 | 3.1E-02 | 8.1E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 113| +1.48E+02 | +5.8E-06 | -2.2E+03 | 3.4E-08 | 4.3E-02 | 6.2E-08 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 7| +2.50E+02 | -1.4E-03 | +6.7E-02 | 6.2E-02 | 1.8E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 37| +1.48E+02 | -2.2E-03 | +6.4E-01 | 6.2E-02 | 3.6E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 22| +1.94E+02 | -1.1E+01 | +9.7E-01 | 5.0E-01 | 3.2E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 3| +7.29E+07 | -4.1E+08 | +8.9E-01 | 5.0E-01 | 1.6E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 41| +1.48E+02 | -5.0E-06 | +1.0E+00 | 3.1E-02 | 1.6E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 115| +1.59E+02 | -1.3E-06 | +2.0E+00 | 8.3E-03 | 1.1E-02 | 5.1E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(WARNING) Stopping as function difference 1.35E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:09 fides(INFO) 114| +1.48E+02 | +4.0E-06 | -6.1E+03 | 8.6E-09 | 4.3E-02 | 1.6E-08 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 38| +1.48E+02 | -7.8E-04 | +5.2E-01 | 6.2E-02 | 1.0E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 8| +2.50E+02 | +9.3E-04 | -6.0E-02 | 1.6E-02 | 1.6E+00 | 4.3E-02 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 4| +1.11E+07 | -6.2E+07 | +8.9E-01 | 5.0E-01 | 2.4E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:09.842 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0854 and h = 2.28973e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.842 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0854: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 42| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 3.1E-02 | 2.1E-02 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 23| +1.94E+02 | +8.6E+00 | -1.9E+00 | 1.0E+00 | 4.2E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 9| +2.50E+02 | -8.1E-03 | +6.5E-01 | 3.9E-03 | 1.6E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 5| +1.70E+06 | -9.4E+06 | +9.0E-01 | 5.0E-01 | 3.6E+07 | 9.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 115| +1.48E+02 | +6.4E-06 | -3.9E+04 | 2.1E-09 | 4.3E-02 | 3.9E-09 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 39| +1.48E+02 | -5.9E-04 | +5.2E-01 | 6.2E-02 | 1.7E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 43| +1.48E+02 | -1.3E-06 | +1.2E+00 | 1.5E-02 | 3.1E-02 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(WARNING) Stopping as function difference 1.27E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 10| +2.50E+02 | -1.5E-07 | +5.2E-03 | 3.9E-03 | 6.7E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:09.892 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.732 and h = 3.21785e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.892 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.732: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 116| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.4E-10 | 4.3E-02 | 9.8E-10 | 2d |0\n", - "2023-02-17 16:05:09.895 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 123.347 and h = 8.2092e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.895 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 123.347: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 24| +1.94E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 4.2E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 40| +1.48E+02 | -2.6E-04 | +3.5E-01 | 6.2E-02 | 6.7E-01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 11| +2.50E+02 | +3.9E-08 | -1.6E-03 | 9.8E-04 | 6.7E-02 | 2.7E-03 | 2d |0\n", - "2023-02-17 16:05:09.925 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 147.013 and h = 1.79811e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.925 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 147.013: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 25| +1.94E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 4.2E+01 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 58| +1.71E+02 | -5.8E+00 | +6.8E-01 | 1.1E-01 | 4.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 6| +2.63E+05 | -1.4E+06 | +9.0E-01 | 5.0E-01 | 5.5E+06 | 9.5E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 117| +1.48E+02 | +2.3E-06 | -2.2E+05 | 1.3E-10 | 4.3E-02 | 2.4E-10 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 41| +1.48E+02 | -1.8E-04 | +2.6E-01 | 6.2E-02 | 9.3E-01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 12| +2.50E+02 | -8.1E-06 | +3.2E-01 | 2.4E-04 | 6.7E-02 | 6.4E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 26| +1.92E+02 | -1.3E+00 | +9.9E-01 | 1.6E-02 | 4.2E+01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 118| +1.48E+02 | +2.9E-06 | -1.1E+06 | 3.4E-11 | 4.3E-02 | 6.1E-11 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 13| +2.50E+02 | -2.2E-06 | +2.1E-01 | 2.4E-04 | 4.2E-02 | 4.3E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 0| +1.10E+14 | NaN | NaN | 1.0E+00 | 5.1E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:09 fides(INFO) 42| +1.48E+02 | -2.5E-05 | +3.3E-02 | 6.2E-02 | 5.0E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 27| +1.90E+02 | -2.4E+00 | +9.8E-01 | 3.1E-02 | 4.1E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 0| +5.57E+12 | NaN | NaN | 1.0E+00 | 2.6E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:10 fides(INFO) 119| +1.48E+02 | +4.2E-06 | -6.5E+06 | 8.4E-12 | 4.3E-02 | 1.5E-11 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 7| +4.11E+04 | -2.2E+05 | +9.0E-01 | 5.0E-01 | 8.6E+05 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 14| +2.50E+02 | -2.9E-06 | +7.3E-01 | 6.1E-05 | 3.2E-02 | 1.6E-04 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 43| +1.48E+02 | -2.6E-04 | +7.3E-01 | 1.6E-02 | 7.4E-01 | 7.1E-03 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 1| +1.08E+08 | -1.1E+14 | +8.2E-02 | 1.0E+00 | 5.1E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 28| +1.86E+02 | -4.3E+00 | +9.8E-01 | 6.2E-02 | 4.0E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 1| +1.50E+09 | -5.6E+12 | +8.6E-02 | 1.0E+00 | 2.6E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 15| +2.50E+02 | -1.1E-09 | +9.6E-03 | 6.1E-05 | 4.5E-03 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 120| +1.48E+02 | +1.6E-06 | -9.7E+06 | 2.1E-12 | 4.3E-02 | 3.8E-12 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 2| +2.44E+08 | -1.3E+09 | +8.9E-01 | 2.5E-01 | 6.5E+09 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 44| +1.48E+02 | -5.8E-05 | +8.4E-01 | 1.6E-02 | 3.6E-01 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 16| +2.50E+02 | -5.9E-08 | +6.6E-01 | 1.5E-05 | 4.5E-03 | 2.7E-05 | 2d |1\n", - "2023-02-17 16:05:10 fides(WARNING) Stopping as function difference 5.90E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:10 fides(INFO) 121| +1.48E+02 | +3.6E-06 | -8.8E+07 | 5.2E-13 | 4.3E-02 | 9.5E-13 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 29| +1.77E+02 | -9.0E+00 | +1.1E+00 | 1.2E-01 | 4.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 3| +3.70E+07 | -2.1E+08 | +8.9E-01 | 5.0E-01 | 1.0E+09 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 2| +1.69E+07 | -9.1E+07 | +8.9E-01 | 2.5E-01 | 3.5E+08 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 45| +1.48E+02 | -3.7E-05 | +8.4E-01 | 3.1E-02 | 3.0E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 59| +1.63E+02 | -8.4E+00 | +8.7E-01 | 1.1E-01 | 8.0E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 122| +1.48E+02 | +3.2E-06 | -3.2E+08 | 1.3E-13 | 4.3E-02 | 2.4E-13 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 30| +1.62E+02 | -1.5E+01 | +8.6E-01 | 2.5E-01 | 4.9E+01 | 5.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 4| +5.64E+06 | -3.1E+07 | +8.9E-01 | 5.0E-01 | 1.5E+08 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 46| +1.48E+02 | -4.9E-05 | +9.3E-01 | 6.2E-02 | 2.4E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 3| +2.62E+06 | -1.4E+07 | +8.9E-01 | 5.0E-01 | 5.5E+07 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 123| +1.48E+02 | +3.2E-06 | -1.3E+09 | 3.3E-14 | 4.3E-02 | 6.0E-14 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 8| +6.58E+03 | -3.5E+04 | +9.0E-01 | 5.0E-01 | 1.3E+05 | 9.2E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 31| +1.62E+02 | +7.2E+01 | -5.0E+00 | 5.0E-01 | 1.0E+02 | 9.7E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 5| +8.66E+05 | -4.8E+06 | +8.9E-01 | 5.0E-01 | 2.3E+07 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10.187 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.882 and h = 4.14838e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:10.188 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.882: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:10 fides(INFO) 47| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 2.7E-01 | 4.4E-02 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 0| +4.28E+11 | NaN | NaN | 1.0E+00 | 2.0E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:10 fides(INFO) 4| +4.31E+05 | -2.2E+06 | +8.6E-01 | 1.0E+00 | 8.6E+06 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 124| +1.48E+02 | +6.8E-06 | -1.1E+10 | 8.2E-15 | 4.3E-02 | 1.5E-14 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 6| +1.34E+05 | -7.3E+05 | +9.0E-01 | 1.0E+00 | 3.5E+06 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 32| +1.55E+02 | -7.2E+00 | +7.5E-01 | 1.2E-01 | 1.0E+02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 48| +1.48E+02 | -1.9E-05 | +7.7E-01 | 3.1E-02 | 2.7E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 7| +2.10E+04 | -1.1E+05 | +9.0E-01 | 1.0E+00 | 5.4E+05 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 9| +1.18E+03 | -5.4E+03 | +9.0E-01 | 5.0E-01 | 2.1E+04 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 125| +1.48E+02 | +4.5E-06 | -2.8E+10 | 2.0E-15 | 4.3E-02 | 3.7E-15 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 5| +6.72E+04 | -3.6E+05 | +9.0E-01 | 2.0E+00 | 1.3E+06 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 8| +3.45E+03 | -1.8E+04 | +9.0E-01 | 1.0E+00 | 8.5E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 1| +5.82E+05 | -4.3E+11 | +8.9E-02 | 1.0E+00 | 2.0E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 33| +1.53E+02 | -2.2E+00 | +1.0E+00 | 1.2E-01 | 6.5E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 49| +1.48E+02 | -2.3E-05 | +8.5E-01 | 6.2E-02 | 1.5E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 126| +1.48E+02 | +3.8E-06 | -9.7E+10 | 5.1E-16 | 4.3E-02 | 9.3E-16 | 2d |0\n", - "2023-02-17 16:05:10 fides(WARNING) Stopping as trust region radius 1.28E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:10 fides(INFO) 9| +7.11E+02 | -2.7E+03 | +9.0E-01 | 1.0E+00 | 1.3E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 2| +9.11E+04 | -4.9E+05 | +9.0E-01 | 2.5E-01 | 2.7E+06 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 10| +3.53E+02 | -8.3E+02 | +9.0E-01 | 5.0E-01 | 3.4E+03 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 6| +1.05E+04 | -5.7E+04 | +9.0E-01 | 2.0E+00 | 2.1E+05 | 9.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 34| +1.51E+02 | -1.9E+00 | +4.6E-01 | 2.5E-01 | 3.0E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 3| +1.46E+04 | -7.6E+04 | +9.0E-01 | 5.0E-01 | 4.2E+05 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 50| +1.48E+02 | +2.8E-05 | -7.2E-01 | 1.2E-01 | 2.0E-01 | 3.8E-02 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 10| +2.99E+02 | -4.1E+02 | +9.0E-01 | 1.0E+00 | 2.1E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 11| +2.39E+02 | -1.1E+02 | +9.0E-01 | 1.0E+00 | 5.2E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 4| +4.15E+03 | -1.0E+04 | +6.8E-01 | 1.0E+00 | 6.6E+04 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 11| +2.35E+02 | -6.4E+01 | +1.0E+00 | 1.0E+00 | 3.1E+02 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 7| +1.72E+03 | -8.8E+03 | +9.1E-01 | 2.0E+00 | 3.2E+04 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 35| +1.50E+02 | -6.8E-01 | +2.3E-01 | 2.5E-01 | 9.0E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 5| +8.28E+02 | -3.3E+03 | +9.0E-01 | 1.0E+00 | 1.3E+04 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 51| +1.48E+02 | -1.4E-05 | +8.8E-01 | 3.1E-02 | 2.0E-01 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 6| +3.15E+02 | -5.1E+02 | +9.0E-01 | 2.0E+00 | 2.1E+03 | 3.5E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 12| +2.20E+02 | -1.9E+01 | +1.1E+00 | 1.0E+00 | 6.1E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 36| +1.49E+02 | -7.8E-01 | +9.3E-01 | 6.2E-02 | 4.3E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 52| +1.48E+02 | -9.5E-07 | +6.4E-02 | 6.2E-02 | 9.3E-02 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 12| +2.35E+02 | +9.0E+01 | -2.0E+01 | 2.0E+00 | 2.8E+01 | 4.0E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 8| +3.86E+02 | -1.3E+03 | +9.3E-01 | 2.0E+00 | 4.9E+03 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 7| +2.51E+02 | -6.4E+01 | +8.4E-01 | 4.0E+00 | 3.2E+02 | 2.3E+00 | trr |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 60| +1.63E+02 | +2.1E+01 | -2.5E+00 | 2.3E-01 | 3.8E+01 | 4.6E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 13| +2.35E+02 | +2.0E+01 | -2.5E+00 | 5.0E-01 | 2.8E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 8| +2.34E+02 | -1.7E+01 | +5.1E+00 | 4.0E+00 | 2.5E+01 | 4.2E+00 | trr |1\n", - "2023-02-17 16:05:10 fides(INFO) 53| +1.48E+02 | -5.7E-06 | +4.3E-01 | 1.6E-02 | 1.2E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 37| +1.48E+02 | -6.9E-01 | +6.4E-01 | 1.2E-01 | 3.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 14| +2.32E+02 | -3.5E+00 | +5.1E-01 | 1.2E-01 | 2.8E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 13| +2.20E+02 | +2.1E+02 | -1.3E+01 | 2.0E+00 | 3.4E+01 | 5.8E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 0| +5.24E+12 | NaN | NaN | 1.0E+00 | 2.4E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:10 fides(INFO) 15| +2.29E+02 | -3.1E+00 | +8.1E-01 | 1.2E-01 | 2.9E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 9| +2.06E+02 | -1.8E+02 | +1.0E+00 | 2.0E+00 | 7.0E+02 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:10.466 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.274 and h = 2.21878e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:10.467 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.274: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:10 fides(INFO) 54| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 6.9E-02 | 3.8E-03 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 38| +1.48E+02 | -3.1E-01 | +7.9E-01 | 1.2E-01 | 3.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 14| +2.20E+02 | +1.6E+00 | -6.3E-01 | 5.0E-01 | 3.4E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 9| +2.34E+02 | +1.1E+04 | -3.1E+02 | 4.0E+00 | 4.9E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 16| +2.29E+02 | +2.5E-01 | -1.9E-01 | 2.5E-01 | 2.2E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 55| +1.48E+02 | -5.4E-06 | +6.4E+00 | 3.9E-03 | 6.9E-02 | 9.5E-04 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 39| +1.48E+02 | -1.1E-01 | +1.8E-01 | 2.5E-01 | 1.5E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 1| +7.80E+10 | -5.2E+12 | +8.7E-02 | 1.0E+00 | 2.4E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 15| +2.14E+02 | -5.5E+00 | +7.8E-01 | 1.2E-01 | 3.4E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 10| +2.34E+02 | +1.5E+03 | -4.5E+01 | 1.0E+00 | 4.9E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 17| +2.27E+02 | -1.8E+00 | +7.9E-01 | 6.2E-02 | 2.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 56| +1.48E+02 | +3.0E-06 | -3.9E+00 | 7.8E-03 | 5.3E-02 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 2| +1.16E+10 | -6.6E+10 | +8.9E-01 | 2.5E-01 | 3.6E+11 | 7.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 40| +1.48E+02 | -2.4E-01 | +3.2E-01 | 6.2E-02 | 2.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 18| +2.23E+02 | -3.4E+00 | +1.1E+00 | 1.2E-01 | 2.1E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 10| +2.06E+02 | +2.3E+02 | -1.4E+01 | 2.0E+00 | 8.5E+01 | 5.7E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 11| +2.17E+02 | -1.7E+01 | +6.3E-01 | 2.5E-01 | 4.9E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 16| +2.06E+02 | -8.7E+00 | +1.1E+00 | 2.5E-01 | 3.9E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 57| +1.48E+02 | +1.8E-06 | -4.1E+00 | 2.0E-03 | 5.3E-02 | 4.7E-04 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 19| +2.23E+02 | -8.5E-01 | +1.2E-01 | 2.5E-01 | 2.0E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 12| +2.16E+02 | -1.2E+00 | +5.0E-01 | 2.5E-01 | 2.4E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 41| +1.48E+02 | -9.5E-02 | +7.6E-01 | 6.2E-02 | 1.2E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 17| +2.06E+02 | -3.9E+00 | -9.5E-02 | 5.0E-01 | 3.1E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 3| +1.74E+09 | -9.9E+09 | +8.9E-01 | 5.0E-01 | 5.3E+10 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 13| +2.16E+02 | +5.5E-01 | -3.9E-01 | 2.5E-01 | 1.7E+01 | 7.3E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 20| +2.19E+02 | -3.7E+00 | +8.4E-01 | 6.2E-02 | 4.9E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 58| +1.48E+02 | +9.3E-07 | -2.6E+00 | 4.9E-04 | 5.3E-02 | 1.2E-04 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 42| +1.48E+02 | -7.8E-02 | +9.5E-01 | 1.2E-01 | 8.4E+00 | 9.7E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 11| +1.91E+02 | -1.5E+01 | +1.4E+00 | 5.0E-01 | 8.5E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 14| +2.15E+02 | -4.4E-01 | +3.1E-01 | 6.2E-02 | 1.7E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 18| +2.01E+02 | -4.8E+00 | +7.6E-01 | 1.2E-01 | 3.1E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 4| +2.61E+08 | -1.5E+09 | +8.9E-01 | 1.0E+00 | 8.0E+09 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 61| +1.63E+02 | -1.1E-01 | +9.7E-03 | 5.6E-02 | 3.8E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 21| +2.17E+02 | -2.3E+00 | +8.0E-01 | 1.2E-01 | 2.4E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 59| +1.48E+02 | -2.1E-08 | +6.2E-02 | 1.2E-04 | 5.3E-02 | 3.3E-05 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 43| +1.48E+02 | -7.0E-02 | +8.7E-01 | 2.5E-01 | 5.8E+00 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 15| +2.14E+02 | -6.6E-01 | +6.0E-01 | 6.2E-02 | 1.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 5| +3.95E+07 | -2.2E+08 | +8.9E-01 | 1.0E+00 | 1.2E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 19| +1.95E+02 | -5.8E+00 | +6.8E-01 | 2.5E-01 | 4.1E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 22| +2.14E+02 | -2.5E+00 | +5.9E-01 | 2.5E-01 | 1.9E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 60| +1.48E+02 | +2.8E-06 | -4.1E+00 | 3.1E-05 | 5.3E-02 | 3.5E-05 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 44| +1.48E+02 | +7.5E-02 | -1.7E+00 | 5.0E-01 | 3.6E+00 | 2.1E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 12| +1.91E+02 | +4.2E+01 | -2.3E+00 | 1.0E+00 | 4.9E+01 | 2.1E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 6| +6.00E+06 | -3.3E+07 | +8.9E-01 | 1.0E+00 | 1.8E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 16| +2.14E+02 | -4.9E-03 | -2.0E-02 | 6.2E-02 | 1.2E+01 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 23| +2.07E+02 | -6.8E+00 | +9.6E-01 | 2.5E-01 | 3.3E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 20| +1.89E+02 | -6.2E+00 | +3.7E-01 | 2.5E-01 | 3.9E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 61| +1.48E+02 | +3.0E-06 | -7.2E+00 | 7.6E-06 | 5.3E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 17| +2.14E+02 | -2.3E-01 | +8.6E-01 | 1.6E-02 | 1.2E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 7| +9.19E+05 | -5.1E+06 | +8.9E-01 | 1.0E+00 | 2.8E+07 | 9.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 45| +1.48E+02 | -1.3E-02 | +6.0E-01 | 1.0E-01 | 3.6E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 62| +1.48E+02 | +2.6E-06 | -2.1E+01 | 1.9E-06 | 5.3E-02 | 3.2E-06 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 24| +1.82E+02 | -2.5E+01 | +1.2E+00 | 5.0E-01 | 2.5E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 18| +2.14E+02 | -3.3E-01 | +9.6E-01 | 3.1E-02 | 1.0E+01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 21| +1.80E+02 | -8.9E+00 | +8.7E-01 | 2.5E-01 | 7.0E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 13| +1.78E+02 | -1.3E+01 | +7.1E-01 | 2.5E-01 | 4.9E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 8| +1.42E+05 | -7.8E+05 | +8.9E-01 | 1.0E+00 | 4.2E+06 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 46| +1.48E+02 | +2.7E-02 | -6.9E-01 | 1.0E-01 | 2.4E+00 | 9.6E-02 | 2d |0\n", - "2023-02-17 16:05:10.784 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 37.9918 and h = 5.00215e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:10.784 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 37.9918: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:10 fides(INFO) 25| +1.82E+02 | +0.0E+00 | +0.0E+00 | 1.0E+00 | 9.0E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 19| +2.13E+02 | -4.8E-01 | +8.3E-01 | 6.2E-02 | 9.0E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 63| +1.48E+02 | +6.0E-06 | -1.9E+02 | 4.8E-07 | 5.3E-02 | 8.1E-07 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 22| +1.63E+02 | -1.7E+01 | +1.1E+00 | 5.0E-01 | 6.2E+01 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 47| +1.48E+02 | -6.8E-03 | +6.0E-01 | 2.5E-02 | 2.4E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 9| +2.22E+04 | -1.2E+05 | +9.0E-01 | 1.0E+00 | 6.5E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 20| +2.13E+02 | -1.1E-01 | +2.1E-01 | 1.2E-01 | 1.1E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 26| +1.68E+02 | -1.4E+01 | +8.3E-01 | 2.5E-01 | 9.0E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 14| +1.71E+02 | -6.9E+00 | +9.9E-01 | 2.5E-01 | 1.1E+02 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 64| +1.48E+02 | +1.3E-06 | -1.6E+02 | 1.2E-07 | 5.3E-02 | 2.0E-07 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 23| +1.59E+02 | -4.3E+00 | +9.0E-01 | 1.0E+00 | 6.6E+01 | 1.7E+00 | trr |1\n", - "2023-02-17 16:05:10 fides(INFO) 48| +1.48E+02 | -3.0E-03 | +9.9E-01 | 2.5E-02 | 2.1E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 62| +1.60E+02 | -2.7E+00 | +1.0E+00 | 1.4E-02 | 1.1E+02 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 21| +2.13E+02 | -3.8E-01 | +7.0E-01 | 3.1E-02 | 1.2E+01 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 27| +1.64E+02 | -4.1E+00 | +9.1E-01 | 5.0E-01 | 7.4E+01 | 5.4E-01 | trr |1\n", - "2023-02-17 16:05:10 fides(INFO) 65| +1.48E+02 | +3.1E-06 | -1.5E+03 | 3.0E-08 | 5.3E-02 | 5.1E-08 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 22| +2.13E+02 | -1.9E-01 | +8.6E-01 | 3.1E-02 | 7.4E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 49| +1.48E+02 | -4.2E-03 | +9.5E-01 | 5.0E-02 | 1.8E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 10| +3.64E+03 | -1.9E+04 | +9.0E-01 | 1.0E+00 | 1.0E+05 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 24| +1.59E+02 | +1.6E+03 | -1.2E+02 | 1.0E+00 | 7.0E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:10.914 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.723 and h = 3.11306e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:10.914 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.723: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:10 fides(INFO) 66| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.5E-09 | 5.3E-02 | 1.3E-08 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 28| +1.64E+02 | -1.6E+00 | -3.3E-01 | 5.0E-01 | 6.9E+01 | 9.0E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 15| +1.61E+02 | -1.0E+01 | +9.0E-01 | 5.0E-01 | 7.3E+01 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 23| +2.13E+02 | -2.4E-02 | +8.0E-02 | 6.2E-02 | 6.5E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 50| +1.48E+02 | -5.6E-03 | +9.2E-01 | 1.0E-01 | 1.4E+00 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 67| +1.48E+02 | +1.9E-06 | -1.5E+04 | 1.9E-09 | 5.3E-02 | 3.2E-09 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 24| +2.13E+02 | -2.2E-01 | +8.3E-01 | 1.6E-02 | 1.2E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 29| +1.59E+02 | -5.6E+00 | +8.1E-01 | 1.2E-01 | 6.9E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 11| +7.26E+02 | -2.9E+03 | +9.0E-01 | 2.0E+00 | 1.6E+04 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 25| +1.52E+02 | -7.0E+00 | +5.3E-01 | 2.3E-01 | 7.0E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 51| +1.48E+02 | +1.6E-02 | -2.0E+00 | 2.0E-01 | 1.1E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 16| +1.56E+02 | -4.5E+00 | +9.9E-02 | 1.0E+00 | 7.5E+01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 25| +2.12E+02 | -9.8E-02 | +5.4E-01 | 3.1E-02 | 6.8E+00 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 68| +1.48E+02 | -5.6E-07 | +1.8E+04 | 4.7E-10 | 5.3E-02 | 7.9E-10 | 2d |1\n", - "2023-02-17 16:05:10 fides(WARNING) Stopping as function difference 5.55E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 30| +1.53E+02 | -5.5E+00 | +1.0E+00 | 2.5E-01 | 7.0E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 26| +1.49E+02 | -2.9E+00 | +9.7E-01 | 2.3E-01 | 1.4E+02 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 52| +1.48E+02 | -1.2E-03 | +4.3E-01 | 5.0E-02 | 1.1E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 26| +2.12E+02 | -2.7E-02 | +1.2E-01 | 3.1E-02 | 6.2E+00 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 31| +1.53E+02 | +2.0E+01 | -3.2E+00 | 5.0E-01 | 5.6E+01 | 7.8E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 12| +7.26E+02 | +9.4E+03 | -2.8E+01 | 2.0E+00 | 2.5E+03 | 3.8E+00 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 17| +1.53E+02 | -3.1E+00 | +1.0E+00 | 2.5E-01 | 7.8E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 63| +1.59E+02 | -1.2E+00 | +8.4E-01 | 2.8E-02 | 3.6E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 27| +1.49E+02 | +1.3E+00 | -6.9E-01 | 4.7E-01 | 3.3E+01 | 6.9E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 53| +1.48E+02 | +1.1E-03 | -2.3E-01 | 5.0E-02 | 9.6E-01 | 1.7E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 27| +2.12E+02 | -9.1E-02 | +9.0E-01 | 7.8E-03 | 8.7E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 32| +1.51E+02 | -2.6E+00 | +7.3E-01 | 1.2E-01 | 5.6E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 28| +2.12E+02 | -8.2E-02 | +7.4E-01 | 1.6E-02 | 6.4E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 54| +1.48E+02 | -9.9E-04 | +5.4E-01 | 1.3E-02 | 9.6E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:11.099 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 208.039 and h = 1.4974e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:11.099 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 208.039: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:11 fides(INFO) 13| +7.26E+02 | +0.0E+00 | +0.0E+00 | 4.3E-01 | 2.5E+03 | 8.8E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 28| +1.48E+02 | -8.3E-01 | +4.9E-01 | 1.2E-01 | 3.3E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 18| +1.53E+02 | -3.9E-01 | +1.0E+00 | 5.0E-01 | 4.0E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 33| +1.49E+02 | -1.2E+00 | +8.6E-01 | 1.2E-01 | 5.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 29| +2.12E+02 | -6.2E-02 | +8.4E-01 | 1.6E-02 | 4.5E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 55| +1.48E+02 | -7.5E-04 | +9.4E-01 | 1.3E-02 | 2.1E+00 | 5.8E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 0| +3.71E+02 | NaN | NaN | 1.0E+00 | 1.4E+02 | NaN | NaN |1\n", - "2023-02-17 16:05:11.147 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 172.17 and h = 5.26904e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:11.147 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 172.17: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:11 fides(INFO) 34| +1.49E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 3.2E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 30| +2.12E+02 | -5.4E-02 | +4.9E-01 | 3.1E-02 | 4.9E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 29| +1.48E+02 | -4.0E-01 | +4.6E-01 | 1.2E-01 | 4.7E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 14| +3.80E+02 | -3.5E+02 | +8.5E-01 | 1.1E-01 | 2.5E+03 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 19| +1.52E+02 | -8.7E-01 | +7.8E-01 | 1.0E+00 | 2.3E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 56| +1.48E+02 | -7.6E-04 | +9.4E-01 | 2.5E-02 | 4.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 1| +3.36E+02 | -3.5E+01 | +2.1E-02 | 1.0E+00 | 1.4E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 31| +2.12E+02 | -8.0E-03 | +3.5E-02 | 3.1E-02 | 5.3E+00 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 35| +1.49E+02 | -6.2E-01 | +7.4E-01 | 6.2E-02 | 3.2E+01 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 30| +1.48E+02 | -1.2E-01 | +7.7E-01 | 1.2E-01 | 9.1E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 57| +1.48E+02 | -4.3E-04 | +4.5E-01 | 5.0E-02 | 9.4E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 2| +2.85E+02 | -5.1E+01 | +9.0E-01 | 2.5E-01 | 1.8E+02 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 20| +1.51E+02 | -3.6E-01 | +9.9E-01 | 2.0E+00 | 3.9E+01 | 1.0E+00 | trr |1\n", - "2023-02-17 16:05:11 fides(INFO) 32| +2.12E+02 | -8.0E-02 | +8.8E-01 | 7.8E-03 | 7.6E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 15| +2.32E+02 | -1.5E+02 | +8.8E-01 | 2.1E-01 | 8.4E+02 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 36| +1.48E+02 | -3.6E-01 | +7.7E-01 | 6.2E-02 | 4.0E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 31| +1.48E+02 | -9.2E-02 | +9.3E-01 | 2.3E-01 | 5.5E+00 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 33| +2.12E+02 | -5.0E-02 | +6.1E-01 | 1.6E-02 | 5.1E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 58| +1.48E+02 | +2.6E-03 | -1.2E+00 | 5.0E-02 | 4.0E-01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 21| +1.51E+02 | +1.7E-01 | -1.2E+00 | 2.0E+00 | 4.7E+00 | 9.1E-01 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 3| +2.67E+02 | -1.8E+01 | +1.7E-01 | 5.0E-01 | 5.6E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 37| +1.48E+02 | -3.0E-01 | +4.8E-01 | 1.2E-01 | 1.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 32| +1.48E+02 | +7.5E-03 | -8.6E-02 | 4.7E-01 | 5.0E+00 | 5.5E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 59| +1.48E+02 | +1.5E-04 | -1.6E-01 | 1.3E-02 | 4.0E-01 | 5.0E-03 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 34| +2.12E+02 | -2.2E-02 | +4.2E-01 | 1.6E-02 | 3.6E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 16| +2.11E+02 | -2.1E+01 | +8.5E-01 | 4.3E-01 | 1.3E+02 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 4| +2.52E+02 | -1.5E+01 | +8.1E-01 | 1.2E-01 | 1.1E+02 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 64| +1.57E+02 | -1.7E+00 | +8.4E-01 | 5.6E-02 | 2.9E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 22| +1.51E+02 | -9.5E-02 | +1.0E+00 | 2.3E-01 | 4.7E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 38| +1.48E+02 | +1.0E+00 | -9.7E-01 | 1.2E-01 | 3.1E+01 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 35| +2.12E+02 | -4.0E-02 | +5.6E-01 | 1.6E-02 | 4.7E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 33| +1.48E+02 | -3.1E-02 | +7.6E-01 | 1.2E-01 | 5.0E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 60| +1.48E+02 | -3.9E-05 | +6.2E-02 | 3.1E-03 | 4.0E-01 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 5| +2.52E+02 | -4.8E-02 | -2.8E-02 | 2.5E-01 | 2.8E+01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 36| +2.12E+02 | -1.6E-02 | +3.3E-01 | 1.6E-02 | 3.4E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 23| +1.51E+02 | -2.1E-01 | +1.3E+00 | 4.7E-01 | 6.5E+00 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 39| +1.48E+02 | -2.6E-01 | +6.8E-01 | 3.1E-02 | 3.1E+01 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 34| +1.48E+02 | -4.3E-02 | +6.3E-01 | 2.3E-01 | 3.8E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 61| +1.48E+02 | -3.7E-04 | +9.5E-01 | 7.9E-04 | 1.8E+00 | 5.5E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 37| +2.12E+02 | -3.5E-02 | +5.2E-01 | 1.6E-02 | 4.4E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 6| +2.50E+02 | -2.0E+00 | +7.3E-01 | 6.2E-02 | 2.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 24| +1.51E+02 | -4.8E-01 | +1.0E+00 | 9.4E-01 | 9.9E+00 | 3.2E-01 | trr |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 40| +1.48E+02 | -5.2E-02 | +8.1E-01 | 3.1E-02 | 6.5E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 35| +1.48E+02 | +1.5E-01 | -1.1E+00 | 2.3E-01 | 6.3E+00 | 2.5E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 62| +1.48E+02 | -3.1E-05 | +1.0E+00 | 1.6E-03 | 1.5E-01 | 6.9E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 38| +2.12E+02 | -1.4E-02 | +2.9E-01 | 1.6E-02 | 3.2E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 17| +2.11E+02 | +1.7E+02 | -1.5E+01 | 8.5E-01 | 3.1E+01 | 1.6E+00 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 7| +2.50E+02 | -1.8E-02 | +1.2E-01 | 6.2E-02 | 5.1E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 41| +1.48E+02 | -7.6E-02 | +9.4E-01 | 6.2E-02 | 1.3E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 25| +1.51E+02 | +4.7E-01 | -4.4E-01 | 9.4E-01 | 4.8E+01 | 3.7E-01 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 36| +1.48E+02 | -2.2E-02 | +5.4E-01 | 5.8E-02 | 6.3E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 39| +2.12E+02 | -3.0E-02 | +4.8E-01 | 1.6E-02 | 4.1E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 8| +2.50E+02 | -3.2E-02 | +3.2E-01 | 1.6E-02 | 4.1E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 63| +1.48E+02 | -3.8E-05 | +9.4E-01 | 3.1E-03 | 2.3E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 42| +1.48E+02 | -5.1E-02 | +5.7E-01 | 1.2E-01 | 4.1E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 40| +2.12E+02 | -1.2E-02 | +2.7E-01 | 1.6E-02 | 3.0E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 37| +1.48E+02 | -1.1E-02 | +8.5E-01 | 5.8E-02 | 2.4E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 26| +1.50E+02 | -4.2E-01 | +9.8E-01 | 4.7E-02 | 4.8E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 64| +1.48E+02 | -5.1E-05 | +8.8E-01 | 6.3E-03 | 8.6E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 43| +1.48E+02 | +1.4E-01 | -8.1E-01 | 1.2E-01 | 5.6E+00 | 8.6E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 18| +2.08E+02 | -3.4E+00 | +4.1E-01 | 1.6E-01 | 3.1E+01 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 9| +2.50E+02 | -7.8E-03 | +1.9E-01 | 1.6E-02 | 2.7E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 41| +2.12E+02 | -2.6E-02 | +4.5E-01 | 1.6E-02 | 3.9E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 65| +1.48E+02 | -7.4E-05 | +9.6E-01 | 1.3E-02 | 1.8E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 38| +1.48E+02 | -1.4E-02 | +7.9E-01 | 1.2E-01 | 5.3E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 27| +1.50E+02 | -4.1E-01 | +8.8E-01 | 9.4E-02 | 1.3E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 65| +1.56E+02 | -1.1E+00 | +4.5E-01 | 1.1E-01 | 5.5E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 10| +2.50E+02 | -1.2E-02 | +7.4E-01 | 3.9E-03 | 2.1E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 39| +1.48E+02 | -4.5E-03 | +1.6E-01 | 2.3E-01 | 1.6E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 44| +1.48E+02 | -3.4E-02 | +5.7E-01 | 3.1E-02 | 5.6E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 42| +2.12E+02 | -1.1E-02 | +2.6E-01 | 1.6E-02 | 2.9E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 66| +1.48E+02 | -1.1E-04 | +9.1E-01 | 2.5E-02 | 8.0E-02 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 11| +2.50E+02 | +7.2E-06 | -9.2E-03 | 3.9E-03 | 3.7E-01 | 9.9E-03 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 28| +1.50E+02 | -1.2E-01 | +8.3E-01 | 1.9E-01 | 1.8E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 43| +2.12E+02 | -2.3E-02 | +4.3E-01 | 1.6E-02 | 3.6E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 45| +1.48E+02 | -1.6E-02 | +8.7E-01 | 3.1E-02 | 6.4E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 19| +1.99E+02 | -8.5E+00 | +9.5E-01 | 1.6E-01 | 9.1E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 67| +1.48E+02 | -7.9E-05 | +3.8E-01 | 5.0E-02 | 2.9E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 40| +1.48E+02 | -1.7E-02 | +3.7E-01 | 5.8E-02 | 5.5E+00 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 12| +2.50E+02 | -4.0E-04 | +5.9E-01 | 9.8E-04 | 3.7E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 29| +1.50E+02 | +5.5E-03 | -2.2E-01 | 3.8E-01 | 2.0E+00 | 9.9E-02 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 46| +1.48E+02 | -2.2E-02 | +9.1E-01 | 6.2E-02 | 2.7E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 44| +2.12E+02 | -1.1E-02 | +2.7E-01 | 1.6E-02 | 2.7E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 68| +1.48E+02 | +1.4E-03 | -1.9E+00 | 5.0E-02 | 2.0E-01 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 41| +1.48E+02 | -3.0E-03 | +3.5E-01 | 5.8E-02 | 1.4E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 13| +2.50E+02 | -3.5E-08 | +1.6E-03 | 9.8E-04 | 5.8E-02 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 45| +2.12E+02 | -2.0E-02 | +4.1E-01 | 1.6E-02 | 3.4E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 30| +1.50E+02 | -9.2E-03 | +7.6E-01 | 3.5E-02 | 2.0E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 69| +1.48E+02 | +4.1E-05 | -1.5E-01 | 1.3E-02 | 2.0E-01 | 3.6E-03 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 47| +1.48E+02 | +7.5E-03 | -2.5E-01 | 1.2E-01 | 4.2E+00 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 20| +1.94E+02 | -5.8E+00 | +5.3E-01 | 3.2E-01 | 4.2E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 42| +1.48E+02 | -3.9E-03 | +6.6E-01 | 5.8E-02 | 3.8E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 46| +2.12E+02 | -1.1E-02 | +2.8E-01 | 1.6E-02 | 2.6E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 14| +2.50E+02 | -2.2E-06 | +1.1E-01 | 2.4E-04 | 5.8E-02 | 6.4E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 70| +1.48E+02 | -2.8E-05 | +1.9E-01 | 3.1E-03 | 2.0E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 48| +1.48E+02 | -8.3E-03 | +7.3E-01 | 3.1E-02 | 4.2E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 31| +1.50E+02 | -4.4E-03 | +8.8E-01 | 7.0E-02 | 4.0E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 43| +1.48E+02 | -1.6E-03 | +7.3E-01 | 5.8E-02 | 6.7E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 47| +2.12E+02 | -1.8E-02 | +4.1E-01 | 1.6E-02 | 3.2E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 15| +2.50E+02 | -4.5E-06 | +9.0E-01 | 6.1E-05 | 5.1E-02 | 1.1E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 32| +1.50E+02 | -4.5E-04 | +7.2E-01 | 1.4E-01 | 9.6E-01 | 1.2E-02 | trr |1\n", - "2023-02-17 16:05:11 fides(INFO) 49| +1.48E+02 | -7.2E-03 | +7.7E-01 | 3.1E-02 | 1.7E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 71| +1.48E+02 | -7.9E-05 | +9.3E-01 | 7.9E-04 | 8.5E-01 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:11.838 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.1643 and h = 1.42051e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:11.838 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.1643: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:11 fides(INFO) 44| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.8E-02 | 1.5E+00 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 48| +2.12E+02 | -1.1E-02 | +3.0E-01 | 1.6E-02 | 2.5E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 50| +1.48E+02 | -4.3E-03 | +3.0E-01 | 6.2E-02 | 3.9E+00 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 16| +2.50E+02 | -3.0E-06 | +6.0E-01 | 1.2E-04 | 3.2E-02 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 49| +2.12E+02 | -1.7E-02 | +4.2E-01 | 1.6E-02 | 3.0E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 33| +1.50E+02 | +6.5E-05 | -2.0E-01 | 1.4E-01 | 5.2E-01 | 1.0E-02 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 72| +1.48E+02 | -5.7E-06 | +1.6E+00 | 1.6E-03 | 5.8E-02 | 3.7E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 45| +1.48E+02 | -6.5E-04 | +9.4E-01 | 1.5E-02 | 1.5E+00 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 73| +1.48E+02 | -2.7E-06 | +4.9E-01 | 3.1E-03 | 5.4E-02 | 7.1E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 66| +1.55E+02 | -8.8E-01 | +4.3E-01 | 1.1E-01 | 1.9E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 21| +1.86E+02 | -7.9E+00 | +5.9E-01 | 3.2E-01 | 1.2E+02 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 17| +2.50E+02 | +1.6E-11 | -1.2E-04 | 1.2E-04 | 4.9E-03 | 3.1E-04 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 50| +2.12E+02 | -1.2E-02 | +3.3E-01 | 1.6E-02 | 2.3E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 51| +1.48E+02 | +1.1E-01 | -2.7E+00 | 6.2E-02 | 1.7E+00 | 5.5E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 46| +1.48E+02 | -8.0E-04 | +9.8E-01 | 2.9E-02 | 2.7E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 34| +1.50E+02 | -1.4E-04 | +8.5E-01 | 6.2E-03 | 5.2E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 74| +1.48E+02 | -6.6E-06 | +1.4E+00 | 3.1E-03 | 3.8E-02 | 6.7E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 51| +2.12E+02 | -1.7E-02 | +4.4E-01 | 1.6E-02 | 2.8E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 18| +2.50E+02 | +1.6E-11 | -1.2E-04 | 3.1E-05 | 4.9E-03 | 7.8E-05 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 47| +1.48E+02 | -1.1E-03 | +9.0E-01 | 5.8E-02 | 7.6E-01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 52| +1.48E+02 | +3.0E-02 | -1.1E+00 | 1.6E-02 | 1.7E+00 | 3.2E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 35| +1.50E+02 | -2.7E-05 | +5.7E-01 | 1.2E-02 | 1.4E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 75| +1.48E+02 | -7.1E-06 | +8.7E-01 | 6.3E-03 | 5.6E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 22| +1.82E+02 | -3.9E+00 | +9.2E-01 | 3.2E-01 | 1.2E+02 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 52| +2.12E+02 | -1.2E-02 | +3.7E-01 | 1.6E-02 | 2.2E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 19| +2.50E+02 | -6.4E-08 | +7.9E-01 | 7.6E-06 | 4.9E-03 | 2.0E-05 | 2d |1\n", - "2023-02-17 16:05:12 fides(WARNING) Stopping as function difference 6.38E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:12 fides(INFO) 48| +1.48E+02 | -1.4E-03 | +7.4E-01 | 1.2E-01 | 3.1E-01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 53| +1.48E+02 | -7.1E-03 | +6.3E-01 | 3.9E-03 | 1.7E+00 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 36| +1.50E+02 | -1.1E-06 | +9.1E-02 | 1.2E-02 | 1.4E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 53| +2.12E+02 | -1.6E-02 | +4.7E-01 | 1.6E-02 | 2.6E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 76| +1.48E+02 | -1.3E-05 | +9.6E-01 | 1.3E-02 | 3.3E-02 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:12.047 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.6153 and h = 1.60736e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:12.047 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.6153: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:12 fides(INFO) 49| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 1.1E+00 | 6.9E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 54| +1.48E+02 | -6.0E-04 | +9.7E-01 | 3.9E-03 | 1.6E+00 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 23| +1.81E+02 | -1.1E+00 | +9.7E-01 | 6.3E-01 | 4.5E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 54| +2.12E+02 | -1.3E-02 | +1.0E+00 | 1.6E-02 | 2.0E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 77| +1.48E+02 | -2.3E-05 | +1.1E+00 | 2.5E-02 | 6.5E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 37| +1.50E+02 | -4.6E-06 | +3.9E-01 | 3.1E-03 | 1.9E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 50| +1.48E+02 | -7.1E-04 | +7.0E-01 | 2.9E-02 | 1.1E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 55| +1.48E+02 | -8.9E-04 | +9.4E-01 | 7.8E-03 | 5.8E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 55| +2.12E+02 | -2.9E-02 | +1.0E+00 | 3.1E-02 | 1.4E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 24| +1.81E+02 | +6.2E+00 | -4.8E+00 | 1.3E+00 | 3.3E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 0| +1.34E+06 | NaN | NaN | 1.0E+00 | 6.1E+06 | NaN | NaN |1\n", - "2023-02-17 16:05:12 fides(INFO) 78| +1.48E+02 | -1.6E-05 | +6.3E-01 | 5.0E-02 | 3.3E-02 | 7.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 38| +1.50E+02 | +1.1E-07 | -3.6E-02 | 3.1E-03 | 5.1E-02 | 9.7E-04 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 56| +1.48E+02 | -1.2E-03 | +9.6E-01 | 1.6E-02 | 1.3E+00 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 56| +2.12E+02 | -2.7E-02 | +1.1E+00 | 6.2E-02 | 2.3E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 51| +1.48E+02 | -2.6E-04 | +1.0E+00 | 2.9E-02 | 2.2E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 1| +1.14E+03 | -1.3E+06 | +1.1E-01 | 1.0E+00 | 6.1E+06 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 25| +1.80E+02 | -3.4E-01 | +3.2E-01 | 3.2E-01 | 3.3E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 79| +1.48E+02 | +2.3E-04 | -3.5E+00 | 5.0E-02 | 4.3E-02 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 57| +2.11E+02 | -3.2E-02 | +2.9E-01 | 1.2E-01 | 1.1E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 39| +1.50E+02 | -1.2E-06 | +8.9E-01 | 7.8E-04 | 5.1E-02 | 2.4E-04 | 2d |1\n", - "2023-02-17 16:05:12 fides(WARNING) Stopping as function difference 1.19E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:12 fides(INFO) 2| +4.50E+02 | -6.9E+02 | +9.0E-01 | 2.5E-01 | 3.7E+03 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 57| +1.48E+02 | -2.0E-03 | +9.0E-01 | 3.1E-02 | 4.0E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 52| +1.48E+02 | -4.2E-04 | +9.7E-01 | 5.8E-02 | 1.7E-01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 3| +2.97E+02 | -1.5E+02 | +9.1E-01 | 5.0E-01 | 6.1E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 58| +2.11E+02 | -1.8E-01 | +1.4E+00 | 1.2E-01 | 6.7E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 67| +1.55E+02 | +1.3E+00 | -1.5E+00 | 1.1E-01 | 3.3E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 80| +1.48E+02 | -7.5E-07 | +3.6E-02 | 1.3E-02 | 4.3E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 53| +1.48E+02 | -6.1E-04 | +9.6E-01 | 1.2E-01 | 2.1E-01 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 26| +1.79E+02 | -1.2E+00 | +8.4E-01 | 3.2E-01 | 3.8E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 58| +1.48E+02 | -6.3E-04 | +1.6E-01 | 6.2E-02 | 1.3E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 4| +2.51E+02 | -4.7E+01 | +7.6E-01 | 1.0E+00 | 9.8E+01 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 59| +2.11E+02 | -3.6E-01 | +1.2E+00 | 2.5E-01 | 2.9E+00 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 81| +1.48E+02 | -6.3E-06 | +8.9E-01 | 3.1E-03 | 6.2E-02 | 4.4E-04 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 54| +1.48E+02 | -4.3E-04 | +7.1E-01 | 2.3E-01 | 9.3E-02 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 5| +2.27E+02 | -2.3E+01 | +4.0E+00 | 2.0E+00 | 2.5E+01 | 3.8E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 59| +1.48E+02 | +7.6E-03 | -9.4E-01 | 1.6E-02 | 9.2E-01 | 1.7E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 60| +2.07E+02 | -3.5E+00 | +2.6E+00 | 5.0E-01 | 2.8E+00 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 27| +1.79E+02 | +4.8E+00 | -5.3E+00 | 6.3E-01 | 1.9E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 82| +1.48E+02 | -2.3E-06 | +8.6E-01 | 6.3E-03 | 7.4E-02 | 5.6E-04 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 55| +1.48E+02 | +1.9E-02 | -1.2E+01 | 2.3E-01 | 2.7E-01 | 7.0E-02 | trr |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 60| +1.48E+02 | -3.8E-04 | +7.3E-02 | 3.9E-03 | 9.2E-01 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 0| +9.11E+06 | NaN | NaN | 1.0E+00 | 4.2E+07 | NaN | NaN |1\n", - "2023-02-17 16:05:12 fides(INFO) 61| +2.07E+02 | +1.0E+02 | -8.3E+00 | 1.0E+00 | 9.9E+00 | 2.6E+00 | trr |0\n", - "2023-02-17 16:05:12 fides(INFO) 6| +2.27E+02 | +1.5E+04 | -7.0E+02 | 4.0E+00 | 4.2E+01 | 1.2E+01 | trr |0\n", - "2023-02-17 16:05:12 fides(INFO) 28| +1.79E+02 | -9.2E-02 | +1.9E-01 | 1.6E-01 | 1.9E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 83| +1.48E+02 | -9.2E-07 | +6.3E-01 | 1.3E-02 | 9.9E-03 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(WARNING) Stopping as function difference 9.23E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:12 fides(INFO) 56| +1.48E+02 | +5.4E-04 | -8.0E-01 | 5.7E-02 | 2.7E-01 | 1.7E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 1| +1.64E+03 | -9.1E+06 | +1.1E-01 | 1.0E+00 | 4.2E+07 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 62| +2.01E+02 | -6.2E+00 | +1.4E+00 | 2.5E-01 | 9.9E+00 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 61| +1.48E+02 | -1.8E-03 | +9.5E-01 | 9.8E-04 | 3.9E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 7| +2.27E+02 | +2.6E+02 | -1.2E+01 | 1.0E+00 | 4.2E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 57| +1.48E+02 | -1.1E-04 | +5.4E-01 | 1.4E-02 | 2.7E-01 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 62| +1.48E+02 | -3.2E-04 | +8.2E-01 | 2.0E-03 | 5.3E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 8| +2.18E+02 | -9.3E+00 | +4.2E-01 | 2.5E-01 | 4.2E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 2| +5.48E+02 | -1.1E+03 | +9.0E-01 | 2.5E-01 | 5.9E+03 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 29| +1.79E+02 | -1.0E-01 | +9.7E-01 | 3.9E-02 | 1.9E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 63| +1.72E+02 | -2.9E+01 | +2.0E+00 | 5.0E-01 | 3.6E+01 | 8.7E-01 | trr |1\n", - "2023-02-17 16:05:12 fides(INFO) 58| +1.48E+02 | -1.8E-05 | +1.0E+00 | 1.4E-02 | 2.9E-01 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 9| +2.14E+02 | -4.2E+00 | +5.9E-01 | 2.5E-01 | 4.8E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 3| +3.02E+02 | -2.5E+02 | +9.1E-01 | 5.0E-01 | 1.0E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 63| +1.48E+02 | -2.5E-04 | +8.9E-01 | 3.9E-03 | 7.8E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 64| +1.72E+02 | +8.7E+02 | -2.7E+01 | 5.0E-01 | 4.9E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 4| +2.58E+02 | -4.4E+01 | +8.4E-01 | 1.0E+00 | 1.9E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 10| +2.13E+02 | -2.6E-01 | +1.0E-01 | 2.5E-01 | 1.5E+01 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 30| +1.79E+02 | -9.2E-02 | +8.6E-01 | 7.9E-02 | 1.6E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 68| +1.55E+02 | +9.2E-01 | -1.4E+00 | 2.8E-02 | 3.3E+01 | 7.5E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 59| +1.48E+02 | -1.7E-05 | +1.1E+00 | 2.8E-02 | 4.8E-02 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 64| +1.48E+02 | -2.2E-04 | +8.0E-01 | 7.8E-03 | 2.3E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 5| +2.58E+02 | +2.7E+03 | -2.1E+02 | 2.0E+00 | 3.1E+01 | 4.1E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 0| +1.54E+13 | NaN | NaN | 1.0E+00 | 7.1E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:12 fides(INFO) 65| +1.69E+02 | -3.0E+00 | +2.5E-01 | 1.2E-01 | 4.9E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 11| +2.12E+02 | -1.3E+00 | +7.0E-01 | 6.2E-02 | 2.4E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 60| +1.48E+02 | -1.7E-05 | +8.7E-01 | 5.7E-02 | 8.5E-02 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 6| +2.58E+02 | +4.5E+02 | -3.0E+01 | 5.0E-01 | 3.1E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 65| +1.48E+02 | -2.4E-04 | +9.0E-01 | 1.6E-02 | 3.5E-01 | 7.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 31| +1.79E+02 | -1.4E-01 | +7.6E-01 | 1.6E-01 | 1.5E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 1| +1.08E+07 | -1.5E+13 | +8.4E-02 | 1.0E+00 | 7.1E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 66| +1.60E+02 | -8.8E+00 | +7.2E-01 | 1.2E-01 | 9.3E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 12| +2.12E+02 | -5.9E-01 | +1.0E+00 | 6.2E-02 | 7.5E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 7| +2.50E+02 | -7.4E+00 | +8.2E-01 | 1.2E-01 | 3.1E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 66| +1.48E+02 | -3.3E-04 | +8.6E-01 | 3.1E-02 | 1.7E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 61| +1.48E+02 | +1.0E-04 | -2.8E+00 | 1.1E-01 | 4.4E-02 | 2.1E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 13| +2.11E+02 | -8.7E-01 | +9.4E-01 | 1.2E-01 | 7.4E+00 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 32| +1.79E+02 | +2.8E-01 | -1.5E+00 | 3.2E-01 | 1.0E+01 | 5.4E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 67| +1.53E+02 | -7.3E+00 | +6.4E-01 | 1.2E-01 | 6.9E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 8| +2.50E+02 | +4.4E-01 | -5.8E-01 | 2.5E-01 | 1.0E+01 | 6.1E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 2| +1.65E+06 | -9.1E+06 | +8.9E-01 | 2.5E-01 | 5.0E+07 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 62| +1.48E+02 | +2.2E-06 | -1.6E-01 | 2.8E-02 | 4.4E-02 | 5.2E-03 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 67| +1.48E+02 | +4.0E-04 | -5.8E-01 | 6.2E-02 | 4.1E-01 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 9| +2.50E+02 | +3.7E-01 | -5.0E-01 | 6.2E-02 | 1.0E+01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 14| +2.09E+02 | -1.7E+00 | +7.7E-01 | 2.5E-01 | 1.0E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 68| +1.51E+02 | -1.7E+00 | +3.2E-01 | 1.2E-01 | 4.7E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:12.603 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1383 and h = 1.69942e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:12.603 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1383: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:12 fides(INFO) 3| +2.56E+05 | -1.4E+06 | +9.0E-01 | 5.0E-01 | 7.7E+06 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 33| +1.79E+02 | -5.1E-02 | +6.3E-01 | 7.9E-02 | 1.0E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 63| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.1E-03 | 4.4E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:12.618 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.406 and h = 2.66838e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:12.619 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.406: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:12 fides(INFO) 68| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 4.1E-01 | 7.5E-03 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 10| +2.50E+02 | -3.0E-01 | +8.5E-01 | 1.6E-02 | 1.0E+01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 15| +2.04E+02 | -5.2E+00 | +1.0E+00 | 5.0E-01 | 1.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 4| +4.44E+04 | -2.1E+05 | +8.7E-01 | 1.0E+00 | 1.2E+06 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 69| +1.51E+02 | +1.6E+01 | -2.7E+00 | 1.2E-01 | 9.4E+01 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 11| +2.50E+02 | +3.0E-02 | -2.0E-01 | 3.1E-02 | 4.8E+00 | 7.8E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 64| +1.48E+02 | +7.2E-07 | -2.8E-01 | 1.8E-03 | 4.4E-02 | 3.4E-04 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 69| +1.48E+02 | -6.7E-05 | +9.3E-01 | 3.9E-03 | 4.1E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 5| +7.00E+03 | -3.7E+04 | +9.0E-01 | 2.0E+00 | 2.0E+05 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 34| +1.79E+02 | -6.4E-02 | +8.2E-01 | 7.9E-02 | 1.1E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 12| +2.50E+02 | -6.6E-02 | +8.1E-01 | 7.8E-03 | 4.8E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 16| +2.04E+02 | +1.6E+02 | -1.8E+01 | 1.0E+00 | 1.5E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 70| +1.48E+02 | -6.2E-05 | +8.7E-01 | 7.8E-03 | 5.2E-02 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 65| +1.48E+02 | +2.0E-06 | -1.0E+00 | 4.4E-04 | 4.4E-02 | 1.2E-04 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 70| +1.51E+02 | +4.9E-01 | -1.4E-01 | 3.1E-02 | 9.4E+01 | 8.4E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 6| +1.25E+03 | -5.7E+03 | +9.0E-01 | 2.0E+00 | 3.1E+04 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 13| +2.50E+02 | +1.2E-03 | -6.2E-02 | 1.6E-02 | 1.8E+00 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 17| +1.98E+02 | -6.1E+00 | +1.2E+00 | 2.5E-01 | 1.5E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 35| +1.79E+02 | +1.4E-02 | -2.4E-01 | 1.6E-01 | 7.4E+00 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 71| +1.50E+02 | -1.3E+00 | +8.2E-01 | 7.8E-03 | 9.4E+01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 66| +1.48E+02 | +2.5E-07 | -1.3E-01 | 1.1E-04 | 4.4E-02 | 9.0E-05 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 71| +1.48E+02 | -1.0E-04 | +9.8E-01 | 1.6E-02 | 6.5E-02 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 7| +3.67E+02 | -8.8E+02 | +9.0E-01 | 2.0E+00 | 4.9E+03 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 14| +2.50E+02 | -9.9E-03 | +7.1E-01 | 3.9E-03 | 1.8E+00 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 69| +1.55E+02 | +7.1E-02 | -1.6E-01 | 7.0E-03 | 3.3E+01 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 67| +1.48E+02 | +2.2E-06 | -1.4E+00 | 2.8E-05 | 4.4E-02 | 5.5E-05 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 18| +1.98E+02 | +2.5E+01 | -2.4E+00 | 5.0E-01 | 3.0E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 72| +1.48E+02 | -1.5E-04 | +9.8E-01 | 3.1E-02 | 1.3E-01 | 8.8E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 72| +1.49E+02 | -6.0E-01 | +9.0E-01 | 1.6E-02 | 4.2E+01 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 15| +2.50E+02 | -2.1E-05 | +8.1E-02 | 3.9E-03 | 2.0E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 8| +2.31E+02 | -1.4E+02 | +9.2E-01 | 2.0E+00 | 7.7E+02 | 4.4E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 36| +1.79E+02 | -2.2E-02 | +7.6E-01 | 3.9E-02 | 7.4E+00 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 19| +1.90E+02 | -7.6E+00 | +1.2E+00 | 1.2E-01 | 3.0E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 16| +2.50E+02 | -8.1E-05 | +3.5E-01 | 9.8E-04 | 2.0E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 68| +1.48E+02 | +2.6E-06 | -4.9E+00 | 6.9E-06 | 4.4E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 73| +1.49E+02 | -7.1E-01 | +7.0E-01 | 3.1E-02 | 2.8E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 73| +1.48E+02 | -1.1E-04 | +6.6E-01 | 6.2E-02 | 4.3E-02 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 9| +2.31E+02 | +1.1E+02 | -4.5E+00 | 4.0E+00 | 1.1E+02 | 7.3E+00 | trr |0\n", - "2023-02-17 16:05:12 fides(INFO) 17| +2.50E+02 | -6.3E-06 | +7.5E-02 | 9.8E-04 | 1.2E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:12.833 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 213.323 and h = 2.13543e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:12.833 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 213.323: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:12 fides(INFO) 37| +1.79E+02 | +0.0E+00 | +0.0E+00 | 7.9E-02 | 7.9E+00 | 3.5E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 20| +1.73E+02 | -1.7E+01 | +1.0E+00 | 2.5E-01 | 3.0E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 18| +2.50E+02 | -3.5E-05 | +7.9E-01 | 2.4E-04 | 1.1E-01 | 4.7E-04 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 74| +1.48E+02 | +3.6E-03 | -7.0E+00 | 6.2E-02 | 1.5E-01 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 69| +1.48E+02 | +4.9E-06 | -3.5E+01 | 1.7E-06 | 4.4E-02 | 3.5E-06 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 74| +1.48E+02 | -2.2E-01 | +3.5E-01 | 3.1E-02 | 3.8E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 10| +2.22E+02 | -9.6E+00 | +2.2E-01 | 6.3E-01 | 1.1E+02 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 19| +2.50E+02 | -3.1E-06 | +3.2E-01 | 4.9E-04 | 3.5E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 38| +1.79E+02 | -1.9E-02 | +1.0E+00 | 2.0E-02 | 7.9E+00 | 9.5E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 75| +1.48E+02 | -3.0E-01 | +7.4E-01 | 3.1E-02 | 3.5E+01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 70| +1.48E+02 | +2.1E-06 | -6.0E+01 | 4.3E-07 | 4.4E-02 | 8.7E-07 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 21| +1.73E+02 | +1.7E+01 | -1.4E+00 | 5.0E-01 | 1.0E+02 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 75| +1.48E+02 | +9.0E-05 | -4.7E-01 | 1.6E-02 | 1.5E-01 | 4.9E-03 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 20| +2.50E+02 | -2.9E-06 | +3.3E-01 | 4.9E-04 | 3.3E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 11| +2.09E+02 | -1.3E+01 | +1.0E+00 | 1.6E-01 | 6.5E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 76| +1.48E+02 | -7.8E-02 | +3.8E-01 | 3.1E-02 | 1.5E+01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 76| +1.48E+02 | -3.6E-05 | +6.2E-01 | 3.9E-03 | 1.5E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 71| +1.48E+02 | +3.0E-06 | -3.3E+02 | 1.1E-07 | 4.4E-02 | 2.2E-07 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 22| +1.63E+02 | -1.1E+01 | +9.1E-01 | 1.2E-01 | 1.0E+02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 21| +2.50E+02 | -3.0E-06 | +3.5E-01 | 4.9E-04 | 3.2E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 12| +1.95E+02 | -1.4E+01 | +6.0E-01 | 3.2E-01 | 4.7E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 39| +1.79E+02 | -2.0E-02 | +9.2E-01 | 3.9E-02 | 6.4E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 22| +2.50E+02 | -2.9E-06 | +3.6E-01 | 4.9E-04 | 3.0E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 77| +1.48E+02 | -2.7E-01 | +6.9E-01 | 3.1E-02 | 1.5E+01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 77| +1.48E+02 | -6.3E-06 | +7.1E-01 | 3.9E-03 | 2.1E-01 | 4.2E-04 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 23| +1.52E+02 | -1.1E+01 | +8.0E-01 | 2.5E-01 | 5.0E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 72| +1.48E+02 | +6.4E-06 | -2.8E+03 | 2.7E-08 | 4.4E-02 | 5.4E-08 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 70| +1.55E+02 | -1.2E-01 | +7.8E-01 | 1.8E-03 | 3.3E+01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 13| +1.77E+02 | -1.8E+01 | +8.9E-01 | 3.2E-01 | 9.3E+01 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 23| +2.50E+02 | -3.0E-06 | +3.9E-01 | 4.9E-04 | 2.9E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 24| +1.52E+02 | +6.1E+00 | -1.7E+00 | 5.0E-01 | 7.6E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 78| +1.48E+02 | +3.6E+00 | -5.2E+00 | 3.1E-02 | 1.4E+01 | 6.0E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 40| +1.78E+02 | -3.1E-02 | +1.0E+00 | 7.9E-02 | 6.4E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 73| +1.48E+02 | +4.2E-06 | -7.4E+03 | 6.8E-09 | 4.4E-02 | 1.4E-08 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 78| +1.48E+02 | +2.4E-06 | -7.3E-01 | 3.9E-03 | 2.8E-02 | 5.0E-04 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 24| +2.50E+02 | -2.9E-06 | +4.0E-01 | 4.9E-04 | 2.8E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 14| +1.77E+02 | +5.3E+01 | -3.5E+00 | 6.3E-01 | 1.2E+02 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 79| +1.48E+02 | +1.1E-01 | -5.8E-01 | 7.8E-03 | 1.4E+01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 25| +2.50E+02 | -3.0E-06 | +4.2E-01 | 4.9E-04 | 2.7E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 79| +1.48E+02 | -4.3E-06 | +2.8E+00 | 9.8E-04 | 2.8E-02 | 1.4E-04 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 41| +1.78E+02 | +6.6E-03 | -2.4E-01 | 1.6E-01 | 4.1E+00 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 25| +1.50E+02 | -1.9E+00 | +7.9E-01 | 1.2E-01 | 7.6E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 26| +2.50E+02 | -2.9E-06 | +4.3E-01 | 4.9E-04 | 2.6E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 15| +1.63E+02 | -1.4E+01 | +1.0E+00 | 1.6E-01 | 1.2E+02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 74| +1.48E+02 | +1.7E-07 | -1.2E+03 | 1.7E-09 | 4.4E-02 | 3.4E-09 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 80| +1.48E+02 | -2.9E-02 | +6.0E-01 | 2.0E-03 | 1.4E+01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 80| +1.48E+02 | -1.0E-06 | +6.1E-01 | 2.0E-03 | 7.0E-02 | 2.9E-04 | 2d |1\n", - "2023-02-17 16:05:13 fides(WARNING) Stopping as function difference 1.05E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:13 fides(INFO) 27| +2.50E+02 | -3.0E-06 | +4.5E-01 | 4.9E-04 | 2.6E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 16| +1.53E+02 | -9.5E+00 | +8.3E-01 | 3.2E-01 | 6.9E+01 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 26| +1.49E+02 | -1.3E+00 | +6.4E-01 | 2.4E-01 | 3.7E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 42| +1.78E+02 | -9.1E-03 | +7.5E-01 | 3.9E-02 | 4.1E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 81| +1.48E+02 | -2.3E-02 | +9.7E-01 | 2.0E-03 | 6.0E+00 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 28| +2.50E+02 | -2.9E-06 | +4.6E-01 | 4.9E-04 | 2.5E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 75| +1.48E+02 | +2.5E-06 | -7.2E+04 | 4.2E-10 | 4.4E-02 | 8.5E-10 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 27| +1.49E+02 | +1.6E+01 | -5.5E+00 | 2.4E-01 | 2.8E+01 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 29| +2.50E+02 | -3.0E-06 | +4.8E-01 | 4.9E-04 | 2.4E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 17| +1.50E+02 | -3.6E+00 | +5.4E-01 | 6.3E-01 | 1.1E+02 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 43| +1.78E+02 | -8.1E-03 | +7.5E-01 | 3.9E-02 | 4.5E+00 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 82| +1.48E+02 | -3.2E-02 | +8.8E-01 | 3.9E-03 | 6.4E+00 | 8.4E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 76| +1.48E+02 | +3.9E-06 | -4.5E+05 | 1.1E-10 | 4.4E-02 | 2.1E-10 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 30| +2.50E+02 | -2.9E-06 | +4.8E-01 | 4.9E-04 | 2.3E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 28| +1.48E+02 | -3.0E-01 | +3.1E-01 | 5.9E-02 | 2.8E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 18| +1.50E+02 | +3.2E+00 | -1.1E+00 | 6.3E-01 | 1.1E+02 | 6.8E-01 | trr |0\n", - "2023-02-17 16:05:13 fides(INFO) 83| +1.48E+02 | -6.3E-03 | +2.3E-01 | 7.8E-03 | 7.4E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 31| +2.50E+02 | -3.0E-06 | +5.0E-01 | 4.9E-04 | 2.3E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 44| +1.78E+02 | +1.6E-03 | -1.3E-01 | 7.9E-02 | 3.3E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 77| +1.48E+02 | +2.5E-06 | -1.1E+06 | 2.6E-11 | 4.4E-02 | 5.3E-11 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 71| +1.55E+02 | -5.4E-02 | +9.5E-01 | 3.5E-03 | 1.5E+01 | 9.6E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 29| +1.48E+02 | -2.9E-01 | +8.0E-01 | 5.9E-02 | 2.2E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 84| +1.48E+02 | -1.7E-02 | +9.2E-01 | 2.0E-03 | 6.3E+00 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 19| +1.48E+02 | -1.3E+00 | +9.3E-01 | 7.1E-02 | 1.1E+02 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 32| +2.50E+02 | -2.9E-06 | +5.0E-01 | 4.9E-04 | 2.3E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 0| +4.20E+12 | NaN | NaN | 1.0E+00 | 1.9E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:13 fides(INFO) 45| +1.78E+02 | -4.2E-03 | +7.6E-01 | 2.0E-02 | 3.3E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 78| +1.48E+02 | +1.2E-06 | -2.1E+06 | 6.6E-12 | 4.4E-02 | 1.3E-11 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 30| +1.48E+02 | -8.3E-02 | +2.7E-01 | 1.2E-01 | 2.0E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 33| +2.50E+02 | -3.0E-06 | +5.2E-01 | 4.9E-04 | 2.2E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 20| +1.48E+02 | -5.8E-01 | +7.8E-01 | 1.4E-01 | 2.7E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 85| +1.48E+02 | -1.7E-02 | +6.1E-01 | 3.9E-03 | 4.6E+00 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 79| +1.48E+02 | +2.4E-06 | -1.7E+07 | 1.6E-12 | 4.4E-02 | 3.3E-12 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 1| +4.24E+06 | -4.2E+12 | +8.7E-02 | 1.0E+00 | 1.9E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 34| +2.50E+02 | -2.9E-06 | +5.2E-01 | 4.9E-04 | 2.2E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 31| +1.48E+02 | +2.7E-01 | -4.4E-01 | 1.2E-01 | 1.4E+01 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 46| +1.78E+02 | -7.1E-03 | +9.8E-01 | 3.9E-02 | 3.6E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 80| +1.48E+02 | +3.1E-06 | -8.9E+07 | 4.1E-13 | 4.4E-02 | 8.3E-13 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 35| +2.50E+02 | -3.0E-06 | +5.3E-01 | 4.9E-04 | 2.2E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 21| +1.48E+02 | -2.0E-01 | +8.9E-01 | 2.8E-01 | 3.7E+01 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 86| +1.48E+02 | -4.8E-03 | +2.6E-01 | 3.9E-03 | 7.3E+00 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 2| +6.58E+05 | -3.6E+06 | +9.0E-01 | 2.5E-01 | 2.0E+07 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:13.358 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 83.5233 and h = 1.6975e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:13.358 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 83.5233: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:13 fides(INFO) 32| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.0E-02 | 1.4E+01 | 3.4E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 36| +2.50E+02 | -2.9E-06 | +5.3E-01 | 4.9E-04 | 2.1E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 87| +1.48E+02 | -9.4E-03 | +9.6E-01 | 3.9E-03 | 7.9E+00 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 22| +1.48E+02 | +2.8E+00 | -2.5E+01 | 5.7E-01 | 6.0E+00 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 47| +1.78E+02 | -8.8E-05 | -1.8E-02 | 7.9E-02 | 2.6E+00 | 9.4E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 81| +1.48E+02 | +2.6E-06 | -3.0E+08 | 1.0E-13 | 4.4E-02 | 2.1E-13 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 37| +2.50E+02 | -3.0E-06 | +5.4E-01 | 4.9E-04 | 2.1E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 33| +1.48E+02 | -7.7E-02 | +9.2E-01 | 7.4E-03 | 1.4E+01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 3| +1.03E+05 | -5.5E+05 | +9.0E-01 | 5.0E-01 | 3.0E+06 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 38| +2.50E+02 | -2.9E-06 | +5.5E-01 | 4.9E-04 | 2.1E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 82| +1.48E+02 | +3.6E-06 | -1.7E+09 | 2.6E-14 | 4.4E-02 | 5.2E-14 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 23| +1.48E+02 | -3.8E-02 | +6.6E-01 | 1.4E-01 | 6.0E+00 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 88| +1.48E+02 | -1.4E-02 | +7.3E-01 | 7.8E-03 | 2.8E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 34| +1.48E+02 | -8.4E-02 | +8.0E-01 | 1.5E-02 | 1.5E+01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 4| +1.64E+04 | -8.7E+04 | +9.0E-01 | 1.0E+00 | 4.7E+05 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 48| +1.78E+02 | -2.9E-03 | +7.7E-01 | 2.0E-02 | 2.6E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 39| +2.50E+02 | -3.0E-06 | +5.5E-01 | 4.9E-04 | 2.1E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 24| +1.48E+02 | +9.8E-02 | -1.6E+00 | 1.4E-01 | 6.6E+00 | 9.3E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 83| +1.48E+02 | +3.4E-06 | -6.2E+09 | 6.4E-15 | 4.4E-02 | 1.3E-14 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 5| +2.79E+03 | -1.4E+04 | +9.0E-01 | 1.0E+00 | 7.5E+04 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 89| +1.48E+02 | -6.6E-03 | +3.9E-01 | 7.8E-03 | 6.7E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 25| +1.48E+02 | -1.5E-02 | +5.9E-01 | 3.5E-02 | 6.6E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 40| +2.50E+02 | -4.6E-06 | +1.0E+00 | 4.9E-04 | 2.0E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 35| +1.48E+02 | -5.7E-02 | +8.0E-01 | 3.0E-02 | 1.1E+01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 49| +1.78E+02 | -4.1E-03 | +8.1E-01 | 3.9E-02 | 2.8E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 90| +1.48E+02 | +1.6E-02 | -2.4E+00 | 7.8E-03 | 1.9E+00 | 9.1E-03 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 84| +1.48E+02 | -2.4E-07 | +1.8E+09 | 1.6E-15 | 4.4E-02 | 3.2E-15 | 2d |1\n", - "2023-02-17 16:05:13 fides(WARNING) Stopping as function difference 2.41E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:13 fides(INFO) 41| +2.50E+02 | -7.0E-06 | +1.0E+00 | 9.8E-04 | 4.2E-03 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 6| +6.31E+02 | -2.2E+03 | +9.1E-01 | 2.0E+00 | 1.2E+04 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 36| +1.48E+02 | -4.6E-02 | +9.4E-01 | 5.9E-02 | 7.7E+00 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 26| +1.48E+02 | -1.1E-02 | +4.4E-01 | 3.5E-02 | 3.6E+00 | 9.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 50| +1.78E+02 | +1.3E-02 | -1.2E+00 | 7.9E-02 | 2.0E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 42| +2.50E+02 | -1.4E-05 | +1.0E+00 | 2.0E-03 | 8.9E-03 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 91| +1.48E+02 | +9.8E-03 | -2.5E+00 | 2.0E-03 | 1.9E+00 | 4.1E-03 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 72| +1.55E+02 | -7.6E-02 | +8.2E-01 | 7.0E-03 | 7.5E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 7| +2.91E+02 | -3.4E+02 | +9.1E-01 | 2.0E+00 | 1.9E+03 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 37| +1.48E+02 | -4.3E-02 | +7.8E-01 | 1.2E-01 | 5.8E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 92| +1.48E+02 | -3.6E-04 | +2.2E-01 | 4.9E-04 | 1.9E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 27| +1.48E+02 | +7.5E-04 | -3.3E-02 | 3.5E-02 | 6.6E+00 | 3.5E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 43| +2.50E+02 | -2.8E-05 | +1.1E+00 | 3.9E-03 | 1.2E-02 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 51| +1.78E+02 | -1.9E-03 | +5.6E-01 | 2.0E-02 | 2.0E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 44| +2.50E+02 | -5.6E-05 | +1.0E+00 | 7.8E-03 | 2.1E-02 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 28| +1.48E+02 | -8.1E-03 | +8.1E-01 | 8.9E-03 | 6.6E+00 | 9.0E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 38| +1.48E+02 | +6.4E-01 | -6.7E+00 | 2.4E-01 | 2.4E+00 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 93| +1.48E+02 | -5.6E-04 | +9.9E-01 | 1.2E-04 | 3.3E+00 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 8| +2.21E+02 | -7.0E+01 | +1.3E+00 | 2.0E+00 | 2.7E+02 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 45| +2.50E+02 | -1.1E-04 | +1.0E+00 | 1.6E-02 | 3.9E-02 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 39| +1.48E+02 | +1.0E-02 | -3.2E-01 | 5.9E-02 | 2.4E+00 | 7.5E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 29| +1.48E+02 | -3.2E-03 | +9.3E-01 | 1.8E-02 | 1.4E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 52| +1.78E+02 | -1.9E-03 | +7.9E-01 | 2.0E-02 | 2.2E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 0| +2.98E+09 | NaN | NaN | 1.0E+00 | 1.4E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:13 fides(INFO) 46| +2.50E+02 | -2.3E-04 | +1.0E+00 | 3.1E-02 | 7.6E-02 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 94| +1.48E+02 | -6.8E-04 | +1.0E+00 | 2.4E-04 | 2.5E+00 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 9| +2.21E+02 | +4.6E+04 | -1.3E+03 | 2.0E+00 | 5.2E+01 | 5.8E+00 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 47| +2.50E+02 | -5.0E-04 | +1.1E+00 | 6.2E-02 | 1.5E-01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 30| +1.48E+02 | -3.8E-03 | +9.6E-01 | 3.5E-02 | 2.2E+00 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 40| +1.48E+02 | -6.9E-03 | +6.8E-01 | 1.5E-02 | 2.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 53| +1.78E+02 | -1.1E-03 | +3.1E-01 | 3.9E-02 | 1.8E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 48| +2.50E+02 | -1.4E-03 | +1.3E+00 | 1.2E-01 | 3.0E-01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 95| +1.48E+02 | -9.9E-04 | +9.4E-01 | 4.9E-04 | 1.4E+00 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 1| +4.43E+04 | -3.0E+09 | +9.3E-02 | 1.0E+00 | 1.4E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 41| +1.48E+02 | -2.9E-03 | +8.7E-01 | 1.5E-02 | 2.7E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 31| +1.48E+02 | +2.1E-03 | -4.0E-01 | 7.1E-02 | 8.2E-01 | 4.7E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 10| +2.21E+02 | +2.8E+01 | -8.8E-01 | 5.0E-01 | 5.2E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 49| +2.50E+02 | -6.8E-03 | +1.9E+00 | 2.5E-01 | 5.9E-01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 42| +1.48E+02 | -4.1E-03 | +9.5E-01 | 3.0E-02 | 1.6E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 54| +1.78E+02 | -1.1E-03 | +1.7E-01 | 3.9E-02 | 1.8E+00 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 2| +8.94E+03 | -3.5E+04 | +8.9E-01 | 2.5E-01 | 1.8E+05 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 50| +2.50E+02 | -8.2E-02 | +3.3E+00 | 5.0E-01 | 1.2E+00 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 32| +1.48E+02 | -1.2E-03 | +5.4E-01 | 1.8E-02 | 8.2E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 96| +1.48E+02 | -7.9E-04 | +7.9E-01 | 9.8E-04 | 1.1E+00 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 3| +1.58E+03 | -7.4E+03 | +9.0E-01 | 5.0E-01 | 3.1E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 11| +2.06E+02 | -1.5E+01 | +9.3E-01 | 1.2E-01 | 5.2E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 51| +2.47E+02 | -2.4E+00 | +4.8E+00 | 1.0E+00 | 2.3E+00 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 43| +1.48E+02 | -3.4E-03 | +6.6E-01 | 5.9E-02 | 1.6E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 73| +1.55E+02 | +3.3E-02 | -5.3E-01 | 1.4E-02 | 7.1E+00 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 55| +1.78E+02 | -1.5E-03 | +7.4E-01 | 9.9E-03 | 1.3E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 33| +1.48E+02 | -1.3E-03 | +4.6E-01 | 1.8E-02 | 2.0E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 4| +4.27E+02 | -1.2E+03 | +9.0E-01 | 1.0E+00 | 4.9E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 97| +1.48E+02 | -7.3E-04 | +9.8E-01 | 2.0E-03 | 2.3E+00 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 52| +2.31E+02 | -1.6E+01 | +1.8E+00 | 2.0E+00 | 8.6E+00 | 3.7E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 12| +2.01E+02 | -4.9E+00 | +2.2E-01 | 2.5E-01 | 4.3E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 5| +2.63E+02 | -1.6E+02 | +8.8E-01 | 1.0E+00 | 7.8E+02 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 44| +1.48E+02 | +2.6E-02 | -2.3E+00 | 5.9E-02 | 1.0E+00 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 34| +1.48E+02 | -2.1E-04 | +1.2E-01 | 1.8E-02 | 5.5E-01 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:13.862 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.905 and h = 3.56407e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:13.862 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.905: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:13 fides(INFO) 98| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.9E-03 | 5.3E-01 | 5.2E-03 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 56| +1.78E+02 | -8.2E-04 | +8.0E-01 | 9.9E-03 | 1.4E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 53| +2.31E+02 | +7.7E+00 | -1.1E+00 | 4.0E+00 | 4.1E+01 | 1.2E+00 | trr |0\n", - "2023-02-17 16:05:13 fides(INFO) 45| +1.48E+02 | -5.8E-04 | +1.4E-01 | 1.5E-02 | 1.0E+00 | 8.4E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 35| +1.48E+02 | -5.4E-04 | +7.7E-01 | 4.4E-03 | 6.2E-01 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 54| +2.30E+02 | -1.7E+00 | +7.7E-01 | 1.4E-01 | 4.1E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 99| +1.48E+02 | -1.6E-04 | +9.8E-01 | 9.8E-04 | 5.3E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 6| +2.50E+02 | -1.3E+01 | +7.5E-01 | 1.0E+00 | 1.0E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 13| +1.98E+02 | -3.5E+00 | +9.4E-01 | 6.2E-02 | 6.6E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 57| +1.78E+02 | -8.6E-04 | +9.3E-01 | 2.0E-02 | 1.3E+00 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 46| +1.48E+02 | -1.0E-03 | +8.9E-01 | 3.7E-03 | 2.1E+00 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 36| +1.48E+02 | -3.2E-04 | +6.8E-01 | 8.9E-03 | 4.2E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 55| +2.23E+02 | -6.5E+00 | +1.2E+00 | 2.8E-01 | 3.8E+01 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 100| +1.48E+02 | -2.6E-04 | +1.0E+00 | 2.0E-03 | 8.1E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 7| +2.44E+02 | -5.2E+00 | +6.4E+00 | 1.0E+00 | 5.5E+00 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 47| +1.48E+02 | -5.4E-04 | +7.1E-01 | 7.4E-03 | 3.9E-01 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 14| +1.94E+02 | -3.5E+00 | +9.2E-01 | 1.2E-01 | 5.1E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 58| +1.78E+02 | -1.2E-03 | +1.0E+00 | 3.9E-02 | 1.2E+00 | 9.6E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 56| +2.17E+02 | -6.6E+00 | +9.8E-01 | 5.5E-01 | 2.0E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 37| +1.48E+02 | -1.8E-04 | +9.1E-01 | 8.9E-03 | 2.2E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 101| +1.48E+02 | -4.1E-04 | +1.0E+00 | 3.9E-03 | 4.2E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 8| +2.16E+02 | -2.8E+01 | +2.0E+00 | 2.0E+00 | 1.5E+01 | 3.6E+00 | 2d |1\n", - "2023-02-17 16:05:14.026 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.61 and h = 3.16878e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:14.027 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.61: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:14 fides(INFO) 48| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.4E-03 | 4.4E-01 | 5.5E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 57| +2.17E+02 | +8.6E+01 | -1.7E+01 | 1.1E+00 | 3.3E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 38| +1.48E+02 | -2.4E-04 | +7.6E-01 | 1.8E-02 | 5.1E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 102| +1.48E+02 | -8.5E-04 | +1.0E+00 | 7.8E-03 | 4.6E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 59| +1.78E+02 | +1.8E-03 | -1.0E+00 | 7.9E-02 | 8.1E-01 | 7.2E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 9| +2.16E+02 | +8.9E+02 | -4.8E+01 | 4.0E+00 | 2.7E+01 | 1.2E+01 | trr |0\n", - "2023-02-17 16:05:14 fides(INFO) 58| +2.15E+02 | -2.0E+00 | +6.1E-01 | 2.5E-01 | 3.3E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 49| +1.48E+02 | -9.2E-05 | +1.0E+00 | 1.9E-03 | 4.4E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 39| +1.48E+02 | +6.3E-04 | -1.1E+00 | 3.5E-02 | 2.0E-01 | 6.4E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 10| +2.16E+02 | +2.9E+01 | -3.6E+00 | 1.0E+00 | 2.7E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 103| +1.48E+02 | -1.4E-03 | +9.5E-01 | 1.6E-02 | 2.9E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 60| +1.78E+02 | -3.3E-04 | +5.7E-01 | 2.0E-02 | 8.1E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 59| +2.12E+02 | -2.4E+00 | +4.6E-01 | 2.5E-01 | 2.7E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 50| +1.48E+02 | -1.5E-04 | +9.8E-01 | 3.7E-03 | 2.6E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 40| +1.48E+02 | -1.0E-04 | +5.6E-01 | 8.9E-03 | 2.0E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 15| +1.93E+02 | -1.1E+00 | +1.0E+00 | 2.5E-01 | 2.3E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 11| +2.16E+02 | +3.8E+00 | -6.2E-01 | 2.5E-01 | 2.7E+01 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 60| +2.12E+02 | +1.1E-01 | -1.1E-01 | 2.5E-01 | 1.4E+01 | 6.6E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 104| +1.48E+02 | -2.0E-03 | +7.5E-01 | 3.1E-02 | 3.1E-01 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 74| +1.55E+02 | -1.7E-02 | +7.7E-01 | 3.5E-03 | 7.1E+00 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 61| +1.78E+02 | +3.3E-05 | -4.2E-02 | 2.0E-02 | 8.6E-01 | 3.1E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 51| +1.48E+02 | -2.3E-04 | +9.2E-01 | 7.4E-03 | 3.9E-01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 41| +1.48E+02 | -7.7E-05 | +6.2E-01 | 8.9E-03 | 2.4E-01 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 61| +2.12E+02 | -6.5E-01 | +8.3E-01 | 6.3E-02 | 1.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 12| +2.13E+02 | -2.9E+00 | +8.4E-01 | 6.2E-02 | 2.7E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 105| +1.48E+02 | -2.8E-03 | +5.0E-01 | 6.2E-02 | 1.1E+00 | 9.7E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 52| +1.48E+02 | -3.8E-04 | +9.7E-01 | 1.5E-02 | 2.0E-01 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 62| +2.11E+02 | -8.7E-01 | +6.2E-01 | 1.3E-01 | 1.0E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 13| +2.12E+02 | -7.5E-01 | +3.3E-01 | 1.2E-01 | 1.3E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 42| +1.48E+02 | -6.0E-05 | +7.2E-01 | 8.9E-03 | 1.4E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 62| +1.78E+02 | -2.5E-04 | +8.1E-01 | 4.9E-03 | 8.6E-01 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 106| +1.48E+02 | +2.0E-03 | -5.2E-01 | 6.2E-02 | 2.4E+00 | 9.6E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 14| +2.10E+02 | -2.1E+00 | +6.9E-01 | 1.2E-01 | 2.6E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 53| +1.48E+02 | -3.9E-04 | +7.0E-01 | 3.0E-02 | 4.1E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 63| +2.10E+02 | -5.7E-01 | +6.0E-01 | 1.3E-01 | 8.3E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 43| +1.48E+02 | -4.0E-05 | +5.8E-01 | 8.9E-03 | 1.5E-01 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 16| +1.78E+02 | -1.5E+01 | +2.2E+00 | 5.0E-01 | 2.3E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 15| +2.09E+02 | -1.5E+00 | +5.4E-01 | 1.2E-01 | 1.3E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 63| +1.78E+02 | -1.9E-04 | +8.1E-01 | 9.9E-03 | 6.8E-01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 107| +1.48E+02 | -2.9E-04 | +1.3E-01 | 1.6E-02 | 2.4E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 54| +1.48E+02 | +1.5E-03 | -1.5E+00 | 3.0E-02 | 2.5E-01 | 7.4E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 64| +2.10E+02 | -2.4E-01 | +1.9E-01 | 1.3E-01 | 1.1E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 44| +1.48E+02 | -3.1E-05 | +5.4E-01 | 8.9E-03 | 1.1E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 16| +2.06E+02 | -2.5E+00 | +6.6E-01 | 1.2E-01 | 2.8E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 64| +1.78E+02 | -2.5E-04 | +6.8E-01 | 2.0E-02 | 7.5E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 65| +2.09E+02 | -5.4E-01 | +8.9E-01 | 3.1E-02 | 1.5E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 108| +1.48E+02 | -1.0E-03 | +9.3E-01 | 3.9E-03 | 2.9E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 55| +1.48E+02 | -4.0E-05 | +1.0E-01 | 7.4E-03 | 2.5E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 45| +1.48E+02 | -1.9E-05 | +3.1E-01 | 8.9E-03 | 1.3E-01 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 17| +2.04E+02 | -2.1E+00 | +7.1E-01 | 1.2E-01 | 1.8E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 66| +2.09E+02 | -2.2E-01 | +9.8E-01 | 6.3E-02 | 4.9E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 109| +1.48E+02 | +3.4E-03 | -7.9E+00 | 7.8E-03 | 2.8E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 46| +1.48E+02 | -8.9E-07 | +1.1E-02 | 8.9E-03 | 1.1E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 65| +1.78E+02 | +2.6E-04 | -3.7E-01 | 2.0E-02 | 5.4E-01 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 56| +1.48E+02 | -1.5E-04 | +8.9E-01 | 1.9E-03 | 9.1E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 18| +2.02E+02 | -1.9E+00 | +6.4E-01 | 1.2E-01 | 2.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 17| +1.78E+02 | +1.7E+01 | -1.2E+00 | 1.0E+00 | 7.0E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 67| +2.09E+02 | -1.6E-01 | +9.0E-01 | 1.3E-01 | 2.5E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 47| +1.48E+02 | -3.0E-05 | +7.1E-01 | 2.2E-03 | 1.5E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 110| +1.48E+02 | +3.3E-05 | -1.2E-01 | 1.8E-03 | 2.8E-01 | 3.1E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 57| +1.48E+02 | -6.5E-05 | +8.0E-01 | 3.7E-03 | 1.0E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 19| +2.00E+02 | -2.7E+00 | +9.3E-01 | 1.2E-01 | 1.9E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 75| +1.55E+02 | -2.8E-02 | +9.4E-01 | 7.0E-03 | 4.5E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 68| +2.09E+02 | -5.2E-02 | +8.6E-01 | 2.5E-01 | 2.9E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 66| +1.78E+02 | -1.5E-04 | +6.9E-01 | 4.9E-03 | 5.4E-01 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 20| +1.90E+02 | -1.0E+01 | +1.7E+00 | 2.5E-01 | 2.1E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 48| +1.48E+02 | -7.8E-06 | +9.6E-01 | 2.2E-03 | 8.6E-02 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 111| +1.48E+02 | -1.4E-04 | +8.9E-01 | 4.6E-04 | 2.8E-01 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 69| +2.09E+02 | -4.3E-02 | +2.1E+00 | 2.5E-01 | 1.3E+00 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 58| +1.48E+02 | -5.8E-05 | +8.5E-01 | 7.4E-03 | 8.8E-02 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 18| +1.69E+02 | -9.4E+00 | +1.2E+00 | 2.5E-01 | 7.0E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 21| +1.90E+02 | -2.0E+00 | -3.4E-01 | 5.0E-01 | 2.8E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 49| +1.48E+02 | -5.0E-06 | +6.4E-01 | 4.4E-03 | 4.3E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 67| +1.78E+02 | -1.0E-04 | +1.0E+00 | 4.9E-03 | 6.0E-01 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 112| +1.48E+02 | -5.3E-05 | +1.0E+00 | 9.2E-04 | 4.0E-01 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 59| +1.48E+02 | -7.7E-05 | +8.0E-01 | 1.5E-02 | 7.8E-02 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 70| +2.09E+02 | -1.1E-01 | +2.5E+00 | 2.5E-01 | 9.3E-01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 22| +1.80E+02 | -9.2E+00 | +1.2E+00 | 1.2E-01 | 2.8E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 50| +1.48E+02 | +3.8E-08 | -7.5E-03 | 4.4E-03 | 7.3E-02 | 8.8E-04 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 113| +1.48E+02 | -3.5E-05 | +8.3E-01 | 1.8E-03 | 1.4E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 71| +2.08E+02 | -3.1E-01 | +2.3E+00 | 2.5E-01 | 1.8E+00 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 68| +1.78E+02 | -1.2E-04 | +9.3E-01 | 9.9E-03 | 5.1E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 60| +1.48E+02 | +1.9E-04 | -9.5E-01 | 3.0E-02 | 2.2E-01 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:14.613 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 95.0225 and h = 1.18628e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:14.613 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 95.0225: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:14 fides(INFO) 23| +1.80E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 4.4E+01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 72| +2.08E+02 | +1.8E+00 | -6.1E+00 | 2.5E-01 | 4.0E+00 | 7.0E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 114| +1.48E+02 | +1.2E-04 | -3.3E+00 | 3.7E-03 | 1.3E-01 | 4.6E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 51| +1.48E+02 | -3.1E-06 | +1.7E+00 | 1.1E-03 | 7.3E-02 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 69| +1.78E+02 | -1.8E-04 | +1.0E+00 | 2.0E-02 | 5.1E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 24| +1.75E+02 | -5.4E+00 | +1.1E+00 | 6.2E-02 | 4.4E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 61| +1.48E+02 | -3.8E-05 | +6.0E-01 | 7.4E-03 | 2.2E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 115| +1.48E+02 | -1.4E-05 | +8.8E-01 | 9.2E-04 | 1.3E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 73| +2.08E+02 | -1.6E-01 | +1.2E+00 | 6.3E-02 | 4.0E+00 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 52| +1.48E+02 | +6.0E-06 | -1.3E+00 | 2.2E-03 | 2.1E-02 | 6.8E-04 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 70| +1.78E+02 | +1.2E-05 | -4.9E-02 | 3.9E-02 | 3.7E-01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 25| +1.64E+02 | -1.1E+01 | +1.0E+00 | 1.2E-01 | 4.7E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 62| +1.48E+02 | -1.9E-05 | +4.9E-01 | 7.4E-03 | 7.7E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 116| +1.48E+02 | +1.0E-04 | -4.8E+00 | 1.8E-03 | 1.2E-01 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 74| +2.08E+02 | -5.9E-01 | +7.0E-01 | 1.3E-01 | 4.8E+00 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 26| +1.57E+02 | -7.6E+00 | +4.7E-01 | 2.5E-01 | 5.9E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 71| +1.78E+02 | -6.7E-05 | +7.6E-01 | 9.9E-03 | 3.7E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 53| +1.48E+02 | -2.0E-07 | +6.1E-02 | 5.5E-04 | 2.1E-02 | 3.8E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 75| +2.06E+02 | -1.4E+00 | +1.5E+00 | 1.3E-01 | 1.0E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 63| +1.48E+02 | -2.3E-05 | +5.6E-01 | 7.4E-03 | 1.8E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 117| +1.48E+02 | +7.5E-05 | -5.2E+00 | 4.6E-04 | 1.2E-01 | 5.2E-04 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 19| +1.66E+02 | -3.0E+00 | +7.7E-01 | 5.0E-01 | 3.8E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 27| +1.50E+02 | -6.6E+00 | +9.6E-01 | 2.5E-01 | 1.7E+02 | 7.0E-01 | trr |1\n", - "2023-02-17 16:05:14.763 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.642 and h = 2.79391e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:14.763 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.642: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:14 fides(INFO) 64| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.4E-03 | 6.7E-02 | 8.8E-04 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 76| +2.03E+02 | -3.5E+00 | +1.3E+00 | 2.5E-01 | 5.5E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 54| +1.48E+02 | -1.4E-06 | +7.5E-01 | 1.4E-04 | 1.1E-01 | 6.5E-05 | 2d |1\n", - "2023-02-17 16:05:14 fides(WARNING) Stopping as function difference 1.43E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:14 fides(INFO) 118| +1.48E+02 | +6.3E-05 | -5.4E+00 | 1.1E-04 | 1.2E-01 | 1.9E-04 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 72| +1.78E+02 | +1.7E-05 | -9.9E-02 | 2.0E-02 | 4.0E-01 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 28| +1.48E+02 | -1.5E+00 | +6.2E-01 | 2.5E-01 | 5.0E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 77| +2.03E+02 | +1.6E+01 | -1.5E+00 | 5.0E-01 | 2.5E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 76| +1.55E+02 | +1.6E-01 | -3.1E+00 | 1.4E-02 | 4.2E+00 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 65| +1.48E+02 | -4.5E-06 | +3.7E-01 | 1.9E-03 | 6.7E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 119| +1.48E+02 | -1.9E-06 | +4.0E-01 | 2.9E-05 | 1.2E-01 | 5.0E-05 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 29| +1.48E+02 | -5.2E-01 | +9.7E-01 | 2.5E-01 | 4.8E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 78| +1.99E+02 | -3.4E+00 | +6.4E-01 | 1.3E-01 | 2.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 73| +1.78E+02 | -5.0E-05 | +7.9E-01 | 4.9E-03 | 4.0E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 66| +1.48E+02 | -1.1E-05 | +1.6E+00 | 1.9E-03 | 1.8E-01 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 120| +1.48E+02 | -7.8E-07 | +4.1E-01 | 2.9E-05 | 1.2E-01 | 5.0E-05 | 2d |1\n", - "2023-02-17 16:05:14 fides(WARNING) Stopping as function difference 7.82E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:14 fides(INFO) 79| +1.97E+02 | -2.9E+00 | +8.4E-01 | 1.3E-01 | 1.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 30| +1.48E+02 | -8.9E-02 | +2.0E-01 | 5.0E-01 | 1.9E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 67| +1.48E+02 | -6.2E-06 | +8.9E-01 | 3.7E-03 | 2.7E-02 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 74| +1.78E+02 | -5.3E-05 | +8.0E-01 | 9.9E-03 | 3.2E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 0| +1.31E+11 | NaN | NaN | 1.0E+00 | 6.0E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:14 fides(INFO) 68| +1.48E+02 | -1.0E-05 | +8.4E-01 | 7.4E-03 | 8.4E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 31| +1.48E+02 | +8.5E-04 | -1.1E-02 | 1.2E-01 | 1.9E+01 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 80| +1.97E+02 | +8.7E+00 | -2.4E+00 | 2.5E-01 | 1.3E+01 | 4.3E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 75| +1.78E+02 | +2.1E-05 | -1.6E-01 | 2.0E-02 | 3.4E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 1| +1.53E+05 | -1.3E+11 | +8.8E-02 | 1.0E+00 | 6.0E+11 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 69| +1.48E+02 | -4.2E-06 | +2.3E-01 | 1.5E-02 | 3.2E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 20| +1.60E+02 | -6.0E+00 | +4.7E-01 | 1.0E+00 | 9.6E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 81| +1.96E+02 | -6.2E-01 | +5.3E-01 | 6.3E-02 | 1.3E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 32| +1.48E+02 | -1.7E-01 | +7.8E-01 | 3.1E-02 | 1.9E+01 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 76| +1.78E+02 | -3.6E-05 | +7.8E-01 | 4.9E-03 | 3.4E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 70| +1.48E+02 | -1.3E-05 | +5.2E-01 | 3.7E-03 | 9.4E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 82| +1.96E+02 | -3.9E-01 | +4.5E-01 | 6.3E-02 | 9.0E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 33| +1.48E+02 | -4.3E-02 | +2.9E-01 | 6.2E-02 | 8.1E+00 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 2| +2.41E+04 | -1.3E+05 | +9.0E-01 | 2.5E-01 | 7.0E+05 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 0| +4.56E+02 | NaN | NaN | 1.0E+00 | 3.3E+02 | NaN | NaN |1\n", - "2023-02-17 16:05:15 fides(INFO) 83| +1.95E+02 | -3.7E-01 | +6.8E-01 | 6.3E-02 | 1.0E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 3| +3.93E+03 | -2.0E+04 | +9.0E-01 | 5.0E-01 | 1.1E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 77| +1.78E+02 | -3.7E-05 | +7.0E-01 | 9.9E-03 | 2.7E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 71| +1.48E+02 | -3.5E-08 | +8.7E-03 | 3.7E-03 | 4.8E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 21| +1.56E+02 | -4.0E+00 | +9.7E-01 | 1.0E+00 | 1.2E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 34| +1.48E+02 | -2.6E-02 | +6.1E-01 | 6.2E-02 | 9.4E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 1| +3.80E+02 | -7.6E+01 | +1.2E-01 | 1.0E+00 | 3.3E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 77| +1.55E+02 | -1.0E-02 | +6.0E-01 | 3.5E-03 | 4.2E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 4| +7.99E+02 | -3.1E+03 | +9.0E-01 | 1.0E+00 | 1.7E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 72| +1.48E+02 | +4.6E-07 | -8.5E-01 | 9.3E-04 | 2.2E-02 | 1.9E-04 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 84| +1.95E+02 | -3.1E-01 | +9.2E-01 | 6.3E-02 | 7.2E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 2| +3.36E+02 | -4.5E+01 | +9.9E-01 | 2.5E-01 | 6.4E+01 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 78| +1.78E+02 | -1.3E-05 | +1.8E-01 | 9.9E-03 | 2.8E-01 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 35| +1.48E+02 | -1.2E-02 | +5.0E-01 | 6.2E-02 | 4.9E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 5| +3.19E+02 | -4.8E+02 | +9.0E-01 | 1.0E+00 | 2.7E+03 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 3| +2.66E+02 | -7.0E+01 | +8.4E-01 | 5.0E-01 | 6.2E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 73| +1.48E+02 | +1.7E-07 | -9.3E-01 | 2.3E-04 | 2.2E-02 | 4.8E-05 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 22| +1.56E+02 | +6.1E+01 | -2.3E+01 | 2.0E+00 | 4.1E+01 | 4.8E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 6| +2.51E+02 | -6.8E+01 | +8.8E-01 | 1.0E+00 | 4.1E+02 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 85| +1.95E+02 | -3.7E-01 | +8.4E-01 | 1.3E-01 | 2.8E+00 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 36| +1.48E+02 | -7.6E-03 | +3.8E-01 | 6.2E-02 | 5.9E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 79| +1.78E+02 | -3.6E-05 | +7.1E-01 | 2.5E-03 | 2.1E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 4| +2.66E+02 | +6.6E+03 | -3.4E+02 | 1.0E+00 | 3.9E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 74| +1.48E+02 | -6.6E-08 | +7.5E-01 | 5.8E-05 | 2.2E-02 | 1.3E-05 | 2d |1\n", - "2023-02-17 16:05:15 fides(WARNING) Stopping as function difference 6.57E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:15 fides(INFO) 7| +2.22E+02 | -2.9E+01 | +2.1E+00 | 2.0E+00 | 4.3E+01 | 3.6E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 37| +1.48E+02 | +4.8E-03 | -2.8E-01 | 6.2E-02 | 2.0E+00 | 5.5E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 86| +1.94E+02 | -7.3E-01 | +1.3E+00 | 2.5E-01 | 5.0E+00 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 23| +1.56E+02 | +4.9E+00 | -7.2E+00 | 5.0E-01 | 4.1E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 5| +2.50E+02 | -1.6E+01 | +7.2E-01 | 2.5E-01 | 3.9E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 80| +1.78E+02 | -1.6E-05 | +9.2E-01 | 2.5E-03 | 2.3E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 38| +1.48E+02 | -3.9E-03 | +6.1E-01 | 1.6E-02 | 2.0E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 8| +2.22E+02 | +4.5E+03 | -3.9E+02 | 4.0E+00 | 3.4E+01 | 4.6E+00 | trr |0\n", - "2023-02-17 16:05:15 fides(INFO) 6| +2.50E+02 | +5.5E-01 | -4.4E-01 | 2.5E-01 | 1.5E+01 | 6.1E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 87| +1.94E+02 | +9.1E-01 | -1.2E+00 | 5.0E-01 | 4.6E+00 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 9| +2.22E+02 | +4.8E+01 | -3.1E+00 | 3.9E-01 | 3.4E+01 | 9.8E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 39| +1.48E+02 | -2.4E-03 | +9.7E-01 | 1.6E-02 | 4.5E+00 | 6.1E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 7| +2.50E+02 | -3.7E-01 | +2.7E-01 | 6.2E-02 | 1.5E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 81| +1.78E+02 | -1.6E-05 | +9.1E-01 | 4.9E-03 | 2.0E-01 | 5.7E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 88| +1.93E+02 | -3.2E-01 | +8.6E-01 | 1.3E-01 | 4.6E+00 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 24| +1.55E+02 | -5.8E-01 | +1.1E+00 | 1.2E-01 | 4.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 8| +2.50E+02 | -1.7E-01 | +2.8E-01 | 6.2E-02 | 1.1E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 40| +1.48E+02 | -8.2E-04 | +9.4E-01 | 3.1E-02 | 1.2E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 10| +2.15E+02 | -6.9E+00 | +8.9E-01 | 9.8E-02 | 3.4E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 82| +1.78E+02 | -2.3E-05 | +9.9E-01 | 9.9E-03 | 2.0E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 9| +2.50E+02 | +7.5E-02 | -2.7E-01 | 6.2E-02 | 6.6E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 0| +9.51E+13 | NaN | NaN | 1.0E+00 | 4.4E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:15 fides(INFO) 89| +1.93E+02 | -3.5E-01 | +1.9E-01 | 2.5E-01 | 7.6E+00 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 41| +1.48E+02 | -1.1E-03 | +9.5E-01 | 6.2E-02 | 1.5E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 11| +2.15E+02 | +7.1E+00 | -1.3E+00 | 2.0E-01 | 2.3E+01 | 4.8E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 1| +9.94E+08 | -9.5E+13 | +8.3E-02 | 1.0E+00 | 4.4E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 78| +1.55E+02 | +3.9E-02 | -1.6E+00 | 3.5E-03 | 4.8E+00 | 9.1E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 10| +2.50E+02 | -1.4E-01 | +7.0E-01 | 1.6E-02 | 6.6E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 90| +1.92E+02 | -8.5E-01 | +1.3E+00 | 6.3E-02 | 1.9E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 25| +1.55E+02 | -3.0E-01 | +4.9E-01 | 2.5E-01 | 2.2E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 12| +2.13E+02 | -2.2E+00 | +8.7E-01 | 4.9E-02 | 2.3E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 42| +1.48E+02 | -2.5E-04 | +1.9E-01 | 1.2E-01 | 8.2E-01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 83| +1.78E+02 | +6.7E-06 | -1.6E-01 | 2.0E-02 | 1.6E-01 | 6.2E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 11| +2.50E+02 | -2.0E-04 | +2.5E-02 | 1.6E-02 | 1.2E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 2| +1.55E+08 | -8.4E+08 | +8.9E-01 | 2.5E-01 | 4.3E+09 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 13| +2.13E+02 | +7.7E-01 | -6.4E-01 | 9.8E-02 | 1.3E+01 | 2.5E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 91| +1.91E+02 | -1.4E+00 | +1.2E+00 | 1.3E-01 | 9.2E+00 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 43| +1.48E+02 | +5.0E-04 | -1.5E-01 | 3.1E-02 | 8.2E-01 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 84| +1.78E+02 | -1.0E-05 | +7.3E-01 | 4.9E-03 | 1.6E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 3| +2.36E+07 | -1.3E+08 | +8.9E-01 | 5.0E-01 | 6.6E+08 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 12| +2.50E+02 | -2.7E-03 | +4.2E-01 | 3.9E-03 | 1.1E+00 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 26| +1.54E+02 | -6.9E-01 | +6.5E-01 | 2.5E-01 | 4.8E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 14| +2.13E+02 | -5.2E-01 | +8.2E-01 | 2.4E-02 | 1.3E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 4| +3.63E+06 | -2.0E+07 | +8.9E-01 | 5.0E-01 | 1.0E+08 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 92| +1.86E+02 | -4.8E+00 | +1.6E+00 | 2.5E-01 | 1.1E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 44| +1.48E+02 | -6.6E-04 | +5.4E-01 | 7.8E-03 | 8.2E-01 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 13| +2.50E+02 | -9.0E-05 | +4.1E-02 | 3.9E-03 | 6.0E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 5| +5.63E+05 | -3.1E+06 | +9.0E-01 | 5.0E-01 | 1.5E+07 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 85| +1.78E+02 | -9.9E-06 | +7.6E-01 | 4.9E-03 | 1.7E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 6| +8.81E+04 | -4.7E+05 | +9.0E-01 | 5.0E-01 | 2.4E+06 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 45| +1.48E+02 | -3.3E-04 | +9.6E-01 | 7.8E-03 | 1.6E+00 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 15| +2.13E+02 | +1.8E-03 | -2.9E-02 | 4.9E-02 | 6.7E+00 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 14| +2.50E+02 | -9.1E-04 | +7.7E-01 | 9.8E-04 | 5.7E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 86| +1.78E+02 | -3.4E-06 | +1.5E-01 | 9.9E-03 | 1.4E-01 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 7| +1.40E+04 | -7.4E+04 | +9.0E-01 | 5.0E-01 | 3.7E+05 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 15| +2.50E+02 | -8.7E-07 | +7.3E-03 | 2.0E-03 | 1.4E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 16| +2.13E+02 | -1.0E-01 | +8.0E-01 | 1.2E-02 | 6.7E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 46| +1.48E+02 | -1.5E-04 | +6.8E-01 | 1.6E-02 | 3.0E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 27| +1.54E+02 | -7.9E-02 | +7.8E-01 | 2.5E-01 | 1.3E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 8| +2.39E+03 | -1.2E+04 | +9.0E-01 | 5.0E-01 | 5.9E+04 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 9| +5.58E+02 | -1.8E+03 | +9.0E-01 | 5.0E-01 | 9.4E+03 | 5.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 93| +1.86E+02 | +1.8E+02 | -2.9E+01 | 5.0E-01 | 5.7E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 16| +2.50E+02 | -5.7E-05 | +6.4E-01 | 4.9E-04 | 1.4E-01 | 8.6E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 17| +2.12E+02 | -1.4E-01 | +9.7E-01 | 2.4E-02 | 5.6E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 47| +1.48E+02 | -4.1E-05 | +4.3E-01 | 1.6E-02 | 2.4E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 87| +1.78E+02 | -1.5E-05 | +6.0E-01 | 2.5E-03 | 1.4E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 10| +2.88E+02 | -2.7E+02 | +9.0E-01 | 5.0E-01 | 1.5E+03 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 17| +2.50E+02 | -3.3E-07 | +5.3E-01 | 4.9E-04 | 7.1E-03 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(WARNING) Stopping as function difference 3.30E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:15 fides(INFO) 11| +2.50E+02 | -3.8E+01 | +8.3E-01 | 1.0E+00 | 2.3E+02 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 18| +2.12E+02 | -1.4E-01 | +6.8E-01 | 4.9E-02 | 5.5E+00 | 6.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 48| +1.48E+02 | -1.5E-05 | +8.3E-01 | 1.6E-02 | 1.2E-01 | 2.9E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 28| +1.54E+02 | -9.8E-03 | +3.6E-01 | 5.0E-01 | 4.1E+00 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 12| +2.50E+02 | -4.6E-01 | +3.9E-01 | 2.0E+00 | 1.7E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 88| +1.78E+02 | -3.8E-06 | +9.1E-01 | 2.5E-03 | 1.1E-01 | 3.0E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 94| +1.86E+02 | +2.7E+00 | -1.2E+00 | 1.3E-01 | 5.7E+01 | 3.4E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 79| +1.55E+02 | -1.3E-03 | +1.4E-01 | 8.8E-04 | 4.8E+00 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 49| +1.48E+02 | -3.8E-06 | +2.6E-01 | 3.1E-02 | 1.1E-01 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 19| +2.12E+02 | +2.0E-02 | -9.4E-02 | 4.9E-02 | 6.4E+00 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 13| +2.50E+02 | +1.4E-01 | -3.5E-01 | 2.0E+00 | 7.7E+00 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 89| +1.78E+02 | -5.9E-06 | +1.0E+00 | 4.9E-03 | 1.1E-01 | 4.1E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 29| +1.54E+02 | +1.0E-02 | -3.5E-01 | 5.0E-01 | 5.2E+00 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 14| +2.50E+02 | -1.6E-01 | +8.5E-01 | 1.1E-02 | 7.7E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 95| +1.84E+02 | -1.7E+00 | +1.5E+00 | 3.1E-02 | 5.7E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 50| +1.48E+02 | +5.1E-07 | -2.8E-02 | 3.1E-02 | 5.9E-02 | 5.0E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 20| +2.12E+02 | -1.1E-01 | +7.9E-01 | 1.2E-02 | 6.4E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 15| +2.50E+02 | +1.1E-02 | -1.4E-01 | 2.1E-02 | 3.7E+00 | 4.7E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 0| +1.65E+08 | NaN | NaN | 1.0E+00 | 7.6E+08 | NaN | NaN |1\n", - "2023-02-17 16:05:15 fides(INFO) 16| +2.50E+02 | -3.2E-02 | +8.5E-01 | 4.5E-03 | 3.7E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 90| +1.78E+02 | -7.3E-06 | +9.2E-01 | 9.9E-03 | 9.7E-02 | 7.4E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 51| +1.48E+02 | -8.3E-06 | +1.3E+00 | 7.8E-03 | 5.9E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 96| +1.79E+02 | -5.4E+00 | +8.9E-01 | 6.3E-02 | 6.0E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 21| +2.12E+02 | -4.7E-02 | +5.4E-01 | 2.4E-02 | 4.1E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 17| +2.50E+02 | +1.2E-03 | -6.3E-02 | 9.1E-03 | 1.8E+00 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 30| +1.54E+02 | -1.1E-02 | +7.8E-01 | 1.2E-01 | 5.2E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 52| +1.48E+02 | -3.3E-06 | +9.8E-01 | 1.6E-02 | 9.9E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 1| +1.45E+03 | -1.6E+08 | +1.0E-01 | 1.0E+00 | 7.6E+08 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 18| +2.50E+02 | -7.4E-03 | +8.6E-01 | 2.1E-03 | 1.8E+00 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 22| +2.12E+02 | -4.8E-02 | +3.7E-01 | 2.4E-02 | 5.9E+00 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 97| +1.70E+02 | -9.0E+00 | +9.8E-01 | 1.3E-01 | 3.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 91| +1.78E+02 | -3.8E-06 | +3.7E-01 | 2.0E-02 | 9.5E-02 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 19| +2.50E+02 | +1.4E-04 | -3.0E-02 | 4.2E-03 | 8.9E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 2| +5.27E+02 | -9.2E+02 | +9.2E-01 | 2.5E-01 | 4.8E+03 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:15.736 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1579 and h = 1.35913e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:15.736 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1579: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:15 fides(INFO) 53| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.1E-02 | 2.0E-02 | 4.7E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 20| +2.50E+02 | -1.8E-03 | +8.6E-01 | 1.0E-03 | 8.9E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 3| +2.92E+02 | -2.3E+02 | +8.9E-01 | 5.0E-01 | 9.8E+02 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 23| +2.12E+02 | -1.5E-02 | +1.2E-01 | 2.4E-02 | 4.6E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 31| +1.54E+02 | -3.4E-03 | +9.9E-01 | 2.5E-01 | 1.7E+00 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 92| +1.78E+02 | +5.7E-04 | -5.2E+00 | 2.0E-02 | 5.3E-02 | 2.6E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 4| +2.59E+02 | -3.3E+01 | +8.3E-01 | 1.0E+00 | 1.4E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 21| +2.50E+02 | +1.6E-05 | -1.5E-02 | 2.1E-03 | 4.4E-01 | 5.3E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 54| +1.48E+02 | -2.6E-06 | +2.0E+00 | 7.8E-03 | 2.0E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 98| +1.70E+02 | +5.5E+00 | -4.3E-01 | 2.5E-01 | 4.4E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 24| +2.12E+02 | -5.1E-02 | +8.9E-01 | 6.1E-03 | 6.2E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 22| +2.50E+02 | -4.4E-04 | +8.6E-01 | 5.1E-04 | 4.4E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 5| +2.59E+02 | +1.3E+03 | -8.4E+01 | 2.0E+00 | 3.4E+01 | 4.2E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 32| +1.54E+02 | -3.9E-03 | +1.1E+00 | 5.0E-01 | 1.7E+00 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:15.818 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 212.759 and h = 2.82009e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:15.818 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 212.759: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:15 fides(INFO) 55| +1.48E+02 | +1.5E-06 | -8.5E-01 | 1.6E-02 | 4.7E-02 | 2.3E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 23| +2.50E+02 | +2.0E-06 | -7.3E-03 | 1.0E-03 | 2.2E-01 | 2.6E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 93| +1.78E+02 | +0.0E+00 | +0.0E+00 | 4.9E-03 | 5.3E-02 | 6.5E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 6| +2.59E+02 | +2.7E+02 | -1.7E+01 | 5.0E-01 | 3.4E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 24| +2.50E+02 | -1.1E-04 | +8.6E-01 | 2.5E-04 | 2.2E-01 | 6.5E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 99| +1.66E+02 | -3.9E+00 | +8.5E-01 | 6.3E-02 | 4.4E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 80| +1.55E+02 | -1.4E-03 | +1.0E+00 | 2.2E-04 | 4.5E+00 | 4.0E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 25| +2.12E+02 | -3.8E-02 | +6.7E-01 | 1.2E-02 | 4.4E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 56| +1.48E+02 | +2.2E-06 | -3.4E+00 | 3.9E-03 | 4.7E-02 | 5.8E-04 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 7| +2.51E+02 | -8.3E+00 | +8.3E-01 | 1.2E-01 | 3.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 33| +1.54E+02 | -1.9E-03 | +6.5E-01 | 1.0E+00 | 4.6E-01 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 25| +2.50E+02 | +2.5E-07 | -3.6E-03 | 5.0E-04 | 1.1E-01 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 94| +1.78E+02 | -5.8E-06 | +6.8E-01 | 1.2E-03 | 5.3E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 8| +2.51E+02 | +4.1E+00 | -1.5E+00 | 2.5E-01 | 1.8E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 26| +2.50E+02 | -2.7E-05 | +8.6E-01 | 1.3E-04 | 1.1E-01 | 3.2E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 26| +2.12E+02 | -2.3E-02 | +6.2E-01 | 1.2E-02 | 3.1E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 57| +1.48E+02 | +2.5E-06 | -7.1E+00 | 9.8E-04 | 4.7E-02 | 1.5E-04 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 27| +2.50E+02 | +3.1E-08 | -1.8E-03 | 2.5E-04 | 5.5E-02 | 6.5E-04 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 34| +1.54E+02 | +9.7E-02 | -7.4E+00 | 1.0E+00 | 6.1E-01 | 2.0E-01 | trr |0\n", - "2023-02-17 16:05:15 fides(INFO) 9| +2.50E+02 | -1.3E+00 | +5.9E-01 | 6.2E-02 | 1.8E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 100| +1.66E+02 | -9.8E-02 | -4.1E-02 | 1.3E-01 | 2.9E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 27| +2.12E+02 | -2.8E-02 | +6.3E-01 | 1.2E-02 | 3.8E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 95| +1.78E+02 | -1.4E-06 | +8.2E-01 | 1.2E-03 | 6.4E-02 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(WARNING) Stopping as function difference 1.45E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 10| +2.50E+02 | -1.3E-02 | +1.4E-01 | 6.2E-02 | 4.1E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 28| +2.50E+02 | -6.7E-06 | +8.6E-01 | 6.2E-05 | 5.5E-02 | 1.6E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 58| +1.48E+02 | +1.1E-06 | -3.8E+00 | 2.4E-04 | 4.7E-02 | 3.8E-05 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 29| +2.50E+02 | +3.8E-09 | -9.0E-04 | 1.2E-04 | 2.7E-02 | 3.2E-04 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 35| +1.54E+02 | -2.4E-03 | +5.6E-01 | 5.1E-02 | 6.1E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 28| +2.12E+02 | -1.5E-02 | +4.4E-01 | 1.2E-02 | 2.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 11| +2.50E+02 | -3.7E-03 | +4.7E-02 | 1.6E-02 | 3.3E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 101| +1.65E+02 | -1.3E+00 | +7.3E-01 | 3.1E-02 | 2.9E+01 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 30| +2.50E+02 | -1.7E-06 | +8.6E-01 | 3.1E-05 | 2.7E-02 | 8.1E-05 | 2d |1\n", - "2023-02-17 16:05:15 fides(WARNING) Stopping as function difference 1.67E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:15 fides(INFO) 59| +1.48E+02 | +4.8E-06 | -1.8E+01 | 6.1E-05 | 4.7E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 12| +2.50E+02 | -2.0E-02 | +8.9E-01 | 3.9E-03 | 3.3E+00 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 29| +2.12E+02 | -2.6E-02 | +5.8E-01 | 1.2E-02 | 3.7E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 36| +1.54E+02 | -4.8E-04 | +1.0E+00 | 5.1E-02 | 2.1E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 13| +2.50E+02 | -9.9E-03 | +5.1E-01 | 7.8E-03 | 2.0E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 102| +1.63E+02 | -1.7E+00 | +9.7E-01 | 3.1E-02 | 4.7E+01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 60| +1.48E+02 | +3.8E-06 | -1.5E+01 | 1.5E-05 | 4.7E-02 | 1.1E-05 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 30| +2.12E+02 | -1.1E-02 | +3.6E-01 | 1.2E-02 | 2.7E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 14| +2.50E+02 | -1.2E-05 | +3.7E-03 | 7.8E-03 | 6.2E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 61| +1.48E+02 | +3.9E-06 | -1.5E+01 | 3.8E-06 | 4.7E-02 | 1.0E-05 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 37| +1.54E+02 | -7.3E-04 | +1.2E+00 | 1.0E-01 | 2.7E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 15| +2.50E+02 | -1.2E-03 | +6.7E-01 | 2.0E-03 | 6.4E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 31| +2.12E+02 | -2.3E-02 | +5.4E-01 | 1.2E-02 | 3.5E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 0| +1.33E+09 | NaN | NaN | 1.0E+00 | 6.1E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:16 fides(INFO) 62| +1.48E+02 | +5.4E-06 | -5.3E+01 | 9.5E-07 | 4.7E-02 | 2.5E-06 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 16| +2.50E+02 | -1.4E-05 | +9.4E-01 | 2.0E-03 | 1.1E-02 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 38| +1.54E+02 | -6.6E-05 | +8.6E-02 | 2.0E-01 | 1.3E-01 | 2.8E-02 | trr |1\n", - "2023-02-17 16:05:16 fides(INFO) 103| +1.61E+02 | -2.2E+00 | +8.5E-01 | 6.3E-02 | 2.6E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 81| +1.55E+02 | -1.9E-03 | +1.0E+00 | 4.4E-04 | 3.4E+00 | 8.2E-04 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 1| +1.40E+06 | -1.3E+09 | +9.5E-02 | 1.0E+00 | 6.1E+09 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 0| +1.62E+11 | NaN | NaN | 1.0E+00 | 7.4E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:16 fides(INFO) 32| +2.12E+02 | -9.2E-03 | +3.1E-01 | 1.2E-02 | 2.6E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 17| +2.50E+02 | -2.8E-05 | +9.4E-01 | 3.9E-03 | 1.7E-02 | 9.3E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 63| +1.48E+02 | +3.6E-06 | -1.3E+02 | 2.4E-07 | 4.7E-02 | 6.2E-07 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 18| +2.50E+02 | -5.7E-05 | +9.7E-01 | 7.8E-03 | 2.0E-02 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 39| +1.54E+02 | +4.5E-03 | -1.1E+00 | 3.1E-02 | 2.8E-01 | 6.3E-02 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 104| +1.58E+02 | -2.9E+00 | +6.1E-01 | 1.3E-01 | 4.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 2| +2.19E+05 | -1.2E+06 | +9.0E-01 | 2.5E-01 | 6.4E+06 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 64| +1.48E+02 | +4.2E-06 | -5.8E+02 | 6.0E-08 | 4.7E-02 | 1.6E-07 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 1| +1.28E+06 | -1.6E+11 | +9.1E-02 | 1.0E+00 | 7.4E+11 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 33| +2.12E+02 | -2.0E-02 | +5.0E-01 | 1.2E-02 | 3.3E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 19| +2.50E+02 | -1.2E-04 | +1.0E+00 | 1.6E-02 | 2.3E-02 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 3| +3.43E+04 | -1.8E+05 | +9.0E-01 | 5.0E-01 | 1.0E+06 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 2| +2.77E+05 | -1.0E+06 | +8.9E-01 | 2.5E-01 | 4.6E+06 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 40| +1.54E+02 | -8.6E-04 | +6.4E-01 | 7.8E-03 | 2.8E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 20| +2.50E+02 | -2.6E-04 | +1.0E+00 | 3.1E-02 | 2.5E-02 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 65| +1.48E+02 | +8.8E-07 | -4.9E+02 | 1.5E-08 | 4.7E-02 | 3.9E-08 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 34| +2.12E+02 | -8.1E-03 | +2.9E-01 | 1.2E-02 | 2.4E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 105| +1.54E+02 | -4.0E+00 | +6.8E-01 | 1.3E-01 | 3.6E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 4| +5.57E+03 | -2.9E+04 | +9.0E-01 | 5.0E-01 | 1.6E+05 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 21| +2.50E+02 | -6.0E-04 | +1.1E+00 | 6.2E-02 | 2.9E-02 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 3| +4.42E+04 | -2.3E+05 | +9.0E-01 | 5.0E-01 | 8.5E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:16.222 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0112 and h = 1.07533e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:16.223 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0112: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:16 fides(INFO) 66| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.7E-09 | 4.7E-02 | 9.7E-09 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 22| +2.50E+02 | -1.6E-03 | +1.2E+00 | 1.2E-01 | 3.1E-02 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 41| +1.54E+02 | -1.2E-04 | +6.7E-01 | 7.8E-03 | 8.3E-02 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 35| +2.12E+02 | -1.7E-02 | +4.6E-01 | 1.2E-02 | 3.1E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 5| +1.06E+03 | -4.5E+03 | +9.0E-01 | 5.0E-01 | 2.5E+04 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 4| +7.14E+03 | -3.7E+04 | +9.0E-01 | 1.0E+00 | 1.3E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 67| +1.48E+02 | +3.4E-06 | -3.0E+04 | 9.3E-10 | 4.7E-02 | 2.4E-09 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 23| +2.50E+02 | -5.9E-03 | +1.5E+00 | 2.5E-01 | 3.8E-02 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 5| +1.28E+03 | -5.9E+03 | +9.0E-01 | 1.0E+00 | 2.1E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 6| +3.55E+02 | -7.0E+02 | +9.0E-01 | 5.0E-01 | 3.9E+03 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 36| +2.12E+02 | -7.5E-03 | +2.8E-01 | 1.2E-02 | 2.3E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 106| +1.54E+02 | +5.3E+00 | -1.0E+00 | 1.3E-01 | 5.8E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 42| +1.54E+02 | -4.8E-05 | +2.0E-01 | 7.8E-03 | 1.1E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 24| +2.50E+02 | -3.9E-02 | +2.2E+00 | 5.0E-01 | 4.4E-02 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 6| +3.74E+02 | -9.1E+02 | +9.0E-01 | 1.0E+00 | 3.4E+03 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 68| +1.48E+02 | +3.9E-06 | -1.4E+05 | 2.3E-10 | 4.7E-02 | 6.1E-10 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 7| +2.50E+02 | -1.1E+02 | +9.0E-01 | 1.0E+00 | 6.1E+02 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 37| +2.12E+02 | -1.5E-02 | +4.4E-01 | 1.2E-02 | 2.9E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 7| +2.55E+02 | -1.2E+02 | +8.7E-01 | 1.0E+00 | 5.3E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 43| +1.54E+02 | -1.0E-04 | +7.9E-01 | 1.9E-03 | 7.6E-02 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 69| +1.48E+02 | +3.7E-07 | -5.2E+04 | 5.8E-11 | 4.7E-02 | 1.5E-10 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 25| +2.49E+02 | -8.1E-01 | +4.2E+00 | 1.0E+00 | 1.7E-01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 38| +2.12E+02 | -7.2E-03 | +2.8E-01 | 1.2E-02 | 2.2E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 8| +2.41E+02 | -8.4E+00 | +7.4E-01 | 2.0E+00 | 7.8E+01 | 5.9E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 107| +1.54E+02 | +1.2E+00 | -4.8E-01 | 3.1E-02 | 5.8E+01 | 7.7E-02 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 8| +2.39E+02 | -1.5E+01 | +1.2E+00 | 2.0E+00 | 5.9E+01 | 3.9E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 26| +2.32E+02 | -1.7E+01 | +5.1E+00 | 2.0E+00 | 2.5E+00 | 3.9E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 82| +1.55E+02 | -2.1E-03 | +9.8E-01 | 8.8E-04 | 1.7E+00 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 44| +1.54E+02 | -5.7E-05 | +5.3E-01 | 3.9E-03 | 1.0E-01 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 70| +1.48E+02 | +4.8E-06 | -2.7E+06 | 1.5E-11 | 4.7E-02 | 3.8E-11 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 9| +2.33E+02 | -8.3E+00 | +6.4E-01 | 2.0E+00 | 1.4E+01 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 39| +2.12E+02 | -1.3E-02 | +4.2E-01 | 1.2E-02 | 2.7E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 9| +2.39E+02 | +6.9E+01 | -1.4E+01 | 4.0E+00 | 3.0E+01 | 6.6E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 108| +1.54E+02 | -5.2E-01 | +5.3E-01 | 7.9E-03 | 5.8E+01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 45| +1.54E+02 | -4.4E-05 | +4.6E-01 | 3.9E-03 | 5.3E-02 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 71| +1.48E+02 | +4.2E-06 | -9.5E+06 | 3.6E-12 | 4.7E-02 | 9.5E-12 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 27| +2.32E+02 | +2.5E+02 | -1.5E+01 | 4.0E+00 | 3.4E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 40| +2.12E+02 | -7.0E-03 | +2.8E-01 | 1.2E-02 | 2.1E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 10| +2.39E+02 | +1.5E+01 | -1.6E+00 | 1.0E+00 | 3.0E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 28| +2.32E+02 | +1.9E+01 | -1.8E+00 | 1.0E+00 | 3.4E+01 | 3.0E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 72| +1.48E+02 | +2.0E-06 | -1.8E+07 | 9.1E-13 | 4.7E-02 | 2.4E-12 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 11| +2.39E+02 | +1.2E+01 | -1.4E+00 | 2.5E-01 | 3.0E+01 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 46| +1.54E+02 | -2.3E-05 | +2.1E-01 | 3.9E-03 | 7.2E-02 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 109| +1.53E+02 | -2.4E-01 | +9.9E-01 | 7.9E-03 | 2.4E+01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 41| +2.12E+02 | -1.2E-02 | +4.1E-01 | 1.2E-02 | 2.5E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 29| +2.32E+02 | +8.3E+00 | -8.0E-01 | 2.5E-01 | 3.4E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 12| +2.36E+02 | -3.5E+00 | +8.4E-01 | 6.2E-02 | 3.0E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 73| +1.48E+02 | -2.3E-06 | +8.4E+07 | 2.3E-13 | 4.7E-02 | 5.9E-13 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 30| +2.28E+02 | -4.1E+00 | +8.7E-01 | 6.2E-02 | 3.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 42| +2.12E+02 | -6.9E-03 | +3.0E-01 | 1.2E-02 | 2.0E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 13| +2.35E+02 | -1.2E+00 | +3.5E-01 | 1.2E-01 | 1.8E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 47| +1.54E+02 | -3.3E-05 | +8.3E-01 | 9.7E-04 | 4.4E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 31| +2.28E+02 | -3.7E-01 | +6.1E-02 | 1.2E-01 | 2.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 74| +1.48E+02 | +1.5E-07 | -2.7E+06 | 4.5E-13 | 4.7E-02 | 1.2E-12 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 110| +1.53E+02 | -3.9E-01 | +9.0E-01 | 1.6E-02 | 1.9E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 14| +2.31E+02 | -3.8E+00 | +7.4E-01 | 1.2E-01 | 3.9E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 43| +2.12E+02 | -1.1E-02 | +4.2E-01 | 1.2E-02 | 2.4E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 32| +2.26E+02 | -1.8E+00 | +9.2E-01 | 3.1E-02 | 4.1E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 48| +1.54E+02 | -2.4E-05 | +5.7E-01 | 1.9E-03 | 6.1E-02 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 75| +1.48E+02 | +6.5E-06 | -4.7E+08 | 1.1E-13 | 4.7E-02 | 3.0E-13 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 10| +2.13E+02 | -1.9E+01 | +7.5E-01 | 2.0E+00 | 3.3E+01 | 4.2E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 15| +2.29E+02 | -1.6E+00 | +4.5E-01 | 1.2E-01 | 2.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 33| +2.24E+02 | -1.9E+00 | +8.4E-01 | 6.2E-02 | 2.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 44| +2.12E+02 | -7.0E-03 | +3.2E-01 | 1.2E-02 | 1.9E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 16| +2.26E+02 | -3.5E+00 | +7.4E-01 | 1.2E-01 | 3.7E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 76| +1.48E+02 | +8.7E-06 | -2.5E+09 | 2.8E-14 | 4.7E-02 | 7.4E-14 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 111| +1.52E+02 | -5.8E-01 | +9.4E-01 | 3.1E-02 | 2.7E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 49| +1.54E+02 | -1.9E-05 | +6.5E-01 | 1.9E-03 | 4.1E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 34| +2.21E+02 | -3.0E+00 | +1.1E+00 | 1.2E-01 | 1.7E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 17| +2.24E+02 | -1.4E+00 | +4.0E-01 | 1.2E-01 | 2.0E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 45| +2.12E+02 | -1.0E-02 | +4.3E-01 | 1.2E-02 | 2.2E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 83| +1.55E+02 | -1.3E-03 | +9.9E-01 | 1.8E-03 | 1.6E+00 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 11| +2.13E+02 | +2.1E+02 | -1.6E+01 | 2.0E+00 | 4.2E+01 | 4.5E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 77| +1.48E+02 | +5.7E-06 | -6.6E+09 | 7.1E-15 | 4.7E-02 | 1.9E-14 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 35| +2.19E+02 | -2.2E+00 | +8.5E-01 | 2.5E-01 | 1.8E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 18| +2.21E+02 | -3.2E+00 | +7.2E-01 | 1.2E-01 | 3.6E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 50| +1.54E+02 | -1.3E-05 | +4.5E-01 | 1.9E-03 | 3.6E-02 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 46| +2.12E+02 | -1.9E-02 | +9.6E-01 | 1.2E-02 | 1.8E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 36| +2.18E+02 | -8.3E-01 | +7.5E-02 | 5.0E-01 | 2.4E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 78| +1.48E+02 | +5.4E-06 | -2.5E+10 | 1.8E-15 | 4.7E-02 | 4.6E-15 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 112| +1.52E+02 | -7.8E-01 | +9.4E-01 | 6.3E-02 | 1.6E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 19| +2.20E+02 | -8.3E-01 | +2.6E-01 | 1.2E-01 | 1.9E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 37| +2.14E+02 | -3.7E+00 | +6.3E-01 | 1.2E-01 | 4.5E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 51| +1.54E+02 | -9.0E-06 | +2.6E-01 | 1.9E-03 | 3.7E-02 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 47| +2.12E+02 | +3.2E-02 | -8.1E-01 | 2.4E-02 | 1.2E+00 | 5.0E-02 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 79| +1.48E+02 | +4.8E-06 | -8.8E+10 | 4.4E-16 | 4.7E-02 | 1.2E-15 | 2d |0\n", - "2023-02-17 16:05:16 fides(WARNING) Stopping as trust region radius 1.11E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:16 fides(INFO) 38| +2.13E+02 | -1.1E+00 | +5.1E-01 | 1.2E-01 | 1.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 20| +2.18E+02 | -2.7E+00 | +6.5E-01 | 1.2E-01 | 3.5E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 48| +2.12E+02 | -5.5E-03 | +5.2E-01 | 6.1E-03 | 1.2E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 52| +1.54E+02 | -7.7E-06 | +2.0E-01 | 1.9E-03 | 2.8E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 39| +2.11E+02 | -2.1E+00 | +7.8E-01 | 1.2E-01 | 2.4E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 113| +1.51E+02 | -4.7E-01 | +5.7E-01 | 1.3E-01 | 1.7E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 21| +2.16E+02 | -1.1E+00 | +4.0E-01 | 1.2E-01 | 1.7E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 49| +2.12E+02 | -7.4E-03 | +9.4E-01 | 6.1E-03 | 1.3E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 53| +1.54E+02 | -9.0E-06 | +8.1E-01 | 4.9E-04 | 3.4E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 40| +2.07E+02 | -3.4E+00 | +8.4E-01 | 2.5E-01 | 1.2E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 22| +2.13E+02 | -3.0E+00 | +7.8E-01 | 1.2E-01 | 2.8E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:16.826 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 106.88 and h = 5.48355e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:16.826 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 106.88: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:16 fides(INFO) 12| +2.13E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 4.2E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 41| +1.94E+02 | -1.4E+01 | +1.5E+00 | 5.0E-01 | 2.6E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 50| +2.12E+02 | +1.7E-03 | -1.1E-01 | 1.2E-02 | 1.1E+00 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 54| +1.54E+02 | -6.0E-06 | +5.0E-01 | 9.7E-04 | 2.3E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 23| +2.06E+02 | -7.7E+00 | +1.2E+00 | 2.5E-01 | 1.6E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 84| +1.55E+02 | -2.7E-03 | +1.0E+00 | 3.5E-03 | 1.4E+00 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 0| +1.85E+12 | NaN | NaN | 1.0E+00 | 8.5E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:16 fides(INFO) 51| +2.12E+02 | -3.0E-03 | +6.5E-01 | 3.1E-03 | 1.1E+00 | 6.3E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 42| +1.94E+02 | +5.1E+03 | -1.7E+02 | 1.0E+00 | 2.9E+01 | 2.2E+00 | trr |0\n", - "2023-02-17 16:05:16 fides(INFO) 1| +2.10E+09 | -1.8E+12 | +8.5E-02 | 1.0E+00 | 8.5E+12 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 24| +1.95E+02 | -1.0E+01 | +5.5E-01 | 5.0E-01 | 3.5E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 55| +1.54E+02 | -4.6E-06 | +4.7E-01 | 9.7E-04 | 1.7E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 114| +1.51E+02 | -1.6E-01 | +3.1E-01 | 1.3E-01 | 2.1E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 52| +2.12E+02 | -3.7E-03 | +1.0E+00 | 3.1E-03 | 1.0E+00 | 6.3E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 43| +1.77E+02 | -1.7E+01 | +1.0E+00 | 2.5E-01 | 2.9E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 2| +3.18E+08 | -1.8E+09 | +8.9E-01 | 2.5E-01 | 9.7E+09 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 25| +1.75E+02 | -2.1E+01 | +8.6E-01 | 5.0E-01 | 6.0E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 56| +1.54E+02 | -2.9E-06 | +2.9E-01 | 9.7E-04 | 2.0E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 44| +1.77E+02 | -1.2E+01 | -2.0E-01 | 5.0E-01 | 9.9E+01 | 9.3E-01 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 53| +2.12E+02 | -7.0E-03 | +1.0E+00 | 6.1E-03 | 9.4E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 3| +4.90E+07 | -2.7E+08 | +8.9E-01 | 5.0E-01 | 1.5E+09 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 26| +1.75E+02 | +6.0E+04 | -1.8E+03 | 1.0E+00 | 1.0E+02 | 2.2E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 4| +7.48E+06 | -4.2E+07 | +8.9E-01 | 1.0E+00 | 2.2E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 57| +1.54E+02 | -3.3E-06 | +3.1E-01 | 9.7E-04 | 1.4E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 45| +1.66E+02 | -1.1E+01 | +9.8E-01 | 1.2E-01 | 9.9E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 54| +2.12E+02 | -1.2E-02 | +9.9E-01 | 1.2E-02 | 8.8E-01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 27| +1.63E+02 | -1.2E+01 | +3.7E-01 | 2.5E-01 | 1.0E+02 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 5| +1.15E+06 | -6.3E+06 | +9.0E-01 | 1.0E+00 | 3.4E+07 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 13| +2.06E+02 | -7.3E+00 | +8.5E-01 | 1.2E-01 | 4.2E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 58| +1.54E+02 | -6.0E-07 | +6.4E-02 | 9.7E-04 | 1.5E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 6| +1.78E+05 | -9.7E+05 | +9.0E-01 | 1.0E+00 | 5.2E+06 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 46| +1.52E+02 | -1.4E+01 | +7.9E-01 | 2.5E-01 | 5.9E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 115| +1.51E+02 | -5.6E-03 | +1.3E-02 | 1.3E-01 | 1.6E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 55| +2.12E+02 | -2.5E-02 | +1.0E+00 | 2.4E-02 | 8.6E-01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 28| +1.51E+02 | -1.2E+01 | +9.6E-01 | 2.5E-01 | 2.5E+02 | 3.5E-01 | trr |1\n", - "2023-02-17 16:05:17 fides(INFO) 7| +2.79E+04 | -1.5E+05 | +9.0E-01 | 1.0E+00 | 8.1E+05 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 8| +4.54E+03 | -2.3E+04 | +9.0E-01 | 1.0E+00 | 1.3E+05 | 7.8E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 85| +1.55E+02 | -3.3E-03 | +1.0E+00 | 7.0E-03 | 1.9E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 47| +1.51E+02 | -1.3E+00 | +2.1E-01 | 5.0E-01 | 1.1E+02 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 59| +1.54E+02 | -3.0E-06 | +8.6E-01 | 2.4E-04 | 1.1E-02 | 5.2E-04 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 56| +2.12E+02 | -5.2E-02 | +1.1E+00 | 4.9E-02 | 8.0E-01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 29| +1.51E+02 | +5.9E-01 | -2.9E-01 | 2.5E-01 | 5.0E+01 | 3.4E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 9| +8.89E+02 | -3.7E+03 | +9.0E-01 | 1.0E+00 | 2.0E+04 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 57| +2.11E+02 | -1.3E-01 | +1.2E+00 | 9.8E-02 | 1.0E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 48| +1.48E+02 | -2.4E+00 | +8.2E-01 | 1.0E-01 | 6.8E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 10| +3.24E+02 | -5.7E+02 | +9.1E-01 | 1.0E+00 | 3.1E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 30| +1.50E+02 | -1.3E+00 | +8.1E-01 | 6.2E-02 | 5.0E+01 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 60| +1.54E+02 | -2.0E-06 | +5.0E-01 | 4.9E-04 | 2.5E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 11| +2.37E+02 | -8.7E+01 | +9.3E-01 | 1.0E+00 | 4.7E+02 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 58| +2.11E+02 | -5.4E-01 | +1.7E+00 | 2.0E-01 | 2.1E+00 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 116| +1.51E+02 | -1.1E-01 | +9.8E-01 | 1.1E-02 | 7.6E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 49| +1.48E+02 | +1.3E+00 | -1.0E+00 | 2.1E-01 | 2.4E+01 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 31| +1.49E+02 | -7.1E-01 | +6.6E-01 | 1.2E-01 | 4.5E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 61| +1.54E+02 | -9.9E-07 | +4.4E-01 | 4.9E-04 | 9.8E-03 | 8.5E-04 | 2d |1\n", - "2023-02-17 16:05:17 fides(WARNING) Stopping as function difference 9.90E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:17.202 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 148.098 and h = 1.74276e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:17.202 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 148.098: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 59| +2.07E+02 | -3.4E+00 | +2.0E+00 | 3.9E-01 | 5.3E+00 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 50| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.1E-02 | 2.4E+01 | 7.1E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 12| +2.37E+02 | -3.1E+01 | -7.3E-01 | 2.0E+00 | 5.8E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 32| +1.49E+02 | +9.6E-02 | -1.4E-01 | 1.2E-01 | 3.0E+01 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 60| +2.07E+02 | +9.3E+01 | -1.7E+01 | 7.8E-01 | 1.9E+01 | 2.0E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 13| +2.24E+02 | -1.2E+01 | +9.6E-01 | 5.0E-01 | 5.8E+01 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 61| +2.05E+02 | -2.5E+00 | +9.4E-01 | 2.0E-01 | 1.9E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 33| +1.48E+02 | -7.1E-01 | +8.5E-01 | 3.1E-02 | 3.0E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 51| +1.48E+02 | -3.1E-01 | +8.5E-01 | 1.3E-02 | 2.4E+01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 14| +1.90E+02 | -1.6E+01 | +1.2E+00 | 2.5E-01 | 3.8E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 62| +1.99E+02 | -5.8E+00 | +3.0E-01 | 3.9E-01 | 3.2E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:17.296 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 87.7814 and h = 9.97554e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:17.296 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 87.7814: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:17 fides(INFO) 52| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.6E-02 | 2.4E+01 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 117| +1.51E+02 | -4.8E-02 | +9.2E-01 | 2.3E-02 | 5.6E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 34| +1.48E+02 | -2.2E-01 | +9.6E-01 | 6.2E-02 | 2.2E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 14| +2.24E+02 | -3.2E+00 | -2.8E+00 | 1.0E+00 | 3.1E+01 | 1.6E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 0| +8.25E+04 | NaN | NaN | 1.0E+00 | 3.8E+05 | NaN | NaN |1\n", - "2023-02-17 16:05:17 fides(INFO) 53| +1.48E+02 | -1.0E-01 | +9.5E-01 | 6.4E-03 | 2.4E+01 | 8.8E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 63| +1.93E+02 | -6.6E+00 | +1.4E-01 | 3.9E-01 | 8.1E+01 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:17.330 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.4894 and h = 1.16646e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:17.330 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.4894: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:17 fides(INFO) 35| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 1.2E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 1| +3.63E+02 | -8.2E+04 | +1.1E-01 | 1.0E+00 | 3.8E+05 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 15| +2.20E+02 | -4.9E+00 | +3.7E-01 | 2.5E-01 | 3.1E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 36| +1.48E+02 | -1.2E-01 | +9.0E-01 | 3.1E-02 | 1.2E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 64| +1.81E+02 | -1.2E+01 | +7.5E-01 | 9.8E-02 | 9.0E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 2| +3.32E+02 | -3.2E+01 | +9.2E-01 | 2.5E-01 | 5.0E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 54| +1.48E+02 | -1.2E-01 | +7.6E-01 | 1.3E-02 | 1.3E+01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 118| +1.51E+02 | -6.4E-03 | +1.5E+00 | 2.3E-02 | 1.2E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 16| +2.06E+02 | -1.3E+01 | +8.9E-01 | 2.5E-01 | 7.9E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 86| +1.55E+02 | -4.1E-03 | +3.6E-01 | 1.4E-02 | 1.0E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 65| +1.81E+02 | +1.6E+00 | -1.2E-01 | 2.0E-01 | 4.8E+01 | 5.1E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 3| +2.77E+02 | -5.5E+01 | +9.8E-01 | 5.0E-01 | 3.7E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 37| +1.48E+02 | -1.1E-01 | +9.5E-01 | 6.2E-02 | 2.1E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 4| +2.77E+02 | +6.4E+03 | -2.6E+02 | 1.0E+00 | 4.2E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 55| +1.48E+02 | -6.5E-02 | +9.1E-01 | 2.6E-02 | 1.9E+01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 66| +1.75E+02 | -5.4E+00 | +9.3E-01 | 4.9E-02 | 4.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 17| +1.79E+02 | -2.8E+01 | +1.1E+00 | 5.0E-01 | 3.2E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 38| +1.48E+02 | -9.0E-02 | +8.3E-01 | 1.2E-01 | 6.6E+00 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 5| +2.54E+02 | -2.2E+01 | +9.0E-01 | 2.5E-01 | 4.2E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 56| +1.48E+02 | -2.7E-02 | +5.2E-01 | 5.1E-02 | 4.7E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 67| +1.67E+02 | -8.4E+00 | +1.0E+00 | 9.8E-02 | 4.1E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 18| +1.79E+02 | +1.8E+03 | -8.2E+01 | 1.0E+00 | 1.2E+02 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 6| +2.54E+02 | +7.1E+01 | -7.5E+00 | 5.0E-01 | 2.8E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:17.483 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 125.418 and h = 8.51644e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:17.484 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 125.418: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:17 fides(INFO) 119| +1.51E+02 | +0.0E+00 | +0.0E+00 | 2.3E-02 | 1.5E+00 | 4.8E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 39| +1.48E+02 | +1.2E+00 | -5.1E+00 | 2.5E-01 | 7.7E+00 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 7| +2.50E+02 | -4.2E+00 | +5.5E-01 | 1.2E-01 | 2.8E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 57| +1.48E+02 | -1.1E-02 | +2.6E-01 | 5.1E-02 | 9.1E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 68| +1.62E+02 | -4.6E+00 | +4.8E-01 | 2.0E-01 | 4.1E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 40| +1.48E+02 | +1.3E-03 | -2.5E-02 | 6.2E-02 | 7.7E+00 | 7.9E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 15| +1.90E+02 | +4.9E+01 | -2.8E+00 | 5.0E-01 | 4.8E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 19| +1.60E+02 | -1.8E+01 | +8.4E-01 | 2.5E-01 | 1.2E+02 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 8| +2.50E+02 | -1.6E-01 | +2.4E-01 | 1.2E-01 | 1.2E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 58| +1.48E+02 | +8.0E-02 | -1.2E+00 | 5.1E-02 | 2.2E+00 | 5.7E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 69| +1.51E+02 | -1.2E+01 | +7.2E-01 | 2.0E-01 | 6.9E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 20| +1.51E+02 | -9.0E+00 | +8.8E-01 | 5.0E-01 | 1.2E+02 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 9| +2.50E+02 | -9.9E-02 | +2.6E-01 | 3.1E-02 | 7.6E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 41| +1.48E+02 | -2.0E-02 | +7.7E-01 | 1.6E-02 | 7.7E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 10| +2.50E+02 | -3.4E-02 | +1.9E-01 | 3.1E-02 | 5.7E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 59| +1.48E+02 | -5.2E-04 | +4.9E-03 | 1.3E-02 | 2.2E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 70| +1.51E+02 | +2.7E+02 | -7.7E+00 | 2.0E-01 | 8.4E+01 | 5.1E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 120| +1.51E+02 | -5.6E-03 | +1.1E+00 | 5.7E-03 | 1.5E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 21| +1.50E+02 | -1.9E+00 | +3.7E-01 | 1.0E+00 | 9.2E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 42| +1.48E+02 | -1.3E-02 | +7.5E-01 | 3.1E-02 | 3.5E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 11| +2.50E+02 | -5.3E-02 | +7.6E-01 | 7.8E-03 | 4.2E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 71| +1.51E+02 | +1.2E+01 | -1.2E+00 | 4.9E-02 | 8.4E+01 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 60| +1.48E+02 | -8.1E-03 | +9.6E-01 | 3.2E-03 | 6.6E+00 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 12| +2.50E+02 | +1.6E-04 | -2.7E-02 | 1.6E-02 | 1.0E+00 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 22| +1.50E+02 | +9.7E+01 | -1.7E+01 | 1.0E+00 | 7.2E+01 | 1.9E+00 | trr |0\n", - "2023-02-17 16:05:17 fides(INFO) 43| +1.48E+02 | -1.5E-02 | +8.0E-01 | 6.2E-02 | 4.6E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 87| +1.55E+02 | -1.2E-02 | +1.1E+00 | 1.4E-02 | 6.1E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 13| +2.50E+02 | -1.8E-03 | +3.1E-01 | 3.9E-03 | 1.0E+00 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 72| +1.49E+02 | -1.3E+00 | +4.8E-01 | 1.2E-02 | 8.4E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 61| +1.48E+02 | -5.4E-03 | +8.5E-01 | 6.4E-03 | 2.1E+00 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 44| +1.48E+02 | +4.5E-02 | -1.5E+00 | 1.2E-01 | 1.9E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 23| +1.50E+02 | +2.6E+00 | -9.5E-01 | 2.2E-01 | 7.2E+01 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:17.680 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 148.597 and h = 1.48907e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:17.680 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 148.597: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:17 fides(INFO) 121| +1.51E+02 | +0.0E+00 | +0.0E+00 | 1.1E-02 | 6.2E-01 | 2.4E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 14| +2.50E+02 | -2.2E-04 | +8.8E-02 | 3.9E-03 | 6.6E-01 | 7.3E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 73| +1.49E+02 | -5.1E-01 | +9.6E-01 | 1.2E-02 | 2.2E+01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 62| +1.48E+02 | -5.5E-03 | +7.8E-01 | 1.3E-02 | 2.6E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 45| +1.48E+02 | -6.4E-03 | +5.3E-01 | 3.1E-02 | 1.9E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 15| +2.50E+02 | -9.5E-04 | +7.9E-01 | 9.8E-04 | 5.9E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 24| +1.49E+02 | -8.5E-01 | +7.4E-01 | 5.6E-02 | 7.2E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 74| +1.48E+02 | -3.2E-01 | +5.1E-01 | 2.4E-02 | 1.9E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 16| +1.80E+02 | -9.8E+00 | +1.0E+00 | 1.2E-01 | 4.8E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 16| +2.50E+02 | +8.8E-07 | -5.1E-03 | 2.0E-03 | 1.8E-01 | 4.9E-03 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 63| +1.48E+02 | -2.1E-03 | +5.0E-01 | 2.6E-02 | 8.1E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 46| +1.48E+02 | -5.4E-03 | +6.0E-01 | 3.1E-02 | 3.7E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 25| +1.48E+02 | -8.8E-01 | +6.9E-01 | 5.6E-02 | 2.2E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 17| +2.50E+02 | -8.9E-05 | +5.8E-01 | 4.9E-04 | 1.8E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 75| +1.48E+02 | -3.1E-01 | +5.0E-01 | 2.4E-02 | 4.2E+01 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 18| +2.50E+02 | -7.5E-09 | +1.1E-03 | 4.9E-04 | 3.3E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 64| +1.48E+02 | -1.5E-03 | +7.8E-01 | 2.6E-02 | 9.6E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 47| +1.48E+02 | -2.6E-03 | +4.2E-01 | 3.1E-02 | 9.2E-01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 26| +1.48E+02 | -9.9E-02 | +9.3E-01 | 5.6E-02 | 2.5E+01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 76| +1.48E+02 | -1.6E-01 | +2.8E-01 | 2.4E-02 | 3.0E+01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 122| +1.51E+02 | -3.0E-03 | +1.0E+00 | 2.8E-03 | 6.2E-01 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 19| +2.50E+02 | -2.1E-06 | +3.4E-01 | 1.2E-04 | 3.3E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:17 fides(WARNING) Stopping as function difference 2.08E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:17 fides(INFO) 65| +1.48E+02 | -1.5E-03 | +6.6E-01 | 5.1E-02 | 5.0E-01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 77| +1.48E+02 | +1.9E-01 | -3.5E-01 | 2.4E-02 | 1.5E+01 | 5.6E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 48| +1.48E+02 | -1.7E-03 | +2.6E-01 | 3.1E-02 | 1.8E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 27| +1.48E+02 | -6.9E-02 | +8.6E-01 | 1.1E-01 | 1.3E+01 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 49| +1.48E+02 | -3.8E-04 | +5.7E-02 | 3.1E-02 | 4.7E-01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 66| +1.48E+02 | +1.0E-03 | -2.9E-01 | 5.1E-02 | 6.0E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 28| +1.48E+02 | +5.3E-02 | -7.0E-01 | 2.2E-01 | 1.2E+01 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 78| +1.48E+02 | -1.1E-01 | +7.5E-01 | 6.1E-03 | 1.5E+01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 88| +1.55E+02 | -1.1E-02 | +8.7E-01 | 2.8E-02 | 1.3E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 50| +1.48E+02 | -1.8E-03 | +7.2E-01 | 7.8E-03 | 1.6E+00 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 67| +1.48E+02 | -7.1E-04 | +6.3E-01 | 1.3E-02 | 6.0E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 79| +1.48E+02 | -8.6E-02 | +7.4E-01 | 6.1E-03 | 1.9E+01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 123| +1.51E+02 | -5.0E-03 | +1.0E+00 | 5.7E-03 | 7.4E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 17| +1.67E+02 | -1.3E+01 | +1.1E+00 | 2.5E-01 | 7.2E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 29| +1.48E+02 | -2.5E-02 | +7.2E-01 | 5.6E-02 | 1.2E+01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 0| +3.21E+02 | NaN | NaN | 1.0E+00 | 6.2E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:17 fides(INFO) 51| +1.48E+02 | -5.0E-04 | +8.4E-01 | 7.8E-03 | 8.3E-01 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 80| +1.48E+02 | -6.5E-02 | +7.7E-01 | 6.1E-03 | 1.1E+01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 30| +1.48E+02 | -1.4E-02 | +7.1E-01 | 5.6E-02 | 6.4E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 68| +1.48E+02 | -4.2E-04 | +9.6E-01 | 1.3E-02 | 9.6E-01 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 1| +3.21E+02 | +4.3E+02 | -5.6E+00 | 1.0E+00 | 6.2E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 52| +1.48E+02 | -4.8E-04 | +9.1E-01 | 1.6E-02 | 3.7E-01 | 6.7E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 81| +1.48E+02 | -5.7E-02 | +7.3E-01 | 1.2E-02 | 1.1E+01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 69| +1.48E+02 | -5.7E-04 | +9.7E-01 | 2.6E-02 | 2.2E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 31| +1.48E+02 | -9.6E-03 | +6.6E-01 | 5.6E-02 | 7.1E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 82| +1.48E+02 | -5.1E-02 | +9.4E-01 | 1.2E-02 | 8.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 53| +1.48E+02 | -6.3E-04 | +8.3E-01 | 3.1E-02 | 4.0E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 2| +2.80E+02 | -4.0E+01 | +9.6E-01 | 2.5E-01 | 6.2E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 70| +1.48E+02 | -6.9E-04 | +8.2E-01 | 5.1E-02 | 4.2E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 32| +1.48E+02 | -5.2E-03 | +5.1E-01 | 5.6E-02 | 3.7E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 83| +1.48E+02 | +2.1E-03 | -4.7E-02 | 2.4E-02 | 5.7E+00 | 4.9E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 124| +1.51E+02 | -9.3E-03 | +1.0E+00 | 1.1E-02 | 6.5E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 71| +1.48E+02 | +4.6E-03 | -3.1E+00 | 1.0E-01 | 2.6E-01 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 3| +2.54E+02 | -2.6E+01 | +2.9E-01 | 5.0E-01 | 5.4E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 54| +1.48E+02 | +8.5E-04 | -6.5E-01 | 6.2E-02 | 2.8E-01 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 33| +1.48E+02 | -3.3E-03 | +3.4E-01 | 5.6E-02 | 4.6E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 84| +1.48E+02 | -1.1E-02 | +4.4E-01 | 6.1E-03 | 5.7E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 72| +1.48E+02 | -6.8E-05 | +1.2E-01 | 2.6E-02 | 2.6E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 55| +1.48E+02 | -2.2E-04 | +4.7E-01 | 1.6E-02 | 2.8E-01 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 34| +1.48E+02 | -8.4E-04 | +8.3E-02 | 5.6E-02 | 2.3E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 4| +2.28E+02 | -2.7E+01 | +3.4E-01 | 5.0E-01 | 1.6E+02 | 1.3E+00 | trr |1\n", - "2023-02-17 16:05:18 fides(INFO) 85| +1.48E+02 | -1.9E-02 | +7.3E-01 | 6.1E-03 | 1.0E+01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 73| +1.48E+02 | -2.6E-04 | +7.4E-01 | 6.4E-03 | 6.1E-01 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 89| +1.55E+02 | -3.5E-03 | +3.7E-01 | 5.6E-02 | 1.2E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 35| +1.48E+02 | -3.9E-03 | +7.8E-01 | 1.4E-02 | 3.9E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 56| +1.48E+02 | -2.6E-04 | +5.1E-01 | 1.6E-02 | 7.1E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 5| +2.10E+02 | -1.8E+01 | +8.4E-01 | 5.0E-01 | 1.2E+02 | 9.0E-01 | trr |1\n", - "2023-02-17 16:05:18 fides(INFO) 125| +1.51E+02 | -1.5E-02 | +9.6E-01 | 2.3E-02 | 5.4E-01 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 36| +1.48E+02 | -8.9E-04 | +5.7E-01 | 2.8E-02 | 1.3E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 86| +1.48E+02 | -6.1E-03 | +8.1E-01 | 6.1E-03 | 3.6E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 74| +1.48E+02 | -6.5E-05 | +8.5E-01 | 6.4E-03 | 1.4E-01 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 18| +1.59E+02 | -7.6E+00 | +9.5E-01 | 5.0E-01 | 6.4E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 57| +1.48E+02 | -9.3E-05 | +2.6E-01 | 1.6E-02 | 2.3E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 6| +2.10E+02 | +3.0E+00 | -1.1E+00 | 1.0E+00 | 1.7E+01 | 2.6E+00 | trr |0\n", - "2023-02-17 16:05:18 fides(INFO) 87| +1.48E+02 | -6.2E-03 | +9.5E-01 | 1.2E-02 | 2.9E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 75| +1.48E+02 | -7.0E-05 | +8.8E-01 | 1.3E-02 | 5.9E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 37| +1.48E+02 | -4.7E-04 | +8.7E-01 | 2.8E-02 | 1.2E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 58| +1.48E+02 | -1.4E-04 | +2.8E-01 | 1.6E-02 | 6.3E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 88| +1.48E+02 | -5.2E-03 | +6.7E-01 | 2.4E-02 | 1.8E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:18.263 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.9411 and h = 2.74506e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:18.263 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.9411: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:18 fides(INFO) 76| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.6E-02 | 1.4E-01 | 6.6E-03 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 7| +2.10E+02 | +3.5E+00 | -8.7E-01 | 2.5E-01 | 1.7E+01 | 6.7E-01 | trr |0\n", - "2023-02-17 16:05:18 fides(INFO) 38| +1.48E+02 | -5.7E-04 | +9.4E-01 | 5.6E-02 | 8.1E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 59| +1.48E+02 | +4.7E-05 | -1.0E-01 | 1.6E-02 | 2.5E-01 | 4.0E-03 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 8| +2.07E+02 | -2.5E+00 | +1.0E+00 | 6.2E-02 | 1.7E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 89| +1.48E+02 | -5.3E-03 | +9.7E-01 | 2.4E-02 | 1.4E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 126| +1.51E+02 | -8.3E-03 | +6.3E-01 | 4.5E-02 | 4.6E-01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 77| +1.48E+02 | -3.5E-05 | +1.1E+00 | 6.4E-03 | 1.4E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 39| +1.48E+02 | -2.7E-04 | +5.7E-01 | 1.1E-01 | 1.1E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 9| +2.06E+02 | -1.6E+00 | +9.2E-01 | 1.2E-01 | 1.2E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 60| +1.48E+02 | -9.4E-05 | +5.6E-01 | 3.9E-03 | 2.5E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 78| +1.48E+02 | -5.3E-05 | +9.6E-01 | 1.3E-02 | 7.7E-02 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 90| +1.48E+02 | -7.1E-03 | +1.0E+00 | 4.9E-02 | 5.7E-01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 40| +1.48E+02 | +7.1E-03 | -3.8E+00 | 1.1E-01 | 3.3E-01 | 4.1E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 61| +1.48E+02 | -6.1E-05 | +9.4E-01 | 3.9E-03 | 6.2E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 10| +2.00E+02 | -5.3E+00 | +1.7E+00 | 2.5E-01 | 1.4E+01 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 91| +1.48E+02 | -1.3E-02 | +1.1E+00 | 9.8E-02 | 9.1E-01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 79| +1.48E+02 | -8.0E-05 | +9.8E-01 | 2.6E-02 | 8.9E-02 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 41| +1.48E+02 | +4.1E-05 | -6.7E-02 | 2.8E-02 | 3.3E-01 | 1.0E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 62| +1.48E+02 | -4.5E-05 | +1.0E+00 | 7.8E-03 | 7.3E-02 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 11| +2.00E+02 | +1.7E+01 | -1.7E+00 | 5.0E-01 | 2.0E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 80| +1.48E+02 | -8.7E-05 | +1.0E+00 | 5.1E-02 | 4.7E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 19| +1.58E+02 | -1.4E+00 | +1.0E+00 | 1.0E+00 | 7.7E+01 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 92| +1.48E+02 | -9.6E-03 | +3.9E-01 | 2.0E-01 | 2.3E+00 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 42| +1.48E+02 | -1.2E-04 | +6.2E-01 | 7.0E-03 | 3.3E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 90| +1.55E+02 | +1.5E-03 | -2.4E-01 | 5.6E-02 | 2.7E+00 | 2.9E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 127| +1.51E+02 | -3.0E-03 | +9.3E-01 | 4.5E-02 | 4.5E-01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 12| +1.93E+02 | -6.9E+00 | +1.3E+00 | 1.2E-01 | 2.0E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 63| +1.48E+02 | -5.4E-05 | +7.4E-01 | 1.6E-02 | 2.2E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 81| +1.48E+02 | +2.1E-05 | -8.9E-01 | 1.0E-01 | 6.7E-02 | 7.8E-03 | trr |0\n", - "2023-02-17 16:05:18 fides(INFO) 43| +1.48E+02 | -7.2E-05 | +9.8E-01 | 7.0E-03 | 7.4E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 93| +1.48E+02 | -9.5E-03 | +7.6E-01 | 2.0E-01 | 5.4E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 64| +1.48E+02 | -1.5E-05 | +2.0E-01 | 1.6E-02 | 8.4E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 13| +1.73E+02 | -2.0E+01 | +1.1E+00 | 2.5E-01 | 3.5E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 82| +1.48E+02 | -4.9E-06 | +4.6E-01 | 1.8E-02 | 6.7E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 65| +1.48E+02 | -4.3E-05 | +6.9E-01 | 3.9E-03 | 2.9E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 94| +1.48E+02 | -5.2E-03 | +1.5E+00 | 2.0E-01 | 1.6E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 44| +1.48E+02 | -2.0E-05 | +8.0E-01 | 1.4E-02 | 2.1E-01 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 14| +1.68E+02 | -5.9E+00 | +3.9E-01 | 5.0E-01 | 7.4E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 83| +1.48E+02 | +1.2E-05 | -7.1E-01 | 1.8E-02 | 2.7E-02 | 3.9E-03 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 95| +1.48E+02 | -1.4E-03 | +5.1E-01 | 2.0E-01 | 7.2E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 45| +1.48E+02 | -2.4E-05 | +9.3E-01 | 2.8E-02 | 2.8E-01 | 5.8E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 66| +1.48E+02 | -4.5E-06 | +4.3E-01 | 3.9E-03 | 5.4E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 128| +1.51E+02 | -3.9E-04 | +1.4E+00 | 4.5E-02 | 4.1E-01 | 7.7E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 84| +1.48E+02 | -3.8E-06 | +7.6E-01 | 4.5E-03 | 2.7E-02 | 9.9E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 15| +1.60E+02 | -7.7E+00 | +8.1E-01 | 5.0E-01 | 1.3E+02 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 67| +1.48E+02 | -1.0E-05 | +1.6E+00 | 3.9E-03 | 2.2E-02 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 46| +1.48E+02 | -2.3E-05 | +7.7E-01 | 5.6E-02 | 1.6E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 96| +1.48E+02 | -8.4E-04 | +9.8E-01 | 2.0E-01 | 4.1E-01 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:18.650 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 183.579 and h = 4.55724e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:18.650 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 183.579: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:18 fides(INFO) 16| +1.60E+02 | +0.0E+00 | +0.0E+00 | 1.0E+00 | 5.0E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 85| +1.48E+02 | -6.5E-07 | +2.4E-01 | 9.0E-03 | 3.3E-02 | 8.6E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 47| +1.48E+02 | +3.8E-04 | -4.8E+00 | 1.1E-01 | 1.4E-01 | 2.1E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 97| +1.48E+02 | -2.0E-04 | +1.7E+00 | 2.0E-01 | 2.4E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 68| +1.48E+02 | -1.4E-05 | +1.3E+00 | 7.8E-03 | 6.2E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 86| +1.48E+02 | -1.4E-06 | +2.3E+00 | 2.2E-03 | 1.7E-02 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(WARNING) Stopping as function difference 1.44E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:18 fides(INFO) 17| +1.56E+02 | -3.5E+00 | +9.4E-01 | 2.3E-01 | 5.0E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 20| +1.58E+02 | +1.5E+00 | -8.6E+00 | 2.0E+00 | 4.7E+01 | 6.1E+00 | trr |0\n", - "2023-02-17 16:05:18 fides(INFO) 69| +1.48E+02 | -9.0E-06 | +5.7E-01 | 1.6E-02 | 2.4E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 48| +1.48E+02 | -8.7E-07 | +2.8E-02 | 2.8E-02 | 1.4E-01 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 98| +1.48E+02 | -9.9E-04 | +1.5E+00 | 2.0E-01 | 1.8E-01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 129| +1.51E+02 | -1.2E-04 | +9.9E-01 | 4.5E-02 | 9.1E-02 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 18| +1.49E+02 | -6.9E+00 | +1.3E+00 | 4.6E-01 | 5.0E+01 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 70| +1.48E+02 | -1.0E-05 | +9.8E-01 | 1.6E-02 | 4.7E-02 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 49| +1.48E+02 | -1.5E-05 | +5.9E-01 | 7.0E-03 | 1.5E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 99| +1.48E+02 | -1.2E-03 | +1.4E+00 | 2.0E-01 | 2.6E-01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 19| +1.49E+02 | +2.1E+01 | -9.6E+00 | 9.2E-01 | 5.3E+01 | 8.2E-01 | trr |0\n", - "2023-02-17 16:05:18 fides(INFO) 71| +1.48E+02 | +7.4E-05 | -3.6E+00 | 3.1E-02 | 2.6E-02 | 2.3E-03 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 91| +1.55E+02 | -2.5E-03 | +6.8E-01 | 2.8E-03 | 2.7E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 50| +1.48E+02 | -4.1E-06 | +1.4E+00 | 7.0E-03 | 1.1E-01 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 100| +1.48E+02 | -7.8E-04 | +1.4E+00 | 2.0E-01 | 3.0E-01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:18.802 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 68.1343 and h = 1.67073e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:18.803 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 68.1343: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 20| +1.49E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 5.3E+01 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 51| +1.48E+02 | -5.2E-07 | +2.2E-01 | 1.4E-02 | 7.3E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 72| +1.48E+02 | +2.5E-05 | -2.1E+00 | 7.8E-03 | 2.6E-02 | 9.4E-04 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 0| +3.01E+13 | NaN | NaN | 1.0E+00 | 1.4E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 130| +1.51E+02 | -4.7E-06 | +1.1E+00 | 4.5E-02 | 9.5E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 21| +1.49E+02 | -5.5E-01 | +9.1E-01 | 2.9E-02 | 5.3E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 101| +1.48E+02 | -3.8E-04 | +1.4E+00 | 2.0E-01 | 1.5E-01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 52| +1.48E+02 | -1.1E-06 | +9.4E-01 | 3.5E-03 | 7.1E-02 | 4.6E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(WARNING) Stopping as function difference 1.13E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:18 fides(INFO) 73| +1.48E+02 | +1.7E-05 | -1.9E+00 | 2.0E-03 | 2.6E-02 | 7.1E-04 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 1| +1.44E+07 | -3.0E+13 | +8.2E-02 | 1.0E+00 | 1.4E+14 | 3.2E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 2| +2.20E+06 | -1.2E+07 | +8.9E-01 | 2.5E-01 | 6.6E+07 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 22| +1.48E+02 | -5.6E-01 | +9.4E-01 | 5.8E-02 | 1.4E+01 | 9.7E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 74| +1.48E+02 | +1.6E-05 | -2.0E+00 | 4.9E-04 | 2.6E-02 | 6.6E-04 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 3| +3.38E+05 | -1.9E+06 | +8.9E-01 | 5.0E-01 | 1.0E+07 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 102| +1.48E+02 | -1.8E-04 | +1.4E+00 | 2.0E-01 | 2.6E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 4| +5.60E+04 | -2.8E+05 | +8.7E-01 | 1.0E+00 | 1.6E+06 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 21| +1.56E+02 | -1.6E+00 | +9.1E-01 | 5.0E-01 | 4.7E+01 | 1.3E+00 | trr |1\n", - "2023-02-17 16:05:18 fides(INFO) 23| +1.48E+02 | +4.3E-01 | -9.2E-01 | 1.2E-01 | 7.4E+00 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 5| +8.88E+03 | -4.7E+04 | +9.0E-01 | 1.0E+00 | 2.4E+05 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 75| +1.48E+02 | -2.6E-06 | +4.8E-01 | 1.2E-04 | 2.6E-02 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 103| +1.48E+02 | -1.1E-04 | +1.4E+00 | 2.0E-01 | 3.4E-02 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 6| +1.56E+03 | -7.3E+03 | +9.0E-01 | 1.0E+00 | 3.8E+04 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 7| +4.27E+02 | -1.1E+03 | +9.0E-01 | 1.0E+00 | 5.9E+03 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 24| +1.48E+02 | -9.2E-02 | +6.4E-01 | 2.9E-02 | 7.4E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 76| +1.48E+02 | -2.1E-07 | +2.3E-01 | 1.2E-04 | 8.4E-02 | 3.4E-05 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 131| +1.51E+02 | -6.9E-07 | +1.3E+00 | 4.5E-02 | 2.8E-02 | 1.9E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(WARNING) Stopping as function difference 6.90E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:18 fides(INFO) 104| +1.48E+02 | -5.9E-05 | +1.5E+00 | 3.9E-01 | 4.0E-02 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 8| +2.64E+02 | -1.6E+02 | +8.9E-01 | 1.0E+00 | 9.2E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 0| +1.63E+13 | NaN | NaN | 1.0E+00 | 7.5E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:19 fides(INFO) 77| +1.48E+02 | -2.0E-06 | +2.0E+01 | 3.1E-05 | 1.5E-02 | 1.3E-05 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 9| +2.33E+02 | -3.1E+01 | +1.2E+00 | 1.0E+00 | 1.2E+02 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 25| +1.48E+02 | -1.3E-01 | +9.4E-01 | 2.9E-02 | 4.3E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 1| +1.16E+07 | -1.6E+13 | +8.4E-02 | 1.0E+00 | 7.5E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 105| +1.48E+02 | -2.8E-05 | +1.3E+00 | 3.9E-01 | 1.3E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 92| +1.55E+02 | -8.4E-04 | +9.3E-01 | 2.8E-03 | 1.3E+00 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 26| +1.48E+02 | -1.5E-01 | +8.7E-01 | 5.8E-02 | 5.6E+00 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 10| +2.33E+02 | +6.2E+01 | -1.3E+01 | 2.0E+00 | 2.2E+01 | 6.0E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 78| +1.48E+02 | +3.4E-07 | -2.4E+00 | 6.1E-05 | 2.4E-02 | 1.6E-05 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 2| +1.77E+06 | -9.8E+06 | +8.9E-01 | 2.5E-01 | 5.3E+07 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 106| +1.48E+02 | -1.5E-05 | +1.3E+00 | 3.9E-01 | 8.3E-03 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 11| +2.33E+02 | +2.3E+00 | -6.2E-01 | 5.0E-01 | 2.2E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 3| +2.75E+05 | -1.5E+06 | +9.0E-01 | 5.0E-01 | 8.2E+06 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 27| +1.48E+02 | -7.8E-02 | +2.0E-01 | 1.2E-01 | 3.8E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 107| +1.48E+02 | -1.3E-05 | +2.0E+00 | 3.9E-01 | 1.5E-02 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 79| +1.48E+02 | +2.0E-06 | -2.1E+01 | 1.5E-05 | 2.4E-02 | 8.3E-06 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 12| +2.32E+02 | -8.6E-01 | +1.8E-01 | 1.2E-01 | 2.2E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 4| +4.30E+04 | -2.3E+05 | +9.0E-01 | 1.0E+00 | 1.3E+06 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:19.102 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 82.3892 and h = 4.26295e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:19.102 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 82.3892: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:19 fides(INFO) 28| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.9E-02 | 1.3E+01 | 5.4E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 13| +2.30E+02 | -1.8E+00 | +9.1E-01 | 3.1E-02 | 3.9E+01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 0| +6.04E+09 | NaN | NaN | 1.0E+00 | 2.2E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:19 fides(INFO) 108| +1.48E+02 | +1.4E-06 | -6.3E-01 | 3.9E-01 | 5.6E-03 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 22| +1.54E+02 | -2.6E+00 | +1.0E+00 | 5.0E-01 | 8.5E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 14| +2.28E+02 | -1.9E+00 | +8.6E-01 | 6.2E-02 | 2.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 5| +6.90E+03 | -3.6E+04 | +9.0E-01 | 1.0E+00 | 2.0E+05 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 1| +2.23E+07 | -6.0E+09 | +1.2E-01 | 1.0E+00 | 2.2E+10 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 80| +1.48E+02 | +1.0E-06 | -1.2E+01 | 3.8E-06 | 2.4E-02 | 6.6E-06 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 29| +1.48E+02 | -7.8E-02 | +9.0E-01 | 7.2E-03 | 1.3E+01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 109| +1.48E+02 | +2.0E-06 | -1.5E+00 | 6.6E-02 | 5.6E-03 | 5.7E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 15| +2.25E+02 | -3.3E+00 | +1.1E+00 | 1.2E-01 | 1.8E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 2| +3.45E+06 | -1.9E+07 | +8.9E-01 | 2.5E-01 | 1.0E+08 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:19.180 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 82.9518 and h = 2.54562e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:19 fides(INFO) 81| +1.48E+02 | -1.2E-07 | +3.7E+00 | 9.5E-07 | 2.4E-02 | 1.7E-06 | 2d |1\n", - "2023-02-17 16:05:19.180 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 82.9518: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:19 fides(WARNING) Stopping as function difference 1.25E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 30| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.4E-02 | 5.8E+00 | 2.6E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 16| +2.22E+02 | -3.0E+00 | +1.0E+00 | 2.5E-01 | 2.0E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 110| +1.48E+02 | +3.0E-06 | -7.9E+00 | 1.7E-02 | 5.6E-03 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 3| +5.50E+05 | -2.9E+06 | +9.0E-01 | 5.0E-01 | 1.6E+07 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 31| +1.48E+02 | -3.2E-02 | +9.3E-01 | 3.6E-03 | 5.8E+00 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 17| +2.22E+02 | +4.3E+00 | -1.1E+00 | 5.0E-01 | 2.1E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 6| +1.26E+03 | -5.6E+03 | +9.0E-01 | 1.0E+00 | 3.1E+04 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 93| +1.55E+02 | -9.2E-04 | +7.3E-01 | 5.6E-03 | 3.2E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 4| +9.46E+04 | -4.6E+05 | +8.9E-01 | 1.0E+00 | 2.4E+06 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 111| +1.48E+02 | +2.3E-06 | -2.3E+01 | 4.1E-03 | 5.6E-03 | 3.5E-03 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 32| +1.48E+02 | -3.9E-02 | +8.8E-01 | 7.2E-03 | 4.9E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 18| +2.21E+02 | -5.4E-01 | +8.7E-02 | 1.2E-01 | 2.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 5| +1.50E+04 | -8.0E+04 | +9.0E-01 | 2.0E+00 | 3.8E+05 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 112| +1.48E+02 | +4.4E-06 | -1.5E+02 | 1.0E-03 | 5.6E-03 | 8.8E-04 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 19| +2.20E+02 | -1.7E+00 | +8.8E-01 | 3.1E-02 | 3.9E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 6| +2.54E+03 | -1.3E+04 | +9.0E-01 | 2.0E+00 | 6.0E+04 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 7| +3.71E+02 | -8.8E+02 | +9.0E-01 | 1.0E+00 | 4.9E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 33| +1.48E+02 | -4.7E-02 | +7.9E-01 | 1.4E-02 | 5.2E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 20| +2.18E+02 | -1.3E+00 | +7.5E-01 | 6.2E-02 | 2.5E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 113| +1.48E+02 | +1.9E-06 | -1.8E+02 | 2.6E-04 | 5.6E-03 | 2.2E-04 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 7| +5.75E+02 | -2.0E+03 | +9.0E-01 | 2.0E+00 | 9.6E+03 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 0| +2.54E+08 | NaN | NaN | 1.0E+00 | 1.2E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:19 fides(INFO) 23| +1.53E+02 | -4.4E-01 | +1.0E+00 | 1.0E+00 | 3.4E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:19.338 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 142.009 and h = 2.78692e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:19 fides(INFO) 8| +2.31E+02 | -1.4E+02 | +9.2E-01 | 1.0E+00 | 7.8E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:19.339 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 142.009: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:19 fides(INFO) 34| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.9E-02 | 2.5E+00 | 4.4E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 21| +2.17E+02 | -1.0E+00 | +4.8E-01 | 1.2E-01 | 1.5E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 114| +1.48E+02 | +2.9E-06 | -4.5E+02 | 6.5E-05 | 5.6E-03 | 5.5E-05 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 1| +4.19E+03 | -2.5E+08 | +1.0E-01 | 1.0E+00 | 1.2E+09 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 8| +2.83E+02 | -2.9E+02 | +8.9E-01 | 2.0E+00 | 1.5E+03 | 4.2E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 22| +2.16E+02 | -1.5E+00 | +5.7E-01 | 1.2E-01 | 3.0E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 9| +2.31E+02 | -7.6E+00 | -1.7E-01 | 2.0E+00 | 1.1E+02 | 4.0E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 35| +1.48E+02 | -1.3E-02 | +8.5E-01 | 7.2E-03 | 2.5E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 115| +1.48E+02 | +3.9E-06 | -7.4E+02 | 1.6E-05 | 5.6E-03 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 9| +2.34E+02 | -5.0E+01 | +1.1E+00 | 4.0E+00 | 2.2E+02 | 3.3E+00 | trr |1\n", - "2023-02-17 16:05:19 fides(INFO) 23| +2.16E+02 | +4.5E-01 | -3.4E-01 | 1.2E-01 | 1.5E+01 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 2| +1.07E+03 | -3.1E+03 | +9.1E-01 | 2.5E-01 | 1.3E+04 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 36| +1.48E+02 | -1.6E-02 | +8.0E-01 | 1.4E-02 | 2.6E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 116| +1.48E+02 | +4.0E-06 | -7.9E+02 | 4.0E-06 | 5.6E-03 | 3.9E-06 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 10| +2.34E+02 | +9.9E+02 | -4.3E+01 | 4.0E+00 | 3.4E+01 | 1.2E+01 | trr |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 10| +2.08E+02 | -2.3E+01 | +9.7E-01 | 5.0E-01 | 1.1E+02 | 9.7E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 24| +2.15E+02 | -6.5E-01 | +8.1E-01 | 3.1E-02 | 1.5E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 3| +4.00E+02 | -6.7E+02 | +9.1E-01 | 5.0E-01 | 2.7E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 37| +1.48E+02 | -2.3E-02 | +8.8E-01 | 2.9E-02 | 1.8E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 25| +2.14E+02 | -8.9E-01 | +9.9E-01 | 6.2E-02 | 1.2E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 117| +1.48E+02 | +4.0E-06 | -8.0E+02 | 1.0E-06 | 5.6E-03 | 1.9E-06 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 11| +1.66E+02 | -4.2E+01 | +9.2E-01 | 9.9E-01 | 4.1E+01 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 11| +2.34E+02 | +6.6E+01 | -5.4E+00 | 1.0E+00 | 3.4E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 4| +2.93E+02 | -1.1E+02 | +8.8E-01 | 1.0E+00 | 4.5E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 12| +2.34E+02 | +2.7E+00 | -3.2E-01 | 2.5E-01 | 3.4E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 26| +2.14E+02 | -7.9E-01 | +7.4E-01 | 1.2E-01 | 1.2E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 38| +1.48E+02 | -4.1E-02 | +7.8E-01 | 5.8E-02 | 2.5E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 118| +1.48E+02 | +1.7E-07 | -6.1E+01 | 2.5E-07 | 5.6E-03 | 6.3E-07 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 94| +1.55E+02 | -5.9E-04 | +9.5E-01 | 5.6E-03 | 9.6E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 5| +2.57E+02 | -3.6E+01 | +8.3E-01 | 2.0E+00 | 6.1E+01 | 3.7E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 12| +1.66E+02 | +4.4E+01 | -2.9E+00 | 2.0E+00 | 1.7E+02 | 1.3E+00 | trr |0\n", - "2023-02-17 16:05:19 fides(INFO) 27| +2.14E+02 | +4.2E-01 | -3.2E-01 | 1.2E-01 | 1.4E+01 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 13| +2.29E+02 | -4.4E+00 | +9.0E-01 | 6.2E-02 | 3.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 24| +1.53E+02 | -8.0E-01 | +5.0E-01 | 2.0E+00 | 3.9E+01 | 8.7E-01 | trr |1\n", - "2023-02-17 16:05:19 fides(INFO) 6| +2.57E+02 | +4.7E+01 | -1.1E+01 | 4.0E+00 | 3.5E+01 | 1.1E+01 | trr |0\n", - "2023-02-17 16:05:19 fides(INFO) 39| +1.48E+02 | +1.5E-01 | -1.8E+00 | 1.2E-01 | 1.7E+00 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 14| +2.26E+02 | -3.0E+00 | +5.2E-01 | 1.2E-01 | 2.6E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 28| +2.13E+02 | -6.3E-01 | +7.9E-01 | 3.1E-02 | 1.4E+01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 119| +1.48E+02 | +6.6E-07 | -8.0E+02 | 6.3E-08 | 5.6E-03 | 1.6E-07 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 7| +2.57E+02 | +1.8E+02 | -1.1E+01 | 9.4E-01 | 3.5E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 13| +1.56E+02 | -1.1E+01 | +9.8E-01 | 1.2E-01 | 1.7E+02 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 25| +1.53E+02 | +7.1E+01 | -7.4E+01 | 2.0E+00 | 1.6E+01 | 5.5E+00 | trr |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 40| +1.48E+02 | -9.7E-03 | +3.9E-01 | 2.9E-02 | 1.7E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 15| +2.22E+02 | -4.1E+00 | +8.2E-01 | 1.2E-01 | 3.8E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 29| +2.12E+02 | -4.7E-01 | +7.7E-01 | 6.2E-02 | 8.9E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 8| +2.57E+02 | +8.1E-01 | -1.4E-01 | 2.3E-01 | 3.5E+01 | 6.2E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 120| +1.48E+02 | +4.5E-06 | -2.1E+04 | 1.6E-08 | 5.6E-03 | 3.9E-08 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 14| +1.52E+02 | -3.8E+00 | +3.0E-01 | 2.4E-01 | 6.1E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 16| +2.19E+02 | -3.8E+00 | +4.6E-01 | 2.5E-01 | 2.4E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 41| +1.48E+02 | +7.5E-03 | -2.4E-01 | 2.9E-02 | 1.5E+00 | 4.2E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 9| +2.53E+02 | -4.7E+00 | +9.2E-01 | 5.9E-02 | 3.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 121| +1.48E+02 | +4.3E-06 | -7.9E+04 | 4.0E-09 | 5.6E-03 | 9.9E-09 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 30| +2.12E+02 | -4.6E-01 | +5.7E-01 | 1.2E-01 | 1.2E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 10| +2.51E+02 | -1.7E+00 | +2.9E-01 | 1.2E-01 | 2.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 17| +2.12E+02 | -7.1E+00 | +7.3E-01 | 2.5E-01 | 5.7E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 26| +1.52E+02 | -5.0E-01 | +8.9E-01 | 5.0E-01 | 1.6E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 42| +1.48E+02 | -5.7E-03 | +4.9E-01 | 7.2E-03 | 1.5E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 31| +2.12E+02 | -2.4E-01 | +2.0E-01 | 1.2E-01 | 1.1E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 122| +1.48E+02 | +1.7E-08 | -1.3E+03 | 9.9E-10 | 5.6E-03 | 2.5E-09 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 15| +1.49E+02 | -3.3E+00 | +9.6E-01 | 2.4E-01 | 1.8E+02 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 18| +2.10E+02 | -1.2E+00 | +6.9E-02 | 2.5E-01 | 2.5E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 11| +2.50E+02 | -7.6E-01 | +3.8E-01 | 1.2E-01 | 2.2E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 27| +1.52E+02 | +4.7E+01 | -1.8E+02 | 1.0E+00 | 2.4E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 32| +2.11E+02 | -6.6E-01 | +7.8E-01 | 3.1E-02 | 1.8E+01 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 12| +2.50E+02 | +2.4E-01 | -3.5E-01 | 1.2E-01 | 1.0E+01 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 123| +1.48E+02 | +5.2E-07 | -1.6E+05 | 2.5E-10 | 5.6E-03 | 6.2E-10 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 16| +1.49E+02 | +8.2E+00 | -5.7E+00 | 4.8E-01 | 3.0E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 43| +1.48E+02 | -4.2E-03 | +8.9E-01 | 7.2E-03 | 4.1E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 19| +2.05E+02 | -5.1E+00 | +8.5E-01 | 6.2E-02 | 6.3E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 13| +2.50E+02 | -3.4E-01 | +5.9E-01 | 2.9E-02 | 1.0E+01 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 33| +2.11E+02 | -4.2E-01 | +8.2E-01 | 6.2E-02 | 7.7E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 124| +1.48E+02 | +4.5E-07 | -5.3E+05 | 6.2E-11 | 5.6E-03 | 1.5E-10 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 17| +1.48E+02 | -5.2E-01 | +5.3E-01 | 1.2E-01 | 3.0E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 28| +1.52E+02 | -1.9E-01 | +1.0E+00 | 2.5E-01 | 2.4E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 20| +2.02E+02 | -3.3E+00 | +7.9E-01 | 1.2E-01 | 3.1E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 44| +1.48E+02 | -3.9E-03 | +9.1E-01 | 1.4E-02 | 4.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 14| +2.50E+02 | -9.7E-04 | +4.9E-02 | 2.9E-02 | 1.8E+00 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 34| +2.10E+02 | -3.5E-01 | +4.0E-01 | 1.2E-01 | 7.9E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 125| +1.48E+02 | +4.6E-06 | -2.2E+07 | 1.5E-11 | 5.6E-03 | 3.9E-11 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 15| +2.50E+02 | -9.4E-04 | +5.5E-02 | 7.3E-03 | 1.7E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 18| +1.48E+02 | -2.8E-01 | +8.6E-01 | 1.2E-01 | 2.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 45| +1.48E+02 | -7.6E-03 | +9.4E-01 | 2.9E-02 | 5.2E-01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 21| +1.94E+02 | -8.3E+00 | +1.3E+00 | 2.5E-01 | 1.8E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 29| +1.52E+02 | +1.9E+00 | -5.6E+00 | 5.0E-01 | 1.5E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 35| +2.09E+02 | -9.0E-01 | +6.7E-01 | 1.2E-01 | 1.5E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 95| +1.55E+02 | -6.7E-04 | +1.3E+00 | 1.1E-02 | 1.6E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 16| +2.50E+02 | -4.3E-03 | +9.0E-01 | 1.8E-03 | 1.6E+00 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 126| +1.48E+02 | +5.1E-06 | -9.7E+07 | 3.9E-12 | 5.6E-03 | 9.6E-12 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 46| +1.48E+02 | -5.5E-03 | +6.3E-01 | 5.8E-02 | 8.4E-01 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 19| +1.48E+02 | +1.9E-01 | -6.3E-01 | 2.4E-01 | 8.0E+00 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 17| +2.50E+02 | -3.0E-03 | +6.2E-01 | 3.7E-03 | 1.0E+00 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 22| +1.94E+02 | +3.1E+01 | -2.3E+00 | 5.0E-01 | 3.7E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 36| +2.09E+02 | -3.3E-01 | +3.2E-01 | 1.2E-01 | 8.2E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 30| +1.52E+02 | -3.1E-01 | +9.7E-01 | 1.2E-01 | 1.5E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 127| +1.48E+02 | -1.7E-08 | +1.3E+06 | 9.7E-13 | 5.6E-03 | 2.4E-12 | 2d |1\n", - "2023-02-17 16:05:19 fides(WARNING) Stopping as function difference 1.67E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:19 fides(INFO) 47| +1.48E+02 | +2.1E-01 | -7.9E+00 | 5.8E-02 | 9.7E-01 | 8.8E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 20| +1.48E+02 | -2.8E-02 | +9.8E-02 | 6.0E-02 | 8.0E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 18| +2.50E+02 | -2.0E-06 | +3.5E-02 | 3.7E-03 | 9.8E-02 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 23| +1.84E+02 | -9.8E+00 | +1.2E+00 | 1.2E-01 | 3.7E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 37| +2.08E+02 | -9.1E-01 | +6.3E-01 | 1.2E-01 | 1.6E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 48| +1.48E+02 | +1.0E-02 | -8.1E-01 | 1.4E-02 | 9.7E-01 | 2.4E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 19| +2.50E+02 | -9.4E-07 | +1.6E-02 | 9.2E-04 | 9.8E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 24| +1.67E+02 | -1.7E+01 | +8.6E-01 | 2.5E-01 | 4.5E+01 | 5.5E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 38| +2.08E+02 | -5.2E-01 | +4.6E-01 | 1.2E-01 | 8.6E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 21| +1.48E+02 | -8.0E-02 | +8.6E-01 | 1.5E-02 | 1.4E+01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 31| +1.52E+02 | -6.6E-02 | +1.2E-01 | 2.5E-01 | 6.1E+00 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 20| +2.50E+02 | -2.8E-05 | +6.5E-01 | 2.3E-04 | 9.7E-02 | 6.0E-04 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 49| +1.48E+02 | -1.7E-03 | +3.6E-01 | 3.6E-03 | 9.7E-01 | 7.1E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 25| +1.67E+02 | +7.7E+01 | -4.4E+00 | 5.0E-01 | 1.3E+02 | 9.0E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 39| +2.06E+02 | -1.4E+00 | +7.7E-01 | 1.2E-01 | 1.6E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 22| +1.48E+02 | -5.0E-02 | +7.1E-01 | 3.0E-02 | 5.9E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 21| +2.50E+02 | -1.4E-07 | +5.9E-01 | 2.3E-04 | 4.1E-03 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:19 fides(WARNING) Stopping as function difference 1.37E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:19 fides(INFO) 32| +1.51E+02 | -1.0E-01 | +9.6E-01 | 6.2E-02 | 2.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 50| +1.48E+02 | -1.3E-03 | +9.2E-01 | 3.6E-03 | 3.0E+00 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 26| +1.55E+02 | -1.1E+01 | +9.2E-01 | 1.2E-01 | 1.3E+02 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 40| +2.03E+02 | -3.1E+00 | +1.2E+00 | 2.5E-01 | 1.0E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 23| +1.48E+02 | -1.2E-02 | +8.1E-01 | 3.0E-02 | 6.3E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 51| +1.48E+02 | -4.9E-04 | +9.2E-01 | 7.2E-03 | 2.8E-01 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 41| +1.81E+02 | -2.3E+01 | +1.5E+00 | 5.0E-01 | 2.1E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 24| +1.48E+02 | -7.0E-03 | +7.9E-01 | 6.0E-02 | 1.6E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 33| +1.51E+02 | -9.7E-02 | +1.0E+00 | 1.2E-01 | 1.1E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 96| +1.55E+02 | -1.3E-03 | +1.1E+00 | 2.2E-02 | 3.0E-01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 27| +1.52E+02 | -3.2E+00 | +4.0E-01 | 2.5E-01 | 6.4E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 0| +3.19E+13 | NaN | NaN | 1.0E+00 | 1.1E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:19 fides(INFO) 52| +1.48E+02 | -9.3E-04 | +9.5E-01 | 1.4E-02 | 2.6E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 1| +3.54E+10 | -3.2E+13 | +1.1E-01 | 1.0E+00 | 1.1E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 25| +1.48E+02 | -7.8E-03 | +9.6E-01 | 1.2E-01 | 2.7E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 28| +1.48E+02 | -4.0E+00 | +7.6E-01 | 2.5E-01 | 7.2E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 42| +1.81E+02 | +8.1E+03 | -2.3E+02 | 1.0E+00 | 8.1E+01 | 2.2E+00 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 2| +5.33E+09 | -3.0E+10 | +8.9E-01 | 2.5E-01 | 1.6E+11 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 0| +4.83E+06 | NaN | NaN | 1.0E+00 | 1.7E+07 | NaN | NaN |1\n", - "2023-02-17 16:05:20 fides(INFO) 53| +1.48E+02 | -4.7E-04 | +4.8E-01 | 2.9E-02 | 2.0E-01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 34| +1.51E+02 | -1.9E-01 | +1.1E+00 | 2.5E-01 | 1.1E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 26| +1.48E+02 | -1.0E-02 | +9.4E-01 | 2.4E-01 | 9.7E-01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 3| +8.04E+08 | -4.5E+09 | +8.9E-01 | 5.0E-01 | 2.4E+10 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 29| +1.48E+02 | +1.0E+01 | -8.3E+00 | 5.0E-01 | 4.5E+01 | 6.2E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 43| +1.67E+02 | -1.4E+01 | +5.2E-01 | 2.5E-01 | 8.1E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 4| +1.22E+08 | -6.8E+08 | +8.9E-01 | 5.0E-01 | 3.7E+09 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 54| +1.48E+02 | +1.7E-02 | -4.2E+00 | 2.9E-02 | 3.8E-01 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 27| +1.48E+02 | -5.8E-03 | +5.0E-01 | 4.8E-01 | 1.2E+00 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 1| +1.28E+04 | -4.8E+06 | +1.4E-01 | 1.0E+00 | 1.7E+07 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 5| +1.86E+07 | -1.0E+08 | +8.9E-01 | 5.0E-01 | 5.6E+08 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 30| +1.48E+02 | -1.8E-01 | +2.6E-01 | 1.2E-01 | 4.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 6| +2.87E+06 | -1.6E+07 | +8.9E-01 | 5.0E-01 | 8.6E+07 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 55| +1.48E+02 | +1.3E-03 | -7.8E-01 | 7.2E-03 | 3.8E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 28| +1.48E+02 | +4.0E-01 | -9.0E+00 | 4.8E-01 | 1.4E+00 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 44| +1.57E+02 | -9.8E+00 | +9.7E-01 | 2.5E-01 | 1.6E+02 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 2| +2.26E+03 | -1.1E+04 | +9.0E-01 | 2.5E-01 | 5.8E+04 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 31| +1.48E+02 | -2.7E-01 | +4.4E-01 | 1.2E-01 | 2.3E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 35| +1.51E+02 | -3.5E-01 | +1.4E+00 | 5.0E-01 | 1.1E+01 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 7| +4.44E+05 | -2.4E+06 | +9.0E-01 | 1.0E+00 | 1.3E+07 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 56| +1.48E+02 | -9.3E-05 | +1.2E-01 | 1.8E-03 | 3.8E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 29| +1.48E+02 | +1.1E-02 | -5.7E-01 | 1.2E-01 | 1.4E+00 | 7.4E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 45| +1.57E+02 | +2.2E+01 | -2.4E+00 | 5.0E-01 | 4.8E+01 | 9.0E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 3| +6.12E+02 | -1.6E+03 | +8.5E-01 | 5.0E-01 | 9.1E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 8| +6.95E+04 | -3.7E+05 | +9.0E-01 | 1.0E+00 | 2.0E+06 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 32| +1.48E+02 | +2.6E-01 | -6.8E-01 | 1.2E-01 | 1.5E+01 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 36| +1.51E+02 | +2.5E+00 | -4.0E+00 | 1.0E+00 | 1.0E+01 | 1.4E+00 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 4| +2.63E+02 | -3.5E+02 | +9.2E-01 | 1.0E+00 | 1.5E+03 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 57| +1.48E+02 | -3.2E-04 | +9.4E-01 | 4.5E-04 | 1.7E+00 | 6.7E-04 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 9| +1.11E+04 | -5.8E+04 | +9.0E-01 | 1.0E+00 | 3.2E+05 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 30| +1.48E+02 | -3.2E-03 | +5.3E-01 | 3.0E-02 | 1.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 46| +1.53E+02 | -3.8E+00 | +7.6E-01 | 1.2E-01 | 4.8E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 5| +2.12E+02 | -5.1E+01 | +8.7E-01 | 1.0E+00 | 1.8E+02 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 33| +1.48E+02 | -8.0E-02 | +6.2E-01 | 3.1E-02 | 1.5E+01 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 97| +1.55E+02 | -2.7E-03 | +1.0E+00 | 4.5E-02 | 3.4E-01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 10| +1.93E+03 | -9.2E+03 | +9.0E-01 | 1.0E+00 | 5.0E+04 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 58| +1.48E+02 | -3.7E-05 | +8.7E-01 | 9.0E-04 | 1.5E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 31| +1.48E+02 | -9.8E-04 | +9.6E-01 | 3.0E-02 | 2.6E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 34| +1.48E+02 | -5.9E-02 | +9.7E-01 | 3.1E-02 | 2.0E+01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 37| +1.50E+02 | -4.3E-01 | +1.1E+00 | 2.4E-01 | 1.0E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 11| +4.92E+02 | -1.4E+03 | +9.1E-01 | 1.0E+00 | 7.9E+03 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 47| +1.51E+02 | -2.3E+00 | +6.2E-01 | 2.5E-01 | 6.9E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 32| +1.48E+02 | -4.7E-04 | +9.4E-01 | 6.0E-02 | 3.0E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 6| +2.12E+02 | +2.6E+02 | -1.2E+01 | 2.0E+00 | 6.4E+01 | 5.9E+00 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 59| +1.48E+02 | -3.2E-05 | +7.2E-01 | 1.8E-03 | 2.2E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:20.266 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0723 and h = 2.40154e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.267 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0723: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) 35| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 1.1E+01 | 4.3E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 12| +2.72E+02 | -2.2E+02 | +9.0E-01 | 1.0E+00 | 1.3E+03 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 7| +1.99E+02 | -1.3E+01 | +6.2E-01 | 5.0E-01 | 6.4E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 60| +1.48E+02 | -2.0E-05 | +9.7E-01 | 1.8E-03 | 7.1E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 48| +1.49E+02 | -1.6E+00 | +8.2E-01 | 2.5E-01 | 3.4E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 38| +1.50E+02 | -5.7E-01 | +8.6E-01 | 4.7E-01 | 1.0E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 33| +1.48E+02 | -4.8E-04 | +6.8E-01 | 1.2E-01 | 6.7E-01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 8| +1.74E+02 | -2.6E+01 | +1.1E+00 | 5.0E-01 | 6.9E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 13| +2.42E+02 | -3.0E+01 | +9.2E-01 | 2.0E+00 | 1.8E+02 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 36| +1.48E+02 | -1.9E-02 | +9.6E-01 | 1.6E-02 | 1.1E+01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 61| +1.48E+02 | -3.3E-05 | +9.9E-01 | 3.6E-03 | 8.7E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 9| +1.74E+02 | +1.6E+03 | -7.8E+01 | 1.0E+00 | 5.5E+01 | 1.9E+00 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 39| +1.50E+02 | -1.8E-01 | +6.6E-01 | 9.5E-01 | 1.6E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 49| +1.49E+02 | +2.9E+00 | -2.1E+00 | 5.0E-01 | 2.9E+01 | 7.0E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 14| +2.31E+02 | -1.2E+01 | +1.7E+00 | 2.0E+00 | 1.5E+01 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 34| +1.48E+02 | +1.2E-03 | -1.0E+00 | 1.2E-01 | 3.1E-01 | 4.8E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 37| +1.48E+02 | -3.0E-02 | +9.8E-01 | 3.1E-02 | 1.2E+01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 10| +1.64E+02 | -9.2E+00 | +4.7E-01 | 2.5E-01 | 5.5E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 62| +1.48E+02 | -3.8E-05 | +1.0E+00 | 7.2E-03 | 4.1E-02 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 35| +1.48E+02 | -1.4E-04 | +3.2E-01 | 3.0E-02 | 3.1E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 50| +1.49E+02 | -3.2E-01 | +5.7E-01 | 1.2E-01 | 2.9E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 15| +2.31E+02 | +7.6E+01 | -9.5E+00 | 4.0E+00 | 1.8E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:20.371 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 169.86 and h = 3.38851e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.371 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 169.86: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 40| +1.50E+02 | +0.0E+00 | +0.0E+00 | 9.5E-01 | 4.9E+00 | 1.2E-01 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 11| +1.53E+02 | -1.1E+01 | +8.7E-01 | 2.5E-01 | 1.4E+02 | 5.7E-01 | trr |1\n", - "2023-02-17 16:05:20 fides(INFO) 38| +1.48E+02 | -2.8E-02 | +9.4E-01 | 6.2E-02 | 8.8E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 12| +1.53E+02 | +1.6E+01 | -2.6E+00 | 2.5E-01 | 1.0E+02 | 4.8E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 63| +1.48E+02 | +8.3E-05 | -4.8E+00 | 1.4E-02 | 2.5E-02 | 2.6E-03 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 16| +2.23E+02 | -7.1E+00 | +1.5E+00 | 1.0E+00 | 1.8E+01 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 36| +1.48E+02 | -2.0E-04 | +7.1E-01 | 3.0E-02 | 9.1E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 39| +1.48E+02 | -2.8E-02 | +7.5E-01 | 1.2E-01 | 7.4E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 51| +1.48E+02 | -4.1E-01 | +8.9E-01 | 1.2E-01 | 2.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 41| +1.50E+02 | -1.4E-02 | +8.3E-01 | 5.6E-02 | 4.9E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 98| +1.55E+02 | -6.4E-03 | +1.1E+00 | 8.9E-02 | 3.8E-01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 13| +1.49E+02 | -4.1E+00 | +8.9E-01 | 6.2E-02 | 1.0E+02 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 64| +1.48E+02 | +3.4E-06 | -4.5E-01 | 2.1E-03 | 2.5E-02 | 6.6E-04 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 37| +1.48E+02 | -7.0E-05 | +8.9E-01 | 3.0E-02 | 1.5E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 42| +1.50E+02 | -4.2E-03 | +8.2E-01 | 1.1E-01 | 2.0E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 40| +1.48E+02 | +5.1E-01 | -5.3E+00 | 2.5E-01 | 4.6E+00 | 2.8E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 17| +2.23E+02 | +7.1E+02 | -4.1E+01 | 2.0E+00 | 4.3E+01 | 4.5E+00 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 14| +1.48E+02 | -1.4E+00 | +4.5E-01 | 1.2E-01 | 3.6E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 52| +1.48E+02 | -4.3E-01 | +9.3E-01 | 2.5E-01 | 2.0E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 65| +1.48E+02 | +2.7E-06 | -1.2E+00 | 5.2E-04 | 2.5E-02 | 1.7E-04 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 15| +1.47E+02 | -7.8E-01 | +9.0E-01 | 1.2E-01 | 6.5E+01 | 8.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 38| +1.48E+02 | -1.0E-04 | +8.9E-01 | 6.0E-02 | 2.1E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 41| +1.48E+02 | +3.7E-03 | -1.2E-01 | 6.2E-02 | 4.6E+00 | 6.9E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 18| +2.04E+02 | -2.0E+01 | +9.7E-01 | 5.0E-01 | 4.3E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 53| +1.48E+02 | +3.2E-01 | -6.9E-01 | 5.0E-01 | 1.5E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 43| +1.50E+02 | -1.3E-03 | +9.6E-01 | 2.3E-01 | 9.1E-01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 16| +1.46E+02 | -4.7E-01 | +7.8E-01 | 2.5E-01 | 1.7E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 66| +1.48E+02 | +2.9E-06 | -3.5E+00 | 1.3E-04 | 2.5E-02 | 5.5E-05 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 54| +1.48E+02 | -1.1E-01 | +6.7E-01 | 1.2E-01 | 1.5E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 42| +1.48E+02 | -7.8E-03 | +7.4E-01 | 1.6E-02 | 4.6E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 19| +2.04E+02 | -9.9E+00 | -1.7E+00 | 1.0E+00 | 6.2E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 39| +1.48E+02 | -2.5E-05 | +1.5E-01 | 1.2E-01 | 1.1E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 44| +1.50E+02 | -1.2E-04 | +6.0E-01 | 4.5E-01 | 6.7E-01 | 3.3E-02 | trr |1\n", - "2023-02-17 16:05:20 fides(INFO) 17| +1.46E+02 | +7.2E+00 | -7.9E+00 | 5.0E-01 | 4.2E+01 | 4.1E-01 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 67| +1.48E+02 | +1.3E-06 | -3.1E+00 | 3.2E-05 | 2.5E-02 | 3.0E-05 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 43| +1.48E+02 | -3.4E-03 | +8.9E-01 | 1.6E-02 | 3.5E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 40| +1.48E+02 | -4.7E-05 | +1.3E-01 | 3.0E-02 | 2.5E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 55| +1.48E+02 | -1.0E-01 | +4.5E-01 | 1.2E-01 | 1.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 20| +1.85E+02 | -1.8E+01 | +9.9E-01 | 2.5E-01 | 6.2E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 18| +1.46E+02 | -3.0E-01 | +5.9E-01 | 5.2E-02 | 4.2E+01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 99| +1.55E+02 | -1.5E-02 | +1.1E+00 | 1.8E-01 | 1.2E+00 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 45| +1.50E+02 | +7.4E-05 | -4.2E-01 | 4.5E-01 | 1.1E-01 | 9.1E-03 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 19| +1.46E+02 | +3.2E-01 | -1.4E+00 | 5.2E-02 | 1.0E+01 | 9.8E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 68| +1.48E+02 | +4.7E-06 | -1.6E+01 | 8.1E-06 | 2.5E-02 | 2.1E-05 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 44| +1.48E+02 | -3.0E-03 | +9.2E-01 | 3.1E-02 | 3.2E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 56| +1.48E+02 | +1.0E-03 | -1.4E-02 | 1.2E-01 | 2.1E+01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 41| +1.48E+02 | -5.8E-05 | +7.2E-01 | 7.5E-03 | 2.3E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 21| +1.74E+02 | -1.2E+01 | +1.5E-01 | 5.0E-01 | 5.0E+01 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 20| +1.46E+02 | -4.0E-02 | +5.6E-01 | 1.3E-02 | 1.0E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 69| +1.48E+02 | +5.0E-06 | -4.4E+01 | 2.0E-06 | 2.5E-02 | 5.4E-06 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 46| +1.50E+02 | -5.6E-05 | +7.1E-01 | 1.1E-02 | 1.1E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 42| +1.48E+02 | -1.7E-05 | +7.9E-01 | 7.5E-03 | 2.3E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 57| +1.48E+02 | -9.3E-02 | +8.0E-01 | 3.1E-02 | 2.1E+01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 45| +1.48E+02 | -3.3E-03 | +8.3E-01 | 6.2E-02 | 2.5E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 22| +1.51E+02 | -2.2E+01 | +9.1E-01 | 1.2E-01 | 2.9E+02 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 21| +1.46E+02 | -5.9E-02 | +9.1E-01 | 1.3E-02 | 7.7E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 43| +1.48E+02 | -6.8E-06 | +3.7E-01 | 1.5E-02 | 6.3E-02 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 47| +1.50E+02 | -1.7E-05 | +1.1E+00 | 1.1E-02 | 2.5E-01 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 70| +1.48E+02 | +5.2E-06 | -1.7E+02 | 5.1E-07 | 2.5E-02 | 1.3E-06 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 46| +1.48E+02 | +1.7E-02 | -2.3E+00 | 1.2E-01 | 2.0E+00 | 8.2E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 23| +1.49E+02 | -2.8E+00 | +8.9E-01 | 2.5E-01 | 7.8E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 58| +1.48E+02 | -4.0E-02 | +7.7E-01 | 6.2E-02 | 3.8E+00 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 100| +1.55E+02 | +9.5E-01 | -3.5E+01 | 3.6E-01 | 7.2E+00 | 9.0E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 22| +1.46E+02 | -7.1E-02 | +9.0E-01 | 2.6E-02 | 1.3E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 23| +1.46E+02 | +4.0E-02 | -3.5E-01 | 5.2E-02 | 4.5E+00 | 8.6E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 71| +1.48E+02 | +6.4E-06 | -8.1E+02 | 1.3E-07 | 2.5E-02 | 3.4E-07 | 2d |0\n", - "2023-02-17 16:05:20.687 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 144.786 and h = 1.83601e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.687 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 144.786: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) 44| +1.48E+02 | -9.4E-06 | +5.6E-01 | 1.5E-02 | 2.3E-01 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 24| +1.49E+02 | +2.3E+00 | -1.8E+00 | 5.0E-01 | 4.3E+01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 48| +1.50E+02 | -7.6E-06 | +8.9E-01 | 2.2E-02 | 7.1E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 59| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 7.8E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 47| +1.48E+02 | -1.0E-03 | +3.8E-01 | 3.1E-02 | 2.0E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 24| +1.46E+02 | -3.0E-02 | +7.4E-01 | 1.3E-02 | 4.5E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 45| +1.48E+02 | -5.4E-06 | +1.5E+00 | 1.5E-02 | 3.2E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 25| +1.48E+02 | -5.5E-01 | +7.1E-01 | 1.2E-01 | 4.3E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 49| +1.50E+02 | -1.4E-06 | +5.6E-01 | 4.3E-02 | 7.3E-02 | 6.6E-04 | trr |1\n", - "2023-02-17 16:05:20 fides(WARNING) Stopping as function difference 1.40E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 60| +1.48E+02 | -1.9E-02 | +9.7E-01 | 3.1E-02 | 7.8E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 25| +1.46E+02 | -3.0E-02 | +8.3E-01 | 1.3E-02 | 8.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 48| +1.48E+02 | -2.4E-04 | +8.1E-02 | 3.1E-02 | 1.5E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 72| +1.48E+02 | +5.9E-06 | -3.0E+03 | 3.2E-08 | 2.5E-02 | 8.4E-08 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 26| +1.46E+02 | -2.7E-02 | +5.9E-01 | 2.6E-02 | 3.9E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 46| +1.48E+02 | -4.8E-06 | +8.4E-01 | 3.0E-02 | 3.2E-02 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 61| +1.48E+02 | -2.6E-02 | +7.4E-01 | 6.2E-02 | 2.8E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 26| +1.48E+02 | -1.0E-01 | +3.2E-01 | 1.2E-01 | 2.6E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 49| +1.48E+02 | -1.2E-03 | +7.9E-01 | 7.8E-03 | 1.9E+00 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 27| +1.46E+02 | -6.5E-03 | +5.1E-02 | 2.6E-02 | 3.4E+00 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 47| +1.48E+02 | -1.0E-05 | +1.3E+00 | 6.0E-02 | 2.2E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 27| +1.48E+02 | -2.0E-01 | +3.4E-01 | 1.2E-01 | 2.1E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 101| +1.55E+02 | -1.2E-02 | +8.2E-01 | 8.9E-02 | 7.2E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 50| +1.48E+02 | -4.1E-04 | +6.0E-01 | 1.6E-02 | 8.3E-01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 73| +1.48E+02 | +2.7E-06 | -5.5E+03 | 7.9E-09 | 2.5E-02 | 2.1E-08 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 62| +1.48E+02 | -2.6E-02 | +7.2E-01 | 6.2E-02 | 8.3E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 28| +1.46E+02 | -3.5E-02 | +8.3E-01 | 6.5E-03 | 9.0E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:20.822 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.271 and h = 1.32109e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.822 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.271: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) 28| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 2.6E+01 | 3.4E-01 | 2d |0\n", - "2023-02-17 16:05:20.824 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0627 and h = 1.71793e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.824 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0627: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) 48| +1.48E+02 | +2.9E-05 | -2.9E+00 | 1.2E-01 | 2.1E-02 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 74| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.0E-09 | 2.5E-02 | 5.2E-09 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 29| +1.46E+02 | -1.6E-02 | +4.7E-01 | 1.3E-02 | 2.2E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 51| +1.48E+02 | -1.9E-04 | +9.1E-01 | 1.6E-02 | 7.3E-01 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 63| +1.48E+02 | -1.5E-02 | +6.0E-01 | 6.2E-02 | 1.7E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 30| +1.46E+02 | -8.6E-03 | +6.4E-01 | 1.3E-02 | 2.5E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 0| +1.14E+14 | NaN | NaN | 1.0E+00 | 5.2E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:20 fides(INFO) 49| +1.48E+02 | +1.5E-06 | -4.3E-01 | 3.0E-02 | 2.1E-02 | 5.0E-03 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 29| +1.48E+02 | -1.8E-01 | +7.1E-01 | 3.1E-02 | 2.6E+01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 52| +1.48E+02 | -2.4E-04 | +9.8E-01 | 3.1E-02 | 5.8E-01 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 75| +1.48E+02 | +6.1E-06 | -2.0E+05 | 5.0E-10 | 2.5E-02 | 1.3E-09 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 31| +1.46E+02 | -2.3E-03 | +1.4E-01 | 1.3E-02 | 2.2E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 64| +1.48E+02 | -2.0E-02 | +6.5E-01 | 6.2E-02 | 7.4E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 30| +1.48E+02 | -1.1E-02 | +2.7E-01 | 3.1E-02 | 3.2E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 50| +1.48E+02 | +6.8E-07 | -6.8E-01 | 7.5E-03 | 2.1E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 76| +1.48E+02 | +3.5E-06 | -4.5E+05 | 1.2E-10 | 2.5E-02 | 3.3E-10 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 1| +6.16E+07 | -1.1E+14 | +8.2E-02 | 1.0E+00 | 5.2E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 53| +1.48E+02 | -2.0E-04 | +8.3E-01 | 6.2E-02 | 5.2E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 32| +1.46E+02 | -4.8E-03 | +7.4E-01 | 3.2E-03 | 1.8E+00 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:20.925 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 86.193 and h = 1.72762e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.925 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 86.193: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) 65| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 1.4E+00 | 5.2E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 51| +1.48E+02 | +2.2E-06 | -7.3E+00 | 1.9E-03 | 2.1E-02 | 3.2E-04 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 31| +1.48E+02 | -1.7E-02 | +7.5E-01 | 3.1E-02 | 9.8E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 77| +1.48E+02 | +2.6E-06 | -1.3E+06 | 3.1E-11 | 2.5E-02 | 8.2E-11 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 33| +1.46E+02 | -3.1E-03 | +8.2E-01 | 3.2E-03 | 2.9E+00 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 54| +1.48E+02 | +4.9E-03 | -1.1E+01 | 1.2E-01 | 2.3E-01 | 4.1E-02 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 2| +9.41E+06 | -5.2E+07 | +8.9E-01 | 2.5E-01 | 2.8E+08 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 66| +1.48E+02 | -4.0E-03 | +6.3E-01 | 1.6E-02 | 1.4E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 32| +1.48E+02 | -4.0E-03 | +6.8E-01 | 3.1E-02 | 1.9E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 34| +1.46E+02 | -4.0E-03 | +9.9E-01 | 6.5E-03 | 1.5E+00 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 52| +1.48E+02 | +4.0E-06 | -3.4E+01 | 4.7E-04 | 2.1E-02 | 7.9E-05 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 78| +1.48E+02 | +4.4E-08 | -9.1E+04 | 7.7E-12 | 2.5E-02 | 2.0E-11 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 102| +1.55E+02 | +3.2E-01 | -5.2E+00 | 1.8E-01 | 5.9E+00 | 3.7E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 67| +1.48E+02 | -5.2E-03 | +9.5E-01 | 1.6E-02 | 4.9E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 55| +1.48E+02 | +1.4E-04 | -6.3E-01 | 3.0E-02 | 2.3E-01 | 1.0E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 35| +1.46E+02 | -5.8E-03 | +8.9E-01 | 1.3E-02 | 1.6E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 3| +1.45E+06 | -8.0E+06 | +8.9E-01 | 5.0E-01 | 4.3E+07 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 53| +1.48E+02 | +4.1E-07 | -5.7E+00 | 1.2E-04 | 2.1E-02 | 2.1E-05 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 33| +1.48E+02 | -1.0E-03 | +6.5E-01 | 3.1E-02 | 2.0E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 36| +1.46E+02 | +4.0E-02 | -2.7E+00 | 2.6E-02 | 1.8E+00 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 56| +1.48E+02 | -4.8E-05 | +5.9E-01 | 7.6E-03 | 2.3E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 68| +1.48E+02 | -5.3E-03 | +9.9E-01 | 3.1E-02 | 7.2E-01 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 79| +1.48E+02 | +5.2E-06 | -4.3E+07 | 1.9E-12 | 2.5E-02 | 5.1E-12 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 54| +1.48E+02 | +1.5E-06 | -2.5E+01 | 2.9E-05 | 2.1E-02 | 7.5E-06 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 57| +1.48E+02 | -2.3E-05 | +9.9E-01 | 7.6E-03 | 3.4E-01 | 9.9E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 37| +1.46E+02 | -1.2E-03 | +2.5E-01 | 6.5E-03 | 1.8E+00 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 34| +1.48E+02 | -9.9E-06 | +6.5E-03 | 3.1E-02 | 4.8E-01 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 4| +2.35E+05 | -1.2E+06 | +8.8E-01 | 1.0E+00 | 6.7E+06 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 69| +1.48E+02 | -8.6E-03 | +9.2E-01 | 6.2E-02 | 1.8E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 80| +1.48E+02 | +8.5E-07 | -2.8E+07 | 4.8E-13 | 2.5E-02 | 1.3E-12 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 38| +1.46E+02 | -3.3E-03 | +4.5E-01 | 6.5E-03 | 1.1E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 55| +1.48E+02 | +1.8E-06 | -3.0E+01 | 7.3E-06 | 2.1E-02 | 5.7E-06 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 35| +1.48E+02 | -6.9E-04 | +7.4E-01 | 7.8E-03 | 1.4E+00 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 58| +1.48E+02 | -1.9E-05 | +1.1E+00 | 1.5E-02 | 1.3E-01 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 39| +1.46E+02 | -3.8E-04 | +7.1E-02 | 6.5E-03 | 2.5E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 70| +1.48E+02 | -1.3E-02 | +8.6E-01 | 1.2E-01 | 8.4E-01 | 9.7E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 81| +1.48E+02 | +7.9E-07 | -1.1E+08 | 1.2E-13 | 2.5E-02 | 3.2E-13 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 103| +1.55E+02 | +3.8E-02 | -1.3E+00 | 4.5E-02 | 5.9E+00 | 9.2E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 56| +1.48E+02 | -5.9E-07 | +1.0E+01 | 1.8E-06 | 2.1E-02 | 4.8E-06 | 2d |1\n", - "2023-02-17 16:05:21 fides(WARNING) Stopping as function difference 5.86E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 40| +1.46E+02 | -1.7E-03 | +8.7E-01 | 1.6E-03 | 8.0E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 36| +1.48E+02 | -1.1E-04 | +7.6E-01 | 7.8E-03 | 3.1E-01 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 59| +1.48E+02 | -1.1E-05 | +5.4E-01 | 3.0E-02 | 1.8E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 5| +2.86E+04 | -2.1E+05 | +8.8E-01 | 2.0E+00 | 1.0E+06 | 3.7E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 71| +1.48E+02 | +1.8E-02 | -5.8E-01 | 2.5E-01 | 3.0E+00 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 82| +1.48E+02 | +3.6E-06 | -1.9E+09 | 3.0E-14 | 2.5E-02 | 8.0E-14 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 41| +1.46E+02 | -1.3E-03 | +6.2E-01 | 3.2E-03 | 7.2E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 37| +1.48E+02 | -8.5E-05 | +8.3E-01 | 1.6E-02 | 2.0E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 60| +1.48E+02 | +1.0E-04 | -1.9E+00 | 3.0E-02 | 8.2E-02 | 4.3E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 72| +1.48E+02 | -6.5E-03 | +6.5E-01 | 6.2E-02 | 3.0E+00 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 42| +1.46E+02 | -7.8E-04 | +9.8E-01 | 3.2E-03 | 9.5E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 83| +1.48E+02 | +4.0E-06 | -8.4E+09 | 7.6E-15 | 2.5E-02 | 2.0E-14 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 38| +1.48E+02 | -9.7E-05 | +8.8E-01 | 3.1E-02 | 3.3E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 61| +1.48E+02 | +4.5E-07 | -2.4E-02 | 7.6E-03 | 8.2E-02 | 1.1E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 43| +1.46E+02 | -1.2E-03 | +1.0E+00 | 6.5E-03 | 6.6E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 73| +1.48E+02 | -4.4E-03 | +6.8E-01 | 6.2E-02 | 8.8E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 84| +1.48E+02 | +4.2E-06 | -3.6E+10 | 1.9E-15 | 2.5E-02 | 5.0E-15 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 39| +1.48E+02 | +1.3E-04 | -6.9E-01 | 6.2E-02 | 1.3E-01 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 62| +1.48E+02 | -5.9E-06 | +6.7E-01 | 1.9E-03 | 8.2E-02 | 3.4E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 44| +1.46E+02 | -1.6E-03 | +8.3E-01 | 1.3E-02 | 5.3E-01 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 74| +1.48E+02 | -4.4E-03 | +6.7E-01 | 6.2E-02 | 2.7E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 6| +4.64E+03 | -2.4E+04 | +9.0E-01 | 2.0E+00 | 1.3E+05 | 3.3E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 0| +2.13E+14 | NaN | NaN | 1.0E+00 | 9.8E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:21 fides(INFO) 85| +1.48E+02 | +4.1E-06 | -1.4E+11 | 4.7E-16 | 2.5E-02 | 1.3E-15 | 2d |0\n", - "2023-02-17 16:05:21 fides(WARNING) Stopping as trust region radius 1.18E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:21 fides(INFO) 45| +1.46E+02 | +6.0E-02 | -8.1E+00 | 2.6E-02 | 8.6E-01 | 4.3E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 40| +1.48E+02 | -3.5E-05 | +6.0E-01 | 1.6E-02 | 1.3E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 63| +1.48E+02 | -7.1E-06 | +1.8E+00 | 1.9E-03 | 1.7E-01 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 75| +1.48E+02 | -2.8E-03 | +6.2E-01 | 6.2E-02 | 7.4E-01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 104| +1.55E+02 | +2.6E-03 | -1.4E-01 | 1.1E-02 | 5.9E+00 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 46| +1.46E+02 | +1.8E-03 | -7.5E-01 | 6.5E-03 | 8.6E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 7| +9.19E+02 | -3.7E+03 | +8.9E-01 | 2.0E+00 | 2.0E+04 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 64| +1.48E+02 | +1.4E-06 | -3.1E-01 | 3.8E-03 | 3.7E-02 | 4.5E-04 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 41| +1.48E+02 | -3.0E-05 | +7.2E-01 | 1.6E-02 | 2.0E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 47| +1.46E+02 | -4.0E-04 | +6.0E-01 | 1.6E-03 | 8.6E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 1| +1.48E+11 | -2.1E+14 | +8.2E-02 | 1.0E+00 | 9.8E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 76| +1.48E+02 | -2.8E-03 | +5.6E-01 | 6.2E-02 | 2.2E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 8| +9.19E+02 | -1.4E+02 | -2.0E-01 | 2.0E+00 | 3.3E+03 | 2.2E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 65| +1.48E+02 | +2.1E-06 | -5.0E-01 | 9.5E-04 | 3.7E-02 | 3.1E-04 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 42| +1.48E+02 | -2.1E-05 | +6.9E-01 | 1.6E-02 | 9.4E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 48| +1.46E+02 | -1.9E-04 | +9.8E-01 | 1.6E-03 | 3.7E-01 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 2| +2.20E+10 | -1.3E+11 | +8.9E-01 | 2.5E-01 | 6.8E+11 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 77| +1.48E+02 | -1.4E-03 | +3.5E-01 | 6.2E-02 | 6.9E-01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 49| +1.46E+02 | -3.0E-04 | +1.0E+00 | 3.2E-03 | 6.2E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 9| +3.33E+02 | -5.9E+02 | +9.0E-01 | 2.1E-01 | 3.3E+03 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 43| +1.48E+02 | -1.2E-05 | +5.2E-01 | 1.6E-02 | 9.8E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 66| +1.48E+02 | -1.4E-07 | +3.5E-02 | 2.4E-04 | 3.7E-02 | 2.9E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 0| +1.86E+09 | NaN | NaN | 1.0E+00 | 8.6E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:21 fides(INFO) 50| +1.46E+02 | -5.1E-04 | +9.6E-01 | 6.5E-03 | 2.8E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 78| +1.48E+02 | -1.7E-03 | +3.0E-01 | 6.2E-02 | 2.3E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 3| +3.28E+09 | -1.9E+10 | +8.9E-01 | 5.0E-01 | 1.0E+11 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 10| +2.28E+02 | -1.0E+02 | +8.6E-01 | 4.3E-01 | 5.4E+02 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 1| +2.20E+06 | -1.9E+09 | +9.9E-02 | 1.0E+00 | 8.6E+09 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 44| +1.48E+02 | -1.5E-05 | +6.7E-01 | 1.6E-02 | 7.9E-02 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 51| +1.46E+02 | +7.0E-04 | -9.2E-01 | 1.3E-02 | 8.4E-01 | 6.3E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 67| +1.48E+02 | -8.6E-07 | +5.3E+00 | 5.9E-05 | 3.3E-02 | 1.2E-05 | 2d |1\n", - "2023-02-17 16:05:21 fides(WARNING) Stopping as function difference 8.62E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:21 fides(INFO) 105| +1.55E+02 | -8.5E-03 | +6.0E-01 | 2.8E-03 | 5.9E+00 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 79| +1.48E+02 | +1.1E-03 | -2.0E-01 | 6.2E-02 | 8.1E-01 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 11| +1.88E+02 | -4.1E+01 | +1.6E+00 | 8.5E-01 | 1.3E+02 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 4| +4.92E+08 | -2.8E+09 | +8.9E-01 | 5.0E-01 | 1.5E+10 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 52| +1.46E+02 | -1.7E-04 | +6.4E-01 | 3.2E-03 | 8.4E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 45| +1.48E+02 | -1.1E-06 | +4.7E-02 | 1.6E-02 | 7.5E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 2| +3.47E+05 | -1.9E+06 | +9.0E-01 | 2.5E-01 | 1.0E+07 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 80| +1.48E+02 | -7.4E-04 | +2.9E-01 | 1.6E-02 | 8.1E-01 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 12| +1.88E+02 | +1.8E+02 | -1.0E+01 | 8.5E-01 | 4.7E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 53| +1.46E+02 | -1.7E-05 | +4.3E-02 | 3.2E-03 | 1.7E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 5| +7.40E+07 | -4.2E+08 | +8.9E-01 | 5.0E-01 | 2.3E+09 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 46| +1.48E+02 | -1.1E-05 | +1.1E+00 | 3.9E-03 | 8.3E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 81| +1.48E+02 | -1.4E-03 | +9.3E-01 | 1.6E-02 | 3.1E+00 | 8.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 3| +5.82E+04 | -2.9E+05 | +9.0E-01 | 5.0E-01 | 1.6E+06 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 54| +1.46E+02 | -1.8E-04 | +7.2E-01 | 8.1E-04 | 6.2E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 13| +1.72E+02 | -1.6E+01 | +8.2E-01 | 2.1E-01 | 4.7E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 4| +1.27E+04 | -4.5E+04 | +7.8E-01 | 1.0E+00 | 2.5E+05 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 6| +1.12E+07 | -6.3E+07 | +8.9E-01 | 5.0E-01 | 3.4E+08 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 55| +1.46E+02 | -4.9E-05 | +7.7E-01 | 8.1E-04 | 1.8E-01 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 47| +1.48E+02 | +1.1E-06 | -2.4E-01 | 7.8E-03 | 5.0E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 14| +1.61E+02 | -1.0E+01 | +5.5E-01 | 4.3E-01 | 5.5E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 82| +1.48E+02 | -4.8E-04 | +9.7E-01 | 3.1E-02 | 2.5E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 5| +2.16E+03 | -1.1E+04 | +9.0E-01 | 2.0E+00 | 4.1E+04 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 56| +1.46E+02 | -5.8E-05 | +1.0E+00 | 1.6E-03 | 2.9E-01 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 48| +1.48E+02 | +4.3E-07 | -2.9E-01 | 2.0E-03 | 5.0E-02 | 3.3E-04 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 0| +9.42E+04 | NaN | NaN | 1.0E+00 | 4.3E+05 | NaN | NaN |1\n", - "2023-02-17 16:05:21 fides(INFO) 6| +5.05E+02 | -1.7E+03 | +9.0E-01 | 2.0E+00 | 6.5E+03 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 7| +1.68E+06 | -9.5E+06 | +8.9E-01 | 5.0E-01 | 5.1E+07 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 83| +1.48E+02 | -7.6E-04 | +9.7E-01 | 6.2E-02 | 2.2E-01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 15| +1.52E+02 | -9.5E+00 | +8.4E-01 | 4.3E-01 | 8.5E+01 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 57| +1.46E+02 | -1.3E-04 | +9.6E-01 | 3.2E-03 | 1.2E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 7| +2.69E+02 | -2.4E+02 | +8.8E-01 | 2.0E+00 | 1.0E+03 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 49| +1.48E+02 | +2.5E-06 | -4.3E+00 | 4.9E-04 | 5.0E-02 | 8.3E-05 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 1| +4.02E+02 | -9.4E+04 | +1.2E-01 | 1.0E+00 | 4.3E+05 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 8| +2.44E+02 | -2.5E+01 | +9.0E-01 | 2.0E+00 | 1.4E+02 | 4.3E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 106| +1.55E+02 | -1.7E-03 | +4.9E-01 | 2.8E-03 | 3.7E+00 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 58| +1.46E+02 | -2.9E-05 | +1.6E-01 | 6.5E-03 | 6.3E-01 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 16| +1.52E+02 | +2.5E+00 | -4.9E-01 | 8.5E-01 | 5.2E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 8| +2.49E+05 | -1.4E+06 | +9.0E-01 | 5.0E-01 | 7.7E+06 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 84| +1.48E+02 | -1.0E-03 | +9.5E-01 | 1.2E-01 | 2.7E-01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 2| +3.63E+02 | -3.9E+01 | +9.5E-01 | 2.5E-01 | 5.8E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 50| +1.48E+02 | +1.3E-06 | -3.5E+00 | 1.2E-04 | 5.0E-02 | 2.5E-05 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 59| +1.46E+02 | -9.3E-05 | +2.7E-01 | 1.6E-03 | 1.9E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 3| +2.89E+02 | -7.4E+01 | +9.9E-01 | 5.0E-01 | 5.2E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 17| +1.46E+02 | -6.0E+00 | +9.8E-01 | 2.1E-01 | 5.2E+01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 9| +3.71E+04 | -2.1E+05 | +9.0E-01 | 5.0E-01 | 1.1E+06 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 60| +1.46E+02 | -3.1E-05 | +2.5E-01 | 1.6E-03 | 2.7E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 85| +1.48E+02 | -6.7E-04 | +8.8E-01 | 2.5E-01 | 1.1E-01 | 8.8E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 51| +1.48E+02 | +2.4E-06 | -7.9E+00 | 3.1E-05 | 5.0E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 4| +2.89E+02 | +3.2E+03 | -6.9E+01 | 1.0E+00 | 4.9E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 18| +1.42E+02 | -4.2E+00 | +5.2E-01 | 4.3E-01 | 5.2E+01 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 61| +1.46E+02 | -5.1E-06 | +5.1E-02 | 1.6E-03 | 1.2E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 86| +1.48E+02 | +7.0E-04 | -8.6E+00 | 5.0E-01 | 2.1E-01 | 2.3E-02 | trr |0\n", - "2023-02-17 16:05:21 fides(INFO) 5| +2.60E+02 | -2.9E+01 | +9.1E-01 | 2.5E-01 | 4.9E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 10| +5.74E+03 | -3.1E+04 | +9.0E-01 | 5.0E-01 | 1.7E+05 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 52| +1.48E+02 | -2.4E-06 | +8.4E+00 | 7.6E-06 | 5.0E-02 | 1.3E-05 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 62| +1.46E+02 | -2.6E-05 | +8.0E-01 | 4.0E-04 | 1.6E-01 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 87| +1.48E+02 | +1.8E-05 | -4.6E-01 | 3.8E-02 | 2.1E-01 | 5.9E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 6| +2.60E+02 | +3.1E+02 | -1.7E+01 | 5.0E-01 | 3.4E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 11| +1.04E+03 | -4.7E+03 | +9.0E-01 | 5.0E-01 | 2.6E+04 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 19| +1.40E+02 | -1.5E+00 | +5.0E-01 | 4.3E-01 | 2.9E+01 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 9| +2.44E+02 | -6.9E+00 | -1.6E+00 | 4.0E+00 | 1.8E+01 | 5.3E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 63| +1.46E+02 | -1.6E-05 | +5.8E-01 | 8.1E-04 | 7.3E-02 | 7.1E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 53| +1.48E+02 | +8.9E-07 | -2.2E+00 | 1.5E-05 | 4.0E-02 | 2.2E-05 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 7| +2.51E+02 | -9.0E+00 | +8.5E-01 | 1.2E-01 | 3.4E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 88| +1.48E+02 | -8.8E-06 | +5.9E-01 | 9.4E-03 | 2.1E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 12| +3.35E+02 | -7.1E+02 | +9.0E-01 | 5.0E-01 | 4.1E+03 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 10| +2.43E+02 | -1.2E+00 | +3.1E-02 | 1.0E+00 | 1.8E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 64| +1.46E+02 | -1.0E-05 | +9.0E-01 | 8.1E-04 | 7.2E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 107| +1.55E+02 | -1.4E-03 | +1.0E+00 | 2.8E-03 | 2.7E+00 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 8| +2.51E+02 | +5.4E+00 | -1.8E+00 | 2.5E-01 | 1.8E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 54| +1.48E+02 | +3.1E-06 | -2.0E+01 | 3.8E-06 | 4.0E-02 | 5.6E-06 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 20| +1.40E+02 | +3.8E-01 | -3.1E-01 | 4.3E-01 | 1.7E+01 | 7.8E-01 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 11| +2.36E+02 | -6.8E+00 | +7.5E-01 | 2.5E-01 | 5.1E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 65| +1.46E+02 | -1.5E-05 | +9.4E-01 | 1.6E-03 | 1.6E-01 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 89| +1.48E+02 | -4.1E-06 | +1.2E+00 | 9.4E-03 | 6.7E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 9| +2.50E+02 | -1.3E+00 | +5.9E-01 | 6.2E-02 | 1.8E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 55| +1.48E+02 | +7.0E-06 | -1.6E+02 | 9.5E-07 | 4.0E-02 | 1.4E-06 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 13| +2.29E+02 | -1.1E+02 | +9.1E-01 | 5.0E-01 | 6.3E+02 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 66| +1.46E+02 | -1.3E-05 | +4.4E-01 | 3.2E-03 | 6.0E-02 | 7.6E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 12| +2.30E+02 | -6.2E+00 | +5.1E-01 | 5.0E-01 | 2.2E+01 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 21| +1.40E+02 | -3.0E-01 | +6.9E-01 | 1.1E-01 | 1.7E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 10| +2.50E+02 | -1.2E-02 | +1.3E-01 | 6.2E-02 | 4.0E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 90| +1.48E+02 | -4.1E-06 | +8.8E-01 | 1.9E-02 | 6.2E-02 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 56| +1.48E+02 | +3.7E-06 | -3.4E+02 | 2.4E-07 | 4.0E-02 | 3.5E-07 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 67| +1.46E+02 | +1.6E-04 | -1.2E+00 | 3.2E-03 | 2.0E-01 | 3.4E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 13| +2.16E+02 | -1.4E+01 | +8.9E-01 | 5.0E-01 | 6.9E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 11| +2.50E+02 | +6.8E-03 | -1.1E-01 | 1.6E-02 | 3.2E+00 | 4.1E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 91| +1.48E+02 | -3.6E-06 | +6.4E-01 | 3.8E-02 | 4.7E-02 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 14| +1.99E+02 | -3.0E+01 | +1.2E+00 | 5.0E-01 | 9.7E+01 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 68| +1.46E+02 | -1.8E-05 | +4.8E-01 | 8.1E-04 | 2.0E-01 | 8.5E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 57| +1.48E+02 | +2.6E-06 | -9.5E+02 | 6.0E-08 | 4.0E-02 | 8.7E-08 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 12| +2.50E+02 | -2.4E-02 | +8.5E-01 | 3.9E-03 | 3.2E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 22| +1.40E+02 | -2.6E-01 | +9.3E-01 | 1.1E-01 | 2.2E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 69| +1.46E+02 | -4.8E-06 | +7.2E-01 | 8.1E-04 | 5.7E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 92| +1.48E+02 | -3.3E-06 | +8.1E-01 | 3.8E-02 | 3.1E-02 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 14| +1.56E+02 | -6.0E+01 | +1.6E+00 | 1.0E+00 | 3.9E+01 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 58| +1.48E+02 | +2.7E-06 | -4.0E+03 | 1.5E-08 | 4.0E-02 | 2.2E-08 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 13| +2.50E+02 | +8.1E-04 | -5.6E-02 | 7.8E-03 | 1.6E+00 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 15| +1.99E+02 | +3.0E+01 | -3.5E+00 | 1.0E+00 | 5.7E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 70| +1.46E+02 | -4.4E-06 | +8.6E-01 | 8.1E-04 | 6.3E-02 | 2.4E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 108| +1.55E+02 | -1.2E-03 | +9.4E-01 | 5.6E-03 | 1.1E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 93| +1.48E+02 | +3.3E-04 | -8.6E+00 | 7.5E-02 | 3.7E-02 | 1.2E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 15| +1.56E+02 | +1.1E+02 | -8.8E+00 | 2.0E+00 | 1.0E+02 | 9.6E-01 | trr |0\n", - "2023-02-17 16:05:21 fides(INFO) 14| +2.50E+02 | -5.9E-03 | +8.5E-01 | 2.0E-03 | 1.6E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 23| +1.39E+02 | -3.7E-01 | +9.2E-01 | 2.1E-01 | 1.3E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 59| +1.48E+02 | +2.1E-06 | -1.2E+04 | 3.7E-09 | 4.0E-02 | 5.4E-09 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 71| +1.46E+02 | -5.9E-06 | +7.5E-01 | 1.6E-03 | 5.1E-02 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 15| +2.50E+02 | +7.7E-05 | -2.5E-02 | 3.9E-03 | 7.4E-01 | 1.0E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 16| +1.52E+02 | -4.2E+00 | +9.5E-01 | 1.2E-01 | 1.0E+02 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 16| +1.81E+02 | -1.8E+01 | +1.1E+00 | 2.5E-01 | 5.7E+01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 72| +1.46E+02 | -5.3E-06 | +5.5E-01 | 1.6E-03 | 5.0E-02 | 5.7E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 94| +1.48E+02 | +1.0E-05 | -6.8E-01 | 1.9E-02 | 3.7E-02 | 2.9E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 60| +1.48E+02 | +4.7E-06 | -1.1E+05 | 9.3E-10 | 4.0E-02 | 1.4E-09 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 16| +2.50E+02 | -1.3E-03 | +8.3E-01 | 9.8E-04 | 7.4E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 17| +1.50E+02 | -2.0E+00 | +3.0E-01 | 2.3E-01 | 4.8E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 73| +1.46E+02 | +9.2E-06 | -4.0E-01 | 1.6E-03 | 5.9E-02 | 9.5E-04 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 24| +1.39E+02 | -4.8E-01 | +8.3E-01 | 4.3E-01 | 1.1E+01 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 95| +1.48E+02 | -5.7E-06 | +1.4E+00 | 4.7E-03 | 3.7E-02 | 7.4E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 61| +1.48E+02 | +1.6E-06 | -1.5E+05 | 2.3E-10 | 4.0E-02 | 3.4E-10 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 17| +2.50E+02 | +4.9E-06 | -8.7E-03 | 2.0E-03 | 3.2E-01 | 5.0E-03 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 74| +1.46E+02 | -4.1E-06 | +6.5E-01 | 4.0E-04 | 5.9E-02 | 2.5E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 17| +1.67E+02 | -1.4E+01 | +8.9E-01 | 5.0E-01 | 5.8E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +2.50E+02 | -2.7E-04 | +8.0E-01 | 4.9E-04 | 3.2E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +1.48E+02 | -1.6E+00 | +6.1E-01 | 2.3E-01 | 1.2E+02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 96| +1.48E+02 | +3.9E-06 | -6.2E+00 | 9.4E-03 | 2.9E-02 | 9.1E-04 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 75| +1.46E+02 | -3.8E-07 | +2.3E-01 | 4.0E-04 | 6.0E-02 | 5.3E-05 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 62| +1.48E+02 | +3.3E-06 | -1.2E+06 | 5.8E-11 | 4.0E-02 | 8.5E-11 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 25| +1.39E+02 | +4.3E+00 | -5.8E+00 | 8.5E-01 | 1.2E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:22.041 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.735 and h = 9.95199e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.041 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.735: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 19| +2.50E+02 | -2.6E-07 | +4.0E-03 | 9.8E-04 | 1.0E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 19| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.3E-01 | 2.6E+01 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:22.048 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.486 and h = 4.04049e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.048 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.486: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 97| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.4E-03 | 2.9E-02 | 2.3E-04 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 76| +1.46E+02 | -1.5E-06 | +3.3E+00 | 1.0E-04 | 4.1E-02 | 1.9E-05 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 63| +1.48E+02 | +3.0E-06 | -4.5E+06 | 1.5E-11 | 4.0E-02 | 2.1E-11 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 20| +2.50E+02 | -2.9E-05 | +7.8E-01 | 2.4E-04 | 1.0E-01 | 4.4E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +1.59E+02 | -7.7E+00 | +1.0E+00 | 1.0E+00 | 1.1E+02 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 109| +1.55E+02 | -2.1E-03 | +9.3E-01 | 1.1E-02 | 1.7E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 77| +1.46E+02 | -8.5E-07 | +1.0E+00 | 2.0E-04 | 4.6E-02 | 3.6E-05 | 2d |1\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as function difference 8.51E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 20| +1.48E+02 | -2.1E-01 | +8.8E-01 | 5.8E-02 | 2.6E+01 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +2.50E+02 | -2.3E-07 | +4.5E-02 | 4.9E-04 | 2.9E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 64| +1.48E+02 | +6.6E-06 | -3.9E+07 | 3.6E-12 | 4.0E-02 | 5.3E-12 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 98| +1.48E+02 | +5.9E-06 | -3.8E+01 | 5.9E-04 | 2.9E-02 | 5.7E-05 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 26| +1.39E+02 | -1.5E-02 | +3.4E-02 | 2.1E-01 | 1.2E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +1.48E+02 | -1.6E-01 | +7.1E-01 | 1.2E-01 | 2.0E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 99| +1.48E+02 | +3.9E-06 | -3.0E+01 | 1.5E-04 | 2.9E-02 | 1.7E-05 | 2d |0\n", - "2023-02-17 16:05:22.130 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0572 and h = 1.54133e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.131 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0572: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 22| +2.50E+02 | -4.9E-07 | +1.0E-01 | 1.2E-04 | 2.8E-02 | 3.2E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 65| +1.48E+02 | +0.0E+00 | +0.0E+00 | 9.1E-13 | 4.0E-02 | 1.3E-12 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 23| +2.50E+02 | -1.1E-06 | +9.0E-01 | 3.1E-05 | 2.5E-02 | 5.5E-05 | 2d |1\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as function difference 1.13E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:22 fides(INFO) 22| +1.48E+02 | -3.6E-02 | +2.4E-01 | 1.2E-01 | 1.7E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 19| +1.59E+02 | +1.8E+02 | -1.6E+02 | 2.0E+00 | 5.3E+01 | 5.5E+00 | trr |0\n", - "2023-02-17 16:05:22 fides(INFO) 66| +1.48E+02 | +3.3E-06 | -3.1E+08 | 2.3E-13 | 4.0E-02 | 3.3E-13 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 100| +1.48E+02 | +5.2E-06 | -4.3E+01 | 3.7E-05 | 2.9E-02 | 9.3E-06 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 27| +1.39E+02 | -1.3E-01 | +6.0E-01 | 5.3E-02 | 8.7E+00 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 110| +1.55E+02 | -5.9E-03 | +1.0E+00 | 2.2E-02 | 1.2E+00 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 23| +1.48E+02 | -9.7E-02 | +4.3E-01 | 2.9E-02 | 1.1E+01 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 67| +1.48E+02 | +3.5E-06 | -1.3E+09 | 5.7E-14 | 4.0E-02 | 8.3E-14 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 101| +1.48E+02 | -7.2E-07 | +6.0E+00 | 9.2E-06 | 2.9E-02 | 8.6E-06 | 2d |1\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as function difference 7.25E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 0| +1.05E+12 | NaN | NaN | 1.0E+00 | 4.6E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 20| +1.58E+02 | -1.4E+00 | +1.0E+00 | 5.0E-01 | 5.3E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 68| +1.48E+02 | +4.8E-07 | -7.4E+08 | 1.4E-14 | 4.0E-02 | 2.1E-14 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 24| +1.48E+02 | -3.9E-02 | +6.7E-01 | 2.9E-02 | 5.8E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 1| +2.02E+10 | -1.0E+12 | +9.0E-02 | 1.0E+00 | 4.6E+12 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 28| +1.39E+02 | -9.6E-02 | +9.6E-01 | 5.3E-02 | 2.9E+01 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 0| +8.11E+12 | NaN | NaN | 1.0E+00 | 2.8E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:22 fides(INFO) 69| +1.48E+02 | +4.7E-06 | -2.9E+10 | 3.6E-15 | 4.0E-02 | 5.2E-15 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 2| +3.02E+09 | -1.7E+10 | +8.9E-01 | 2.5E-01 | 9.3E+10 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 25| +1.48E+02 | -9.3E-03 | +8.9E-01 | 2.9E-02 | 7.5E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 1| +3.54E+10 | -8.1E+12 | +1.1E-01 | 1.0E+00 | 2.8E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 3| +4.53E+08 | -2.6E+09 | +8.9E-01 | 5.0E-01 | 1.4E+10 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:22.287 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.9304 and h = 2.14384e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.287 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.9304: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 26| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.8E-02 | 2.0E+00 | 4.0E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 70| +1.48E+02 | +3.6E-06 | -8.7E+10 | 8.9E-16 | 4.0E-02 | 1.3E-15 | 2d |0\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as trust region radius 2.22E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:22 fides(INFO) 2| +5.99E+09 | -2.9E+10 | +8.8E-01 | 2.5E-01 | 1.4E+11 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 29| +1.39E+02 | -4.5E-02 | +7.2E-01 | 1.1E-01 | 3.4E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 111| +1.55E+02 | -3.6E-03 | +2.9E-01 | 4.5E-02 | 1.0E+00 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +1.54E+02 | -3.7E+00 | +1.0E+00 | 1.0E+00 | 6.3E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 0| +3.21E+07 | NaN | NaN | 1.0E+00 | 1.5E+08 | NaN | NaN |1\n", - "2023-02-17 16:05:22 fides(INFO) 4| +6.84E+07 | -3.8E+08 | +8.9E-01 | 5.0E-01 | 2.1E+09 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 27| +1.48E+02 | -6.2E-03 | +7.5E-01 | 1.5E-02 | 2.0E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 3| +8.99E+08 | -5.1E+09 | +8.9E-01 | 5.0E-01 | 2.1E+10 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 5| +1.04E+07 | -5.8E+07 | +8.9E-01 | 5.0E-01 | 3.1E+08 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 4| +1.36E+08 | -7.6E+08 | +8.9E-01 | 5.0E-01 | 3.2E+09 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 30| +1.39E+02 | +1.1E-02 | -1.3E-01 | 1.1E-01 | 4.2E+00 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 28| +1.48E+02 | -4.2E-03 | +9.5E-01 | 2.9E-02 | 5.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 1| +1.89E+03 | -3.2E+07 | +1.1E-01 | 1.0E+00 | 1.5E+08 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 6| +1.58E+06 | -8.8E+06 | +8.9E-01 | 5.0E-01 | 4.8E+07 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 5| +2.07E+07 | -1.2E+08 | +8.9E-01 | 5.0E-01 | 4.8E+08 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 2| +6.31E+02 | -1.3E+03 | +9.1E-01 | 2.5E-01 | 6.5E+03 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 29| +1.48E+02 | -2.8E-03 | +8.5E-01 | 5.8E-02 | 1.3E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 3| +3.08E+02 | -3.2E+02 | +9.0E-01 | 5.0E-01 | 1.3E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 6| +3.16E+06 | -1.7E+07 | +8.9E-01 | 5.0E-01 | 7.3E+07 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 22| +1.54E+02 | +2.2E+00 | -3.0E+01 | 2.0E+00 | 5.7E+01 | 6.3E+00 | trr |0\n", - "2023-02-17 16:05:22 fides(INFO) 7| +2.44E+05 | -1.3E+06 | +8.9E-01 | 5.0E-01 | 7.3E+06 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 31| +1.39E+02 | -1.9E-02 | +7.2E-01 | 2.7E-02 | 4.2E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 4| +2.56E+02 | -5.2E+01 | +8.6E-01 | 1.0E+00 | 2.0E+02 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 0| +3.56E+11 | NaN | NaN | 1.0E+00 | 1.6E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:22 fides(INFO) 7| +4.86E+05 | -2.7E+06 | +8.9E-01 | 5.0E-01 | 1.1E+07 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 30| +1.48E+02 | -1.3E-03 | +3.4E-01 | 1.2E-01 | 2.6E+00 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 8| +3.80E+04 | -2.1E+05 | +9.0E-01 | 5.0E-01 | 1.1E+06 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 112| +1.55E+02 | -2.6E-03 | +2.1E-01 | 4.5E-02 | 2.8E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 5| +2.56E+02 | +6.0E+02 | -5.2E+01 | 2.0E+00 | 3.0E+01 | 2.5E+00 | trr |0\n", - "2023-02-17 16:05:22 fides(INFO) 8| +7.55E+04 | -4.1E+05 | +9.0E-01 | 5.0E-01 | 1.7E+06 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 1| +3.50E+05 | -3.6E+11 | +8.7E-02 | 1.0E+00 | 1.6E+12 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 9| +6.10E+03 | -3.2E+04 | +9.0E-01 | 5.0E-01 | 1.7E+05 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 9| +1.19E+04 | -6.4E+04 | +9.0E-01 | 5.0E-01 | 2.7E+05 | 7.8E-01 | 2d |1\n", - "2023-02-17 16:05:22.461 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2575 and h = 1.54213e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.461 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2575: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 6| +2.56E+02 | +9.4E+01 | -7.3E+00 | 3.7E-01 | 3.0E+01 | 9.5E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 31| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 8.9E-01 | 6.5E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 32| +1.39E+02 | -1.3E-02 | +9.9E-01 | 2.7E-02 | 4.0E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 2| +5.43E+04 | -3.0E+05 | +9.0E-01 | 2.5E-01 | 1.6E+06 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 7| +2.50E+02 | -5.5E+00 | +8.5E-01 | 9.3E-02 | 3.0E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 10| +2.01E+03 | -9.9E+03 | +9.0E-01 | 5.0E-01 | 4.1E+04 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 10| +1.14E+03 | -5.0E+03 | +9.0E-01 | 5.0E-01 | 2.7E+04 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 8| +2.50E+02 | +1.6E+00 | -9.5E-01 | 1.9E-01 | 1.4E+01 | 3.7E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 32| +1.48E+02 | +2.9E-03 | -5.7E-01 | 2.9E-02 | 8.9E-01 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 11| +4.82E+02 | -1.5E+03 | +9.0E-01 | 5.0E-01 | 6.4E+03 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 23| +1.53E+02 | -7.1E-01 | +1.0E+00 | 5.0E-01 | 5.7E+01 | 1.6E+00 | trr |1\n", - "2023-02-17 16:05:22 fides(INFO) 3| +8.67E+03 | -4.6E+04 | +9.0E-01 | 5.0E-01 | 2.5E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 33| +1.38E+02 | -2.4E-02 | +9.7E-01 | 5.3E-02 | 3.0E+00 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 9| +2.50E+02 | -8.0E-01 | +6.2E-01 | 4.6E-02 | 1.4E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 11| +3.71E+02 | -7.7E+02 | +9.0E-01 | 5.0E-01 | 4.3E+03 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 12| +2.60E+02 | -2.2E+02 | +8.9E-01 | 5.0E-01 | 9.9E+02 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 4| +1.54E+03 | -7.1E+03 | +9.0E-01 | 1.0E+00 | 3.9E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 10| +2.50E+02 | -3.1E-02 | +6.2E-01 | 4.6E-02 | 2.1E+00 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 11| +2.50E+02 | -3.3E-02 | +6.6E-01 | 4.6E-02 | 1.8E+00 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 33| +1.48E+02 | +1.3E-03 | -3.4E-01 | 7.3E-03 | 8.9E-01 | 9.9E-03 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 12| +2.59E+02 | -1.1E+02 | +8.8E-01 | 5.0E-01 | 6.6E+02 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 13| +2.37E+02 | -2.4E+01 | +8.6E-01 | 5.0E-01 | 1.3E+02 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 12| +2.50E+02 | -4.3E-02 | +7.5E-01 | 4.6E-02 | 2.0E+00 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 5| +4.32E+02 | -1.1E+03 | +9.0E-01 | 1.0E+00 | 6.1E+03 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 113| +1.55E+02 | -6.3E-03 | +1.0E+00 | 1.1E-02 | 5.7E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 34| +1.48E+02 | -1.5E-03 | +6.4E-01 | 1.8E-03 | 8.9E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 13| +2.50E+02 | -9.2E+00 | +7.3E-01 | 5.0E-01 | 8.8E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 34| +1.38E+02 | -4.0E-02 | +9.5E-01 | 1.1E-01 | 4.8E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 13| +2.49E+02 | -4.7E-02 | +7.7E-01 | 4.6E-02 | 1.7E+00 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 14| +2.14E+02 | -2.2E+01 | +1.2E+00 | 1.0E+00 | 2.0E+01 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 6| +2.67E+02 | -1.6E+02 | +8.8E-01 | 1.0E+00 | 9.6E+02 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 14| +2.49E+02 | -1.3E-01 | +1.0E+00 | 9.3E-02 | 1.9E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 7| +2.50E+02 | -1.7E+01 | +7.9E-01 | 1.0E+00 | 1.3E+02 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 35| +1.48E+02 | -3.8E-04 | +9.6E-01 | 1.8E-03 | 1.8E+00 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 15| +2.49E+02 | -4.1E-01 | +1.3E+00 | 1.9E-01 | 2.0E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 14| +2.50E+02 | +2.3E-02 | -1.5E-01 | 5.0E-01 | 5.1E+00 | 6.6E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 15| +2.14E+02 | +3.3E+02 | -3.4E+01 | 2.0E+00 | 3.7E+01 | 6.2E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 8| +2.50E+02 | -1.4E-01 | -1.0E+00 | 2.0E+00 | 3.8E+00 | 4.9E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 35| +1.38E+02 | -6.2E-02 | +8.9E-01 | 2.1E-01 | 2.3E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 16| +2.47E+02 | -2.3E+00 | +1.9E+00 | 3.7E-01 | 2.8E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 15| +2.50E+02 | -6.3E-02 | +8.5E-01 | 6.4E-03 | 5.1E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 36| +1.48E+02 | -2.0E-04 | +5.0E-01 | 3.6E-03 | 3.0E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 16| +2.14E+02 | +2.2E+00 | -3.4E-01 | 5.0E-01 | 3.7E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 17| +2.40E+02 | -6.7E+00 | +1.8E+00 | 7.4E-01 | 7.3E+00 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 9| +2.50E+02 | -8.6E-03 | +1.0E-01 | 5.0E-01 | 3.8E+00 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 24| +1.52E+02 | -1.9E+00 | +9.2E-01 | 1.0E+00 | 4.7E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 16| +2.50E+02 | +2.3E-03 | -6.4E-02 | 1.3E-02 | 2.5E+00 | 3.1E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 17| +2.09E+02 | -5.2E+00 | +6.9E-01 | 1.2E-01 | 3.7E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +2.36E+02 | -4.1E+00 | +1.3E+00 | 1.5E+00 | 1.4E+01 | 4.4E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 36| +1.38E+02 | +5.4E-02 | -6.1E-01 | 4.3E-01 | 4.7E+00 | 4.4E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 10| +2.50E+02 | +5.7E-03 | -9.2E-02 | 1.2E-01 | 3.3E+00 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 37| +1.48E+02 | -1.8E-04 | +9.6E-01 | 3.6E-03 | 1.1E+00 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 25| +1.52E+02 | +5.7E+01 | -6.0E+02 | 2.0E+00 | 3.9E+01 | 5.8E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 17| +2.50E+02 | -1.4E-02 | +8.5E-01 | 3.0E-03 | 2.5E+00 | 7.7E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +2.06E+02 | -2.6E+00 | +5.9E-01 | 1.2E-01 | 3.1E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 11| +2.50E+02 | +5.8E-03 | -9.2E-02 | 3.1E-02 | 3.3E+00 | 7.5E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 114| +1.55E+02 | -1.9E-03 | +1.2E+00 | 2.2E-02 | 1.1E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 19| +2.36E+02 | +9.5E+01 | -9.3E+00 | 3.0E+00 | 2.4E+01 | 8.5E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 12| +2.50E+02 | -3.3E-02 | +6.7E-01 | 7.8E-03 | 3.3E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 38| +1.48E+02 | -7.9E-05 | +9.6E-01 | 7.3E-03 | 1.5E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +2.50E+02 | +2.5E-04 | -2.9E-02 | 6.0E-03 | 1.2E+00 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 37| +1.38E+02 | -2.0E-02 | +6.4E-01 | 1.1E-01 | 4.7E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 19| +2.06E+02 | -8.1E-01 | +2.2E-01 | 1.2E-01 | 2.8E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 13| +2.50E+02 | -2.2E-06 | +1.8E-02 | 7.8E-03 | 1.5E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 20| +2.36E+02 | +9.2E+00 | -1.7E+00 | 7.4E-01 | 2.4E+01 | 2.1E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 19| +2.50E+02 | -3.4E-03 | +8.5E-01 | 1.5E-03 | 1.2E+00 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 39| +1.48E+02 | -1.2E-04 | +9.2E-01 | 1.5E-02 | 2.8E-01 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 14| +2.50E+02 | -9.8E-07 | +7.6E-03 | 2.0E-03 | 1.5E-01 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 20| +2.04E+02 | -1.3E+00 | +8.4E-01 | 3.1E-02 | 2.9E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +2.36E+02 | +3.0E+00 | -6.5E-01 | 1.9E-01 | 2.4E+01 | 4.5E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 20| +2.50E+02 | +2.5E-05 | -1.2E-02 | 2.9E-03 | 6.1E-01 | 7.3E-03 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 38| +1.38E+02 | +9.9E-02 | -1.4E+00 | 1.1E-01 | 1.7E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 15| +2.50E+02 | -5.2E-05 | +5.1E-01 | 4.9E-04 | 1.5E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 40| +1.48E+02 | -2.0E-04 | +9.7E-01 | 2.9E-02 | 1.2E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 22| +2.34E+02 | -2.0E+00 | +8.8E-01 | 4.6E-02 | 2.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +2.04E+02 | -9.6E-02 | +1.7E-01 | 6.2E-02 | 1.4E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +2.50E+02 | -8.4E-04 | +8.5E-01 | 7.4E-04 | 6.1E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 16| +2.50E+02 | -3.3E-07 | +1.2E-02 | 4.9E-04 | 6.6E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 115| +1.55E+02 | -6.8E-04 | +2.8E-01 | 2.2E-02 | 1.1E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 23| +2.32E+02 | -1.8E+00 | +7.1E-01 | 9.3E-02 | 1.8E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 41| +1.48E+02 | -2.8E-04 | +8.2E-01 | 5.8E-02 | 3.4E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 22| +2.50E+02 | -1.8E-05 | +3.2E-02 | 1.5E-03 | 3.0E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 22| +2.04E+02 | -2.9E-01 | +8.3E-01 | 1.6E-02 | 1.4E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 39| +1.38E+02 | -7.3E-03 | +3.5E-01 | 2.7E-02 | 1.7E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 17| +2.50E+02 | -1.2E-05 | +7.5E-01 | 1.2E-04 | 6.6E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 24| +2.30E+02 | -2.1E+00 | +7.9E-01 | 9.3E-02 | 2.5E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 42| +1.48E+02 | -2.1E-04 | +4.1E-01 | 1.2E-01 | 1.7E-01 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 23| +2.50E+02 | -1.7E-04 | +8.8E-01 | 3.7E-04 | 2.9E-01 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +2.50E+02 | -7.4E-08 | +6.3E-02 | 2.4E-04 | 1.4E-02 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 23| +2.04E+02 | -7.7E-02 | +6.5E-01 | 3.1E-02 | 6.0E+00 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 25| +2.30E+02 | -2.2E-01 | -7.7E-02 | 1.9E-01 | 1.9E+01 | 4.3E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 40| +1.38E+02 | -8.6E-03 | +8.7E-01 | 2.7E-02 | 5.2E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 19| +2.50E+02 | -4.4E-07 | +4.3E-01 | 6.1E-05 | 1.4E-02 | 1.2E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as function difference 4.40E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:22 fides(INFO) 24| +2.50E+02 | -7.4E-05 | +4.8E-01 | 7.4E-04 | 1.7E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 43| +1.48E+02 | +2.0E-03 | -1.1E+00 | 1.2E-01 | 5.7E-01 | 5.6E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 24| +2.04E+02 | -3.4E-02 | +4.1E-01 | 3.1E-02 | 4.5E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 26| +2.29E+02 | -1.3E+00 | +8.9E-01 | 4.6E-02 | 1.9E+01 | 9.9E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 25| +2.50E+02 | +2.5E-08 | -1.1E-03 | 7.4E-04 | 6.5E-02 | 7.7E-04 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 41| +1.38E+02 | -9.0E-03 | +8.7E-01 | 5.3E-02 | 1.2E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 25| +2.04E+02 | -5.4E-02 | +6.3E-01 | 3.1E-02 | 4.4E+00 | 9.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 27| +2.26E+02 | -2.5E+00 | +1.1E+00 | 9.3E-02 | 1.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 116| +1.55E+02 | -3.1E-03 | +1.5E+00 | 2.2E-02 | 2.6E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 44| +1.48E+02 | -2.7E-04 | +5.1E-01 | 2.9E-02 | 5.7E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 26| +2.50E+02 | -9.4E-06 | +8.5E-01 | 8.1E-05 | 6.5E-02 | 2.0E-04 | 2d |1\n", - "2023-02-17 16:05:22.937 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 176.485 and h = 1.21791e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.937 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 176.485: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 26| +2.04E+02 | -4.1E-02 | +6.5E-01 | 3.1E-02 | 3.2E+00 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 26| +1.52E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 3.9E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 28| +2.24E+02 | -2.5E+00 | +6.5E-01 | 1.9E-01 | 1.8E+01 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 42| +1.38E+02 | -1.0E-02 | +4.5E-01 | 1.1E-01 | 1.6E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 27| +2.50E+02 | -4.3E-07 | +7.1E-02 | 1.6E-04 | 3.2E-02 | 3.6E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 45| +1.48E+02 | -6.2E-05 | +9.4E-01 | 2.9E-02 | 1.8E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 29| +2.20E+02 | -3.2E+00 | +6.5E-01 | 1.9E-01 | 3.8E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 27| +2.04E+02 | -5.0E-02 | +7.9E-01 | 3.1E-02 | 2.9E+00 | 9.3E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 28| +2.50E+02 | -1.9E-06 | +8.5E-01 | 4.0E-05 | 3.0E-02 | 8.7E-05 | 2d |1\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as function difference 1.85E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:22 fides(INFO) 46| +1.48E+02 | -8.3E-05 | +9.3E-01 | 5.8E-02 | 8.7E-02 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 43| +1.38E+02 | +2.4E-02 | -4.4E-01 | 1.1E-01 | 1.9E+00 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 28| +2.03E+02 | -1.0E-01 | +1.0E+00 | 6.2E-02 | 2.3E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 30| +2.20E+02 | -1.4E-01 | -3.0E-02 | 1.9E-01 | 1.9E+01 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 0| +2.00E+11 | NaN | NaN | 1.0E+00 | 1.1E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:23 fides(INFO) 47| +1.48E+02 | -9.9E-05 | +8.3E-01 | 1.2E-01 | 7.9E-02 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 29| +2.03E+02 | -2.9E-01 | +1.2E+00 | 1.2E-01 | 2.1E+00 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 31| +2.19E+02 | -1.3E+00 | +8.0E-01 | 4.6E-02 | 1.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 44| +1.38E+02 | -9.8E-03 | +6.3E-01 | 2.7E-02 | 1.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 48| +1.48E+02 | +1.9E-03 | -6.7E+00 | 2.3E-01 | 7.6E-02 | 7.1E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 117| +1.55E+02 | -2.6E-03 | +1.3E+00 | 2.2E-02 | 1.2E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 30| +2.02E+02 | -1.2E+00 | +1.6E+00 | 2.5E-01 | 2.8E+00 | 7.5E-01 | 2d |1\n", - "2023-02-17 16:05:23.071 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 104.324 and h = 1.41477e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.071 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 104.324: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) 27| +1.52E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 3.9E+01 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 1| +1.25E+05 | -2.0E+11 | +8.0E-02 | 1.0E+00 | 1.1E+12 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 32| +2.17E+02 | -2.0E+00 | +1.1E+00 | 9.3E-02 | 1.7E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 0| +1.23E+09 | NaN | NaN | 1.0E+00 | 6.1E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:23 fides(INFO) 45| +1.38E+02 | -4.0E-03 | +9.7E-01 | 2.7E-02 | 2.4E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 49| +1.48E+02 | +3.2E-05 | -3.0E-01 | 5.8E-02 | 7.6E-02 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 31| +1.94E+02 | -7.9E+00 | +2.2E+00 | 5.0E-01 | 3.7E+00 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 33| +2.15E+02 | -2.1E+00 | +8.2E-01 | 1.9E-01 | 1.6E+01 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 50| +1.48E+02 | -1.7E-05 | +5.5E-01 | 1.5E-02 | 7.6E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 34| +2.11E+02 | -3.7E+00 | +8.1E-01 | 3.7E-01 | 2.3E+01 | 9.5E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 32| +1.94E+02 | +4.8E+02 | -2.6E+01 | 1.0E+00 | 2.5E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 2| +5.32E+04 | -7.2E+04 | +9.6E-01 | 2.5E-01 | 5.9E+05 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 1| +5.39E+04 | -1.2E+09 | +8.5E-02 | 1.0E+00 | 6.1E+09 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 46| +1.38E+02 | -6.8E-03 | +9.8E-01 | 5.3E-02 | 1.0E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 51| +1.48E+02 | -6.5E-06 | +8.5E-01 | 1.5E-02 | 5.4E-02 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 35| +1.95E+02 | -1.7E+01 | +6.7E-01 | 7.4E-01 | 2.2E+01 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 33| +1.79E+02 | -1.5E+01 | +1.5E+00 | 2.5E-01 | 2.5E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 28| +1.51E+02 | -2.0E-01 | +1.0E+00 | 3.1E-02 | 3.9E+01 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 118| +1.55E+02 | -1.6E-03 | +1.3E+00 | 4.5E-02 | 4.2E-01 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 2| +8.35E+03 | -4.6E+04 | +9.0E-01 | 2.5E-01 | 2.7E+05 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 52| +1.48E+02 | -9.5E-06 | +7.9E-01 | 2.9E-02 | 2.8E-02 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 47| +1.38E+02 | -1.2E-02 | +9.5E-01 | 1.1E-01 | 1.2E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 36| +1.95E+02 | +7.6E+01 | -2.6E+00 | 7.4E-01 | 1.1E+02 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 34| +1.79E+02 | +3.4E+01 | -2.2E+00 | 5.0E-01 | 4.6E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 37| +1.78E+02 | -1.7E+01 | +8.4E-01 | 1.9E-01 | 1.1E+02 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 53| +1.48E+02 | -1.7E-05 | +9.4E-01 | 5.8E-02 | 4.8E-02 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 3| +1.39E+03 | -7.0E+03 | +9.1E-01 | 5.0E-01 | 4.2E+04 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 3| +1.25E+04 | -4.1E+04 | +9.4E-01 | 5.0E-01 | 1.9E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 35| +1.69E+02 | -1.1E+01 | +1.1E+00 | 1.2E-01 | 4.6E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 48| +1.38E+02 | -1.3E-02 | +5.7E-01 | 2.1E-01 | 1.2E+00 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 38| +1.66E+02 | -1.2E+01 | +5.0E-01 | 3.7E-01 | 4.7E+01 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 54| +1.48E+02 | -1.7E-05 | +9.6E-01 | 1.2E-01 | 1.8E-02 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 119| +1.55E+02 | -1.1E-03 | +1.6E+00 | 4.5E-02 | 4.4E-01 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 36| +1.55E+02 | -1.4E+01 | +8.2E-01 | 2.5E-01 | 6.2E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 4| +3.46E+02 | -1.0E+03 | +9.1E-01 | 1.0E+00 | 6.8E+03 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 39| +1.59E+02 | -6.7E+00 | +3.1E-01 | 3.7E-01 | 1.2E+02 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 55| +1.48E+02 | +5.0E-04 | -1.2E+01 | 2.3E-01 | 4.5E-02 | 2.3E-02 | trr |0\n", - "2023-02-17 16:05:23 fides(INFO) 49| +1.38E+02 | +3.6E-01 | -3.5E+00 | 2.1E-01 | 1.4E+00 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 37| +1.49E+02 | -5.6E+00 | +7.6E-01 | 5.0E-01 | 1.1E+02 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 40| +1.51E+02 | -8.9E+00 | +9.5E-01 | 3.7E-01 | 2.3E+02 | 3.2E-01 | trr |1\n", - "2023-02-17 16:05:23 fides(INFO) 56| +1.48E+02 | +1.9E-05 | -1.0E+00 | 3.7E-02 | 4.5E-02 | 5.8E-03 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 5| +1.68E+02 | -1.8E+02 | +1.0E+00 | 1.0E+00 | 1.2E+03 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 29| +1.51E+02 | -3.0E-01 | +1.0E+00 | 6.2E-02 | 2.3E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 50| +1.38E+02 | +5.8E-03 | -2.0E-01 | 5.3E-02 | 1.4E+00 | 4.8E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 41| +1.51E+02 | +2.8E+00 | -1.1E+00 | 3.7E-01 | 4.8E+01 | 5.7E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 38| +1.49E+02 | +3.5E+00 | -1.7E+00 | 1.0E+00 | 5.2E+01 | 7.7E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 57| +1.48E+02 | -1.9E-06 | +3.4E-01 | 9.3E-03 | 4.5E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 120| +1.55E+02 | -1.7E-03 | +1.3E+00 | 4.5E-02 | 6.1E-01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 6| +1.68E+02 | -3.3E+00 | -2.8E-02 | 1.0E+00 | 2.0E+02 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:23.374 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 77.935 and h = 1.24774e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.374 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 77.935: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) 39| +1.49E+02 | +0.0E+00 | +0.0E+00 | 1.7E-01 | 5.2E+01 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 42| +1.49E+02 | -1.3E+00 | +7.7E-01 | 8.8E-02 | 4.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 51| +1.38E+02 | -5.3E-03 | +5.0E-01 | 1.3E-02 | 1.4E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 58| +1.48E+02 | +4.1E-07 | -6.7E-01 | 9.3E-03 | 4.6E-02 | 1.4E-03 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 4| +2.02E+03 | -1.0E+04 | +9.0E-01 | 1.0E+00 | 4.4E+04 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 43| +1.49E+02 | +8.8E-01 | -6.9E-01 | 1.8E-01 | 3.0E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 40| +1.49E+02 | -5.5E-01 | +8.8E-01 | 4.2E-02 | 5.2E+01 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 7| +1.58E+02 | -1.1E+01 | +8.9E-01 | 2.1E-01 | 2.0E+02 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 59| +1.48E+02 | +3.4E-07 | -9.7E-01 | 2.3E-03 | 4.6E-02 | 3.4E-04 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 52| +1.38E+02 | -4.2E-03 | +9.7E-01 | 1.3E-02 | 6.4E+00 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 44| +1.49E+02 | -3.3E-01 | +7.0E-01 | 4.4E-02 | 3.0E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 41| +1.48E+02 | -4.1E-01 | +9.6E-01 | 8.3E-02 | 4.1E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 8| +1.52E+02 | -5.7E+00 | +8.5E-01 | 4.3E-01 | 6.1E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 121| +1.55E+02 | -4.6E-03 | +1.2E+00 | 8.9E-02 | 7.5E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 60| +1.48E+02 | +8.9E-07 | -3.2E+00 | 5.8E-04 | 4.6E-02 | 8.6E-05 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 42| +1.48E+02 | -4.1E-01 | +8.1E-01 | 1.7E-01 | 3.2E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 45| +1.48E+02 | -4.8E-01 | +7.5E-01 | 4.4E-02 | 2.3E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 53| +1.38E+02 | -1.8E-03 | +9.8E-01 | 2.7E-02 | 6.1E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 61| +1.48E+02 | +3.9E-07 | -1.5E+00 | 1.5E-04 | 4.6E-02 | 2.4E-05 | 2d |0\n", - "2023-02-17 16:05:23.546 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 133.055 and h = 1.26071e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.546 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 133.055: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 30| +1.51E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 9.4E+00 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 46| +1.48E+02 | +4.8E-02 | -1.0E-01 | 8.8E-02 | 2.6E+01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 43| +1.48E+02 | +9.7E-02 | -2.0E-01 | 3.3E-01 | 2.9E+01 | 3.7E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 9| +1.50E+02 | -2.2E+00 | +1.0E+00 | 8.5E-01 | 3.9E+01 | 8.0E-01 | trr |1\n", - "2023-02-17 16:05:23 fides(INFO) 54| +1.38E+02 | -3.5E-03 | +9.7E-01 | 5.3E-02 | 8.2E-01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 47| +1.48E+02 | -1.7E-01 | +8.0E-01 | 2.2E-02 | 2.6E+01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 62| +1.48E+02 | -1.0E-06 | +4.0E+00 | 3.6E-05 | 4.6E-02 | 1.2E-05 | 2d |1\n", - "2023-02-17 16:05:23 fides(WARNING) Stopping as function difference 1.03E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:23 fides(INFO) 5| +4.14E+02 | -1.6E+03 | +9.0E-01 | 1.0E+00 | 6.8E+03 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 122| +1.55E+02 | -1.0E-02 | +1.1E+00 | 1.8E-01 | 1.4E+00 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 44| +1.48E+02 | -1.8E-01 | +8.0E-01 | 8.3E-02 | 2.9E+01 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 10| +1.50E+02 | -4.8E-02 | +2.5E-01 | 8.5E-01 | 8.1E+00 | 1.0E+00 | trr |1\n", - "2023-02-17 16:05:23 fides(INFO) 55| +1.38E+02 | -5.4E-03 | +8.0E-01 | 1.1E-01 | 7.0E-01 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 48| +1.48E+02 | -2.3E-01 | +9.6E-01 | 4.4E-02 | 9.2E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 45| +1.48E+02 | -1.7E-01 | +8.5E-01 | 1.7E-01 | 1.7E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 123| +1.55E+02 | +6.4E-01 | -5.7E+01 | 3.6E-01 | 6.7E+00 | 9.0E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 49| +1.48E+02 | -1.6E-01 | +5.9E-01 | 8.8E-02 | 1.4E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 56| +1.38E+02 | +2.1E-01 | -6.3E+00 | 2.1E-01 | 6.0E-01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 11| +1.50E+02 | -3.8E-02 | +9.0E-01 | 1.7E-01 | 1.3E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 46| +1.48E+02 | +1.0E-01 | -4.8E-01 | 3.3E-01 | 1.8E+01 | 3.3E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 50| +1.48E+02 | +2.8E-01 | -5.9E-01 | 8.8E-02 | 6.2E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 31| +1.51E+02 | -1.4E-02 | +1.0E+00 | 3.1E-02 | 9.4E+00 | 8.9E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 47| +1.48E+02 | -7.1E-02 | +7.5E-01 | 8.3E-02 | 1.8E+01 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 57| +1.38E+02 | +6.1E-03 | -6.6E-01 | 5.3E-02 | 6.0E-01 | 4.0E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 0| +3.10E+09 | NaN | NaN | 1.0E+00 | 1.4E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:23 fides(INFO) 12| +1.50E+02 | -1.9E-02 | +8.5E-01 | 3.4E-01 | 2.0E+00 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 124| +1.55E+02 | -6.6E-03 | +9.7E-01 | 8.9E-02 | 6.7E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:23.765 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.64 and h = 4.19393e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.765 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.64: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) 48| +1.48E+02 | +0.0E+00 | +0.0E+00 | 8.3E-02 | 8.8E+00 | 6.9E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 51| +1.48E+02 | -1.1E-01 | +6.1E-01 | 2.2E-02 | 6.2E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 13| +1.50E+02 | -5.3E-03 | +3.6E-01 | 6.8E-01 | 1.7E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 58| +1.38E+02 | -1.4E-03 | +5.2E-01 | 1.3E-02 | 6.0E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 52| +1.48E+02 | -4.2E-02 | +8.8E-01 | 2.2E-02 | 9.9E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 1| +1.92E+04 | -3.1E+09 | +9.3E-02 | 1.0E+00 | 1.4E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 49| +1.48E+02 | -2.9E-02 | +8.6E-01 | 2.1E-02 | 8.8E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 125| +1.55E+02 | -2.5E-02 | +3.9E-01 | 1.8E-01 | 1.0E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 14| +1.50E+02 | +2.2E-03 | -2.6E-01 | 6.8E-01 | 3.5E+00 | 2.8E-01 | trr |0\n", - "2023-02-17 16:05:23 fides(INFO) 53| +1.48E+02 | -6.1E-02 | +9.5E-01 | 4.4E-02 | 2.4E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 50| +1.48E+02 | -2.5E-02 | +9.6E-01 | 4.2E-02 | 1.2E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 2| +3.81E+03 | -1.5E+04 | +9.1E-01 | 2.5E-01 | 6.4E+04 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 59| +1.38E+02 | -1.1E-03 | +9.5E-01 | 1.3E-02 | 2.1E+00 | 8.8E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 6| +1.81E+02 | -2.3E+02 | +8.9E-01 | 1.0E+00 | 1.0E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 54| +1.48E+02 | -7.4E-02 | +9.0E-01 | 8.8E-02 | 4.8E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 3| +8.29E+02 | -3.0E+03 | +9.1E-01 | 5.0E-01 | 1.2E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 15| +1.50E+02 | -3.8E-03 | +8.1E-01 | 1.2E-01 | 3.5E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 51| +1.48E+02 | -1.9E-02 | +9.5E-01 | 8.3E-02 | 5.1E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 60| +1.38E+02 | -1.6E-03 | +9.7E-01 | 2.7E-02 | 4.5E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 126| +1.55E+02 | +3.1E-01 | -3.2E+00 | 1.8E-01 | 1.2E+01 | 3.4E-01 | 2d |0\n", - "2023-02-17 16:05:23.931 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 87.6304 and h = 1.05166e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.932 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 87.6304: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) 32| +1.51E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 4.6E+00 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 4| +8.29E+02 | +5.6E+03 | -3.0E+01 | 1.0E+00 | 2.0E+03 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 16| +1.50E+02 | -7.9E-04 | +7.6E-01 | 2.4E-01 | 9.1E-01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 55| +1.48E+02 | +3.2E-02 | -5.2E-01 | 1.8E-01 | 1.5E+00 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 52| +1.48E+02 | -1.9E-02 | +8.1E-01 | 1.7E-01 | 5.9E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 5| +3.55E+02 | -4.7E+02 | +8.8E-01 | 2.5E-01 | 2.0E+03 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:23.973 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 146.739 and h = 3.74372e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.973 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 146.739: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) 56| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.8E-02 | 1.5E+00 | 5.0E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 61| +1.38E+02 | -2.6E-03 | +9.1E-01 | 5.3E-02 | 1.8E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 53| +1.48E+02 | +4.3E-02 | -1.3E+00 | 3.3E-01 | 2.9E+00 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 17| +1.50E+02 | -1.6E-04 | +8.4E-01 | 4.7E-01 | 5.0E-01 | 1.7E-01 | trr |1\n", - "2023-02-17 16:05:24 fides(INFO) 127| +1.55E+02 | -3.7E-02 | +7.2E-01 | 4.2E-02 | 1.2E+01 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 6| +2.54E+02 | -1.0E+02 | +8.0E-01 | 5.0E-01 | 3.6E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 57| +1.48E+02 | -1.1E-02 | +6.4E-01 | 9.6E-03 | 1.5E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 54| +1.48E+02 | -5.9E-03 | +4.5E-01 | 8.3E-02 | 2.9E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 62| +1.38E+02 | +1.8E-03 | -3.4E-01 | 1.1E-01 | 4.0E-01 | 6.8E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 7| +1.53E+02 | -2.7E+01 | +8.6E-01 | 1.0E+00 | 1.5E+02 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 18| +1.50E+02 | +5.0E-05 | -4.1E-01 | 4.7E-01 | 3.0E-01 | 7.7E-03 | trr |0\n", - "2023-02-17 16:05:24 fides(INFO) 7| +2.49E+02 | -4.7E+00 | +6.1E-01 | 1.0E+00 | 4.7E+01 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 55| +1.48E+02 | -3.9E-03 | +2.8E-01 | 8.3E-02 | 3.9E+00 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 58| +1.48E+02 | -7.2E-03 | +8.7E-01 | 9.6E-03 | 3.6E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:24.073 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.1719 and h = 1.21884e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.073 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.1719: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 33| +1.51E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 4.6E+00 | 3.8E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 128| +1.55E+02 | -2.4E-02 | +1.9E-01 | 4.2E-02 | 1.1E+01 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 19| +1.50E+02 | -4.8E-05 | +8.1E-01 | 1.4E-02 | 3.0E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 8| +2.43E+02 | -5.9E+00 | +1.1E+00 | 1.0E+00 | 1.6E+01 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 63| +1.38E+02 | -1.0E-03 | +5.1E-01 | 2.7E-02 | 4.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 56| +1.48E+02 | +6.8E-03 | -4.1E-01 | 8.3E-02 | 1.9E+00 | 5.1E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 59| +1.48E+02 | -6.9E-03 | +7.8E-01 | 1.9E-02 | 5.3E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 9| +2.27E+02 | -1.6E+01 | +1.6E+00 | 2.0E+00 | 2.4E+01 | 4.5E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 20| +1.50E+02 | -1.1E-05 | +5.6E-01 | 2.9E-02 | 7.4E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 8| +1.52E+02 | -7.5E-01 | +9.6E-01 | 1.0E+00 | 4.2E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 64| +1.38E+02 | -1.5E-03 | +4.7E-01 | 2.7E-02 | 1.9E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 60| +1.48E+02 | -1.4E-02 | +8.8E-01 | 3.8E-02 | 1.2E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 57| +1.48E+02 | -1.5E-03 | +1.6E-01 | 2.1E-02 | 1.9E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 129| +1.54E+02 | -7.7E-02 | +1.1E+00 | 1.0E-02 | 2.3E+01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 10| +2.27E+02 | +4.8E+03 | -1.3E+02 | 4.0E+00 | 3.3E+01 | 7.2E+00 | trr |0\n", - "2023-02-17 16:05:24 fides(INFO) 21| +1.50E+02 | -5.6E-07 | +7.6E-02 | 2.9E-02 | 7.5E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 9| +1.52E+02 | +8.3E+01 | -9.5E+01 | 2.0E+00 | 1.9E+01 | 4.7E+00 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 58| +1.48E+02 | -4.3E-03 | +9.4E-01 | 5.2E-03 | 5.9E+00 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 61| +1.48E+02 | +6.8E-03 | -3.8E-01 | 7.7E-02 | 6.1E-01 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 65| +1.38E+02 | -1.0E-03 | +4.3E-01 | 2.7E-02 | 4.5E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 22| +1.50E+02 | -2.7E-06 | +4.9E-01 | 7.1E-03 | 1.9E-02 | 8.7E-04 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 11| +2.27E+02 | +4.6E+01 | -4.0E+00 | 7.1E-01 | 3.3E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 130| +1.54E+02 | -3.0E-02 | +1.1E+00 | 2.1E-02 | 3.5E+00 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 62| +1.48E+02 | -3.8E-03 | +6.9E-01 | 1.9E-02 | 6.1E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 59| +1.48E+02 | -6.3E-04 | +8.8E-01 | 1.0E-02 | 5.0E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:24.245 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 78.9074 and h = 1.0834e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.246 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 78.9074: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 34| +1.51E+02 | +0.0E+00 | +0.0E+00 | 3.9E-03 | 4.6E+00 | 8.9E-03 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 10| +1.52E+02 | +2.3E+00 | -1.1E+01 | 5.0E-01 | 1.9E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 66| +1.38E+02 | -8.5E-04 | +3.4E-01 | 2.7E-02 | 6.9E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 12| +2.22E+02 | -5.2E+00 | +4.5E-01 | 1.8E-01 | 3.3E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 23| +1.50E+02 | -2.8E-08 | +4.4E-02 | 7.1E-03 | 2.2E-02 | 4.1E-04 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 60| +1.48E+02 | -6.7E-04 | +8.4E-01 | 2.1E-02 | 6.4E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 63| +1.48E+02 | -2.7E-03 | +3.8E-01 | 1.9E-02 | 6.8E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 13| +2.14E+02 | -7.9E+00 | +9.1E-01 | 1.8E-01 | 4.9E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 131| +1.54E+02 | -4.3E-02 | +1.0E+00 | 4.2E-02 | 3.0E+00 | 9.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 61| +1.48E+02 | -7.4E-04 | +9.1E-01 | 4.2E-02 | 2.8E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 64| +1.48E+02 | -4.7E-03 | +5.2E-01 | 1.9E-02 | 3.0E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 24| +1.50E+02 | -1.5E-07 | +6.6E-01 | 1.8E-03 | 1.2E-02 | 1.4E-04 | 2d |1\n", - "2023-02-17 16:05:24 fides(WARNING) Stopping as function difference 1.50E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:24 fides(INFO) 67| +1.38E+02 | -9.3E-04 | +3.2E-01 | 2.7E-02 | 5.1E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 14| +1.95E+02 | -1.9E+01 | +1.3E+00 | 3.5E-01 | 2.8E+01 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 11| +1.52E+02 | -1.4E-01 | +9.4E-01 | 1.2E-01 | 1.9E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 65| +1.48E+02 | -2.7E-03 | +6.2E-01 | 1.9E-02 | 6.9E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 62| +1.48E+02 | -1.1E-03 | +9.6E-01 | 8.3E-02 | 3.9E-01 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 132| +1.54E+02 | -7.1E-02 | +8.4E-01 | 8.3E-02 | 5.1E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 15| +1.95E+02 | -8.5E+00 | -3.6E-01 | 7.1E-01 | 5.5E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 68| +1.38E+02 | -5.6E-04 | +1.7E-01 | 2.7E-02 | 7.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 63| +1.48E+02 | -1.3E-03 | +9.2E-01 | 1.7E-01 | 2.4E-01 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 66| +1.48E+02 | -1.3E-03 | +3.5E-01 | 1.9E-02 | 6.8E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 16| +1.83E+02 | -1.2E+01 | +1.0E+00 | 1.8E-01 | 5.5E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 12| +1.52E+02 | -2.0E-01 | +7.0E-01 | 2.5E-01 | 2.1E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 67| +1.48E+02 | +5.7E-03 | -9.3E-01 | 1.9E-02 | 6.4E-01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 69| +1.38E+02 | -8.7E-04 | +7.9E-01 | 6.7E-03 | 6.0E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 0| +1.22E+14 | NaN | NaN | 1.0E+00 | 5.6E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:24 fides(INFO) 64| +1.48E+02 | +1.8E-03 | -1.7E+00 | 3.3E-01 | 1.5E-01 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:24.455 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 111.961 and h = 1.39991e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.456 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 111.961: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 133| +1.54E+02 | -5.6E-02 | +8.6E-01 | 1.7E-01 | 1.0E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 35| +1.51E+02 | +0.0E+00 | +0.0E+00 | 9.8E-04 | 4.6E+00 | 2.2E-03 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 65| +1.48E+02 | -1.2E-04 | +2.7E-01 | 8.3E-02 | 1.5E-01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 68| +1.48E+02 | -9.4E-04 | +4.7E-01 | 4.8E-03 | 6.4E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 17| +1.67E+02 | -1.6E+01 | +6.3E-01 | 3.5E-01 | 5.7E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 70| +1.38E+02 | -6.7E-04 | +7.7E-01 | 1.3E-02 | 8.1E-01 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 13| +1.52E+02 | -1.9E-01 | +8.0E-01 | 2.5E-01 | 1.9E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 1| +7.10E+07 | -1.2E+14 | +8.3E-02 | 1.0E+00 | 5.6E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 18| +1.54E+02 | -1.3E+01 | +9.7E-01 | 3.5E-01 | 2.2E+02 | 9.7E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 69| +1.48E+02 | -5.4E-04 | +8.7E-01 | 4.8E-03 | 1.3E+00 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 66| +1.48E+02 | +1.3E-03 | -7.9E-01 | 8.3E-02 | 5.7E-01 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 134| +1.54E+02 | -1.9E-02 | +6.2E-01 | 1.7E-01 | 2.8E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 2| +1.09E+07 | -6.0E+07 | +8.9E-01 | 2.5E-01 | 3.3E+08 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 71| +1.38E+02 | -8.8E-04 | +9.9E-01 | 2.7E-02 | 3.1E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 19| +1.54E+02 | +6.4E+00 | -9.7E-01 | 7.1E-01 | 7.2E+01 | 9.7E-01 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 67| +1.48E+02 | -3.0E-04 | +6.2E-01 | 2.1E-02 | 5.7E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 70| +1.48E+02 | -5.7E-04 | +9.2E-01 | 9.6E-03 | 1.8E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 3| +1.68E+06 | -9.2E+06 | +8.9E-01 | 5.0E-01 | 5.0E+07 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 14| +1.52E+02 | -2.2E-01 | +6.7E-01 | 5.0E-01 | 2.5E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:24.587 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.6128 and h = 1.23616e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.587 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.6128: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 36| +1.51E+02 | +0.0E+00 | +0.0E+00 | 2.4E-04 | 4.6E+00 | 5.6E-04 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 72| +1.38E+02 | -1.7E-03 | +9.8E-01 | 5.3E-02 | 3.0E-01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 71| +1.48E+02 | -1.0E-03 | +8.9E-01 | 1.9E-02 | 2.1E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:24.596 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.536 and h = 2.82335e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.596 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.536: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 68| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.1E-02 | 5.3E-02 | 7.1E-03 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 20| +1.51E+02 | -3.6E+00 | +8.2E-01 | 1.8E-01 | 7.2E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 135| +1.54E+02 | -2.3E-02 | +1.0E+00 | 1.7E-01 | 1.2E+01 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 4| +2.61E+05 | -1.4E+06 | +9.0E-01 | 5.0E-01 | 7.7E+06 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 69| +1.48E+02 | -1.4E-05 | +1.1E+00 | 5.2E-03 | 5.3E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 5| +4.12E+04 | -2.2E+05 | +9.0E-01 | 5.0E-01 | 1.2E+06 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 72| +1.48E+02 | +1.4E-03 | -1.1E+00 | 3.8E-02 | 2.3E-01 | 4.4E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 21| +1.50E+02 | -1.2E+00 | +4.4E-01 | 3.5E-01 | 7.7E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 73| +1.38E+02 | -2.9E-03 | +9.1E-01 | 1.1E-01 | 4.1E-01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 6| +6.71E+03 | -3.4E+04 | +9.0E-01 | 5.0E-01 | 1.9E+05 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 70| +1.48E+02 | -2.2E-05 | +9.2E-01 | 1.0E-02 | 1.0E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:24.659 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.443 and h = 3.24687e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.659 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.443: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 73| +1.48E+02 | +0.0E+00 | +0.0E+00 | 9.6E-03 | 2.3E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 136| +1.54E+02 | -1.3E-02 | +1.6E+00 | 1.7E-01 | 4.5E+00 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 22| +1.49E+02 | -3.9E-01 | +7.7E-02 | 3.5E-01 | 4.4E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 15| +1.51E+02 | -2.6E-01 | +1.2E+00 | 5.0E-01 | 1.4E+01 | 6.9E-01 | trr |1\n", - "2023-02-17 16:05:24 fides(INFO) 7| +1.25E+03 | -5.5E+03 | +9.0E-01 | 5.0E-01 | 3.0E+04 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 74| +1.38E+02 | +4.9E-02 | -5.3E+00 | 2.1E-01 | 2.9E-01 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 71| +1.48E+02 | -3.9E-05 | +9.4E-01 | 2.1E-02 | 5.5E-02 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 74| +1.48E+02 | -9.3E-05 | +8.1E-01 | 2.4E-03 | 2.3E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 8| +3.91E+02 | -8.6E+02 | +9.0E-01 | 5.0E-01 | 4.8E+03 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 23| +1.48E+02 | -9.5E-01 | +5.1E-01 | 8.9E-02 | 5.4E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 16| +1.51E+02 | -1.2E-01 | +8.4E-01 | 1.0E+00 | 7.2E+00 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:24.718 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 94.2389 and h = 1.24166e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.718 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 94.2389: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 37| +1.51E+02 | +0.0E+00 | +0.0E+00 | 6.1E-05 | 4.6E+00 | 1.4E-04 | 2d |0\n", - "2023-02-17 16:05:24.727 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.508 and h = 2.16248e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.728 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.508: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 72| +1.48E+02 | +0.0E+00 | +0.0E+00 | 4.2E-02 | 8.9E-02 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 75| +1.38E+02 | +1.4E-03 | -5.2E-01 | 5.3E-02 | 2.9E-01 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 75| +1.48E+02 | -1.6E-04 | +9.4E-01 | 4.8E-03 | 8.1E-02 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 9| +2.62E+02 | -1.3E+02 | +8.9E-01 | 5.0E-01 | 7.6E+02 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 24| +1.48E+02 | -2.6E-01 | +9.1E-01 | 8.9E-02 | 2.8E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 137| +1.54E+02 | -1.6E-02 | +1.2E+00 | 1.7E-01 | 1.7E+00 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 76| +1.48E+02 | -2.8E-04 | +9.8E-01 | 9.6E-03 | 1.8E-01 | 9.1E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 17| +1.51E+02 | -3.1E-01 | +1.5E+00 | 2.0E+00 | 7.5E+00 | 3.3E-01 | trr |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 10| +2.50E+02 | -1.2E+01 | +7.8E-01 | 5.0E-01 | 1.1E+02 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 73| +1.48E+02 | -2.4E-05 | +1.2E+00 | 1.0E-02 | 8.9E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 76| +1.38E+02 | -3.9E-04 | +4.8E-01 | 1.3E-02 | 2.9E-01 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 25| +1.48E+02 | -3.1E-01 | +8.7E-01 | 1.8E-01 | 2.9E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 77| +1.48E+02 | -2.8E-04 | +8.3E-01 | 1.9E-02 | 8.6E-02 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 11| +2.50E+02 | -2.3E-04 | +3.8E-02 | 1.0E+00 | 9.9E-01 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 74| +1.48E+02 | -3.3E-05 | +9.7E-01 | 2.1E-02 | 5.8E-02 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 138| +1.54E+02 | -5.5E-03 | +1.5E+00 | 1.7E-01 | 1.5E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 18| +1.51E+02 | -4.8E-01 | +9.4E-01 | 2.0E+00 | 9.3E+00 | 4.2E-01 | trr |1\n", - "2023-02-17 16:05:24 fides(INFO) 26| +1.48E+02 | +3.8E-01 | -1.6E+00 | 3.5E-01 | 1.4E+01 | 3.3E-01 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 12| +2.50E+02 | +1.4E-04 | -2.8E-02 | 2.5E-01 | 9.4E-01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:24.822 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2018 and h = 1.60887e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.822 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2018: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 78| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.8E-02 | 1.8E-01 | 1.2E-02 | trr |0\n", - "2023-02-17 16:05:24 fides(INFO) 77| +1.38E+02 | -5.1E-04 | +8.1E-01 | 1.3E-02 | 1.2E+00 | 6.7E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 75| +1.48E+02 | -5.3E-05 | +9.5E-01 | 4.2E-02 | 7.3E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 19| +1.51E+02 | +1.9E+00 | -1.3E+00 | 2.0E+00 | 4.8E+01 | 5.6E-01 | trr |0\n", - "2023-02-17 16:05:24 fides(INFO) 27| +1.48E+02 | -4.3E-02 | +5.1E-01 | 8.9E-02 | 1.4E+01 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 13| +2.50E+02 | +1.5E-04 | -3.1E-02 | 6.2E-02 | 9.4E-01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 78| +1.38E+02 | -6.3E-04 | +9.5E-01 | 2.7E-02 | 1.9E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 79| +1.48E+02 | +1.3E-04 | -5.2E-01 | 6.2E-03 | 1.8E-01 | 3.0E-03 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 38| +1.51E+02 | -1.5E-04 | +1.0E+00 | 1.5E-05 | 4.6E+00 | 3.5E-05 | 2d |1\n", - "2023-02-17 16:05:24.882 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.542 and h = 2.33234e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.882 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.542: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 76| +1.48E+02 | +0.0E+00 | +0.0E+00 | 8.3E-02 | 4.4E-02 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 139| +1.54E+02 | -5.3E-03 | +1.4E+00 | 1.7E-01 | 1.0E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 28| +1.48E+02 | -4.4E-02 | +4.8E-01 | 8.9E-02 | 1.2E+01 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 20| +1.50E+02 | -7.5E-01 | +8.6E-01 | 8.9E-02 | 4.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 14| +2.50E+02 | +1.5E-04 | -3.1E-02 | 1.6E-02 | 9.4E-01 | 3.8E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 80| +1.48E+02 | -4.6E-05 | +6.3E-01 | 1.5E-03 | 1.8E-01 | 7.9E-04 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 77| +1.48E+02 | -2.1E-05 | +9.8E-01 | 2.1E-02 | 4.4E-02 | 5.7E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 79| +1.38E+02 | -9.6E-04 | +6.9E-01 | 5.3E-02 | 2.3E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 29| +1.48E+02 | -2.1E-02 | +2.8E-01 | 8.9E-02 | 1.3E+01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 15| +2.50E+02 | -1.1E-03 | +2.1E-01 | 3.9E-03 | 9.4E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 81| +1.48E+02 | -1.7E-05 | +1.0E+00 | 1.5E-03 | 1.6E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 21| +1.50E+02 | -2.0E-01 | +6.3E-01 | 1.8E-01 | 1.2E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 78| +1.48E+02 | -3.4E-05 | +9.7E-01 | 4.2E-02 | 6.2E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 140| +1.54E+02 | -3.3E-03 | +1.4E+00 | 1.7E-01 | 7.0E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 80| +1.38E+02 | +3.6E-03 | -9.5E-01 | 5.3E-02 | 3.3E-01 | 2.7E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 16| +2.50E+02 | -1.1E-03 | +8.7E-01 | 9.8E-04 | 7.3E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 30| +1.48E+02 | +3.0E-02 | -3.0E-01 | 8.9E-02 | 6.8E+00 | 7.7E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 82| +1.48E+02 | -2.5E-05 | +9.9E-01 | 3.1E-03 | 5.1E-02 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 79| +1.48E+02 | -4.2E-05 | +9.6E-01 | 8.3E-02 | 3.2E-02 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 22| +1.50E+02 | -1.2E-02 | +9.6E-01 | 1.8E-01 | 3.8E+00 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:24.994 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 94.9704 and h = 1.35144e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.994 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 94.9704: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 39| +1.51E+02 | +0.0E+00 | +0.0E+00 | 3.1E-05 | 4.5E+00 | 7.0E-05 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 31| +1.48E+02 | -2.3E-02 | +2.8E-01 | 2.2E-02 | 6.8E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 17| +2.50E+02 | -3.1E-04 | +3.4E-01 | 2.0E-03 | 4.0E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 81| +1.38E+02 | -5.1E-04 | +5.1E-01 | 1.3E-02 | 3.3E-01 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 83| +1.48E+02 | -3.0E-05 | +8.6E-01 | 6.2E-03 | 1.1E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 80| +1.48E+02 | -1.2E-05 | +4.7E-01 | 1.7E-01 | 4.8E-02 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 23| +1.50E+02 | +3.0E-02 | -8.8E-01 | 3.6E-01 | 1.2E+00 | 1.4E-01 | trr |0\n", - "2023-02-17 16:05:25 fides(INFO) 32| +1.48E+02 | -2.0E-02 | +8.0E-01 | 2.2E-02 | 8.7E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 141| +1.54E+02 | -1.8E-03 | +1.4E+00 | 1.7E-01 | 7.8E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 18| +2.50E+02 | +2.5E-06 | -8.0E-03 | 2.0E-03 | 2.4E-01 | 4.8E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 84| +1.48E+02 | +1.5E-04 | -2.3E+00 | 1.2E-02 | 5.4E-02 | 3.0E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 81| +1.48E+02 | +2.1E-03 | -1.3E+01 | 1.7E-01 | 6.4E-02 | 2.1E-02 | trr |0\n", - "2023-02-17 16:05:25 fides(INFO) 82| +1.38E+02 | -3.2E-04 | +8.1E-01 | 1.3E-02 | 4.4E-01 | 6.1E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 33| +1.48E+02 | -5.4E-03 | +5.8E-01 | 4.4E-02 | 2.6E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 24| +1.50E+02 | -3.2E-03 | +8.6E-01 | 1.7E-02 | 1.2E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 19| +2.50E+02 | -1.6E-04 | +7.2E-01 | 4.9E-04 | 2.4E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 85| +1.48E+02 | -2.4E-06 | +1.0E-01 | 3.1E-03 | 5.4E-02 | 8.1E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 82| +1.48E+02 | +8.3E-05 | -1.2E+00 | 1.5E-02 | 6.4E-02 | 5.2E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 34| +1.48E+02 | -1.9E-03 | +8.6E-01 | 4.4E-02 | 3.0E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 20| +2.50E+02 | +2.0E-09 | -4.6E-04 | 4.9E-04 | 2.9E-02 | 1.2E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 83| +1.38E+02 | -5.3E-04 | +8.8E-01 | 2.7E-02 | 1.8E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 25| +1.50E+02 | -4.6E-03 | +9.1E-01 | 3.3E-02 | 1.4E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 142| +1.54E+02 | -1.0E-03 | +1.4E+00 | 1.7E-01 | 2.3E-01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 86| +1.48E+02 | -9.0E-06 | +8.1E-01 | 7.7E-04 | 1.3E-01 | 7.8E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 35| +1.48E+02 | -1.3E-03 | +5.8E-01 | 8.9E-02 | 7.3E-01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 83| +1.48E+02 | -7.3E-06 | +3.4E-01 | 3.7E-03 | 6.4E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:25.130 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 137.357 and h = 1.59524e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.130 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 137.357: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 40| +1.51E+02 | +0.0E+00 | +0.0E+00 | 7.6E-06 | 4.5E+00 | 1.7E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 21| +2.50E+02 | -8.2E-07 | +1.7E-01 | 1.2E-04 | 2.9E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 84| +1.38E+02 | -3.0E-04 | +1.8E-01 | 5.3E-02 | 2.2E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 87| +1.48E+02 | -6.9E-06 | +8.7E-01 | 1.5E-03 | 2.7E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 26| +1.50E+02 | -5.2E-03 | +8.9E-01 | 6.6E-02 | 1.7E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 36| +1.48E+02 | -6.0E-04 | +6.7E-01 | 8.9E-02 | 1.6E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 84| +1.48E+02 | -1.8E-06 | +7.3E-01 | 3.7E-03 | 1.3E-01 | 2.7E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 22| +2.50E+02 | -1.1E-06 | +8.8E-01 | 3.1E-05 | 2.3E-02 | 5.9E-05 | 2d |1\n", - "2023-02-17 16:05:25 fides(WARNING) Stopping as function difference 1.08E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:25 fides(INFO) 88| +1.48E+02 | -6.1E-06 | +9.5E-01 | 3.1E-03 | 2.1E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 37| +1.48E+02 | +3.6E-04 | -4.3E-01 | 8.9E-02 | 3.6E-01 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 85| +1.48E+02 | -2.1E-06 | +3.2E+01 | 3.7E-03 | 1.1E-02 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 27| +1.50E+02 | -2.7E-03 | +6.6E-01 | 1.3E-01 | 9.8E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 143| +1.54E+02 | -6.4E-04 | +1.3E+00 | 3.3E-01 | 5.7E-01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 85| +1.38E+02 | -8.7E-04 | +3.8E-01 | 1.3E-02 | 4.6E-01 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 89| +1.48E+02 | -4.8E-06 | +5.7E-01 | 6.2E-03 | 3.0E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 86| +1.48E+02 | +2.4E-06 | -4.6E+01 | 7.5E-03 | 7.2E-03 | 4.1E-04 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 28| +1.50E+02 | -2.5E-04 | +1.6E-01 | 1.3E-01 | 1.1E+00 | 2.6E-02 | trr |1\n", - "2023-02-17 16:05:25 fides(INFO) 38| +1.48E+02 | -1.9E-04 | +7.0E-01 | 2.2E-02 | 3.6E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 86| +1.38E+02 | -3.5E-04 | +5.9E-01 | 1.3E-02 | 7.5E-01 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 90| +1.48E+02 | +2.6E-04 | -6.1E+00 | 6.2E-03 | 3.6E-02 | 3.9E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 144| +1.54E+02 | -3.3E-04 | +1.3E+00 | 3.3E-01 | 1.9E-01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 87| +1.48E+02 | -2.2E-06 | +1.0E+02 | 1.9E-03 | 7.2E-03 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 29| +1.50E+02 | -8.3E-04 | +5.2E-01 | 3.0E-02 | 1.8E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 41| +1.51E+02 | -1.8E-05 | +9.7E-01 | 1.9E-06 | 4.5E+00 | 4.3E-06 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 39| +1.48E+02 | -6.3E-05 | +9.4E-01 | 2.2E-02 | 5.1E-01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 0| +1.23E+14 | NaN | NaN | 1.0E+00 | 5.6E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:25 fides(INFO) 87| +1.38E+02 | -2.5E-04 | +7.5E-01 | 1.3E-02 | 1.9E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 91| +1.48E+02 | +9.5E-05 | -3.5E+00 | 1.5E-03 | 3.6E-02 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 88| +1.48E+02 | -2.4E-07 | +7.6E+00 | 3.7E-03 | 5.7E-03 | 2.0E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(WARNING) Stopping as function difference 2.41E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 30| +1.50E+02 | -7.7E-05 | +5.7E-01 | 3.0E-02 | 7.1E-01 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 40| +1.48E+02 | -5.6E-05 | +8.4E-01 | 4.4E-02 | 2.2E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 1| +9.94E+11 | -1.2E+14 | +8.2E-02 | 1.0E+00 | 5.6E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 92| +1.48E+02 | +2.8E-05 | -1.5E+00 | 3.9E-04 | 3.6E-02 | 8.5E-04 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 145| +1.54E+02 | -1.5E-04 | +1.4E+00 | 3.3E-01 | 1.8E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 88| +1.38E+02 | -4.1E-04 | +7.3E-01 | 2.7E-02 | 2.6E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 41| +1.48E+02 | -6.0E-05 | +7.6E-01 | 8.9E-02 | 2.7E-01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 31| +1.50E+02 | +3.6E-05 | -4.1E-01 | 3.0E-02 | 6.1E-02 | 6.7E-03 | trr |0\n", - "2023-02-17 16:05:25 fides(INFO) 2| +1.47E+11 | -8.5E+11 | +8.9E-01 | 2.5E-01 | 4.6E+12 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 93| +1.48E+02 | -1.7E-06 | +2.5E-01 | 9.6E-05 | 3.6E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 3| +2.19E+10 | -1.3E+11 | +8.9E-01 | 5.0E-01 | 6.8E+11 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 146| +1.54E+02 | -9.2E-05 | +1.4E+00 | 3.3E-01 | 9.7E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 42| +1.48E+02 | +6.0E-04 | -3.3E+00 | 1.8E-01 | 1.3E-01 | 5.3E-02 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 32| +1.50E+02 | -2.9E-05 | +7.7E-01 | 6.3E-03 | 6.1E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 89| +1.38E+02 | -1.5E-04 | +1.6E-01 | 2.7E-02 | 2.0E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 4| +3.26E+09 | -1.9E+10 | +8.9E-01 | 5.0E-01 | 1.0E+11 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:25.403 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 85.3984 and h = 1.13613e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.403 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 85.3984: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) 42| +1.51E+02 | +0.0E+00 | +0.0E+00 | 3.8E-06 | 4.5E+00 | 8.7E-06 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 5| +4.90E+08 | -2.8E+09 | +8.9E-01 | 5.0E-01 | 1.5E+10 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 94| +1.48E+02 | -3.3E-06 | +1.2E+01 | 2.4E-05 | 4.5E-02 | 1.3E-05 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 43| +1.48E+02 | -5.1E-06 | +7.7E-02 | 4.4E-02 | 1.3E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 6| +7.38E+07 | -4.2E+08 | +8.9E-01 | 5.0E-01 | 2.3E+09 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 0| +2.27E+09 | NaN | NaN | 1.0E+00 | 1.0E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:25 fides(INFO) 147| +1.54E+02 | -4.9E-05 | +1.4E+00 | 3.3E-01 | 2.1E-02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 33| +1.50E+02 | -1.1E-05 | +8.4E-01 | 1.3E-02 | 1.4E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 90| +1.38E+02 | -4.6E-04 | +6.0E-01 | 6.7E-03 | 3.9E-01 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 95| +1.48E+02 | +4.0E-06 | -2.2E+01 | 4.8E-05 | 1.4E-02 | 2.8E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 7| +1.12E+07 | -6.3E+07 | +8.9E-01 | 5.0E-01 | 3.4E+08 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 44| +1.48E+02 | -3.1E-05 | +7.5E-01 | 1.1E-02 | 2.4E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 8| +1.70E+06 | -9.5E+06 | +8.9E-01 | 5.0E-01 | 5.1E+07 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 34| +1.50E+02 | -2.3E-06 | +5.8E-01 | 2.5E-02 | 7.8E-02 | 7.5E-04 | trr |1\n", - "2023-02-17 16:05:25 fides(INFO) 96| +1.48E+02 | +2.7E-06 | -1.6E+01 | 1.2E-05 | 1.4E-02 | 2.5E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 148| +1.54E+02 | -2.6E-05 | +1.4E+00 | 3.3E-01 | 9.4E-03 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 1| +1.35E+06 | -2.3E+09 | +9.7E-02 | 1.0E+00 | 1.0E+10 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 45| +1.48E+02 | -9.5E-06 | +7.1E-01 | 2.2E-02 | 6.8E-02 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 9| +2.61E+05 | -1.4E+06 | +8.9E-01 | 5.0E-01 | 7.8E+06 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 97| +1.48E+02 | +3.7E-06 | -4.7E+01 | 3.0E-06 | 1.4E-02 | 7.1E-06 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 91| +1.38E+02 | -1.1E-04 | +9.8E-01 | 6.7E-03 | 3.1E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 2| +2.13E+05 | -1.1E+06 | +9.0E-01 | 2.5E-01 | 6.4E+06 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 35| +1.50E+02 | +2.5E-06 | -5.7E-01 | 2.5E-02 | 2.0E-02 | 1.5E-03 | trr |0\n", - "2023-02-17 16:05:25 fides(INFO) 149| +1.54E+02 | -1.4E-05 | +1.4E+00 | 3.3E-01 | 9.2E-03 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 10| +4.03E+04 | -2.2E+05 | +9.0E-01 | 5.0E-01 | 1.2E+06 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 46| +1.48E+02 | -6.7E-06 | +1.2E+00 | 2.2E-02 | 5.2E-02 | 5.8E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 3| +3.59E+04 | -1.8E+05 | +9.0E-01 | 5.0E-01 | 9.9E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 98| +1.48E+02 | +2.4E-06 | -1.1E+02 | 7.5E-07 | 1.4E-02 | 1.8E-06 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 92| +1.38E+02 | -2.4E-04 | +1.0E+00 | 1.3E-02 | 1.3E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 36| +1.50E+02 | -1.6E-06 | +8.4E-01 | 4.0E-04 | 2.0E-02 | 3.8E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 47| +1.48E+02 | -5.3E-06 | +6.6E-01 | 4.4E-02 | 5.8E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 4| +5.68E+03 | -3.0E+04 | +9.0E-01 | 1.0E+00 | 1.6E+05 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 11| +6.40E+03 | -3.4E+04 | +9.0E-01 | 5.0E-01 | 1.8E+05 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:25.573 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 114.461 and h = 1.42314e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.573 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 114.461: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 150| +1.54E+02 | -7.9E-06 | +1.4E+00 | 3.3E-01 | 5.7E-03 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 43| +1.51E+02 | +0.0E+00 | +0.0E+00 | 9.5E-07 | 4.5E+00 | 2.2E-06 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 99| +1.48E+02 | +3.0E-06 | -5.4E+02 | 1.9E-07 | 1.4E-02 | 4.4E-07 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 5| +1.04E+03 | -4.6E+03 | +9.0E-01 | 1.0E+00 | 2.4E+04 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:25.590 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 168.763 and h = 2.32709e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.590 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 168.763: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) 93| +1.38E+02 | -4.2E-04 | +9.8E-01 | 2.7E-02 | 4.6E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 37| +1.50E+02 | +0.0E+00 | +0.0E+00 | 7.9E-04 | 1.1E-02 | 3.5E-04 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 48| +1.48E+02 | -7.0E-06 | +1.2E+00 | 4.4E-02 | 3.3E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 12| +1.18E+03 | -5.2E+03 | +9.0E-01 | 5.0E-01 | 2.9E+04 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 6| +3.31E+02 | -7.1E+02 | +9.0E-01 | 1.0E+00 | 3.9E+03 | 7.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 151| +1.54E+02 | -4.2E-06 | +1.3E+00 | 3.3E-01 | 3.9E-03 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 100| +1.48E+02 | -5.3E-08 | +3.8E+01 | 4.7E-08 | 1.4E-02 | 1.1E-07 | 2d |1\n", - "2023-02-17 16:05:25 fides(WARNING) Stopping as function difference 5.31E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:25 fides(INFO) 13| +3.70E+02 | -8.1E+02 | +9.0E-01 | 5.0E-01 | 4.5E+03 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 38| +1.50E+02 | -1.7E-07 | +8.8E-01 | 2.0E-04 | 1.1E-02 | 8.9E-05 | 2d |1\n", - "2023-02-17 16:05:25 fides(WARNING) Stopping as function difference 1.74E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:25 fides(INFO) 94| +1.38E+02 | -6.3E-04 | +7.2E-01 | 5.3E-02 | 1.1E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 49| +1.48E+02 | +4.9E-06 | -5.2E-01 | 8.9E-02 | 3.7E-02 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 7| +2.31E+02 | -1.0E+02 | +8.7E-01 | 1.0E+00 | 6.2E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 14| +2.52E+02 | -1.2E+02 | +8.8E-01 | 5.0E-01 | 6.9E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 152| +1.54E+02 | -2.1E-06 | +1.3E+00 | 3.3E-01 | 2.0E-03 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 50| +1.48E+02 | +7.5E-07 | -2.5E-01 | 2.2E-02 | 3.7E-02 | 4.6E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 95| +1.38E+02 | +9.1E-03 | -2.5E+00 | 5.3E-02 | 2.5E-01 | 2.5E-02 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 15| +2.40E+02 | -1.2E+01 | +7.3E-01 | 1.0E+00 | 9.1E+01 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 8| +2.04E+02 | -2.7E+01 | +1.1E+00 | 2.0E+00 | 1.1E+02 | 3.7E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 153| +1.54E+02 | -6.1E-07 | +8.8E-01 | 3.3E-01 | 1.2E-03 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(WARNING) Stopping as function difference 6.15E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:25 fides(INFO) 51| +1.48E+02 | +2.3E-07 | -2.4E-01 | 5.5E-03 | 3.7E-02 | 1.1E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 16| +2.32E+02 | -8.6E+00 | +1.1E+00 | 1.0E+00 | 1.7E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 96| +1.38E+02 | -1.4E-04 | +1.4E-01 | 1.3E-02 | 2.5E-01 | 6.3E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 9| +2.04E+02 | +1.3E+03 | -5.7E+01 | 4.0E+00 | 4.6E+01 | 4.4E+00 | trr |0\n", - "2023-02-17 16:05:25.751 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 95.8575 and h = 1.32106e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.752 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 95.8575: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) 44| +1.51E+02 | +0.0E+00 | +0.0E+00 | 2.4E-07 | 4.5E+00 | 5.4E-07 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 17| +2.22E+02 | -9.5E+00 | +2.3E-01 | 2.0E+00 | 4.5E+01 | 4.4E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 52| +1.48E+02 | +3.1E-06 | -8.0E+00 | 1.4E-03 | 3.7E-02 | 2.8E-04 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 0| +7.56E+07 | NaN | NaN | 1.0E+00 | 3.5E+08 | NaN | NaN |1\n", - "2023-02-17 16:05:25 fides(INFO) 18| +2.05E+02 | -1.7E+01 | +9.8E-01 | 5.0E-01 | 5.7E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 53| +1.48E+02 | +9.8E-07 | -4.0E+00 | 3.5E-04 | 3.7E-02 | 7.2E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 10| +2.04E+02 | +2.9E+01 | -1.7E+00 | 6.1E-01 | 4.6E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 97| +1.38E+02 | -2.1E-04 | +8.1E-01 | 3.3E-03 | 4.0E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 0| +2.91E+12 | NaN | NaN | 1.0E+00 | 1.3E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:25 fides(INFO) 54| +1.48E+02 | +3.3E-06 | -1.6E+01 | 8.7E-05 | 3.7E-02 | 2.1E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 1| +1.15E+03 | -7.6E+07 | +1.0E-01 | 1.0E+00 | 3.5E+08 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 19| +2.05E+02 | +3.0E+02 | -1.6E+01 | 1.0E+00 | 3.0E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 11| +1.95E+02 | -9.4E+00 | +9.2E-01 | 1.5E-01 | 4.6E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 98| +1.38E+02 | -1.5E-04 | +7.3E-01 | 6.7E-03 | 1.9E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 55| +1.48E+02 | +1.8E-06 | -9.2E+00 | 2.2E-05 | 3.7E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 2| +4.22E+02 | -7.3E+02 | +8.9E-01 | 2.5E-01 | 3.0E+03 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 20| +1.93E+02 | -1.3E+01 | +1.1E+00 | 2.5E-01 | 3.0E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 0| +2.93E+04 | NaN | NaN | 1.0E+00 | 1.3E+05 | NaN | NaN |1\n", - "2023-02-17 16:05:25 fides(INFO) 1| +1.31E+08 | -2.9E+12 | +8.7E-02 | 1.0E+00 | 1.3E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 12| +1.84E+02 | -1.1E+01 | +7.4E-01 | 3.0E-01 | 5.2E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 3| +2.61E+02 | -1.6E+02 | +8.8E-01 | 5.0E-01 | 5.4E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 99| +1.38E+02 | -8.6E-05 | +9.8E-01 | 6.7E-03 | 1.3E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 56| +1.48E+02 | +1.6E-06 | -8.3E+00 | 5.4E-06 | 3.7E-02 | 9.9E-06 | 2d |0\n", - "2023-02-17 16:05:25.897 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 100 and h = 1.26382e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.897 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 100: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) 45| +1.51E+02 | +0.0E+00 | +0.0E+00 | 6.0E-08 | 4.5E+00 | 1.4E-07 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 21| +1.93E+02 | -1.0E+01 | -3.3E-02 | 5.0E-01 | 4.8E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 1| +4.30E+02 | -2.9E+04 | +1.2E-01 | 1.0E+00 | 1.3E+05 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 4| +2.55E+02 | -6.8E+00 | +2.9E-01 | 1.0E+00 | 7.7E+01 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 57| +1.48E+02 | +3.5E-07 | -4.8E+00 | 1.4E-06 | 3.7E-02 | 2.5E-06 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 13| +1.77E+02 | -7.0E+00 | +4.1E-01 | 3.0E-01 | 8.9E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 22| +1.84E+02 | -8.9E+00 | +1.1E+00 | 1.2E-01 | 4.8E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 5| +2.42E+02 | -1.3E+01 | +1.2E+00 | 1.0E+00 | 5.6E+01 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 2| +2.22E+07 | -1.1E+08 | +8.9E-01 | 2.5E-01 | 5.9E+08 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 100| +1.38E+02 | -1.7E-04 | +9.9E-01 | 1.3E-02 | 7.1E-02 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 2| +3.86E+02 | -4.4E+01 | +9.8E-01 | 2.5E-01 | 6.2E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:25.952 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1525 and h = 2.26931e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.952 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1525: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) 58| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.4E-07 | 3.7E-02 | 6.2E-07 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 14| +1.69E+02 | -8.1E+00 | +8.9E-01 | 3.0E-01 | 1.1E+02 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 6| +2.42E+02 | +3.1E+02 | -1.0E+01 | 2.0E+00 | 2.7E+01 | 3.9E+00 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 23| +1.68E+02 | -1.5E+01 | +9.7E-01 | 2.5E-01 | 5.6E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 3| +3.17E+02 | -6.8E+01 | +8.3E-01 | 5.0E-01 | 5.8E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 59| +1.48E+02 | +2.8E-08 | -5.6E+00 | 8.5E-08 | 3.7E-02 | 1.5E-07 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 101| +1.38E+02 | -3.3E-04 | +9.9E-01 | 2.7E-02 | 1.3E-01 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 7| +2.13E+02 | -2.9E+01 | +1.5E+00 | 5.0E-01 | 2.7E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 15| +1.63E+02 | -5.7E+00 | +4.1E-01 | 6.1E-01 | 5.5E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 3| +4.98E+06 | -1.7E+07 | +9.0E-01 | 5.0E-01 | 9.1E+07 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 24| +1.55E+02 | -1.3E+01 | +6.9E-01 | 5.0E-01 | 7.3E+01 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 4| +2.26E+02 | -9.2E+01 | +8.5E-01 | 1.0E+00 | 8.9E+01 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 60| +1.48E+02 | +3.1E-06 | -2.4E+03 | 2.1E-08 | 3.7E-02 | 3.9E-08 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 16| +1.58E+02 | -5.4E+00 | +9.5E-01 | 6.1E-01 | 1.7E+02 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 8| +1.99E+02 | -1.5E+01 | +1.2E+00 | 1.0E+00 | 5.0E+01 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 102| +1.38E+02 | -6.2E-04 | +9.6E-01 | 5.3E-02 | 7.0E-02 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 25| +1.53E+02 | -2.5E+00 | +2.0E-01 | 5.0E-01 | 5.7E+01 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 61| +1.48E+02 | +1.3E-06 | -4.1E+03 | 5.3E-09 | 3.7E-02 | 9.7E-09 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 4| +8.59E+05 | -4.1E+06 | +9.0E-01 | 1.0E+00 | 1.6E+07 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 17| +1.58E+02 | +1.8E+01 | -3.5E+00 | 1.2E+00 | 3.4E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 5| +2.26E+02 | +5.5E+02 | -2.1E+01 | 2.0E+00 | 2.7E+01 | 5.1E+00 | trr |0\n", - "2023-02-17 16:05:26 fides(INFO) 103| +1.38E+02 | +2.9E-03 | -2.1E+00 | 1.1E-01 | 8.4E-02 | 3.3E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 26| +1.48E+02 | -4.2E+00 | +6.1E-01 | 1.1E-01 | 6.8E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 46| +1.51E+02 | -1.3E-07 | +8.7E-01 | 1.5E-08 | 4.5E+00 | 3.4E-08 | 2d |1\n", - "2023-02-17 16:05:26 fides(WARNING) Stopping as function difference 1.29E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:26 fides(INFO) 18| +1.55E+02 | -3.1E+00 | +8.8E-01 | 2.1E-01 | 3.4E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 9| +1.99E+02 | +5.3E+04 | -1.3E+03 | 2.0E+00 | 5.0E+01 | 4.7E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 62| +1.48E+02 | +5.8E-07 | -7.3E+03 | 1.3E-09 | 3.7E-02 | 2.4E-09 | 2d |0\n", - "2023-02-17 16:05:26.114 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 201.089 and h = 7.23664e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:26.115 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 201.089: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:26 fides(INFO) 27| +1.48E+02 | +1.3E+00 | -9.5E-01 | 1.1E-01 | 3.1E+01 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 6| +2.26E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 2.7E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 5| +1.43E+05 | -7.2E+05 | +9.0E-01 | 2.0E+00 | 2.8E+06 | 2.3E+00 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 63| +1.48E+02 | +2.9E-06 | -1.5E+05 | 3.3E-10 | 3.7E-02 | 6.0E-10 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 104| +1.38E+02 | -1.3E-04 | +3.0E-01 | 2.7E-02 | 8.4E-02 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 19| +1.50E+02 | -4.7E+00 | +1.1E+00 | 4.3E-01 | 6.0E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 10| +1.61E+02 | -3.7E+01 | +1.1E+00 | 5.0E-01 | 5.0E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 7| +2.21E+02 | -4.9E+00 | +8.1E-01 | 1.2E-01 | 2.7E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 28| +1.48E+02 | -4.9E-01 | +6.8E-01 | 2.8E-02 | 3.1E+01 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 64| +1.48E+02 | +2.7E-06 | -5.3E+05 | 8.3E-11 | 3.7E-02 | 1.5E-10 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 20| +1.50E+02 | +4.7E-01 | -2.8E-01 | 8.5E-01 | 1.5E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 8| +2.08E+02 | -1.2E+01 | +1.4E+00 | 2.5E-01 | 2.0E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 105| +1.38E+02 | +4.1E-03 | -2.1E+00 | 2.7E-02 | 2.1E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 11| +1.56E+02 | -4.6E+00 | +9.5E-01 | 1.0E+00 | 9.1E+01 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 29| +1.48E+02 | -2.1E-01 | +9.2E-01 | 2.8E-02 | 2.5E+01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 6| +2.37E+04 | -1.2E+05 | +9.0E-01 | 2.0E+00 | 4.7E+05 | 3.6E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 65| +1.48E+02 | +2.7E-06 | -2.2E+06 | 2.1E-11 | 3.7E-02 | 3.8E-11 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 21| +1.49E+02 | -1.1E+00 | +8.3E-01 | 1.3E-01 | 1.5E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 9| +2.08E+02 | -1.5E+00 | -1.2E-01 | 5.0E-01 | 3.3E+01 | 8.1E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 0| +1.12E+14 | NaN | NaN | 1.0E+00 | 5.2E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:26 fides(INFO) 12| +1.56E+02 | +7.5E+01 | -7.0E+00 | 2.0E+00 | 8.2E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 106| +1.38E+02 | +6.0E-04 | -7.9E-01 | 6.7E-03 | 2.1E-01 | 5.7E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 30| +1.48E+02 | -8.9E-02 | +8.5E-01 | 5.7E-02 | 1.0E+01 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 66| +1.48E+02 | +2.9E-06 | -9.2E+06 | 5.2E-12 | 3.7E-02 | 9.4E-12 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 10| +2.02E+02 | -6.1E+00 | +9.3E-01 | 1.2E-01 | 3.3E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 1| +8.83E+08 | -1.1E+14 | +8.3E-02 | 1.0E+00 | 5.2E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 22| +1.49E+02 | +2.3E+01 | -9.5E+00 | 2.6E-01 | 1.0E+01 | 4.8E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 13| +1.51E+02 | -5.3E+00 | +7.2E-01 | 1.9E-01 | 8.2E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 31| +1.48E+02 | +4.5E-01 | -5.9E+00 | 1.1E-01 | 6.9E+00 | 3.2E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 107| +1.38E+02 | +6.3E-05 | -1.6E-01 | 1.7E-03 | 2.1E-01 | 3.1E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 67| +1.48E+02 | +2.1E-06 | -2.7E+07 | 1.3E-12 | 3.7E-02 | 2.4E-12 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 11| +1.93E+02 | -9.1E+00 | +9.1E-01 | 2.5E-01 | 4.4E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:26.296 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 93.9924 and h = 1.89086e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:26.296 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 93.9924: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:26 fides(INFO) 23| +1.49E+02 | +1.5E-02 | -4.4E-02 | 6.5E-02 | 1.0E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 14| +1.51E+02 | +0.0E+00 | +0.0E+00 | 1.9E-01 | 9.0E+01 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 32| +1.48E+02 | +4.8E-04 | -1.8E-02 | 2.8E-02 | 6.9E+00 | 7.9E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 108| +1.38E+02 | -1.1E-04 | +7.5E-01 | 4.2E-04 | 2.1E-01 | 8.8E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 68| +1.48E+02 | +2.5E-06 | -1.3E+08 | 3.2E-13 | 3.7E-02 | 5.9E-13 | 2d |0\n", - "2023-02-17 16:05:26.333 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 149.627 and h = 2.79486e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:26.333 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 149.627: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:26 fides(INFO) 24| +1.49E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 1.0E+01 | 3.1E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 15| +1.50E+02 | -1.4E+00 | +9.4E-01 | 4.7E-02 | 9.0E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 12| +1.81E+02 | -1.2E+01 | +8.2E-01 | 5.0E-01 | 5.6E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 2| +1.73E+08 | -7.1E+08 | +8.9E-01 | 2.5E-01 | 3.8E+09 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 7| +3.98E+03 | -2.0E+04 | +9.1E-01 | 4.0E+00 | 7.7E+04 | 1.9E+00 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 33| +1.48E+02 | -1.0E-02 | +8.0E-01 | 7.1E-03 | 6.9E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 69| +1.48E+02 | +2.7E-06 | -5.5E+08 | 8.1E-14 | 3.7E-02 | 1.5E-13 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 25| +1.49E+02 | -5.8E-02 | +9.4E-01 | 4.1E-03 | 1.0E+01 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 109| +1.38E+02 | -2.5E-05 | +9.9E-01 | 4.2E-04 | 4.1E-01 | 1.6E-04 | 2d |1\n", - "2023-02-17 16:05:26.373 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 110.183 and h = 2.72528e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:26 fides(INFO) 16| +1.49E+02 | -7.5E-01 | +6.9E-01 | 9.4E-02 | 4.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:26.374 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 110.183: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:26 fides(INFO) 13| +1.81E+02 | +0.0E+00 | +0.0E+00 | 1.0E+00 | 7.6E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 34| +1.48E+02 | -6.1E-03 | +9.6E-01 | 1.4E-02 | 3.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 3| +3.61E+07 | -1.4E+08 | +9.1E-01 | 5.0E-01 | 6.1E+08 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 70| +1.48E+02 | +2.6E-06 | -2.2E+09 | 2.0E-14 | 3.7E-02 | 3.7E-14 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 26| +1.49E+02 | -9.6E-02 | +9.2E-01 | 8.1E-03 | 9.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 110| +1.38E+02 | +1.9E-05 | -1.5E-01 | 8.3E-04 | 1.2E-01 | 1.8E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 8| +3.98E+03 | +2.9E+07 | -4.3E+04 | 4.0E+00 | 1.2E+04 | 4.0E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 17| +1.48E+02 | -6.0E-01 | +9.6E-01 | 9.4E-02 | 5.3E+01 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 14| +1.76E+02 | -5.2E+00 | +7.9E-01 | 2.5E-01 | 7.6E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 35| +1.48E+02 | -9.4E-03 | +8.1E-01 | 2.8E-02 | 3.3E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 71| +1.48E+02 | +1.6E-06 | -5.3E+09 | 5.0E-15 | 3.7E-02 | 9.2E-15 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 4| +5.59E+06 | -3.1E+07 | +8.9E-01 | 1.0E+00 | 1.2E+08 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 27| +1.49E+02 | -1.1E-01 | +6.8E-01 | 1.6E-02 | 9.8E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 18| +1.48E+02 | -4.1E-01 | +6.9E-01 | 1.9E-01 | 3.0E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 15| +1.73E+02 | -2.9E+00 | +8.6E-01 | 5.0E-01 | 5.1E+01 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 111| +1.38E+02 | -3.0E-05 | +7.7E-01 | 2.1E-04 | 1.2E-01 | 4.6E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 36| +1.48E+02 | +2.9E-02 | -4.5E+00 | 5.7E-02 | 5.5E-01 | 7.5E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 72| +1.48E+02 | +8.8E-07 | -1.2E+10 | 1.3E-15 | 3.7E-02 | 2.3E-15 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 28| +1.49E+02 | -9.7E-02 | +9.9E-01 | 1.6E-02 | 6.3E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 5| +8.13E+05 | -4.8E+06 | +9.0E-01 | 2.0E+00 | 1.8E+07 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 19| +1.48E+02 | +2.1E-01 | -3.7E-01 | 1.9E-01 | 3.0E+01 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 37| +1.48E+02 | +2.2E-04 | -1.1E-01 | 1.4E-02 | 5.5E-01 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 73| +1.48E+02 | +2.8E-06 | -1.4E+11 | 3.2E-16 | 3.7E-02 | 5.8E-16 | 2d |0\n", - "2023-02-17 16:05:26 fides(WARNING) Stopping as trust region radius 7.88E-17 is smaller than machine precision.\n", - "2023-02-17 16:05:26 fides(INFO) 16| +1.72E+02 | -9.8E-01 | +8.3E-01 | 1.0E+00 | 2.6E+01 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 112| +1.38E+02 | -9.3E-06 | +1.0E+00 | 4.2E-04 | 1.9E-01 | 1.3E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 20| +1.48E+02 | -1.8E-01 | +7.7E-01 | 4.7E-02 | 3.0E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 29| +1.48E+02 | -1.4E-01 | +9.6E-01 | 3.2E-02 | 1.2E+01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 38| +1.48E+02 | -4.4E-04 | +7.1E-01 | 3.5E-03 | 5.5E-01 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 6| +1.19E+05 | -6.9E+05 | +9.0E-01 | 2.0E+00 | 2.8E+06 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 113| +1.38E+02 | -1.2E-05 | +9.3E-01 | 8.3E-04 | 7.9E-02 | 2.6E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 30| +1.48E+02 | -2.6E-01 | +9.8E-01 | 6.5E-02 | 5.8E+00 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 21| +1.48E+02 | -1.0E-01 | +8.8E-01 | 9.4E-02 | 1.6E+01 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 17| +1.71E+02 | -6.7E-01 | +7.0E-01 | 2.0E+00 | 1.5E+01 | 1.0E+00 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 39| +1.48E+02 | +7.8E-05 | -1.8E-01 | 3.5E-03 | 5.8E-01 | 9.0E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 9| +8.88E+02 | -3.1E+03 | +8.6E-01 | 7.6E-01 | 1.2E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 7| +1.79E+04 | -1.0E+05 | +9.0E-01 | 2.0E+00 | 4.3E+05 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 22| +1.48E+02 | -7.5E-02 | +7.7E-01 | 1.9E-01 | 1.6E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 31| +1.48E+02 | -2.5E-01 | +7.2E-01 | 1.3E-01 | 1.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 114| +1.38E+02 | -2.0E-05 | +9.2E-01 | 1.7E-03 | 1.1E-01 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 40| +1.48E+02 | -1.1E-04 | +7.4E-01 | 8.8E-04 | 5.8E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 18| +1.71E+02 | -8.7E-02 | +4.2E-01 | 2.0E+00 | 2.1E+01 | 2.5E-01 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 23| +1.48E+02 | +1.0E+00 | -5.4E+00 | 3.8E-01 | 7.1E+00 | 3.7E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 8| +2.80E+03 | -1.5E+04 | +9.0E-01 | 2.0E+00 | 6.7E+04 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 32| +1.48E+02 | +5.2E-01 | -1.0E+00 | 1.3E-01 | 5.8E+00 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 0| +7.69E+12 | NaN | NaN | 1.0E+00 | 3.5E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:26 fides(INFO) 115| +1.38E+02 | -3.9E-05 | +1.0E+00 | 3.3E-03 | 7.0E-02 | 9.6E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 41| +1.48E+02 | -2.5E-05 | +7.3E-01 | 8.8E-04 | 2.7E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 24| +1.48E+02 | -9.4E-03 | +8.2E-02 | 9.4E-02 | 7.1E+00 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 19| +1.71E+02 | -2.1E-03 | +1.3E-02 | 2.0E+00 | 6.5E+00 | 4.3E-01 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 33| +1.48E+02 | -9.3E-02 | +3.4E-01 | 3.2E-02 | 5.8E+00 | 6.9E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 9| +5.59E+02 | -2.2E+03 | +9.1E-01 | 2.0E+00 | 1.1E+04 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 42| +1.48E+02 | -1.5E-05 | +1.1E+00 | 8.8E-04 | 2.2E-01 | 3.7E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 116| +1.38E+02 | -7.0E-05 | +9.8E-01 | 6.7E-03 | 7.5E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 25| +1.48E+02 | -3.0E-02 | +8.9E-01 | 2.4E-02 | 8.6E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 34| +1.48E+02 | -9.3E-02 | +5.2E-01 | 3.2E-02 | 6.4E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 1| +8.84E+10 | -7.6E+12 | +8.6E-02 | 1.0E+00 | 3.5E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 10| +2.39E+02 | -3.2E+02 | +9.2E-01 | 2.0E+00 | 1.7E+03 | 5.7E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 20| +1.71E+02 | -5.0E-02 | +6.7E-01 | 1.5E-01 | 7.6E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 43| +1.48E+02 | -1.3E-05 | +8.4E-01 | 1.8E-03 | 1.8E-01 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 26| +1.48E+02 | -1.0E-02 | +2.2E-01 | 4.7E-02 | 3.3E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 35| +1.48E+02 | -4.0E-02 | +9.7E-01 | 3.2E-02 | 5.5E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 117| +1.38E+02 | -1.4E-04 | +9.9E-01 | 1.3E-02 | 6.5E-02 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 44| +1.48E+02 | -8.8E-06 | +6.8E-01 | 3.5E-03 | 1.4E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 21| +1.71E+02 | -5.9E-03 | +8.5E-01 | 1.5E-01 | 4.6E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 11| +2.39E+02 | +7.4E+01 | -1.7E+00 | 4.0E+00 | 2.7E+02 | 8.5E+00 | trr |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 10| +2.85E+02 | -6.0E+02 | +9.2E-01 | 1.5E+00 | 2.6E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 36| +1.48E+02 | -6.2E-02 | +9.5E-01 | 6.5E-02 | 2.1E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 2| +1.32E+10 | -7.5E+10 | +8.9E-01 | 2.5E-01 | 4.1E+11 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 27| +1.48E+02 | -1.3E-02 | +9.3E-01 | 1.2E-02 | 1.0E+01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 118| +1.38E+02 | -2.7E-04 | +1.0E+00 | 2.7E-02 | 6.5E-02 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 45| +1.48E+02 | +2.4E-04 | -4.5E+00 | 3.5E-03 | 5.8E-02 | 6.0E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 22| +1.71E+02 | -3.4E-05 | +7.4E-02 | 2.9E-01 | 3.5E-01 | 1.5E-02 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 12| +1.96E+02 | -4.3E+01 | +9.1E-01 | 7.1E-01 | 2.7E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:26.755 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.898 and h = 2.79382e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:26 fides(INFO) 37| +1.48E+02 | -6.4E-02 | +7.6E-01 | 1.3E-01 | 3.7E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:26.755 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.898: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:26 fides(INFO) 28| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.4E-02 | 9.6E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 3| +1.99E+09 | -1.1E+10 | +8.9E-01 | 5.0E-01 | 6.1E+10 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 46| +1.48E+02 | +2.3E-05 | -9.3E-01 | 8.8E-04 | 5.8E-02 | 1.5E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 13| +1.96E+02 | +2.8E+01 | -2.9E+00 | 1.4E+00 | 6.6E+01 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 119| +1.38E+02 | -4.7E-04 | +9.0E-01 | 5.3E-02 | 6.5E-02 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 4| +3.02E+08 | -1.7E+09 | +8.9E-01 | 5.0E-01 | 9.2E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 29| +1.48E+02 | -6.8E-04 | +8.6E-01 | 5.9E-03 | 9.6E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 38| +1.48E+02 | +7.6E-01 | -5.7E+00 | 2.6E-01 | 2.5E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 23| +1.71E+02 | -3.2E-06 | +7.1E-03 | 2.9E-02 | 4.0E-01 | 3.5E-03 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 47| +1.48E+02 | -3.7E-06 | +2.9E-01 | 2.2E-04 | 5.8E-02 | 4.0E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 5| +4.59E+07 | -2.6E+08 | +8.9E-01 | 5.0E-01 | 1.4E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 14| +1.84E+02 | -1.2E+01 | +1.0E+00 | 3.5E-01 | 6.6E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 30| +1.48E+02 | -5.9E-04 | +8.9E-01 | 1.2E-02 | 1.6E+00 | 6.7E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 120| +1.38E+02 | +1.4E-02 | -6.7E+00 | 1.1E-01 | 1.0E-01 | 3.3E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 11| +2.85E+02 | +3.6E+02 | -7.6E+00 | 1.5E+00 | 3.1E+02 | 2.2E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 39| +1.48E+02 | +4.4E-03 | -7.9E-02 | 5.5E-02 | 2.5E+00 | 4.2E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 24| +1.71E+02 | +1.4E-06 | -3.8E-03 | 2.8E-03 | 3.8E-01 | 3.8E-03 | trr |0\n", - "2023-02-17 16:05:26 fides(INFO) 48| +1.48E+02 | +2.7E-07 | -1.8E-01 | 2.2E-04 | 8.8E-02 | 1.5E-04 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 15| +1.83E+02 | -1.2E+00 | +1.6E-01 | 7.1E-01 | 1.9E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 6| +7.05E+06 | -3.9E+07 | +8.9E-01 | 5.0E-01 | 2.1E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 31| +1.48E+02 | -4.6E-04 | +8.6E-01 | 2.4E-02 | 6.7E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 40| +1.48E+02 | -1.3E-02 | +5.9E-01 | 1.4E-02 | 2.5E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 121| +1.38E+02 | +4.9E-04 | -8.2E-01 | 2.7E-02 | 1.0E-01 | 8.3E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 49| +1.48E+02 | -5.0E-06 | +4.4E+00 | 5.5E-05 | 8.8E-02 | 4.3E-05 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 16| +1.79E+02 | -3.4E+00 | +8.2E-01 | 1.8E-01 | 5.0E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 25| +1.71E+02 | -1.7E-04 | +7.8E-01 | 4.6E-04 | 3.8E-01 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 7| +1.09E+06 | -6.0E+06 | +9.0E-01 | 1.0E+00 | 3.2E+07 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 32| +1.48E+02 | -3.8E-04 | +9.3E-01 | 4.7E-02 | 1.1E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 41| +1.48E+02 | -8.9E-03 | +9.5E-01 | 1.4E-02 | 6.2E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 50| +1.48E+02 | +5.8E-06 | -1.8E+01 | 1.1E-04 | 2.2E-02 | 6.4E-05 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 17| +1.71E+02 | -8.0E+00 | +1.6E+00 | 3.5E-01 | 1.5E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 33| +1.48E+02 | -4.0E-04 | +8.7E-01 | 9.4E-02 | 4.7E-01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 8| +1.70E+05 | -9.2E+05 | +9.0E-01 | 1.0E+00 | 5.0E+06 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 26| +1.71E+02 | -3.4E-05 | +4.9E-01 | 9.3E-04 | 2.3E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 122| +1.38E+02 | -7.0E-05 | +4.2E-01 | 6.7E-03 | 1.0E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 42| +1.48E+02 | -9.9E-03 | +9.7E-01 | 2.7E-02 | 9.7E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 51| +1.48E+02 | +2.8E-06 | -1.4E+01 | 2.8E-05 | 2.2E-02 | 2.2E-05 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 18| +1.71E+02 | +3.0E+02 | -2.0E+01 | 7.1E-01 | 2.1E+01 | 1.6E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 34| +1.48E+02 | +7.5E-04 | -1.0E+00 | 1.9E-01 | 6.7E-01 | 8.2E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 9| +2.68E+04 | -1.4E+05 | +9.0E-01 | 1.0E+00 | 7.8E+05 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 43| +1.48E+02 | -1.2E-02 | +7.9E-01 | 5.5E-02 | 2.6E+00 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 52| +1.48E+02 | +3.9E-06 | -2.4E+01 | 6.9E-06 | 2.2E-02 | 1.5E-05 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 19| +1.62E+02 | -9.5E+00 | +1.2E+00 | 1.8E-01 | 2.1E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 123| +1.38E+02 | -8.0E-05 | +7.7E-01 | 6.7E-03 | 3.9E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 12| +2.47E+02 | -3.8E+01 | +7.3E-01 | 2.0E-01 | 3.1E+02 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 27| +1.71E+02 | -7.2E-07 | +3.4E-02 | 9.3E-04 | 7.9E-02 | 7.6E-04 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 10| +4.40E+03 | -2.2E+04 | +9.0E-01 | 1.0E+00 | 1.2E+05 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 35| +1.48E+02 | -1.6E-04 | +6.1E-01 | 4.7E-02 | 6.7E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 44| +1.48E+02 | +3.3E-02 | -1.1E+00 | 1.1E-01 | 1.1E+00 | 6.7E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 20| +1.53E+02 | -8.3E+00 | +5.2E-01 | 3.5E-01 | 3.1E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 53| +1.48E+02 | +1.4E-06 | -1.6E+01 | 1.7E-06 | 2.2E-02 | 4.9E-06 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 124| +1.38E+02 | -1.1E-04 | +9.2E-01 | 1.3E-02 | 6.8E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 11| +8.75E+02 | -3.5E+03 | +9.1E-01 | 1.0E+00 | 1.9E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 28| +1.71E+02 | -9.9E-06 | +6.4E-01 | 1.4E-04 | 7.1E-02 | 3.4E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 36| +1.48E+02 | -8.3E-05 | +4.2E-01 | 4.7E-02 | 2.9E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 45| +1.48E+02 | -3.6E-03 | +3.1E-01 | 2.7E-02 | 1.1E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 12| +3.28E+02 | -5.5E+02 | +9.1E-01 | 1.0E+00 | 3.0E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 54| +1.48E+02 | +4.5E-06 | -1.7E+02 | 4.3E-07 | 2.2E-02 | 1.2E-06 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 21| +1.51E+02 | -2.4E+00 | +9.6E-01 | 3.5E-01 | 1.2E+02 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 125| +1.38E+02 | -1.1E-04 | +3.9E-01 | 2.7E-02 | 7.3E-02 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 37| +1.48E+02 | -7.3E-05 | +3.1E-01 | 4.7E-02 | 5.2E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 13| +2.49E+02 | -7.9E+01 | +9.0E-01 | 2.0E+00 | 4.7E+02 | 1.3E+00 | trr |1\n", - "2023-02-17 16:05:27 fides(INFO) 29| +1.71E+02 | -8.2E-07 | +8.0E+00 | 1.4E-04 | 2.1E-02 | 2.3E-05 | trr |1\n", - "2023-02-17 16:05:27 fides(WARNING) Stopping as function difference 8.24E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:27 fides(INFO) 46| +1.48E+02 | -6.3E-03 | +4.6E-01 | 2.7E-02 | 3.9E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 55| +1.48E+02 | +7.1E-07 | -1.1E+02 | 1.1E-07 | 2.2E-02 | 3.1E-07 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 22| +1.51E+02 | +4.0E-01 | -2.8E-01 | 7.1E-01 | 6.6E+01 | 8.1E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 38| +1.48E+02 | +4.8E-05 | -1.8E-01 | 4.7E-02 | 2.2E-01 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 126| +1.38E+02 | +1.9E-03 | -1.4E+00 | 2.7E-02 | 2.1E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 14| +2.29E+02 | -1.9E+01 | +1.5E+00 | 2.0E+00 | 5.4E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 47| +1.48E+02 | -2.5E-03 | +3.4E-01 | 2.7E-02 | 9.6E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 56| +1.48E+02 | +3.7E-06 | -2.2E+03 | 2.7E-08 | 2.2E-02 | 7.7E-08 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 23| +1.49E+02 | -1.6E+00 | +7.3E-01 | 1.8E-01 | 6.6E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 39| +1.48E+02 | -5.2E-05 | +4.6E-01 | 1.2E-02 | 2.2E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 15| +2.29E+02 | +6.1E+02 | -3.0E+01 | 2.0E+00 | 3.3E+01 | 3.5E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 13| +2.19E+02 | -2.8E+01 | +8.4E-01 | 2.0E-01 | 3.5E+02 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 127| +1.38E+02 | -1.5E-04 | +4.1E-01 | 6.7E-03 | 2.1E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 24| +1.48E+02 | -9.6E-01 | +9.8E-01 | 1.8E-01 | 6.9E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 48| +1.48E+02 | -3.0E-03 | +3.5E-01 | 2.7E-02 | 2.3E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 16| +2.08E+02 | -2.2E+01 | +7.3E-01 | 5.0E-01 | 3.3E+01 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 40| +1.48E+02 | -4.7E-05 | +8.9E-01 | 1.2E-02 | 6.1E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 57| +1.48E+02 | +4.0E-06 | -9.6E+03 | 6.7E-09 | 2.2E-02 | 1.9E-08 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 49| +1.48E+02 | +1.4E-03 | -1.9E-01 | 2.7E-02 | 8.8E-01 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 25| +1.48E+02 | -6.1E-01 | +8.2E-01 | 3.5E-01 | 2.9E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 0| +2.51E+02 | NaN | NaN | 1.0E+00 | 2.5E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:27 fides(INFO) 128| +1.38E+02 | -6.6E-05 | +6.4E-01 | 6.7E-03 | 1.8E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:27.170 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0041 and h = 1.08836e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:27 fides(INFO) 17| +1.74E+02 | -3.4E+01 | +1.1E+00 | 5.0E-01 | 9.3E+01 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:27.171 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0041: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:27 fides(INFO) 41| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.4E-02 | 8.5E-02 | 7.8E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 58| +1.48E+02 | +3.8E-06 | -3.6E+04 | 1.7E-09 | 2.2E-02 | 4.8E-09 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 50| +1.48E+02 | -1.3E-03 | +4.4E-01 | 6.8E-03 | 8.8E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 26| +1.48E+02 | +4.7E+01 | -3.5E+01 | 7.1E-01 | 2.0E+01 | 1.7E+00 | trr |0\n", - "2023-02-17 16:05:27 fides(INFO) 42| +1.48E+02 | -1.7E-05 | +1.0E+00 | 5.9E-03 | 8.5E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 1| +2.51E+02 | +2.0E+04 | -8.4E+02 | 1.0E+00 | 2.5E+01 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 129| +1.38E+02 | -5.4E-05 | +8.4E-01 | 6.7E-03 | 7.3E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 59| +1.48E+02 | +5.9E-06 | -2.3E+05 | 4.2E-10 | 2.2E-02 | 1.2E-09 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 18| +1.74E+02 | +6.4E+03 | -2.0E+02 | 1.0E+00 | 6.8E+01 | 2.0E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 51| +1.48E+02 | -1.3E-03 | +9.3E-01 | 6.8E-03 | 2.7E+00 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 27| +1.48E+02 | +1.3E+00 | -2.3E+00 | 1.8E-01 | 2.0E+01 | 4.2E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 43| +1.48E+02 | -1.3E-05 | +9.4E-01 | 1.2E-02 | 2.3E-01 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 2| +2.51E+02 | +2.4E+01 | -1.9E+00 | 2.5E-01 | 2.5E+01 | 6.1E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 19| +1.56E+02 | -1.7E+01 | +8.1E-01 | 2.5E-01 | 6.8E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 130| +1.38E+02 | -1.0E-04 | +9.2E-01 | 1.3E-02 | 7.2E-02 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 28| +1.48E+02 | -9.1E-02 | +4.6E-01 | 4.4E-02 | 2.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 52| +1.48E+02 | -8.8E-04 | +9.8E-01 | 1.4E-02 | 2.9E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 14| +2.17E+02 | -2.3E+00 | +5.5E-01 | 4.1E-01 | 1.8E+02 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 60| +1.48E+02 | +2.3E-06 | -3.4E+05 | 1.1E-10 | 2.2E-02 | 3.0E-10 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 44| +1.48E+02 | -1.3E-05 | +8.1E-01 | 2.4E-02 | 4.7E-02 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 131| +1.38E+02 | -8.7E-05 | +3.3E-01 | 2.7E-02 | 6.9E-02 | 6.1E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 20| +1.53E+02 | -3.2E+00 | +9.5E-01 | 5.0E-01 | 1.1E+02 | 1.6E+00 | trr |1\n", - "2023-02-17 16:05:27 fides(INFO) 53| +1.48E+02 | -1.4E-03 | +9.6E-01 | 2.7E-02 | 4.2E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 29| +1.48E+02 | -4.4E-02 | +4.4E-01 | 4.4E-02 | 8.9E+00 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 3| +2.49E+02 | -2.2E+00 | +6.1E-01 | 6.2E-02 | 2.5E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 61| +1.48E+02 | +3.9E-06 | -2.4E+06 | 2.6E-11 | 2.2E-02 | 7.5E-11 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 45| +1.48E+02 | -1.6E-05 | +5.7E-01 | 4.7E-02 | 1.4E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 54| +1.48E+02 | -1.7E-03 | +9.2E-01 | 5.5E-02 | 2.8E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 21| +1.53E+02 | +7.2E-01 | -2.2E-01 | 1.0E+00 | 5.8E+01 | 7.4E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 30| +1.48E+02 | -2.2E-02 | +5.1E-01 | 4.4E-02 | 5.5E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 132| +1.38E+02 | +1.2E-02 | -5.8E+00 | 2.7E-02 | 1.7E-01 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 4| +2.49E+02 | -1.8E-01 | +6.1E-01 | 6.2E-02 | 5.3E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 62| +1.48E+02 | +4.5E-06 | -1.1E+07 | 6.6E-12 | 2.2E-02 | 1.9E-11 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 46| +1.48E+02 | +1.8E-05 | -4.3E-01 | 4.7E-02 | 6.3E-02 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 15| +2.16E+02 | -6.6E-01 | +7.3E-01 | 4.1E-01 | 1.6E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 22| +1.50E+02 | -2.6E+00 | +7.4E-01 | 2.5E-01 | 5.8E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 55| +1.48E+02 | +6.0E-03 | -4.5E+00 | 1.1E-01 | 1.3E-01 | 8.0E-02 | trr |0\n", - "2023-02-17 16:05:27 fides(INFO) 31| +1.48E+02 | -4.7E-03 | +7.7E-01 | 4.4E-02 | 2.8E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 63| +1.48E+02 | +4.3E-06 | -4.2E+07 | 1.6E-12 | 2.2E-02 | 4.7E-12 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 133| +1.38E+02 | +3.1E-03 | -3.3E+00 | 6.7E-03 | 1.7E-01 | 9.3E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 47| +1.48E+02 | -4.0E-06 | +2.3E-01 | 1.2E-02 | 6.3E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 5| +2.49E+02 | -2.8E-01 | +7.8E-01 | 6.2E-02 | 5.7E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 56| +1.48E+02 | -1.2E-04 | +2.0E-01 | 2.7E-02 | 1.3E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 23| +1.49E+02 | -1.4E+00 | +9.5E-01 | 2.5E-01 | 8.6E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 64| +1.48E+02 | +4.0E-06 | -1.6E+08 | 4.1E-13 | 2.2E-02 | 1.2E-12 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 32| +1.48E+02 | -2.6E-03 | +4.8E-01 | 8.9E-02 | 2.2E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 134| +1.38E+02 | +1.1E-04 | -2.6E-01 | 1.7E-03 | 1.7E-01 | 3.3E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 48| +1.48E+02 | -6.0E-06 | +7.3E-01 | 2.9E-03 | 2.5E-01 | 8.3E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 24| +1.48E+02 | -6.4E-01 | +9.9E-01 | 5.0E-01 | 3.4E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 65| +1.48E+02 | +4.5E-06 | -6.9E+08 | 1.0E-13 | 2.2E-02 | 2.9E-13 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 16| +2.16E+02 | -5.6E-02 | -4.4E-01 | 4.1E-01 | 6.3E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 57| +1.48E+02 | -2.0E-04 | +2.5E-01 | 6.8E-03 | 3.9E-01 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 6| +2.48E+02 | -6.8E-01 | +1.1E+00 | 1.2E-01 | 5.1E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 33| +1.48E+02 | +1.3E-02 | -1.0E+00 | 8.9E-02 | 1.0E+00 | 9.4E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 49| +1.48E+02 | -3.0E-06 | +3.2E+00 | 2.9E-03 | 1.8E-02 | 8.2E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 58| +1.48E+02 | -1.6E-04 | +8.9E-01 | 1.7E-03 | 1.1E+00 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 25| +1.48E+02 | -4.2E-01 | +6.1E-01 | 1.0E+00 | 3.0E+01 | 9.1E-01 | trr |1\n", - "2023-02-17 16:05:27 fides(INFO) 135| +1.38E+02 | -8.8E-05 | +7.3E-01 | 4.2E-04 | 1.7E-01 | 8.4E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 66| +1.48E+02 | +4.6E-06 | -2.9E+09 | 2.6E-14 | 2.2E-02 | 7.3E-14 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 34| +1.48E+02 | -2.0E-03 | +4.5E-01 | 2.2E-02 | 1.0E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 7| +2.45E+02 | -3.4E+00 | +1.6E+00 | 2.5E-01 | 8.1E+00 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 50| +1.48E+02 | +2.1E-07 | -1.4E-01 | 5.9E-03 | 1.7E-02 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 59| +1.48E+02 | -5.6E-05 | +8.7E-01 | 3.4E-03 | 1.0E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 26| +1.48E+02 | +1.1E+01 | -1.8E+01 | 1.0E+00 | 1.1E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 17| +2.16E+02 | -3.1E-01 | +1.0E+00 | 9.4E-02 | 6.3E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 67| +1.48E+02 | +4.1E-06 | -1.0E+10 | 6.4E-15 | 2.2E-02 | 1.8E-14 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 136| +1.38E+02 | -1.0E-05 | +9.1E-01 | 4.2E-04 | 1.9E-01 | 1.3E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 35| +1.48E+02 | -9.6E-04 | +8.6E-01 | 2.2E-02 | 2.5E+00 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 51| +1.48E+02 | +5.1E-07 | -1.2E+00 | 1.5E-03 | 1.7E-02 | 4.0E-04 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 60| +1.48E+02 | -5.0E-05 | +8.3E-01 | 6.8E-03 | 1.2E-01 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 27| +1.48E+02 | -9.3E-02 | +2.3E-01 | 1.5E-01 | 1.1E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 68| +1.48E+02 | +4.0E-06 | -4.0E+10 | 1.6E-15 | 2.2E-02 | 4.6E-15 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 36| +1.48E+02 | -6.1E-04 | +8.1E-01 | 4.4E-02 | 4.8E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 8| +2.19E+02 | -2.6E+01 | +2.2E+00 | 5.0E-01 | 1.7E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 137| +1.38E+02 | -1.5E-05 | +8.9E-01 | 8.3E-04 | 9.0E-02 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 52| +1.48E+02 | -2.6E-07 | +1.9E+00 | 3.7E-04 | 1.7E-02 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(WARNING) Stopping as function difference 2.58E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:27 fides(INFO) 61| +1.48E+02 | -7.9E-05 | +9.1E-01 | 1.4E-02 | 8.3E-02 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 28| +1.48E+02 | -7.1E-02 | +6.8E-01 | 3.7E-02 | 1.9E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:27.544 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2248 and h = 8.49905e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:27.544 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2248: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:27 fides(INFO) 37| +1.48E+02 | +0.0E+00 | +0.0E+00 | 8.9E-02 | 1.2E+00 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 69| +1.48E+02 | +3.9E-06 | -1.5E+11 | 4.0E-16 | 2.2E-02 | 1.1E-15 | 2d |0\n", - "2023-02-17 16:05:27 fides(WARNING) Stopping as trust region radius 1.00E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:27 fides(INFO) 18| +2.16E+02 | -4.3E-02 | -8.6E-02 | 1.9E-01 | 3.2E+00 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 138| +1.38E+02 | -2.0E-05 | +9.4E-01 | 1.7E-03 | 1.7E-01 | 3.9E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 38| +1.48E+02 | -2.5E-04 | +8.3E-01 | 2.2E-02 | 1.2E+00 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 62| +1.48E+02 | +3.9E-05 | -2.8E-01 | 2.7E-02 | 1.8E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 29| +1.48E+02 | -1.9E-02 | +7.6E-01 | 3.7E-02 | 3.9E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 139| +1.38E+02 | -2.5E-05 | +8.7E-01 | 3.3E-03 | 6.6E-02 | 7.2E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 63| +1.48E+02 | -3.2E-05 | +7.2E-01 | 6.8E-03 | 1.8E-01 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 39| +1.48E+02 | -1.2E-04 | +6.4E-01 | 4.4E-02 | 2.7E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 30| +1.48E+02 | -9.9E-03 | +9.2E-01 | 7.3E-02 | 7.7E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 9| +2.19E+02 | +2.1E+02 | -9.7E+00 | 1.0E+00 | 5.0E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 19| +2.16E+02 | -3.8E-02 | +1.1E+00 | 4.7E-02 | 3.2E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 140| +1.38E+02 | -4.7E-05 | +1.0E+00 | 6.7E-03 | 8.1E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 64| +1.48E+02 | -2.2E-05 | +6.9E-01 | 6.8E-03 | 6.6E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 40| +1.48E+02 | +3.4E-05 | -1.3E-01 | 4.4E-02 | 3.9E-01 | 9.7E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 31| +1.48E+02 | +2.0E-03 | -4.7E-01 | 1.5E-01 | 1.1E+00 | 6.4E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 0| +1.95E+08 | NaN | NaN | 1.0E+00 | 9.0E+08 | NaN | NaN |1\n", - "2023-02-17 16:05:27 fides(INFO) 65| +1.48E+02 | -2.0E-05 | +5.7E-01 | 6.8E-03 | 1.9E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 41| +1.48E+02 | -6.6E-05 | +7.8E-01 | 1.1E-02 | 3.9E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 32| +1.48E+02 | -1.7E-03 | +7.5E-01 | 3.7E-02 | 1.1E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 0| +2.66E+08 | NaN | NaN | 1.0E+00 | 1.2E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:27 fides(INFO) 141| +1.38E+02 | -9.1E-05 | +9.9E-01 | 1.3E-02 | 6.1E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 10| +2.05E+02 | -1.4E+01 | +6.8E-01 | 2.5E-01 | 5.0E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 1| +4.07E+04 | -1.9E+08 | +9.8E-02 | 1.0E+00 | 9.0E+08 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 1| +2.73E+05 | -2.7E+08 | +1.0E-01 | 1.0E+00 | 1.2E+09 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 42| +1.48E+02 | -4.0E-05 | +9.3E-01 | 2.2E-02 | 1.9E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 66| +1.48E+02 | -8.2E-06 | +3.1E-01 | 6.8E-03 | 6.1E-02 | 9.2E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 20| +2.15E+02 | -8.7E-02 | +1.1E+00 | 9.4E-02 | 1.9E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 33| +1.48E+02 | -4.0E-05 | +2.0E-02 | 7.3E-02 | 2.0E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 142| +1.38E+02 | -1.8E-04 | +9.8E-01 | 2.7E-02 | 6.5E-02 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 2| +4.40E+04 | -2.3E+05 | +9.0E-01 | 2.5E-01 | 1.2E+06 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 2| +7.17E+03 | -3.4E+04 | +9.0E-01 | 2.5E-01 | 1.8E+05 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 43| +1.48E+02 | -5.0E-05 | +9.1E-01 | 4.4E-02 | 1.7E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 34| +1.48E+02 | -1.0E-03 | +3.6E-01 | 1.8E-02 | 5.7E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 67| +1.48E+02 | -1.5E-05 | +4.3E-01 | 6.8E-03 | 1.6E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 3| +7.74E+03 | -3.6E+04 | +9.0E-01 | 5.0E-01 | 2.0E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 11| +2.01E+02 | -5.0E+00 | +7.0E-01 | 2.5E-01 | 4.0E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 143| +1.38E+02 | +2.2E-04 | -6.8E-01 | 5.3E-02 | 6.5E-02 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 3| +1.60E+03 | -5.6E+03 | +9.1E-01 | 5.0E-01 | 2.9E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 21| +2.15E+02 | +4.3E-01 | -3.7E+00 | 1.9E-01 | 1.0E+01 | 4.4E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 4| +1.72E+03 | -6.0E+03 | +9.1E-01 | 1.0E+00 | 3.1E+04 | 6.8E-01 | trr |1\n", - "2023-02-17 16:05:27 fides(INFO) 44| +1.48E+02 | -5.3E-06 | +7.9E-02 | 8.9E-02 | 1.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 35| +1.48E+02 | -2.9E-04 | +5.8E-01 | 1.8E-02 | 1.2E+00 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 68| +1.48E+02 | +9.2E-06 | -2.6E-01 | 6.8E-03 | 6.7E-02 | 1.0E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 4| +1.60E+03 | +3.5E+04 | -8.3E+01 | 1.0E+00 | 5.0E+03 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 5| +4.61E+02 | -1.3E+03 | +9.0E-01 | 1.0E+00 | 5.3E+03 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 45| +1.48E+02 | +2.0E-04 | -7.1E-01 | 2.2E-02 | 1.1E-01 | 9.5E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 144| +1.38E+02 | -6.1E-05 | +5.7E-01 | 1.3E-02 | 6.5E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 36| +1.48E+02 | -9.1E-05 | +5.8E-01 | 1.8E-02 | 2.9E-01 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 69| +1.48E+02 | -4.2E-06 | +3.3E-01 | 1.7E-03 | 6.7E-02 | 3.4E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 6| +2.62E+02 | -2.0E+02 | +9.0E-01 | 2.0E+00 | 8.5E+02 | 3.9E+00 | trr |1\n", - "2023-02-17 16:05:27 fides(INFO) 46| +1.48E+02 | -3.2E-05 | +3.6E-01 | 5.5E-03 | 1.1E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 12| +1.93E+02 | -7.4E+00 | +1.1E+00 | 2.5E-01 | 3.8E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 37| +1.48E+02 | -7.0E-05 | +7.1E-01 | 1.8E-02 | 3.1E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 22| +2.15E+02 | -6.4E-02 | +1.1E+00 | 4.7E-02 | 1.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 5| +5.17E+02 | -1.1E+03 | +9.1E-01 | 2.5E-01 | 5.0E+03 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 70| +1.48E+02 | -4.4E-06 | +8.2E-01 | 1.7E-03 | 1.8E-01 | 3.6E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 7| +2.38E+02 | -2.4E+01 | +1.0E+00 | 4.0E+00 | 1.1E+02 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 145| +1.38E+02 | +7.2E-05 | -2.9E-01 | 1.3E-02 | 1.4E-01 | 3.7E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 47| +1.48E+02 | -1.6E-05 | +9.2E-01 | 5.5E-03 | 3.7E-01 | 6.5E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 6| +3.08E+02 | -2.1E+02 | +6.8E-01 | 5.0E-01 | 1.0E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 38| +1.48E+02 | -5.1E-05 | +7.0E-01 | 1.8E-02 | 2.8E-01 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 71| +1.48E+02 | -6.4E-06 | +1.8E+00 | 3.4E-03 | 2.1E-02 | 6.6E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 8| +2.38E+02 | +3.4E+01 | -6.3E+00 | 4.0E+00 | 2.4E+01 | 5.0E+00 | trr |0\n", - "2023-02-17 16:05:27 fides(INFO) 48| +1.48E+02 | -6.9E-07 | +3.5E-01 | 1.1E-02 | 3.3E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(WARNING) Stopping as function difference 6.90E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:27 fides(INFO) 146| +1.38E+02 | -4.5E-05 | +6.7E-01 | 3.3E-03 | 1.4E-01 | 9.2E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 7| +2.51E+02 | -5.7E+01 | +8.4E-01 | 5.0E-01 | 2.8E+02 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 23| +2.15E+02 | -1.2E-01 | +7.2E-01 | 9.4E-02 | 6.2E+00 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 9| +2.38E+02 | +1.6E+00 | -3.9E-01 | 6.9E-01 | 2.4E+01 | 2.0E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 39| +1.48E+02 | -4.4E-05 | +7.4E-01 | 1.8E-02 | 2.0E-01 | 2.9E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 13| +1.81E+02 | -1.2E+01 | +8.0E-01 | 5.0E-01 | 3.3E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 72| +1.48E+02 | -4.6E-06 | +7.9E-01 | 6.8E-03 | 5.6E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 8| +2.50E+02 | -1.1E+00 | +3.8E-01 | 1.0E+00 | 2.6E+01 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 40| +1.48E+02 | -2.9E-05 | +5.3E-01 | 1.8E-02 | 2.7E-01 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 73| +1.48E+02 | +6.7E-07 | -7.7E-02 | 1.4E-02 | 2.2E-02 | 1.5E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 147| +1.38E+02 | -2.1E-05 | +9.9E-01 | 3.3E-03 | 7.8E-02 | 6.0E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 10| +2.38E+02 | +1.3E+00 | -3.2E-01 | 1.7E-01 | 2.4E+01 | 4.3E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 24| +2.15E+02 | -1.6E-01 | +7.4E-01 | 9.4E-02 | 2.9E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 41| +1.48E+02 | -1.4E-05 | +2.8E-01 | 1.8E-02 | 1.4E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 148| +1.38E+02 | -4.1E-05 | +1.0E+00 | 6.7E-03 | 5.9E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 74| +1.48E+02 | -3.8E-06 | +1.3E+00 | 3.4E-03 | 2.2E-02 | 3.8E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 11| +2.36E+02 | -1.9E+00 | +8.7E-01 | 4.3E-02 | 2.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 14| +1.81E+02 | -6.7E-01 | -5.0E-01 | 1.0E+00 | 1.5E+02 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 42| +1.48E+02 | -4.4E-06 | +5.4E-02 | 1.8E-02 | 2.5E-01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 9| +2.50E+02 | -2.9E-01 | +2.9E-02 | 1.0E+00 | 1.2E+01 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 75| +1.48E+02 | +1.6E-06 | -3.0E-01 | 6.8E-03 | 6.5E-02 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 149| +1.38E+02 | -7.9E-05 | +9.9E-01 | 1.3E-02 | 6.8E-02 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 25| +2.15E+02 | -1.7E-01 | +9.8E-01 | 9.4E-02 | 3.2E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 12| +2.34E+02 | -2.2E+00 | +9.0E-01 | 8.6E-02 | 1.9E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 0| +1.64E+11 | NaN | NaN | 1.0E+00 | 7.4E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:28 fides(INFO) 43| +1.48E+02 | -1.9E-05 | +3.6E-01 | 4.6E-03 | 1.3E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 76| +1.48E+02 | +1.3E-06 | -7.2E-01 | 1.7E-03 | 6.5E-02 | 4.1E-04 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 150| +1.38E+02 | -1.5E-04 | +1.0E+00 | 2.7E-02 | 5.9E-02 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 10| +2.49E+02 | -8.7E-01 | +5.1E-01 | 2.5E-01 | 1.7E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 13| +2.30E+02 | -3.6E+00 | +9.8E-01 | 1.7E-01 | 2.4E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:28.049 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.543 and h = 3.35461e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:28.049 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.543: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:28 fides(INFO) 44| +1.48E+02 | +0.0E+00 | +0.0E+00 | 4.6E-03 | 3.8E-01 | 7.2E-04 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 26| +2.15E+02 | -1.5E-01 | +3.9E-01 | 1.9E-01 | 1.9E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 1| +3.11E+09 | -1.6E+11 | +8.7E-02 | 1.0E+00 | 7.4E+11 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 77| +1.48E+02 | +1.7E-06 | -2.1E+00 | 4.3E-04 | 6.5E-02 | 1.0E-04 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 15| +1.69E+02 | -1.2E+01 | +9.8E-01 | 2.5E-01 | 1.5E+02 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 151| +1.38E+02 | -2.5E-04 | +8.5E-01 | 5.3E-02 | 6.2E-02 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 45| +1.48E+02 | -1.7E-05 | +9.9E-01 | 1.1E-03 | 3.8E-01 | 2.0E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 14| +2.28E+02 | -2.3E+00 | +2.9E-01 | 3.4E-01 | 2.2E+01 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 78| +1.48E+02 | -2.3E-06 | +3.9E+00 | 1.1E-04 | 6.5E-02 | 3.0E-05 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 2| +4.64E+08 | -2.6E+09 | +8.9E-01 | 2.5E-01 | 1.4E+10 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 15| +2.18E+02 | -9.5E+00 | +8.3E-01 | 3.4E-01 | 5.4E+01 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 46| +1.48E+02 | -3.4E-06 | +1.0E+00 | 2.3E-03 | 4.0E-02 | 3.6E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 11| +2.48E+02 | -6.8E-01 | +4.8E-01 | 2.5E-01 | 1.0E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 152| +1.38E+02 | +3.9E-02 | -1.9E+01 | 1.1E-01 | 9.7E-02 | 3.4E-02 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 27| +2.15E+02 | -1.9E-01 | +1.1E+00 | 1.9E-01 | 3.5E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 16| +1.69E+02 | +8.9E+00 | -1.6E+00 | 5.0E-01 | 4.4E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 79| +1.48E+02 | +4.9E-06 | -2.7E+01 | 2.1E-04 | 6.9E-03 | 5.2E-05 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 16| +1.99E+02 | -1.9E+01 | +4.2E-01 | 6.9E-01 | 2.5E+01 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 47| +1.48E+02 | -6.6E-06 | +2.6E+00 | 4.6E-03 | 1.0E-01 | 6.3E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 3| +6.92E+07 | -3.9E+08 | +8.9E-01 | 5.0E-01 | 2.1E+09 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 153| +1.38E+02 | +1.9E-03 | -3.0E+00 | 2.7E-02 | 9.7E-02 | 8.6E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 80| +1.48E+02 | +5.6E-06 | -7.7E+01 | 5.3E-05 | 6.9E-03 | 1.8E-05 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 12| +2.47E+02 | -1.4E+00 | +7.4E-01 | 2.5E-01 | 1.5E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 17| +1.99E+02 | +3.2E+01 | -2.2E+00 | 6.9E-01 | 1.5E+02 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 48| +1.48E+02 | +2.7E-06 | -7.8E-01 | 9.2E-03 | 2.6E-02 | 1.2E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 28| +2.14E+02 | -1.7E-01 | +1.4E-01 | 3.8E-01 | 1.4E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 154| +1.38E+02 | +5.5E-07 | -6.5E-03 | 6.7E-03 | 9.7E-02 | 2.2E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 4| +1.04E+07 | -5.9E+07 | +8.9E-01 | 5.0E-01 | 3.2E+08 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 81| +1.48E+02 | +3.1E-06 | -6.9E+01 | 1.3E-05 | 6.9E-03 | 1.2E-05 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 17| +1.65E+02 | -3.7E+00 | +8.2E-01 | 1.2E-01 | 4.4E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 18| +1.77E+02 | -2.1E+01 | +9.9E-01 | 1.7E-01 | 1.5E+02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 49| +1.48E+02 | +1.4E-06 | -6.6E-01 | 2.3E-03 | 2.6E-02 | 3.3E-04 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 13| +2.46E+02 | -1.4E+00 | +6.1E-01 | 2.5E-01 | 1.2E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 82| +1.48E+02 | +3.2E-07 | -1.0E+01 | 3.3E-06 | 6.9E-03 | 7.3E-06 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 155| +1.38E+02 | -3.3E-05 | +7.0E-01 | 1.7E-03 | 9.7E-02 | 6.3E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 5| +1.56E+06 | -8.8E+06 | +8.9E-01 | 5.0E-01 | 4.8E+07 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 29| +2.14E+02 | -9.0E-02 | +1.1E+00 | 9.4E-02 | 3.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 50| +1.48E+02 | -1.0E-06 | +5.9E-01 | 5.7E-04 | 2.6E-02 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(WARNING) Stopping as function difference 1.02E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:28 fides(INFO) 19| +1.59E+02 | -1.9E+01 | +6.8E-01 | 3.4E-01 | 5.9E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 14| +2.43E+02 | -2.7E+00 | +7.9E-01 | 2.5E-01 | 2.1E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 6| +2.38E+05 | -1.3E+06 | +8.9E-01 | 5.0E-01 | 7.2E+06 | 9.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 156| +1.38E+02 | -1.4E-05 | +9.4E-01 | 1.7E-03 | 2.4E-01 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 83| +1.48E+02 | -4.8E-08 | +4.5E+00 | 8.3E-07 | 6.9E-03 | 1.8E-06 | 2d |1\n", - "2023-02-17 16:05:28 fides(WARNING) Stopping as function difference 4.76E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:28 fides(INFO) 18| +1.55E+02 | -9.5E+00 | +1.1E+00 | 2.5E-01 | 8.3E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +1.59E+02 | +4.1E+01 | -3.7E+00 | 3.4E-01 | 1.8E+02 | 5.7E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 7| +3.68E+04 | -2.0E+05 | +8.9E-01 | 5.0E-01 | 1.1E+06 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 15| +2.38E+02 | -4.9E+00 | +6.5E-01 | 5.0E-01 | 1.9E+01 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 30| +2.14E+02 | -9.2E-02 | +9.5E-01 | 1.9E-01 | 3.3E+00 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 157| +1.38E+02 | -1.6E-05 | +9.6E-01 | 3.3E-03 | 6.0E-02 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 8| +5.70E+03 | -3.1E+04 | +9.0E-01 | 5.0E-01 | 1.7E+05 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 21| +1.51E+02 | -8.1E+00 | +8.9E-01 | 7.6E-02 | 1.8E+02 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 19| +1.55E+02 | -2.7E-01 | -1.1E-02 | 5.0E-01 | 5.1E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 16| +2.29E+02 | -9.2E+00 | +9.1E-01 | 5.0E-01 | 4.7E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 158| +1.38E+02 | -3.1E-05 | +1.0E+00 | 6.7E-03 | 6.2E-02 | 9.3E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 9| +9.75E+02 | -4.7E+03 | +9.1E-01 | 5.0E-01 | 2.6E+04 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 22| +1.49E+02 | -2.0E+00 | +9.2E-01 | 1.5E-01 | 5.5E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 31| +2.14E+02 | -1.3E-01 | +7.0E-01 | 3.8E-01 | 5.3E+00 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 17| +2.29E+02 | +2.5E+01 | -3.2E+00 | 1.0E+00 | 2.9E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 0| +2.62E+11 | NaN | NaN | 1.0E+00 | 9.5E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 10| +7.50E+02 | -2.2E+02 | +9.7E-02 | 5.0E-01 | 3.8E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 159| +1.38E+02 | -5.8E-05 | +9.6E-01 | 1.3E-02 | 5.8E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 23| +1.49E+02 | +1.6E+00 | -1.6E+00 | 3.0E-01 | 3.7E+01 | 4.1E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 0| +1.18E+11 | NaN | NaN | 1.0E+00 | 5.4E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +1.55E+02 | -9.1E-01 | +9.5E-01 | 9.3E-02 | 5.1E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 18| +2.29E+02 | -1.8E-01 | -1.2E-01 | 2.5E-01 | 2.9E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 32| +2.14E+02 | -1.0E-01 | +1.2E+00 | 3.8E-01 | 1.3E+01 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 11| +2.94E+02 | -4.6E+02 | +9.2E-01 | 1.2E-01 | 3.6E+03 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 24| +1.48E+02 | -3.3E-01 | +7.0E-01 | 7.6E-02 | 3.7E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 1| +1.03E+09 | -2.6E+11 | +1.1E-01 | 1.0E+00 | 9.5E+11 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 1| +2.17E+08 | -1.2E+11 | +9.2E-02 | 1.0E+00 | 5.4E+11 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 160| +1.38E+02 | -3.8E-05 | +2.4E-01 | 2.7E-02 | 6.0E-02 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 12| +1.83E+02 | -1.1E+02 | +8.5E-01 | 2.5E-01 | 9.1E+02 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 19| +2.26E+02 | -3.1E+00 | +8.9E-01 | 6.2E-02 | 2.9E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 25| +1.48E+02 | -3.2E-01 | +5.8E-01 | 7.6E-02 | 2.8E+01 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 21| +1.54E+02 | -2.6E-01 | +8.2E-01 | 1.9E-01 | 1.3E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 2| +1.56E+08 | -8.7E+08 | +8.9E-01 | 2.5E-01 | 4.7E+09 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 33| +2.14E+02 | -1.2E-01 | +5.8E-01 | 7.5E-01 | 7.1E+00 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 2| +3.32E+07 | -1.8E+08 | +8.9E-01 | 2.5E-01 | 1.0E+09 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 13| +1.72E+02 | -1.1E+01 | +7.6E-01 | 5.0E-01 | 1.5E+02 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 161| +1.38E+02 | +8.5E-05 | -2.1E-01 | 6.7E-03 | 1.5E-01 | 3.5E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +2.20E+02 | -5.6E+00 | +1.1E+00 | 1.2E-01 | 2.9E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 3| +2.38E+07 | -1.3E+08 | +8.9E-01 | 5.0E-01 | 7.2E+08 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 3| +5.10E+06 | -2.8E+07 | +8.9E-01 | 5.0E-01 | 1.5E+08 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 26| +1.48E+02 | -6.1E-02 | +1.6E-01 | 7.6E-02 | 1.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 22| +1.54E+02 | -1.4E-01 | +8.6E-01 | 3.7E-01 | 2.2E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 34| +2.14E+02 | -5.4E-02 | +1.4E+00 | 7.5E-01 | 1.1E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 14| +1.71E+02 | -6.7E-01 | +6.7E-01 | 1.0E+00 | 1.3E+01 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 162| +1.38E+02 | -7.4E-05 | +6.9E-01 | 1.7E-03 | 1.5E-01 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 21| +2.09E+02 | -1.1E+01 | +1.2E+00 | 2.5E-01 | 3.0E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 4| +3.66E+06 | -2.0E+07 | +8.9E-01 | 1.0E+00 | 1.1E+08 | 9.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 27| +1.48E+02 | -1.1E-01 | +8.0E-01 | 1.9E-02 | 2.2E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 4| +7.91E+05 | -4.3E+06 | +8.9E-01 | 1.0E+00 | 2.3E+07 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 23| +1.54E+02 | -1.2E-02 | +5.1E-01 | 7.4E-01 | 3.6E+00 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 5| +5.66E+05 | -3.1E+06 | +9.0E-01 | 1.0E+00 | 1.7E+07 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 22| +2.09E+02 | -1.8E+00 | -5.4E-01 | 5.0E-01 | 3.1E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 28| +1.48E+02 | -7.0E-02 | +6.5E-01 | 3.8E-02 | 1.4E+01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 5| +1.24E+05 | -6.7E+05 | +9.0E-01 | 2.0E+00 | 3.6E+06 | 3.9E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 163| +1.38E+02 | -1.7E-05 | +8.1E-01 | 1.7E-03 | 2.0E-01 | 3.5E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 35| +2.14E+02 | -7.3E-02 | +7.5E-01 | 7.5E-01 | 3.2E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 15| +1.71E+02 | -1.3E-01 | +6.7E-01 | 1.0E+00 | 1.1E+01 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 6| +8.84E+04 | -4.8E+05 | +9.0E-01 | 1.0E+00 | 2.6E+06 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 6| +1.96E+04 | -1.0E+05 | +9.0E-01 | 4.0E+00 | 5.6E+05 | 1.6E+00 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 164| +1.38E+02 | -1.4E-05 | +8.9E-01 | 3.3E-03 | 6.0E-02 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 29| +1.48E+02 | -3.8E-02 | +5.8E-01 | 3.8E-02 | 1.0E+01 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 23| +2.03E+02 | -6.0E+00 | +9.5E-01 | 1.2E-01 | 3.1E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 24| +1.54E+02 | +1.5E-01 | -5.1E+00 | 7.4E-01 | 1.8E+00 | 3.3E-01 | trr |0\n", - "2023-02-17 16:05:28 fides(INFO) 7| +1.40E+04 | -7.4E+04 | +9.0E-01 | 1.0E+00 | 4.0E+05 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 7| +3.28E+03 | -1.6E+04 | +9.0E-01 | 4.0E+00 | 8.8E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 16| +1.71E+02 | -2.8E-02 | +8.1E-01 | 1.0E+00 | 4.7E+00 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 36| +2.14E+02 | -1.6E-02 | +1.4E+00 | 7.5E-01 | 4.7E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 30| +1.48E+02 | -3.1E-02 | +6.4E-01 | 3.8E-02 | 1.1E+01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 24| +1.89E+02 | -1.5E+01 | +1.1E+00 | 2.5E-01 | 4.0E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 8| +2.35E+03 | -1.2E+04 | +9.1E-01 | 1.0E+00 | 6.3E+04 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 8| +7.03E+02 | -2.6E+03 | +9.0E-01 | 4.0E+00 | 1.4E+04 | 1.2E+00 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 25| +1.54E+02 | -4.9E-03 | +8.4E-01 | 9.0E-02 | 1.8E+00 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 31| +1.48E+02 | -1.9E-02 | +3.9E-01 | 3.8E-02 | 5.1E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 165| +1.38E+02 | -2.5E-05 | +9.2E-01 | 6.7E-03 | 6.2E-02 | 8.1E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 9| +5.18E+02 | -1.8E+03 | +9.2E-01 | 1.0E+00 | 9.9E+03 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 17| +1.71E+02 | -3.2E-03 | +5.4E-01 | 2.0E+00 | 1.8E+00 | 1.1E+00 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 25| +1.89E+02 | -5.0E+00 | -3.4E-01 | 5.0E-01 | 4.5E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 37| +2.14E+02 | -1.0E-02 | +1.0E+00 | 7.5E-01 | 2.2E+00 | 7.6E-03 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 9| +3.02E+02 | -4.0E+02 | +9.1E-01 | 4.0E+00 | 2.2E+03 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 10| +2.23E+02 | -3.0E+02 | +1.0E+00 | 1.0E+00 | 1.4E+03 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 32| +1.48E+02 | -6.2E-03 | +1.7E-01 | 3.8E-02 | 3.7E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 26| +1.54E+02 | -4.2E-03 | +6.5E-01 | 1.8E-01 | 1.8E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 26| +1.79E+02 | -9.6E+00 | +1.0E+00 | 1.2E-01 | 4.5E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 166| +1.38E+02 | -4.1E-05 | +7.2E-01 | 1.3E-02 | 5.9E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 18| +1.71E+02 | -1.2E-04 | +4.8E-02 | 2.0E+00 | 1.5E+00 | 1.4E-02 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 11| +1.99E+02 | -2.4E+01 | +9.8E-01 | 1.0E+00 | 1.4E+02 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 10| +2.21E+02 | -8.1E+01 | +1.2E+00 | 4.0E+00 | 3.3E+02 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 38| +2.14E+02 | -3.0E-04 | +9.4E-01 | 7.5E-01 | 2.2E+00 | 1.4E-03 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 33| +1.48E+02 | -5.0E-03 | +7.4E-01 | 9.5E-03 | 1.3E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 27| +1.59E+02 | -2.0E+01 | +1.0E+00 | 2.5E-01 | 5.8E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 167| +1.38E+02 | +3.5E-04 | -1.9E+00 | 1.3E-02 | 7.7E-02 | 3.8E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 27| +1.54E+02 | -1.4E-03 | +3.8E-01 | 1.8E-01 | 7.8E-01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 19| +1.71E+02 | -7.2E-06 | +2.1E-03 | 3.0E-02 | 1.1E+00 | 1.4E-02 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 12| +1.99E+02 | +4.4E+02 | -2.4E+01 | 2.0E+00 | 3.8E+01 | 5.9E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 34| +1.48E+02 | -2.3E-03 | +7.8E-01 | 9.5E-03 | 2.1E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 28| +1.59E+02 | +3.0E+01 | -2.1E+00 | 5.0E-01 | 7.3E+01 | 8.5E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 11| +2.21E+02 | +1.1E+03 | -3.7E+01 | 4.0E+00 | 2.9E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 39| +2.14E+02 | -4.6E-06 | +1.7E-01 | 7.5E-01 | 2.1E+00 | 5.5E-04 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 28| +1.54E+02 | +2.0E-04 | -6.0E-02 | 1.8E-01 | 8.8E-01 | 9.4E-02 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 168| +1.38E+02 | +1.7E-05 | -3.1E-01 | 3.3E-03 | 7.7E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +1.71E+02 | -5.6E-04 | +2.6E-01 | 5.2E-03 | 1.1E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 12| +2.21E+02 | +7.1E+00 | -1.2E+00 | 1.0E+00 | 2.9E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 13| +1.94E+02 | -4.5E+00 | +2.2E-01 | 5.0E-01 | 3.8E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 35| +1.48E+02 | -9.7E-04 | +5.6E-01 | 1.9E-02 | 5.3E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 29| +1.53E+02 | -6.7E+00 | +8.1E-01 | 1.2E-01 | 7.3E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 169| +1.38E+02 | -9.1E-08 | +2.9E-03 | 8.3E-04 | 7.7E-02 | 7.0E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 13| +2.21E+02 | +3.5E+00 | -4.7E-01 | 2.5E-01 | 2.9E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 36| +1.48E+02 | -8.1E-04 | +5.1E-01 | 1.9E-02 | 1.0E+00 | 5.7E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 40| +2.14E+02 | +1.2E-07 | -1.1E-02 | 3.8E-04 | 2.1E+00 | 5.3E-04 | trr |0\n", - "2023-02-17 16:05:28 fides(INFO) 29| +1.54E+02 | -1.0E-03 | +8.0E-01 | 4.5E-02 | 8.8E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 30| +1.50E+02 | -2.8E+00 | +9.0E-01 | 2.5E-01 | 7.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 14| +1.85E+02 | -9.2E+00 | +8.7E-01 | 1.2E-01 | 8.4E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 21| +1.71E+02 | -3.1E-04 | +3.0E-01 | 5.2E-03 | 9.2E-01 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 37| +1.48E+02 | -4.7E-04 | +3.1E-01 | 1.9E-02 | 5.6E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 170| +1.38E+02 | -1.2E-05 | +9.8E-01 | 2.1E-04 | 3.7E-01 | 6.8E-05 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 31| +1.50E+02 | +4.9E+00 | -1.6E+00 | 5.0E-01 | 4.5E+01 | 5.3E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 14| +2.18E+02 | -3.3E+00 | +8.5E-01 | 6.2E-02 | 2.9E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 15| +1.75E+02 | -1.0E+01 | +1.3E+00 | 2.5E-01 | 3.6E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 30| +1.54E+02 | -6.0E-04 | +8.2E-01 | 9.0E-02 | 4.2E-01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 22| +1.71E+02 | -7.9E-06 | +1.7E-02 | 5.2E-03 | 6.1E-01 | 5.8E-03 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 15| +2.16E+02 | -1.6E+00 | +5.3E-01 | 1.2E-01 | 1.8E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 38| +1.48E+02 | +2.2E-04 | -1.2E-01 | 1.9E-02 | 5.9E-01 | 6.9E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 41| +2.14E+02 | -4.4E-06 | +8.1E-01 | 6.5E-05 | 2.1E+00 | 1.4E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 32| +1.49E+02 | -1.2E+00 | +7.2E-01 | 1.2E-01 | 4.5E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 16| +1.75E+02 | +1.1E+01 | -1.4E+00 | 5.0E-01 | 2.4E+01 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 171| +1.38E+02 | -3.4E-06 | +1.1E+00 | 4.2E-04 | 6.3E-02 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 31| +1.54E+02 | -8.5E-04 | +1.1E+00 | 1.8E-01 | 3.8E-01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 16| +2.14E+02 | -2.7E+00 | +7.2E-01 | 1.2E-01 | 2.9E+01 | 2.7E-01 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 39| +1.48E+02 | -3.9E-04 | +6.1E-01 | 4.8E-03 | 5.9E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 23| +1.71E+02 | -1.4E-04 | +7.8E-01 | 5.8E-04 | 5.0E-01 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 33| +1.48E+02 | -4.1E-01 | +5.5E-01 | 1.2E-01 | 4.4E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 17| +1.69E+02 | -6.2E+00 | +1.0E+00 | 1.2E-01 | 2.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 42| +2.14E+02 | -5.0E-07 | +1.8E-01 | 1.3E-04 | 2.1E+00 | 2.4E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 17| +2.11E+02 | -2.5E+00 | +6.5E-01 | 1.2E-01 | 1.8E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 172| +1.38E+02 | -3.1E-06 | +8.8E-01 | 8.3E-04 | 6.2E-02 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 32| +1.54E+02 | +6.3E-04 | -7.2E-01 | 3.6E-01 | 3.1E-01 | 8.3E-02 | trr |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 40| +1.48E+02 | -2.2E-04 | +9.6E-01 | 4.8E-03 | 1.0E+00 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 24| +1.71E+02 | -7.0E-05 | +5.8E-01 | 1.2E-03 | 3.1E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 34| +1.48E+02 | -3.8E-01 | +3.7E-01 | 1.2E-01 | 2.5E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 18| +1.60E+02 | -8.5E+00 | +8.5E-01 | 2.5E-01 | 5.6E+01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 18| +2.08E+02 | -3.3E+00 | +7.6E-01 | 1.2E-01 | 2.7E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 173| +1.38E+02 | -3.5E-06 | +5.5E-01 | 1.7E-03 | 6.6E-02 | 1.9E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 41| +1.48E+02 | -3.0E-04 | +7.9E-01 | 9.5E-03 | 2.6E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 33| +1.54E+02 | -3.0E-04 | +8.0E-01 | 6.8E-02 | 3.1E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 43| +2.14E+02 | -9.1E-07 | +8.2E-01 | 3.2E-05 | 2.1E+00 | 7.5E-05 | 2d |1\n", - "2023-02-17 16:05:28 fides(WARNING) Stopping as function difference 9.13E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:28 fides(INFO) 25| +1.71E+02 | -1.7E-06 | +5.8E-02 | 1.2E-03 | 1.4E-01 | 1.7E-03 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 35| +1.48E+02 | +7.8E-02 | -1.1E-01 | 1.2E-01 | 1.2E+01 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 19| +2.07E+02 | -5.6E-01 | +4.8E-02 | 2.5E-01 | 1.8E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 19| +1.55E+02 | -4.8E+00 | +7.9E-01 | 5.0E-01 | 7.0E+01 | 1.1E+00 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 174| +1.38E+02 | -8.5E-06 | +1.4E+00 | 1.7E-03 | 6.0E-02 | 1.9E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 42| +1.48E+02 | -9.6E-05 | +1.7E-01 | 1.9E-02 | 6.1E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +2.03E+02 | -4.2E+00 | +8.2E-01 | 6.2E-02 | 5.4E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 26| +1.71E+02 | -8.6E-06 | +6.6E-01 | 1.6E-04 | 1.2E-01 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 36| +1.48E+02 | -2.3E-01 | +7.6E-01 | 3.1E-02 | 1.2E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 34| +1.54E+02 | -4.5E-04 | +9.8E-01 | 1.4E-01 | 1.6E-01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +1.55E+02 | +9.5E+01 | -1.1E+01 | 5.0E-01 | 1.0E+02 | 8.3E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 43| +1.48E+02 | +8.2E-05 | -1.4E-01 | 4.8E-03 | 3.2E-01 | 3.7E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 21| +2.01E+02 | -2.5E+00 | +8.8E-01 | 1.2E-01 | 2.0E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 37| +1.48E+02 | -7.8E-02 | +3.5E-01 | 6.2E-02 | 8.4E+00 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 175| +1.38E+02 | -1.2E-05 | +9.7E-01 | 3.3E-03 | 6.1E-02 | 3.7E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 35| +1.54E+02 | +2.2E-03 | -2.9E+00 | 2.7E-01 | 1.7E-01 | 4.6E-02 | trr |0\n", - "2023-02-17 16:05:29 fides(INFO) 21| +1.50E+02 | -5.4E+00 | +7.7E-01 | 1.2E-01 | 1.0E+02 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 27| +1.71E+02 | -3.9E-06 | +7.3E-01 | 1.6E-04 | 8.2E-02 | 4.3E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 22| +1.91E+02 | -9.1E+00 | +1.5E+00 | 2.5E-01 | 1.5E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 44| +1.48E+02 | -7.1E-05 | +1.9E-01 | 1.2E-03 | 3.2E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 38| +1.48E+02 | -3.2E-02 | +7.9E-01 | 6.2E-02 | 8.4E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 36| +1.54E+02 | -1.8E-04 | +5.5E-01 | 4.0E-02 | 1.7E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 22| +1.48E+02 | -1.6E+00 | +9.7E-01 | 2.3E-01 | 6.7E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 176| +1.38E+02 | -2.4E-05 | +9.9E-01 | 6.7E-03 | 6.0E-02 | 7.3E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 28| +1.71E+02 | +3.9E-07 | -4.3E+00 | 1.6E-04 | 1.2E-02 | 8.0E-05 | trr |0\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 0| +6.60E+09 | NaN | NaN | 1.0E+00 | 3.1E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:29 fides(INFO) 45| +1.48E+02 | -2.1E-04 | +9.7E-01 | 3.0E-04 | 1.4E+00 | 3.5E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 23| +1.83E+02 | -9.0E+00 | +3.6E-01 | 5.0E-01 | 4.0E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 39| +1.48E+02 | -3.0E-02 | +8.4E-01 | 1.2E-01 | 3.2E+00 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 23| +1.48E+02 | +4.3E+00 | -2.3E+00 | 4.7E-01 | 2.0E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 177| +1.38E+02 | -4.6E-05 | +1.0E+00 | 1.3E-02 | 5.9E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 46| +1.48E+02 | -8.0E-06 | +8.6E-01 | 6.0E-04 | 1.0E-01 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 37| +1.54E+02 | -1.1E-04 | +7.1E-01 | 4.0E-02 | 2.1E-01 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 29| +1.71E+02 | -8.7E-07 | +1.4E+01 | 7.2E-06 | 1.2E-02 | 2.0E-05 | 2d |1\n", - "2023-02-17 16:05:29 fides(WARNING) Stopping as function difference 8.74E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 40| +1.48E+02 | -1.8E-02 | +9.1E-01 | 2.5E-01 | 7.6E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 1| +1.51E+04 | -6.6E+09 | +9.3E-02 | 1.0E+00 | 3.1E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 24| +1.68E+02 | -1.5E+01 | +4.4E-01 | 5.0E-01 | 1.5E+02 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 24| +1.48E+02 | -2.4E-01 | +1.5E-01 | 1.2E-01 | 2.0E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 47| +1.48E+02 | -1.1E-05 | +9.5E-01 | 1.2E-03 | 1.2E-01 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 38| +1.54E+02 | -1.7E-04 | +1.0E+00 | 4.0E-02 | 7.8E-02 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 41| +1.48E+02 | +3.3E-02 | -4.0E+00 | 5.0E-01 | 1.2E+00 | 2.3E-01 | trr |0\n", - "2023-02-17 16:05:29 fides(INFO) 178| +1.38E+02 | -8.5E-05 | +9.6E-01 | 2.7E-02 | 6.0E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 25| +1.53E+02 | -1.4E+01 | +9.8E-01 | 5.0E-01 | 2.3E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 25| +1.48E+02 | -5.3E-01 | +9.5E-01 | 2.9E-02 | 7.0E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 2| +2.53E+03 | -1.3E+04 | +9.0E-01 | 2.5E-01 | 6.9E+04 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 48| +1.48E+02 | -1.8E-05 | +9.1E-01 | 2.4E-03 | 5.7E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 42| +1.48E+02 | -1.2E-03 | +3.0E-01 | 1.1E-01 | 1.2E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 39| +1.54E+02 | +1.2E-04 | -6.4E-01 | 7.9E-02 | 2.0E-01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 179| +1.38E+02 | +1.3E-03 | -6.4E+00 | 5.3E-02 | 6.0E-02 | 7.7E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 26| +1.53E+02 | +3.2E+02 | -3.1E+01 | 1.0E+00 | 6.2E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 26| +1.48E+02 | -3.4E-02 | +5.9E-01 | 5.9E-02 | 5.9E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 49| +1.48E+02 | -3.0E-05 | +1.0E+00 | 4.8E-03 | 1.1E-01 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 43| +1.48E+02 | +9.5E-03 | -8.5E-01 | 1.1E-01 | 1.2E+00 | 5.1E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 3| +6.21E+02 | -1.9E+03 | +8.4E-01 | 5.0E-01 | 1.1E+04 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 40| +1.54E+02 | -5.4E-05 | +7.3E-01 | 2.0E-02 | 2.0E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 27| +1.52E+02 | -1.1E+00 | +8.3E-02 | 2.5E-01 | 6.2E+01 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 27| +1.48E+02 | -2.3E-02 | +9.6E-01 | 5.9E-02 | 1.1E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 180| +1.38E+02 | +3.9E-05 | -6.4E-01 | 1.3E-02 | 6.0E-02 | 2.0E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 50| +1.48E+02 | -4.5E-05 | +9.5E-01 | 9.5E-03 | 5.2E-02 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 44| +1.48E+02 | -2.0E-03 | +5.8E-01 | 2.7E-02 | 1.2E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 4| +2.36E+02 | -3.8E+02 | +9.2E-01 | 1.0E+00 | 1.8E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 0| +5.61E+08 | NaN | NaN | 1.0E+00 | 2.6E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:29 fides(INFO) 28| +1.50E+02 | -2.1E+00 | +9.6E-01 | 6.2E-02 | 1.1E+02 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 28| +1.48E+02 | -1.8E-02 | +9.0E-01 | 1.2E-01 | 3.7E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 51| +1.48E+02 | -5.8E-05 | +8.2E-01 | 1.9E-02 | 1.5E-01 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 41| +1.54E+02 | -3.1E-05 | +9.9E-01 | 2.0E-02 | 1.2E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 45| +1.48E+02 | -4.5E-04 | +9.8E-01 | 2.7E-02 | 1.0E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 181| +1.38E+02 | -7.5E-06 | +4.3E-01 | 3.3E-03 | 6.0E-02 | 5.3E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 29| +1.49E+02 | -1.3E+00 | +6.3E-01 | 1.2E-01 | 5.7E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 29| +1.48E+02 | -8.7E-03 | +3.6E-01 | 2.3E-01 | 6.4E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 42| +1.54E+02 | -4.7E-05 | +1.1E+00 | 4.0E-02 | 7.7E-02 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 5| +1.91E+02 | -4.6E+01 | +9.0E-01 | 1.0E+00 | 2.2E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 52| +1.48E+02 | +2.7E-04 | -2.1E+00 | 3.8E-02 | 7.7E-02 | 4.2E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 46| +1.48E+02 | -5.9E-04 | +9.7E-01 | 5.4E-02 | 5.5E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 182| +1.38E+02 | -1.4E-05 | +7.1E-01 | 3.3E-03 | 1.1E-01 | 5.2E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 30| +1.48E+02 | -7.0E-01 | +9.6E-01 | 1.2E-01 | 6.6E+01 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 1| +3.31E+06 | -5.6E+08 | +9.3E-02 | 1.0E+00 | 2.6E+09 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 30| +1.48E+02 | +1.2E-01 | -1.8E+00 | 2.3E-01 | 2.7E+00 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 6| +1.91E+02 | +2.4E+03 | -1.7E+02 | 2.0E+00 | 8.4E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 53| +1.48E+02 | -1.1E-05 | +2.4E-01 | 9.5E-03 | 7.7E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 47| +1.48E+02 | -7.4E-04 | +9.4E-01 | 1.1E-01 | 6.0E-01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 43| +1.54E+02 | -2.6E-05 | +6.6E-01 | 7.9E-02 | 3.3E-02 | 5.6E-03 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 31| +1.48E+02 | -2.1E-01 | +6.2E-01 | 2.5E-01 | 2.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 183| +1.38E+02 | -9.0E-06 | +5.6E-01 | 3.3E-03 | 6.0E-02 | 4.8E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 31| +1.48E+02 | +2.1E-02 | -5.5E-01 | 5.9E-02 | 2.7E+00 | 5.7E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 54| +1.48E+02 | -2.1E-05 | +8.9E-01 | 2.4E-03 | 1.9E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 7| +1.82E+02 | -8.3E+00 | +4.1E-01 | 3.4E-01 | 8.4E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 48| +1.48E+02 | +2.9E-03 | -3.3E+00 | 2.2E-01 | 3.3E-01 | 7.3E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 44| +1.54E+02 | +7.6E-04 | -3.9E+00 | 7.9E-02 | 4.7E-02 | 2.2E-02 | trr |0\n", - "2023-02-17 16:05:29 fides(INFO) 32| +1.48E+02 | +9.3E-01 | -1.5E+00 | 2.5E-01 | 1.4E+01 | 3.5E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 2| +5.08E+05 | -2.8E+06 | +8.9E-01 | 2.5E-01 | 1.5E+07 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 32| +1.48E+02 | +5.0E-03 | -1.8E-01 | 1.5E-02 | 2.7E+00 | 2.6E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 55| +1.48E+02 | -5.0E-06 | +4.1E-01 | 4.8E-03 | 3.3E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 184| +1.38E+02 | -1.1E-05 | +7.4E-01 | 3.3E-03 | 6.7E-02 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 49| +1.48E+02 | -9.9E-05 | +2.9E-01 | 5.4E-02 | 3.3E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 45| +1.54E+02 | -2.4E-05 | +2.8E-01 | 2.6E-03 | 4.7E-02 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 8| +1.76E+02 | -6.8E+00 | +9.1E-01 | 3.4E-01 | 1.4E+02 | 6.9E-01 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 33| +1.48E+02 | -9.9E-02 | +5.2E-01 | 6.2E-02 | 1.4E+01 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 56| +1.48E+02 | -8.0E-06 | +1.0E+00 | 4.8E-03 | 2.4E-02 | 9.2E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 50| +1.48E+02 | +6.2E-04 | -6.9E-01 | 5.4E-02 | 3.7E-01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 33| +1.48E+02 | -1.1E-02 | +7.8E-01 | 3.7E-03 | 2.7E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 185| +1.38E+02 | -1.0E-05 | +7.5E-01 | 3.3E-03 | 5.9E-02 | 4.3E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 46| +1.54E+02 | +7.5E-06 | -1.5E-01 | 2.6E-03 | 3.9E-02 | 5.2E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 34| +1.48E+02 | -2.1E-02 | +9.4E-01 | 6.2E-02 | 6.9E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 51| +1.48E+02 | -1.5E-04 | +5.0E-01 | 1.3E-02 | 3.7E-01 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 57| +1.48E+02 | -5.0E-06 | +2.9E-01 | 9.5E-03 | 1.2E-01 | 2.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 34| +1.48E+02 | -1.4E-03 | +9.6E-01 | 7.3E-03 | 2.8E+00 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 9| +1.74E+02 | -1.6E+00 | +7.8E-01 | 3.4E-01 | 5.5E+01 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 3| +7.83E+04 | -4.3E+05 | +9.0E-01 | 5.0E-01 | 2.4E+06 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 186| +1.38E+02 | -1.1E-05 | +3.9E-01 | 6.7E-03 | 6.3E-02 | 8.3E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 35| +1.48E+02 | -8.4E-03 | +5.5E-01 | 1.2E-01 | 6.1E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 47| +1.54E+02 | -1.1E-05 | +7.6E-01 | 6.5E-04 | 3.9E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 35| +1.48E+02 | -2.5E-03 | +6.0E-01 | 1.5E-02 | 1.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:29.462 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.596 and h = 3.12958e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.463 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.596: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 52| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.3E-02 | 6.4E-01 | 3.5E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 58| +1.48E+02 | +1.8E-04 | -3.9E+00 | 9.5E-03 | 4.7E-02 | 2.1E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 10| +1.73E+02 | -7.7E-01 | +9.5E-01 | 6.9E-01 | 4.2E+01 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 187| +1.38E+02 | +5.3E-05 | -6.2E-01 | 6.7E-03 | 7.1E-02 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 36| +1.48E+02 | -2.4E-03 | +9.5E-01 | 1.5E-02 | 4.4E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 36| +1.48E+02 | +6.4E-02 | -1.6E+00 | 1.2E-01 | 2.7E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 48| +1.54E+02 | -5.0E-06 | +4.5E-01 | 1.3E-03 | 3.3E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 59| +1.48E+02 | +1.4E-04 | -3.5E+00 | 2.4E-03 | 4.7E-02 | 1.7E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 53| +1.48E+02 | -5.8E-05 | +1.0E+00 | 3.4E-03 | 6.4E-01 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 4| +1.05E+04 | -6.8E+04 | +9.2E-01 | 1.0E+00 | 3.6E+05 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 11| +1.73E+02 | +1.0E+00 | -1.1E+01 | 1.4E+00 | 3.5E+01 | 4.2E+00 | trr |0\n", - "2023-02-17 16:05:29 fides(INFO) 37| +1.48E+02 | -9.3E-04 | +9.9E-01 | 2.9E-02 | 5.3E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 37| +1.48E+02 | -2.3E-03 | +1.3E-01 | 3.1E-02 | 2.7E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 188| +1.38E+02 | -1.2E-05 | +5.2E-01 | 1.7E-03 | 7.1E-02 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 60| +1.48E+02 | +7.4E-05 | -2.1E+00 | 6.0E-04 | 4.7E-02 | 1.4E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 49| +1.54E+02 | -3.5E-06 | +2.4E-01 | 1.3E-03 | 1.9E-02 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 54| +1.48E+02 | -6.1E-05 | +8.6E-01 | 6.7E-03 | 1.6E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 38| +1.48E+02 | -1.1E-03 | +8.8E-01 | 5.9E-02 | 1.4E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 12| +1.73E+02 | -3.5E-01 | +8.9E-01 | 3.4E-01 | 3.5E+01 | 3.5E-01 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 38| +1.48E+02 | -5.6E-03 | +8.4E-01 | 7.8E-03 | 5.9E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 61| +1.48E+02 | -9.7E-06 | +7.4E-01 | 1.5E-04 | 4.7E-02 | 3.4E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 55| +1.48E+02 | -3.7E-05 | +1.0E+00 | 1.3E-02 | 3.6E-01 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 50| +1.54E+02 | -3.9E-06 | +8.2E-01 | 3.2E-04 | 3.4E-02 | 6.5E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 189| +1.38E+02 | -6.8E-06 | +1.4E+00 | 1.7E-03 | 1.0E-01 | 1.4E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 5| +1.58E+03 | -8.9E+03 | +9.1E-01 | 1.0E+00 | 5.1E+04 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 39| +1.48E+02 | -1.4E-03 | +7.7E-01 | 1.2E-01 | 3.9E-01 | 8.9E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 62| +1.48E+02 | -1.4E-06 | +2.5E+00 | 1.5E-04 | 4.0E-02 | 4.1E-05 | 2d |1\n", - "2023-02-17 16:05:29 fides(WARNING) Stopping as function difference 1.39E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:29 fides(INFO) 39| +1.48E+02 | -6.2E-04 | +5.2E-01 | 1.6E-02 | 9.1E-01 | 7.7E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 13| +1.72E+02 | -5.0E-01 | +9.4E-01 | 3.4E-01 | 3.3E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 56| +1.48E+02 | -3.2E-05 | +8.2E-01 | 2.7E-02 | 8.1E-02 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 51| +1.54E+02 | -2.7E-06 | +5.6E-01 | 6.5E-04 | 1.7E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 190| +1.38E+02 | -8.2E-06 | +9.3E-01 | 3.3E-03 | 5.6E-02 | 2.7E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 40| +1.48E+02 | +4.2E-03 | -8.8E-01 | 2.3E-01 | 1.3E+00 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 40| +1.48E+02 | -2.9E-04 | +8.9E-01 | 1.6E-02 | 1.1E+00 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 57| +1.48E+02 | -2.9E-06 | +4.1E-02 | 5.4E-02 | 2.0E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 6| +3.68E+02 | -1.2E+03 | +9.1E-01 | 1.0E+00 | 7.7E+03 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 14| +1.72E+02 | -5.4E-01 | +8.9E-01 | 6.9E-01 | 2.5E+01 | 2.3E-01 | trr |1\n", - "2023-02-17 16:05:29.645 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2931 and h = 1.46524e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.646 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2931: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 191| +1.38E+02 | -1.7E-05 | +9.7E-01 | 6.7E-03 | 5.7E-02 | 5.4E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 41| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.1E-02 | 5.5E-01 | 6.7E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 41| +1.48E+02 | -9.1E-04 | +5.9E-01 | 5.9E-02 | 1.3E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 52| +1.54E+02 | -3.4E-06 | +7.2E-01 | 6.5E-04 | 1.3E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 58| +1.48E+02 | +1.1E-04 | -6.7E-01 | 1.3E-02 | 1.3E-01 | 3.6E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 42| +1.48E+02 | -2.0E-04 | +9.3E-01 | 7.8E-03 | 5.5E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 42| +1.48E+02 | -4.5E-04 | +7.8E-01 | 5.9E-02 | 3.3E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 15| +1.71E+02 | -4.7E-01 | +6.5E-01 | 6.9E-01 | 3.4E+01 | 4.7E-01 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 192| +1.38E+02 | -3.2E-05 | +9.5E-01 | 1.3E-02 | 5.6E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 53| +1.54E+02 | -1.3E-06 | +3.1E-01 | 6.5E-04 | 3.5E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 59| +1.48E+02 | +3.1E-05 | -3.2E-01 | 3.4E-03 | 1.3E-01 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(WARNING) Stopping as function difference 1.26E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:29 fides(INFO) 7| +1.81E+02 | -1.9E+02 | +1.1E+00 | 1.0E+00 | 1.1E+03 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 0| +4.31E+02 | NaN | NaN | 1.0E+00 | 6.4E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:29 fides(INFO) 43| +1.48E+02 | -6.0E-04 | +7.4E-01 | 1.2E-01 | 5.4E-01 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 16| +1.71E+02 | -8.5E-02 | +3.1E-01 | 6.9E-01 | 2.2E+01 | 1.6E-01 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 43| +1.48E+02 | -1.5E-04 | +9.8E-01 | 1.6E-02 | 9.1E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:29.722 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.674 and h = 2.5308e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.722 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.674: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 60| +1.48E+02 | +0.0E+00 | +0.0E+00 | 8.4E-04 | 1.3E-01 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 193| +1.38E+02 | -5.4E-05 | +8.1E-01 | 2.7E-02 | 5.6E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 1| +2.68E+02 | -1.6E+02 | +8.8E-01 | 1.0E+00 | 6.4E+01 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 44| +1.48E+02 | +1.2E-04 | -1.3E-01 | 1.2E-01 | 2.9E-01 | 6.7E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 44| +1.48E+02 | -9.5E-05 | +9.2E-01 | 3.1E-02 | 3.0E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 61| +1.48E+02 | -3.1E-05 | +6.7E-01 | 2.1E-04 | 1.3E-01 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 2| +2.68E+02 | +6.4E+00 | -4.0E-01 | 2.0E+00 | 6.9E+01 | 5.3E+00 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 17| +1.71E+02 | -6.9E-03 | +3.4E-02 | 6.9E-01 | 8.4E+00 | 2.1E-01 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 194| +1.38E+02 | +1.7E-02 | -2.5E+01 | 5.3E-02 | 7.3E-02 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 45| +1.48E+02 | -1.5E-04 | +4.0E-01 | 2.9E-02 | 2.9E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 3| +2.56E+02 | -1.2E+01 | +3.6E-01 | 5.0E-01 | 6.9E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 45| +1.48E+02 | -4.2E-05 | +4.1E-01 | 6.2E-02 | 3.9E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 62| +1.48E+02 | -7.6E-06 | +1.0E+00 | 2.1E-04 | 2.4E-01 | 7.2E-05 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 18| +1.71E+02 | -6.8E-02 | +6.9E-01 | 5.3E-02 | 1.1E+01 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:29.791 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.178 and h = 3.56278e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.792 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.178: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 46| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.9E-02 | 1.0E+00 | 1.7E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 4| +2.56E+02 | -7.5E-01 | -8.6E-03 | 5.0E-01 | 5.0E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 195| +1.38E+02 | +9.0E-04 | -3.9E+00 | 1.3E-02 | 7.3E-02 | 5.1E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 63| +1.48E+02 | -1.3E-06 | +7.7E-02 | 4.2E-04 | 6.3E-02 | 5.6E-04 | 2d |1\n", - "2023-02-17 16:05:29.809 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2503 and h = 4.35246e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.810 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2503: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 46| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 1.7E-01 | 2.7E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 0| +1.96E+13 | NaN | NaN | 1.0E+00 | 9.0E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:29 fides(INFO) 19| +1.71E+02 | -6.8E-03 | +8.7E-01 | 5.3E-02 | 4.4E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 5| +2.50E+02 | -6.6E+00 | +7.1E-01 | 1.2E-01 | 5.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 47| +1.48E+02 | -1.6E-04 | +9.5E-01 | 7.3E-03 | 1.0E+00 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:29.840 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.3921 and h = 2.28356e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.840 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.3921: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 47| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 1.7E-01 | 7.1E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 196| +1.38E+02 | +1.2E-05 | -2.0E-01 | 3.3E-03 | 7.3E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 1| +6.60E+10 | -2.0E+13 | +8.5E-02 | 1.0E+00 | 9.0E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 64| +1.48E+02 | -1.3E-05 | +8.6E-01 | 1.1E-04 | 3.5E-01 | 8.5E-05 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 6| +2.50E+02 | +9.0E-03 | -1.0E-01 | 1.2E-01 | 3.9E+00 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 48| +1.48E+02 | -6.7E-05 | +9.9E-01 | 1.5E-02 | 8.9E-02 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 20| +1.71E+02 | -7.8E-05 | +1.6E-01 | 1.1E-01 | 3.3E-01 | 2.2E-02 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 48| +1.48E+02 | +7.9E-05 | -5.5E-01 | 3.9E-03 | 1.7E-01 | 2.5E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 197| +1.38E+02 | -1.0E-05 | +6.2E-01 | 8.3E-04 | 7.3E-02 | 3.5E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 8| +1.81E+02 | +1.4E+03 | -5.6E+01 | 1.0E+00 | 6.2E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 65| +1.48E+02 | +6.1E-07 | -2.6E+00 | 2.1E-04 | 2.3E-02 | 4.3E-05 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 7| +2.50E+02 | +9.1E-03 | -1.1E-01 | 3.1E-02 | 3.9E+00 | 7.9E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 2| +9.84E+09 | -5.6E+10 | +8.9E-01 | 2.5E-01 | 3.0E+11 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 49| +1.48E+02 | -1.0E-04 | +9.3E-01 | 2.9E-02 | 1.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 49| +1.48E+02 | +3.9E-05 | -3.4E-01 | 9.8E-04 | 1.7E-01 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 8| +2.50E+02 | -4.4E-02 | +7.3E-01 | 7.8E-03 | 3.9E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:29.904 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.594 and h = 2.06348e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.904 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.594: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 66| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.3E-05 | 2.3E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 198| +1.38E+02 | -3.1E-06 | +8.4E-01 | 8.3E-04 | 1.1E-01 | 1.1E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 21| +1.71E+02 | -7.4E-06 | +1.8E-02 | 7.8E-03 | 2.4E-01 | 1.5E-02 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 3| +1.47E+09 | -8.4E+09 | +8.9E-01 | 5.0E-01 | 4.5E+10 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 50| +1.48E+02 | -1.7E-04 | +9.9E-01 | 5.9E-02 | 1.1E-01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 9| +2.50E+02 | -1.5E-05 | +2.5E-03 | 7.8E-03 | 8.8E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 50| +1.48E+02 | -4.0E-05 | +7.2E-01 | 2.4E-04 | 1.7E-01 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 67| +1.48E+02 | -8.0E-07 | +7.1E+00 | 1.3E-05 | 2.3E-02 | 9.6E-06 | 2d |1\n", - "2023-02-17 16:05:29 fides(WARNING) Stopping as function difference 7.96E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:29.943 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 142.796 and h = 9.16043e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.943 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 142.796: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 9| +1.81E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 6.2E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 199| +1.38E+02 | -3.5E-06 | +9.6E-01 | 1.7E-03 | 5.6E-02 | 1.2E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 22| +1.71E+02 | -1.0E-04 | +8.5E-01 | 1.4E-03 | 2.6E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 10| +2.50E+02 | -2.0E-03 | +7.4E-01 | 2.0E-03 | 8.8E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 51| +1.48E+02 | -2.4E-04 | +9.6E-01 | 1.2E-01 | 1.6E-01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 4| +2.21E+08 | -1.3E+09 | +8.9E-01 | 5.0E-01 | 6.8E+09 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 51| +1.48E+02 | -3.7E-06 | +1.4E+00 | 2.4E-04 | 1.2E-01 | 7.4E-05 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 11| +2.50E+02 | -4.5E-06 | +7.9E-03 | 2.0E-03 | 3.0E-01 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 200| +1.38E+02 | -6.0E-06 | +8.9E-01 | 3.3E-03 | 5.7E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 23| +1.71E+02 | -8.5E-05 | +7.9E-01 | 2.8E-03 | 2.5E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 5| +3.34E+07 | -1.9E+08 | +8.9E-01 | 5.0E-01 | 1.0E+09 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 52| +1.48E+02 | -1.7E-04 | +6.7E-01 | 2.3E-01 | 1.1E-01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 52| +1.48E+02 | -4.6E-06 | +7.7E-01 | 4.9E-04 | 5.9E-02 | 2.5E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.50E+02 | -2.4E-04 | +7.8E-01 | 4.9E-04 | 3.0E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 53| +1.48E+02 | +6.6E-03 | -7.8E+00 | 2.3E-01 | 1.1E-01 | 8.2E-02 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 201| +1.38E+02 | -1.2E-05 | +9.3E-01 | 6.7E-03 | 5.6E-02 | 4.4E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 53| +1.48E+02 | -3.2E-06 | +7.1E-01 | 9.8E-04 | 1.7E-01 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 6| +5.07E+06 | -2.8E+07 | +8.9E-01 | 5.0E-01 | 1.5E+08 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.50E+02 | +7.8E-08 | -1.9E-03 | 9.8E-04 | 8.6E-02 | 2.5E-03 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +1.74E+02 | -6.7E+00 | +1.0E+00 | 6.2E-02 | 6.2E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 24| +1.71E+02 | +2.2E-07 | -2.3E-02 | 5.6E-03 | 5.8E-02 | 2.9E-03 | trr |0\n", - "2023-02-17 16:05:30 fides(INFO) 54| +1.48E+02 | +1.6E-04 | -5.2E-01 | 5.9E-02 | 1.1E-01 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +2.28E+12 | NaN | NaN | 1.0E+00 | 1.0E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.50E+02 | -2.0E-05 | +5.5E-01 | 2.4E-04 | 8.6E-02 | 6.3E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 54| +1.48E+02 | -1.5E-06 | +3.4E-01 | 9.8E-04 | 2.9E-02 | 4.2E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +7.72E+05 | -4.3E+06 | +8.9E-01 | 5.0E-01 | 2.3E+07 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 202| +1.38E+02 | +8.6E-05 | -2.1E+00 | 1.3E-02 | 5.8E-02 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 25| +1.71E+02 | -2.5E-06 | +5.2E-01 | 5.4E-04 | 5.8E-02 | 7.3E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 55| +1.48E+02 | -5.5E-05 | +6.3E-01 | 1.5E-02 | 1.1E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.50E+02 | -1.5E-09 | +5.4E-04 | 2.4E-04 | 2.1E-02 | 5.7E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 1| +1.65E+06 | -2.3E+12 | +8.4E-02 | 1.0E+00 | 1.0E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 55| +1.48E+02 | -2.0E-06 | +2.7E+00 | 9.8E-04 | 1.6E-02 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +1.17E+05 | -6.5E+05 | +9.0E-01 | 5.0E-01 | 3.5E+06 | 7.8E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 203| +1.38E+02 | -3.9E-07 | +2.2E-02 | 3.3E-03 | 5.8E-02 | 5.5E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 26| +1.71E+02 | -1.8E-06 | +1.2E+00 | 5.4E-04 | 2.4E-02 | 7.2E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.50E+02 | -1.2E-06 | +5.3E-01 | 6.1E-05 | 2.1E-02 | 1.6E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(WARNING) Stopping as function difference 1.18E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:30 fides(INFO) 56| +1.48E+02 | -9.7E-06 | +9.7E-01 | 1.5E-02 | 1.6E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 2| +2.54E+05 | -1.4E+06 | +8.9E-01 | 2.5E-01 | 7.6E+06 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 56| +1.48E+02 | -1.7E-06 | +1.4E+00 | 2.0E-03 | 3.8E-02 | 3.2E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +1.64E+02 | -1.1E+01 | +1.0E+00 | 1.2E-01 | 7.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 9| +1.73E+04 | -1.0E+05 | +9.0E-01 | 5.0E-01 | 5.4E+05 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +4.01E+04 | -2.1E+05 | +9.0E-01 | 5.0E-01 | 1.2E+06 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 204| +1.38E+02 | -8.3E-06 | +7.3E-01 | 8.3E-04 | 1.1E-01 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 57| +1.48E+02 | -7.1E-06 | +8.7E-01 | 2.9E-02 | 2.3E-02 | 8.4E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 57| +1.48E+02 | +1.9E-06 | -1.0E+00 | 3.9E-03 | 1.5E-02 | 6.1E-04 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 27| +1.71E+02 | -1.8E-07 | +2.2E+00 | 1.1E-03 | 7.0E-03 | 1.4E-04 | trr |1\n", - "2023-02-17 16:05:30 fides(WARNING) Stopping as function difference 1.82E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:30 fides(INFO) 58| +1.48E+02 | -1.0E-05 | +8.7E-01 | 5.9E-02 | 5.2E-02 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 4| +6.41E+03 | -3.4E+04 | +9.0E-01 | 1.0E+00 | 1.8E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.55E+03 | -1.5E+04 | +9.1E-01 | 5.0E-01 | 7.9E+04 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 58| +1.48E+02 | -6.8E-07 | +1.3E+00 | 9.8E-04 | 1.5E-02 | 1.5E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(WARNING) Stopping as function difference 6.84E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:30 fides(INFO) 205| +1.38E+02 | +2.7E-07 | -1.3E-01 | 8.3E-04 | 5.9E-02 | 9.0E-05 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 59| +1.48E+02 | -7.1E-07 | +4.5E-02 | 1.2E-01 | 1.8E-02 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 5| +1.18E+03 | -5.2E+03 | +9.0E-01 | 1.0E+00 | 2.8E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +1.58E+05 | NaN | NaN | 1.0E+00 | 7.3E+05 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +4.97E+02 | -2.1E+03 | +9.2E-01 | 5.0E-01 | 1.1E+04 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 206| +1.38E+02 | -4.1E-07 | +6.9E-01 | 2.1E-04 | 5.9E-02 | 2.4E-05 | 2d |1\n", - "2023-02-17 16:05:30 fides(WARNING) Stopping as function difference 4.10E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:30 fides(INFO) 6| +3.76E+02 | -8.1E+02 | +9.0E-01 | 1.0E+00 | 4.5E+03 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 60| +1.48E+02 | +3.6E-05 | -6.2E-01 | 2.9E-02 | 6.8E-02 | 5.8E-03 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 1| +4.19E+02 | -1.6E+05 | +1.2E-01 | 1.0E+00 | 7.3E+05 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +2.56E+02 | -1.2E+02 | +8.9E-01 | 2.0E+00 | 6.9E+02 | 2.8E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +2.21E+05 | NaN | NaN | 1.0E+00 | 1.0E+06 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) 61| +1.48E+02 | +2.9E-06 | -1.4E-01 | 7.3E-03 | 6.8E-02 | 1.5E-03 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.27E+02 | -2.9E+01 | +1.5E+00 | 2.0E+00 | 8.4E+01 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +1.56E+02 | -7.7E+00 | +9.1E-01 | 2.5E-01 | 4.1E+01 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 2| +3.80E+02 | -3.9E+01 | +9.6E-01 | 2.5E-01 | 5.6E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 1| +6.40E+02 | -2.2E+05 | +1.2E-01 | 1.0E+00 | 1.0E+06 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.28E+02 | -2.7E+02 | +9.4E-01 | 5.0E-01 | 1.7E+03 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 62| +1.48E+02 | -2.5E-06 | +2.5E-01 | 1.8E-03 | 6.8E-02 | 4.4E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +3.65E+13 | NaN | NaN | 1.0E+00 | 1.7E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +3.05E+02 | -7.6E+01 | +1.0E+00 | 5.0E-01 | 5.3E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 2| +2.99E+02 | -3.4E+02 | +9.0E-01 | 2.5E-01 | 1.9E+03 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.27E+02 | +1.7E+02 | -1.3E+01 | 2.0E+00 | 2.9E+01 | 5.1E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 1| +2.37E+07 | -3.7E+13 | +8.4E-02 | 1.0E+00 | 1.7E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:30.345 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1296 and h = 1.64695e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:30.345 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1296: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:30 fides(INFO) 63| +1.48E+02 | +0.0E+00 | +0.0E+00 | 4.6E-04 | 2.1E-01 | 9.3E-05 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 2| +3.64E+06 | -2.0E+07 | +8.9E-01 | 2.5E-01 | 1.1E+08 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +2.52E+02 | -4.6E+01 | +8.5E-01 | 5.0E-01 | 2.9E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.27E+02 | +9.6E+00 | -1.1E+00 | 5.0E-01 | 2.9E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 4| +3.05E+02 | +1.3E+03 | -2.5E+01 | 1.0E+00 | 5.2E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 64| +1.48E+02 | -9.4E-06 | +1.7E+00 | 1.1E-04 | 2.1E-01 | 5.4E-05 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +5.63E+05 | -3.1E+06 | +9.0E-01 | 5.0E-01 | 1.7E+07 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 4| +2.50E+02 | -1.8E+00 | +4.6E-01 | 1.0E+00 | 3.3E+01 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +1.90E+02 | -3.8E+01 | +1.1E+00 | 5.0E-01 | 3.0E+02 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +2.24E+02 | -3.8E+00 | +5.5E-01 | 1.2E-01 | 2.9E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 4| +8.80E+04 | -4.7E+05 | +9.0E-01 | 1.0E+00 | 2.6E+06 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 5| +2.71E+02 | -3.4E+01 | +9.7E-01 | 2.5E-01 | 5.2E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 65| +1.48E+02 | -1.7E-06 | +1.0E+00 | 2.3E-04 | 2.7E-02 | 1.3E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 5| +2.49E+02 | -8.0E-01 | +3.2E-01 | 1.0E+00 | 1.2E+01 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.21E+02 | -2.6E+00 | +7.8E-01 | 1.2E-01 | 2.8E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +3.47E+09 | NaN | NaN | 1.0E+00 | 1.6E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) 5| +1.40E+04 | -7.4E+04 | +9.0E-01 | 2.0E+00 | 4.0E+05 | 4.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 6| +2.41E+03 | -1.2E+04 | +9.0E-01 | 4.0E+00 | 6.4E+04 | 1.6E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.20E+02 | -1.0E+00 | +1.8E-01 | 2.5E-01 | 2.0E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 6| +2.71E+02 | +6.3E+01 | -2.0E+00 | 5.0E-01 | 4.7E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 6| +2.31E+02 | -1.8E+01 | +3.1E+00 | 1.0E+00 | 1.8E+01 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 66| +1.48E+02 | +6.5E-07 | -4.6E-01 | 4.6E-04 | 1.1E-01 | 8.1E-05 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 1| +9.62E+04 | -3.5E+09 | +9.3E-02 | 1.0E+00 | 1.6E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +5.71E+02 | -1.8E+03 | +9.0E-01 | 4.0E+00 | 1.0E+04 | 1.3E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +1.52E+02 | -4.1E+00 | +8.1E-01 | 5.0E-01 | 6.9E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 14| +1.83E+02 | -7.2E+00 | +1.3E-01 | 1.0E+00 | 6.8E+01 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +2.57E+02 | -1.4E+01 | +9.3E-01 | 1.2E-01 | 4.7E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 67| +1.48E+02 | -1.1E-06 | +7.9E-01 | 1.1E-04 | 1.1E-01 | 3.2E-05 | 2d |1\n", - "2023-02-17 16:05:30 fides(WARNING) Stopping as function difference 1.10E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.17E+02 | -3.4E+00 | +8.3E-01 | 6.2E-02 | 4.6E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.87E+02 | -2.8E+02 | +9.0E-01 | 4.0E+00 | 1.6E+03 | 1.7E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +2.31E+02 | +4.8E+02 | -2.3E+01 | 2.0E+00 | 4.2E+01 | 3.3E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 2| +2.00E+04 | -7.6E+04 | +9.0E-01 | 2.5E-01 | 4.1E+05 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.14E+02 | -2.4E+00 | +8.0E-01 | 1.2E-01 | 2.2E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.57E+02 | +4.1E+00 | -3.7E-01 | 2.5E-01 | 3.5E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.36E+02 | -5.1E+01 | +1.0E+00 | 4.0E+00 | 2.4E+02 | 3.6E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 14| +1.51E+02 | -7.1E-01 | +9.7E-01 | 1.0E+00 | 3.9E+01 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.31E+02 | +4.3E+01 | -2.0E+00 | 5.0E-01 | 4.2E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.13E+02 | -1.5E+00 | +2.6E-01 | 2.5E-01 | 1.9E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +4.16E+03 | -1.6E+04 | +9.1E-01 | 5.0E-01 | 7.0E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.52E+02 | -4.9E+00 | +9.1E-01 | 6.2E-02 | 3.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.36E+02 | +1.2E+02 | -1.1E+01 | 4.0E+00 | 2.7E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 17| +2.06E+02 | -6.8E+00 | +7.3E-01 | 2.5E-01 | 4.5E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.21E+02 | -9.8E+00 | +8.4E-01 | 1.2E-01 | 4.2E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +1.51E+02 | +2.3E+02 | -1.3E+02 | 2.0E+00 | 2.1E+01 | 5.0E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 4| +4.16E+03 | +2.1E+04 | -1.9E+01 | 1.0E+00 | 1.3E+04 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 18| +2.02E+02 | -3.6E+00 | +4.2E-01 | 2.5E-01 | 2.3E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +2.36E+02 | +1.1E+01 | -1.6E+00 | 1.0E+00 | 2.7E+01 | 3.0E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 15| +1.74E+02 | -9.1E+00 | +1.1E+00 | 2.5E-01 | 1.1E+02 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.51E+02 | -5.6E-01 | +8.0E-02 | 1.2E-01 | 2.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +4.48E+11 | NaN | NaN | 1.0E+00 | 2.1E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.08E+02 | -1.3E+01 | +9.7E-01 | 2.5E-01 | 3.5E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 1| +6.61E+05 | -4.5E+11 | +9.0E-02 | 1.0E+00 | 2.1E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +1.51E+02 | +2.5E+00 | -7.2E+00 | 5.0E-01 | 2.1E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 2| +1.04E+05 | -5.6E+05 | +9.0E-01 | 2.5E-01 | 3.0E+06 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +1.91E+02 | -1.1E+01 | +9.8E-01 | 2.5E-01 | 5.1E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 5| +9.03E+02 | -3.3E+03 | +8.9E-01 | 2.5E-01 | 1.3E+04 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.36E+02 | +1.2E+01 | -1.7E+00 | 2.5E-01 | 2.7E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 11| +2.50E+02 | -1.3E+00 | +8.7E-01 | 3.1E-02 | 2.9E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +1.96E+02 | -1.2E+01 | +1.1E+00 | 5.0E-01 | 4.4E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +1.51E+02 | -2.6E-01 | +1.1E+00 | 1.2E-01 | 2.1E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +1.66E+04 | -8.7E+04 | +9.0E-01 | 5.0E-01 | 4.8E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 20| +1.60E+02 | -3.1E+01 | +1.1E+00 | 5.0E-01 | 3.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 4| +2.84E+03 | -1.4E+04 | +9.0E-01 | 1.0E+00 | 7.5E+04 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.33E+02 | -3.0E+00 | +8.3E-01 | 6.2E-02 | 2.7E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.49E+02 | -5.2E-01 | +5.0E-01 | 6.2E-02 | 1.5E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 6| +3.44E+02 | -5.6E+02 | +8.3E-01 | 5.0E-01 | 2.3E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +1.96E+02 | +5.5E+02 | -3.7E+01 | 1.0E+00 | 2.8E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 5| +6.37E+02 | -2.2E+03 | +9.0E-01 | 2.0E+00 | 1.2E+04 | 3.6E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 21| +1.60E+02 | +1.1E+01 | -1.4E+00 | 1.0E+00 | 7.4E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 6| +2.97E+02 | -3.4E+02 | +9.0E-01 | 4.0E+00 | 1.9E+03 | 1.2E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.31E+02 | -1.5E+00 | +5.1E-01 | 1.2E-01 | 1.7E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 18| +1.51E+02 | -3.1E-01 | +4.8E-01 | 2.5E-01 | 2.3E+01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.49E+02 | -8.7E-02 | +3.0E-01 | 6.2E-02 | 5.5E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +2.51E+02 | -4.5E+01 | +8.5E-01 | 4.0E+00 | 3.0E+02 | 1.9E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +2.52E+02 | -9.2E+01 | +8.4E-01 | 1.0E+00 | 3.8E+02 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +1.90E+02 | -6.9E+00 | +7.1E-01 | 2.5E-01 | 2.8E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 22| +1.53E+02 | -6.1E+00 | +8.1E-01 | 2.1E-01 | 7.4E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.36E+02 | -1.5E+01 | +2.5E+00 | 4.0E+00 | 3.1E+01 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.29E+02 | -2.5E+00 | +6.4E-01 | 1.2E-01 | 3.3E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.50E+02 | -1.7E+00 | +4.4E-01 | 2.0E+00 | 3.2E+01 | 4.8E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.49E+02 | -1.5E-01 | +5.0E-01 | 6.2E-02 | 6.3E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +1.50E+02 | -5.3E-01 | +6.8E-01 | 2.5E-01 | 3.6E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +1.61E+02 | -1.3E+01 | +7.9E-01 | 5.0E-01 | 1.8E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.50E+02 | +1.4E-01 | -6.4E-01 | 2.0E+00 | 1.3E+01 | 4.5E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 14| +1.78E+02 | -1.2E+01 | +7.8E-01 | 2.5E-01 | 4.4E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 23| +1.49E+02 | -4.1E+00 | +1.0E+00 | 4.1E-01 | 8.8E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.29E+02 | -6.7E-03 | -6.1E-02 | 1.2E-01 | 1.9E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.36E+02 | +5.5E+02 | -2.5E+01 | 4.0E+00 | 4.0E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.50E+02 | +4.2E-01 | -3.8E-01 | 5.0E-01 | 1.3E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 24| +1.49E+02 | +4.1E+00 | -2.1E+00 | 8.2E-01 | 3.2E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.49E+02 | -1.2E-01 | +4.8E-01 | 6.2E-02 | 4.7E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +2.28E+02 | -9.9E-01 | +8.9E-01 | 3.1E-02 | 1.9E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 20| +1.50E+02 | -2.7E-01 | +8.7E-01 | 2.5E-01 | 2.2E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.36E+02 | +1.1E+02 | -5.7E+00 | 1.0E+00 | 4.0E+01 | 3.0E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 11| +2.50E+02 | +4.2E-01 | -3.8E-01 | 1.2E-01 | 1.3E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 15| +1.78E+02 | +1.8E+01 | -1.4E+00 | 5.0E-01 | 1.4E+02 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 25| +1.49E+02 | -7.4E-01 | +7.0E-01 | 1.3E-01 | 3.2E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +2.35E+02 | -1.8E+00 | +3.1E-02 | 2.5E-01 | 4.0E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 18| +2.26E+02 | -1.3E+00 | +9.6E-01 | 6.2E-02 | 1.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.49E+02 | -1.6E-01 | +5.9E-01 | 6.2E-02 | 5.5E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.50E+02 | -5.6E-01 | +6.9E-01 | 3.1E-02 | 1.3E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 21| +1.50E+02 | -1.7E-01 | +8.5E-01 | 5.0E-01 | 1.8E+01 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +1.65E+02 | -1.2E+01 | +9.1E-01 | 1.2E-01 | 1.4E+02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.28E+02 | -6.2E+00 | +8.7E-01 | 6.2E-02 | 7.4E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 26| +1.49E+02 | +1.0E+00 | -1.1E+00 | 1.3E-01 | 2.6E+01 | 2.1E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 19| +2.24E+02 | -2.0E+00 | +1.0E+00 | 1.2E-01 | 1.7E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.50E+02 | +1.7E-04 | -3.3E-02 | 3.1E-02 | 9.4E-01 | 7.1E-02 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 17| +2.49E+02 | -1.4E-01 | +5.4E-01 | 6.2E-02 | 4.6E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.25E+02 | -3.4E+00 | +6.6E-01 | 1.2E-01 | 3.9E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 27| +1.48E+02 | -2.0E-01 | +6.3E-01 | 3.4E-02 | 2.6E+01 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 22| +1.50E+02 | -2.2E-02 | +6.3E-01 | 1.0E+00 | 7.7E+00 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +1.59E+02 | -1.6E+00 | +1.2E-01 | 1.0E+00 | 9.8E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +1.58E+02 | -7.5E+00 | +6.2E-01 | 2.5E-01 | 4.3E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 20| +2.24E+02 | +7.3E-01 | -3.6E-01 | 2.5E-01 | 1.8E+01 | 7.2E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.50E+02 | +1.7E-04 | -3.3E-02 | 7.8E-03 | 9.4E-01 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.25E+02 | -5.0E-02 | -4.1E-02 | 1.2E-01 | 1.9E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 18| +2.49E+02 | -1.8E-01 | +6.1E-01 | 6.2E-02 | 5.9E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.50E+02 | -2.6E-03 | +7.2E-01 | 2.0E-03 | 9.4E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 28| +1.48E+02 | -2.4E-01 | +9.2E-01 | 3.4E-02 | 1.9E+01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.24E+02 | -9.3E-01 | +8.8E-01 | 3.1E-02 | 1.9E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 21| +2.23E+02 | -1.1E+00 | +6.2E-01 | 6.2E-02 | 1.8E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 23| +1.50E+02 | +3.1E-01 | -5.5E+00 | 1.0E+00 | 8.4E-01 | 6.0E-01 | trr |0\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.50E+02 | +3.7E-07 | -4.0E-03 | 2.0E-03 | 1.3E-01 | 4.5E-03 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.23E+02 | -1.5E+00 | +1.0E+00 | 6.2E-02 | 1.7E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 29| +1.48E+02 | -1.7E-01 | +8.2E-01 | 6.7E-02 | 2.4E+01 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 18| +1.54E+02 | -4.1E+00 | +8.4E-01 | 2.5E-01 | 1.0E+02 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 22| +2.22E+02 | -1.1E+00 | +8.6E-01 | 6.2E-02 | 1.7E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +2.48E+02 | -1.6E-01 | +5.0E-01 | 6.2E-02 | 5.4E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +2.50E+02 | -3.4E-05 | +3.6E-01 | 4.9E-04 | 1.3E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +2.20E+02 | -2.5E+00 | +1.1E+00 | 1.2E-01 | 1.8E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 24| +1.50E+02 | -9.6E-03 | +7.0E-01 | 1.5E-01 | 8.4E-01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 23| +2.22E+02 | -4.0E-01 | +2.1E-01 | 1.2E-01 | 1.3E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 30| +1.48E+02 | -1.7E-01 | +5.7E-01 | 1.3E-01 | 8.5E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 18| +2.20E+02 | +3.6E-01 | -2.5E-01 | 2.5E-01 | 1.7E+01 | 7.3E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 18| +2.50E+02 | -1.3E-07 | +3.4E-03 | 4.9E-04 | 7.7E-02 | 9.5E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +1.53E+02 | -9.0E-01 | +1.1E+00 | 5.0E-01 | 2.3E+01 | 7.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 18| +1.57E+02 | -2.5E+00 | +9.3E-01 | 1.0E-01 | 9.0E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 20| +2.48E+02 | -2.3E-01 | +5.8E-01 | 6.2E-02 | 7.3E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 24| +2.21E+02 | -1.2E+00 | +8.7E-01 | 3.1E-02 | 2.9E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +2.50E+02 | -1.6E-05 | +8.0E-01 | 1.2E-04 | 7.7E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +2.19E+02 | -9.3E-01 | +6.1E-01 | 6.2E-02 | 1.7E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 25| +1.50E+02 | -1.6E-03 | +6.7E-01 | 1.5E-01 | 1.6E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 31| +1.48E+02 | +7.5E-01 | -1.3E+00 | 1.3E-01 | 1.8E+01 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 20| +2.50E+02 | +1.2E-09 | -3.8E-04 | 2.4E-04 | 2.5E-02 | 5.7E-04 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 25| +2.20E+02 | -8.9E-01 | +7.1E-01 | 6.2E-02 | 1.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 20| +2.18E+02 | -1.1E+00 | +8.1E-01 | 6.2E-02 | 2.0E+01 | 9.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 20| +1.52E+02 | -4.3E-01 | +9.7E-01 | 1.0E+00 | 1.7E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 32| +1.48E+02 | -1.1E-01 | +5.7E-01 | 3.4E-02 | 1.8E+01 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 21| +2.48E+02 | -1.7E-01 | +4.1E-01 | 6.2E-02 | 6.5E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 26| +1.50E+02 | -5.0E-04 | +1.0E+00 | 1.5E-01 | 6.9E-01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 21| +2.50E+02 | -1.9E-06 | +6.5E-01 | 6.1E-05 | 2.5E-02 | 1.5E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(WARNING) Stopping as function difference 1.86E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:31 fides(INFO) 21| +2.18E+02 | -1.1E-01 | -1.6E-02 | 1.2E-01 | 1.4E+01 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 26| +2.19E+02 | -4.6E-01 | +4.8E-01 | 6.2E-02 | 1.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 21| +1.52E+02 | +1.4E+02 | -2.2E+02 | 2.0E+00 | 1.4E+01 | 5.2E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 33| +1.48E+02 | -3.9E-02 | +9.1E-01 | 3.4E-02 | 4.2E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 27| +1.50E+02 | -6.0E-04 | +1.1E+00 | 2.9E-01 | 4.8E-01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 22| +2.18E+02 | -5.6E-01 | +8.8E-01 | 3.1E-02 | 1.4E+01 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 19| +1.55E+02 | -1.4E+00 | +9.8E-01 | 2.0E-01 | 8.1E+01 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 27| +2.19E+02 | -7.9E-01 | +6.5E-01 | 6.2E-02 | 1.7E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 22| +1.52E+02 | -1.5E-01 | +3.0E-01 | 5.0E-01 | 1.4E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 22| +2.48E+02 | -3.1E-01 | +5.6E-01 | 6.2E-02 | 9.4E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 23| +2.16E+02 | -1.1E+00 | +1.0E+00 | 6.2E-02 | 1.4E+01 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +1.48E+02 | -4.8E-02 | +8.4E-01 | 6.7E-02 | 1.0E+01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 28| +1.50E+02 | -1.5E-04 | +8.2E-01 | 5.9E-01 | 2.9E-01 | 2.6E-01 | trr |1\n", - "2023-02-17 16:05:31 fides(INFO) 23| +1.52E+02 | -6.4E-01 | +5.2E-01 | 5.0E-01 | 2.3E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 28| +2.18E+02 | -3.0E-01 | +3.4E-01 | 6.2E-02 | 1.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 24| +2.16E+02 | -3.9E-01 | +2.1E-01 | 1.2E-01 | 1.3E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 35| +1.48E+02 | -4.7E-02 | +6.4E-01 | 1.3E-01 | 2.3E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +1.50E+02 | +4.7E-04 | -3.2E+00 | 5.9E-01 | 3.3E-01 | 1.5E-02 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 23| +2.48E+02 | -1.9E-01 | +3.4E-01 | 6.2E-02 | 7.7E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +2.17E+02 | -7.2E-01 | +6.0E-01 | 6.2E-02 | 1.7E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 25| +2.15E+02 | -1.1E+00 | +8.5E-01 | 3.1E-02 | 2.8E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 24| +1.51E+02 | -1.1E+00 | +8.7E-01 | 5.0E-01 | 2.0E+01 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 20| +1.55E+02 | +4.0E-01 | -7.4E-01 | 4.0E-01 | 2.1E+01 | 8.1E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 0| +2.59E+04 | NaN | NaN | 1.0E+00 | 9.1E+04 | NaN | NaN |1\n", - "2023-02-17 16:05:31 fides(INFO) 36| +1.48E+02 | +1.9E-01 | -1.3E+00 | 1.3E-01 | 3.7E+00 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 30| +2.17E+02 | -1.7E-01 | +2.0E-01 | 6.2E-02 | 1.0E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 26| +2.14E+02 | -5.0E-01 | +5.4E-01 | 6.2E-02 | 1.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 30| +1.50E+02 | -3.1E-05 | +4.5E-01 | 1.9E-02 | 3.3E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 24| +2.47E+02 | -4.2E-01 | +5.7E-01 | 6.2E-02 | 1.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 25| +1.51E+02 | +2.8E+00 | -1.7E+00 | 1.0E+00 | 9.0E+00 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 31| +2.17E+02 | -3.9E-01 | +9.0E-01 | 1.6E-02 | 1.7E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +1.48E+02 | -2.1E-02 | +5.1E-01 | 3.4E-02 | 3.7E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 27| +2.14E+02 | +3.1E-03 | -3.1E-02 | 6.2E-02 | 1.1E+01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 1| +3.75E+02 | -2.6E+04 | +1.6E-01 | 1.0E+00 | 9.1E+04 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 31| +1.50E+02 | -6.2E-06 | +2.9E-01 | 1.9E-02 | 8.7E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 26| +1.50E+02 | -4.1E-01 | +6.1E-01 | 2.5E-01 | 9.0E+00 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 32| +2.17E+02 | -3.9E-01 | +7.9E-01 | 3.1E-02 | 1.2E+01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 28| +2.14E+02 | -2.7E-01 | +8.7E-01 | 1.6E-02 | 1.1E+01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 38| +1.48E+02 | -9.7E-03 | +9.2E-01 | 3.4E-02 | 1.6E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 2| +2.83E+02 | -9.2E+01 | +8.9E-01 | 2.5E-01 | 5.4E+02 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 25| +2.47E+02 | -2.2E-01 | +3.1E-01 | 6.2E-02 | 9.0E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 21| +1.55E+02 | -5.6E-01 | +8.6E-01 | 1.0E-01 | 2.1E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +2.14E+02 | -2.7E-01 | +7.9E-01 | 3.1E-02 | 9.4E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 33| +2.16E+02 | -5.8E-01 | +8.9E-01 | 6.2E-02 | 7.8E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 32| +1.50E+02 | +4.8E-08 | -4.0E-03 | 1.9E-02 | 5.6E-02 | 2.3E-03 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 3| +2.58E+02 | -2.5E+01 | +7.7E-01 | 5.0E-01 | 8.1E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 39| +1.48E+02 | -1.4E-02 | +9.3E-01 | 6.7E-02 | 2.1E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 27| +1.50E+02 | -1.8E-01 | +7.7E-01 | 2.5E-01 | 1.2E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +2.16E+02 | -3.3E-01 | +5.0E-01 | 1.2E-01 | 9.8E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 30| +2.14E+02 | -3.0E-01 | +5.8E-01 | 6.2E-02 | 1.1E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 26| +2.46E+02 | -5.9E-01 | +6.0E-01 | 6.2E-02 | 1.5E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 4| +2.58E+02 | +1.1E+02 | -6.5E+00 | 1.0E+00 | 4.0E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 35| +2.16E+02 | +1.9E-01 | -2.3E-01 | 1.2E-01 | 1.1E+01 | 3.5E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 33| +1.50E+02 | -3.2E-06 | +7.8E-01 | 4.7E-03 | 5.6E-02 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 40| +1.48E+02 | -1.6E-02 | +7.7E-01 | 1.3E-01 | 1.0E+00 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 28| +1.50E+02 | -2.1E-01 | +6.2E-01 | 5.0E-01 | 4.7E+00 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 31| +2.14E+02 | +1.2E-01 | -1.9E-01 | 6.2E-02 | 1.1E+01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 22| +1.54E+02 | -4.5E-01 | +6.0E-01 | 2.0E-01 | 4.9E+01 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 36| +2.15E+02 | -4.1E-01 | +6.8E-01 | 3.1E-02 | 1.1E+01 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 5| +2.57E+02 | -1.5E+00 | +1.0E-02 | 2.5E-01 | 4.0E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 41| +1.48E+02 | +3.6E-01 | -7.2E+00 | 2.7E-01 | 1.4E+00 | 2.6E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 32| +2.13E+02 | -2.7E-01 | +8.5E-01 | 1.6E-02 | 1.1E+01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +1.50E+02 | -7.5E-07 | +3.3E-01 | 9.5E-03 | 1.0E-02 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(WARNING) Stopping as function difference 7.48E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:31 fides(INFO) 27| +2.46E+02 | -3.1E-01 | +3.6E-01 | 6.2E-02 | 1.0E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +1.50E+02 | -1.2E-01 | +6.6E-01 | 5.0E-01 | 4.0E+00 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +2.15E+02 | -2.6E-01 | +9.4E-01 | 3.1E-02 | 6.6E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 6| +2.52E+02 | -5.5E+00 | +8.5E-01 | 6.2E-02 | 6.6E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 33| +2.13E+02 | -1.5E-01 | +5.5E-01 | 3.1E-02 | 7.8E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 42| +1.48E+02 | +6.2E-03 | -3.4E-01 | 6.7E-02 | 1.4E+00 | 6.6E-02 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 23| +1.54E+02 | -2.0E-01 | +8.9E-01 | 2.0E-01 | 2.7E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 38| +2.15E+02 | -1.4E-01 | +3.4E-01 | 6.2E-02 | 6.1E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 7| +2.50E+02 | -1.2E+00 | +4.0E-01 | 1.2E-01 | 2.8E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 30| +1.50E+02 | -6.3E-02 | +7.3E-01 | 5.0E-01 | 7.0E+00 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +2.13E+02 | -2.0E-01 | +6.4E-01 | 3.1E-02 | 9.9E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 43| +1.48E+02 | -3.6E-03 | +6.9E-01 | 1.7E-02 | 1.4E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 28| +2.45E+02 | -7.9E-01 | +6.6E-01 | 6.2E-02 | 1.8E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 39| +2.15E+02 | -3.2E-01 | +4.3E-01 | 6.2E-02 | 1.3E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 35| +2.13E+02 | -9.1E-02 | +3.8E-01 | 3.1E-02 | 7.1E+00 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 8| +2.50E+02 | +3.4E-01 | -3.3E-01 | 1.2E-01 | 1.3E+01 | 2.8E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 24| +1.54E+02 | -2.9E-02 | +9.1E-01 | 4.0E-01 | 5.9E+00 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 44| +1.48E+02 | -1.3E-03 | +9.1E-01 | 1.7E-02 | 5.8E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 40| +2.15E+02 | +1.3E-02 | -5.0E-02 | 6.2E-02 | 8.2E+00 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 31| +1.50E+02 | -1.4E-02 | +7.5E-01 | 5.0E-01 | 2.7E+00 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 36| +2.13E+02 | -1.7E-01 | +5.7E-01 | 3.1E-02 | 9.8E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 0| +1.98E+12 | NaN | NaN | 1.0E+00 | 9.2E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +2.45E+02 | -5.1E-01 | +5.1E-01 | 6.2E-02 | 1.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 9| +2.50E+02 | -5.2E-01 | +6.9E-01 | 3.1E-02 | 1.3E+01 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 41| +2.14E+02 | -2.0E-01 | +8.1E-01 | 1.6E-02 | 8.2E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 45| +1.48E+02 | -2.2E-03 | +9.8E-01 | 3.4E-02 | 7.0E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +2.13E+02 | -4.2E-02 | +1.9E-01 | 3.1E-02 | 6.4E+00 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 25| +1.54E+02 | -7.2E-03 | +5.1E-01 | 8.0E-01 | 4.4E+00 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 1| +1.26E+06 | -2.0E+12 | +8.4E-02 | 1.0E+00 | 9.2E+12 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 32| +1.50E+02 | -3.6E-03 | +7.3E-01 | 1.0E+00 | 1.0E+00 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 10| +2.50E+02 | -3.9E-03 | +9.2E-02 | 3.1E-02 | 2.7E+00 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 42| +2.14E+02 | -1.4E-01 | +7.0E-01 | 3.1E-02 | 5.2E+00 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 38| +2.13E+02 | -1.0E-01 | +9.1E-01 | 7.8E-03 | 9.6E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 46| +1.48E+02 | -3.1E-03 | +9.4E-01 | 6.7E-02 | 5.8E-01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 30| +2.44E+02 | -9.4E-01 | +7.3E-01 | 6.2E-02 | 2.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 2| +1.83E+05 | -1.1E+06 | +9.0E-01 | 2.5E-01 | 6.0E+06 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 39| +2.12E+02 | -9.9E-02 | +7.8E-01 | 1.6E-02 | 7.3E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 43| +2.14E+02 | -1.5E-01 | +5.9E-01 | 3.1E-02 | 7.3E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 26| +1.54E+02 | +3.7E-01 | -1.2E+01 | 8.0E-01 | 1.1E+00 | 5.0E-01 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 33| +1.50E+02 | +1.0E-04 | -2.0E-01 | 1.0E+00 | 5.6E-01 | 7.4E-01 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 11| +2.50E+02 | -1.3E-02 | +4.3E-01 | 7.8E-03 | 2.3E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 3| +2.56E+04 | -1.6E+05 | +9.0E-01 | 5.0E-01 | 9.0E+05 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 47| +1.48E+02 | -2.5E-03 | +8.0E-01 | 1.3E-01 | 5.2E-01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 40| +2.12E+02 | -1.4E-01 | +8.8E-01 | 3.1E-02 | 5.2E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 44| +2.14E+02 | -7.0E-02 | +3.1E-01 | 3.1E-02 | 5.7E+00 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 31| +2.43E+02 | -7.3E-01 | +6.4E-01 | 6.2E-02 | 1.3E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 27| +1.54E+02 | -5.4E-03 | +7.0E-01 | 1.3E-01 | 1.1E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 12| +2.50E+02 | -3.1E-04 | +4.0E-02 | 7.8E-03 | 1.1E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 4| +3.89E+03 | -2.2E+04 | +9.0E-01 | 5.0E-01 | 1.4E+05 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 48| +1.48E+02 | +8.2E-02 | -1.4E+01 | 2.7E-01 | 2.9E-01 | 1.4E-01 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 41| +2.12E+02 | -4.7E-02 | +3.2E-01 | 6.2E-02 | 5.9E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 45| +2.14E+02 | -1.6E-01 | +5.6E-01 | 3.1E-02 | 8.2E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +1.50E+02 | -2.0E-04 | +7.7E-01 | 2.2E-01 | 5.6E-01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 5| +7.42E+02 | -3.1E+03 | +9.0E-01 | 5.0E-01 | 2.2E+04 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 13| +2.50E+02 | -3.1E-03 | +7.4E-01 | 2.0E-03 | 1.0E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 49| +1.48E+02 | +3.1E-03 | -1.1E+00 | 3.9E-02 | 2.9E-01 | 3.4E-02 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 46| +2.14E+02 | -5.6E-02 | +2.8E-01 | 3.1E-02 | 5.3E+00 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 32| +2.42E+02 | -9.5E-01 | +7.7E-01 | 6.2E-02 | 2.0E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 42| +2.12E+02 | +3.4E-02 | -1.5E-01 | 6.2E-02 | 6.3E+00 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 28| +1.54E+02 | -5.8E-04 | +6.8E-01 | 1.3E-01 | 3.7E-01 | 9.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 35| +1.50E+02 | -7.7E-05 | +9.2E-01 | 4.3E-01 | 2.5E-01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 6| +2.75E+02 | -4.7E+02 | +9.0E-01 | 5.0E-01 | 3.5E+03 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 14| +2.50E+02 | +1.1E-06 | -6.0E-03 | 2.0E-03 | 1.8E-01 | 4.2E-03 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 47| +2.14E+02 | -1.5E-01 | +5.3E-01 | 3.1E-02 | 8.0E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 43| +2.12E+02 | -1.2E-01 | +6.9E-01 | 1.6E-02 | 6.3E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 50| +1.48E+02 | -3.3E-04 | +4.1E-01 | 9.8E-03 | 2.9E-01 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 7| +2.05E+02 | -7.0E+01 | +9.3E-01 | 5.0E-01 | 5.4E+02 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +1.54E+02 | -2.3E-04 | +1.1E+00 | 1.3E-01 | 4.2E-01 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 33| +2.41E+02 | -8.7E-01 | +4.6E-01 | 1.2E-01 | 1.3E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 15| +2.50E+02 | -9.7E-05 | +5.8E-01 | 4.9E-04 | 1.8E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 44| +2.12E+02 | -6.4E-02 | +9.8E-01 | 1.6E-02 | 4.1E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 48| +2.14E+02 | -4.6E-02 | +2.4E-01 | 3.1E-02 | 5.2E+00 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 36| +1.50E+02 | -2.2E-05 | +8.7E-01 | 8.7E-01 | 1.9E-01 | 2.4E-01 | trr |1\n", - "2023-02-17 16:05:31 fides(INFO) 51| +1.48E+02 | -1.3E-04 | +8.5E-01 | 9.8E-03 | 8.1E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 45| +2.12E+02 | -6.6E-02 | +6.7E-01 | 3.1E-02 | 3.7E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 16| +2.50E+02 | -8.5E-09 | +1.2E-03 | 4.9E-04 | 3.3E-02 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 49| +2.13E+02 | -8.7E-02 | +9.0E-01 | 7.8E-03 | 7.8E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 30| +1.54E+02 | -3.4E-04 | +1.1E+00 | 2.5E-01 | 1.7E-01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +1.50E+02 | +6.1E-06 | -4.5E-01 | 8.7E-01 | 5.5E-02 | 6.1E-03 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 8| +2.05E+02 | +1.8E+02 | -1.8E+01 | 1.0E+00 | 9.1E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 34| +2.39E+02 | -1.9E+00 | +9.3E-01 | 1.2E-01 | 1.2E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 52| +1.48E+02 | -8.1E-05 | +8.2E-01 | 2.0E-02 | 1.1E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 46| +2.12E+02 | -2.9E-02 | +2.3E-01 | 3.1E-02 | 5.5E+00 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 50| +2.13E+02 | -8.5E-02 | +7.7E-01 | 1.6E-02 | 5.5E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 9| +1.94E+02 | -1.0E+01 | +1.1E+00 | 2.5E-01 | 9.1E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 17| +2.50E+02 | -1.7E-06 | +2.8E-01 | 1.2E-04 | 3.2E-02 | 3.2E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(WARNING) Stopping as function difference 1.72E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:31 fides(INFO) 38| +1.50E+02 | -5.2E-06 | +8.5E-01 | 1.9E-02 | 5.5E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 53| +1.48E+02 | +6.9E-05 | -3.9E-01 | 3.9E-02 | 1.6E-01 | 5.4E-03 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 31| +1.54E+02 | -5.0E-04 | +1.6E+00 | 5.1E-01 | 1.7E-01 | 2.1E-01 | trr |1\n", - "2023-02-17 16:05:31 fides(INFO) 47| +2.12E+02 | -5.5E-02 | +8.1E-01 | 7.8E-03 | 4.7E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 51| +2.13E+02 | -1.0E-01 | +7.1E-01 | 3.1E-02 | 3.9E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 10| +1.94E+02 | +1.3E+01 | -2.6E+00 | 5.0E-01 | 3.1E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 39| +1.50E+02 | -1.3E-06 | +6.0E-01 | 3.7E-02 | 2.5E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(WARNING) Stopping as function difference 1.28E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:31 fides(INFO) 54| +1.48E+02 | -3.9E-05 | +7.0E-01 | 9.8E-03 | 1.6E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 48| +2.12E+02 | -1.1E-02 | +2.9E-01 | 1.6E-02 | 3.0E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 35| +2.39E+02 | +2.7E+00 | -6.9E-01 | 2.5E-01 | 1.2E+01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 32| +1.54E+02 | -1.9E-04 | +1.5E+00 | 5.1E-01 | 1.0E-01 | 5.0E-03 | trr |1\n", - "2023-02-17 16:05:31 fides(INFO) 52| +2.13E+02 | -7.9E-02 | +4.2E-01 | 3.1E-02 | 6.2E+00 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 11| +1.89E+02 | -5.4E+00 | +1.0E+00 | 1.2E-01 | 3.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 55| +1.48E+02 | -2.8E-05 | +7.7E-01 | 9.8E-03 | 1.4E-01 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 49| +2.12E+02 | -3.4E-02 | +4.8E-01 | 1.6E-02 | 4.5E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 53| +2.13E+02 | -2.6E-02 | +1.3E-01 | 3.1E-02 | 5.3E+00 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 36| +2.38E+02 | -9.4E-01 | +7.2E-01 | 6.2E-02 | 1.2E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 33| +1.54E+02 | +3.4E-04 | -2.4E+00 | 5.1E-01 | 7.8E-02 | 1.6E-02 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 12| +1.76E+02 | -1.3E+01 | +1.4E+00 | 2.5E-01 | 1.9E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 50| +2.12E+02 | -4.7E-03 | +1.0E-01 | 1.6E-02 | 3.1E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 0| +1.57E+13 | NaN | NaN | 1.0E+00 | 5.8E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:31 fides(INFO) 56| +1.48E+02 | -3.1E-05 | +6.2E-01 | 2.0E-02 | 8.4E-02 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 54| +2.13E+02 | -8.6E-02 | +8.9E-01 | 7.8E-03 | 7.6E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 1| +4.16E+10 | -1.6E+13 | +1.0E-01 | 1.0E+00 | 5.8E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 51| +2.12E+02 | -2.2E-02 | +9.0E-01 | 3.9E-03 | 4.3E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 13| +1.63E+02 | -1.2E+01 | +4.7E-01 | 5.0E-01 | 6.1E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 57| +1.48E+02 | +4.0E-05 | -4.4E-01 | 2.0E-02 | 1.7E-01 | 7.6E-03 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 55| +2.13E+02 | -7.2E-02 | +7.1E-01 | 1.6E-02 | 5.2E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +1.54E+02 | -3.3E-05 | +5.3E-01 | 1.8E-03 | 7.8E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +2.37E+02 | -9.8E-01 | +8.0E-01 | 6.2E-02 | 1.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 2| +6.23E+09 | -3.5E+10 | +8.9E-01 | 2.5E-01 | 1.9E+11 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 52| +2.12E+02 | -2.0E-02 | +7.2E-01 | 7.8E-03 | 3.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 0| +6.30E+11 | NaN | NaN | 1.0E+00 | 2.6E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:31 fides(INFO) 56| +2.13E+02 | -5.4E-02 | +7.6E-01 | 1.6E-02 | 3.5E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 3| +9.33E+08 | -5.3E+09 | +8.9E-01 | 5.0E-01 | 2.9E+10 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 14| +1.63E+02 | -2.2E+00 | -6.9E-02 | 5.0E-01 | 1.4E+02 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 58| +1.48E+02 | -1.9E-05 | +6.9E-01 | 4.9E-03 | 1.7E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 4| +1.41E+08 | -7.9E+08 | +8.9E-01 | 5.0E-01 | 4.3E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 35| +1.54E+02 | -2.2E-05 | +4.6E-01 | 1.8E-03 | 4.6E-02 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 53| +2.12E+02 | -1.5E-02 | +8.3E-01 | 7.8E-03 | 2.4E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 1| +9.99E+09 | -6.2E+11 | +9.9E-02 | 1.0E+00 | 2.6E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 38| +2.35E+02 | -2.1E+00 | +1.0E+00 | 1.2E-01 | 1.5E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 5| +2.13E+07 | -1.2E+08 | +8.9E-01 | 5.0E-01 | 6.4E+08 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 57| +2.13E+02 | -4.7E-02 | +4.1E-01 | 3.1E-02 | 4.3E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 15| +1.58E+02 | -4.9E+00 | +9.8E-01 | 1.2E-01 | 1.4E+02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 59| +1.48E+02 | -7.7E-06 | +1.2E+00 | 4.9E-03 | 4.8E-02 | 8.9E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 2| +1.49E+09 | -8.5E+09 | +8.9E-01 | 2.5E-01 | 4.6E+10 | 7.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 54| +2.12E+02 | -1.2E-02 | +4.2E-01 | 1.6E-02 | 2.6E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 6| +3.24E+06 | -1.8E+07 | +8.9E-01 | 5.0E-01 | 9.7E+07 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 58| +2.13E+02 | +1.6E-02 | -1.0E-01 | 3.1E-02 | 5.3E+00 | 7.5E-02 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 36| +1.54E+02 | -2.1E-05 | +4.1E-01 | 1.8E-03 | 5.1E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 16| +1.57E+02 | -1.5E+00 | +3.8E-01 | 2.5E-01 | 6.9E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 7| +4.97E+05 | -2.7E+06 | +8.9E-01 | 5.0E-01 | 1.5E+07 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 3| +2.25E+08 | -1.3E+09 | +8.9E-01 | 5.0E-01 | 6.9E+09 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 60| +1.48E+02 | -9.4E-06 | +9.5E-01 | 9.8E-03 | 6.0E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 55| +2.12E+02 | -2.9E-03 | +5.7E-02 | 1.6E-02 | 2.9E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 59| +2.13E+02 | -7.0E-02 | +8.5E-01 | 7.8E-03 | 5.3E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 8| +7.70E+04 | -4.2E+05 | +9.0E-01 | 5.0E-01 | 2.3E+06 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 4| +3.41E+07 | -1.9E+08 | +8.9E-01 | 1.0E+00 | 1.0E+09 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 17| +1.52E+02 | -4.8E+00 | +8.1E-01 | 2.5E-01 | 7.9E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 39| +2.34E+02 | -1.3E+00 | +2.5E-01 | 2.5E-01 | 1.3E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +1.54E+02 | -1.1E-05 | +2.7E-01 | 1.8E-03 | 3.5E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 61| +1.48E+02 | -1.1E-05 | +8.1E-01 | 2.0E-02 | 3.6E-02 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 56| +2.12E+02 | -2.0E-02 | +8.9E-01 | 3.9E-03 | 3.8E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 9| +1.21E+04 | -6.5E+04 | +9.0E-01 | 5.0E-01 | 3.5E+05 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 60| +2.13E+02 | -3.8E-02 | +5.5E-01 | 1.6E-02 | 3.5E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 5| +5.17E+06 | -2.9E+07 | +8.9E-01 | 2.0E+00 | 1.6E+08 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 10| +2.08E+03 | -1.0E+04 | +9.0E-01 | 1.0E+00 | 5.5E+04 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 57| +2.12E+02 | -1.4E-02 | +6.3E-01 | 7.8E-03 | 2.7E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 38| +1.54E+02 | -1.4E-05 | +3.0E-01 | 1.8E-03 | 4.1E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 18| +1.50E+02 | -2.0E+00 | +7.7E-01 | 5.0E-01 | 3.8E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 62| +1.48E+02 | +2.3E-05 | -1.4E+00 | 3.9E-02 | 4.1E-02 | 7.0E-03 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 11| +5.14E+02 | -1.6E+03 | +9.0E-01 | 1.0E+00 | 8.6E+03 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 6| +7.88E+05 | -4.4E+06 | +8.9E-01 | 2.0E+00 | 2.4E+07 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 61| +2.13E+02 | -5.5E-02 | +6.7E-01 | 1.6E-02 | 4.4E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 58| +2.12E+02 | -6.6E-03 | +4.9E-01 | 7.8E-03 | 1.9E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 12| +2.76E+02 | -2.4E+02 | +9.0E-01 | 2.0E+00 | 1.3E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 40| +2.29E+02 | -4.7E+00 | +9.8E-01 | 2.5E-01 | 3.4E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 63| +1.48E+02 | -6.0E-06 | +1.1E+00 | 9.8E-03 | 4.1E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 7| +1.21E+05 | -6.7E+05 | +9.0E-01 | 2.0E+00 | 3.6E+06 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 62| +2.13E+02 | -3.4E-02 | +5.3E-01 | 1.6E-02 | 3.3E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 39| +1.54E+02 | -5.5E-06 | +1.5E-01 | 1.8E-03 | 2.8E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 59| +2.12E+02 | -1.1E-02 | +5.8E-01 | 7.8E-03 | 2.4E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 13| +2.29E+02 | -4.7E+01 | +1.2E+00 | 2.0E+00 | 1.9E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 19| +1.50E+02 | -2.3E-01 | +2.2E-01 | 1.0E+00 | 3.0E+01 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 8| +1.88E+04 | -1.0E+05 | +9.0E-01 | 2.0E+00 | 5.5E+05 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 64| +1.48E+02 | +2.3E-05 | -1.7E+00 | 2.0E-02 | 3.1E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 63| +2.13E+02 | -5.2E-02 | +6.4E-01 | 1.6E-02 | 4.4E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 41| +2.23E+02 | -6.6E+00 | +7.2E-01 | 5.0E-01 | 2.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 60| +2.12E+02 | -4.4E-03 | +3.3E-01 | 7.8E-03 | 1.9E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 9| +3.09E+03 | -1.6E+04 | +9.0E-01 | 2.0E+00 | 8.5E+04 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 40| +1.54E+02 | -9.6E-06 | +8.3E-01 | 4.5E-04 | 3.4E-02 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 64| +2.13E+02 | -2.9E-02 | +4.7E-01 | 1.6E-02 | 3.2E+00 | 3.3E-02 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 65| +1.48E+02 | -1.8E-07 | +3.5E-02 | 4.9E-03 | 3.1E-02 | 3.8E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 14| +2.29E+02 | +1.4E+02 | -1.6E+01 | 2.0E+00 | 3.1E+01 | 6.1E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 61| +2.12E+02 | -1.0E-02 | +5.5E-01 | 7.8E-03 | 2.4E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 20| +1.50E+02 | -1.9E-01 | +5.3E-01 | 1.1E-01 | 6.9E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 10| +6.63E+02 | -2.4E+03 | +9.0E-01 | 2.0E+00 | 1.3E+04 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 42| +2.23E+02 | +8.6E+01 | -6.9E+00 | 5.0E-01 | 3.5E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 15| +2.29E+02 | +1.4E+01 | -1.6E+00 | 5.0E-01 | 3.1E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 41| +1.54E+02 | -6.5E-06 | +4.4E-01 | 9.0E-04 | 2.3E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 65| +2.13E+02 | -5.0E-02 | +6.2E-01 | 1.6E-02 | 4.4E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 62| +2.12E+02 | -3.3E-03 | +2.7E-01 | 7.8E-03 | 1.7E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 11| +2.93E+02 | -3.7E+02 | +9.0E-01 | 2.0E+00 | 2.1E+03 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 66| +1.48E+02 | -5.5E-06 | +2.3E+00 | 1.2E-03 | 1.0E-01 | 2.7E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 66| +2.13E+02 | -2.5E-02 | +4.2E-01 | 1.6E-02 | 3.1E+00 | 3.4E-02 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 16| +2.25E+02 | -3.9E+00 | +5.3E-01 | 1.2E-01 | 3.1E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 43| +2.23E+02 | +2.0E+00 | -3.7E-01 | 1.2E-01 | 3.5E+01 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 12| +2.43E+02 | -5.0E+01 | +8.8E-01 | 2.0E+00 | 3.1E+02 | 5.0E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 42| +1.54E+02 | -4.7E-06 | +4.2E-01 | 9.0E-04 | 1.7E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 17| +2.23E+02 | -2.1E+00 | +7.1E-01 | 1.2E-01 | 2.6E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 67| +1.48E+02 | +3.7E-06 | -3.2E+00 | 2.4E-03 | 1.2E-02 | 4.0E-04 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 63| +2.12E+02 | -9.0E-03 | +5.1E-01 | 7.8E-03 | 2.3E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 67| +2.12E+02 | -5.1E-02 | +6.3E-01 | 1.6E-02 | 4.4E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 18| +2.23E+02 | +1.0E-01 | -8.0E-02 | 1.2E-01 | 2.1E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 68| +1.48E+02 | +3.3E-06 | -9.3E+00 | 6.1E-04 | 1.2E-02 | 1.0E-04 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 43| +1.54E+02 | -3.0E-06 | +2.6E-01 | 9.0E-04 | 2.0E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 64| +2.12E+02 | -2.6E-03 | +2.3E-01 | 7.8E-03 | 1.6E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 68| +2.12E+02 | -2.2E-02 | +3.9E-01 | 1.6E-02 | 3.0E+00 | 3.4E-02 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 21| +1.50E+02 | -1.3E-01 | +8.2E-01 | 1.1E-01 | 1.7E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 44| +2.21E+02 | -1.6E+00 | +8.1E-01 | 3.1E-02 | 3.5E+01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 13| +2.38E+02 | -5.3E+00 | +7.2E-01 | 4.0E+00 | 3.5E+01 | 4.4E+00 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 19| +2.22E+02 | -1.1E+00 | +8.8E-01 | 3.1E-02 | 2.1E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 65| +2.12E+02 | -5.3E-03 | +9.0E-01 | 2.0E-03 | 2.1E+00 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 69| +1.48E+02 | +4.7E-06 | -3.2E+01 | 1.5E-04 | 1.2E-02 | 3.0E-05 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 69| +2.12E+02 | -5.1E-02 | +6.3E-01 | 1.6E-02 | 4.3E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 44| +1.54E+02 | -3.8E-06 | +3.3E-01 | 9.0E-04 | 1.4E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 14| +2.38E+02 | +7.8E+01 | -6.4E+00 | 4.0E+00 | 2.2E+01 | 9.9E+00 | trr |0\n", - "2023-02-17 16:05:32 fides(INFO) 66| +2.12E+02 | -5.2E-03 | +7.6E-01 | 3.9E-03 | 1.7E+00 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 20| +2.20E+02 | -1.2E+00 | +8.6E-01 | 6.2E-02 | 1.7E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 45| +2.18E+02 | -2.6E+00 | +7.3E-01 | 6.2E-02 | 4.6E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 22| +1.50E+02 | -5.4E-02 | +5.6E-01 | 2.2E-01 | 5.4E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 70| +1.48E+02 | +5.5E-06 | -5.8E+01 | 3.8E-05 | 1.2E-02 | 1.6E-05 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 70| +2.12E+02 | -2.0E-02 | +3.8E-01 | 1.6E-02 | 2.8E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 67| +2.12E+02 | -6.3E-03 | +7.2E-01 | 7.8E-03 | 1.3E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 21| +2.19E+02 | -1.8E+00 | +8.4E-01 | 1.2E-01 | 2.0E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 45| +1.54E+02 | -4.9E-07 | +5.4E-02 | 9.0E-04 | 1.6E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 46| +2.16E+02 | -1.9E+00 | +8.6E-01 | 6.2E-02 | 4.8E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 71| +2.12E+02 | -5.0E-02 | +6.2E-01 | 1.6E-02 | 4.3E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:32.264 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.574 and h = 2.93244e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:32.264 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.574: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:32 fides(INFO) 71| +1.48E+02 | +0.0E+00 | +0.0E+00 | 9.6E-06 | 1.2E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 68| +2.12E+02 | -3.9E-03 | +3.5E-01 | 7.8E-03 | 1.7E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 22| +2.19E+02 | +8.3E-01 | -3.6E-01 | 2.5E-01 | 1.9E+01 | 7.2E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 23| +1.50E+02 | -1.1E-02 | +8.8E-01 | 2.2E-01 | 4.1E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 46| +1.54E+02 | -2.9E-06 | +8.7E-01 | 2.2E-04 | 1.1E-02 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 72| +2.12E+02 | -2.8E-02 | +5.4E-01 | 1.6E-02 | 2.8E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 15| +2.38E+02 | +2.5E+01 | -5.3E+00 | 9.2E-01 | 2.2E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 69| +2.12E+02 | -2.3E-03 | +1.9E-01 | 7.8E-03 | 1.6E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 23| +2.17E+02 | -1.2E+00 | +6.1E-01 | 6.2E-02 | 1.9E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 47| +2.14E+02 | -2.2E+00 | +9.6E-01 | 1.2E-01 | 3.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 72| +1.48E+02 | +2.8E-06 | -6.0E+01 | 2.4E-06 | 1.2E-02 | 5.2E-06 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 73| +2.12E+02 | -4.1E-02 | +6.2E-01 | 1.6E-02 | 3.7E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 24| +1.50E+02 | -8.9E-03 | +9.2E-01 | 4.5E-01 | 3.6E+00 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 47| +1.54E+02 | -2.3E-06 | +5.4E-01 | 4.5E-04 | 2.7E-02 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 24| +2.16E+02 | -1.0E+00 | +8.6E-01 | 6.2E-02 | 1.8E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 70| +2.12E+02 | -4.8E-03 | +8.9E-01 | 2.0E-03 | 1.9E+00 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 48| +2.11E+02 | -3.0E+00 | +8.7E-01 | 2.5E-01 | 3.3E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 73| +1.48E+02 | +6.7E-06 | -4.9E+02 | 6.0E-07 | 1.2E-02 | 1.3E-06 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 74| +2.12E+02 | -2.8E-02 | +5.2E-01 | 1.6E-02 | 2.8E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 16| +2.38E+02 | +3.1E+00 | -8.3E-01 | 2.3E-01 | 2.2E+01 | 5.6E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 25| +2.16E+02 | -1.4E-01 | +3.3E-02 | 1.2E-01 | 1.4E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 71| +2.12E+02 | -3.8E-03 | +6.9E-01 | 3.9E-03 | 1.4E+00 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 25| +1.50E+02 | -3.7E-03 | +5.2E-01 | 9.0E-01 | 7.0E-01 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 48| +1.54E+02 | -1.2E-06 | +4.9E-01 | 4.5E-04 | 9.6E-03 | 9.5E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(WARNING) Stopping as function difference 1.19E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:32 fides(INFO) 75| +2.12E+02 | -4.0E-02 | +6.2E-01 | 1.6E-02 | 3.6E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 74| +1.48E+02 | +1.4E-05 | -4.0E+03 | 1.5E-07 | 1.2E-02 | 3.2E-07 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 49| +2.11E+02 | +1.6E+01 | -5.2E+00 | 5.0E-01 | 4.0E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 26| +2.15E+02 | -1.2E+00 | +8.7E-01 | 3.1E-02 | 3.1E+01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 72| +2.12E+02 | -2.6E-03 | +6.5E-01 | 3.9E-03 | 1.1E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 17| +2.36E+02 | -1.9E+00 | +8.2E-01 | 5.8E-02 | 2.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 76| +2.12E+02 | -2.7E-02 | +5.2E-01 | 1.6E-02 | 2.7E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 26| +1.50E+02 | +8.2E-03 | -2.7E+00 | 9.0E-01 | 1.5E+00 | 1.2E-01 | trr |0\n", - "2023-02-17 16:05:32 fides(INFO) 73| +2.12E+02 | -3.2E-03 | +6.7E-01 | 3.9E-03 | 1.3E+00 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 27| +2.14E+02 | -8.0E-01 | +6.6E-01 | 6.2E-02 | 1.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 75| +1.48E+02 | +1.5E-05 | -1.6E+04 | 3.7E-08 | 1.2E-02 | 8.1E-08 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 50| +2.09E+02 | -1.9E+00 | +1.1E+00 | 1.2E-01 | 4.0E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 77| +2.12E+02 | -3.9E-02 | +6.2E-01 | 1.6E-02 | 3.5E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 28| +2.14E+02 | -2.5E-01 | +2.8E-01 | 6.2E-02 | 1.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 74| +2.12E+02 | -2.0E-03 | +5.4E-01 | 3.9E-03 | 1.0E+00 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 18| +2.33E+02 | -2.9E+00 | +1.2E+00 | 1.2E-01 | 1.6E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 76| +1.48E+02 | +8.1E-06 | -3.6E+04 | 9.3E-09 | 1.2E-02 | 2.0E-08 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 27| +1.50E+02 | -8.3E-04 | +5.5E-01 | 7.9E-02 | 1.5E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 51| +2.05E+02 | -4.8E+00 | +8.5E-01 | 2.5E-01 | 3.2E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 78| +2.12E+02 | -2.8E-02 | +5.3E-01 | 1.6E-02 | 2.7E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 75| +2.12E+02 | -3.0E-03 | +6.3E-01 | 3.9E-03 | 1.2E+00 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 29| +2.13E+02 | -7.1E-01 | +5.8E-01 | 6.2E-02 | 1.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 79| +2.12E+02 | -3.9E-02 | +6.3E-01 | 1.6E-02 | 3.4E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 77| +1.48E+02 | +4.7E-06 | -8.4E+04 | 2.3E-09 | 1.2E-02 | 5.0E-09 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 28| +1.50E+02 | -1.7E-04 | +3.6E-01 | 7.9E-02 | 4.4E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 52| +1.90E+02 | -1.5E+01 | +6.2E-01 | 5.0E-01 | 1.1E+02 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 30| +2.13E+02 | -6.8E-02 | +6.2E-02 | 6.2E-02 | 1.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 76| +2.12E+02 | -1.7E-03 | +4.8E-01 | 3.9E-03 | 9.9E-01 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 0| +7.90E+12 | NaN | NaN | 1.0E+00 | 3.6E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 80| +2.12E+02 | -2.8E-02 | +5.5E-01 | 1.6E-02 | 2.6E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 78| +1.48E+02 | +7.0E-06 | -5.0E+05 | 5.8E-10 | 1.2E-02 | 1.3E-09 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 31| +2.13E+02 | -4.1E-01 | +9.0E-01 | 1.6E-02 | 2.0E+01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 19| +2.30E+02 | -2.6E+00 | +9.5E-01 | 2.3E-01 | 1.6E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 29| +1.50E+02 | -2.5E-05 | +8.4E-02 | 7.9E-02 | 4.0E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 77| +2.12E+02 | -2.7E-03 | +6.0E-01 | 3.9E-03 | 1.2E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 53| +1.90E+02 | +6.1E+01 | -4.4E+00 | 5.0E-01 | 5.1E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 81| +2.12E+02 | -5.6E-02 | +1.0E+00 | 1.6E-02 | 3.3E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 32| +2.12E+02 | -3.7E-01 | +7.5E-01 | 3.1E-02 | 1.4E+01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 1| +3.35E+08 | -7.9E+12 | +8.5E-02 | 1.0E+00 | 3.6E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 79| +1.48E+02 | +3.2E-06 | -9.2E+05 | 1.5E-10 | 1.2E-02 | 3.2E-10 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 78| +2.12E+02 | -1.5E-03 | +4.4E-01 | 3.9E-03 | 9.3E-01 | 8.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 30| +1.50E+02 | -8.0E-05 | +7.1E-01 | 2.0E-02 | 9.9E-02 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 54| +1.87E+02 | -2.3E+00 | +3.4E-01 | 1.2E-01 | 5.1E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 82| +2.12E+02 | -8.3E-02 | +7.8E-01 | 3.1E-02 | 2.2E+00 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 33| +2.12E+02 | -4.2E-01 | +7.5E-01 | 6.2E-02 | 8.8E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 20| +2.25E+02 | -5.8E+00 | +1.0E+00 | 4.6E-01 | 2.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 79| +2.12E+02 | -2.5E-03 | +5.7E-01 | 3.9E-03 | 1.1E+00 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 2| +5.08E+07 | -2.8E+08 | +8.9E-01 | 2.5E-01 | 1.5E+09 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 34| +2.12E+02 | -2.1E-01 | +3.8E-01 | 6.2E-02 | 1.3E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 83| +2.12E+02 | -1.8E-01 | +1.0E+00 | 6.2E-02 | 3.2E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 31| +1.50E+02 | -1.2E-05 | +7.5E-01 | 2.0E-02 | 1.7E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 55| +1.85E+02 | -2.3E+00 | +6.9E-01 | 1.2E-01 | 3.8E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 80| +1.48E+02 | +3.5E-06 | -4.0E+06 | 3.6E-11 | 1.2E-02 | 7.9E-11 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 80| +2.12E+02 | -1.3E-03 | +4.1E-01 | 3.9E-03 | 8.9E-01 | 9.3E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 3| +7.21E+06 | -4.4E+07 | +9.0E-01 | 5.0E-01 | 2.3E+08 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 21| +2.20E+02 | -4.1E+00 | +2.9E-01 | 9.2E-01 | 2.3E+01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 35| +2.12E+02 | +1.1E-01 | -2.1E-01 | 6.2E-02 | 9.5E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 84| +2.11E+02 | -2.7E-01 | +9.6E-01 | 1.2E-01 | 2.2E+00 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 32| +1.50E+02 | -2.2E-06 | +8.6E-01 | 2.0E-02 | 3.6E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 81| +2.12E+02 | -2.2E-03 | +5.4E-01 | 3.9E-03 | 1.1E+00 | 7.7E-03 | 2d |1\n", - "2023-02-17 16:05:32.617 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.317 and h = 3.91293e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:32.617 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.317: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:32 fides(INFO) 81| +1.48E+02 | +0.0E+00 | +0.0E+00 | 9.1E-12 | 1.2E-02 | 2.0E-11 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 56| +1.77E+02 | -8.4E+00 | +6.6E-01 | 1.2E-01 | 5.5E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 36| +2.12E+02 | -2.3E-01 | +8.4E-01 | 1.6E-02 | 9.5E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 85| +2.10E+02 | -1.2E+00 | +1.4E+00 | 2.5E-01 | 4.8E+00 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 82| +2.12E+02 | -1.2E-03 | +3.9E-01 | 3.9E-03 | 8.5E-01 | 9.6E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 4| +1.01E+06 | -6.2E+06 | +9.0E-01 | 5.0E-01 | 3.3E+07 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 22| +2.20E+02 | -5.1E+00 | -4.0E-01 | 9.2E-01 | 6.9E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 33| +1.50E+02 | -1.8E-06 | +8.9E-01 | 3.9E-02 | 4.4E-02 | 6.3E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 82| +1.48E+02 | +5.5E-06 | -1.0E+08 | 2.3E-12 | 1.2E-02 | 4.9E-12 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 37| +2.11E+02 | -1.4E-01 | +5.8E-01 | 3.1E-02 | 7.1E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 57| +1.73E+02 | -3.1E+00 | +5.8E-01 | 1.2E-01 | 4.6E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 86| +2.10E+02 | +2.5E+01 | -1.0E+01 | 5.0E-01 | 8.4E+00 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 83| +2.12E+02 | -2.0E-03 | +5.2E-01 | 3.9E-03 | 1.0E+00 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 38| +2.11E+02 | -1.6E-01 | +5.7E-01 | 3.1E-02 | 9.6E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 5| +1.44E+05 | -8.7E+05 | +9.0E-01 | 5.0E-01 | 4.9E+06 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 87| +2.10E+02 | -6.9E-01 | +8.2E-01 | 1.2E-01 | 8.4E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 34| +1.50E+02 | -1.8E-06 | +1.1E+00 | 7.9E-02 | 3.4E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 84| +2.12E+02 | -1.1E-03 | +3.8E-01 | 3.9E-03 | 8.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 58| +1.71E+02 | -2.9E+00 | +4.1E-01 | 1.2E-01 | 4.3E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 23| +2.07E+02 | -1.3E+01 | +8.3E-01 | 2.3E-01 | 6.9E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 39| +2.11E+02 | -6.7E-02 | +2.8E-01 | 3.1E-02 | 6.8E+00 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 83| +1.48E+02 | +1.4E-05 | -9.9E+08 | 5.7E-13 | 1.2E-02 | 1.2E-12 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 85| +2.12E+02 | -1.8E-03 | +5.0E-01 | 3.9E-03 | 9.7E-01 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 88| +2.10E+02 | +8.6E+00 | -4.0E+00 | 2.5E-01 | 6.7E+00 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 59| +1.71E+02 | +2.7E+01 | -2.7E+00 | 1.2E-01 | 4.9E+01 | 3.3E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 6| +2.12E+04 | -1.2E+05 | +9.0E-01 | 5.0E-01 | 7.5E+05 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 35| +1.50E+02 | +2.5E-06 | -2.2E+00 | 1.6E-01 | 1.5E-02 | 8.4E-03 | trr |0\n", - "2023-02-17 16:05:32 fides(INFO) 40| +2.11E+02 | -1.6E-01 | +5.3E-01 | 3.1E-02 | 9.8E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 86| +2.12E+02 | -1.1E-03 | +3.7E-01 | 3.9E-03 | 7.8E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 24| +1.83E+02 | -2.4E+01 | +1.0E+00 | 4.6E-01 | 3.3E+01 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 89| +2.10E+02 | +3.0E-01 | -4.3E-01 | 6.2E-02 | 6.7E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 84| +1.48E+02 | +5.3E-06 | -1.6E+09 | 1.4E-13 | 1.2E-02 | 3.1E-13 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 87| +2.12E+02 | -1.7E-03 | +4.8E-01 | 3.9E-03 | 9.2E-01 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 41| +2.11E+02 | -3.4E-02 | +1.6E-01 | 3.1E-02 | 6.2E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 60| +1.70E+02 | -3.8E-01 | +1.0E-01 | 3.1E-02 | 4.9E+01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 7| +3.31E+03 | -1.8E+04 | +9.0E-01 | 5.0E-01 | 1.2E+05 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 90| +2.09E+02 | -1.3E-01 | +6.3E-01 | 1.6E-02 | 6.7E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 36| +1.50E+02 | -1.7E-07 | +3.1E-01 | 2.3E-02 | 1.5E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(WARNING) Stopping as function difference 1.68E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:32 fides(INFO) 25| +1.83E+02 | +3.9E+02 | -1.7E+01 | 9.2E-01 | 1.2E+02 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 85| +1.48E+02 | +4.3E-06 | -5.0E+09 | 3.6E-14 | 1.2E-02 | 7.7E-14 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 88| +2.12E+02 | -1.0E-03 | +3.7E-01 | 3.9E-03 | 7.5E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 42| +2.11E+02 | -9.6E-02 | +9.0E-01 | 7.8E-03 | 9.5E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 91| +2.09E+02 | -5.5E-02 | +1.0E+00 | 1.6E-02 | 1.9E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 61| +1.69E+02 | -8.6E-01 | +1.0E+00 | 7.8E-03 | 6.9E+01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 8| +6.40E+02 | -2.7E+03 | +9.0E-01 | 5.0E-01 | 1.9E+04 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 89| +2.12E+02 | -1.5E-03 | +4.7E-01 | 3.9E-03 | 8.8E-01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 26| +1.70E+02 | -1.3E+01 | +8.7E-01 | 2.3E-01 | 1.2E+02 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 86| +1.48E+02 | +2.5E-06 | -1.1E+10 | 8.9E-15 | 1.2E-02 | 1.9E-14 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 43| +2.11E+02 | -9.0E-02 | +7.5E-01 | 1.6E-02 | 7.2E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 92| +2.09E+02 | -1.1E-01 | +1.1E+00 | 3.1E-02 | 1.4E+00 | 8.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 62| +1.68E+02 | -1.1E+00 | +1.0E+00 | 1.6E-02 | 5.1E+01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 27| +1.56E+02 | -1.4E+01 | +1.0E+00 | 4.6E-01 | 8.3E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 44| +2.11E+02 | -1.1E-01 | +7.6E-01 | 3.1E-02 | 5.0E+00 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 9| +2.48E+02 | -3.9E+02 | +8.9E-01 | 5.0E-01 | 3.0E+03 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 90| +2.12E+02 | -1.0E-03 | +3.8E-01 | 3.9E-03 | 7.3E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 93| +2.09E+02 | -2.0E-01 | +1.1E+00 | 6.2E-02 | 1.6E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 45| +2.11E+02 | -6.0E-02 | +3.5E-01 | 6.2E-02 | 6.6E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 0| +6.35E+09 | NaN | NaN | 1.0E+00 | 2.9E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:32 fides(INFO) 87| +1.48E+02 | +1.1E-05 | -2.0E+11 | 2.2E-15 | 1.2E-02 | 4.8E-15 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 91| +2.12E+02 | -1.4E-03 | +4.6E-01 | 3.9E-03 | 8.4E-01 | 8.8E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 28| +1.56E+02 | +1.8E+01 | -2.7E+00 | 9.2E-01 | 9.3E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 63| +1.67E+02 | -1.1E+00 | +9.9E-01 | 3.1E-02 | 2.6E+01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 94| +2.09E+02 | -5.6E-01 | +1.3E+00 | 1.2E-01 | 3.5E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 10| +1.84E+02 | -6.4E+01 | +9.0E-01 | 5.0E-01 | 5.2E+02 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 1| +1.83E+04 | -6.4E+09 | +9.6E-02 | 1.0E+00 | 2.9E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 46| +2.11E+02 | +4.8E-03 | -3.8E-02 | 6.2E-02 | 6.0E+00 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 92| +2.12E+02 | -2.2E-03 | +1.0E+00 | 3.9E-03 | 7.0E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 88| +1.48E+02 | +6.8E-06 | -5.1E+11 | 5.6E-16 | 1.2E-02 | 1.2E-15 | 2d |0\n", - "2023-02-17 16:05:32 fides(WARNING) Stopping as trust region radius 1.39E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:32 fides(INFO) 2| +3.15E+03 | -1.5E+04 | +9.0E-01 | 2.5E-01 | 8.3E+04 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 29| +1.51E+02 | -4.4E+00 | +8.7E-01 | 2.1E-01 | 9.3E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 95| +2.06E+02 | -2.5E+00 | +1.9E+00 | 2.5E-01 | 7.4E+00 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 64| +1.66E+02 | -1.5E+00 | +9.8E-01 | 6.2E-02 | 2.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 47| +2.11E+02 | -1.0E-01 | +6.7E-01 | 1.6E-02 | 6.0E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 93| +2.12E+02 | -3.6E-03 | +9.0E-01 | 7.8E-03 | 5.2E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 3| +7.50E+02 | -2.4E+03 | +9.0E-01 | 5.0E-01 | 1.3E+04 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 11| +1.69E+02 | -1.5E+01 | +1.0E+00 | 5.0E-01 | 2.0E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 96| +1.98E+02 | -8.5E+00 | +6.2E-01 | 5.0E-01 | 1.4E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 30| +1.51E+02 | +4.7E-01 | -1.9E-01 | 4.2E-01 | 5.9E+01 | 5.4E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 4| +3.61E+02 | -3.9E+02 | +9.0E-01 | 1.0E+00 | 2.1E+03 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 48| +2.11E+02 | -6.1E-02 | +9.3E-01 | 1.6E-02 | 4.3E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 94| +2.12E+02 | -1.3E-03 | +5.8E-01 | 1.6E-02 | 6.0E-01 | 8.8E-03 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 65| +1.62E+02 | -3.3E+00 | +7.9E-01 | 1.2E-01 | 2.3E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 97| +1.98E+02 | +5.1E-01 | -1.2E-01 | 5.0E-01 | 6.8E+01 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 5| +2.64E+02 | -9.7E+01 | +9.2E-01 | 2.0E+00 | 3.3E+02 | 4.8E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 49| +2.10E+02 | -2.6E-02 | +3.1E-01 | 3.1E-02 | 3.8E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 31| +1.50E+02 | -1.1E+00 | +8.4E-01 | 1.1E-01 | 5.9E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 95| +2.12E+02 | -3.1E-03 | +8.9E-01 | 1.6E-02 | 7.0E-01 | 1.4E-02 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 66| +1.60E+02 | -2.1E+00 | +1.2E-01 | 2.5E-01 | 5.4E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 6| +2.64E+02 | +1.6E+03 | -1.1E+02 | 4.0E+00 | 4.8E+01 | 9.8E+00 | trr |0\n", - "2023-02-17 16:05:33.009 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 193.843 and h = 3.10514e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33 fides(INFO) 12| +1.66E+02 | -3.1E+00 | +6.3E-01 | 1.0E+00 | 5.1E+01 | 4.0E-01 | trr |1\n", - "2023-02-17 16:05:33.009 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 193.843: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 50| +2.10E+02 | -5.4E-02 | +3.1E-01 | 3.1E-02 | 6.7E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 98| +1.98E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 6.8E+01 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 32| +1.49E+02 | -1.1E+00 | +6.4E-01 | 2.1E-01 | 4.0E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 0| +3.27E+09 | NaN | NaN | 1.0E+00 | 1.5E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:33 fides(INFO) 7| +2.64E+02 | +1.7E+02 | -8.1E+00 | 1.0E+00 | 4.8E+01 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 96| +2.12E+02 | -3.0E-03 | +8.3E-01 | 1.6E-02 | 6.4E-01 | 1.7E-02 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 67| +1.57E+02 | -3.6E+00 | +7.7E-01 | 6.2E-02 | 1.2E+02 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 8| +2.52E+02 | -1.2E+01 | +5.7E-01 | 2.5E-01 | 4.8E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 51| +2.10E+02 | -5.6E-03 | +2.7E-02 | 3.1E-02 | 4.9E+00 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 99| +1.94E+02 | -3.6E+00 | +1.0E+00 | 3.1E-02 | 6.8E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 1| +1.38E+04 | -3.3E+09 | +9.8E-02 | 1.0E+00 | 1.5E+10 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 33| +1.49E+02 | +2.8E-01 | -2.0E-01 | 2.1E-01 | 5.9E+01 | 2.8E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 97| +2.12E+02 | -3.2E-03 | +9.8E-01 | 1.6E-02 | 7.1E-01 | 1.5E-02 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 9| +2.51E+02 | -5.9E-01 | +1.5E-01 | 2.5E-01 | 2.7E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 68| +1.57E+02 | +2.0E-01 | -2.9E-01 | 1.2E-01 | 2.8E+01 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 52| +2.10E+02 | -7.1E-02 | +8.6E-01 | 7.8E-03 | 6.9E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:33.067 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 57.1283 and h = 7.30369e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33.067 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 57.1283: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 100| +1.94E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 6.4E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 98| +2.12E+02 | -2.4E-03 | +8.6E-01 | 1.6E-02 | 4.5E-01 | 2.3E-02 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 10| +2.50E+02 | -1.2E+00 | +6.1E-01 | 6.2E-02 | 2.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 2| +2.98E+03 | -1.1E+04 | +8.9E-01 | 2.5E-01 | 5.4E+04 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 13| +1.59E+02 | -6.5E+00 | +1.0E+00 | 1.0E+00 | 1.5E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 34| +1.48E+02 | -6.5E-01 | +8.4E-01 | 5.3E-02 | 5.9E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 11| +2.50E+02 | -1.4E-02 | +1.5E-01 | 6.2E-02 | 4.0E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 53| +2.10E+02 | -3.3E-02 | +5.1E-01 | 1.6E-02 | 4.4E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 101| +1.92E+02 | -1.7E+00 | +1.0E+00 | 1.6E-02 | 6.4E+01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 99| +2.12E+02 | -3.5E-03 | +8.7E-01 | 3.1E-02 | 6.8E-01 | 2.9E-02 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 69| +1.56E+02 | -3.2E-01 | +7.2E-01 | 3.1E-02 | 2.8E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 12| +2.50E+02 | +9.1E-05 | -7.4E-03 | 1.6E-02 | 3.2E+00 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 35| +1.48E+02 | -2.7E-01 | +7.1E-01 | 1.1E-01 | 1.7E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 3| +6.53E+02 | -2.3E+03 | +9.0E-01 | 5.0E-01 | 9.7E+03 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 54| +2.10E+02 | -1.5E-02 | +2.9E-01 | 1.6E-02 | 3.3E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 13| +2.50E+02 | -2.4E-02 | +8.6E-01 | 3.9E-03 | 3.2E+00 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 102| +1.89E+02 | -3.4E+00 | +9.9E-01 | 3.1E-02 | 6.3E+01 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 100| +2.12E+02 | -2.3E-03 | +6.3E-01 | 3.1E-02 | 7.1E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 36| +1.48E+02 | -2.0E-01 | +8.9E-01 | 1.1E-01 | 2.2E+01 | 9.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 70| +1.56E+02 | -2.7E-01 | +7.6E-01 | 3.1E-02 | 1.5E+01 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 55| +2.10E+02 | -3.2E-02 | +4.9E-01 | 1.6E-02 | 4.4E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 101| +2.12E+02 | -7.8E-05 | +1.3E-02 | 3.1E-02 | 4.8E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 14| +2.50E+02 | -9.3E-04 | +5.2E-02 | 7.8E-03 | 1.7E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 4| +2.90E+02 | -3.6E+02 | +8.9E-01 | 1.0E+00 | 1.6E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 103| +1.82E+02 | -6.4E+00 | +9.9E-01 | 6.2E-02 | 6.0E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 15| +2.50E+02 | -4.8E-03 | +8.9E-01 | 2.0E-03 | 1.6E+00 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 37| +1.48E+02 | -1.6E-01 | +8.1E-01 | 2.1E-01 | 5.7E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 5| +2.49E+02 | -4.1E+01 | +8.3E-01 | 1.0E+00 | 2.3E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 56| +2.10E+02 | -1.3E-02 | +2.7E-01 | 1.6E-02 | 3.1E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 102| +2.12E+02 | -4.0E-03 | +8.9E-01 | 7.8E-03 | 1.1E+00 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 71| +1.56E+02 | +1.4E-01 | -5.0E-01 | 6.2E-02 | 6.1E+00 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 16| +2.50E+02 | -2.4E-03 | +5.0E-01 | 3.9E-03 | 9.6E-01 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 104| +1.72E+02 | -1.0E+01 | +1.0E+00 | 1.2E-01 | 5.4E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 17| +2.50E+02 | -1.6E-04 | +2.1E-01 | 3.9E-03 | 3.2E-01 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 6| +2.35E+02 | -1.5E+01 | +3.9E+00 | 2.0E+00 | 1.2E+01 | 3.9E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 57| +2.10E+02 | -2.7E-02 | +4.5E-01 | 1.6E-02 | 4.1E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 103| +2.12E+02 | -4.4E-03 | +8.0E-01 | 1.6E-02 | 3.7E-01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 38| +1.48E+02 | +4.2E+00 | -8.7E+00 | 4.2E-01 | 3.2E+00 | 4.8E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 72| +1.56E+02 | -1.1E-01 | +8.6E-01 | 1.6E-02 | 6.1E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 18| +2.50E+02 | -3.1E-04 | +6.8E-01 | 9.8E-04 | 3.2E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 105| +1.65E+02 | -7.2E+00 | +4.9E-01 | 2.5E-01 | 4.9E+01 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 104| +2.12E+02 | -8.0E-03 | +1.1E+00 | 3.1E-02 | 5.8E-01 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 58| +2.10E+02 | -1.2E-02 | +2.6E-01 | 1.6E-02 | 3.0E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 19| +2.50E+02 | -6.7E-05 | +9.8E-01 | 9.8E-04 | 3.0E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 14| +1.59E+02 | -1.8E+00 | -4.6E-01 | 2.0E+00 | 3.8E+01 | 2.5E+00 | trr |0\n", - "2023-02-17 16:05:33 fides(INFO) 39| +1.48E+02 | +5.4E-02 | -3.5E-01 | 1.1E-01 | 3.2E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 73| +1.56E+02 | -5.4E-02 | +6.5E-01 | 3.1E-02 | 3.4E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 7| +2.35E+02 | +4.0E+02 | -1.8E+01 | 4.0E+00 | 3.5E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:33.259 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 68.9213 and h = 1.14762e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33.259 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 68.9213: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) 106| +1.65E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 1.3E+02 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 20| +2.50E+02 | -7.7E-05 | +8.7E-01 | 2.0E-03 | 4.1E-02 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 105| +2.12E+02 | -2.0E-02 | +1.1E+00 | 6.2E-02 | 3.6E-01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 59| +2.10E+02 | -2.4E-02 | +4.3E-01 | 1.6E-02 | 3.9E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 8| +2.35E+02 | +3.4E+01 | -3.0E+00 | 1.0E+00 | 3.5E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 21| +2.50E+02 | -1.6E-04 | +8.3E-01 | 3.9E-03 | 6.9E-02 | 9.1E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 40| +1.48E+02 | -3.1E-02 | +6.8E-01 | 2.6E-02 | 3.2E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 106| +2.12E+02 | -7.4E-02 | +1.4E+00 | 1.2E-01 | 6.9E-01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 60| +2.10E+02 | -1.1E-02 | +2.6E-01 | 1.6E-02 | 2.9E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 22| +2.50E+02 | -3.1E-04 | +8.6E-01 | 7.8E-03 | 9.6E-02 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 74| +1.56E+02 | -8.2E-02 | +1.1E+00 | 3.1E-02 | 3.8E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 107| +1.61E+02 | -4.2E+00 | +3.4E-01 | 6.2E-02 | 1.3E+02 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 9| +2.35E+02 | +5.7E+00 | -5.3E-01 | 2.5E-01 | 3.5E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 41| +1.48E+02 | -1.2E-02 | +9.6E-01 | 2.6E-02 | 3.4E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 23| +2.50E+02 | -6.5E-04 | +9.2E-01 | 1.6E-02 | 1.1E-01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 107| +2.11E+02 | -4.3E-01 | +2.1E+00 | 2.5E-01 | 1.7E+00 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 10| +2.30E+02 | -4.4E+00 | +8.8E-01 | 6.2E-02 | 3.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 61| +2.10E+02 | -2.1E-02 | +4.2E-01 | 1.6E-02 | 3.6E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 24| +2.50E+02 | -1.4E-03 | +9.8E-01 | 3.1E-02 | 1.3E-01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 108| +1.54E+02 | -7.0E+00 | +9.0E-01 | 6.2E-02 | 1.5E+02 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 75| +1.56E+02 | -1.7E-01 | +1.2E+00 | 6.2E-02 | 2.6E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 42| +1.48E+02 | -1.9E-02 | +9.5E-01 | 5.3E-02 | 1.5E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 108| +2.11E+02 | +8.4E+00 | -8.0E+00 | 5.0E-01 | 4.3E+00 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 25| +2.50E+02 | -3.2E-03 | +1.1E+00 | 6.2E-02 | 1.4E-01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 11| +2.29E+02 | -1.1E+00 | +2.1E-01 | 1.2E-01 | 2.3E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 62| +2.10E+02 | -1.1E-02 | +2.7E-01 | 1.6E-02 | 2.7E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 109| +1.52E+02 | -2.0E+00 | +3.1E-01 | 1.2E-01 | 5.2E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 26| +2.50E+02 | -8.3E-03 | +1.2E+00 | 1.2E-01 | 1.6E-01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 12| +2.28E+02 | -1.7E+00 | +9.2E-01 | 3.1E-02 | 3.9E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 76| +1.55E+02 | -3.3E-01 | +1.0E+00 | 1.2E-01 | 5.5E+00 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 109| +2.11E+02 | -6.0E-01 | +1.4E+00 | 1.2E-01 | 4.3E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 63| +2.10E+02 | -1.9E-02 | +4.1E-01 | 1.6E-02 | 3.4E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 43| +1.48E+02 | -3.2E-02 | +9.6E-01 | 1.1E-01 | 2.1E+00 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 15| +1.56E+02 | -3.0E+00 | +1.0E+00 | 5.0E-01 | 3.8E+01 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 27| +2.50E+02 | -2.8E-02 | +1.4E+00 | 2.5E-01 | 1.8E-01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 13| +2.26E+02 | -1.9E+00 | +8.6E-01 | 6.2E-02 | 2.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 110| +1.52E+02 | +9.5E-01 | -2.4E-01 | 1.2E-01 | 7.2E+01 | 2.8E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 110| +2.11E+02 | +1.5E+00 | -1.5E+00 | 2.5E-01 | 3.6E+00 | 6.7E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 28| +2.50E+02 | -1.6E-01 | +2.0E+00 | 5.0E-01 | 2.7E-01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 64| +2.10E+02 | -1.1E-02 | +2.9E-01 | 1.6E-02 | 2.6E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 44| +1.48E+02 | -3.8E-02 | +8.5E-01 | 2.1E-01 | 1.1E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 14| +2.23E+02 | -3.2E+00 | +1.1E+00 | 1.2E-01 | 1.8E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 77| +1.55E+02 | -5.7E-01 | +6.7E-01 | 2.5E-01 | 2.2E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 29| +2.47E+02 | -2.1E+00 | +3.2E+00 | 1.0E+00 | 5.1E-01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 111| +1.50E+02 | -1.5E+00 | +5.5E-01 | 3.1E-02 | 7.2E+01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 65| +2.10E+02 | -1.8E-02 | +4.2E-01 | 1.6E-02 | 3.2E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 111| +2.10E+02 | -3.6E-01 | +1.0E+00 | 6.2E-02 | 3.6E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 45| +1.48E+02 | +2.0E-01 | -3.7E+00 | 4.2E-01 | 1.7E+00 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 15| +2.20E+02 | -3.0E+00 | +1.0E+00 | 2.5E-01 | 1.9E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 30| +2.29E+02 | -1.8E+01 | +3.6E+00 | 2.0E+00 | 6.4E+00 | 3.7E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 112| +2.10E+02 | +2.5E-01 | -5.3E-01 | 1.2E-01 | 2.0E+00 | 3.3E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 66| +2.10E+02 | -1.2E-02 | +3.2E-01 | 1.6E-02 | 2.4E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 112| +1.49E+02 | -1.0E+00 | +5.1E-01 | 3.1E-02 | 7.3E+01 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 46| +1.48E+02 | -6.6E-03 | +2.8E-01 | 1.1E-01 | 1.7E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 16| +2.19E+02 | -9.4E-01 | +1.1E-04 | 5.0E-01 | 2.0E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 78| +1.54E+02 | -9.4E-01 | +1.4E+00 | 2.5E-01 | 3.1E+01 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 31| +2.29E+02 | +1.9E+02 | -1.3E+01 | 4.0E+00 | 2.7E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 113| +2.10E+02 | -1.2E-01 | +8.6E-01 | 3.1E-02 | 2.0E+00 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 17| +2.12E+02 | -6.7E+00 | +7.7E-01 | 1.2E-01 | 6.3E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 67| +2.10E+02 | -1.7E-02 | +4.3E-01 | 1.6E-02 | 3.0E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 47| +1.48E+02 | +4.7E-02 | -7.8E-01 | 1.1E-01 | 2.3E+00 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 113| +1.49E+02 | -4.6E-01 | +6.7E-01 | 3.1E-02 | 3.7E+01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 32| +2.29E+02 | +5.4E+00 | -1.3E+00 | 1.0E+00 | 2.7E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 114| +2.10E+02 | +1.7E-01 | -1.4E+00 | 6.2E-02 | 1.2E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 18| +2.03E+02 | -8.4E+00 | +1.3E+00 | 2.5E-01 | 1.6E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 68| +2.10E+02 | -1.2E-02 | +3.6E-01 | 1.6E-02 | 2.3E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 114| +1.48E+02 | -2.7E-01 | +6.1E-01 | 3.1E-02 | 2.5E+01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 33| +2.29E+02 | +6.5E+00 | -9.9E-01 | 2.5E-01 | 2.7E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 48| +1.48E+02 | -9.9E-03 | +5.8E-01 | 2.6E-02 | 2.3E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 115| +2.10E+02 | -3.1E-02 | +7.8E-01 | 1.6E-02 | 1.2E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 69| +2.10E+02 | -1.7E-02 | +4.6E-01 | 1.6E-02 | 2.8E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 19| +1.77E+02 | -2.6E+01 | +8.4E-01 | 5.0E-01 | 3.1E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 34| +2.26E+02 | -2.9E+00 | +8.2E-01 | 6.2E-02 | 2.7E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 16| +1.52E+02 | -4.3E+00 | +9.0E-01 | 1.0E+00 | 6.9E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 115| +1.48E+02 | -2.6E-01 | +4.1E-01 | 3.1E-02 | 2.4E+01 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 49| +1.48E+02 | -2.2E-03 | +9.7E-01 | 2.6E-02 | 1.0E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 116| +2.10E+02 | -3.2E-03 | +5.8E-02 | 3.1E-02 | 1.4E+00 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 35| +2.25E+02 | -1.6E+00 | +5.6E-01 | 1.2E-01 | 1.8E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 79| +1.52E+02 | -2.0E+00 | +1.2E+00 | 2.5E-01 | 4.5E+01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 20| +1.77E+02 | +1.3E+02 | -6.6E+00 | 1.0E+00 | 1.2E+02 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 70| +2.10E+02 | -1.3E-02 | +4.0E-01 | 1.6E-02 | 2.1E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 50| +1.48E+02 | -3.6E-03 | +9.6E-01 | 5.3E-02 | 4.1E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 116| +1.48E+02 | -1.2E-01 | +3.0E-01 | 3.1E-02 | 2.0E+01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 36| +2.22E+02 | -2.3E+00 | +6.6E-01 | 1.2E-01 | 3.3E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 17| +1.52E+02 | +1.8E+02 | -7.2E+02 | 2.0E+00 | 3.9E+01 | 5.7E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 117| +2.10E+02 | -3.4E-02 | +1.0E+00 | 7.8E-03 | 3.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 21| +1.61E+02 | -1.6E+01 | +8.7E-01 | 2.5E-01 | 1.2E+02 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 71| +2.10E+02 | -1.6E-02 | +5.0E-01 | 1.6E-02 | 2.5E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 117| +1.48E+02 | -1.9E-01 | +4.4E-01 | 3.1E-02 | 3.2E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 37| +2.22E+02 | -2.2E-01 | +6.1E-03 | 1.2E-01 | 1.8E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 118| +2.10E+02 | -1.7E-02 | +1.1E+00 | 1.6E-02 | 9.4E-01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 51| +1.48E+02 | -5.1E-03 | +9.5E-01 | 1.1E-01 | 7.8E-01 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 72| +2.10E+02 | -1.3E-02 | +4.6E-01 | 1.6E-02 | 2.0E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 38| +2.20E+02 | -1.7E+00 | +8.8E-01 | 3.1E-02 | 4.1E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 119| +2.10E+02 | -3.2E-02 | +1.1E+00 | 3.1E-02 | 4.9E-01 | 8.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 22| +1.58E+02 | -3.4E+00 | +9.4E-01 | 5.0E-01 | 8.1E+01 | 2.5E-01 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 118| +1.48E+02 | -1.5E-01 | +6.8E-01 | 3.1E-02 | 2.3E+01 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 52| +1.48E+02 | -4.2E-03 | +8.8E-01 | 2.1E-01 | 2.5E-01 | 9.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 73| +2.10E+02 | -1.7E-02 | +5.4E-01 | 1.6E-02 | 2.3E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 39| +2.19E+02 | -1.4E+00 | +7.6E-01 | 6.2E-02 | 2.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 120| +2.10E+02 | -7.6E-02 | +1.2E+00 | 6.2E-02 | 7.9E-01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 23| +1.58E+02 | +2.3E+01 | -2.0E+00 | 5.0E-01 | 6.3E+01 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 119| +1.48E+02 | -1.3E-02 | +9.6E-02 | 3.1E-02 | 9.5E+00 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 74| +2.10E+02 | -2.2E-02 | +8.2E-01 | 1.6E-02 | 1.8E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 53| +1.48E+02 | +5.1E-03 | -5.4E+00 | 4.2E-01 | 6.0E-01 | 3.2E-02 | trr |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 40| +2.18E+02 | -1.3E+00 | +6.3E-01 | 1.2E-01 | 1.5E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 80| +1.52E+02 | +2.0E+00 | -2.0E+00 | 5.0E-01 | 1.3E+01 | 4.0E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 121| +2.10E+02 | -2.1E-01 | +1.4E+00 | 1.2E-01 | 1.5E+00 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 18| +1.51E+02 | -2.5E-01 | +1.0E+00 | 5.0E-01 | 3.9E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 75| +2.10E+02 | +5.0E-04 | -1.5E-02 | 3.1E-02 | 1.8E+00 | 8.5E-02 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 24| +1.53E+02 | -5.5E+00 | +7.9E-01 | 1.2E-01 | 6.3E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 120| +1.48E+02 | -5.1E-02 | +7.8E-01 | 7.8E-03 | 1.5E+01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 41| +2.16E+02 | -1.4E+00 | +5.9E-01 | 1.2E-01 | 2.7E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 54| +1.48E+02 | -3.6E-05 | +8.2E-02 | 4.3E-02 | 6.0E-01 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 122| +2.10E+02 | -1.2E-01 | +9.6E-02 | 2.5E-01 | 2.9E+00 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 76| +2.10E+02 | -1.2E-02 | +6.1E-01 | 7.8E-03 | 1.8E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 25| +1.49E+02 | -3.1E+00 | +9.8E-01 | 2.5E-01 | 7.7E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 42| +2.16E+02 | +1.5E-01 | -1.5E-01 | 1.2E-01 | 1.4E+01 | 3.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 19| +1.51E+02 | +3.2E+00 | -2.2E+01 | 1.0E+00 | 3.2E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 123| +2.09E+02 | -7.1E-01 | +1.0E+00 | 6.2E-02 | 6.6E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 121| +1.48E+02 | -1.2E-02 | +4.5E-01 | 1.6E-02 | 3.9E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 55| +1.48E+02 | -2.5E-04 | +5.2E-01 | 1.1E-02 | 2.8E-01 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 77| +2.10E+02 | -9.6E-03 | +1.0E+00 | 7.8E-03 | 1.4E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 43| +2.16E+02 | -6.4E-01 | +8.1E-01 | 3.1E-02 | 1.4E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 26| +1.49E+02 | +1.0E+01 | -3.8E+00 | 5.0E-01 | 4.1E+01 | 5.4E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 124| +2.08E+02 | -6.2E-01 | +1.2E+00 | 1.2E-01 | 4.4E+00 | 3.0E-01 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 122| +1.48E+02 | +2.6E-03 | -4.2E-01 | 1.6E-02 | 4.5E+00 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 56| +1.48E+02 | -3.8E-05 | +9.3E-01 | 1.1E-02 | 2.9E-01 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 81| +1.51E+02 | -3.0E-01 | +5.1E-01 | 4.2E-02 | 1.3E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 78| +2.10E+02 | -1.9E-02 | +9.2E-01 | 1.6E-02 | 1.2E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 44| +2.15E+02 | -9.0E-01 | +1.0E+00 | 6.2E-02 | 1.2E+01 | 9.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 27| +1.49E+02 | -9.2E-01 | +6.2E-01 | 1.2E-01 | 4.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 125| +2.07E+02 | -7.8E-01 | +4.3E-01 | 2.5E-01 | 5.8E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:33.825 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.2839 and h = 1.60631e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33.825 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.2839: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) 123| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.0E-03 | 4.5E+00 | 5.4E-03 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 79| +2.10E+02 | -4.0E-02 | +1.0E+00 | 3.1E-02 | 1.3E+00 | 8.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 57| +1.48E+02 | -5.9E-05 | +1.1E+00 | 2.1E-02 | 8.0E-02 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 20| +1.51E+02 | -3.6E-01 | +1.0E+00 | 2.5E-01 | 3.2E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 45| +2.14E+02 | -9.4E-01 | +8.3E-01 | 1.2E-01 | 1.2E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:33.840 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.3559 and h = 3.61223e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33.840 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.3559: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) 28| +1.49E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 4.1E+01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 80| +2.10E+02 | -8.6E-02 | +1.1E+00 | 6.2E-02 | 1.2E+00 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 46| +2.14E+02 | -3.7E-01 | +1.4E-01 | 2.5E-01 | 1.3E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 126| +2.05E+02 | -2.3E+00 | +8.8E-01 | 2.5E-01 | 3.5E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 124| +1.48E+02 | -2.3E-03 | +7.2E-01 | 4.9E-04 | 4.5E+00 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 21| +1.51E+02 | +8.5E-01 | -3.0E+00 | 5.0E-01 | 3.0E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 29| +1.48E+02 | -3.0E-01 | +8.8E-01 | 3.1E-02 | 4.1E+01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 81| +2.10E+02 | -2.1E-01 | +1.2E+00 | 1.2E-01 | 1.5E+00 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 47| +2.12E+02 | -1.5E+00 | +6.9E-01 | 6.2E-02 | 2.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 127| +2.05E+02 | +4.3E+00 | -5.9E-01 | 5.0E-01 | 3.1E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 125| +1.48E+02 | -4.1E-04 | +9.8E-01 | 4.9E-04 | 1.2E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 48| +2.11E+02 | -5.6E-01 | +9.0E-01 | 6.2E-02 | 7.2E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 82| +2.09E+02 | -5.2E-01 | +1.1E+00 | 2.5E-01 | 2.9E+00 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 82| +1.51E+02 | -3.2E-01 | +8.7E-01 | 4.2E-02 | 1.6E+01 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 30| +1.48E+02 | -2.3E-01 | +8.7E-01 | 6.2E-02 | 2.4E+01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 128| +2.04E+02 | -1.4E+00 | +2.6E-01 | 1.2E-01 | 3.1E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 49| +2.10E+02 | -1.2E+00 | +1.0E+00 | 1.2E-01 | 1.0E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 126| +1.48E+02 | -4.1E-04 | +9.0E-01 | 9.8E-04 | 7.5E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 22| +1.51E+02 | -2.5E-01 | +1.1E+00 | 1.2E-01 | 3.0E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 83| +2.09E+02 | +1.6E+00 | -4.9E+00 | 5.0E-01 | 7.9E+00 | 8.8E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 129| +2.01E+02 | -2.9E+00 | +1.4E+00 | 1.2E-01 | 2.3E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 31| +1.48E+02 | -2.1E-01 | +9.6E-01 | 1.2E-01 | 2.6E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 127| +1.48E+02 | -5.3E-04 | +1.0E+00 | 2.0E-03 | 8.9E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 50| +2.06E+02 | -4.3E+00 | +1.3E+00 | 2.5E-01 | 9.8E+00 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 84| +2.09E+02 | -3.8E-01 | +1.3E+00 | 1.2E-01 | 7.9E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 130| +1.93E+02 | -7.4E+00 | +1.2E+00 | 2.5E-01 | 1.8E+01 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 58| +1.48E+02 | -5.7E-05 | +8.0E-01 | 4.3E-02 | 1.2E-01 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 23| +1.50E+02 | -6.3E-01 | +1.3E+00 | 2.5E-01 | 1.6E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 32| +1.48E+02 | -1.6E-01 | +8.7E-01 | 2.5E-01 | 1.3E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:33.989 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.0831 and h = 2.15215e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33.989 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.0831: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) 128| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.9E-03 | 5.3E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 85| +2.09E+02 | +5.0E+00 | -2.1E+00 | 2.5E-01 | 6.4E+00 | 6.7E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 51| +1.84E+02 | -2.2E+01 | +1.4E+00 | 5.0E-01 | 2.4E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 59| +1.48E+02 | +2.4E-04 | -3.0E+00 | 8.5E-02 | 4.8E-02 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 83| +1.51E+02 | -2.7E-01 | +1.1E+00 | 8.5E-02 | 2.0E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 131| +1.93E+02 | +9.2E+01 | -6.7E+00 | 5.0E-01 | 5.0E+01 | 1.3E+00 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 129| +1.48E+02 | -2.6E-04 | +1.0E+00 | 9.8E-04 | 5.3E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 33| +1.48E+02 | +8.8E-01 | -5.0E+00 | 5.0E-01 | 8.7E+00 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 86| +2.08E+02 | -3.4E-01 | +3.6E-01 | 6.2E-02 | 6.4E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 24| +1.50E+02 | +4.2E-01 | -1.1E+00 | 5.0E-01 | 2.0E+01 | 9.8E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 52| +1.84E+02 | +3.1E+02 | -1.3E+01 | 1.0E+00 | 3.1E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 60| +1.48E+02 | -1.5E-05 | +4.8E-01 | 2.1E-02 | 4.8E-02 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 130| +1.48E+02 | -4.7E-04 | +1.0E+00 | 2.0E-03 | 4.8E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 34| +1.48E+02 | -2.2E-02 | +2.7E-01 | 1.2E-01 | 8.7E+00 | 6.9E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 87| +2.08E+02 | -7.9E-01 | +1.3E+00 | 6.2E-02 | 1.0E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 132| +1.88E+02 | -4.9E+00 | +8.5E-01 | 1.2E-01 | 5.0E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 53| +1.68E+02 | -1.6E+01 | +1.3E+00 | 2.5E-01 | 3.1E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 61| +1.48E+02 | +4.6E-05 | -6.0E-01 | 2.1E-02 | 9.3E-02 | 3.1E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 88| +2.06E+02 | -1.5E+00 | +1.0E+00 | 1.2E-01 | 5.4E+00 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 25| +1.50E+02 | -1.9E-01 | +8.9E-01 | 1.2E-01 | 2.0E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 131| +1.48E+02 | -6.7E-04 | +1.0E+00 | 3.9E-03 | 4.8E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 133| +1.77E+02 | -1.1E+01 | +5.5E-01 | 2.5E-01 | 7.0E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 35| +1.48E+02 | +1.5E-01 | -8.7E-01 | 1.2E-01 | 4.8E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 84| +1.51E+02 | +5.3E-03 | -8.8E-02 | 1.7E-01 | 4.7E+00 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 89| +2.03E+02 | -3.1E+00 | +1.0E+00 | 2.5E-01 | 1.2E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 54| +1.68E+02 | +1.4E+01 | -1.4E+00 | 5.0E-01 | 4.5E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 62| +1.48E+02 | -1.5E-05 | +6.2E-01 | 5.3E-03 | 9.3E-02 | 8.1E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 132| +1.48E+02 | -1.2E-03 | +7.6E-01 | 7.8E-03 | 3.1E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 134| +1.71E+02 | -6.0E+00 | +4.0E-01 | 2.5E-01 | 7.0E+01 | 3.7E-01 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 36| +1.48E+02 | -2.7E-02 | +5.4E-01 | 2.9E-02 | 4.8E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 26| +1.50E+02 | -1.8E-01 | +6.7E-01 | 2.5E-01 | 1.3E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 90| +2.03E+02 | +2.6E+01 | -5.3E+00 | 5.0E-01 | 4.2E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 55| +1.59E+02 | -8.5E+00 | +1.0E+00 | 1.2E-01 | 4.5E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 63| +1.48E+02 | -3.2E-06 | +6.1E-01 | 5.3E-03 | 1.4E-01 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 37| +1.48E+02 | -9.3E-03 | +9.8E-01 | 2.9E-02 | 7.3E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 135| +1.58E+02 | -1.3E+01 | +1.1E+00 | 2.5E-01 | 1.1E+02 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 133| +1.48E+02 | -2.4E-03 | +1.1E+00 | 1.6E-02 | 1.6E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 91| +2.01E+02 | -1.8E+00 | +1.1E+00 | 1.2E-01 | 4.2E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 64| +1.48E+02 | -4.0E-06 | +1.0E+00 | 5.3E-03 | 2.9E-02 | 6.8E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 27| +1.50E+02 | -1.6E-01 | +8.2E-01 | 2.5E-01 | 1.8E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 38| +1.48E+02 | -8.7E-03 | +9.5E-01 | 5.9E-02 | 3.6E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 134| +1.48E+02 | -2.7E-03 | +9.5E-01 | 3.1E-02 | 6.9E-01 | 9.1E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 56| +1.52E+02 | -7.3E+00 | +4.1E-01 | 2.5E-01 | 7.5E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 136| +1.58E+02 | +3.0E+02 | -1.7E+01 | 5.0E-01 | 5.6E+01 | 1.3E+00 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 85| +1.51E+02 | -5.1E-02 | +9.5E-01 | 1.1E-02 | 4.7E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 92| +2.01E+02 | +4.2E-01 | -1.6E-01 | 2.5E-01 | 5.3E+01 | 6.7E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 65| +1.48E+02 | -6.0E-06 | +1.2E+00 | 1.1E-02 | 8.5E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:34.214 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 87.9354 and h = 3.13883e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.215 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 87.9354: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 135| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.3E-02 | 3.9E-01 | 2.4E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 39| +1.48E+02 | -1.1E-02 | +9.2E-01 | 1.2E-01 | 3.9E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 57| +1.49E+02 | -3.2E+00 | +9.6E-01 | 2.5E-01 | 1.8E+02 | 3.3E-01 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 137| +1.51E+02 | -7.0E+00 | +8.7E-01 | 1.2E-01 | 5.6E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:34.229 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 171.577 and h = 4.79171e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.229 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 171.577: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 28| +1.50E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 1.3E+01 | 7.9E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 66| +1.48E+02 | -2.5E-06 | +3.6E-01 | 2.1E-02 | 2.2E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 93| +1.98E+02 | -3.1E+00 | +1.4E+00 | 6.2E-02 | 5.3E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:34.243 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.485 and h = 3.02269e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.243 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.485: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 136| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.0E-03 | 3.9E-01 | 5.9E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 40| +1.48E+02 | +3.8E-02 | -2.4E+00 | 2.4E-01 | 2.1E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 58| +1.49E+02 | +2.5E-02 | -6.7E-02 | 2.5E-01 | 3.6E+01 | 2.6E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 138| +1.51E+02 | +2.6E+01 | -4.4E+00 | 2.5E-01 | 2.9E+01 | 5.4E-01 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 137| +1.48E+02 | -2.2E-04 | +1.3E+00 | 5.1E-04 | 3.9E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 94| +1.85E+02 | -1.3E+01 | +8.7E-01 | 1.2E-01 | 5.3E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 41| +1.48E+02 | -2.2E-03 | +3.7E-01 | 5.9E-02 | 2.1E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 67| +1.48E+02 | +2.4E-05 | -1.2E+00 | 2.1E-02 | 6.1E-02 | 4.2E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 59| +1.48E+02 | -9.4E-01 | +6.2E-01 | 6.2E-02 | 3.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 139| +1.51E+02 | +2.2E+00 | -7.1E-01 | 6.2E-02 | 2.9E+01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 29| +1.50E+02 | -4.7E-02 | +1.1E+00 | 1.2E-01 | 1.3E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 138| +1.48E+02 | -1.4E-04 | +9.0E-01 | 1.0E-03 | 3.1E-01 | 2.9E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 42| +1.48E+02 | +7.8E-04 | -8.4E-02 | 5.9E-02 | 2.1E+00 | 2.5E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 68| +1.48E+02 | -5.0E-06 | +8.5E-01 | 5.3E-03 | 6.1E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 95| +1.85E+02 | +1.1E+01 | -5.7E-01 | 2.5E-01 | 4.4E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 140| +1.50E+02 | -7.8E-01 | +7.2E-01 | 1.6E-02 | 2.9E+01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 60| +1.48E+02 | -1.2E-01 | +5.3E-01 | 6.2E-02 | 2.1E+01 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 86| +1.51E+02 | -3.4E-02 | +8.2E-01 | 2.2E-02 | 4.0E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 139| +1.48E+02 | -1.4E-04 | +9.4E-01 | 2.0E-03 | 6.3E-01 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 43| +1.48E+02 | -2.4E-03 | +6.7E-01 | 1.5E-02 | 2.1E+00 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 69| +1.48E+02 | +2.7E-06 | -1.5E+00 | 1.1E-02 | 2.0E-02 | 8.0E-04 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 96| +1.78E+02 | -6.7E+00 | +9.9E-01 | 6.2E-02 | 4.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 141| +1.49E+02 | -6.6E-01 | +7.9E-01 | 1.6E-02 | 3.9E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 61| +1.48E+02 | -4.4E-02 | +3.7E-01 | 6.2E-02 | 4.7E+00 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 30| +1.50E+02 | -8.0E-02 | +1.1E+00 | 2.5E-01 | 9.0E+00 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 140| +1.48E+02 | -7.6E-05 | +9.2E-01 | 4.1E-03 | 1.6E-01 | 8.2E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 70| +1.48E+02 | -1.8E-07 | +3.3E-01 | 2.7E-03 | 2.0E-02 | 2.0E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(WARNING) Stopping as function difference 1.75E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:34 fides(INFO) 44| +1.48E+02 | -1.3E-03 | +9.7E-01 | 1.5E-02 | 2.6E+00 | 5.7E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 97| +1.68E+02 | -1.0E+01 | +7.8E-01 | 1.2E-01 | 4.6E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 62| +1.48E+02 | -2.8E-02 | +6.4E-01 | 6.2E-02 | 9.5E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 142| +1.49E+02 | -5.3E-01 | +8.5E-01 | 3.1E-02 | 2.9E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 141| +1.48E+02 | -2.0E-06 | +2.8E-01 | 4.1E-03 | 4.6E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 31| +1.50E+02 | -9.7E-03 | +8.3E-01 | 5.0E-01 | 1.7E+00 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 98| +1.68E+02 | +5.6E+00 | -6.0E-01 | 2.5E-01 | 8.1E+01 | 5.1E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 45| +1.48E+02 | -1.4E-03 | +9.5E-01 | 2.9E-02 | 1.2E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 143| +1.48E+02 | -4.9E-01 | +5.0E-01 | 6.2E-02 | 2.6E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 63| +1.48E+02 | -1.1E-02 | +8.1E-01 | 6.2E-02 | 3.5E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 142| +1.48E+02 | -3.4E-06 | +1.7E+00 | 4.1E-03 | 2.9E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 32| +1.50E+02 | -3.3E-03 | +8.7E-01 | 1.0E+00 | 1.4E+00 | 1.1E+00 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 46| +1.48E+02 | -1.2E-03 | +7.6E-01 | 5.9E-02 | 1.8E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 87| +1.51E+02 | +3.9E-03 | -4.8E-01 | 4.4E-02 | 5.0E+00 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 99| +1.63E+02 | -4.6E+00 | +9.4E-01 | 6.2E-02 | 8.1E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 144| +1.48E+02 | -2.2E-02 | +1.2E-02 | 6.2E-02 | 5.3E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 64| +1.48E+02 | -1.2E-02 | +8.5E-01 | 1.2E-01 | 4.7E+00 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 143| +1.48E+02 | +4.7E-07 | -8.7E+00 | 4.1E-03 | 5.6E-03 | 4.0E-04 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 100| +1.59E+02 | -4.3E+00 | +6.8E-01 | 1.2E-01 | 5.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 0| +3.57E+10 | NaN | NaN | 1.0E+00 | 1.6E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:34 fides(INFO) 33| +1.50E+02 | -3.0E-04 | +2.6E-01 | 2.0E+00 | 1.8E+00 | 2.2E-01 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 47| +1.48E+02 | +4.6E-03 | -1.6E+00 | 1.2E-01 | 7.2E-01 | 3.2E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 65| +1.48E+02 | +4.4E-03 | -3.2E-01 | 2.5E-01 | 1.6E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 145| +1.48E+02 | -2.3E-01 | +7.6E-01 | 1.6E-02 | 2.7E+01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 144| +1.48E+02 | -2.7E-06 | +1.0E+02 | 3.5E-05 | 5.6E-03 | 9.9E-05 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 1| +9.25E+07 | -3.6E+10 | +9.0E-02 | 1.0E+00 | 1.6E+11 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 48| +1.48E+02 | -3.9E-04 | +3.5E-01 | 2.9E-02 | 7.2E-01 | 8.2E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 101| +1.54E+02 | -5.0E+00 | +6.0E-01 | 1.2E-01 | 7.3E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 34| +1.50E+02 | -2.1E-05 | +2.1E-02 | 2.0E+00 | 6.3E-01 | 7.8E-03 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 66| +1.48E+02 | -3.3E-03 | +6.8E-01 | 6.2E-02 | 1.6E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 146| +1.48E+02 | -2.9E-01 | +7.0E-01 | 3.1E-02 | 2.6E+01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 2| +1.42E+07 | -7.8E+07 | +8.9E-01 | 2.5E-01 | 4.3E+08 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 145| +1.48E+02 | +4.4E-06 | -2.5E+02 | 7.0E-05 | 2.1E-03 | 1.9E-04 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 3| +2.21E+06 | -1.2E+07 | +8.9E-01 | 5.0E-01 | 6.5E+07 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 49| +1.48E+02 | -4.3E-04 | +3.5E-01 | 2.9E-02 | 1.3E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 88| +1.51E+02 | -3.1E-03 | +5.4E-01 | 2.1E-03 | 5.0E+00 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 67| +1.48E+02 | -2.3E-03 | +7.4E-01 | 6.2E-02 | 2.1E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 102| +1.54E+02 | +3.8E+01 | -3.4E+00 | 1.2E-01 | 6.3E+01 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 35| +1.50E+02 | +1.6E-05 | -1.7E-02 | 1.7E-02 | 1.0E+00 | 6.4E-03 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 146| +1.48E+02 | +2.9E-06 | -3.8E+02 | 1.7E-05 | 2.1E-03 | 4.7E-05 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 147| +1.48E+02 | +2.9E-02 | -1.3E-01 | 3.1E-02 | 1.9E+01 | 3.7E-02 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 68| +1.48E+02 | -1.4E-03 | +6.5E-01 | 6.2E-02 | 8.2E-01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 4| +3.72E+05 | -1.8E+06 | +8.7E-01 | 1.0E+00 | 1.0E+07 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 103| +1.53E+02 | -5.3E-01 | +1.6E-01 | 3.1E-02 | 6.3E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 50| +1.48E+02 | +1.6E-05 | -1.7E-02 | 2.9E-02 | 5.0E-01 | 8.3E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 36| +1.50E+02 | -3.8E-04 | +7.9E-01 | 1.9E-03 | 1.0E+00 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 148| +1.48E+02 | -5.6E-02 | +6.9E-01 | 7.2E-03 | 1.9E+01 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 147| +1.48E+02 | +4.4E-06 | -1.7E+03 | 4.4E-06 | 2.1E-03 | 1.2E-05 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 5| +5.76E+04 | -3.1E+05 | +9.0E-01 | 2.0E+00 | 1.6E+06 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 69| +1.48E+02 | -1.1E-03 | +5.8E-01 | 6.2E-02 | 1.5E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 51| +1.48E+02 | -2.3E-04 | +5.3E-01 | 7.3E-03 | 5.0E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 104| +1.52E+02 | -1.0E+00 | +9.8E-01 | 7.8E-03 | 7.3E+01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 37| +1.50E+02 | -8.4E-05 | +7.0E-01 | 3.7E-03 | 4.0E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 148| +1.48E+02 | +3.9E-06 | -3.1E+03 | 1.1E-06 | 2.1E-03 | 2.9E-06 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 149| +1.48E+02 | -3.8E-02 | +9.5E-01 | 7.2E-03 | 1.3E+01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 6| +8.73E+03 | -4.9E+04 | +9.1E-01 | 2.0E+00 | 2.4E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 7| +1.35E+03 | -7.4E+03 | +9.2E-01 | 2.0E+00 | 3.6E+04 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 52| +1.48E+02 | -2.0E-04 | +9.5E-01 | 7.3E-03 | 1.2E+00 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 70| +1.48E+02 | +2.4E-04 | -1.2E-01 | 6.2E-02 | 5.1E-01 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 89| +1.51E+02 | -1.1E-03 | +9.9E-01 | 2.1E-03 | 2.5E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 150| +1.48E+02 | -4.4E-02 | +7.2E-01 | 1.4E-02 | 9.4E+00 | 1.5E-02 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 105| +1.52E+02 | -5.8E-01 | +9.3E-01 | 1.6E-02 | 3.5E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 149| +1.48E+02 | +1.5E-06 | -1.8E+03 | 2.7E-07 | 2.1E-03 | 7.0E-07 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 38| +1.50E+02 | -6.5E-07 | +9.2E-02 | 3.7E-03 | 1.4E-01 | 8.8E-04 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 8| +3.49E+02 | -1.0E+03 | +9.2E-01 | 2.0E+00 | 5.1E+03 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 53| +1.48E+02 | -1.6E-04 | +9.0E-01 | 1.5E-02 | 2.1E-01 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:34.687 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.782 and h = 3.80467e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.687 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.782: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 71| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 5.1E-01 | 7.7E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 151| +1.48E+02 | -5.6E-02 | +8.8E-01 | 1.4E-02 | 2.0E+01 | 6.5E-03 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 106| +1.51E+02 | -9.8E-01 | +8.8E-01 | 3.1E-02 | 3.1E+01 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 9| +2.27E+02 | -1.2E+02 | +9.2E-01 | 2.0E+00 | 7.5E+02 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 150| +1.48E+02 | +3.1E-06 | -9.7E+03 | 6.8E-08 | 2.1E-03 | 1.7E-07 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 39| +1.50E+02 | +1.8E-06 | -2.3E-01 | 9.3E-04 | 3.1E-02 | 1.2E-03 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 54| +1.48E+02 | -9.1E-05 | +5.3E-01 | 2.9E-02 | 4.0E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 72| +1.48E+02 | -1.6E-04 | +2.2E-01 | 3.9E-03 | 5.1E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 10| +2.27E+02 | +5.9E+01 | -4.5E+00 | 2.0E+00 | 1.2E+02 | 3.0E+00 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 152| +1.48E+02 | -2.0E-02 | +9.5E-01 | 1.4E-02 | 7.9E+00 | 6.2E-03 | trr |1\n", - "2023-02-17 16:05:34.726 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.665 and h = 2.87681e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.726 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.665: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 151| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.7E-08 | 2.1E-03 | 4.3E-08 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 107| +1.51E+02 | -8.2E-02 | +4.8E-02 | 6.2E-02 | 3.8E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 40| +1.50E+02 | -3.0E-06 | +7.7E-01 | 1.6E-04 | 3.1E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 11| +2.00E+02 | -2.7E+01 | +1.0E+00 | 5.0E-01 | 1.2E+02 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 55| +1.48E+02 | +5.0E-05 | -2.5E-01 | 2.9E-02 | 1.6E-01 | 6.0E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 73| +1.48E+02 | -4.7E-04 | +9.6E-01 | 9.8E-04 | 2.0E+00 | 6.7E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 153| +1.48E+02 | -1.9E-02 | +9.3E-01 | 1.4E-02 | 9.5E+00 | 5.4E-03 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 152| +1.48E+02 | +5.5E-06 | -2.4E+05 | 4.3E-09 | 2.1E-03 | 1.1E-08 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 12| +2.00E+02 | +7.4E+02 | -2.4E+01 | 1.0E+00 | 5.5E+01 | 2.0E+00 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 108| +1.50E+02 | -5.9E-01 | +9.1E-01 | 1.6E-02 | 4.8E+01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 41| +1.50E+02 | -6.4E-08 | +5.8E-02 | 3.1E-04 | 6.1E-02 | 4.8E-04 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 56| +1.48E+02 | -1.9E-05 | +2.0E-01 | 7.3E-03 | 1.6E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 74| +1.48E+02 | -4.6E-05 | +1.0E+00 | 2.0E-03 | 2.3E-01 | 9.9E-04 | 2d |1\n", - "2023-02-17 16:05:34.794 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 81.0378 and h = 4.97343e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.794 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 81.0378: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 154| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.4E-02 | 7.2E+00 | 7.4E-03 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 13| +1.81E+02 | -1.9E+01 | +8.5E-01 | 2.5E-01 | 5.5E+01 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 153| +1.48E+02 | +3.0E-06 | -5.3E+05 | 1.1E-09 | 2.1E-03 | 2.7E-09 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 109| +1.49E+02 | -7.0E-01 | +6.7E-01 | 3.1E-02 | 3.2E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 90| +1.51E+02 | -8.2E-04 | +1.4E+00 | 4.3E-03 | 3.5E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 42| +1.50E+02 | -2.9E-07 | +5.0E-01 | 7.8E-05 | 6.1E-03 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 57| +1.48E+02 | -4.8E-05 | +1.0E+00 | 1.8E-03 | 5.9E-01 | 5.7E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(WARNING) Stopping as function difference 2.88E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:34.819 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.558 and h = 2.71512e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.819 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.558: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 75| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.9E-03 | 2.5E-01 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 155| +1.48E+02 | -8.0E-03 | +9.9E-01 | 1.0E-03 | 7.2E+00 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 14| +1.70E+02 | -1.2E+01 | +7.0E-02 | 5.0E-01 | 5.3E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 154| +1.48E+02 | +4.5E-06 | -3.2E+06 | 2.7E-10 | 2.1E-03 | 6.7E-10 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 110| +1.49E+02 | -8.1E-02 | +8.5E-02 | 3.1E-02 | 3.5E+01 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 15| +1.53E+02 | -1.7E+01 | +7.8E-01 | 1.2E-01 | 3.3E+02 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 76| +1.48E+02 | -2.6E-05 | +9.5E-01 | 9.8E-04 | 2.5E-01 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 58| +1.48E+02 | -1.4E-05 | +1.2E+00 | 3.7E-03 | 4.6E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:34.854 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0766 and h = 1.15392e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.854 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0766: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 155| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.7E-11 | 2.1E-03 | 1.7E-10 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 156| +1.48E+02 | -8.1E-03 | +9.7E-01 | 2.0E-03 | 4.3E+00 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 111| +1.49E+02 | -4.0E-01 | +8.0E-01 | 7.8E-03 | 5.3E+01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 16| +1.51E+02 | -2.4E+00 | +4.9E-01 | 2.5E-01 | 1.2E+02 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 77| +1.48E+02 | -3.2E-05 | +9.4E-01 | 2.0E-03 | 2.0E-01 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 59| +1.48E+02 | -1.3E-05 | +7.5E-01 | 7.3E-03 | 5.0E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 156| +1.48E+02 | +1.5E-06 | -1.7E+07 | 1.7E-11 | 2.1E-03 | 4.2E-11 | 2d |0\n", - "2023-02-17 16:05:34.885 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 224.204 and h = 2.042e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.885 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 224.204: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 91| +1.51E+02 | +0.0E+00 | +0.0E+00 | 8.6E-03 | 3.0E-01 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 157| +1.48E+02 | -1.0E-02 | +9.7E-01 | 4.1E-03 | 7.7E+00 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 17| +1.49E+02 | -1.7E+00 | +6.7E-01 | 2.5E-01 | 4.6E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 78| +1.48E+02 | -5.7E-05 | +9.2E-01 | 3.9E-03 | 1.5E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 112| +1.49E+02 | -2.4E-01 | +8.9E-01 | 1.6E-02 | 1.2E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 60| +1.48E+02 | -2.2E-05 | +9.2E-01 | 1.5E-02 | 3.7E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 157| +1.48E+02 | +5.0E-06 | -2.3E+08 | 4.2E-12 | 2.1E-03 | 1.1E-11 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 0| +8.21E+10 | NaN | NaN | 1.0E+00 | 3.8E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:34 fides(INFO) 18| +1.49E+02 | -4.0E-01 | +4.2E-01 | 2.5E-01 | 3.5E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 158| +1.48E+02 | -6.8E-03 | +9.8E-01 | 8.1E-03 | 4.1E+00 | 4.9E-03 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 79| +1.48E+02 | -9.5E-05 | +9.7E-01 | 7.8E-03 | 2.4E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 113| +1.49E+02 | -1.6E-01 | +6.4E-01 | 3.1E-02 | 1.7E+01 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 1| +1.49E+05 | -8.2E+10 | +9.2E-02 | 1.0E+00 | 3.8E+11 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 19| +1.49E+02 | +5.7E-01 | -4.4E-01 | 2.5E-01 | 2.0E+01 | 4.3E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 158| +1.48E+02 | +2.6E-06 | -4.8E+08 | 1.0E-12 | 2.1E-03 | 2.6E-12 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 61| +1.48E+02 | -3.6E-05 | +1.0E+00 | 2.9E-02 | 7.3E-02 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 159| +1.48E+02 | -9.2E-03 | +9.8E-01 | 8.1E-03 | 6.5E+00 | 4.6E-03 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 20| +1.48E+02 | -3.8E-01 | +7.3E-01 | 6.2E-02 | 2.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 2| +2.36E+04 | -1.2E+05 | +9.0E-01 | 2.5E-01 | 6.8E+05 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 62| +1.48E+02 | -4.2E-05 | +9.5E-01 | 5.9E-02 | 4.2E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 114| +1.49E+02 | +4.0E+00 | -6.2E+00 | 3.1E-02 | 9.9E+00 | 8.1E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 80| +1.48E+02 | -1.6E-04 | +9.7E-01 | 1.6E-02 | 1.1E-01 | 7.3E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 159| +1.48E+02 | +5.8E-06 | -4.2E+09 | 2.6E-13 | 2.1E-03 | 6.6E-13 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 21| +1.48E+02 | -1.1E-01 | +7.5E-01 | 6.2E-02 | 1.8E+01 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 160| +1.48E+02 | -6.5E-03 | +9.9E-01 | 8.1E-03 | 4.8E+00 | 5.4E-03 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 3| +3.99E+03 | -2.0E+04 | +9.0E-01 | 5.0E-01 | 1.1E+05 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 63| +1.48E+02 | +3.0E-04 | -5.9E+00 | 1.2E-01 | 3.1E-02 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 22| +1.48E+02 | -6.1E-02 | +8.7E-01 | 6.2E-02 | 1.2E+01 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 115| +1.49E+02 | +9.3E-02 | -5.0E-01 | 7.8E-03 | 9.9E+00 | 2.1E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 81| +1.48E+02 | -2.5E-04 | +9.5E-01 | 3.1E-02 | 2.6E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 92| +1.51E+02 | -3.7E-04 | +1.0E+00 | 2.1E-03 | 3.0E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 4| +8.79E+02 | -3.1E+03 | +9.0E-01 | 1.0E+00 | 1.7E+04 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 161| +1.48E+02 | -8.2E-03 | +9.9E-01 | 8.1E-03 | 4.9E+00 | 7.2E-03 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 23| +1.48E+02 | -1.1E-01 | +9.5E-01 | 1.2E-01 | 1.1E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 64| +1.48E+02 | +2.6E-06 | -1.2E-01 | 2.9E-02 | 3.1E-02 | 5.4E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 160| +1.48E+02 | +1.3E-06 | -3.9E+09 | 6.5E-14 | 2.1E-03 | 1.6E-13 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 5| +3.46E+02 | -5.3E+02 | +9.2E-01 | 2.0E+00 | 2.7E+03 | 1.9E+00 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 82| +1.48E+02 | -3.9E-04 | +9.3E-01 | 6.2E-02 | 1.5E-01 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 162| +1.48E+02 | -8.6E-03 | +1.0E+00 | 8.1E-03 | 5.3E+00 | 9.9E-03 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 116| +1.49E+02 | -3.1E-02 | +6.4E-01 | 2.0E-03 | 9.9E+00 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 24| +1.48E+02 | -9.6E-02 | +6.6E-01 | 2.5E-01 | 1.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 6| +2.60E+02 | -8.6E+01 | +8.9E-01 | 2.0E+00 | 4.0E+02 | 5.5E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 65| +1.48E+02 | -2.7E-06 | +4.7E-01 | 7.3E-03 | 3.1E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 161| +1.48E+02 | +2.2E-06 | -2.6E+10 | 1.6E-14 | 2.1E-03 | 4.1E-14 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 83| +1.48E+02 | -1.5E-04 | +2.1E-01 | 1.2E-01 | 4.3E-01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 25| +1.48E+02 | +2.4E-01 | -7.0E-01 | 2.5E-01 | 7.1E+00 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 163| +1.48E+02 | -8.3E-03 | +1.0E+00 | 8.1E-03 | 4.7E+00 | 8.9E-03 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 117| +1.49E+02 | -3.0E-02 | +9.2E-01 | 2.0E-03 | 9.7E+00 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 7| +2.25E+02 | -3.5E+01 | +5.1E-01 | 4.0E+00 | 5.5E+01 | 8.5E+00 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 66| +1.48E+02 | -6.6E-07 | +2.7E-01 | 7.3E-03 | 2.2E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as function difference 6.59E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:35 fides(INFO) 84| +1.48E+02 | +6.5E-04 | -4.5E-01 | 3.1E-02 | 4.0E-01 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 26| +1.48E+02 | -6.0E-02 | +3.7E-01 | 6.2E-02 | 7.1E+00 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 162| +1.48E+02 | +1.3E-06 | -5.8E+10 | 4.1E-15 | 2.1E-03 | 1.0E-14 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 8| +2.25E+02 | +4.9E+02 | -3.4E+01 | 4.0E+00 | 3.9E+01 | 9.4E+00 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) 164| +1.48E+02 | -1.6E-02 | +1.0E+00 | 1.6E-02 | 3.1E+00 | 3.0E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 27| +1.48E+02 | -7.2E-02 | +8.4E-01 | 6.2E-02 | 1.9E+01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 118| +1.48E+02 | -4.0E-02 | +8.7E-01 | 3.9E-03 | 7.1E+00 | 8.9E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 85| +1.48E+02 | -2.2E-06 | +8.4E-04 | 7.8E-03 | 4.0E-01 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 163| +1.48E+02 | +1.7E-06 | -3.1E+11 | 1.0E-15 | 2.1E-03 | 2.6E-15 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 28| +1.48E+02 | -7.0E-02 | +1.0E+00 | 1.2E-01 | 3.1E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 93| +1.51E+02 | -6.9E-04 | +1.0E+00 | 4.3E-03 | 3.7E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 9| +2.25E+02 | +1.9E+01 | -1.7E+00 | 8.0E-01 | 3.9E+01 | 1.9E+00 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) 165| +1.48E+02 | -1.1E-02 | +9.8E-01 | 3.3E-02 | 2.5E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 119| +1.48E+02 | -5.0E-02 | +9.3E-01 | 7.8E-03 | 1.0E+01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 86| +1.48E+02 | -4.2E-04 | +9.5E-01 | 2.0E-03 | 1.9E+00 | 8.7E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 29| +1.47E+02 | -1.6E-01 | +1.2E+00 | 2.5E-01 | 7.0E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 164| +1.48E+02 | +1.6E-07 | -1.2E+11 | 2.5E-16 | 2.1E-03 | 6.4E-16 | 2d |0\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as trust region radius 6.35E-17 is smaller than machine precision.\n", - "2023-02-17 16:05:35 fides(INFO) 166| +1.48E+02 | -1.7E-02 | +9.4E-01 | 6.5E-02 | 2.3E+00 | 8.8E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 10| +2.21E+02 | -4.6E+00 | +3.4E-01 | 2.0E-01 | 3.9E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 87| +1.48E+02 | -2.9E-05 | +8.6E-01 | 3.9E-03 | 1.6E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 30| +1.47E+02 | +2.7E-02 | -1.4E-01 | 5.0E-01 | 4.2E+00 | 3.8E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 120| +1.48E+02 | -5.9E-02 | +8.5E-01 | 1.6E-02 | 6.9E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 167| +1.48E+02 | +1.5E-02 | -1.8E+00 | 1.3E-01 | 9.2E-01 | 7.1E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 31| +1.47E+02 | -8.3E-02 | +7.9E-01 | 1.2E-01 | 4.2E+00 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 0| +1.15E+13 | NaN | NaN | 1.0E+00 | 5.3E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:35 fides(INFO) 11| +2.18E+02 | -2.9E+00 | +4.8E-01 | 2.0E-01 | 3.4E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 88| +1.48E+02 | -3.2E-05 | +8.7E-01 | 7.8E-03 | 1.9E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 121| +1.48E+02 | -1.8E-01 | +8.8E-01 | 3.1E-02 | 7.7E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 32| +1.47E+02 | -1.2E-01 | +3.1E-01 | 2.5E-01 | 7.4E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 168| +1.48E+02 | -1.9E-03 | +5.3E-01 | 2.2E-02 | 9.2E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 1| +6.42E+06 | -1.2E+13 | +8.3E-02 | 1.0E+00 | 5.3E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 12| +2.18E+02 | -2.4E-01 | +1.8E-02 | 2.0E-01 | 2.2E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 94| +1.51E+02 | -1.7E-03 | +1.1E+00 | 8.6E-03 | 2.1E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:35.268 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.52 and h = 3.50065e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:35.268 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.52: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:35 fides(INFO) 89| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 7.9E-02 | 4.9E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 33| +1.47E+02 | +8.0E+00 | -4.5E+00 | 2.5E-01 | 1.8E+01 | 3.5E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 122| +1.48E+02 | -3.2E-02 | +1.0E-01 | 6.2E-02 | 4.7E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 13| +2.15E+02 | -2.4E+00 | +8.3E-01 | 5.0E-02 | 3.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 169| +1.48E+02 | -9.4E-04 | +7.6E-01 | 2.2E-02 | 1.2E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 2| +9.82E+05 | -5.4E+06 | +8.9E-01 | 2.5E-01 | 3.0E+07 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 34| +1.47E+02 | -1.5E-01 | +2.6E-01 | 6.2E-02 | 1.8E+01 | 8.8E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 90| +1.48E+02 | -8.6E-06 | +8.2E-01 | 3.9E-03 | 7.9E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 123| +1.48E+02 | -2.3E-01 | +9.9E-01 | 1.6E-02 | 4.0E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 14| +2.15E+02 | -6.2E-01 | +4.3E-01 | 1.0E-01 | 1.4E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 35| +1.47E+02 | -1.4E-01 | +7.0E-01 | 6.2E-02 | 9.2E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 3| +1.52E+05 | -8.3E+05 | +8.9E-01 | 5.0E-01 | 4.5E+06 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 170| +1.48E+02 | +1.6E-03 | -1.8E+00 | 4.4E-02 | 2.6E-01 | 3.0E-02 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 0| +6.05E+13 | NaN | NaN | 1.0E+00 | 2.8E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:35 fides(INFO) 91| +1.48E+02 | -1.4E-05 | +9.9E-01 | 7.8E-03 | 1.0E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 124| +1.48E+02 | -9.1E-02 | +9.3E-01 | 3.1E-02 | 3.9E+00 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 36| +1.47E+02 | -1.2E-01 | +8.4E-01 | 6.2E-02 | 1.3E+01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 15| +2.15E+02 | -8.5E-02 | +3.3E-02 | 1.0E-01 | 1.6E+01 | 2.5E-01 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 4| +3.14E+04 | -1.2E+05 | +7.2E-01 | 1.0E+00 | 7.0E+05 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 1| +3.62E+07 | -6.0E+13 | +8.3E-02 | 1.0E+00 | 2.8E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 171| +1.48E+02 | +7.3E-05 | -1.6E-01 | 7.2E-03 | 2.6E-01 | 6.3E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 37| +1.47E+02 | -2.2E-01 | +9.7E-01 | 1.2E-01 | 9.1E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 92| +1.48E+02 | -2.2E-05 | +1.1E+00 | 1.6E-02 | 5.0E-02 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 125| +1.48E+02 | -1.1E-01 | +8.6E-01 | 6.2E-02 | 4.3E+00 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 2| +5.55E+06 | -3.1E+07 | +8.9E-01 | 2.5E-01 | 1.7E+08 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 16| +2.14E+02 | -7.3E-01 | +8.6E-01 | 2.5E-02 | 2.0E+01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 5| +5.08E+03 | -2.6E+04 | +9.0E-01 | 1.0E+00 | 1.1E+05 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 172| +1.48E+02 | +4.1E-05 | -1.4E-01 | 1.8E-03 | 2.6E-01 | 2.3E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 38| +1.47E+02 | +3.5E+00 | -5.9E+00 | 2.5E-01 | 7.2E+00 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 6| +9.65E+02 | -4.1E+03 | +9.0E-01 | 1.0E+00 | 1.8E+04 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 17| +2.14E+02 | -2.7E-01 | +4.7E-01 | 5.0E-02 | 1.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 126| +1.48E+02 | -1.7E-02 | +7.5E-02 | 1.2E-01 | 4.0E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 3| +8.57E+05 | -4.7E+06 | +9.0E-01 | 5.0E-01 | 2.6E+07 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 93| +1.48E+02 | -2.7E-05 | +8.9E-01 | 3.1E-02 | 9.1E-02 | 8.9E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 39| +1.47E+02 | -9.7E-02 | +3.6E-01 | 6.2E-02 | 7.2E+00 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 7| +3.34E+02 | -6.3E+02 | +9.0E-01 | 1.0E+00 | 2.8E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 173| +1.48E+02 | -9.0E-05 | +4.9E-01 | 4.5E-04 | 2.6E-01 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 4| +1.34E+05 | -7.2E+05 | +9.0E-01 | 1.0E+00 | 3.9E+06 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 18| +2.14E+02 | -8.5E-02 | +1.9E-01 | 5.0E-02 | 8.3E+00 | 1.2E-01 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 127| +1.48E+02 | -8.9E-02 | +8.5E-01 | 3.1E-02 | 6.1E+00 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 95| +1.51E+02 | -3.0E-03 | +1.0E+00 | 1.7E-02 | 5.7E-01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 40| +1.46E+02 | -1.7E-01 | +4.8E-01 | 6.2E-02 | 1.4E+01 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 94| +1.48E+02 | -4.4E-05 | +1.0E+00 | 6.2E-02 | 4.6E-02 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 5| +1.34E+05 | +1.0E+09 | -7.0E+04 | 2.0E+00 | 6.1E+05 | 5.5E+00 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 8| +2.52E+02 | -8.2E+01 | +8.6E-01 | 1.0E+00 | 4.3E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 41| +1.46E+02 | -9.1E-02 | +5.9E-01 | 6.2E-02 | 1.5E+01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 19| +2.13E+02 | -1.9E-01 | +8.8E-01 | 1.3E-02 | 9.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 174| +1.48E+02 | -5.7E-05 | +7.7E-01 | 4.5E-04 | 7.2E-01 | 5.3E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 6| +2.12E+04 | -1.1E+05 | +9.0E-01 | 5.0E-01 | 6.1E+05 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 128| +1.48E+02 | -4.6E-02 | +9.8E-01 | 6.2E-02 | 9.7E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 42| +1.46E+02 | -6.1E-02 | +2.8E-01 | 6.2E-02 | 6.9E+00 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 95| +1.48E+02 | +3.0E-05 | -5.7E-01 | 1.2E-01 | 6.7E-02 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 9| +2.29E+02 | -2.4E+01 | +2.0E+00 | 2.0E+00 | 4.6E+01 | 4.7E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 20| +2.13E+02 | -1.5E-01 | +6.9E-01 | 2.5E-02 | 6.1E+00 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 7| +3.53E+03 | -1.8E+04 | +9.0E-01 | 1.0E+00 | 9.7E+04 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 129| +1.48E+02 | -9.9E-03 | +1.1E+00 | 6.2E-02 | 1.1E+00 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 175| +1.48E+02 | -3.2E-05 | +1.4E+00 | 8.9E-04 | 1.3E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 43| +1.46E+02 | +2.9E-01 | -8.8E-01 | 6.2E-02 | 7.6E+00 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 8| +7.48E+02 | -2.8E+03 | +9.0E-01 | 2.0E+00 | 1.5E+04 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 96| +1.48E+02 | -9.6E-06 | +5.5E-01 | 3.1E-02 | 6.7E-02 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 21| +2.13E+02 | -9.9E-02 | +6.4E-01 | 2.5E-02 | 4.7E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 10| +2.29E+02 | +6.1E+03 | -1.7E+02 | 4.0E+00 | 4.3E+01 | 1.1E+01 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 130| +1.48E+02 | -3.1E-03 | +3.2E-01 | 6.2E-02 | 1.1E+00 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 44| +1.46E+02 | -9.1E-02 | +7.1E-01 | 1.6E-02 | 7.6E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 9| +3.13E+02 | -4.3E+02 | +9.0E-01 | 2.0E+00 | 2.4E+03 | 1.9E+00 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 176| +1.48E+02 | -3.3E-05 | +9.8E-01 | 1.8E-03 | 1.0E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 22| +2.13E+02 | -1.1E-01 | +6.0E-01 | 2.5E-02 | 5.2E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 97| +1.48E+02 | -1.9E-06 | +1.1E-01 | 3.1E-02 | 4.4E-02 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 11| +2.29E+02 | +8.1E+01 | -4.2E+00 | 1.0E+00 | 4.3E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 10| +2.53E+02 | -6.0E+01 | +8.6E-01 | 2.0E+00 | 3.8E+02 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 45| +1.46E+02 | -4.7E-02 | +9.3E-01 | 1.6E-02 | 1.6E+01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:35.557 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0211 and h = 1.41573e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:35.558 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0211: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:35 fides(INFO) 131| +1.48E+02 | -5.1E-03 | +1.0E+00 | 6.2E-02 | 3.1E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 177| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.6E-03 | 8.1E-02 | 4.6E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 11| +2.50E+02 | -2.9E+00 | +5.6E-01 | 2.0E+00 | 4.4E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 12| +2.21E+02 | -7.8E+00 | +3.3E-01 | 2.5E-01 | 4.3E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 23| +2.13E+02 | -8.3E-02 | +5.3E-01 | 2.5E-02 | 5.2E+00 | 5.1E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 98| +1.48E+02 | -9.3E-06 | +8.2E-01 | 7.8E-03 | 1.3E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 96| +1.51E+02 | -5.7E-03 | +1.0E+00 | 3.4E-02 | 4.4E-01 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 46| +1.46E+02 | -7.3E-02 | +1.0E+00 | 3.1E-02 | 4.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 132| +1.48E+02 | -4.2E-04 | +9.5E-01 | 6.2E-02 | 3.6E-01 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 12| +2.50E+02 | +2.0E-02 | -4.4E-02 | 2.0E+00 | 1.3E+01 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 178| +1.48E+02 | -1.5E-05 | +1.0E+00 | 8.9E-04 | 8.1E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 24| +2.13E+02 | -8.0E-02 | +4.7E-01 | 2.5E-02 | 5.0E+00 | 5.5E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 13| +2.15E+02 | -6.1E+00 | +6.8E-01 | 2.5E-01 | 4.7E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 47| +1.46E+02 | -8.9E-02 | +1.0E+00 | 6.2E-02 | 1.4E+01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 99| +1.48E+02 | -4.1E-06 | +8.8E-01 | 1.6E-02 | 2.3E-02 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 13| +2.50E+02 | -3.3E-01 | +8.5E-01 | 1.7E-02 | 1.3E+01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 133| +1.48E+02 | -7.9E-05 | +1.2E+00 | 6.2E-02 | 1.1E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 48| +1.46E+02 | -8.9E-02 | +8.7E-01 | 1.2E-01 | 3.2E+00 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 25| +2.13E+02 | -8.4E-02 | +5.0E-01 | 2.5E-02 | 5.6E+00 | 5.1E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 14| +2.10E+02 | -4.9E+00 | +9.9E-01 | 2.5E-01 | 1.6E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 14| +2.50E+02 | -5.2E-02 | +2.2E-01 | 3.3E-02 | 6.6E+00 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 179| +1.48E+02 | -2.1E-05 | +9.1E-01 | 1.8E-03 | 5.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 100| +1.48E+02 | -3.7E-06 | +6.9E-01 | 3.1E-02 | 1.7E-02 | 5.7E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 49| +1.46E+02 | +2.2E+01 | -5.4E+01 | 2.5E-01 | 3.0E+00 | 4.8E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 15| +2.50E+02 | -5.9E-02 | +7.9E-01 | 8.3E-03 | 4.8E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 134| +1.48E+02 | -1.3E-05 | +1.3E+00 | 6.2E-02 | 2.3E-02 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 15| +1.91E+02 | -1.9E+01 | +1.2E+00 | 5.0E-01 | 2.3E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 26| +2.13E+02 | -5.5E-02 | +3.6E-01 | 2.5E-02 | 4.7E+00 | 5.7E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 180| +1.48E+02 | -3.3E-05 | +1.0E+00 | 3.6E-03 | 4.0E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 16| +2.50E+02 | -9.4E-04 | +4.7E-02 | 1.7E-02 | 1.8E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 50| +1.46E+02 | +4.5E-01 | -2.9E+00 | 6.2E-02 | 3.0E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 101| +1.48E+02 | +4.8E-06 | -9.0E-01 | 3.1E-02 | 4.7E-02 | 5.3E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 135| +1.48E+02 | +2.4E-06 | -2.5E+01 | 6.2E-02 | 7.1E-03 | 3.0E-04 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 27| +2.13E+02 | -9.0E-02 | +5.1E-01 | 2.5E-02 | 6.0E+00 | 5.1E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 17| +2.50E+02 | -7.3E-03 | +7.9E-01 | 2.7E-03 | 1.7E+00 | 6.7E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 16| +1.91E+02 | +4.3E+03 | -9.5E+01 | 1.0E+00 | 5.8E+01 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 51| +1.46E+02 | -1.0E-02 | +2.3E-01 | 1.6E-02 | 3.0E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:35.696 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 87.9877 and h = 3.2767e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:35.696 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 87.9877: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:35 fides(INFO) 181| +1.48E+02 | -1.5E-05 | +5.8E-01 | 7.2E-03 | 3.6E-02 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 102| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.8E-03 | 4.7E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 97| +1.51E+02 | -9.5E-03 | +1.1E+00 | 6.8E-02 | 4.7E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 18| +2.50E+02 | -4.5E-06 | +9.3E-04 | 5.4E-03 | 6.0E-01 | 7.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 136| +1.48E+02 | +1.8E-06 | -3.7E+01 | 5.8E-05 | 7.1E-03 | 7.4E-05 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 28| +2.13E+02 | -4.0E-02 | +2.9E-01 | 2.5E-02 | 4.3E+00 | 5.9E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 52| +1.46E+02 | -5.1E-03 | +8.4E-01 | 3.9E-03 | 3.3E+00 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 17| +1.68E+02 | -2.3E+01 | +9.4E-01 | 2.5E-01 | 5.8E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 103| +1.48E+02 | -9.8E-07 | +1.6E+00 | 2.0E-03 | 4.7E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as function difference 9.82E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:35 fides(INFO) 19| +2.50E+02 | -8.2E-04 | +8.3E-01 | 8.6E-04 | 6.0E-01 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 182| +1.48E+02 | -2.8E-07 | +2.0E-02 | 7.2E-03 | 5.3E-02 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 29| +2.13E+02 | -9.0E-02 | +5.0E-01 | 2.5E-02 | 6.1E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 53| +1.46E+02 | -4.9E-03 | +8.0E-01 | 7.8E-03 | 3.3E+00 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 137| +1.48E+02 | +9.8E-07 | -4.8E+01 | 1.4E-05 | 7.1E-03 | 1.9E-05 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 20| +2.50E+02 | -4.6E-05 | +9.2E-02 | 1.7E-03 | 2.9E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 183| +1.48E+02 | -6.0E-06 | +8.3E-01 | 1.2E-03 | 5.0E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 18| +1.68E+02 | +1.1E+00 | -2.2E-01 | 5.0E-01 | 5.4E+01 | 8.6E-01 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) 54| +1.46E+02 | -7.7E-03 | +9.1E-01 | 1.6E-02 | 2.2E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 30| +2.13E+02 | -2.3E-02 | +1.6E-01 | 2.5E-02 | 4.2E+00 | 5.9E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 21| +2.50E+02 | -1.8E-04 | +7.9E-01 | 4.3E-04 | 2.6E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 138| +1.48E+02 | +7.6E-07 | -6.1E+01 | 3.6E-06 | 7.1E-03 | 5.3E-06 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 22| +2.50E+02 | -5.3E-08 | +8.2E-04 | 8.6E-04 | 9.4E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 55| +1.46E+02 | -1.1E-03 | +7.2E-02 | 3.1E-02 | 3.7E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 184| +1.48E+02 | -3.7E-06 | +3.3E+00 | 2.4E-03 | 4.0E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 19| +1.64E+02 | -4.1E+00 | +8.8E-01 | 7.5E-02 | 5.4E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 31| +2.12E+02 | -6.4E-02 | +9.1E-01 | 6.3E-03 | 6.5E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 98| +1.51E+02 | -1.1E-02 | +1.4E+00 | 1.4E-01 | 6.9E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 23| +2.50E+02 | -2.1E-05 | +8.2E-01 | 1.4E-04 | 9.4E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 139| +1.48E+02 | -6.1E-07 | +7.1E+01 | 9.0E-07 | 7.1E-03 | 1.8E-06 | 2d |1\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as function difference 6.06E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:35 fides(INFO) 56| +1.46E+02 | -8.6E-03 | +5.1E-01 | 7.8E-03 | 2.0E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 32| +2.12E+02 | -6.5E-02 | +7.8E-01 | 1.3E-02 | 4.6E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 24| +2.50E+02 | -1.0E-08 | +8.5E-04 | 2.8E-04 | 4.2E-02 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 185| +1.48E+02 | +2.3E-06 | -1.4E+01 | 2.4E-03 | 7.6E-03 | 1.1E-04 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 20| +1.54E+02 | -9.2E+00 | +7.7E-01 | 1.5E-01 | 7.6E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 57| +1.46E+02 | -2.8E-03 | +9.7E-01 | 7.8E-03 | 2.7E+00 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 0| +2.95E+13 | NaN | NaN | 1.0E+00 | 1.4E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:35 fides(INFO) 33| +2.12E+02 | -1.1E-01 | +9.3E-01 | 2.5E-02 | 2.9E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 25| +2.50E+02 | -4.4E-06 | +8.1E-01 | 6.4E-05 | 4.2E-02 | 1.6E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 186| +1.48E+02 | +2.4E-06 | -2.7E+01 | 1.2E-04 | 7.6E-03 | 3.1E-05 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 58| +1.46E+02 | -3.5E-03 | +9.5E-01 | 1.6E-02 | 1.5E+00 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:35.868 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 143.128 and h = 1.38416e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:35.868 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 143.128: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:35 fides(INFO) 21| +1.54E+02 | +0.0E+00 | +0.0E+00 | 3.0E-01 | 8.2E+01 | 3.2E-01 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) 26| +2.50E+02 | -3.3E-10 | +1.6E-04 | 1.3E-04 | 1.7E-02 | 2.0E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 59| +1.46E+02 | +8.1E-03 | -1.1E+00 | 3.1E-02 | 1.3E+00 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 34| +2.12E+02 | -2.6E-02 | +1.9E-01 | 5.0E-02 | 3.7E+00 | 1.2E-01 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 187| +1.48E+02 | -1.4E-06 | +2.7E+01 | 2.9E-05 | 7.6E-03 | 1.3E-05 | 2d |1\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as function difference 1.37E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:35 fides(INFO) 27| +2.50E+02 | -6.9E-07 | +8.2E-01 | 2.5E-05 | 1.7E-02 | 6.0E-05 | 2d |1\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as function difference 6.87E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:35 fides(INFO) 22| +1.53E+02 | -1.9E+00 | +9.5E-01 | 2.6E-02 | 8.2E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 60| +1.46E+02 | -1.4E-03 | +6.0E-01 | 7.8E-03 | 1.3E+00 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 1| +2.16E+07 | -2.9E+13 | +8.5E-02 | 1.0E+00 | 1.4E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 35| +2.12E+02 | -1.3E-01 | +8.2E-01 | 1.2E-02 | 6.7E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 61| +1.46E+02 | -1.0E-03 | +3.6E-01 | 7.8E-03 | 1.7E+00 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 2| +3.33E+06 | -1.8E+07 | +8.9E-01 | 2.5E-01 | 9.9E+07 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 23| +1.50E+02 | -2.1E+00 | +8.4E-01 | 5.2E-02 | 4.6E+01 | 9.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 36| +2.12E+02 | -3.0E-02 | +2.9E-01 | 2.4E-02 | 3.3E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 62| +1.46E+02 | -2.5E-03 | +3.8E-01 | 7.8E-03 | 9.7E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 99| +1.51E+02 | -9.1E-03 | +1.6E+00 | 1.4E-01 | 1.2E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 0| +5.21E+07 | NaN | NaN | 1.0E+00 | 2.4E+08 | NaN | NaN |1\n", - "2023-02-17 16:05:35 fides(INFO) 3| +5.18E+05 | -2.8E+06 | +9.0E-01 | 5.0E-01 | 1.5E+07 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 24| +1.49E+02 | -1.3E+00 | +9.6E-01 | 1.0E-01 | 4.8E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 37| +2.12E+02 | -1.1E-01 | +6.0E-01 | 2.4E-02 | 6.1E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 63| +1.46E+02 | -1.2E-03 | +1.7E-01 | 7.8E-03 | 1.9E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 1| +2.41E+04 | -5.2E+07 | +1.1E-01 | 1.0E+00 | 2.4E+08 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 4| +8.27E+04 | -4.4E+05 | +8.9E-01 | 1.0E+00 | 2.4E+06 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 64| +1.46E+02 | -1.1E-03 | +7.3E-01 | 2.0E-03 | 8.2E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 0| +2.15E+10 | NaN | NaN | 1.0E+00 | 9.9E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:36 fides(INFO) 25| +1.49E+02 | -1.1E-01 | -3.1E-02 | 2.1E-01 | 3.0E+01 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 5| +1.32E+04 | -6.9E+04 | +9.0E-01 | 2.0E+00 | 3.7E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 38| +2.12E+02 | -5.4E-02 | +4.5E-01 | 2.4E-02 | 3.5E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 2| +4.83E+03 | -1.9E+04 | +8.9E-01 | 2.5E-01 | 1.0E+05 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 65| +1.46E+02 | -3.9E-04 | +7.8E-01 | 2.0E-03 | 9.7E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 1| +3.42E+04 | -2.2E+10 | +9.0E-02 | 1.0E+00 | 9.9E+10 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 6| +2.26E+03 | -1.1E+04 | +9.0E-01 | 2.0E+00 | 5.9E+04 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 3| +9.58E+02 | -3.9E+03 | +8.9E-01 | 5.0E-01 | 1.7E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 39| +2.12E+02 | -1.1E-01 | +6.4E-01 | 2.4E-02 | 5.5E+00 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 26| +1.49E+02 | -6.0E-01 | +7.5E-01 | 5.2E-02 | 3.0E+01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 2| +5.57E+03 | -2.9E+04 | +9.0E-01 | 2.5E-01 | 1.6E+05 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 66| +1.46E+02 | -2.4E-04 | +8.1E-01 | 3.9E-03 | 4.6E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 7| +5.46E+02 | -1.7E+03 | +9.0E-01 | 2.0E+00 | 9.4E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 4| +3.30E+02 | -6.3E+02 | +8.9E-01 | 1.0E+00 | 2.7E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 0| +2.85E+08 | NaN | NaN | 1.0E+00 | 1.2E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 40| +2.12E+02 | -8.0E-02 | +6.0E-01 | 2.4E-02 | 3.6E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 3| +1.06E+03 | -4.5E+03 | +9.0E-01 | 5.0E-01 | 2.4E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 8| +2.84E+02 | -2.6E+02 | +9.0E-01 | 2.0E+00 | 1.5E+03 | 1.7E+00 | trr |1\n", - "2023-02-17 16:05:36 fides(INFO) 67| +1.46E+02 | -3.6E-04 | +7.3E-01 | 7.8E-03 | 4.5E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 27| +1.48E+02 | -3.5E-01 | +9.6E-01 | 5.2E-02 | 4.2E+01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 5| +2.53E+02 | -7.7E+01 | +8.5E-01 | 1.0E+00 | 4.0E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 1| +6.78E+06 | -2.8E+08 | +1.1E-01 | 1.0E+00 | 1.2E+09 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 4| +3.57E+02 | -7.0E+02 | +9.0E-01 | 1.0E+00 | 3.9E+03 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 41| +2.12E+02 | -1.2E-01 | +7.1E-01 | 2.4E-02 | 5.0E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 68| +1.46E+02 | +6.4E-05 | -8.7E-02 | 7.8E-03 | 5.6E-01 | 4.7E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 100| +1.51E+02 | -1.5E-02 | +1.4E+00 | 1.4E-01 | 1.2E+00 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 6| +2.42E+02 | -1.1E+01 | +9.0E-01 | 2.0E+00 | 4.3E+01 | 4.0E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 9| +2.48E+02 | -3.5E+01 | +8.7E-01 | 2.0E+00 | 2.3E+02 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 28| +1.48E+02 | -3.3E-01 | +8.4E-01 | 1.0E-01 | 1.9E+01 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 5| +2.57E+02 | -9.9E+01 | +8.8E-01 | 1.0E+00 | 6.0E+02 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 42| +2.12E+02 | -1.1E-01 | +7.0E-01 | 2.4E-02 | 3.7E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 2| +1.04E+06 | -5.7E+06 | +8.9E-01 | 2.5E-01 | 3.1E+07 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 69| +1.46E+02 | -1.6E-04 | +7.5E-01 | 2.0E-03 | 5.6E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 10| +2.29E+02 | -1.9E+01 | +3.0E+00 | 2.0E+00 | 1.6E+01 | 3.4E+00 | trr |1\n", - "2023-02-17 16:05:36 fides(INFO) 7| +2.42E+02 | +3.2E+01 | -6.3E+00 | 4.0E+00 | 2.8E+01 | 9.0E+00 | trr |0\n", - "2023-02-17 16:05:36 fides(INFO) 6| +2.47E+02 | -1.1E+01 | +7.1E-01 | 2.0E+00 | 7.7E+01 | 4.3E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 29| +1.48E+02 | -1.6E-01 | +5.6E-01 | 2.1E-01 | 3.0E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 43| +2.11E+02 | -1.4E-01 | +7.8E-01 | 2.4E-02 | 4.9E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 8| +2.42E+02 | +9.1E+00 | -1.2E+00 | 8.5E-01 | 2.8E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 70| +1.46E+02 | -1.9E-04 | +9.7E-01 | 3.9E-03 | 3.4E-01 | 9.2E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 3| +1.61E+05 | -8.8E+05 | +8.9E-01 | 5.0E-01 | 4.8E+06 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 7| +2.35E+02 | -1.1E+01 | +1.6E+00 | 2.0E+00 | 1.2E+01 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 71| +1.46E+02 | -2.9E-04 | +1.0E+00 | 7.8E-03 | 5.7E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 44| +2.11E+02 | -2.1E-01 | +7.2E-01 | 4.8E-02 | 3.9E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 9| +2.42E+02 | +7.0E+00 | -1.0E+00 | 2.1E-01 | 2.8E+01 | 5.4E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 30| +1.48E+02 | +4.6E-01 | -1.0E+00 | 2.1E-01 | 9.8E+00 | 2.1E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 4| +2.51E+04 | -1.4E+05 | +9.0E-01 | 1.0E+00 | 7.4E+05 | 9.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 45| +2.11E+02 | -3.2E-01 | +6.7E-01 | 4.8E-02 | 7.9E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 10| +2.40E+02 | -2.8E+00 | +8.5E-01 | 5.3E-02 | 2.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 11| +2.29E+02 | +1.7E+02 | -2.0E+01 | 2.0E+00 | 3.1E+01 | 6.1E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 8| +2.35E+02 | +2.0E+01 | -3.9E+00 | 4.0E+00 | 1.9E+01 | 6.9E+00 | trr |0\n", - "2023-02-17 16:05:36 fides(INFO) 72| +1.46E+02 | -4.4E-04 | +8.1E-01 | 1.6E-02 | 2.4E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 101| +1.51E+02 | -1.8E-02 | +1.3E+00 | 2.7E-01 | 7.5E-01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 11| +2.39E+02 | -5.3E-01 | +1.9E-01 | 1.1E-01 | 1.6E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 73| +1.46E+02 | +1.8E-02 | -8.2E+00 | 3.1E-02 | 9.4E-01 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 31| +1.48E+02 | -9.8E-02 | +3.4E-01 | 5.2E-02 | 9.8E+00 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 12| +2.29E+02 | +1.6E+01 | -1.7E+00 | 5.0E-01 | 3.1E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 46| +2.11E+02 | -3.0E-01 | +5.8E-01 | 4.8E-02 | 6.4E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 9| +2.30E+02 | -5.3E+00 | +1.3E+00 | 5.8E-01 | 1.9E+01 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 5| +4.09E+03 | -2.1E+04 | +9.0E-01 | 1.0E+00 | 1.1E+05 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 12| +2.38E+02 | -1.3E+00 | +9.3E-01 | 2.7E-02 | 3.3E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 74| +1.46E+02 | +5.1E-04 | -6.9E-01 | 7.8E-03 | 9.4E-01 | 7.0E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 13| +2.25E+02 | -4.0E+00 | +5.4E-01 | 1.2E-01 | 3.1E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 32| +1.48E+02 | -8.1E-02 | +5.7E-01 | 5.2E-02 | 1.4E+01 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 47| +2.10E+02 | -4.7E-01 | +7.3E-01 | 4.8E-02 | 9.3E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 13| +2.36E+02 | -1.6E+00 | +8.8E-01 | 5.3E-02 | 2.4E+01 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 10| +2.30E+02 | +1.0E+02 | -1.0E+01 | 1.2E+00 | 2.6E+01 | 3.2E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 75| +1.46E+02 | -1.7E-04 | +6.9E-01 | 2.0E-03 | 9.4E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 14| +2.22E+02 | -2.3E+00 | +7.6E-01 | 1.2E-01 | 2.6E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 6| +8.23E+02 | -3.3E+03 | +9.0E-01 | 1.0E+00 | 1.8E+04 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 14| +2.33E+02 | -2.9E+00 | +1.2E+00 | 1.1E-01 | 1.6E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 48| +2.10E+02 | -4.7E-01 | +7.2E-01 | 4.8E-02 | 7.1E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 33| +1.48E+02 | -5.8E-03 | +9.3E-01 | 5.2E-02 | 3.2E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 76| +1.46E+02 | -3.1E-05 | +9.7E-01 | 2.0E-03 | 1.2E-01 | 3.6E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 15| +2.22E+02 | -5.6E-02 | -5.7E-02 | 2.5E-01 | 2.0E+01 | 7.0E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 11| +2.30E+02 | +2.6E+00 | -4.7E-01 | 2.9E-01 | 2.6E+01 | 7.4E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 15| +2.30E+02 | -3.5E+00 | +8.4E-01 | 2.1E-01 | 1.7E+01 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 77| +1.46E+02 | -4.6E-05 | +9.4E-01 | 3.9E-03 | 2.3E-01 | 6.8E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 49| +2.09E+02 | -5.9E-01 | +8.2E-01 | 4.8E-02 | 9.3E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36.303 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.9015 and h = 7.61275e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.304 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.9015: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) 34| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.0E-01 | 1.7E+00 | 5.2E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 16| +2.21E+02 | -1.4E+00 | +6.8E-01 | 6.2E-02 | 2.0E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 12| +2.27E+02 | -2.9E+00 | +7.8E-01 | 7.2E-02 | 2.6E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 7| +3.14E+02 | -5.1E+02 | +9.1E-01 | 1.0E+00 | 2.8E+03 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 16| +2.25E+02 | -4.4E+00 | +7.3E-01 | 4.3E-01 | 3.9E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 78| +1.46E+02 | -6.6E-05 | +8.2E-01 | 7.8E-03 | 1.3E-01 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 50| +2.08E+02 | -6.4E-01 | +6.0E-01 | 9.6E-02 | 7.2E+00 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 17| +2.20E+02 | -1.4E+00 | +9.6E-01 | 6.2E-02 | 2.0E+01 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 102| +1.51E+02 | -2.4E-03 | +1.0E+00 | 2.7E-01 | 5.9E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 35| +1.48E+02 | -4.0E-03 | +9.1E-01 | 2.6E-02 | 1.7E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 13| +2.23E+02 | -4.2E+00 | +1.2E+00 | 1.4E-01 | 2.0E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 79| +1.46E+02 | +9.6E-04 | -3.1E+00 | 1.6E-02 | 1.3E-01 | 7.5E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 17| +2.22E+02 | -3.2E+00 | +3.5E-01 | 4.3E-01 | 2.3E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 18| +2.18E+02 | -1.7E+00 | +7.2E-01 | 1.2E-01 | 1.6E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 51| +2.07E+02 | -1.0E+00 | +7.3E-01 | 9.6E-02 | 1.4E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 80| +1.46E+02 | -7.0E-06 | +7.6E-02 | 3.9E-03 | 1.3E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 36| +1.48E+02 | -3.5E-03 | +9.6E-01 | 5.2E-02 | 3.8E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 14| +2.17E+02 | -5.5E+00 | +1.1E+00 | 2.9E-01 | 1.9E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 18| +2.14E+02 | -8.1E+00 | +7.4E-01 | 4.3E-01 | 5.5E+01 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 19| +2.16E+02 | -1.6E+00 | +5.9E-01 | 1.2E-01 | 2.6E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 8| +2.20E+02 | -9.4E+01 | +1.1E+00 | 1.0E+00 | 4.0E+02 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 52| +2.07E+02 | -7.1E-01 | +7.6E-01 | 9.6E-02 | 7.0E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 81| +1.46E+02 | -3.8E-05 | +6.6E-01 | 9.8E-04 | 1.7E-01 | 7.2E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 19| +1.99E+02 | -1.5E+01 | +1.1E+00 | 4.3E-01 | 2.5E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 20| +2.16E+02 | -2.7E-01 | +7.9E-02 | 1.2E-01 | 1.7E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 37| +1.48E+02 | -3.9E-03 | +9.0E-01 | 1.0E-01 | 9.8E-01 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 15| +2.02E+02 | -1.6E+01 | +1.0E+00 | 5.8E-01 | 2.8E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 82| +1.46E+02 | -1.1E-05 | +9.3E-01 | 9.8E-04 | 1.4E-01 | 1.9E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 53| +2.05E+02 | -2.0E+00 | +1.1E+00 | 1.9E-01 | 9.8E+00 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 103| +1.51E+02 | -3.1E-04 | +1.7E+00 | 2.7E-01 | 3.0E-01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 21| +2.15E+02 | -1.3E+00 | +8.8E-01 | 3.1E-02 | 3.2E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 16| +2.02E+02 | +3.0E+03 | -1.0E+02 | 1.2E+00 | 6.6E+01 | 2.2E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 20| +1.99E+02 | +1.6E+02 | -7.8E+00 | 8.5E-01 | 4.6E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 83| +1.46E+02 | -1.5E-05 | +1.1E+00 | 2.0E-03 | 9.8E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 38| +1.48E+02 | +7.4E-03 | -1.9E+00 | 2.1E-01 | 2.5E+00 | 9.8E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 54| +1.93E+02 | -1.2E+01 | +2.2E+00 | 3.9E-01 | 1.1E+01 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 22| +2.14E+02 | -1.0E+00 | +7.3E-01 | 6.2E-02 | 2.0E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 21| +1.85E+02 | -1.3E+01 | +1.1E+00 | 2.1E-01 | 4.6E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 84| +1.46E+02 | -2.4E-05 | +9.5E-01 | 3.9E-03 | 9.2E-02 | 5.2E-04 | 2d |1\n", - "2023-02-17 16:05:36.482 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 115.442 and h = 1.5043e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.482 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 115.442: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) 17| +2.02E+02 | +0.0E+00 | +0.0E+00 | 2.9E-01 | 6.6E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 39| +1.48E+02 | -9.4E-04 | +5.3E-01 | 5.2E-02 | 2.5E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 23| +2.13E+02 | -6.4E-01 | +6.7E-01 | 6.2E-02 | 1.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 55| +1.93E+02 | +1.3E+03 | -3.9E+01 | 7.7E-01 | 4.3E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 9| +2.20E+02 | +8.5E+00 | -1.0E+00 | 1.0E+00 | 2.8E+01 | 1.9E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 85| +1.46E+02 | -3.4E-05 | +7.8E-01 | 7.8E-03 | 1.1E-01 | 7.7E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 18| +1.94E+02 | -8.0E+00 | +9.8E-01 | 7.2E-02 | 6.6E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 40| +1.48E+02 | +6.4E-04 | -3.5E-01 | 5.2E-02 | 6.1E-01 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 22| +1.62E+02 | -2.3E+01 | +5.2E-01 | 4.3E-01 | 4.7E+01 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 24| +2.12E+02 | -7.7E-01 | +7.2E-01 | 6.2E-02 | 1.6E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 86| +1.46E+02 | +1.1E-03 | -5.3E+00 | 1.6E-02 | 6.9E-02 | 7.5E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 56| +1.73E+02 | -2.0E+01 | +1.1E+00 | 1.9E-01 | 4.3E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 19| +1.81E+02 | -1.3E+01 | +9.6E-01 | 1.4E-01 | 5.7E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 87| +1.46E+02 | +2.5E-05 | -3.9E-01 | 3.9E-03 | 6.9E-02 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 25| +2.12E+02 | -5.1E-01 | +5.7E-01 | 6.2E-02 | 1.1E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 41| +1.48E+02 | -3.0E-04 | +3.2E-01 | 1.3E-02 | 6.1E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 23| +1.49E+02 | -1.3E+01 | +9.4E-01 | 4.3E-01 | 2.8E+02 | 5.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 57| +1.73E+02 | -2.0E+00 | -1.8E-01 | 3.9E-01 | 4.8E+01 | 8.7E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 104| +1.51E+02 | -7.1E-04 | +1.4E+00 | 2.7E-01 | 1.3E-01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 88| +1.46E+02 | -1.2E-05 | +6.5E-01 | 9.8E-04 | 6.9E-02 | 5.2E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 26| +2.11E+02 | -6.9E-01 | +6.8E-01 | 6.2E-02 | 1.6E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36.568 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 100.881 and h = 9.40868e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.568 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 100.881: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) 42| +1.48E+02 | -5.0E-04 | +8.8E-01 | 1.3E-02 | 1.9E+00 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 10| +2.20E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 2.8E+01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 20| +1.72E+02 | -8.7E+00 | +3.1E-01 | 2.9E-01 | 5.3E+01 | 5.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 24| +1.48E+02 | -7.3E-01 | +4.7E-01 | 8.5E-01 | 8.0E+01 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 58| +1.64E+02 | -8.9E+00 | +9.9E-01 | 9.6E-02 | 4.8E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 89| +1.46E+02 | -4.6E-06 | +8.3E-01 | 9.8E-04 | 1.4E-01 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 27| +2.11E+02 | -4.5E-01 | +5.4E-01 | 6.2E-02 | 9.8E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 21| +1.72E+02 | -1.8E+00 | -7.1E-02 | 2.9E-01 | 2.4E+02 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 43| +1.48E+02 | -1.1E-04 | +9.0E-01 | 2.6E-02 | 2.1E-01 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 25| +1.48E+02 | +8.2E+00 | -5.3E+00 | 8.5E-01 | 1.7E+01 | 4.4E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 90| +1.46E+02 | -1.8E-05 | +7.8E-01 | 2.0E-03 | 5.1E-02 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 28| +2.10E+02 | -6.3E-01 | +6.5E-01 | 6.2E-02 | 1.5E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 59| +1.54E+02 | -1.0E+01 | +7.2E-01 | 1.9E-01 | 5.2E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 26| +1.48E+02 | -4.9E-01 | +5.8E-01 | 8.3E-02 | 1.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 91| +1.46E+02 | +1.4E-05 | -3.1E-01 | 3.9E-03 | 2.1E-01 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 44| +1.48E+02 | -1.4E-04 | +9.9E-01 | 5.2E-02 | 3.7E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 22| +1.56E+02 | -1.6E+01 | +9.7E-01 | 7.2E-02 | 2.4E+02 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 29| +2.10E+02 | -3.8E-01 | +4.6E-01 | 6.2E-02 | 9.0E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 60| +1.50E+02 | -3.9E+00 | +6.4E-01 | 1.9E-01 | 1.1E+02 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 92| +1.46E+02 | -1.2E-05 | +7.8E-01 | 9.8E-04 | 2.1E-01 | 4.0E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 27| +1.48E+02 | -7.2E-02 | +8.4E-01 | 8.3E-02 | 7.9E+00 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 45| +1.48E+02 | -1.5E-04 | +9.1E-01 | 1.0E-01 | 1.3E-01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 23| +1.51E+02 | -4.9E+00 | +8.6E-01 | 1.4E-01 | 6.7E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 30| +2.09E+02 | -5.9E-01 | +6.1E-01 | 6.2E-02 | 1.4E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 105| +1.51E+02 | -5.2E-04 | +1.5E+00 | 2.7E-01 | 4.6E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 93| +1.46E+02 | -6.2E-06 | +9.8E-01 | 2.0E-03 | 6.2E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 61| +1.50E+02 | +1.2E+00 | -5.5E-01 | 1.9E-01 | 4.1E+01 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 28| +1.48E+02 | -2.9E-02 | +8.3E-01 | 1.7E-01 | 5.7E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 11| +2.17E+02 | -3.3E+00 | +8.6E-01 | 6.2E-02 | 2.8E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 24| +1.49E+02 | -2.3E+00 | +9.2E-01 | 2.9E-01 | 8.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 31| +2.09E+02 | -2.9E-01 | +3.5E-01 | 6.2E-02 | 8.5E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 46| +1.48E+02 | +1.1E-03 | -6.5E+00 | 2.1E-01 | 1.4E-01 | 4.4E-02 | trr |0\n", - "2023-02-17 16:05:36 fides(INFO) 94| +1.46E+02 | -8.5E-06 | +1.0E+00 | 3.9E-03 | 4.7E-02 | 2.5E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 62| +1.49E+02 | -1.0E+00 | +8.2E-01 | 4.8E-02 | 4.1E+01 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:36.723 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0685 and h = 1.43602e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.724 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0685: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) 47| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.0E-02 | 1.4E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 29| +1.48E+02 | +1.5E-02 | -2.0E+00 | 3.3E-01 | 1.3E+00 | 1.4E-01 | trr |0\n", - "2023-02-17 16:05:36 fides(INFO) 95| +1.46E+02 | -6.0E-06 | +3.4E-01 | 7.8E-03 | 5.4E-02 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 32| +2.08E+02 | -5.8E-01 | +5.7E-01 | 6.2E-02 | 1.5E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 25| +1.49E+02 | +2.7E+00 | -1.4E+00 | 5.8E-01 | 4.4E+01 | 5.4E-01 | 2d |0\n", - "2023-02-17 16:05:36.750 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.9736 and h = 9.3318e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.751 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.9736: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 30| +1.48E+02 | +0.0E+00 | +0.0E+00 | 4.5E-02 | 1.3E+00 | 3.4E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 48| +1.48E+02 | -1.6E-05 | +7.3E-01 | 1.3E-02 | 1.4E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 96| +1.46E+02 | +5.3E-04 | -3.8E+00 | 7.8E-03 | 5.6E-02 | 4.6E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 63| +1.48E+02 | -5.9E-01 | +8.9E-01 | 9.6E-02 | 3.7E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 106| +1.51E+02 | -4.1E-04 | +1.4E+00 | 2.7E-01 | 3.9E-01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 33| +2.08E+02 | -2.1E-01 | +2.5E-01 | 6.2E-02 | 8.4E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 26| +1.48E+02 | -8.5E-01 | +7.3E-01 | 1.4E-01 | 4.4E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 97| +1.46E+02 | +5.1E-06 | -1.3E-01 | 2.0E-03 | 5.6E-02 | 1.2E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 31| +1.48E+02 | -1.1E-03 | +9.2E-01 | 1.1E-02 | 1.3E+00 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 49| +1.48E+02 | -8.1E-06 | +8.5E-01 | 1.3E-02 | 8.1E-02 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 34| +2.08E+02 | -3.7E-01 | +8.9E-01 | 1.6E-02 | 1.5E+01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 64| +1.48E+02 | +1.1E+00 | -2.2E+00 | 1.9E-01 | 1.7E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 27| +1.48E+02 | -2.4E-01 | +4.3E-01 | 1.4E-01 | 3.8E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 98| +1.46E+02 | -8.5E-06 | +7.8E-01 | 4.9E-04 | 5.6E-02 | 3.2E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 35| +2.07E+02 | -3.5E-01 | +7.8E-01 | 3.1E-02 | 8.6E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 32| +1.48E+02 | -1.3E-03 | +9.6E-01 | 2.3E-02 | 8.0E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 99| +1.46E+02 | -2.1E-06 | +4.7E-01 | 9.8E-04 | 1.2E-01 | 1.9E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 50| +1.48E+02 | -1.3E-05 | +8.9E-01 | 2.5E-02 | 8.6E-02 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 12| +2.13E+02 | -4.1E+00 | +8.7E-01 | 1.2E-01 | 2.1E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 65| +1.48E+02 | -7.9E-02 | +4.5E-01 | 4.8E-02 | 1.7E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 28| +1.48E+02 | -3.4E-01 | +4.6E-01 | 1.4E-01 | 2.4E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 36| +2.07E+02 | -4.3E-01 | +7.5E-01 | 6.2E-02 | 5.2E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 100| +1.46E+02 | -1.6E-06 | +1.1E+00 | 9.8E-04 | 3.4E-02 | 8.0E-05 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 51| +1.48E+02 | -2.1E-05 | +1.1E+00 | 5.0E-02 | 5.5E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 66| +1.48E+02 | -9.3E-02 | +3.0E-01 | 4.8E-02 | 1.5E+01 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 33| +1.48E+02 | -1.8E-03 | +9.3E-01 | 4.5E-02 | 7.7E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 29| +1.48E+02 | +5.7E-02 | -1.4E-01 | 1.4E-01 | 8.4E+00 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 101| +1.46E+02 | -3.4E-06 | +1.3E+00 | 2.0E-03 | 5.1E-02 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 37| +2.07E+02 | -2.2E-01 | +2.3E-01 | 1.2E-01 | 1.2E+01 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 107| +1.51E+02 | -1.8E-04 | +1.4E+00 | 2.7E-01 | 1.4E-01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 52| +1.48E+02 | +8.1E-06 | -3.6E-01 | 1.0E-01 | 5.7E-02 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 67| +1.48E+02 | -1.2E-01 | +4.4E-01 | 4.8E-02 | 2.2E+01 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 34| +1.48E+02 | +3.4E-03 | -1.6E+00 | 9.0E-02 | 5.1E-01 | 6.3E-02 | 2d |0\n", - "2023-02-17 16:05:36.908 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 92.147 and h = 1.45023e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.908 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 92.147: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) 102| +1.46E+02 | -4.4E-06 | +9.2E-01 | 3.9E-03 | 3.3E-02 | 1.8E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 30| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.6E-02 | 8.4E+00 | 5.1E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 38| +2.06E+02 | -7.6E-01 | +8.4E-01 | 3.1E-02 | 1.5E+01 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 103| +1.46E+02 | -6.4E-06 | +7.2E-01 | 7.8E-03 | 3.7E-02 | 5.6E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 53| +1.48E+02 | -5.4E-06 | +7.0E-01 | 2.5E-02 | 5.7E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 68| +1.48E+02 | -4.2E-03 | +3.1E-02 | 4.8E-02 | 6.7E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 35| +1.48E+02 | -3.8E-04 | +5.0E-01 | 2.3E-02 | 5.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 39| +2.05E+02 | -7.9E-01 | +1.3E+00 | 6.2E-02 | 6.2E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 31| +1.48E+02 | -6.6E-02 | +8.6E-01 | 9.0E-03 | 8.4E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 13| +2.07E+02 | -6.6E+00 | +9.3E-01 | 2.5E-01 | 3.4E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 104| +1.46E+02 | +3.6E-04 | -6.0E+00 | 7.8E-03 | 4.1E-02 | 3.6E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 54| +1.48E+02 | -3.0E-06 | +3.6E-01 | 2.5E-02 | 3.8E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 40| +2.05E+02 | -3.2E-01 | +2.5E-01 | 1.2E-01 | 9.9E+00 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 69| +1.48E+02 | -4.3E-02 | +7.4E-01 | 1.2E-02 | 5.4E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 36| +1.48E+02 | +2.3E-04 | -1.9E-01 | 2.3E-02 | 4.6E-01 | 6.1E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 32| +1.48E+02 | -7.1E-02 | +8.8E-01 | 1.8E-02 | 1.4E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 105| +1.46E+02 | +1.0E-05 | -5.9E-01 | 2.0E-03 | 4.1E-02 | 9.1E-04 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 106| +1.46E+02 | -2.5E-06 | +5.3E-01 | 4.9E-04 | 4.1E-02 | 2.4E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 55| +1.48E+02 | +1.3E-06 | -7.4E-02 | 2.5E-02 | 8.9E-02 | 4.4E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 41| +2.03E+02 | -1.5E+00 | +8.0E-01 | 1.2E-01 | 2.2E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 70| +1.48E+02 | -2.3E-02 | +8.2E-01 | 1.2E-02 | 8.6E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 33| +1.48E+02 | -5.4E-02 | +6.1E-01 | 3.6E-02 | 5.4E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 37| +1.48E+02 | -2.9E-04 | +6.6E-01 | 5.6E-03 | 4.6E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 108| +1.51E+02 | -7.8E-05 | +1.4E+00 | 2.7E-01 | 3.0E-02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 107| +1.46E+02 | -5.4E-07 | +8.7E-01 | 4.9E-04 | 5.0E-02 | 3.3E-05 | 2d |1\n", - "2023-02-17 16:05:37 fides(WARNING) Stopping as function difference 5.41E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:37 fides(INFO) 42| +2.01E+02 | -2.1E+00 | +1.0E+00 | 2.5E-01 | 1.1E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 34| +1.48E+02 | -1.5E-02 | +7.8E-01 | 3.6E-02 | 9.3E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 56| +1.48E+02 | -1.0E-06 | +1.8E-01 | 6.3E-03 | 8.9E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 71| +1.48E+02 | -9.8E-03 | +5.5E-01 | 2.4E-02 | 2.6E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 38| +1.48E+02 | -1.6E-04 | +9.8E-01 | 5.6E-03 | 7.9E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 14| +1.96E+02 | -1.0E+01 | +3.0E-01 | 5.0E-01 | 2.9E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:37.056 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.455 and h = 1.39738e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.056 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.455: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 35| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.2E-02 | 2.6E+00 | 4.1E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 43| +1.89E+02 | -1.2E+01 | +1.8E+00 | 5.0E-01 | 1.8E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 57| +1.48E+02 | -2.3E-06 | +6.4E+00 | 1.6E-03 | 2.0E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 39| +1.48E+02 | -2.4E-04 | +9.3E-01 | 1.1E-02 | 2.2E-01 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:37.066 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.258 and h = 3.96555e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.066 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.258: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 72| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.4E-02 | 2.4E+00 | 2.5E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 36| +1.48E+02 | -4.0E-03 | +9.2E-01 | 1.8E-02 | 2.6E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 58| +1.48E+02 | -3.9E-06 | +7.9E+00 | 3.1E-03 | 2.2E-02 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 44| +1.89E+02 | +1.7E+03 | -6.6E+01 | 1.0E+00 | 3.0E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 40| +1.48E+02 | -1.6E-04 | +5.7E-01 | 2.3E-02 | 5.8E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 109| +1.51E+02 | -4.8E-05 | +1.4E+00 | 2.7E-01 | 7.1E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 73| +1.48E+02 | -3.4E-03 | +9.5E-01 | 6.0E-03 | 2.4E+00 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 59| +1.48E+02 | +2.5E-06 | -3.2E+00 | 6.3E-03 | 1.5E-02 | 8.9E-04 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 37| +1.48E+02 | -3.4E-03 | +9.6E-01 | 3.6E-02 | 4.5E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 41| +1.48E+02 | +6.4E-04 | -1.2E+00 | 2.3E-02 | 2.1E-01 | 5.7E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 45| +1.73E+02 | -1.6E+01 | +1.1E+00 | 2.5E-01 | 3.0E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 74| +1.48E+02 | -4.7E-03 | +9.6E-01 | 1.2E-02 | 2.3E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 15| +1.96E+02 | +4.8E+00 | -5.1E-01 | 5.0E-01 | 1.9E+02 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 0| +1.22E+10 | NaN | NaN | 1.0E+00 | 4.7E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 60| +1.48E+02 | +1.3E-06 | -4.3E+00 | 1.6E-03 | 1.5E-02 | 2.2E-04 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 38| +1.48E+02 | -2.8E-03 | +9.2E-01 | 7.2E-02 | 1.8E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 42| +1.48E+02 | -1.9E-05 | +8.4E-02 | 5.6E-03 | 2.1E-01 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 75| +1.48E+02 | -6.1E-03 | +9.4E-01 | 2.4E-02 | 1.8E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 1| +1.13E+08 | -1.2E+10 | +1.1E-01 | 1.0E+00 | 4.7E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 46| +1.73E+02 | +7.8E-01 | -5.9E-01 | 5.0E-01 | 1.1E+02 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 61| +1.48E+02 | +2.9E-07 | -1.6E+00 | 3.9E-04 | 1.5E-02 | 5.9E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 43| +1.48E+02 | -9.0E-05 | +9.2E-01 | 1.4E-03 | 7.8E-01 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 2| +1.71E+07 | -9.6E+07 | +8.9E-01 | 2.5E-01 | 5.2E+08 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 39| +1.48E+02 | -1.9E-03 | +7.9E-01 | 1.4E-01 | 2.8E+00 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 76| +1.48E+02 | -8.8E-03 | +8.6E-01 | 4.8E-02 | 1.5E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 3| +2.61E+06 | -1.4E+07 | +8.9E-01 | 5.0E-01 | 7.9E+07 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 62| +1.48E+02 | +3.0E-06 | -2.0E+01 | 9.8E-05 | 1.5E-02 | 2.5E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 44| +1.48E+02 | -2.9E-05 | +7.9E-01 | 2.8E-03 | 8.0E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 40| +1.48E+02 | +2.2E-02 | -4.4E+00 | 2.9E-01 | 9.5E-01 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 110| +1.51E+02 | -1.6E-05 | +1.1E+00 | 2.7E-01 | 5.2E-02 | 1.4E-01 | trr |1\n", - "2023-02-17 16:05:37 fides(INFO) 47| +1.62E+02 | -1.1E+01 | +9.3E-01 | 1.2E-01 | 1.1E+02 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 77| +1.48E+02 | +2.9E-02 | -2.2E+00 | 9.6E-02 | 9.2E-01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 16| +1.77E+02 | -1.9E+01 | +1.0E+00 | 1.2E-01 | 1.9E+02 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 4| +4.00E+05 | -2.2E+06 | +8.9E-01 | 5.0E-01 | 1.2E+07 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 63| +1.48E+02 | +2.7E-06 | -1.9E+01 | 2.5E-05 | 1.5E-02 | 2.1E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 45| +1.48E+02 | -2.5E-05 | +7.8E-01 | 5.6E-03 | 1.0E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 41| +1.48E+02 | +1.9E-04 | -9.1E-02 | 7.2E-02 | 9.5E-01 | 3.6E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 5| +6.19E+04 | -3.4E+05 | +9.0E-01 | 5.0E-01 | 1.8E+06 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 78| +1.48E+02 | -1.5E-03 | +3.4E-01 | 2.4E-02 | 9.2E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 48| +1.52E+02 | -1.0E+01 | +8.2E-01 | 2.5E-01 | 5.7E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 6| +9.77E+03 | -5.2E+04 | +9.0E-01 | 5.0E-01 | 2.8E+05 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 46| +1.48E+02 | -2.9E-05 | +7.8E-01 | 1.1E-02 | 5.4E-02 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 64| +1.48E+02 | +1.6E-06 | -1.4E+01 | 6.1E-06 | 1.5E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 42| +1.48E+02 | -6.2E-04 | +6.2E-01 | 1.8E-02 | 9.5E-01 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:37.289 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 125.179 and h = 9.55388e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.289 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 125.179: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 49| +1.52E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 9.7E+01 | 6.9E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 7| +1.68E+03 | -8.1E+03 | +9.0E-01 | 1.0E+00 | 4.4E+04 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 79| +1.48E+02 | +1.1E-02 | -1.2E+00 | 2.4E-02 | 9.5E-01 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 47| +1.48E+02 | -3.3E-05 | +5.7E-01 | 2.3E-02 | 1.4E-01 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 65| +1.48E+02 | +3.2E-06 | -7.9E+01 | 1.5E-06 | 1.5E-02 | 3.3E-06 | 2d |0\n", - "2023-02-17 16:05:37.307 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.78 and h = 2.75474e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.307 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.78: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 43| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.8E-02 | 1.8E+00 | 7.5E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 17| +1.68E+02 | -9.1E+00 | +4.9E-01 | 2.5E-01 | 6.8E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 8| +4.19E+02 | -1.3E+03 | +9.2E-01 | 2.0E+00 | 6.8E+03 | 9.3E-01 | trr |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 80| +1.48E+02 | -4.9E-04 | +1.3E-01 | 6.0E-03 | 9.5E-01 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 50| +1.50E+02 | -2.0E+00 | +9.0E-01 | 1.2E-01 | 9.7E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 48| +1.48E+02 | +5.2E-04 | -2.5E+00 | 2.3E-02 | 1.0E-01 | 6.4E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 9| +2.28E+02 | -1.9E+02 | +9.8E-01 | 2.0E+00 | 9.6E+02 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 111| +1.51E+02 | +7.4E-08 | -6.8E-01 | 2.7E-01 | 3.3E-02 | 8.2E-06 | g |0\n", - "2023-02-17 16:05:37 fides(INFO) 44| +1.48E+02 | -3.8E-04 | +9.6E-01 | 4.5E-03 | 1.8E+00 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 66| +1.48E+02 | +5.5E-06 | -5.2E+02 | 3.8E-07 | 1.5E-02 | 8.2E-07 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 10| +2.15E+02 | -1.2E+01 | +8.0E-01 | 2.0E+00 | 9.8E+01 | 5.8E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 81| +1.48E+02 | -1.2E-03 | +9.4E-01 | 1.5E-03 | 3.0E+00 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 49| +1.48E+02 | +1.3E-05 | -1.7E-01 | 5.6E-03 | 1.0E-01 | 1.8E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 51| +1.49E+02 | -1.0E+00 | +8.9E-01 | 2.5E-01 | 4.2E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 45| +1.48E+02 | -2.4E-04 | +9.3E-01 | 9.0E-03 | 4.9E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 67| +1.48E+02 | +1.8E-06 | -6.8E+02 | 9.6E-08 | 1.5E-02 | 2.1E-07 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 82| +1.48E+02 | -4.5E-04 | +8.2E-01 | 3.0E-03 | 3.5E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 50| +1.48E+02 | -9.8E-06 | +2.6E-01 | 1.4E-03 | 1.0E-01 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 52| +1.49E+02 | +1.5E-01 | -1.6E-01 | 5.0E-01 | 3.8E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 46| +1.48E+02 | -1.4E-04 | +9.7E-01 | 1.8E-02 | 1.0E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 68| +1.48E+02 | +3.0E-06 | -4.4E+03 | 2.4E-08 | 1.5E-02 | 5.1E-08 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 83| +1.48E+02 | -5.2E-04 | +8.1E-01 | 6.0E-03 | 6.5E-01 | 7.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 51| +1.48E+02 | -1.8E-05 | +7.9E-01 | 1.4E-03 | 4.2E-01 | 4.0E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 11| +2.15E+02 | +3.2E+02 | -1.9E+01 | 4.0E+00 | 2.5E+01 | 1.1E+01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 53| +1.48E+02 | -5.3E-01 | +7.7E-01 | 1.2E-01 | 3.8E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 47| +1.48E+02 | -7.6E-05 | +9.3E-01 | 3.6E-02 | 2.6E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 69| +1.48E+02 | +2.5E-06 | -1.5E+04 | 6.0E-09 | 1.5E-02 | 1.3E-08 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 84| +1.48E+02 | -6.6E-04 | +9.1E-01 | 1.2E-02 | 1.9E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 52| +1.48E+02 | -4.5E-06 | +1.7E+00 | 2.8E-03 | 3.0E-02 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 54| +1.48E+02 | -3.7E-01 | +7.3E-01 | 2.5E-01 | 3.4E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:37.474 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 159.303 and h = 7.75438e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.474 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 159.303: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 12| +2.00E+02 | -1.6E+01 | +1.4E+00 | 1.0E+00 | 2.5E+01 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 112| +1.51E+02 | +0.0E+00 | +0.0E+00 | 1.1E-06 | 3.3E-02 | 2.0E-06 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 18| +1.57E+02 | -1.1E+01 | +9.8E-01 | 2.5E-01 | 1.6E+02 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 48| +1.48E+02 | -5.7E-05 | +9.4E-01 | 7.2E-02 | 4.9E-01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 85| +1.48E+02 | -1.0E-03 | +8.6E-01 | 2.4E-02 | 2.6E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 53| +1.48E+02 | -4.1E-06 | +1.0E+00 | 5.6E-03 | 2.6E-02 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 55| +1.48E+02 | +2.4E-02 | -7.7E-02 | 2.5E-01 | 2.1E+01 | 2.5E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 70| +1.48E+02 | +2.6E-06 | -6.0E+04 | 1.5E-09 | 1.5E-02 | 3.2E-09 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 13| +2.00E+02 | +1.0E+03 | -3.7E+01 | 2.0E+00 | 6.8E+01 | 5.4E+00 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 49| +1.48E+02 | +7.9E-06 | -1.4E-01 | 1.4E-01 | 1.3E-01 | 5.0E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 86| +1.48E+02 | +2.8E-03 | -1.7E+00 | 4.8E-02 | 2.2E-01 | 5.1E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 54| +1.48E+02 | -5.6E-06 | +9.8E-01 | 1.1E-02 | 2.3E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 71| +1.48E+02 | +2.3E-06 | -2.2E+05 | 3.7E-10 | 1.5E-02 | 8.0E-10 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 56| +1.48E+02 | -2.2E-01 | +5.8E-01 | 6.2E-02 | 2.1E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:37.540 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.636 and h = 2.24351e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.540 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.636: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 50| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.6E-02 | 1.3E-01 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 87| +1.48E+02 | -2.4E-04 | +4.6E-01 | 1.2E-02 | 2.2E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 14| +1.80E+02 | -2.0E+01 | +1.0E+00 | 5.0E-01 | 6.8E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 55| +1.48E+02 | -5.4E-06 | +8.2E-01 | 2.3E-02 | 1.8E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 72| +1.48E+02 | +1.3E-06 | -4.9E+05 | 9.4E-11 | 1.5E-02 | 2.0E-10 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 57| +1.48E+02 | -1.5E-01 | +8.9E-01 | 6.2E-02 | 3.3E+01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 51| +1.48E+02 | -1.2E-05 | +1.0E+00 | 9.0E-03 | 1.3E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 113| +1.51E+02 | -3.4E-08 | +2.6E+00 | 2.8E-07 | 3.3E-02 | 5.1E-07 | 2d |1\n", - "2023-02-17 16:05:37 fides(WARNING) Stopping as function difference 3.41E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:37 fides(INFO) 88| +1.48E+02 | +4.4E-03 | -3.4E+00 | 1.2E-02 | 2.3E-01 | 1.0E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 73| +1.48E+02 | +2.3E-06 | -3.5E+06 | 2.3E-11 | 1.5E-02 | 5.0E-11 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 56| +1.48E+02 | +4.5E-04 | -1.3E+01 | 4.5E-02 | 3.3E-02 | 7.0E-03 | trr |0\n", - "2023-02-17 16:05:37 fides(INFO) 15| +1.80E+02 | +3.8E+01 | -2.6E+00 | 1.0E+00 | 6.9E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 58| +1.48E+02 | -5.0E-02 | +9.0E-01 | 1.2E-01 | 6.5E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 52| +1.48E+02 | -9.2E-06 | +8.9E-01 | 1.8E-02 | 2.2E-01 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 89| +1.48E+02 | +1.6E-03 | -1.8E+00 | 3.0E-03 | 2.3E-01 | 6.7E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 57| +1.48E+02 | +2.1E-05 | -1.3E+00 | 8.4E-03 | 3.3E-02 | 1.8E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 16| +1.77E+02 | -2.5E+00 | +1.5E-01 | 2.5E-01 | 6.9E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 59| +1.48E+02 | -3.1E-02 | +7.5E-01 | 2.5E-01 | 1.0E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 53| +1.48E+02 | -1.5E-05 | +1.2E+00 | 3.6E-02 | 8.0E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 74| +1.48E+02 | -5.7E-07 | +3.5E+06 | 5.8E-12 | 1.5E-02 | 1.3E-11 | 2d |1\n", - "2023-02-17 16:05:37 fides(WARNING) Stopping as function difference 5.75E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:37 fides(INFO) 19| +1.55E+02 | -1.5E+00 | +9.5E-01 | 5.0E-01 | 3.9E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 58| +1.48E+02 | +6.4E-06 | -1.2E+00 | 2.1E-03 | 3.3E-02 | 4.7E-04 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 90| +1.48E+02 | -1.7E-04 | +5.1E-01 | 7.5E-04 | 2.3E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 17| +1.68E+02 | -9.1E+00 | +9.2E-01 | 6.2E-02 | 1.4E+02 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 54| +1.48E+02 | -6.6E-06 | +5.9E-01 | 7.2E-02 | 1.5E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 60| +1.48E+02 | +6.0E-02 | -1.3E+00 | 2.5E-01 | 2.7E+00 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 59| +1.48E+02 | +1.0E-06 | -4.7E-01 | 5.3E-04 | 3.3E-02 | 1.5E-04 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 91| +1.48E+02 | -2.9E-05 | +9.3E-01 | 7.5E-04 | 3.0E-01 | 7.1E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 61| +1.48E+02 | -4.7E-03 | +2.4E-01 | 6.2E-02 | 2.7E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 55| +1.48E+02 | -6.9E-06 | +1.1E+00 | 7.2E-02 | 3.7E-02 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 18| +1.66E+02 | -2.4E+00 | +5.5E-01 | 1.2E-01 | 5.0E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 0| +2.13E+11 | NaN | NaN | 1.0E+00 | 8.4E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:37 fides(INFO) 92| +1.48E+02 | -3.3E-05 | +8.2E-01 | 1.5E-03 | 1.2E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 60| +1.48E+02 | +2.5E-06 | -1.9E+00 | 1.3E-04 | 3.3E-02 | 7.8E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 62| +1.48E+02 | -6.6E-03 | +9.0E-01 | 1.6E-02 | 6.3E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 1| +2.64E+09 | -2.1E+11 | +1.0E-01 | 1.0E+00 | 8.4E+11 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 56| +1.48E+02 | +1.7E-05 | -2.5E+00 | 1.4E-01 | 6.0E-02 | 3.4E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 61| +1.48E+02 | +7.0E-07 | -6.6E-01 | 3.3E-05 | 3.3E-02 | 6.1E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 93| +1.48E+02 | -6.3E-05 | +1.0E+00 | 3.0E-03 | 1.5E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 0| +4.07E+10 | NaN | NaN | 1.0E+00 | 1.9E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:37 fides(INFO) 19| +1.62E+02 | -3.3E+00 | +9.2E-01 | 1.2E-01 | 1.0E+02 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 2| +3.98E+08 | -2.2E+09 | +8.9E-01 | 2.5E-01 | 1.2E+10 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 57| +1.48E+02 | -2.3E-06 | +9.2E-01 | 3.6E-02 | 6.0E-02 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 63| +1.48E+02 | -2.2E-03 | +7.7E-01 | 3.1E-02 | 8.7E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 1| +8.32E+04 | -4.1E+10 | +9.3E-02 | 1.0E+00 | 1.9E+11 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 62| +1.48E+02 | -4.2E-08 | +8.9E-02 | 8.2E-06 | 3.3E-02 | 1.7E-05 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 94| +1.48E+02 | -9.4E-05 | +9.4E-01 | 6.0E-03 | 8.3E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 20| +1.61E+02 | -1.2E+00 | +7.5E-01 | 2.5E-01 | 3.9E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 2| +1.33E+04 | -7.0E+04 | +9.0E-01 | 2.5E-01 | 3.8E+05 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 64| +1.48E+02 | -2.2E-03 | +9.3E-01 | 6.2E-02 | 9.4E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 58| +1.48E+02 | +5.7E-06 | -1.1E+00 | 7.2E-02 | 1.4E-02 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 63| +1.48E+02 | +1.5E-06 | -1.2E+01 | 2.1E-06 | 2.6E-02 | 5.3E-06 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 95| +1.48E+02 | -1.5E-04 | +9.7E-01 | 1.2E-02 | 1.3E-01 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 3| +6.02E+07 | -3.4E+08 | +8.9E-01 | 5.0E-01 | 1.8E+09 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 21| +1.59E+02 | -2.1E+00 | +1.2E+00 | 5.0E-01 | 5.6E+01 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 3| +2.29E+03 | -1.1E+04 | +9.0E-01 | 5.0E-01 | 6.0E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 65| +1.48E+02 | -3.2E-03 | +9.0E-01 | 1.2E-01 | 7.4E-01 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 59| +1.48E+02 | -1.4E-06 | +9.4E-01 | 1.8E-02 | 1.4E-02 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(WARNING) Stopping as function difference 1.40E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:37 fides(INFO) 4| +9.00E+06 | -5.1E+07 | +8.9E-01 | 1.0E+00 | 2.8E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 64| +1.48E+02 | +1.0E-07 | -3.0E+00 | 5.1E-07 | 2.6E-02 | 1.3E-06 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 4| +5.52E+02 | -1.7E+03 | +9.0E-01 | 5.0E-01 | 9.6E+03 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 96| +1.48E+02 | -7.9E-05 | +4.5E-01 | 2.4E-02 | 5.5E-02 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:37.842 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 182.65 and h = 3.35302e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.842 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 182.65: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 22| +1.59E+02 | +0.0E+00 | +0.0E+00 | 1.0E+00 | 2.4E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 5| +1.28E+06 | -7.7E+06 | +9.0E-01 | 1.0E+00 | 4.1E+07 | 9.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 66| +1.48E+02 | +2.0E-03 | -4.1E-01 | 2.5E-01 | 7.8E-01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 5| +2.84E+02 | -2.7E+02 | +9.0E-01 | 1.0E+00 | 1.5E+03 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 97| +1.48E+02 | +4.4E-03 | -5.0E+00 | 2.4E-02 | 1.8E-01 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 65| +1.48E+02 | +1.6E-06 | -1.9E+02 | 1.3E-07 | 2.6E-02 | 3.3E-07 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 23| +1.57E+02 | -2.0E+00 | +1.1E+00 | 2.5E-01 | 2.4E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 6| +1.59E+05 | -1.1E+06 | +9.2E-01 | 1.0E+00 | 5.9E+06 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 20| +1.53E+02 | -2.1E+00 | +4.9E-01 | 1.0E+00 | 3.8E+01 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 67| +1.48E+02 | -1.1E-03 | +7.0E-01 | 6.2E-02 | 7.8E-01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 6| +2.51E+02 | -3.3E+01 | +8.5E-01 | 2.0E+00 | 2.3E+02 | 4.7E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 98| +1.48E+02 | +8.5E-05 | -2.8E-01 | 6.0E-03 | 1.8E-01 | 5.8E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 66| +1.48E+02 | +1.6E-06 | -7.3E+02 | 3.2E-08 | 2.6E-02 | 8.3E-08 | 2d |0\n", - "2023-02-17 16:05:37.902 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 99.1786 and h = 1.85395e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.902 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 99.1786: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 24| +1.57E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 3.9E+01 | 7.0E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 7| +1.73E+04 | -1.4E+05 | +9.4E-01 | 1.0E+00 | 7.7E+05 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 68| +1.48E+02 | -5.4E-04 | +3.6E-01 | 6.2E-02 | 4.7E-01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 7| +2.33E+02 | -1.7E+01 | +8.8E+00 | 4.0E+00 | 1.9E+01 | 6.8E+00 | trr |1\n", - "2023-02-17 16:05:37 fides(INFO) 99| +1.48E+02 | -5.8E-05 | +5.8E-01 | 1.5E-03 | 1.8E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 25| +1.56E+02 | -1.3E+00 | +1.1E+00 | 1.2E-01 | 3.9E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 8| +2.36E+03 | -1.5E+04 | +9.1E-01 | 1.0E+00 | 1.0E+05 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 0| +4.92E+02 | NaN | NaN | 1.0E+00 | 5.7E+02 | NaN | NaN |1\n", - "2023-02-17 16:05:37 fides(INFO) 69| +1.48E+02 | -6.5E-04 | +2.4E-01 | 6.2E-02 | 1.5E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 67| +1.48E+02 | +3.5E-07 | -6.5E+02 | 8.0E-09 | 2.6E-02 | 2.1E-08 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 8| +2.33E+02 | +4.2E+04 | -1.5E+03 | 4.0E+00 | 4.9E+01 | 1.2E+01 | trr |0\n", - "2023-02-17 16:05:37 fides(INFO) 100| +1.48E+02 | -1.8E-05 | +7.8E-01 | 1.5E-03 | 3.8E-01 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 9| +4.73E+02 | -1.9E+03 | +9.0E-01 | 1.0E+00 | 1.7E+04 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 26| +1.53E+02 | -2.9E+00 | +1.1E+00 | 2.5E-01 | 1.7E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 1| +3.93E+02 | -9.9E+01 | +9.5E-02 | 1.0E+00 | 5.7E+02 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 70| +1.48E+02 | -2.9E-04 | +2.1E-01 | 1.6E-02 | 5.8E-01 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 68| +1.48E+02 | +6.4E-06 | -4.8E+04 | 2.0E-09 | 2.6E-02 | 5.2E-09 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 9| +2.33E+02 | +1.7E+03 | -5.1E+01 | 9.5E-01 | 4.9E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 101| +1.48E+02 | -7.5E-06 | +1.2E+00 | 3.0E-03 | 3.4E-02 | 7.8E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 2| +3.48E+02 | -4.5E+01 | +9.9E-01 | 2.5E-01 | 6.3E+01 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 27| +1.49E+02 | -3.5E+00 | +1.0E+00 | 5.0E-01 | 4.1E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 10| +2.06E+02 | -2.7E+02 | +9.0E-01 | 1.0E+00 | 2.8E+03 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 71| +1.48E+02 | -6.4E-04 | +9.5E-01 | 3.9E-03 | 2.3E+00 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 10| +2.15E+02 | -1.9E+01 | +7.0E-01 | 2.4E-01 | 4.9E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 69| +1.48E+02 | +3.9E-06 | -1.2E+05 | 5.0E-10 | 2.6E-02 | 1.3E-09 | 2d |0\n", - "2023-02-17 16:05:38.015 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 86.6153 and h = 1.31e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:38.015 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 86.6153: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:38 fides(INFO) 102| +1.48E+02 | -6.9E-06 | +7.3E-01 | 6.0E-03 | 6.1E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 28| +1.49E+02 | +0.0E+00 | +0.0E+00 | 1.0E+00 | 1.3E+01 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 11| +1.69E+02 | -3.6E+01 | +8.7E-01 | 1.0E+00 | 4.8E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 3| +2.89E+02 | -5.9E+01 | +7.0E-01 | 5.0E-01 | 6.1E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 11| +2.15E+02 | +2.0E+00 | -4.5E-01 | 2.4E-01 | 2.8E+01 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 21| +1.52E+02 | -7.1E-01 | +9.7E-01 | 1.0E+00 | 7.1E+01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 72| +1.48E+02 | -7.3E-05 | +9.1E-01 | 7.8E-03 | 1.5E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 70| +1.48E+02 | +2.3E-06 | -2.8E+05 | 1.3E-10 | 2.6E-02 | 3.2E-10 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 12| +1.61E+02 | -8.0E+00 | +1.2E+00 | 1.0E+00 | 9.2E+01 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 103| +1.48E+02 | +1.4E-05 | -8.5E-01 | 6.0E-03 | 3.6E-02 | 7.7E-04 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 29| +1.49E+02 | -6.6E-01 | +6.7E-01 | 1.4E-01 | 1.3E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 4| +2.66E+02 | -2.3E+01 | +8.8E-01 | 5.0E-01 | 8.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 12| +2.12E+02 | -2.5E+00 | +8.1E-01 | 5.9E-02 | 2.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 13| +1.61E+02 | +1.6E+01 | -2.8E+00 | 2.0E+00 | 2.8E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 22| +1.52E+02 | +3.8E+01 | -2.1E+02 | 2.0E+00 | 8.5E+00 | 5.0E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 73| +1.48E+02 | -1.1E-04 | +9.2E-01 | 1.6E-02 | 1.2E-01 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 71| +1.48E+02 | +1.9E-06 | -9.3E+05 | 3.1E-11 | 2.6E-02 | 8.1E-11 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 30| +1.49E+02 | +1.7E+00 | -1.5E+00 | 1.4E-01 | 1.5E+01 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 104| +1.48E+02 | +1.3E-06 | -2.2E-01 | 1.5E-03 | 3.6E-02 | 2.6E-04 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 14| +1.57E+02 | -4.8E+00 | +1.1E+00 | 3.0E-01 | 2.8E+01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 13| +2.12E+02 | +1.4E-01 | -2.4E-01 | 1.2E-01 | 9.4E+00 | 3.5E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 5| +2.66E+02 | +3.3E+03 | -1.7E+02 | 1.0E+00 | 3.9E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 74| +1.48E+02 | -1.7E-04 | +9.6E-01 | 3.1E-02 | 1.1E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 72| +1.48E+02 | +1.0E-07 | -1.9E+05 | 7.9E-12 | 2.6E-02 | 2.0E-11 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 31| +1.48E+02 | -2.1E-01 | +5.9E-01 | 3.5E-02 | 1.5E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 15| +1.50E+02 | -6.1E+00 | +6.9E-01 | 6.0E-01 | 3.6E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 6| +2.53E+02 | -1.3E+01 | +6.0E-01 | 2.5E-01 | 3.9E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 105| +1.48E+02 | -6.5E-07 | +2.4E-01 | 3.8E-04 | 3.6E-02 | 1.3E-04 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 14| +2.12E+02 | -3.2E-01 | +6.4E-01 | 3.0E-02 | 9.4E+00 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 16| +1.50E+02 | +1.2E+01 | -3.4E+00 | 6.0E-01 | 1.0E+02 | 8.8E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 75| +1.48E+02 | -2.6E-04 | +9.7E-01 | 6.2E-02 | 1.4E-01 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 23| +1.52E+02 | +6.7E-03 | -5.3E-01 | 5.0E-01 | 8.5E+00 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 73| +1.48E+02 | +1.5E-06 | -1.2E+07 | 2.0E-12 | 2.6E-02 | 5.1E-12 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 32| +1.48E+02 | -1.1E-01 | +9.5E-01 | 3.5E-02 | 9.5E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 15| +2.12E+02 | -6.5E-02 | +5.8E-01 | 3.0E-02 | 2.6E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 7| +2.50E+02 | -2.6E+00 | +5.9E-01 | 2.5E-01 | 4.0E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 106| +1.48E+02 | -2.5E-06 | +1.7E+00 | 9.4E-05 | 1.1E-01 | 3.3E-05 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 17| +1.49E+02 | -1.7E+00 | +6.7E-01 | 1.5E-01 | 1.0E+02 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 76| +1.48E+02 | -3.3E-04 | +9.6E-01 | 1.2E-01 | 1.3E-01 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 74| +1.48E+02 | +1.2E-05 | -3.7E+08 | 4.9E-13 | 2.6E-02 | 1.3E-12 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 33| +1.48E+02 | -1.9E-01 | +9.8E-01 | 7.1E-02 | 9.3E+00 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 16| +2.12E+02 | +7.9E-03 | -7.3E-02 | 3.0E-02 | 4.1E+00 | 8.2E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 18| +1.48E+02 | -6.2E-01 | +9.1E-01 | 1.5E-01 | 3.1E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 8| +2.50E+02 | +1.9E-01 | -3.9E-01 | 2.5E-01 | 8.6E+00 | 6.6E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 107| +1.48E+02 | +2.9E-06 | -3.5E+00 | 1.9E-04 | 1.6E-02 | 1.0E-04 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 17| +2.12E+02 | -5.2E-02 | +8.2E-01 | 7.4E-03 | 4.1E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 19| +1.48E+02 | -3.4E-01 | +4.3E-01 | 3.0E-01 | 2.2E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 77| +1.48E+02 | -1.9E-04 | +8.5E-01 | 2.5E-01 | 5.8E-02 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 34| +1.48E+02 | -2.3E-01 | +8.7E-01 | 1.4E-01 | 8.7E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 9| +2.50E+02 | +1.9E-01 | -3.9E-01 | 6.2E-02 | 8.6E+00 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 75| +1.48E+02 | -9.9E-07 | +1.2E+08 | 1.2E-13 | 2.6E-02 | 3.2E-13 | 2d |1\n", - "2023-02-17 16:05:38 fides(WARNING) Stopping as function difference 9.94E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 20| +1.47E+02 | -3.5E-01 | +2.9E-01 | 3.0E-01 | 2.8E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 18| +2.12E+02 | -7.5E-03 | +2.3E-01 | 1.5E-02 | 1.6E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 108| +1.48E+02 | +1.8E-06 | -2.5E+00 | 4.7E-05 | 1.6E-02 | 8.8E-05 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 78| +1.48E+02 | +5.4E-05 | -6.7E+00 | 5.0E-01 | 9.4E-02 | 1.4E-02 | trr |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 10| +2.50E+02 | -2.3E-01 | +7.9E-01 | 1.6E-02 | 8.6E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 35| +1.48E+02 | +1.5E-01 | -4.3E-01 | 2.8E-01 | 6.8E+00 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 21| +1.47E+02 | +8.2E+00 | -4.5E+00 | 3.0E-01 | 1.9E+01 | 4.1E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 19| +2.12E+02 | -1.9E-02 | +8.9E-01 | 3.7E-03 | 3.0E+00 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 109| +1.48E+02 | -1.3E-07 | +4.1E-01 | 1.2E-05 | 1.6E-02 | 2.5E-05 | 2d |1\n", - "2023-02-17 16:05:38 fides(WARNING) Stopping as function difference 1.33E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:38 fides(INFO) 22| +1.47E+02 | -1.2E-01 | +2.0E-01 | 7.6E-02 | 1.9E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 79| +1.48E+02 | -2.9E-06 | +6.9E-01 | 2.9E-02 | 9.4E-02 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 36| +1.48E+02 | -1.0E-01 | +7.1E-01 | 7.1E-02 | 6.8E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 11| +2.50E+02 | +3.2E-03 | -7.4E-02 | 3.1E-02 | 2.9E+00 | 8.1E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 24| +1.52E+02 | -7.9E-02 | +1.1E+00 | 1.2E-01 | 8.5E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 20| +2.12E+02 | -1.3E-02 | +6.4E-01 | 7.4E-03 | 1.8E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 23| +1.47E+02 | -2.2E-01 | +8.8E-01 | 1.9E-02 | 1.9E+01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 12| +2.50E+02 | -2.2E-02 | +5.9E-01 | 7.8E-03 | 2.9E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 80| +1.48E+02 | +2.3E-05 | -1.3E+00 | 2.9E-02 | 3.1E-02 | 3.8E-03 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 37| +1.48E+02 | -2.9E-02 | +2.2E-01 | 7.1E-02 | 9.0E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 21| +2.12E+02 | -2.8E-03 | +3.3E-01 | 7.4E-03 | 8.1E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 24| +1.47E+02 | -1.4E-01 | +9.8E-01 | 3.8E-02 | 3.3E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 81| +1.48E+02 | -2.9E-06 | +5.4E-01 | 7.3E-03 | 3.1E-02 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 13| +2.50E+02 | -1.2E-04 | +2.3E-02 | 7.8E-03 | 9.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 38| +1.48E+02 | -6.1E-02 | +6.2E-01 | 1.8E-02 | 5.7E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 22| +2.12E+02 | -4.2E-03 | +3.3E-01 | 7.4E-03 | 1.4E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 25| +1.47E+02 | -1.7E-01 | +9.8E-01 | 7.6E-02 | 1.0E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 25| +1.52E+02 | -1.3E-01 | +1.1E+00 | 2.5E-01 | 1.6E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 82| +1.48E+02 | +1.2E-06 | -2.3E+00 | 7.3E-03 | 4.7E-02 | 7.2E-04 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 23| +2.12E+02 | -8.3E-04 | +9.7E-02 | 7.4E-03 | 1.1E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 39| +1.48E+02 | -4.0E-02 | +9.7E-01 | 1.8E-02 | 1.6E+01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 14| +2.50E+02 | -2.3E-03 | +6.8E-01 | 2.0E-03 | 8.7E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 26| +1.47E+02 | -1.9E-01 | +9.4E-01 | 1.5E-01 | 2.4E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 24| +2.12E+02 | -3.5E-03 | +8.5E-01 | 1.9E-03 | 1.2E+00 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 83| +1.48E+02 | +2.3E-06 | -6.7E+00 | 1.8E-03 | 4.7E-02 | 1.8E-04 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 27| +1.46E+02 | -2.1E-01 | +5.4E-01 | 3.0E-01 | 7.1E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 15| +2.50E+02 | +8.3E-09 | -1.2E-03 | 2.0E-03 | 3.6E-02 | 5.1E-03 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 40| +1.48E+02 | -2.2E-02 | +9.3E-01 | 3.5E-02 | 2.2E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 26| +1.52E+02 | +7.5E-01 | -4.8E+00 | 5.0E-01 | 8.2E+00 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 0| +1.34E+03 | NaN | NaN | 1.0E+00 | 5.1E+03 | NaN | NaN |1\n", - "2023-02-17 16:05:38 fides(INFO) 28| +1.46E+02 | +1.8E+01 | -1.1E+01 | 3.0E-01 | 1.1E+01 | 4.6E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 25| +2.12E+02 | -8.2E-04 | +3.2E-01 | 3.7E-03 | 5.9E-01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 84| +1.48E+02 | +6.4E-07 | -2.2E+00 | 4.5E-04 | 4.7E-02 | 4.7E-05 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 16| +2.50E+02 | +8.3E-09 | -1.2E-03 | 4.9E-04 | 3.6E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 41| +1.48E+02 | -3.6E-02 | +9.6E-01 | 7.1E-02 | 4.3E+00 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 1| +3.07E+02 | -1.0E+03 | +1.2E-01 | 1.0E+00 | 5.1E+03 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 29| +1.46E+02 | +6.2E-02 | -1.5E-01 | 7.6E-02 | 1.1E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 26| +2.12E+02 | -4.3E-04 | +2.0E-01 | 3.7E-03 | 5.5E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 85| +1.48E+02 | +9.7E-07 | -3.5E+00 | 1.1E-04 | 4.7E-02 | 1.6E-05 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 17| +2.50E+02 | -3.0E-06 | +4.3E-01 | 1.2E-04 | 3.6E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 42| +1.48E+02 | -3.5E-02 | +7.2E-01 | 1.4E-01 | 1.8E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 30| +1.46E+02 | -1.2E-01 | +7.6E-01 | 1.9E-02 | 1.1E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 27| +2.12E+02 | -8.1E-04 | +8.5E-01 | 9.3E-04 | 5.8E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 2| +2.71E+02 | -3.6E+01 | +9.2E-01 | 2.5E-01 | 6.0E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 27| +1.52E+02 | -9.8E-02 | +1.1E+00 | 1.2E-01 | 8.2E+00 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 86| +1.48E+02 | +2.9E-06 | -1.0E+01 | 2.8E-05 | 4.7E-02 | 1.2E-05 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 31| +1.46E+02 | -5.6E-02 | +3.6E-01 | 3.8E-02 | 1.1E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 18| +2.50E+02 | -1.0E-09 | +6.2E-04 | 1.2E-04 | 1.6E-02 | 2.7E-04 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 43| +1.48E+02 | +1.3E-01 | -1.6E+00 | 1.4E-01 | 3.0E+00 | 7.4E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 28| +2.12E+02 | -2.7E-04 | +4.4E-01 | 1.9E-03 | 2.7E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 3| +2.71E+02 | +5.8E+01 | -2.0E+00 | 5.0E-01 | 4.4E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 32| +1.46E+02 | -1.2E-01 | +9.1E-01 | 3.8E-02 | 6.3E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 87| +1.48E+02 | +1.3E-06 | -4.8E+00 | 7.1E-06 | 4.7E-02 | 1.2E-05 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 44| +1.48E+02 | -1.2E-02 | +4.6E-01 | 3.5E-02 | 3.0E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 29| +2.12E+02 | -1.9E-04 | +3.6E-01 | 1.9E-03 | 2.5E-01 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 19| +2.50E+02 | -7.5E-07 | +7.5E-01 | 3.1E-05 | 1.6E-02 | 7.7E-05 | 2d |1\n", - "2023-02-17 16:05:38 fides(WARNING) Stopping as function difference 7.52E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:38 fides(INFO) 28| +1.52E+02 | -1.7E-01 | +7.3E-01 | 2.5E-01 | 1.4E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 4| +2.59E+02 | -1.2E+01 | +9.0E-01 | 1.2E-01 | 4.4E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 33| +1.46E+02 | -5.1E-02 | +5.2E-01 | 7.6E-02 | 1.3E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 88| +1.48E+02 | +5.7E-07 | -4.4E+00 | 1.8E-06 | 4.7E-02 | 3.2E-06 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 30| +2.12E+02 | -1.9E-04 | +4.0E-01 | 1.9E-03 | 2.5E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 45| +1.48E+02 | -6.6E-03 | +4.9E-01 | 3.5E-02 | 2.9E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 34| +1.46E+02 | -2.7E-02 | +2.1E-01 | 7.6E-02 | 3.9E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 31| +2.12E+02 | -1.6E-04 | +4.1E-01 | 1.9E-03 | 2.1E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 35| +1.46E+02 | -3.7E-02 | +5.5E-01 | 1.9E-02 | 4.7E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 5| +2.56E+02 | -3.2E+00 | +7.9E-02 | 2.5E-01 | 3.2E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 89| +1.48E+02 | -1.2E-06 | +3.2E+01 | 4.4E-07 | 4.7E-02 | 8.1E-07 | 2d |1\n", - "2023-02-17 16:05:38 fides(WARNING) Stopping as function difference 1.15E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:38 fides(INFO) 29| +1.52E+02 | -1.9E-01 | +9.5E-01 | 2.5E-01 | 1.7E+01 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 46| +1.48E+02 | -4.5E-03 | +3.6E-01 | 3.5E-02 | 1.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 32| +2.12E+02 | -1.6E-04 | +4.5E-01 | 1.9E-03 | 2.0E-01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 36| +1.46E+02 | -2.1E-02 | +9.8E-01 | 1.9E-02 | 1.1E+01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 6| +2.51E+02 | -4.8E+00 | +8.2E-01 | 6.2E-02 | 6.2E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 37| +1.46E+02 | -1.9E-02 | +1.0E+00 | 3.8E-02 | 3.3E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 33| +2.12E+02 | -1.5E-04 | +4.8E-01 | 1.9E-03 | 1.8E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 47| +1.48E+02 | -6.2E-03 | +4.0E-01 | 3.5E-02 | 4.7E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 38| +1.46E+02 | -3.3E-02 | +9.9E-01 | 7.6E-02 | 4.0E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 7| +2.50E+02 | -6.8E-01 | +4.1E-01 | 1.2E-01 | 2.0E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 34| +2.12E+02 | -1.5E-04 | +5.2E-01 | 1.9E-03 | 1.7E-01 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 30| +1.52E+02 | -1.1E-01 | +5.6E-01 | 5.0E-01 | 1.5E+01 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 48| +1.48E+02 | +2.8E-03 | -2.5E-01 | 3.5E-02 | 1.2E+00 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 39| +1.46E+02 | -4.2E-02 | +9.3E-01 | 1.5E-01 | 3.9E+00 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 35| +2.12E+02 | -1.5E-04 | +5.4E-01 | 1.9E-03 | 1.5E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 8| +2.50E+02 | +2.2E-01 | -4.2E-01 | 1.2E-01 | 8.6E+00 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 49| +1.48E+02 | -1.2E-03 | +2.1E-01 | 8.8E-03 | 1.2E+00 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 40| +1.46E+02 | +2.2E+00 | -2.2E+01 | 3.0E-01 | 2.3E+00 | 2.6E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 36| +2.12E+02 | -1.5E-04 | +5.7E-01 | 1.9E-03 | 1.5E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 9| +2.50E+02 | -2.2E-01 | +4.6E-01 | 3.1E-02 | 8.6E+00 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 41| +1.46E+02 | +5.6E-02 | -1.3E+00 | 7.6E-02 | 2.3E+00 | 6.4E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 31| +1.51E+02 | -1.5E-01 | +1.1E+00 | 5.0E-01 | 1.3E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 50| +1.48E+02 | -2.8E-03 | +9.5E-01 | 2.2E-03 | 4.8E+00 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 37| +2.12E+02 | -1.5E-04 | +5.9E-01 | 1.9E-03 | 1.4E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 42| +1.46E+02 | -6.3E-03 | +5.0E-01 | 1.9E-02 | 2.3E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 10| +2.50E+02 | -1.0E-02 | +1.3E-01 | 3.1E-02 | 3.8E+00 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 38| +2.12E+02 | -1.5E-04 | +6.1E-01 | 1.9E-03 | 1.4E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 51| +1.48E+02 | -6.1E-04 | +9.1E-01 | 4.4E-03 | 4.3E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 43| +1.46E+02 | -4.6E-03 | +7.4E-01 | 1.9E-02 | 2.4E+00 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 32| +1.51E+02 | -2.2E-01 | +1.5E+00 | 1.0E+00 | 1.2E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 39| +2.12E+02 | -1.5E-04 | +6.1E-01 | 1.9E-03 | 1.3E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 11| +2.50E+02 | -2.9E-02 | +6.5E-01 | 7.8E-03 | 3.1E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 44| +1.46E+02 | -4.1E-03 | +8.6E-01 | 1.9E-02 | 2.6E+00 | 8.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 52| +1.48E+02 | -8.0E-04 | +9.0E-01 | 8.8E-03 | 7.9E-01 | 6.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 40| +2.12E+02 | -1.5E-04 | +6.3E-01 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 45| +1.46E+02 | -6.0E-03 | +7.8E-01 | 3.8E-02 | 1.7E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 33| +1.51E+02 | -4.8E-01 | +1.7E+00 | 2.0E+00 | 7.4E+00 | 8.4E-01 | trr |1\n", - "2023-02-17 16:05:38 fides(INFO) 53| +1.48E+02 | -1.2E-03 | +9.6E-01 | 1.8E-02 | 3.0E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 12| +2.50E+02 | -1.2E-04 | +4.4E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 46| +1.46E+02 | +2.2E-02 | -1.2E+00 | 7.6E-02 | 3.3E+00 | 4.7E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 41| +2.12E+02 | -1.5E-04 | +6.2E-01 | 1.9E-03 | 1.3E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 54| +1.48E+02 | -1.8E-03 | +9.4E-01 | 3.5E-02 | 7.2E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 34| +1.51E+02 | +3.7E+00 | -3.8E+00 | 2.0E+00 | 2.9E+01 | 6.4E-01 | trr |0\n", - "2023-02-17 16:05:38 fides(INFO) 47| +1.46E+02 | -3.5E-03 | +5.8E-01 | 1.9E-02 | 3.3E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 42| +2.12E+02 | -1.5E-04 | +6.4E-01 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 13| +2.50E+02 | -1.2E-04 | +4.5E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 48| +1.46E+02 | -3.5E-03 | +5.0E-01 | 1.9E-02 | 1.2E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 43| +2.12E+02 | -1.5E-04 | +6.3E-01 | 1.9E-03 | 1.3E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 55| +1.48E+02 | -2.2E-03 | +8.2E-01 | 7.1E-02 | 3.4E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 35| +1.50E+02 | -4.4E-01 | +1.0E+00 | 1.6E-01 | 2.9E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 14| +2.50E+02 | -1.3E-04 | +4.7E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 49| +1.46E+02 | -3.8E-03 | +4.4E-01 | 1.9E-02 | 4.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 44| +2.12E+02 | -1.6E-04 | +6.4E-01 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 56| +1.48E+02 | +2.8E-02 | -4.9E+00 | 1.4E-01 | 5.2E-01 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 50| +1.46E+02 | -4.1E-04 | +9.3E-02 | 1.9E-02 | 1.2E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 36| +1.50E+02 | -5.2E-01 | +8.3E-01 | 3.2E-01 | 9.2E+00 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 15| +2.50E+02 | -1.3E-04 | +4.8E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 45| +2.12E+02 | -1.5E-04 | +6.3E-01 | 1.9E-03 | 1.3E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 57| +1.48E+02 | +1.2E-04 | -6.6E-02 | 3.5E-02 | 5.2E-01 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 51| +1.46E+02 | -1.5E-03 | +7.5E-01 | 4.7E-03 | 9.2E-01 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 46| +2.12E+02 | -1.6E-04 | +6.4E-01 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 16| +2.50E+02 | -1.4E-04 | +5.0E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 37| +1.50E+02 | -1.0E-02 | +1.2E-02 | 6.3E-01 | 1.0E+01 | 2.8E-01 | trr |1\n", - "2023-02-17 16:05:38 fides(INFO) 52| +1.46E+02 | -1.0E-03 | +7.5E-01 | 9.4E-03 | 1.5E+00 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 58| +1.48E+02 | -4.1E-04 | +7.3E-01 | 8.8E-03 | 5.2E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 47| +2.12E+02 | -1.6E-04 | +6.3E-01 | 1.9E-03 | 1.3E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 17| +2.50E+02 | -1.4E-04 | +5.0E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 53| +1.46E+02 | -2.0E-03 | +9.4E-01 | 9.4E-03 | 6.3E-01 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 38| +1.50E+02 | -5.3E-02 | +2.7E-01 | 1.1E-01 | 7.8E+00 | 1.6E-01 | trr |1\n", - "2023-02-17 16:05:38 fides(INFO) 48| +2.12E+02 | -1.6E-04 | +6.4E-01 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 59| +1.48E+02 | -1.9E-04 | +9.8E-01 | 8.8E-03 | 1.8E-01 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 54| +1.46E+02 | -1.3E-03 | +7.1E-01 | 1.9E-02 | 2.8E+00 | 7.0E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 18| +2.50E+02 | -1.5E-04 | +5.2E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 49| +2.12E+02 | -2.3E-04 | +1.0E+00 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 39| +1.50E+02 | -3.6E-02 | +6.0E-01 | 1.1E-01 | 4.2E+00 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 60| +1.48E+02 | -2.9E-04 | +9.6E-01 | 1.8E-02 | 1.4E-01 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 55| +1.46E+02 | +2.2E-05 | -1.7E-02 | 1.9E-02 | 4.3E-01 | 7.8E-03 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 19| +2.50E+02 | -1.5E-04 | +5.3E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 50| +2.12E+02 | -2.9E-04 | +6.7E-01 | 3.7E-03 | 4.4E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 56| +1.46E+02 | -3.5E-04 | +7.1E-01 | 4.7E-03 | 4.3E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 61| +1.48E+02 | -4.4E-04 | +9.7E-01 | 3.5E-02 | 2.2E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 40| +1.50E+02 | -8.0E-03 | +4.8E-01 | 1.1E-01 | 7.4E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 20| +2.50E+02 | -1.6E-04 | +5.5E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 51| +2.12E+02 | -3.5E-04 | +1.0E+00 | 3.7E-03 | 2.5E-01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 57| +1.46E+02 | -2.4E-04 | +9.8E-01 | 4.7E-03 | 9.6E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 62| +1.48E+02 | -2.8E-04 | +5.6E-01 | 7.1E-02 | 9.4E-02 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 41| +1.50E+02 | +1.7E-03 | -4.2E-01 | 1.1E-01 | 1.9E+00 | 4.2E-02 | trr |0\n", - "2023-02-17 16:05:38 fides(INFO) 58| +1.46E+02 | -3.1E-04 | +1.0E+00 | 9.4E-03 | 3.4E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 52| +2.12E+02 | -4.9E-04 | +9.4E-01 | 7.4E-03 | 9.2E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 21| +2.50E+02 | -1.7E-04 | +5.6E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 63| +1.48E+02 | +1.4E-02 | -6.8E+00 | 7.1E-02 | 2.9E-01 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 59| +1.46E+02 | -5.5E-04 | +9.8E-01 | 1.9E-02 | 4.5E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 53| +2.12E+02 | -6.4E-04 | +8.5E-01 | 1.5E-02 | 1.0E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 42| +1.50E+02 | -1.5E-03 | +7.5E-01 | 2.0E-02 | 1.9E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 22| +2.50E+02 | -1.7E-04 | +5.7E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 54| +2.12E+02 | -2.3E-03 | +5.8E-01 | 3.0E-02 | 1.8E-01 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 60| +1.46E+02 | -8.3E-04 | +9.0E-01 | 3.8E-02 | 3.7E-01 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 64| +1.48E+02 | +3.5E-04 | -4.5E-01 | 1.8E-02 | 2.9E-01 | 9.8E-03 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 43| +1.50E+02 | -1.8E-04 | +9.0E-01 | 2.0E-02 | 5.2E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 61| +1.46E+02 | +1.1E-02 | -4.2E+00 | 7.6E-02 | 2.4E-01 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 23| +2.50E+02 | -1.8E-04 | +5.8E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 55| +2.12E+02 | -5.7E-03 | +1.2E+00 | 3.0E-02 | 7.0E-01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 65| +1.48E+02 | -1.4E-04 | +5.8E-01 | 4.4E-03 | 2.9E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 62| +1.46E+02 | +6.0E-05 | -8.3E-02 | 1.9E-02 | 2.4E-01 | 7.1E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 44| +1.50E+02 | -4.4E-05 | +6.5E-01 | 4.0E-02 | 1.8E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 56| +2.12E+02 | -6.3E-03 | +5.1E-01 | 5.9E-02 | 4.3E-01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 24| +2.50E+02 | -1.9E-04 | +6.0E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 66| +1.48E+02 | -3.8E-05 | +9.7E-01 | 4.4E-03 | 4.8E-01 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 63| +1.46E+02 | -1.7E-04 | +7.3E-01 | 4.7E-03 | 2.4E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 57| +2.12E+02 | -2.1E-02 | +1.4E+00 | 5.9E-02 | 1.5E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 45| +1.50E+02 | -1.5E-06 | +1.0E-01 | 4.0E-02 | 1.9E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 64| +1.46E+02 | -1.0E-04 | +1.0E+00 | 4.7E-03 | 4.9E-01 | 9.3E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 25| +2.50E+02 | -1.9E-04 | +6.0E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 67| +1.48E+02 | -1.5E-05 | +7.8E-01 | 8.8E-03 | 5.7E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 58| +2.12E+02 | -5.7E-02 | +1.5E+00 | 1.2E-01 | 1.2E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 65| +1.46E+02 | -2.8E-04 | +9.3E-01 | 9.4E-03 | 2.0E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 46| +1.50E+02 | +2.1E-06 | -1.1E-01 | 1.0E-02 | 3.4E-02 | 2.7E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 26| +2.50E+02 | -2.0E-04 | +6.2E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 59| +2.11E+02 | -3.5E-01 | +2.2E+00 | 2.4E-01 | 1.9E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 68| +1.48E+02 | -1.8E-05 | +6.3E-01 | 1.8E-02 | 1.4E-01 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 66| +1.46E+02 | -2.4E-04 | +5.5E-01 | 1.9E-02 | 1.0E+00 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 47| +1.50E+02 | -6.1E-06 | +8.0E-01 | 2.5E-03 | 3.4E-02 | 6.8E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 67| +1.46E+02 | +9.1E-04 | -8.5E-01 | 1.9E-02 | 2.3E-01 | 8.1E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 60| +2.11E+02 | +2.7E+00 | -4.0E+00 | 4.7E-01 | 4.4E+00 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 27| +2.50E+02 | -2.1E-04 | +6.3E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 69| +1.48E+02 | +3.4E-05 | -7.1E-01 | 1.8E-02 | 6.2E-02 | 1.8E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 68| +1.46E+02 | -1.5E-04 | +5.2E-01 | 4.7E-03 | 2.3E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 61| +2.11E+02 | -4.6E-01 | +1.4E+00 | 1.2E-01 | 4.4E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 48| +1.50E+02 | -1.9E-06 | +6.3E-01 | 5.0E-03 | 3.1E-02 | 6.8E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 70| +1.48E+02 | -8.7E-06 | +5.0E-01 | 4.4E-03 | 6.2E-02 | 5.5E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 28| +2.50E+02 | -2.2E-04 | +6.4E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 69| +1.46E+02 | -7.2E-05 | +9.6E-01 | 4.7E-03 | 4.5E-01 | 9.2E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 62| +2.10E+02 | -3.7E-01 | +2.5E-01 | 2.4E-01 | 5.1E+00 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 49| +1.50E+02 | -2.3E-07 | +8.0E-01 | 5.0E-03 | 2.7E-02 | 1.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(WARNING) Stopping as function difference 2.33E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:39 fides(INFO) 71| +1.48E+02 | -8.1E-06 | +7.6E-01 | 4.4E-03 | 2.0E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 70| +1.46E+02 | -1.0E-04 | +9.9E-01 | 9.4E-03 | 1.3E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 29| +2.50E+02 | -2.2E-04 | +6.5E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 63| +2.10E+02 | +3.0E-01 | -4.3E-01 | 5.9E-02 | 7.4E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 71| +1.46E+02 | -1.8E-04 | +9.6E-01 | 1.9E-02 | 2.0E-01 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 72| +1.48E+02 | -3.2E-06 | +6.3E-01 | 8.8E-03 | 2.7E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 30| +2.50E+02 | -2.4E-04 | +6.6E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 64| +2.10E+02 | -1.6E-01 | +7.2E-01 | 1.5E-02 | 7.4E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 72| +1.46E+02 | -2.7E-05 | +2.0E-02 | 3.8E-02 | 1.3E-01 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 73| +1.48E+02 | -2.1E-06 | +5.3E-01 | 8.8E-03 | 2.9E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 31| +2.50E+02 | -2.4E-04 | +6.7E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 73| +1.46E+02 | +1.2E-03 | -7.7E-01 | 9.4E-03 | 2.3E-01 | 9.2E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 65| +2.10E+02 | -1.1E-01 | +9.5E-01 | 1.5E-02 | 4.7E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 74| +1.48E+02 | -5.4E-06 | +1.8E+00 | 8.8E-03 | 2.0E-02 | 8.6E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 74| +1.46E+02 | -1.2E-04 | +2.3E-01 | 2.4E-03 | 2.3E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 32| +2.50E+02 | -3.0E-04 | +9.6E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 66| +2.10E+02 | -1.7E-01 | +9.6E-01 | 3.0E-02 | 3.9E+00 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 75| +1.46E+02 | -9.9E-05 | +9.7E-01 | 5.9E-04 | 1.0E+00 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 75| +1.48E+02 | +2.5E-06 | -4.7E-01 | 1.8E-02 | 3.1E-02 | 2.6E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 67| +2.10E+02 | -1.4E-01 | +7.0E-01 | 5.9E-02 | 3.0E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 33| +2.50E+02 | +1.8E-04 | -2.4E-01 | 1.6E-02 | 9.8E-02 | 3.8E-02 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 76| +1.46E+02 | -3.6E-05 | +9.3E-01 | 1.2E-03 | 2.3E-01 | 3.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 68| +2.10E+02 | -1.8E-01 | +9.7E-01 | 5.9E-02 | 4.4E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 76| +1.48E+02 | +1.1E-07 | -6.7E-02 | 4.4E-03 | 3.1E-02 | 6.5E-04 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 77| +1.46E+02 | -2.4E-05 | +8.3E-01 | 2.4E-03 | 1.1E-01 | 4.7E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 34| +2.50E+02 | +8.1E-05 | -3.2E-01 | 3.9E-03 | 9.8E-02 | 9.6E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 69| +2.09E+02 | -2.6E-01 | +1.0E+00 | 1.2E-01 | 2.6E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 78| +1.46E+02 | -3.8E-05 | +9.7E-01 | 4.7E-03 | 3.6E-01 | 6.5E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 77| +1.48E+02 | +2.3E-07 | -4.7E-01 | 1.1E-03 | 3.1E-02 | 1.6E-04 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 70| +2.09E+02 | +7.3E-01 | -2.3E+00 | 2.4E-01 | 2.2E+00 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 79| +1.46E+02 | -5.3E-05 | +9.6E-01 | 9.4E-03 | 8.1E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 35| +2.50E+02 | +5.2E-05 | -4.6E-01 | 9.8E-04 | 9.8E-02 | 2.4E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 78| +1.48E+02 | +2.2E-08 | -1.0E-01 | 2.8E-04 | 3.1E-02 | 4.2E-05 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 80| +1.46E+02 | -6.1E-05 | +5.4E-01 | 1.9E-02 | 1.3E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 71| +2.09E+02 | -1.3E-01 | +1.1E+00 | 5.9E-02 | 2.2E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 36| +2.50E+02 | -2.7E-05 | +5.8E-01 | 2.4E-04 | 9.8E-02 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 79| +1.48E+02 | +2.0E-06 | -1.4E+01 | 6.9E-05 | 3.1E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 81| +1.46E+02 | +1.2E-03 | -2.3E+00 | 1.9E-02 | 1.2E-01 | 7.4E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 72| +2.09E+02 | -4.1E-01 | +1.2E+00 | 1.2E-01 | 2.7E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 37| +2.50E+02 | -9.9E-06 | +6.0E-01 | 2.4E-04 | 4.0E-02 | 5.6E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 82| +1.46E+02 | -3.1E-05 | +2.2E-01 | 4.7E-03 | 1.2E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 80| +1.48E+02 | +4.8E-07 | -3.9E+00 | 1.7E-05 | 3.1E-02 | 8.0E-06 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 73| +2.08E+02 | -3.8E-01 | +3.3E-01 | 2.4E-01 | 3.5E+00 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 83| +1.46E+02 | -2.2E-05 | +7.7E-01 | 1.2E-03 | 1.9E-01 | 4.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 38| +2.50E+02 | -1.0E-05 | +1.0E+00 | 2.4E-04 | 3.1E-02 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 81| +1.48E+02 | +2.0E-06 | -1.7E+01 | 4.3E-06 | 3.1E-02 | 7.5E-06 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 74| +2.07E+02 | -1.3E+00 | +6.8E-01 | 2.4E-01 | 1.3E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 84| +1.46E+02 | -1.6E-05 | +7.2E-01 | 2.4E-03 | 6.8E-02 | 5.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 39| +2.50E+02 | -2.2E-05 | +1.0E+00 | 4.9E-04 | 2.3E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 82| +1.48E+02 | +2.0E-06 | -2.8E+01 | 1.1E-06 | 3.1E-02 | 2.8E-06 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 85| +1.46E+02 | -9.7E-06 | +9.4E-01 | 2.4E-03 | 6.8E-02 | 2.6E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 75| +2.03E+02 | -3.9E+00 | +1.3E+00 | 2.4E-01 | 1.7E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 86| +1.46E+02 | -2.0E-05 | +1.1E+00 | 4.7E-03 | 9.1E-02 | 4.7E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 40| +2.50E+02 | -3.8E-05 | +1.0E+00 | 9.8E-04 | 2.9E-02 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 76| +2.03E+02 | +2.0E+01 | -3.6E+00 | 4.7E-01 | 1.8E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 87| +1.46E+02 | -3.6E-05 | +9.9E-01 | 9.4E-03 | 5.7E-02 | 9.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 83| +1.48E+02 | +9.3E-08 | -4.5E+00 | 2.7E-07 | 3.1E-02 | 7.1E-07 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 77| +2.01E+02 | -2.5E+00 | +1.1E+00 | 1.2E-01 | 1.8E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 41| +2.50E+02 | -8.7E-05 | +1.0E+00 | 2.0E-03 | 2.3E-02 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 88| +1.46E+02 | -5.8E-05 | +9.4E-01 | 1.9E-02 | 1.4E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 84| +1.48E+02 | +2.8E-07 | -5.2E+01 | 6.7E-08 | 3.1E-02 | 1.8E-07 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 78| +2.01E+02 | +7.4E+00 | -9.2E-01 | 2.4E-01 | 1.9E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 89| +1.46E+02 | +7.9E-04 | -5.4E+00 | 3.8E-02 | 5.1E-02 | 5.9E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 42| +2.50E+02 | -1.7E-04 | +1.0E+00 | 3.9E-03 | 3.9E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 79| +1.99E+02 | -1.7E+00 | +7.2E-01 | 5.9E-02 | 1.9E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 90| +1.46E+02 | +1.4E-05 | -3.2E-01 | 9.4E-03 | 5.1E-02 | 1.5E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 85| +1.48E+02 | +1.5E-07 | -1.1E+02 | 1.7E-08 | 3.1E-02 | 4.5E-08 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 43| +2.50E+02 | -3.4E-04 | +1.0E+00 | 7.8E-03 | 3.1E-02 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 91| +1.46E+02 | -8.3E-06 | +6.2E-01 | 2.4E-03 | 5.1E-02 | 3.9E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 80| +1.98E+02 | -1.1E+00 | +8.2E-01 | 5.9E-02 | 1.4E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 86| +1.48E+02 | +4.8E-07 | -1.4E+03 | 4.2E-09 | 3.1E-02 | 1.1E-08 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 92| +1.46E+02 | -8.6E-06 | +1.1E+00 | 2.4E-03 | 1.1E-01 | 2.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 44| +2.50E+02 | -7.2E-04 | +1.1E+00 | 1.6E-02 | 3.8E-02 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 81| +1.96E+02 | -1.8E+00 | +9.3E-01 | 1.2E-01 | 1.2E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 93| +1.46E+02 | -1.1E-05 | +9.3E-01 | 4.7E-03 | 4.1E-02 | 3.5E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 87| +1.48E+02 | +6.8E-07 | -7.9E+03 | 1.1E-09 | 3.1E-02 | 2.8E-09 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 45| +2.50E+02 | -1.6E-03 | +1.1E+00 | 3.1E-02 | 6.3E-02 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 82| +1.93E+02 | -3.0E+00 | +7.0E-01 | 2.4E-01 | 1.6E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 94| +1.46E+02 | -2.0E-05 | +8.7E-01 | 9.4E-03 | 5.5E-02 | 7.7E-04 | 2d |1\n", - "2023-02-17 16:05:39.482 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0965 and h = 4.6875e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:39.482 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0965: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:39 fides(INFO) 88| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.6E-10 | 3.1E-02 | 7.0E-10 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 46| +2.50E+02 | -4.3E-03 | +1.2E+00 | 6.2E-02 | 1.2E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 95| +1.46E+02 | +7.0E-05 | -1.4E+00 | 1.9E-02 | 4.4E-02 | 2.3E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 83| +1.84E+02 | -9.3E+00 | +1.9E+00 | 2.4E-01 | 2.0E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 89| +1.48E+02 | +2.6E-07 | -4.8E+04 | 6.6E-11 | 3.1E-02 | 1.7E-10 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 96| +1.46E+02 | -8.8E-06 | +5.4E-01 | 4.7E-03 | 4.4E-02 | 5.7E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 47| +2.50E+02 | -1.5E-02 | +1.5E+00 | 1.2E-01 | 2.4E-01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 84| +1.84E+02 | +8.0E+01 | -4.0E+00 | 4.7E-01 | 3.5E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 97| +1.46E+02 | -4.7E-07 | +5.4E-03 | 4.7E-03 | 5.9E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 90| +1.48E+02 | +3.6E-07 | -2.7E+05 | 1.6E-11 | 3.1E-02 | 4.3E-11 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 48| +2.50E+02 | -9.1E-02 | +2.2E+00 | 2.5E-01 | 4.8E-01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 98| +1.46E+02 | -1.4E-05 | +6.8E-01 | 1.2E-03 | 6.9E-02 | 4.8E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 85| +1.76E+02 | -8.3E+00 | +1.1E+00 | 1.2E-01 | 3.5E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 91| +1.48E+02 | +1.2E-07 | -3.7E+05 | 4.1E-12 | 3.1E-02 | 1.1E-11 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 99| +1.46E+02 | -4.0E-06 | +1.1E+00 | 1.2E-03 | 8.8E-02 | 1.2E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 49| +2.48E+02 | -1.5E+00 | +4.2E+00 | 5.0E-01 | 1.1E+00 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 86| +1.76E+02 | +8.7E+00 | -8.3E-01 | 2.4E-01 | 5.1E+01 | 5.6E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 100| +1.46E+02 | -4.3E-06 | +9.3E-01 | 2.4E-03 | 3.5E-02 | 1.5E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 92| +1.48E+02 | +4.8E-07 | -5.7E+06 | 1.0E-12 | 3.1E-02 | 2.7E-12 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 50| +2.26E+02 | -2.2E+01 | +3.4E+00 | 1.0E+00 | 7.4E+00 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 101| +1.46E+02 | -8.4E-06 | +9.5E-01 | 4.7E-03 | 4.6E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 93| +1.48E+02 | +4.5E-08 | -2.2E+06 | 2.6E-13 | 3.1E-02 | 6.8E-13 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 87| +1.71E+02 | -4.7E+00 | +1.1E+00 | 5.9E-02 | 5.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 51| +2.26E+02 | +1.6E+02 | -5.5E+00 | 2.0E+00 | 5.0E+01 | 3.9E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 102| +1.46E+02 | -1.2E-05 | +6.8E-01 | 9.4E-03 | 3.4E-02 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 94| +1.48E+02 | +1.9E-08 | -3.6E+06 | 6.4E-14 | 3.1E-02 | 1.7E-13 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 52| +2.12E+02 | -1.4E+01 | +3.7E-01 | 5.0E-01 | 5.0E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 103| +1.46E+02 | +1.1E-04 | -1.9E+00 | 9.4E-03 | 4.5E-02 | 2.5E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 88| +1.62E+02 | -9.0E+00 | +9.6E-01 | 1.2E-01 | 4.4E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 104| +1.46E+02 | -5.5E-06 | +3.6E-01 | 2.4E-03 | 4.5E-02 | 6.2E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 53| +2.12E+02 | +3.3E+02 | -5.8E+00 | 5.0E-01 | 1.3E+02 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 95| +1.48E+02 | -4.2E-08 | +3.2E+07 | 1.6E-14 | 3.1E-02 | 4.2E-14 | 2d |1\n", - "2023-02-17 16:05:39 fides(WARNING) Stopping as function difference 4.16E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:39 fides(INFO) 89| +1.62E+02 | +1.3E+01 | -1.2E+00 | 2.4E-01 | 4.3E+01 | 4.5E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 105| +1.46E+02 | -3.5E-06 | +3.0E-01 | 2.4E-03 | 5.0E-02 | 4.8E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 54| +1.96E+02 | -1.6E+01 | +5.0E-01 | 1.2E-01 | 1.3E+02 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 90| +1.59E+02 | -3.0E+00 | +7.8E-01 | 5.9E-02 | 4.3E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 55| +1.90E+02 | -5.4E+00 | +4.8E-01 | 1.2E-01 | 5.5E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 106| +1.46E+02 | -4.1E-06 | +4.4E-01 | 2.4E-03 | 4.3E-02 | 4.6E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 107| +1.46E+02 | -2.7E-06 | +3.3E-01 | 2.4E-03 | 3.9E-02 | 3.9E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 56| +1.82E+02 | -8.4E+00 | +9.9E-01 | 1.2E-01 | 3.0E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 91| +1.57E+02 | -1.8E+00 | +3.5E-01 | 1.2E-01 | 4.6E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 108| +1.46E+02 | -3.9E-06 | +5.0E-01 | 2.4E-03 | 4.3E-02 | 4.2E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 57| +1.71E+02 | -1.1E+01 | +7.8E-01 | 2.5E-01 | 2.8E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 92| +1.56E+02 | -1.4E+00 | +2.7E-01 | 1.2E-01 | 1.0E+02 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 109| +1.46E+02 | -2.0E-06 | +2.5E-01 | 2.4E-03 | 3.7E-02 | 4.0E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 0| +7.05E+09 | NaN | NaN | 1.0E+00 | 3.2E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 110| +1.46E+02 | -2.5E-06 | +2.8E-01 | 2.4E-03 | 4.4E-02 | 4.7E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 58| +1.71E+02 | +7.6E+02 | -4.2E+01 | 5.0E-01 | 8.6E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 93| +1.54E+02 | -2.0E+00 | +3.0E-01 | 1.2E-01 | 1.1E+02 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 1| +1.37E+04 | -7.0E+09 | +9.2E-02 | 1.0E+00 | 3.2E+10 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 111| +1.46E+02 | -4.3E-06 | +4.3E-01 | 2.4E-03 | 3.9E-02 | 5.1E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 59| +1.70E+02 | -1.3E+00 | +1.0E-01 | 1.2E-01 | 8.6E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:39.776 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 38.3134 and h = 1.45372e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:39.776 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 38.3134: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:39 fides(INFO) 94| +1.54E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 2.4E+01 | 2.1E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 2| +2.35E+03 | -1.1E+04 | +9.0E-01 | 2.5E-01 | 6.2E+04 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 112| +1.46E+02 | +8.4E-07 | -7.0E-02 | 2.4E-03 | 4.8E-02 | 6.5E-04 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 3| +5.66E+02 | -1.8E+03 | +9.0E-01 | 5.0E-01 | 9.8E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 60| +1.64E+02 | -5.7E+00 | +8.9E-01 | 3.1E-02 | 1.2E+02 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 113| +1.46E+02 | -1.6E-06 | +4.4E-01 | 5.9E-04 | 4.8E-02 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 95| +1.53E+02 | -8.4E-01 | +9.9E-01 | 3.0E-02 | 2.4E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 114| +1.46E+02 | -1.3E-06 | +1.1E+00 | 5.9E-04 | 3.3E-02 | 7.7E-05 | 2d |1\n", - "2023-02-17 16:05:39 fides(WARNING) Stopping as function difference 1.28E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:39 fides(INFO) 4| +2.86E+02 | -2.8E+02 | +8.9E-01 | 1.0E+00 | 1.5E+03 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 61| +1.60E+02 | -3.8E+00 | +7.7E-01 | 6.2E-02 | 4.6E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 96| +1.52E+02 | -6.3E-01 | +5.6E-01 | 5.9E-02 | 1.5E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 5| +2.41E+02 | -4.5E+01 | +9.6E-01 | 2.0E+00 | 2.3E+02 | 4.4E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 62| +1.58E+02 | -2.2E+00 | +2.6E-01 | 1.2E-01 | 4.2E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 97| +1.51E+02 | -9.1E-01 | +8.4E-01 | 5.9E-02 | 5.1E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 6| +2.41E+02 | +7.7E+02 | -3.0E+01 | 4.0E+00 | 3.5E+01 | 1.1E+01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 98| +1.50E+02 | -1.3E+00 | +1.1E+00 | 1.2E-01 | 2.0E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 63| +1.53E+02 | -5.3E+00 | +6.5E-01 | 1.2E-01 | 5.5E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 7| +2.41E+02 | +6.3E+01 | -5.5E+00 | 1.0E+00 | 3.5E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 99| +1.50E+02 | +8.1E-01 | -4.9E-01 | 2.4E-01 | 1.4E+01 | 4.2E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 64| +1.53E+02 | +3.7E+00 | -5.1E-01 | 1.2E-01 | 9.3E+01 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 8| +2.41E+02 | +5.7E+00 | -5.6E-01 | 2.5E-01 | 3.5E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 100| +1.49E+02 | -7.2E-01 | +8.7E-01 | 5.9E-02 | 1.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 65| +1.51E+02 | -1.9E+00 | +5.6E-01 | 3.1E-02 | 9.3E+01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 9| +2.36E+02 | -4.3E+00 | +8.8E-01 | 6.2E-02 | 3.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 101| +1.48E+02 | -6.8E-01 | +6.4E-01 | 1.2E-01 | 7.6E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 66| +1.50E+02 | -1.4E+00 | +8.9E-01 | 3.1E-02 | 4.7E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 10| +2.36E+02 | -3.2E-01 | +5.3E-02 | 1.2E-01 | 2.0E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 102| +1.48E+02 | -3.2E-01 | +4.4E-01 | 1.2E-01 | 1.7E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 67| +1.49E+02 | -1.6E-01 | +4.7E-02 | 6.2E-02 | 2.6E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 11| +2.35E+02 | -1.4E+00 | +8.9E-01 | 3.1E-02 | 3.3E+01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 103| +1.48E+02 | -3.9E-01 | +5.5E-01 | 1.2E-01 | 4.8E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 68| +1.48E+02 | -1.2E+00 | +6.0E-01 | 1.6E-02 | 1.1E+02 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 12| +2.34E+02 | -1.2E+00 | +7.6E-01 | 6.2E-02 | 2.0E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 104| +1.48E+02 | -4.4E-02 | +1.7E-01 | 1.2E-01 | 2.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 69| +1.48E+02 | -2.4E-01 | +8.0E-01 | 1.6E-02 | 3.8E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 13| +2.32E+02 | -1.4E+00 | +8.9E-01 | 1.2E-01 | 1.1E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 105| +1.48E+02 | -1.1E-01 | +8.6E-01 | 2.2E-02 | 7.1E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 70| +1.48E+02 | +1.1E-01 | -6.8E-01 | 3.1E-02 | 8.6E+00 | 5.5E-02 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 14| +2.29E+02 | -3.4E+00 | +9.5E-01 | 2.5E-01 | 1.9E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:39.998 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.601 and h = 3.69658e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:39.998 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.601: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:39 fides(INFO) 106| +1.48E+02 | +0.0E+00 | +0.0E+00 | 4.4E-02 | 6.3E+00 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 71| +1.48E+02 | -4.8E-02 | +7.4E-01 | 7.8E-03 | 8.6E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 15| +2.18E+02 | -1.1E+01 | +1.1E+00 | 5.0E-01 | 1.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 107| +1.48E+02 | -1.5E-02 | +7.3E-01 | 3.8E-03 | 6.3E+00 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 72| +1.48E+02 | -6.0E-02 | +7.5E-01 | 7.8E-03 | 1.6E+01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 16| +2.02E+02 | -1.6E+01 | +4.2E-01 | 1.0E+00 | 4.2E+01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 108| +1.48E+02 | -5.3E-03 | +1.1E+00 | 3.8E-03 | 3.7E+00 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 17| +2.02E+02 | +7.0E+02 | -2.2E+01 | 1.0E+00 | 9.6E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 73| +1.48E+02 | +1.9E-02 | -2.4E-01 | 1.6E-02 | 9.7E+00 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 109| +1.48E+02 | -7.6E-03 | +1.1E+00 | 7.5E-03 | 1.4E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 18| +1.84E+02 | -1.8E+01 | +8.6E-01 | 2.5E-01 | 9.6E+01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 74| +1.48E+02 | -2.1E-02 | +6.2E-01 | 3.9E-03 | 9.7E+00 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 110| +1.48E+02 | -1.0E-02 | +1.0E+00 | 1.5E-02 | 1.8E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 19| +1.78E+02 | -6.2E+00 | +6.2E-01 | 5.0E-01 | 4.1E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 75| +1.48E+02 | -2.8E-02 | +7.6E-01 | 3.9E-03 | 1.0E+01 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 111| +1.48E+02 | -1.2E-02 | +1.0E+00 | 3.0E-02 | 8.1E-01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 20| +1.67E+02 | -1.1E+01 | +1.4E+00 | 5.0E-01 | 7.6E+01 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 112| +1.48E+02 | -1.4E-02 | +1.2E+00 | 6.0E-02 | 5.6E-01 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 76| +1.48E+02 | +6.3E-03 | -1.5E-01 | 7.8E-03 | 6.7E+00 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 21| +1.67E+02 | +8.1E+02 | -3.3E+01 | 1.0E+00 | 9.4E+01 | 1.9E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 113| +1.48E+02 | -4.5E-03 | +1.1E+00 | 1.2E-01 | 3.0E-01 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 77| +1.48E+02 | -1.5E-02 | +8.1E-01 | 2.0E-03 | 6.7E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 22| +1.53E+02 | -1.4E+01 | +1.0E+00 | 2.5E-01 | 9.4E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 114| +1.48E+02 | -1.2E-03 | +7.5E-01 | 1.2E-01 | 5.1E-01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 78| +1.48E+02 | -2.5E-02 | +8.2E-01 | 3.9E-03 | 7.3E+00 | 9.0E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 23| +1.53E+02 | +1.1E+02 | -1.2E+01 | 5.0E-01 | 5.0E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 115| +1.48E+02 | -1.6E-04 | +1.0E+00 | 1.2E-01 | 4.1E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 24| +1.49E+02 | -3.9E+00 | +8.2E-01 | 1.2E-01 | 5.0E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 79| +1.48E+02 | -8.8E-03 | +3.4E-01 | 7.8E-03 | 6.3E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 116| +1.48E+02 | -7.5E-06 | +9.8E-01 | 1.2E-01 | 4.2E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 25| +1.48E+02 | -7.0E-01 | +5.6E-01 | 2.5E-01 | 2.7E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 80| +1.48E+02 | -3.2E-02 | +9.0E-01 | 7.8E-03 | 7.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 117| +1.48E+02 | +4.5E-06 | -3.1E+00 | 1.2E-01 | 3.5E-02 | 8.3E-04 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 26| +1.48E+02 | -4.2E-01 | +9.4E-01 | 2.5E-01 | 3.2E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 81| +1.48E+02 | +3.6E-01 | -5.1E+00 | 1.6E-02 | 4.2E+00 | 4.1E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 118| +1.48E+02 | -3.8E-07 | +4.2E-01 | 1.6E-04 | 3.5E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:40 fides(WARNING) Stopping as function difference 3.76E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:40.211 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.5253 and h = 1.64262e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:40.211 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.5253: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:40 fides(INFO) 27| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 2.0E+01 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 82| +1.48E+02 | +1.8E-02 | -7.8E-01 | 3.9E-03 | 4.2E+00 | 1.0E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 28| +1.48E+02 | -1.4E-01 | +9.6E-01 | 1.2E-01 | 2.0E+01 | 9.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 29| +1.48E+02 | -1.0E-01 | +7.2E-01 | 2.5E-01 | 1.5E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 83| +1.48E+02 | -4.3E-03 | +5.1E-01 | 9.8E-04 | 4.2E+00 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 30| +1.48E+02 | +3.2E-01 | -1.5E+00 | 2.5E-01 | 1.3E+01 | 2.8E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 84| +1.48E+02 | -5.8E-03 | +9.7E-01 | 9.8E-04 | 5.3E+00 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 0| +1.71E+03 | NaN | NaN | 1.0E+00 | 6.0E+03 | NaN | NaN |1\n", - "2023-02-17 16:05:40 fides(INFO) 31| +1.48E+02 | -4.1E-02 | +5.4E-01 | 6.2E-02 | 1.3E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 85| +1.48E+02 | -7.2E-03 | +9.3E-01 | 2.0E-03 | 3.0E+00 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 1| +4.70E+02 | -1.2E+03 | +1.3E-01 | 1.0E+00 | 6.0E+03 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 32| +1.48E+02 | -2.5E-02 | +7.6E-01 | 6.2E-02 | 8.2E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 2| +4.23E+02 | -4.7E+01 | +1.0E+00 | 2.5E-01 | 6.4E+01 | 7.5E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 86| +1.48E+02 | -1.1E-02 | +7.6E-01 | 3.9E-03 | 5.2E+00 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 33| +1.48E+02 | -1.2E-02 | +3.5E-01 | 1.2E-01 | 9.1E+00 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 3| +3.42E+02 | -8.1E+01 | +9.0E-01 | 5.0E-01 | 6.3E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 87| +1.48E+02 | -2.8E-03 | +1.2E-01 | 7.8E-03 | 2.9E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 34| +1.48E+02 | +4.8E-02 | -6.0E-01 | 1.2E-01 | 4.6E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 4| +3.42E+02 | +3.9E+02 | -7.8E+00 | 1.0E+00 | 5.6E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 88| +1.48E+02 | -3.8E-03 | +6.2E-01 | 2.0E-03 | 3.2E+00 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 5| +3.06E+02 | -3.6E+01 | +9.8E-01 | 2.5E-01 | 5.6E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 35| +1.48E+02 | -3.9E-03 | +6.5E-02 | 3.1E-02 | 4.6E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 89| +1.48E+02 | -3.4E-03 | +6.5E-01 | 2.0E-03 | 4.4E+00 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 6| +2.83E+02 | -2.3E+01 | +1.7E-01 | 5.0E-01 | 5.0E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 36| +1.48E+02 | -1.4E-02 | +8.8E-01 | 7.8E-03 | 7.7E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 90| +1.48E+02 | -3.3E-03 | +8.6E-01 | 2.0E-03 | 2.5E+00 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 7| +2.58E+02 | -2.5E+01 | +8.3E-01 | 1.2E-01 | 1.9E+02 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 37| +1.48E+02 | -8.0E-03 | +7.2E-01 | 1.6E-02 | 2.9E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 8| +2.53E+02 | -5.1E+00 | +3.2E-01 | 2.5E-01 | 5.0E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 91| +1.48E+02 | -5.2E-03 | +8.1E-01 | 3.9E-03 | 1.5E+00 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 38| +1.48E+02 | -1.8E-03 | +8.9E-01 | 1.6E-02 | 3.0E+00 | 6.3E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 9| +2.52E+02 | -1.1E+00 | +1.5E-01 | 2.5E-01 | 3.5E+01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 39| +1.48E+02 | -1.3E-03 | +8.1E-01 | 3.1E-02 | 7.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 92| +1.48E+02 | +5.4E-04 | -1.7E-01 | 7.8E-03 | 1.6E+00 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 10| +2.50E+02 | -2.2E+00 | +7.5E-01 | 6.2E-02 | 3.0E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 40| +1.48E+02 | -1.5E-03 | +9.1E-01 | 6.2E-02 | 1.7E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 93| +1.48E+02 | -3.9E-04 | +2.7E-01 | 2.0E-03 | 1.6E+00 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 11| +2.50E+02 | -1.9E-02 | +1.1E-01 | 6.2E-02 | 5.5E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 41| +1.48E+02 | -1.7E-04 | +8.4E-02 | 1.2E-01 | 3.6E-01 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 94| +1.48E+02 | -1.3E-03 | +7.0E-01 | 2.0E-03 | 2.6E+00 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 12| +2.50E+02 | -5.5E-02 | +4.8E-01 | 1.6E-02 | 4.8E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 42| +1.48E+02 | -6.0E-04 | +1.4E-01 | 3.1E-02 | 7.5E-01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 95| +1.48E+02 | -3.7E-04 | +8.8E-01 | 2.0E-03 | 6.0E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 13| +2.50E+02 | -3.2E-03 | +1.2E-01 | 1.6E-02 | 2.1E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 43| +1.48E+02 | -7.6E-04 | +7.7E-01 | 7.8E-03 | 1.4E+00 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 14| +2.50E+02 | -9.8E-03 | +6.9E-01 | 3.9E-03 | 1.8E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 96| +1.48E+02 | -4.1E-04 | +9.8E-01 | 3.9E-03 | 4.8E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 44| +1.48E+02 | -1.9E-04 | +5.7E-01 | 1.6E-02 | 4.1E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 15| +2.50E+02 | -2.3E-04 | +7.7E-01 | 3.9E-03 | 1.1E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 97| +1.48E+02 | -5.5E-04 | +9.8E-01 | 7.8E-03 | 2.6E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 45| +1.48E+02 | -6.1E-05 | +1.0E+00 | 1.6E-02 | 2.7E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 16| +2.50E+02 | -4.6E-04 | +8.3E-01 | 7.8E-03 | 1.3E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 98| +1.48E+02 | -9.2E-04 | +1.0E+00 | 1.6E-02 | 1.6E-01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 46| +1.48E+02 | -9.5E-05 | +9.5E-01 | 3.1E-02 | 1.7E-01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 17| +2.50E+02 | -9.6E-04 | +9.0E-01 | 1.6E-02 | 1.5E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 99| +1.48E+02 | -1.7E-03 | +1.1E+00 | 3.1E-02 | 2.2E-01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 47| +1.48E+02 | -1.0E-04 | +9.0E-01 | 6.2E-02 | 2.9E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 18| +2.50E+02 | -2.1E-03 | +9.7E-01 | 3.1E-02 | 1.7E-01 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 100| +1.48E+02 | -3.8E-03 | +1.1E+00 | 6.2E-02 | 4.1E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 48| +1.48E+02 | +4.3E-04 | -3.0E+00 | 1.2E-01 | 8.1E-02 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 19| +2.50E+02 | -4.9E-03 | +1.1E+00 | 6.2E-02 | 2.0E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 49| +1.48E+02 | -1.6E-05 | +3.0E-01 | 3.1E-02 | 8.1E-02 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 20| +2.50E+02 | -1.4E-02 | +1.2E+00 | 1.2E-01 | 2.2E-01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 101| +1.48E+02 | -1.0E-02 | +1.4E+00 | 1.2E-01 | 1.0E+00 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 21| +2.50E+02 | -5.3E-02 | +1.5E+00 | 2.5E-01 | 2.8E-01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 50| +1.48E+02 | +4.5E-05 | -3.8E-01 | 3.1E-02 | 1.3E-01 | 9.3E-03 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 102| +1.48E+02 | -1.9E-02 | +7.9E-01 | 2.5E-01 | 1.3E+00 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 22| +2.49E+02 | -3.6E-01 | +2.3E+00 | 5.0E-01 | 3.6E-01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 51| +1.48E+02 | -2.0E-05 | +5.8E-01 | 7.8E-03 | 1.3E-01 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 23| +2.44E+02 | -4.8E+00 | +3.3E+00 | 1.0E+00 | 1.8E+00 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 103| +1.48E+02 | -1.3E-02 | +6.3E-01 | 5.0E-01 | 7.7E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 52| +1.48E+02 | -8.4E-06 | +1.5E+00 | 7.8E-03 | 6.6E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 24| +2.20E+02 | -2.4E+01 | +1.9E+00 | 2.0E+00 | 1.3E+01 | 4.1E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 104| +1.48E+02 | -3.7E-03 | +3.5E-01 | 5.0E-01 | 2.2E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 53| +1.48E+02 | -7.8E-06 | +7.2E-01 | 1.6E-02 | 4.5E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 54| +1.48E+02 | -8.2E-06 | +1.1E+00 | 1.6E-02 | 6.5E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 25| +2.20E+02 | +4.5E+03 | -1.4E+02 | 4.0E+00 | 2.7E+01 | 7.7E+00 | trr |0\n", - "2023-02-17 16:05:40 fides(INFO) 105| +1.48E+02 | -4.6E-04 | +1.3E-02 | 5.0E-01 | 6.5E-01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 55| +1.48E+02 | -5.8E-06 | +5.9E-01 | 3.1E-02 | 2.3E-02 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:40.669 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 118.415 and h = 1.50222e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:40.669 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 118.415: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:40 fides(INFO) 26| +2.20E+02 | +0.0E+00 | +0.0E+00 | 8.0E-01 | 2.7E+01 | 1.6E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 106| +1.48E+02 | -4.5E-03 | +8.4E-01 | 3.1E-02 | 1.5E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 56| +1.48E+02 | +1.5E-05 | -7.1E-01 | 3.1E-02 | 4.0E-02 | 5.5E-03 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 27| +2.17E+02 | -3.1E+00 | +3.3E-01 | 2.0E-01 | 2.7E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 107| +1.48E+02 | -8.4E-04 | +1.1E+00 | 3.1E-02 | 8.0E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 57| +1.48E+02 | -5.1E-06 | +8.4E-01 | 7.8E-03 | 4.0E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 28| +2.07E+02 | -1.0E+01 | +9.2E-01 | 2.0E-01 | 5.8E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 108| +1.48E+02 | -7.2E-05 | +1.5E+00 | 3.1E-02 | 1.7E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 58| +1.48E+02 | +2.7E-06 | -1.2E+00 | 1.6E-02 | 2.1E-02 | 1.8E-03 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 29| +1.96E+02 | -1.1E+01 | +4.1E-01 | 4.0E-01 | 2.9E+01 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 59| +1.48E+02 | +7.2E-07 | -1.1E+00 | 3.9E-03 | 2.1E-02 | 4.5E-04 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 109| +1.48E+02 | -7.8E-05 | +1.2E+00 | 3.1E-02 | 8.2E-02 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 30| +1.66E+02 | -3.0E+01 | +9.6E-01 | 4.0E-01 | 1.4E+02 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 60| +1.48E+02 | +2.4E-06 | -1.2E+01 | 9.8E-04 | 2.1E-02 | 1.1E-04 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 110| +1.48E+02 | -1.7E-05 | +1.6E+00 | 3.1E-02 | 4.5E-02 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 31| +1.56E+02 | -9.8E+00 | +4.4E-01 | 8.0E-01 | 6.1E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 61| +1.48E+02 | +8.6E-07 | -8.0E+00 | 2.4E-04 | 2.1E-02 | 3.0E-05 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 111| +1.48E+02 | -8.0E-05 | +1.6E+00 | 3.1E-02 | 4.4E-02 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 32| +1.51E+02 | -5.1E+00 | +5.1E-01 | 8.0E-01 | 2.4E+02 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 62| +1.48E+02 | +6.9E-08 | -8.7E-01 | 6.1E-05 | 2.1E-02 | 1.0E-05 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 112| +1.48E+02 | -8.8E-05 | +1.3E+00 | 3.1E-02 | 7.5E-02 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 33| +1.49E+02 | -2.2E+00 | +9.5E-01 | 8.0E-01 | 1.3E+02 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 63| +1.48E+02 | -1.0E-06 | +1.4E+01 | 1.5E-05 | 2.1E-02 | 7.1E-06 | 2d |1\n", - "2023-02-17 16:05:40 fides(WARNING) Stopping as function difference 9.98E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:40 fides(INFO) 113| +1.48E+02 | -1.7E-04 | +1.1E+00 | 6.2E-02 | 8.5E-02 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 34| +1.49E+02 | +2.6E+01 | -1.2E+01 | 1.6E+00 | 3.1E+01 | 4.8E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 35| +1.48E+02 | -5.8E-01 | +2.8E-01 | 4.0E-01 | 3.1E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 114| +1.48E+02 | -2.7E-04 | +1.1E+00 | 1.2E-01 | 8.2E-02 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 36| +1.48E+02 | +1.9E+01 | -8.7E+00 | 4.0E-01 | 3.9E+01 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 115| +1.48E+02 | -2.4E-04 | +1.3E+00 | 2.5E-01 | 9.1E-02 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 0| +1.46E+07 | NaN | NaN | 1.0E+00 | 6.7E+07 | NaN | NaN |1\n", - "2023-02-17 16:05:40.850 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.3823 and h = 1.24949e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:40.850 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.3823: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:40 fides(INFO) 37| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.0E-01 | 3.9E+01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 116| +1.48E+02 | -9.9E-05 | +1.5E+00 | 2.5E-01 | 1.9E-02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 1| +3.72E+02 | -1.5E+07 | +1.1E-01 | 1.0E+00 | 6.7E+07 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 38| +1.48E+02 | -2.9E-01 | +8.5E-01 | 2.5E-02 | 3.9E+01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:40.874 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.9091 and h = 1.33625e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:40.874 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.9091: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:40 fides(INFO) 117| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 1.5E-02 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 2| +2.65E+02 | -1.1E+02 | +8.9E-01 | 2.5E-01 | 6.3E+02 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 39| +1.48E+02 | -9.9E-03 | +2.7E-02 | 5.0E-02 | 5.7E+00 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 3| +2.50E+02 | -1.5E+01 | +7.5E-01 | 5.0E-01 | 9.3E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 118| +1.48E+02 | -1.7E-05 | +1.1E+00 | 4.6E-02 | 1.5E-02 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 40| +1.48E+02 | -6.2E-02 | +9.3E-01 | 1.3E-02 | 1.7E+01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 4| +2.50E+02 | +8.8E-01 | -5.9E-01 | 1.0E+00 | 1.4E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 119| +1.48E+02 | -2.8E-05 | +1.1E+00 | 9.2E-02 | 1.2E-02 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 41| +1.48E+02 | -2.7E-02 | +2.8E-01 | 2.5E-02 | 6.2E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 5| +2.50E+02 | +8.8E-01 | -5.9E-01 | 2.5E-01 | 1.4E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 120| +1.48E+02 | -3.2E-05 | +1.1E+00 | 1.8E-01 | 1.5E-02 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 42| +1.48E+02 | -3.7E-02 | +8.1E-01 | 2.5E-02 | 1.6E+01 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 6| +2.50E+02 | -3.7E-01 | +2.4E-01 | 6.2E-02 | 1.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 121| +1.48E+02 | -2.0E-05 | +1.3E+00 | 3.7E-01 | 4.5E-03 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 43| +1.48E+02 | -9.4E-03 | +9.4E-01 | 5.0E-02 | 1.6E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 7| +2.50E+02 | -2.6E-01 | +8.7E-01 | 1.6E-02 | 1.2E+01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 122| +1.48E+02 | -1.5E-05 | +1.9E+00 | 3.7E-01 | 7.8E-04 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 44| +1.48E+02 | -1.2E-02 | +8.9E-01 | 1.0E-01 | 2.0E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 8| +2.50E+02 | -6.9E-02 | +3.3E-01 | 3.1E-02 | 6.4E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 123| +1.48E+02 | -4.1E-06 | +8.8E-01 | 3.7E-01 | 3.2E-03 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 45| +1.48E+02 | +3.5E-02 | -2.2E+00 | 2.0E-01 | 8.2E-01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 9| +2.50E+02 | +7.7E-03 | -9.9E-02 | 3.1E-02 | 3.6E+00 | 8.4E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 124| +1.48E+02 | -7.2E-06 | +3.1E+00 | 3.7E-01 | 2.4E-03 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 10| +2.50E+02 | -4.0E-02 | +7.1E-01 | 7.8E-03 | 3.6E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 46| +1.48E+02 | -2.5E-03 | +4.2E-01 | 5.0E-02 | 8.2E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 125| +1.48E+02 | +2.8E-07 | -5.5E-01 | 3.7E-01 | 1.1E-03 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 11| +2.50E+02 | +6.8E-06 | -9.8E-03 | 7.8E-03 | 3.5E-01 | 2.1E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 47| +1.48E+02 | +1.0E-03 | -1.0E-01 | 5.0E-02 | 1.0E+00 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 126| +1.48E+02 | +4.5E-06 | -9.3E+00 | 7.2E-02 | 1.1E-03 | 4.5E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 12| +2.50E+02 | +6.8E-06 | -9.8E-03 | 2.0E-03 | 3.5E-01 | 5.1E-03 | 2d |0\n", - "2023-02-17 16:05:41.025 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.328 and h = 3.65848e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:41.025 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.328: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:41 fides(INFO) 48| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.3E-02 | 1.0E+00 | 4.9E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 13| +2.50E+02 | -3.0E-04 | +8.3E-01 | 4.9E-04 | 3.5E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 127| +1.48E+02 | +3.0E-06 | -2.2E+01 | 1.8E-02 | 1.1E-03 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:41.037 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.4913 and h = 1.14652e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:41.037 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.4913: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:41 fides(INFO) 49| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.1E-03 | 1.0E+00 | 1.7E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 128| +1.48E+02 | +5.1E-06 | -1.5E+02 | 4.5E-03 | 1.1E-03 | 2.8E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 14| +2.50E+02 | +4.7E-07 | -4.1E-03 | 9.8E-04 | 1.4E-01 | 2.6E-03 | 2d |0\n", - "2023-02-17 16:05:41.053 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.1737 and h = 1.14938e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:41.053 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.1737: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 50| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.8E-04 | 1.0E+00 | 9.4E-04 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 15| +2.50E+02 | -5.6E-05 | +7.8E-01 | 2.4E-04 | 1.4E-01 | 6.2E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 129| +1.48E+02 | +3.9E-06 | -4.3E+02 | 1.1E-03 | 1.1E-03 | 7.1E-04 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 16| +2.50E+02 | +9.6E-09 | -1.2E-03 | 4.9E-04 | 3.8E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 51| +1.48E+02 | -3.5E-04 | +9.6E-01 | 2.0E-04 | 1.0E+00 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 130| +1.48E+02 | +2.7E-06 | -1.1E+03 | 2.8E-04 | 1.1E-03 | 1.8E-04 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 17| +2.50E+02 | -3.8E-06 | +4.9E-01 | 1.2E-04 | 3.8E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 131| +1.48E+02 | +3.4E-06 | -4.1E+03 | 7.0E-05 | 1.1E-03 | 4.4E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 52| +1.48E+02 | -5.3E-04 | +9.3E-01 | 3.9E-04 | 1.2E+00 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 18| +2.50E+02 | -5.7E-10 | +4.8E-04 | 1.2E-04 | 1.4E-02 | 2.9E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 132| +1.48E+02 | +5.2E-06 | -1.3E+04 | 1.8E-05 | 1.1E-03 | 1.1E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 53| +1.48E+02 | -3.7E-04 | +9.5E-01 | 7.8E-04 | 1.1E+00 | 6.4E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 19| +2.50E+02 | -5.6E-07 | +6.9E-01 | 3.1E-05 | 1.4E-02 | 7.7E-05 | 2d |1\n", - "2023-02-17 16:05:41 fides(WARNING) Stopping as function difference 5.57E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:41 fides(INFO) 133| +1.48E+02 | +2.8E-06 | -9.2E+03 | 4.4E-06 | 1.1E-03 | 2.8E-06 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 54| +1.48E+02 | -4.3E-04 | +9.1E-01 | 1.6E-03 | 1.1E+00 | 8.6E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 134| +1.48E+02 | +4.9E-06 | -1.8E+04 | 1.1E-06 | 1.1E-03 | 8.5E-07 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 55| +1.48E+02 | -4.7E-04 | +9.5E-01 | 3.1E-03 | 1.0E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 135| +1.48E+02 | +1.7E-06 | -6.4E+03 | 2.8E-07 | 1.1E-03 | 5.2E-07 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 56| +1.48E+02 | -6.2E-04 | +8.7E-01 | 6.3E-03 | 7.6E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 136| +1.48E+02 | +2.8E-06 | -1.8E+04 | 6.9E-08 | 1.1E-03 | 1.8E-07 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 57| +1.48E+02 | -7.6E-04 | +9.5E-01 | 1.3E-02 | 1.0E+00 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 137| +1.48E+02 | +2.6E-06 | -5.8E+04 | 1.7E-08 | 1.1E-03 | 4.5E-08 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 58| +1.48E+02 | -1.2E-03 | +9.5E-01 | 2.5E-02 | 4.2E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 138| +1.48E+02 | +4.1E-06 | -3.5E+05 | 4.3E-09 | 1.1E-03 | 1.1E-08 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 59| +1.48E+02 | -1.3E-03 | +6.8E-01 | 5.0E-02 | 1.1E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 139| +1.48E+02 | +4.5E-06 | -1.5E+06 | 1.1E-09 | 1.1E-03 | 2.8E-09 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 60| +1.48E+02 | +2.5E-03 | -8.0E-01 | 5.0E-02 | 4.9E-01 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 140| +1.48E+02 | +3.6E-06 | -4.9E+06 | 2.7E-10 | 1.1E-03 | 7.0E-10 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 61| +1.48E+02 | -2.4E-04 | +1.9E-01 | 1.3E-02 | 4.9E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:41.250 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.431 and h = 4.24931e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:41.250 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.431: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:41 fides(INFO) 141| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.7E-11 | 1.1E-03 | 1.7E-10 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 62| +1.48E+02 | -5.3E-04 | +9.3E-01 | 3.1E-03 | 1.9E+00 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 142| +1.48E+02 | +4.7E-06 | -1.0E+08 | 1.7E-11 | 1.1E-03 | 4.4E-11 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 63| +1.48E+02 | -2.0E-04 | +8.5E-01 | 6.3E-03 | 1.8E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 143| +1.48E+02 | +3.6E-06 | -3.1E+08 | 4.2E-12 | 1.1E-03 | 1.1E-11 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 64| +1.48E+02 | -2.2E-04 | +8.7E-01 | 1.3E-02 | 2.4E-01 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 144| +1.48E+02 | +5.3E-06 | -1.8E+09 | 1.1E-12 | 1.1E-03 | 2.7E-12 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 65| +1.48E+02 | -3.5E-04 | +9.5E-01 | 2.5E-02 | 1.6E-01 | 9.5E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 145| +1.48E+02 | +4.6E-06 | -6.4E+09 | 2.6E-13 | 1.1E-03 | 6.8E-13 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 66| +1.48E+02 | -2.7E-04 | +4.8E-01 | 5.0E-02 | 4.2E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 146| +1.48E+02 | +3.9E-06 | -2.2E+10 | 6.6E-14 | 1.1E-03 | 1.7E-13 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 67| +1.48E+02 | +4.3E-03 | -2.3E+00 | 5.0E-02 | 3.1E-01 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 147| +1.48E+02 | +4.5E-06 | -1.0E+11 | 1.6E-14 | 1.1E-03 | 4.3E-14 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 68| +1.48E+02 | +9.9E-05 | -1.4E-01 | 1.3E-02 | 3.1E-01 | 5.1E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 148| +1.48E+02 | +3.0E-06 | -2.7E+11 | 4.1E-15 | 1.1E-03 | 1.1E-14 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 69| +1.48E+02 | -8.9E-05 | +2.7E-01 | 3.1E-03 | 3.1E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 70| +1.48E+02 | -1.8E-04 | +9.4E-01 | 3.1E-03 | 1.2E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 149| +1.48E+02 | +3.2E-06 | -1.1E+12 | 1.0E-15 | 1.1E-03 | 2.7E-15 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 71| +1.48E+02 | -3.2E-05 | +1.2E+00 | 6.3E-03 | 8.9E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 150| +1.48E+02 | +5.3E-06 | -7.6E+12 | 2.6E-16 | 1.1E-03 | 6.6E-16 | 2d |0\n", - "2023-02-17 16:05:41 fides(WARNING) Stopping as trust region radius 6.41E-17 is smaller than machine precision.\n", - "2023-02-17 16:05:41 fides(INFO) 72| +1.48E+02 | -3.7E-05 | +8.6E-01 | 1.3E-02 | 8.6E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 73| +1.48E+02 | -6.3E-05 | +9.8E-01 | 2.5E-02 | 7.2E-02 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 74| +1.48E+02 | -4.4E-05 | +6.1E-01 | 5.0E-02 | 5.9E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 75| +1.48E+02 | +1.5E-03 | -5.8E+00 | 5.0E-02 | 9.9E-02 | 1.2E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 0| +9.34E+11 | NaN | NaN | 1.0E+00 | 4.2E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:41 fides(INFO) 76| +1.48E+02 | +3.1E-05 | -3.4E-01 | 1.3E-02 | 9.9E-02 | 3.1E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 1| +1.55E+10 | -9.2E+11 | +9.0E-02 | 1.0E+00 | 4.2E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 77| +1.48E+02 | -1.9E-05 | +6.6E-01 | 3.1E-03 | 9.9E-02 | 8.3E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 2| +2.31E+09 | -1.3E+10 | +8.9E-01 | 2.5E-01 | 7.1E+10 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 78| +1.48E+02 | -3.1E-06 | +5.1E-01 | 3.1E-03 | 2.0E-01 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 3| +3.45E+08 | -2.0E+09 | +8.9E-01 | 5.0E-01 | 1.1E+10 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 79| +1.48E+02 | -7.2E-06 | +4.2E+00 | 3.1E-03 | 2.3E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 4| +5.14E+07 | -2.9E+08 | +8.9E-01 | 5.0E-01 | 1.6E+09 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 5| +7.57E+06 | -4.4E+07 | +9.0E-01 | 5.0E-01 | 2.4E+08 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 80| +1.48E+02 | +1.0E-06 | -4.2E-01 | 6.3E-03 | 5.3E-02 | 7.8E-04 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 6| +1.09E+06 | -6.5E+06 | +9.0E-01 | 5.0E-01 | 3.5E+07 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 81| +1.48E+02 | +4.3E-06 | -5.0E+00 | 1.6E-03 | 5.3E-02 | 2.0E-04 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 7| +1.53E+05 | -9.3E+05 | +9.0E-01 | 5.0E-01 | 5.1E+06 | 7.8E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 82| +1.48E+02 | +8.8E-07 | -1.8E+00 | 3.9E-04 | 5.3E-02 | 5.1E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 83| +1.48E+02 | +4.2E-06 | -1.1E+01 | 9.8E-05 | 5.3E-02 | 1.8E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 8| +2.17E+04 | -1.3E+05 | +9.0E-01 | 5.0E-01 | 7.4E+05 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 84| +1.48E+02 | +2.8E-06 | -7.9E+00 | 2.4E-05 | 5.3E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 9| +3.33E+03 | -1.8E+04 | +9.0E-01 | 5.0E-01 | 1.1E+05 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 85| +1.48E+02 | +5.6E-06 | -1.6E+01 | 6.1E-06 | 5.3E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 10| +6.49E+02 | -2.7E+03 | +9.0E-01 | 5.0E-01 | 1.7E+04 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 86| +1.48E+02 | +4.9E-06 | -2.7E+01 | 1.5E-06 | 5.3E-02 | 4.0E-06 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 11| +2.51E+02 | -4.0E+02 | +9.0E-01 | 5.0E-01 | 2.7E+03 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 87| +1.48E+02 | -4.1E-07 | +8.0E+00 | 3.8E-07 | 5.3E-02 | 1.0E-06 | 2d |1\n", - "2023-02-17 16:05:41 fides(WARNING) Stopping as function difference 4.13E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:41 fides(INFO) 12| +1.78E+02 | -7.3E+01 | +9.8E-01 | 5.0E-01 | 4.4E+02 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 13| +1.67E+02 | -1.1E+01 | +5.5E-02 | 1.0E+00 | 8.3E+01 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 14| +1.57E+02 | -1.0E+01 | +9.3E-01 | 2.5E-01 | 1.4E+02 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 0| +8.42E+08 | NaN | NaN | 1.0E+00 | 3.9E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:41 fides(INFO) 15| +1.57E+02 | +1.3E+00 | -3.3E-01 | 5.0E-01 | 5.6E+01 | 7.4E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 1| +1.10E+04 | -8.4E+08 | +9.9E-02 | 1.0E+00 | 3.9E+09 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 16| +1.53E+02 | -4.2E+00 | +7.0E-01 | 1.2E-01 | 5.6E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 2| +2.54E+03 | -8.5E+03 | +9.1E-01 | 2.5E-01 | 3.8E+04 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 17| +1.51E+02 | -1.8E+00 | +9.4E-01 | 1.2E-01 | 9.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 3| +6.03E+02 | -1.9E+03 | +8.9E-01 | 5.0E-01 | 7.7E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 18| +1.50E+02 | -1.3E+00 | +9.4E-01 | 2.5E-01 | 1.8E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 4| +2.82E+02 | -3.2E+02 | +8.9E-01 | 1.0E+00 | 1.3E+03 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 5| +2.50E+02 | -3.2E+01 | +8.0E-01 | 2.0E+00 | 1.8E+02 | 4.0E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 19| +1.50E+02 | +3.5E-01 | -3.1E-01 | 5.0E-01 | 2.1E+01 | 5.6E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 6| +2.33E+02 | -1.7E+01 | +2.8E+01 | 4.0E+00 | 5.7E+00 | 7.6E+00 | trr |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 20| +1.49E+02 | -5.1E-01 | +7.8E-01 | 1.2E-01 | 2.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 7| +2.33E+02 | +9.0E+03 | -4.0E+02 | 8.0E+00 | 4.7E+01 | 7.1E+00 | trr |0\n", - "2023-02-17 16:05:41 fides(INFO) 21| +1.49E+02 | -6.7E-02 | +6.0E-02 | 2.5E-01 | 1.8E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 8| +2.33E+02 | +1.5E+02 | -4.6E+00 | 5.8E-01 | 4.7E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 22| +1.49E+02 | -5.2E-01 | +8.7E-01 | 6.2E-02 | 2.0E+01 | 6.9E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 9| +2.18E+02 | -1.4E+01 | +8.8E-01 | 1.4E-01 | 4.7E+01 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 23| +1.48E+02 | -3.8E-01 | +6.5E-01 | 1.2E-01 | 2.4E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 10| +2.18E+02 | +1.7E+01 | -1.6E+00 | 2.9E-01 | 3.1E+01 | 7.2E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 11| +2.14E+02 | -4.4E+00 | +8.7E-01 | 7.2E-02 | 3.1E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 24| +1.48E+02 | -1.3E-01 | +5.2E-01 | 1.2E-01 | 1.5E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 12| +2.14E+02 | +2.9E+00 | -9.6E-01 | 1.4E-01 | 1.8E+01 | 3.5E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 25| +1.48E+02 | -1.4E-01 | +3.7E-01 | 1.2E-01 | 8.7E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 13| +2.13E+02 | -1.2E+00 | +8.6E-01 | 3.6E-02 | 1.8E+01 | 8.8E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 26| +1.48E+02 | +4.6E-01 | -8.0E-01 | 1.2E-01 | 1.0E+01 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 14| +2.13E+02 | +2.7E-01 | -4.0E-01 | 7.2E-02 | 9.2E+00 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 27| +1.48E+02 | -1.2E-01 | +6.6E-01 | 3.1E-02 | 1.0E+01 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 15| +2.12E+02 | -3.0E-01 | +8.4E-01 | 1.8E-02 | 9.2E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:41.877 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 141.82 and h = 3.32306e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:41.877 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 141.82: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:41 fides(INFO) 28| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.1E-02 | 2.6E+00 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 16| +2.12E+02 | -1.0E-01 | +4.0E-01 | 3.6E-02 | 4.1E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 29| +1.48E+02 | -8.7E-03 | +9.5E-01 | 7.8E-03 | 2.6E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 17| +2.12E+02 | -2.0E-01 | +5.2E-01 | 3.6E-02 | 7.7E+00 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 30| +1.48E+02 | -1.5E-02 | +9.5E-01 | 1.6E-02 | 2.5E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 18| +2.12E+02 | -2.3E-02 | +7.4E-02 | 3.6E-02 | 5.0E+00 | 9.6E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 19| +2.12E+02 | -1.3E-01 | +9.0E-01 | 9.0E-03 | 8.6E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 31| +1.48E+02 | -2.4E-02 | +9.4E-01 | 3.1E-02 | 2.8E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 20| +2.12E+02 | -1.1E-01 | +7.3E-01 | 1.8E-02 | 5.2E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 32| +1.48E+02 | -4.2E-02 | +9.7E-01 | 6.2E-02 | 2.4E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 21| +2.12E+02 | -7.5E-02 | +8.7E-01 | 1.8E-02 | 2.0E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 33| +1.48E+02 | -6.1E-02 | +9.1E-01 | 1.2E-01 | 3.7E+00 | 9.9E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 22| +2.12E+02 | -2.6E-02 | +3.2E-01 | 3.6E-02 | 2.5E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 34| +1.48E+02 | -3.0E-02 | +3.1E-01 | 2.5E-01 | 1.9E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 23| +2.12E+02 | +9.0E-03 | -8.2E-02 | 3.6E-02 | 4.2E+00 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 35| +1.48E+02 | +2.3E+00 | -4.2E+00 | 2.5E-01 | 6.9E+00 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 24| +2.12E+02 | -5.8E-02 | +7.7E-01 | 9.0E-03 | 4.2E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 36| +1.48E+02 | -2.0E-02 | +1.1E-01 | 6.2E-02 | 6.9E+00 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 25| +2.12E+02 | -7.7E-03 | +3.5E-01 | 1.8E-02 | 1.2E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 26| +2.12E+02 | -2.0E-03 | +5.5E-02 | 1.8E-02 | 2.4E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 37| +1.48E+02 | -2.4E-02 | +7.1E-01 | 1.6E-02 | 3.6E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 27| +2.12E+02 | -1.6E-02 | +8.1E-01 | 4.5E-03 | 2.5E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 38| +1.48E+02 | -1.1E-02 | +8.5E-01 | 1.6E-02 | 7.5E+00 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 28| +2.12E+02 | -2.1E-03 | +2.6E-01 | 9.0E-03 | 1.0E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 39| +1.48E+02 | -4.4E-03 | +8.4E-01 | 3.1E-02 | 1.3E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 29| +2.12E+02 | -4.7E-04 | +5.5E-02 | 9.0E-03 | 1.2E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 40| +1.48E+02 | -5.5E-03 | +8.9E-01 | 6.2E-02 | 9.8E-01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 30| +2.12E+02 | -3.8E-03 | +8.3E-01 | 2.3E-03 | 1.2E+00 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 41| +1.48E+02 | -2.2E-03 | +2.4E-01 | 1.2E-01 | 5.8E-01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 31| +2.12E+02 | -6.4E-04 | +3.1E-01 | 4.5E-03 | 5.1E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 42| +1.48E+02 | -4.0E-03 | +2.7E-01 | 3.1E-02 | 1.2E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 32| +2.12E+02 | -3.0E-04 | +1.6E-01 | 4.5E-03 | 5.1E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 43| +1.48E+02 | -9.5E-04 | +1.8E-01 | 3.1E-02 | 1.5E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 33| +2.12E+02 | -7.6E-04 | +8.1E-01 | 1.1E-03 | 5.2E-01 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 44| +1.48E+02 | -9.1E-04 | +6.6E-01 | 7.8E-03 | 4.3E-01 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 34| +2.12E+02 | -2.0E-04 | +5.3E-01 | 2.3E-03 | 1.8E-01 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 35| +2.12E+02 | -1.5E-04 | +4.4E-01 | 2.3E-03 | 1.9E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 45| +1.48E+02 | -4.2E-04 | +9.1E-01 | 7.8E-03 | 1.2E+00 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 36| +2.12E+02 | -1.8E-04 | +5.0E-01 | 2.3E-03 | 1.9E-01 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 46| +1.48E+02 | -3.1E-04 | +8.6E-01 | 1.6E-02 | 1.1E-01 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 37| +2.12E+02 | -1.6E-04 | +4.8E-01 | 2.3E-03 | 1.7E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 47| +1.48E+02 | -4.5E-04 | +9.6E-01 | 3.1E-02 | 3.1E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 38| +2.12E+02 | -1.7E-04 | +5.3E-01 | 2.3E-03 | 1.7E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 48| +1.48E+02 | -4.7E-04 | +7.8E-01 | 6.2E-02 | 1.0E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 39| +2.12E+02 | -1.6E-04 | +5.3E-01 | 2.3E-03 | 1.6E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 49| +1.48E+02 | +1.2E-02 | -9.4E+00 | 1.2E-01 | 2.7E-01 | 2.4E-02 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 40| +2.12E+02 | -1.7E-04 | +5.6E-01 | 2.3E-03 | 1.6E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 41| +2.12E+02 | -1.6E-04 | +5.6E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 50| +1.48E+02 | +2.7E-04 | -4.9E-01 | 3.1E-02 | 2.7E-01 | 6.0E-03 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 42| +2.12E+02 | -1.7E-04 | +5.8E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 51| +1.48E+02 | -1.1E-04 | +6.3E-01 | 7.7E-03 | 2.7E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 43| +2.12E+02 | -1.6E-04 | +5.7E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 52| +1.48E+02 | -4.0E-05 | +9.6E-01 | 7.7E-03 | 2.9E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 44| +2.12E+02 | -1.7E-04 | +5.9E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 53| +1.48E+02 | -5.5E-05 | +9.2E-01 | 1.5E-02 | 6.6E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 45| +2.12E+02 | -1.6E-04 | +5.7E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 54| +1.48E+02 | -5.8E-05 | +6.3E-01 | 3.1E-02 | 1.9E-01 | 9.0E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 46| +2.12E+02 | -1.8E-04 | +5.9E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 55| +1.48E+02 | +2.9E-04 | -1.5E+00 | 3.1E-02 | 1.1E-01 | 4.8E-03 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 47| +2.12E+02 | -1.7E-04 | +5.8E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 56| +1.48E+02 | -1.5E-05 | +2.1E-01 | 7.7E-03 | 1.1E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 48| +2.12E+02 | -1.8E-04 | +5.9E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 57| +1.48E+02 | -1.8E-05 | +6.5E-01 | 1.9E-03 | 3.8E-01 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 49| +2.12E+02 | -1.7E-04 | +5.7E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 50| +2.12E+02 | -1.8E-04 | +5.9E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 58| +1.48E+02 | -7.5E-06 | +1.3E+00 | 1.9E-03 | 3.9E-02 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 51| +2.12E+02 | -1.7E-04 | +5.7E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 59| +1.48E+02 | -6.3E-06 | +8.7E-01 | 3.8E-03 | 3.4E-02 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 52| +2.12E+02 | -1.9E-04 | +5.9E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 60| +1.48E+02 | -8.8E-06 | +1.1E+00 | 7.7E-03 | 2.4E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 53| +2.12E+02 | -1.7E-04 | +5.7E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 61| +1.48E+02 | -1.1E-05 | +9.2E-01 | 1.5E-02 | 3.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 54| +2.12E+02 | -1.9E-04 | +5.9E-01 | 2.3E-03 | 1.6E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 62| +1.48E+02 | -1.2E-05 | +8.7E-01 | 3.1E-02 | 2.7E-02 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 55| +2.12E+02 | -1.8E-04 | +9.9E-01 | 2.3E-03 | 1.6E-01 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 63| +1.48E+02 | +2.0E-04 | -9.4E+00 | 6.2E-02 | 2.6E-02 | 4.2E-03 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) 56| +2.12E+02 | -2.6E-04 | +8.5E-01 | 4.5E-03 | 5.1E-02 | 9.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 64| +1.48E+02 | +8.0E-05 | -4.7E+00 | 1.5E-02 | 2.6E-02 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 57| +2.12E+02 | -5.6E-04 | +9.6E-01 | 9.0E-03 | 2.1E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 58| +2.12E+02 | -8.4E-04 | +9.1E-01 | 1.8E-02 | 8.4E-02 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 65| +1.48E+02 | +6.1E-05 | -4.2E+00 | 3.7E-03 | 2.6E-02 | 1.2E-03 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 59| +2.12E+02 | -1.3E-03 | +6.2E-01 | 3.6E-02 | 2.1E-01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 66| +1.48E+02 | +5.9E-05 | -4.2E+00 | 9.2E-04 | 2.6E-02 | 1.1E-03 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 60| +2.12E+02 | -4.4E-03 | +9.8E-01 | 3.6E-02 | 4.4E-01 | 9.1E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 67| +1.48E+02 | +1.5E-05 | -1.5E+00 | 2.3E-04 | 2.6E-02 | 5.1E-04 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 61| +2.12E+02 | -1.1E-02 | +1.3E+00 | 7.2E-02 | 5.4E-01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 68| +1.48E+02 | -1.4E-06 | +4.7E-01 | 5.8E-05 | 2.6E-02 | 1.3E-04 | 2d |1\n", - "2023-02-17 16:05:42 fides(WARNING) Stopping as function difference 1.38E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:42 fides(INFO) 62| +2.12E+02 | -3.6E-02 | +1.5E+00 | 1.4E-01 | 7.1E-01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 63| +2.11E+02 | -2.5E-01 | +2.4E+00 | 2.9E-01 | 1.5E+00 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 64| +2.11E+02 | +2.4E+00 | -5.0E+00 | 5.8E-01 | 3.3E+00 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 65| +2.11E+02 | -3.8E-01 | +1.5E+00 | 1.4E-01 | 3.3E+00 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 66| +2.10E+02 | -5.4E-01 | +4.7E-01 | 2.9E-01 | 3.4E+00 | 7.5E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 67| +2.10E+02 | +6.8E-01 | -7.8E-01 | 2.9E-01 | 4.7E+00 | 4.5E-01 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 68| +2.10E+02 | -4.3E-02 | +8.2E-02 | 7.2E-02 | 4.7E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 69| +2.10E+02 | -2.4E-01 | +9.2E-01 | 1.8E-02 | 9.5E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 70| +2.10E+02 | -6.3E-02 | +9.4E-01 | 3.6E-02 | 3.3E+00 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 71| +2.10E+02 | -6.0E-02 | +8.9E-01 | 7.2E-02 | 1.8E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 72| +2.10E+02 | -1.1E-01 | +9.6E-01 | 1.4E-01 | 2.1E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 73| +2.10E+02 | -2.5E-01 | +1.3E+00 | 2.9E-01 | 1.2E+00 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 74| +2.10E+02 | +4.9E+00 | -1.4E+01 | 5.8E-01 | 2.4E+00 | 1.5E+00 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) 75| +2.10E+02 | -1.7E-01 | +1.1E+00 | 1.4E-01 | 2.4E+00 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 76| +2.09E+02 | -5.6E-01 | +9.5E-01 | 2.9E-01 | 3.0E+00 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 77| +2.01E+02 | -7.5E+00 | +4.2E+00 | 5.8E-01 | 9.0E+00 | 1.2E+00 | trr |1\n", - "2023-02-17 16:05:42 fides(INFO) 78| +2.01E+02 | +8.4E+00 | -6.4E-01 | 1.2E+00 | 2.8E+01 | 1.4E+00 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) 79| +1.89E+02 | -1.3E+01 | +1.5E+00 | 2.9E-01 | 2.8E+01 | 5.4E-01 | trr |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 80| +1.89E+02 | +2.6E+02 | -1.0E+01 | 5.8E-01 | 4.4E+01 | 1.3E+00 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) 81| +1.83E+02 | -5.8E+00 | +7.1E-01 | 1.4E-01 | 4.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 82| +1.70E+02 | -1.3E+01 | +8.3E-01 | 1.4E-01 | 7.0E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 83| +1.70E+02 | +7.7E+00 | -5.5E-01 | 2.9E-01 | 4.8E+01 | 6.2E-01 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 84| +1.65E+02 | -5.3E+00 | +8.0E-01 | 7.2E-02 | 4.8E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 85| +1.58E+02 | -6.6E+00 | +8.3E-01 | 1.4E-01 | 7.3E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:42.744 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 124.452 and h = 1.67549e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:42.744 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 124.452: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:42 fides(INFO) 86| +1.58E+02 | +0.0E+00 | +0.0E+00 | 2.9E-01 | 7.6E+01 | 5.5E-01 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) 87| +1.54E+02 | -4.1E+00 | +7.1E-01 | 7.2E-02 | 7.6E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 88| +1.51E+02 | -2.7E+00 | +8.3E-01 | 7.2E-02 | 8.1E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 89| +1.49E+02 | -2.5E+00 | +5.7E-01 | 1.4E-01 | 4.4E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 90| +1.49E+02 | +9.5E-01 | -4.4E-01 | 1.4E-01 | 5.9E+01 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 91| +1.49E+02 | +1.7E-01 | -1.5E-01 | 3.6E-02 | 5.9E+01 | 5.1E-02 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 92| +1.48E+02 | -3.6E-01 | +5.3E-01 | 9.0E-03 | 5.9E+01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 93| +1.48E+02 | -8.1E-02 | +9.6E-01 | 9.0E-03 | 1.7E+01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 94| +1.48E+02 | -1.1E-01 | +7.4E-01 | 1.8E-02 | 1.2E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 95| +1.48E+02 | -1.1E-01 | +8.2E-01 | 1.8E-02 | 2.1E+01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 96| +1.48E+02 | -1.3E-01 | +8.8E-01 | 3.6E-02 | 1.2E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 97| +1.48E+02 | -1.6E-01 | +7.5E-01 | 7.2E-02 | 8.8E+00 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 98| +1.48E+02 | +3.0E+00 | -5.2E+00 | 1.4E-01 | 1.5E+01 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 99| +1.48E+02 | +6.0E-02 | -2.5E-01 | 3.6E-02 | 1.5E+01 | 5.6E-02 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 100| +1.48E+02 | -5.5E-02 | +6.6E-01 | 9.0E-03 | 1.5E+01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 101| +1.48E+02 | -2.2E-02 | +8.1E-01 | 9.0E-03 | 5.3E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 102| +1.48E+02 | -2.6E-02 | +5.8E-01 | 1.8E-02 | 5.6E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 103| +1.48E+02 | -3.4E-02 | +9.3E-01 | 1.8E-02 | 8.4E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 104| +1.48E+02 | -5.2E-02 | +8.2E-01 | 3.6E-02 | 4.3E+00 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 105| +1.48E+02 | +5.5E-02 | -6.4E-01 | 7.2E-02 | 5.2E+00 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 106| +1.48E+02 | -1.9E-02 | +7.2E-01 | 1.8E-02 | 5.2E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 107| +1.48E+02 | +4.0E-02 | -1.0E+00 | 1.8E-02 | 2.5E+00 | 2.7E-02 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 108| +1.48E+02 | +2.1E-02 | -1.4E+00 | 4.5E-03 | 2.5E+00 | 8.4E-03 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 109| +1.48E+02 | +6.9E-04 | -1.2E-01 | 1.1E-03 | 2.5E+00 | 2.7E-03 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 110| +1.48E+02 | -1.2E-03 | +7.3E-01 | 2.8E-04 | 2.5E+00 | 6.9E-04 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 111| +1.48E+02 | -1.1E-03 | +9.9E-01 | 2.8E-04 | 2.2E+00 | 5.6E-04 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 112| +1.48E+02 | -2.0E-03 | +1.0E+00 | 5.6E-04 | 2.0E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 113| +1.48E+02 | -3.3E-03 | +9.9E-01 | 1.1E-03 | 2.0E+00 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 114| +1.48E+02 | -3.2E-03 | +9.9E-01 | 2.3E-03 | 1.9E+00 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 115| +1.48E+02 | -5.6E-03 | +9.7E-01 | 4.5E-03 | 2.1E+00 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 116| +1.48E+02 | -1.0E-02 | +9.8E-01 | 9.0E-03 | 2.1E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 117| +1.48E+02 | -2.0E-02 | +1.0E+00 | 1.8E-02 | 1.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 118| +1.48E+02 | -2.3E-02 | +8.0E-01 | 3.6E-02 | 1.5E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 119| +1.48E+02 | -2.5E-02 | +4.5E-01 | 7.2E-02 | 2.4E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 120| +1.48E+02 | +3.0E-02 | -6.7E-01 | 7.2E-02 | 3.0E+00 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 121| +1.48E+02 | -1.6E-02 | +8.0E-01 | 1.8E-02 | 3.0E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 122| +1.48E+02 | -1.4E-02 | +8.7E-01 | 3.6E-02 | 2.4E+00 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 123| +1.48E+02 | -4.8E-03 | +4.7E-01 | 7.2E-02 | 2.4E+00 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 124| +1.48E+02 | -5.4E-03 | +1.1E+00 | 7.2E-02 | 5.7E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 125| +1.48E+02 | -2.8E-04 | +3.9E-01 | 7.2E-02 | 7.7E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 126| +1.48E+02 | -3.6E-04 | +1.0E+00 | 7.2E-02 | 9.5E-01 | 8.8E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 127| +1.48E+02 | -1.4E-05 | +7.5E-01 | 7.2E-02 | 1.4E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 128| +1.48E+02 | -3.9E-06 | +1.3E+00 | 7.2E-02 | 5.9E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 129| +1.48E+02 | +3.8E-06 | -1.5E+02 | 7.2E-02 | 1.1E-02 | 5.7E-05 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 130| +1.48E+02 | +1.3E-06 | -7.0E+01 | 1.2E-05 | 1.1E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 131| +1.48E+02 | +2.4E-06 | -1.7E+02 | 3.0E-06 | 1.1E-02 | 4.1E-06 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 132| +1.48E+02 | +1.2E-06 | -1.1E+02 | 7.4E-07 | 1.1E-02 | 1.4E-06 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 133| +1.48E+02 | -7.8E-07 | +2.3E+02 | 1.9E-07 | 1.1E-02 | 3.4E-07 | 2d |1\n", - "2023-02-17 16:05:43 fides(WARNING) Stopping as function difference 7.78E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 0| +2.42E+10 | NaN | NaN | 1.0E+00 | 1.1E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:43 fides(INFO) 1| +5.45E+04 | -2.4E+10 | +9.3E-02 | 1.0E+00 | 1.1E+11 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 2| +8.86E+03 | -4.6E+04 | +9.0E-01 | 2.5E-01 | 2.5E+05 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 3| +1.61E+03 | -7.3E+03 | +9.0E-01 | 5.0E-01 | 3.9E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 4| +1.61E+03 | +4.5E+02 | -2.3E+00 | 1.0E+00 | 6.3E+03 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 5| +4.66E+02 | -1.1E+03 | +9.0E-01 | 2.5E-01 | 6.3E+03 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 6| +2.88E+02 | -1.8E+02 | +9.0E-01 | 5.0E-01 | 1.0E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 7| +2.57E+02 | -3.2E+01 | +8.7E-01 | 1.0E+00 | 1.5E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 8| +2.57E+02 | +1.7E+02 | -1.3E+01 | 2.0E+00 | 3.2E+01 | 4.1E+00 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 9| +2.57E+02 | +1.4E+02 | -1.0E+01 | 5.0E-01 | 3.2E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 10| +2.50E+02 | -6.5E+00 | +7.3E-01 | 1.2E-01 | 3.2E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 11| +2.50E+02 | +9.7E-02 | -1.6E-01 | 1.2E-01 | 1.1E+01 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 12| +2.50E+02 | -3.3E-01 | +6.1E-01 | 3.1E-02 | 1.1E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 13| +2.50E+02 | -1.0E-02 | +2.0E-01 | 3.1E-02 | 2.9E+00 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 14| +2.50E+02 | -1.7E-02 | +5.1E-01 | 7.8E-03 | 2.5E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 15| +2.50E+02 | -1.9E-03 | +3.6E-01 | 7.8E-03 | 8.3E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 16| +2.50E+02 | -1.6E-03 | +3.5E-01 | 7.8E-03 | 7.2E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 17| +2.50E+02 | -1.8E-03 | +4.2E-01 | 7.8E-03 | 7.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 18| +2.50E+02 | -1.6E-03 | +4.2E-01 | 7.8E-03 | 6.4E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 19| +2.50E+02 | -1.8E-03 | +4.7E-01 | 7.8E-03 | 6.4E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 20| +2.50E+02 | -1.7E-03 | +4.6E-01 | 7.8E-03 | 6.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 21| +2.50E+02 | -1.9E-03 | +5.0E-01 | 7.8E-03 | 6.1E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 22| +2.50E+02 | -1.7E-03 | +4.8E-01 | 7.8E-03 | 5.8E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 23| +2.50E+02 | -2.0E-03 | +5.2E-01 | 7.8E-03 | 6.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 24| +2.50E+02 | -1.8E-03 | +5.0E-01 | 7.8E-03 | 5.8E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 25| +2.50E+02 | -2.0E-03 | +5.3E-01 | 7.8E-03 | 6.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 26| +2.50E+02 | -1.9E-03 | +5.0E-01 | 7.8E-03 | 5.8E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 27| +2.50E+02 | -2.1E-03 | +5.3E-01 | 7.8E-03 | 6.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 28| +2.50E+02 | -2.0E-03 | +5.1E-01 | 7.8E-03 | 5.9E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 29| +2.50E+02 | -2.2E-03 | +5.4E-01 | 7.8E-03 | 6.2E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 30| +2.50E+02 | -2.1E-03 | +5.1E-01 | 7.8E-03 | 6.1E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 31| +2.50E+02 | -2.3E-03 | +5.4E-01 | 7.8E-03 | 6.4E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 32| +2.50E+02 | -2.1E-03 | +5.1E-01 | 7.8E-03 | 6.2E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 33| +2.50E+02 | -2.5E-03 | +5.4E-01 | 7.8E-03 | 6.5E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 34| +2.50E+02 | -2.2E-03 | +5.1E-01 | 7.8E-03 | 6.3E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 35| +2.50E+02 | -4.6E-03 | +1.0E+00 | 7.8E-03 | 6.7E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 36| +2.50E+02 | -7.5E-03 | +9.8E-01 | 1.6E-02 | 2.6E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 37| +2.50E+02 | -1.7E-02 | +1.1E+00 | 3.1E-02 | 6.0E-01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 38| +2.50E+02 | -4.1E-02 | +1.2E+00 | 6.2E-02 | 7.2E-01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 39| +2.49E+02 | -1.2E-01 | +1.4E+00 | 1.2E-01 | 1.2E+00 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 40| +2.49E+02 | -5.2E-01 | +1.8E+00 | 2.5E-01 | 2.3E+00 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 41| +2.45E+02 | -3.6E+00 | +2.2E+00 | 5.0E-01 | 4.7E+00 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 42| +2.33E+02 | -1.2E+01 | +1.5E+00 | 1.0E+00 | 1.5E+01 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 43| +2.27E+02 | -6.1E+00 | +3.3E-01 | 2.0E+00 | 4.0E+01 | 1.9E+00 | trr |1\n", - "2023-02-17 16:05:43 fides(INFO) 44| +2.18E+02 | -9.2E+00 | +1.4E+00 | 2.0E+00 | 4.2E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 45| +2.18E+02 | +8.7E+02 | -6.4E+01 | 2.0E+00 | 4.2E+01 | 3.6E+00 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 46| +2.13E+02 | -4.5E+00 | +3.2E-01 | 5.0E-01 | 4.2E+01 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 47| +2.13E+02 | +3.7E+00 | -7.4E-01 | 5.0E-01 | 3.7E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 48| +2.11E+02 | -2.5E+00 | +4.9E-01 | 1.2E-01 | 3.7E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 49| +2.10E+02 | -3.4E-01 | +3.7E-01 | 1.2E-01 | 1.1E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 50| +2.10E+02 | +7.4E-02 | -1.4E-01 | 1.2E-01 | 5.6E+00 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 51| +2.10E+02 | -1.6E-01 | +6.6E-01 | 3.1E-02 | 5.6E+00 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 52| +2.10E+02 | -7.0E-02 | +8.0E-01 | 3.1E-02 | 2.8E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 53| +2.10E+02 | -2.7E-02 | +8.5E-01 | 6.2E-02 | 1.2E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 54| +2.10E+02 | -2.2E-02 | +1.3E+00 | 1.2E-01 | 8.3E-01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 55| +2.10E+02 | -5.4E-02 | +1.8E+00 | 1.2E-01 | 6.3E-01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 56| +2.10E+02 | -1.4E-01 | +1.9E+00 | 2.5E-01 | 1.7E+00 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 57| +2.10E+02 | +3.2E+00 | -3.0E+01 | 5.0E-01 | 3.1E+00 | 9.9E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 58| +2.10E+02 | -9.1E-02 | +1.1E+00 | 8.6E-02 | 3.1E+00 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 59| +2.09E+02 | -3.9E-01 | +5.6E-01 | 1.7E-01 | 3.4E+00 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 60| +2.08E+02 | -1.0E+00 | +1.5E+00 | 1.7E-01 | 1.1E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 61| +2.08E+02 | +1.2E+01 | -1.1E+01 | 3.4E-01 | 8.2E+00 | 9.3E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 62| +2.08E+02 | -5.2E-01 | +1.0E+00 | 8.6E-02 | 8.2E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 63| +2.08E+02 | +3.6E+00 | -1.8E+00 | 1.7E-01 | 8.9E+00 | 4.4E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 64| +2.07E+02 | -3.3E-01 | +5.1E-01 | 4.3E-02 | 8.9E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 65| +2.07E+02 | -6.0E-01 | +1.2E+00 | 4.3E-02 | 8.4E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 66| +2.06E+02 | -1.1E+00 | +1.0E+00 | 8.6E-02 | 5.5E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 67| +2.05E+02 | -7.5E-01 | +3.2E-01 | 1.7E-01 | 8.6E+00 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 68| +2.02E+02 | -2.9E+00 | +1.4E+00 | 1.7E-01 | 2.4E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 69| +2.00E+02 | -2.6E+00 | +5.0E-01 | 3.4E-01 | 1.4E+01 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 70| +2.00E+02 | +3.8E+00 | -8.3E-01 | 3.4E-01 | 2.9E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 71| +1.97E+02 | -2.7E+00 | +1.1E+00 | 8.6E-02 | 2.9E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 72| +1.96E+02 | -5.2E-01 | +1.4E-01 | 1.7E-01 | 1.8E+01 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 73| +1.95E+02 | -1.4E+00 | +9.5E-01 | 4.3E-02 | 2.5E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 74| +1.94E+02 | -8.7E-01 | +1.1E+00 | 8.6E-02 | 8.7E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 75| +1.92E+02 | -2.3E+00 | +1.3E+00 | 1.7E-01 | 4.5E+00 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 76| +1.85E+02 | -6.7E+00 | +9.0E-01 | 3.4E-01 | 1.5E+01 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 77| +1.78E+02 | -7.2E+00 | +4.8E-01 | 6.9E-01 | 6.7E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 78| +1.78E+02 | +1.2E+02 | -7.6E+00 | 6.9E-01 | 9.0E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 79| +1.67E+02 | -1.1E+01 | +9.0E-01 | 1.1E-01 | 9.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 80| +1.64E+02 | -2.7E+00 | +3.1E-01 | 2.2E-01 | 6.3E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 81| +1.64E+02 | +2.7E+01 | -1.8E+00 | 2.2E-01 | 1.2E+02 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 82| +1.62E+02 | -2.3E+00 | +2.4E-01 | 5.4E-02 | 1.2E+02 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 83| +1.60E+02 | -2.5E+00 | +9.9E-01 | 1.3E-02 | 1.1E+02 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 84| +1.58E+02 | -1.1E+00 | +9.2E-01 | 2.7E-02 | 3.6E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 85| +1.57E+02 | -1.1E+00 | +6.7E-01 | 5.4E-02 | 2.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 86| +1.56E+02 | -8.8E-01 | +6.7E-01 | 5.4E-02 | 4.5E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 87| +1.56E+02 | +1.1E-01 | -1.1E-01 | 5.4E-02 | 1.4E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 88| +1.56E+02 | -3.1E-01 | +7.8E-01 | 1.3E-02 | 1.4E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 89| +1.56E+02 | -4.0E-01 | +9.3E-01 | 2.7E-02 | 1.8E+01 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 90| +1.55E+02 | -6.1E-01 | +1.2E+00 | 5.4E-02 | 1.2E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 91| +1.54E+02 | -1.1E+00 | +1.2E+00 | 1.1E-01 | 1.1E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 92| +1.52E+02 | -1.7E+00 | +7.5E-01 | 2.2E-01 | 2.0E+01 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 93| +1.52E+02 | +9.3E-01 | -6.7E-01 | 4.3E-01 | 4.5E+01 | 4.5E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 94| +1.51E+02 | -9.4E-01 | +9.2E-01 | 5.2E-02 | 4.5E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 95| +1.50E+02 | -9.8E-01 | +9.8E-01 | 1.0E-01 | 1.5E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 96| +1.50E+02 | +8.3E-01 | -5.9E-01 | 2.1E-01 | 3.5E+01 | 4.0E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 97| +1.50E+02 | -7.9E-01 | +9.1E-01 | 5.2E-02 | 3.5E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 98| +1.49E+02 | -4.0E-01 | +3.9E-01 | 1.0E-01 | 1.2E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 99| +1.49E+02 | -2.9E-01 | +3.3E-01 | 1.0E-01 | 4.3E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 100| +1.49E+02 | +5.0E-02 | -6.4E-02 | 1.0E-01 | 4.6E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 101| +1.49E+02 | -4.0E-01 | +8.0E-01 | 1.2E-02 | 4.6E+01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 102| +1.48E+02 | -2.5E-01 | +1.3E+00 | 2.3E-02 | 9.7E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 103| +1.48E+02 | -3.6E-01 | +1.0E+00 | 4.7E-02 | 6.9E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 104| +1.48E+02 | -2.6E-01 | +8.5E-01 | 9.3E-02 | 5.4E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 105| +1.48E+02 | -8.8E-02 | +1.1E+00 | 1.9E-01 | 5.6E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 106| +1.48E+02 | -5.0E-02 | +8.4E-01 | 1.9E-01 | 1.3E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 107| +1.48E+02 | -3.8E-02 | +1.2E+00 | 1.9E-01 | 5.0E+00 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 108| +1.48E+02 | -7.9E-03 | +1.2E+00 | 1.9E-01 | 1.1E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 109| +1.48E+02 | -3.1E-03 | +1.2E+00 | 1.9E-01 | 4.2E-01 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 110| +1.48E+02 | -7.6E-04 | +1.1E+00 | 1.9E-01 | 5.3E-01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 111| +1.48E+02 | -4.2E-05 | +1.2E+00 | 1.9E-01 | 7.0E-02 | 9.0E-03 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 112| +1.48E+02 | -1.6E-06 | +8.5E-01 | 1.9E-01 | 2.7E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 113| +1.48E+02 | -2.4E-06 | +1.5E+00 | 1.9E-01 | 1.8E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 114| +1.48E+02 | -6.7E-06 | +1.6E+00 | 1.9E-01 | 1.2E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 115| +1.48E+02 | -1.4E-05 | +1.5E+00 | 1.9E-01 | 5.9E-02 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 116| +1.48E+02 | -3.4E-05 | +1.5E+00 | 1.9E-01 | 1.3E-01 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 117| +1.48E+02 | -7.2E-05 | +1.6E+00 | 1.9E-01 | 2.1E-01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 118| +1.48E+02 | -1.1E-04 | +1.5E+00 | 1.9E-01 | 2.7E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 119| +1.48E+02 | -8.0E-05 | +1.2E+00 | 1.9E-01 | 2.4E-01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 120| +1.48E+02 | -4.5E-05 | +1.3E+00 | 3.7E-01 | 9.9E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 121| +1.48E+02 | -1.8E-05 | +2.1E+00 | 3.7E-01 | 7.7E-03 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 122| +1.48E+02 | -8.9E-06 | +1.3E+00 | 3.7E-01 | 4.4E-02 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 123| +1.48E+02 | -8.8E-06 | +1.5E+00 | 3.7E-01 | 4.7E-02 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 124| +1.48E+02 | -4.1E-06 | +1.3E+00 | 3.7E-01 | 2.5E-02 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 125| +1.48E+02 | -1.9E-06 | +1.3E+00 | 3.7E-01 | 1.7E-03 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 126| +1.48E+02 | -3.0E-06 | +4.5E+00 | 3.7E-01 | 7.0E-03 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 127| +1.48E+02 | +1.9E-06 | -6.9E+00 | 3.7E-01 | 6.7E-03 | 8.7E-02 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 128| +1.48E+02 | +2.9E-06 | -2.2E+01 | 6.4E-02 | 6.7E-03 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 129| +1.48E+02 | +2.2E-06 | -5.4E+01 | 1.6E-02 | 6.7E-03 | 5.4E-03 | 2d |0\n", - "2023-02-17 16:05:45 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:45 fides(INFO) 130| +1.48E+02 | +3.1E-06 | -2.3E+02 | 4.0E-03 | 6.7E-03 | 1.4E-03 | 2d |0\n", - "2023-02-17 16:05:45 fides(INFO) 131| +1.48E+02 | +2.5E-06 | -3.6E+02 | 1.0E-03 | 6.7E-03 | 3.4E-04 | 2d |0\n", - "2023-02-17 16:05:45 fides(INFO) 132| +1.48E+02 | -9.0E-07 | +1.7E+02 | 2.5E-04 | 6.7E-03 | 8.5E-05 | 2d |1\n", - "2023-02-17 16:05:45 fides(WARNING) Stopping as function difference 8.99E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "Performing parallel task execution on 8 processes.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 5.44 s, sys: 2.02 s, total: 7.45 s\n", - "Wall time: 10min 31s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "%%capture --no-display\n", @@ -8844,26 +437,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "optimizer_results = [\n", " result_lbfgsb,\n", @@ -8891,26 +471,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Run time per start:\n", - "-------------------\n", - "Scipy: L-BFGS-B: 3.741806 s\n", - "Scipy: Powell: 9.719779 s\n", - "Fides: 2.701986 s\n", - "pyswarm: 25.507524 s\n" - ] - } - ], + "outputs": [], "source": [ "print('Average Run time per start:')\n", "print('-------------------')\n", @@ -8947,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -8976,30 +543,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", - "Performing parallel task execution on 8 processes.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 280 ms, sys: 148 ms, total: 427 ms\n", - "Wall time: 2min 1s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "%%capture\n", @@ -9038,22 +588,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1min 26s, sys: 5.81 s, total: 1min 32s\n", - "Wall time: 1min 32s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "%%capture\n", @@ -9078,26 +619,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "image/png": 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HnJGZK8rvtRRpNBojLPvjCEtJGpkJE+ArX4GddirWs1y1qu5EkiSp4bbbhl6/EjZNGb/jjvbmkaQWGHRKeERMjIibRnjtWUDz34RLymPN9gH2iYjfRMRlEXHEADncsU7VrFnjCEtJaocdd4TzzoPFi+F1r3P9K0mSOsG6dXD33Y6wlDTuDNqwzMwNwM0RsVubvv8kYG/gcOA44OyImN5PDnesUzWDNSwdYSlJo3PYYfChD8G3vw1nnll3GkmSdPvtxX2VEZa77AIRNiwldYUqm+5sDyyMiJ9FxEWNW4XX3Qk0b1M2uzzWbAlwUWY+lJm3A7dQNDClkXENS0lqr7e9DY48Et7yFrjqqrrTSJLU2xoNy7lzhz538mR4xCNsWErqCpMqnPPuEV77CmDviNiDolF5LPDyPuf8P4qRlV+OiBkUU8RvG+H3k1zDUpLabcIE+OpX4aCD4OUvh2uvHXpXUkmS1B6LFxf3VaaEQzEt3IalpC4w5AjLzLwUWAxMLr++Ahhyi9DMXA+cCPwIuBH4VmYujIgPRMRR5Wk/ApZHxA3AJcDbMnP5iH4SCVzDUpLGwowZxZTwm26CM86oO40kSb1r8WKYMgV23rna+TYsJXWJIRuWEfE64Hzgi+WhWRQjI4eUmRdn5j6Z+cjM/FB57D2ZeVH5dWbmyZm5X2bun5nnjeinkBoGmxI+bVqxKPX69WObSVJPGvcbxj3vefCc58D73gfj8eeTJKkbLF5cTAePqHb+nDk2LCV1hSprWJ4AHAasBsjMW4Gd2hlKGpGNG2Ht2sGnhEPR1JSkNhv3G8ZFwCc/Wfy9+6531Z1GkqTedPvt1davbJg9G1atcuaZpI5XpWH5QGY+2HgQEZOAbF8kaYQajcjBpoSD61hKUqs8+tFw4olw9tluwCNJUh0WL66+fiXArFnF/Z1998OVpM5SpWF5aUS8E9gqIp4NfBv4bntjSSMwVMOycdxPEyWpdd77XthxRzjpJEg/z5QkacysXg333DO8EZY2LCV1iSoNy1OBZcB1wOuBizPztLamkkai0YgcaEq4IywlqfWmT4cPfQh+9Sv49rfrTiNJUu/405+KexuWksahKg3LN2Xm2Zn50sx8SWaeHREntT2ZNFyNhqUjLCVpbL3mNXDQQfDWtxabm0mSpPa7/fbi3oalpHGoSsPyX/o5dnyLc0ijV3UNSxuWktRaEyfCpz8Nd9wBp59edxpJknrD4sXF/XDWsNxqK9h+exuWkjrepIGeiIjjgJcDe0TERU1PbQvc0+5g0rANNSW80ch0Srgktd5TnwrHHAMf+Qj867/CbrvVnUiSpPFt8WLYemuYMWN4r5s1y4alpI43YMMS+C1wFzAD+HjT8TXAte0MJY3IUFPCHWEpSe11+ulw0UXw9rfDeefVnUaSpPFt8eJiOnjE8F43axYsWdKORJLUMgNOCc/MP2XmL4BnAb/KzEspGpizgWH+jSiNgaprWDrCUpLaY7fd4N//Hb75TfjlL+tOI0nS+LZ48fCmgzc4wlJSF6iyhuUvgSkRMQv4MfAq4Nx2hpJGZKg1LCdPhilTHGEpSe309rfDnDlw0kmwYUPdaSRJGr9uv314G+40zJoFd98N69e3PJIktUqVhmVk5jrgRcDnM/OlwGPaG0sagUYjcurUgc+ZNs0RlpLUTltvXUwNv/pq+NKX6k4jSaooIuZHxIKIWLBs2bK642goK1cWt5E2LDduhL/+tcWhJKl1KjUsI+JJwCuA75fHJrYvkjRCa9YUb5QnDvK/57bbOsJSktrtmGPgKU+B004r3kxJkjpeZp6VmfMyc97MmTPrjqOhNHYIH2nDEpwWLqmjVWlY/hvwDuDCzFwYEXsCl7Q1lTQS99478HTwBkdYSlL7RcCnPw3Ll8MHPlB3GkmSxp9Fi4r7vfYa/mttWErqAkM2LDPz0sw8CjgjIrbJzNsy881VLh4RR0TEzRGxKCJOHeS8F0dERsS8YWSXNrdmDWyzzeDnbLstrFo1NnkkqZc97nHw2tfCZz8LN91UdxpJksaXW28t7m1YShqnhmxYRsT+EXEVsBC4ISKujIgh17CMiInAGcCRwH7AcRGxXz/nTQNOAn4/3PDSZtasGXqE5fTpNiwlaax86EPFUh1ve1vdSSRJGl9uuQV22WXoARv9mTEDttjChqWkjlZlSvgXgZMzc/fM3A04BTi7wusOARaVIzIfBM4Dju7nvA8CHwHur5hZ6t+qVbDddoOfs/32rqcmSWNl5kx45zvhe9+Dn/+87jSSJI0ft94K++wzstdOmAC77mrDUlJHq9KwnJqZf1+zMjN/AQyyDfPfzQLuaHq8pDz2dxFxMDAnM7/PINyxTpVUaVhOn27DUpLG0kknwe67wymnwIYNdaeRJGl8uPVW2Hvvkb9+9my4446hz5OkmlRpWN4WEe+OiLnl7V3AbaP9xhExAfgExYjNQbljnSqp2rBctco3zZI0VqZMgQ9/GK6+Gr761brTSJLU/VatgqVLR9ewnDt3007jktSBqjQsXw3MBL5T3maWx4ZyJzCn6fHs8ljDNOCxwC8iYjFwKHCRG+9oxFauLBqSg2k8707hktrM2QFNXvYyeOIT4bTTYO3autNIktTdGhvujKZhuccexQjLhx5qTSZJarEqu4SvKHcFfzrwtMw8KTNXVLj2FcDeEbFHRGwBHAtc1HTdVZk5IzPnZuZc4DLgqMxcMKKfRL1t48aiCVllhCU4LVxS2zk7oEkEfOITcNdd8LGP1Z1GkqTu1mhYjnQNS4A99yzeQ/35z63JJEktVmWX8CdExHXANcB1EXFNRDx+qNdl5nrgROBHwI3AtzJzYUR8ICKOGm1waTP33guZQ4+w3H774t6GpSSNrX/4B3jJS+CjH4W//KXuNJIkda9bby0+DHzkI0d+jT32KO5vG/Vqb5LUFlWmhH8JeGPTSMgTgC9XuXhmXpyZ+2TmIzPzQ+Wx92TmRf2ce7ijKzVijQZk1RGWK6oMEpYktdSHP1xMPXv3u+tOIklS97r1Vpgzp1gneqQaDcvbb29NJklqsSoNyw2Z+avGg8z8NbC+fZGkEVi1qrh3Srgkda5HPhLe9Cb48pfhmmvqTiNJUne65ZbRrV8JMGsWTJ5sw1JSx6rSsLw0Ir4YEYdHxNMi4vMUG+UcHBEHtzugVEmjYVl10x0blpJUj3e9q1ie461vLZbykCRJw3PrraNvWE6cCLvv7pRwSR1rUoVzDizv39vn+OOABJ7R0kTSSAx3SrgNS0mqx/bbw3vfCyedBD/4ATz3uXUnkiSpeyxfXixvNZoNdxr22MMRlpI61pANy8x8+lgEkUal6gjLadNgwgTXsJSkOr3hDfC5zxWjLP/xH2FSlc9PJUnS33cIH+0ISyh2Cj///NFfR5LaoMqU8L+LiO+1K4g0KlXXsJwwoTjHEZaSVJ8ttih2C7/xRjj77LrTSJLUPW65pbhvRcNyjz2KEZtr1oz+WpLUYsNqWAKz2pJCGq2qU8KhGIVpw1KS6nX00fDUpxbTw1evrjuNJEnd4aabivUnG7t8j4Y7hUvqYMNtWF7VlhTSaK1aBVtuCVOmDH2uDUtJql8EfPzjsGwZ/Nd/1Z1GkqTusGABHHBAMVthtPbcs7h34x1JHWhYDcvMfHW7gkijsnJltdGVUDQsXcNSkuo3bx688pXwyU/C4sV1p5EkqbNt3AiXXw6HHNKa6znCUlIHG7JhGRHXRcS1fW6/iohPRsSOYxFSGtKqVdUblttv7whLSeoU//mfxSiR44+HDRvqTiNJUue69dbifc8Tn9ia6+2wA2y7Lfzxj625niS1UJURlj8Avg+8orx9F1gA/BU4t23JpOFYtWroHcIbnBIuSZ1jzhz47Gfh0kvhYx+rO40kSZ3r8suL+1aNsIyARz8aFi5szfUkqYUmVTjnWZl5cNPj6yLiD5l5cES8sl3BpGFxSrgkda9//mf4/vfhXe+CZz0LHv/4uhNJUs+JiPnAfIDddtut5jTq1+9/D9tsA/vu27prHnAAXHABZBYNTEnqEFVGWE6MiL9/hBMRTwAmlg/XtyWVNFzDGWE5cyasWwdr17Y1kiSpogg480zYeWd4xSuKv6MlSWMqM8/KzHmZOW/mzJl1x1F/Lr+8WP954sShz63qwAPhnnvgL39p3TUlqQWqNCxfC3wpIm6PiMXAl4DXRsRUwG091RmWLy/Wpqxil12K+7/+tX15JEnDs8MO8NWvwi23wCmn1J1GkqTO8sADcPXVrVu/suGAA4r7a65p7XUlaZSGbFhm5hWZuT9wEHBgZh5QHlubmd8a7LURcURE3BwRiyLi1H6ePzkibig38vlZROw+4p9EvWv9eli2bFMjciiPeERxf9dd7cskSRq+ZzyjaFaeeSZ897t1p5EkqXNccw089FDr1q9sOPDAYqbDlVe29rqSNEpVdgnfLiI+AfwM+FlEfDwihlwsMCImAmcARwL7AcdFxH59TrsKmJeZBwDnAx8d7g8gsWxZseZKoxE5lEZj04alpDaKiPkRsSAiFixbtqzuON3jP/4DDjoIXvMauPvuutNIktQZfv/74r7VDcttty023mlcX5I6RJUp4ecAa4Bjyttq4MsVXncIsCgzb8vMB4HzgKObT8jMSzKzsVDVZcDsqsGlv2tM7bZhKamDuBbYCG25JXzta7BmDbz61cUHUpIk9brLL4ddd4XZbXjLfOihcNllsHFj668tSSNUpWH5yMx8b9l4vC0z3w/sWeF1s4A7mh4vKY8N5DXADypcV9rccBuWO+4IkybZsJSkTrXffnD66XDxxfD5z9edRpKk+l1+eetHVzY85SnFngA33NCe60vSCFRpWN4XEU9uPIiIw4D7WhkiIl4JzANOH+B5p9VpYI2GZdU1LCdMKJqbNiwlqXOdcAIceSS89a2+gZIk9bYVK4pN6drVsHza04r7Sy5pz/UlaQSqNCzfAJwREYvLXcI/B7y+wuvuBOY0PZ5dHttMRDwLOA04KjMf6O9CTqvToBqNx513rv6aXXaxYSlJnSwCzjkHpk2DV7yi2B1VkqRe1FhfstU7hDfMnQv77gsXXtie60vSCFTZJfyazDwQOAA4IDMfBzyjwrWvAPaOiD0iYgvgWOCi5hMi4nHAFymalUuHnV6CYoTldtvBVltVf40NS0nqfI94RNG0vPpqePe7604jSVI9vvKV4v1OuxqWEfDSl8Kll8JS35ZL6gxVRlgCkJmrM3N1+fDkCuevB04EfgTcCHwrMxdGxAci4qjytNOBbYBvR8TVEXHRAJeTBvbXv1Zfv7Jhl102TSWXJHWu5z8f3vAG+NjH4Oc/rzuNJElj66674Pzzi43opk5t3/d56UuLTXe+8532fQ9JGoZJI3xdVDkpMy8GLu5z7D1NXz9rhN9f2mSkDctly+Chh2Dy5PbkkiS1xsc/DgsWwN/+VncSSZLG1he/CBs2wBvf2N7v89jHwqMeBd/+dvFBoSTVrPIIyz6ypSmk0RhpwxLg7rtbn0eS1Fpbb12s33XMMXUnkSRp7Dz4YNGwPPJI2Guv9n6vxrTwX/zCaeGSOsKADcuIWBMRq/u5rQF2HcOM0uD++tfqO4Q3NM53HUtJ6g4TRvoZqyRJXeqCC4r3OieeODbf75hj4ElPclCHpI4w4JTwzJw2lkGkEVm7FtasGf4Iy8b5NiwlSZIkdaLPfa4YWfmc54zN99t/f/j1r8fme0nSEByuoO7W+PRvpFPCbVhKkiRJ6jR/+AP89rdwwgnOMpDUk/ybT92t0XAcbsNy552LdVpcn0WSJElSp/nsZ4tdwY8/vu4kklSLke4SLnWGBx+EPfeEXYe5rOrkybB6NWyzTXtySZIkSdJIrFoF3/gG/Ou/wvTpdaeRpFrYsFR3e/rT4Y9/HNlrbVZKkiRJ6jTbbQe//31xL0k9yoalJEmSJEmd5MAD604gSbVyDUtJkiRJkiRJHcOGpSRJkiRJkqSOEZlZd4ZhiYg1wM115xjCDOBvdYcYghlbw4ytYcbR6/R80B0ZH5WZ01pxoYiYD8wvHz4WuL4V122jTv/v0+n5wIytYsbWMGNrdEPGltQu61ZbmLE1zNgaZmyNTs/Y6fmgYt3qxoblgsycV3eOwZixNczYGmZsjU7P2On5oLcz9vLP3iqdng/M2CpmbA0ztkavZuzVn7vVzNgaZmwNM7ZGp2fs9HxQPaNTwiVJkiRJkiR1DBuWkiRJkiRJkjpGNzYsz6o7QAVmbA0ztoYZW6PTM3Z6PujtjL38s7dKp+cDM7aKGVvDjK3Rqxl79eduNTO2hhlbw4yt0ekZOz0fVMzYdWtYSpIkSZIkSRq/unGEpSRJkiRJkqRxyoalJEmSJEmSpI7RlQ3LiHhfRNwZEVeXt+fWnWkgEXFKRGREzKg7S18R8cGIuLb8Hf44InatO1NfEXF6RNxU5rwwIqbXnalZRLw0IhZGxMaImFd3nmYRcURE3BwRiyLi1Lrz9CcizomIpRFxfd1Z+hMRcyLikoi4ofzvfFLdmfqKiCkRcXlEXFNmfH/dmQYSERMj4qqI+F7dWfoTEYsj4rry78QFLb62dasFrFutYe0auU6vW2DtaqVerlvl9a1dLWDtao1OrV2dXreg82uXdau1xlPt6sqGZemTmXlQebu47jD9iYg5wD8Cf647ywBOz8wDMvMg4HvAe2rO05+fAI/NzAOAW4B31Jynr+uBFwG/rDtIs4iYCJwBHAnsBxwXEfvVm6pf5wJH1B1iEOuBUzJzP+BQ4IQO/D0+ADwjMw8EDgKOiIhD6400oJOAG+sOMYSnl3WlHf8Qtm6NnnWrNaxdI3cunV23wNrVSr1et8Da1QrWrtbouNrVJXULOr92Wbdaa9zUrm5uWHaDTwJvBzpyZ6PMXN30cCodmDMzf5yZ68uHlwGz68zTV2bemJk3152jH4cAizLztsx8EDgPOLrmTA+Tmb8E7qk7x0Ay867M/EP59RqKv/hn1Ztqc1m4t3w4ubx13J/liJgNPA/477qzaFDWrVHq9LoF1q7R6PS6BdauVrFudRVr1yhZu0as4+sWdH7tsm61znirXd3csDyxHLJ+TkRsX3eYviLiaODOzLym7iyDiYgPRcQdwCvozE/7mr0a+EHdIbrELOCOpsdL6LC/9LtNRMwFHgf8vuYoD1MO+78aWAr8JDM7LiPwKYo3ExtrzjGYBH4cEVdGxPw2XN+61QLWrXHN2tVi1q5R+RTWLbB2tYS1a9yybrWYdWvUPsU4ql2TxijQsEXET4FH9PPUacAXgA9S/KAfBD5O8RfrmBoi4zsppibUarCMmfl/mXkacFpEvAM4EXjvmAZk6IzlOadRDBX/2lhmK7/3kPk0vkXENsAFwL/1+ZS8I2TmBuCgcr2hCyPisZnZMWvURMTzgaWZeWVEHF5znME8OTPvjIidgJ9ExE3lJ9KVWLdaw7rVGtYuWbtGrlfqFli7WsXa1RrWrt5m3Rqd8Vi7OrZhmZnPqnJeRJxNsRbImBsoY0TsD+wBXBMRUAyp/0NEHJKZfx3DiJV/jxRF6WJqKJ5DZYyI44HnA8/MzDEfdj2M32EnuROY0/R4dnlMwxQRkykK59cy8zt15xlMZq6MiEso1qjpmOIJHAYcFcVi/VOAbSPifzPzlTXn2kxm3lneL42ICymm+VR+42fdag3rVmtYu3qbtWvUeqJula+1drWAtas1urB2WbdaxLrVEuOudnXllPCI2KXp4QvprP9JyMzrMnOnzJybmXMphoYfPNaFcygRsXfTw6OBm+rKMpCIOIJiSPNRmbmu7jxd5Apg74jYIyK2AI4FLqo5U9eJ4l+/XwJuzMxP1J2nPxExs/yUj4jYCng2HfZnOTPfkZmzy78PjwV+3mmFMyKmRsS0xtcUozVaVlusW61h3Rr3rF0tYO0aPevW37+HtasFrF3jmnWrBaxbrTEea1fHjrAcwkcj4iCK6QmLgdfXmqZ7fTgiHkWxvsGfgDfUnKc/nwO2pBgqDHBZZnZMzoh4IfBZYCbw/Yi4OjOfU3MsMnN9RJwI/AiYCJyTmQtrjvUwEfEN4HBgRkQsAd6bmV+qN9VmDgNeBVxXrlcC8M7srF0ydwG+EsUuhROAb2VmLSMgutzOFFM7oKiNX8/MH7bw+tat1rButYC1a+S6oG6BtatXtLtugbWrVaxdLdCJtasb6hZ0Re2ybvWOYdWuqGm0tyRJkiRJkiQ9TFdOCZckSZIkSZI0PtmwlCRJkiRJktQxbFhKkiRJkiRJ6hg2LCVJkiRJkiR1DBuWkiRJkiRJkjqGDUtJkiRJkiRJHcOGpbpORNzbgmvsGBGXRMS9EfG5Ps89PiKui4hFEfGZiIim5z4VEU8tv/5FRNwcEVeXt/NHm2uQvL+IiHntuv4A3/PciHjJEOccHxG7Nj3+74jYr4UZ3hcRd5a/35si4gsRMejfW31ec3VEfLg8/qWIuCYiro2I8yNim/L4iRHx6lZllqQ6dFBtXBwRM0abZbSG+/uIiMMj4h9a+P2tLZJUkTVsc634fQzz+/00IrYfy+8pVWHDUr3qfuDdwFv7ee4LwOuAvcvbEVAUQeDQzPxl07mvyMyDytugzb1OEBGTWnzJ44G/Nywz87WZeUOLv8cnM/MgYD9gf+BpVV9T3k4tj70lMw/MzAOAPwMnlsfPAd7U4syS1I1aVRu70eHAsBqWQ9RUa4skja1ermGj9T/AG+sOIfVlw1LjQkQcFBGXlaPnLmx8QhQRTyiPXR0Rp0fE9QCZuTYzf01R2JqvswuwbWZelpkJfBV4Qfn0i4EfVshybkScGRELIuKWiHh+eXxKRHy5/HTvqoh4+iDX2CoizouIGyPiQmCrpuf+MSJ+FxF/iIhvN40UfG45CvHK8pPD75XH3xcR/xMRvwH+JyLmRsSvytf/oTGiJAqfK0eN/hTYqel7viciroiI6yPirPLclwDzgK+Vv9+tomkkaEQcV/6s10fER5qudW9EfKgc7XhZROw81O+0tAUwBVhRXud1ZaZrIuKCiNh6sBdn5urGz1n+PrM8vg5YHBGHVMwhSV2hztpY1oQfRMTrBsn3yoi4vMzxxYiY2JRtSkRMjYiFEfHYKEZA/jIivl/WqTNj6BH3D6s1ETGzrBlXlLfDImIu8AbgLWWWp/R3Xvn6/mrqz8vMP4uI3crfpbVFkkbBGtZvDeu35kTx/vML5bm3ld/vnCjeS57bdM1+358BFwHHDZZHqoMNS40XXwX+vRw9dx3w3vL4l4HXlyP0NlS4zixgSdPjJeUxgMOAK/uc32jWXR0RpzcdnwscAjwPODMipgAnAJmZ+1MUhK+Ux/vz/wHrMvPR5c/yeIAopii8C3hWZh4MLABOLq/zReDIzHw8MLPP9fYrX3McsBR4dvn6lwGfKc95IfCo8tx/ZvORJp/LzCdk5mMpmn3Pz8zzy+/fGGV6X+PkKKaJfwR4BnAQ8ISIeEH59FTgssw8EPglxaedg3lLRFwN3AXckplXl8e/U2Y6ELgReE3f15S35zTl+jLwV2Bf4LNN5y8AnjJEDknqNnXVxm2A7wLfyMyz+7tgRDyaogYd1pTjFZl5BcUbp/8APgr8b2ZeX77sEIpRi/sBjwReNEjmgWrNpylG4T+B4o3qf2fmYuBMNo3O/1V/5zVdu7mmfhb4Svk7/hqbaipYWyRpNKxhD69hg9Wc7YEnAW8pM3wSeAywf9n8HfD9WWauALaMYsSp1DFsWKrrRcR2wPTMvLQ89BXgqRExHZiWmb8rj399lN9qF2BZn2PNU8Lf1nT8W5m5MTNvBW6jaJA9GfhfgMy8CfgTsM8A3+upTedeC1xbHj+Uosj9pmzi/Quwe3n92zLz9vK8b/S53kVNDcXJwNkRcR3w7fJ6je/5jczckJl/AX7e9PqnR8Tvy9c8g6L4DeYJwC8yc1lmrqcoqE8tn3sQ+F759ZUUzd3BNKaE7wRMjYhjy+OPjWKk6HXAK/pkap4S/qPGwcz8V4op7DdS/COjYSlNU9slqdvVXBv/D/hyZn51kNc9k+LDuCvKevZMYM/yuQ8Az6YYxf/Rptdcnpm3ZeYGijr35EGuP1CteRbwufJ7XgRsG+VMhT4GO6+5pj6JTb/D/+mTydoiSSNgDRuwhg1Wc75bjiC9Drg7M6/LzI3AwvL1g70/A2uWOlCr17OTut2dwOymx7PLYwD3UUxJriKHeDxSAfykHNWx6WDEQUO8bm3T128B7gYOpPjQ4v5+X7Hp2lOAzwPzMvOOiHgf1X8P/XmoLKZQfBpZ6e+hzHwoIn5IUVjPA84FXpCZ10TE8RTrj1W5zoaIOA94O8UntFD8PPcN/CpJ6mnDrY2/AY6IiK83/X3fV1CMEnlHP8/tSDHCZXJ57UYNG05tHajWTKBYr6zvlMG+rx/svLV9Tx6AtUWS6jeeathgHijvNzZ93Xg8CXhoiNdbs9RxHGGprpeZq4AVEdGYdvUq4NLMXAmsiYgnlseP7e/1fa51F7A6Ig6N4l3JP1N8ygbFqLy9KsZ6aURMiIhHUnzadjPwK4qRgETEPsBu5fH+/BJ4eXnuY4EDyuOXAYdFxF7lc1PLa90M7BnFOlyw+ejBvrYD7io/cXsVMLHpe76sXH9lF6CxxmajiP+tHF3SvLnQGmBaP9/jcuBpETEjIiZSTIG/tJ/zKiv/exwG/LE8NA24KyImU/5eB3tt0+8sgKOAm5pO2Qe4vr/XSlI3qrk2vodiveEzBrnsz4CXRMROABGxQ0TsXj73RYqNE75GMX2t4ZCI2KNc9+tlwK+Hyt6PH9O0GU7TB35969lA5/X1Wzb9Dl9BUesbrC2SNALWsAENVnOGMuD7s/L38ghg8QgySW1jw1LdaOuIWNJ0O5liavTpEXEtxZocHyjPfQ3F9OerKdYCWdW4SEQsBj4BHF9epzE1+o0Ua1UtomiO/aA8/n0ePoqveQ3LnzYd/zNFUfgB8IZyhMbngQnlFOZvAsdn5gP07wvANhFxY/mzXAmQmcsodub+Rvmz/g7Yt5ya9kbghxFxJcUbr1X9XbjM8S8RcQ3FVPLGp34XArcCN1CsGfO78nuuBM6meNP1I+CKpmudS7FG59UR8feNgcp/GJwKXAJcA1yZmf/HyDTWsLyeorn6+fL4u4HfU3wKelP/L/27oFgz9DqKaRK7sOn/ESgaoT8ZYT5J6gSdVBsBTgK2ioiP9vMcmXkDxZrMPy7z/QTYJSL+mWJkydeBD1OssfWM8mVXAJ+jeIN5O0XdGq43A/Oi2LDgBorNdqBYr+yFZT17yiDn9fUm4F/Ln+FV5c/dYG2RpGqsYdUMVnMGNcT7s8dTrJm5fgSZpLaJgUc5S90vIrbJzHvLr08FdsnMyn+x93O9X1NsOLNykHPOBb5XbkozZho/a/kJ2RnArZn5ybHM0I0i4nHAyZn5qrqzSNJYqKM2jlZEHA68NTOf367v0UrWFklqD2tY60XEpynWZ/5Z3VmkZo6w1Hj3vHK0xPUUO3X+xyivdwrFVO5O9Lryk8aFFNO+v1hvnK4xg2K0piT1il6qjXWxtkhSe1jDWu96m5XqRI6wlGoUEc9h87VNAG7PzBfWkacuEXEa8NI+h7+dmR+qI48kqTUiYkeKtb76emZmLm/B9X8PbNnn8Ksy87rRXluS1NusYVK9bFhKkiRJkiRJ6hhOCZckSZIkSZLUMWxYSpIkSZIkSeoYNiwlSZIkSZIkdQwblpIkSZIkSZI6hg1LSZIkSZIkSR3DhqUkSZIkSZKkjmHDUpIkSZIkSVLHsGEpSZIkSZIkqWNMqjvAcM2YMSPnzp1bdwxJ0jh05ZVX/i0zZ7biWhExH5gPMHXq1Mfvu+++rbisJEmbaVXtsm5JksZC1boVmTkWeVpm3rx5uWDBgrpjSJLGoYi4MjPntfq61i5JUru0o3ZZtyRJ7VK1brVtSnhEnBMRSyPi+gGej4j4TEQsiohrI+LgdmWRJEmSJEmS1B3auYblucARgzx/JLB3eZsPfKGNWSRJkiRJkiR1gbY1LDPzl8A9g5xyNPDVLFwGTI+IXdqVR+PMn/4Ec+aAU1UkSZIkSZLGlTp3CZ8F3NH0eEl57GEiYn5ELIiIBcuWLRuTcOpwZ50FS5bAt75VdxJJkiRJkiS1UJ0Ny8oy86zMnJeZ82bObMnmrep2DzxQdwJJkiRJkiS1QZ0NyzuBOU2PZ5fHpKH97W/F/Z3+LyNJkiRJkjSe1NmwvAj453K38EOBVZl5V4151E0aSwPYsJQkSZIkSRpXJrXrwhHxDeBwYEZELAHeC0wGyMwzgYuB5wKLgHXAv7Yri8ahpUuL+yVL6s0hSZIkSZKklmpbwzIzjxvi+QROaNf31zjXGGG5ZAlkQkS9eSRJkiRJktQSlRqWEXEU8NTy4aWZ+d32RZIqWLYMJk0qNt9ZvhxmzKg7kSRJkiRJklpgyDUsI+K/gJOAG8rbmyPiP9sdTBrQ2rWwbh087nHFY6eFS5IkSZIkjRtVNt15HvDszDwnM88BjgCe395Y0iAa08FtWEqSJEmSJI07VXcJn9709XZtyCFVZ8NSkiRJkiRp3KqyhuV/AVdFxCVAUKxleWpbU0mDaTQs998fJk60YSlJkiRJkjSODNmwzMxvRMQvgCeUh/49M//a1lTSYJYuLe4f8QjYbjtYubLWOJIkSZIkSWqdARuWEbFvZt4UEQeXhxrD2HaNiF0z8w/tjyf1Y/Xq4n677WCbbeDee+vNI0mSNN7cfz888ACsXz/4bcOGoc9p57kAme25H+q5Ktp9viRJ49RgIyxPBuYDH+/nuQSe0ZZE0lDWri3ut9nGhqWkjhUR8ynqKLvttlvNaSSpHw89BLffDrfcAjffXNw3bn/5S325Jk0qlv2ZNGnw28SJEFG8pl33Qz1XRbvPlyRpHBqwYZmZ88svj8zM+5ufi4gpbU0lDebee2HCBNhySxuWkjpWZp4FnAUwb948h8xIqkcm3HXXwxuSN98Mt91WjGRsmDED9tkH/vEfYa+9YOrUhzcIqzQRR3POhAk27IbL35ckaRyqsunOb4GDKxyTxsbatUWjMsKGpSRJEsCqVQ9vSDa+bsxOAdhqK9h7bzjoIDjmmKJB2bjtsENt8SVJkpoNtoblI4BZwFYR8TiKHcIBtgW2HoNsUv/uvbdoVAJMmwbLl9ebR5IkaSw88EAxKrK/Kdx3373pvAkTYO5ceNSj4KlPLZqRj3pUcT9rVvG8JEnqLXfcAbvtBpdcAocfXneaIQ02wvI5wPHAbOATTcfXAO9sYyZpcGvXFlOUwBGWkiRpfNm4Ee68c/OGZOPrxYuL5xt23rloQj7/+Zs3Jffcs1g6RxoG116WpHHu5z8v7s8+u7sblpn5FeArEfHizLxgDDNJg2seYWnDUpIkdaN77ul/+vatt8J99206b+rUogn5hCfAK1+5afr23nvD9Om1xdf449rLkjTONZaImdId29IMuYZlZl4QEc8DHgNMaTr+gaFeGxFHAJ8GJgL/nZkf7vP8bsBXgOnlOadm5sXD+QHUg+69d/MRlmvW1JtHkiSpikz4+tfhHe8opmU1TJxYjIrcZx941rM2NSUf9SjYZRc3VZEkSaP35z8X913y74ohG5YRcSbFmpVPB/4beAlweYXXTQTOAJ4NLAGuiIiLMvOGptPeBXwrM78QEfsBFwNzh/tDqMesXQs77lh8vc02sG5dscPlxIn15pIkSRrIokXw//1/8NOfFqMlTzppU1Nyjz1g8uS6E0qSpPHsttuK++Z1rztYlV3C/yEzD4iIazPz/RHxceAHFV53CLAoM28DiIjzgKOB5oZlUmziA7Ad8Jfq0dWz7r23WCgWNk0NX7eu2IBHkiSpkzz4IJx+Onzwg8W6kmecAa9/vR+0SpKksbVkSXF/11315qioSsPy/vJ+XUTsCiwHdqnwullA01wXlgBP7HPO+4AfR8SbgKnAs/q7kAtAazNr126+hiUUTUwblpIkqZP86ldFc/LGG+GlL4VPfQp23bXuVJIkqRetXl3cd0nDckKFc74bEdOB04E/AIuBr7fo+x8HnJuZs4HnAv8TEQ/LlJlnZea8zJw3c+bMFn1rda2+a1g2jkmSJHWCe+6B170OnvrUYhbI974H3/qWzUpJklSfRt/k7ruLZfU63KAjLMvm4c8ycyVwQUR8D5iSmasqXPtOYE7T49nlsWavAY4AyMzfRcQUYAawtFp89aSBRlhKkiTVKRO+9jU4+eSiafm2t8F737vpg1ZJkqS6NDYs3rABli+HnXaqN88QBh1hmZkbKTbOaTx+oGKzEuAKYO+I2CMitgCOBS7qc86fgWcCRMSjKXYhX1bx+upF69fDAw84wlKSJHWWW2+FZz8bXvWqYsfvK6+Ej37UZqUkSeoMa9bAXnsVX3fBxjtVpoT/LCJeHDG8fc8zcz1wIvAj4EaK3cAXRsQHIuKo8rRTgNdFxDXAN4DjMzOH833UY9auLe4b//jfeuvift26evJIkqTe9uCD8B//AfvvD1dcUWyq85vfwIEH1p1MkiSp8MAD8NBD8IhHFI+7YNBXlU13Xg+cDKyPiPuBADIztx38ZZCZFwMX9zn2nqavbwAOG1Zi9bZGY9KGpSRJqlvzpjrHHAOf/KTrVEqSpM7TaFA2GpaNwWAdbMgRlpk5LTMnZOYWmblt+XjIZqXUFvfdV9xvtVVx32hYNo5LkiS12z33wGtfu2lTne9/H775TZuVkiSpMzXWrxxPDUupozRGUjYalY6wlCRJYyUT/vd/Yd994dxzi011Fi6E5z637mSSJEkD68KGZZUp4VLn6DvCsnFvw1KSJLXT/ffDi14EP/gBPPGJ8JOfuE6lJEnqDuNxSrjUURxhKalLRMT8iFgQEQuWLVtWdxxJo5EJJ5xQNCs/9Sk31ZEkSd2lMcJy552L+25vWEbExIi4aazCSEPqO8JyypTi3oalpA6TmWdl5rzMnDdz5sy640gajbPOgnPOgXe9C046CSZOrDuRJElSdY2eSeN9Sbc3LDNzA3BzROw2RnmkwfUdYRlRfG3DUpIktcPvfgdvehMccQS87311p5EkSRq+xuCv7baDSZO6omFZZQ3L7YGFEXE58PefKDOPalsqaSB9R1iCDUtJktQef/0rvOQlMGcOfP3rjqyUJEndqbmXMnXqpjUtO1iVhuW7255CqqrvCMvG1zYsJUlSKz30EBxzDKxYAZddBttvX3ciSZKkkWnupUydOj5GWGbmpRGxM/CE8tDlmbm0vbGkAQw0wrJxXJIkqRVOOQV+9atiZOUBB9SdRpIkaeT6jrDsgoblkLuER8QxwOXAS4FjgN9HxEvaHUzqlyMsJUlSu/3P/8BnPwtveQscd1zdaSRJkkanuWHZJT2UKlPCTwOe0BhVGREzgZ8C57czmNSvxh+yxu7gUPyB64I/bJIkqQtcdRXMnw+HHw4f/WjdaSRJkkZv3TrYYotiPe4umaU65AhLYEKfKeDLK75Oar1164oGZcSmY13y6YAkSepwy5fDi14EM2bAN79Z7KIp9YiImB8RCyJiwbJly+qOI0lqpfvu27S03lZbdUXDssq/wn4YET8CvlE+fhlwcfsiSYNo/kPWsPXWcPfd9eSRJEnjw4YN8PKXw1/+UqxdudNOdSeSxlRmngWcBTBv3rysOY4kqZWaeylbb11sKtjhhhwpmZlvoyhcB5S3szLz36tcPCKOiIibI2JRRJw6wDnHRMQNEbEwIr4+nPDqQY0Rls0cYSlJkkbrXe+CH/8YzjgDDjmk7jSSJEmt09xLGUcjLMnMC4ALhnPhiJgInAE8G1gCXBERF2XmDU3n7A28AzgsM1dEhB9la3D33bf5hjtgw1KSJI3OBRfAhz9crF352tfWnUaSJKm1mnspXbIPyIAjLCPi1+X9mohY3XRbExGrK1z7EGBRZt6WmQ8C5wFH9znndcAZmbkCoM9amdLDDTQlvAv+sEmSpA50ww1w/PHwxCfCZz5TdxpJkqTW6zslvJtHWGbmk8v7aSO89izgjqbHS4An9jlnH4CI+A0wEXhfZv6w74UiYj4wH2C33XYbYRyNC/1NCe+S4cySJKnD3HsvvPCFxT/czz8fttyy7kSSJEmt14VTwgddwzIiJkbETW38/pOAvYHDgeOAsyNiet+TMvOszJyXmfNmzpzZxjjqeANNCX/ggWKxfEmSpKo++Um45RY47zyYPbvuNJIkSe1x//2bNyzXrYPs7P3VBm1YZuYG4OaIGMmwxjuBOU2PZ5fHmi0BLsrMhzLzduAWigam1L916x7esGz8obv//rHPI0mSutM998DHPgYveAE8/el1p5EkSWqfvlPCN26Ehx6qN9MQhtwlHNgeWBgRP4uIixq3Cq+7Atg7IvaIiC2AY4G+r/t/FKMriYgZFFPEb6saXj1ooDUswXUsJUlSdR/9KKxZAx/8YN1JJEmS2qu5l9K47/Bp4VV2CX/3SC6cmesj4kTgRxTrU56TmQsj4gPAgsy8qHzuHyPiBmAD8LbMXD6S76ce0V/Dskv+sEmSpA5x113FBjsvfzk89rF1p5EkSWqv+++HKVOKr5sHfW23XX2ZhjBkwzIzL42I3YG9M/OnEbE1RQNySJl5MXBxn2Pvafo6gZPLmzS0/qaEO8JSkiQNx4c+VEyDet/76k4iSZLUfl04wnLIKeER8TrgfOCL5aFZFFO5pbHnlHBJXSIi5kfEgohYsGzZsrrjSGpYvBjOOgte8xrYa6+600iSJLVf3013oON7KFXWsDwBOAxYDZCZtwI7tTOUNCCnhEvqEpl5VmbOy8x5M2fOrDuOpIb3vx8mTIB3j2jVI0mSpO5z330PnxLe4T2UKg3LBzLzwcaDiJgEdPbe5xqfHnoI1q93SrgkSRqZG2+Er34VTjwRZs2qO40kSVL7PfQQbNgw/qaEA5dGxDuBrSLi2cC3ge+2N5bUj8YfJkdYSpKkkXjPe4oPOk89te4kkiRJY+P++4v7Ru+kSwZ9VWlYngosA64DXg9cnJmntTWV1J+BGpZd8odNkiTV6Mor4fzz4eSTYcaMutNIkiSNjUYvpTElvEsGfQ25Szjwpsz8NHB240BEnFQek8ZOoyHZd0p4l/xhkyRJNXrXu2CHHYqGpSRJUq8YxyMs/6WfY8e3OIc0NEdYSpKkkfjVr+CHPyymgm+3Xd1pJEmSxk7fXkqXDPoacIRlRBwHvBzYIyIuanpqW+CedgeTHsY1LCVJ0nBlwjvfCbvsAiecUHcaSZKksdV3SniXDPoabEr4b4G7gBnAx5uOrwGubWcoqV9DTQnv8D9skiSpBj/6Efz61/D5zz/83xCSJEnjXd8p4V0y6GvAhmVm/gn4U0Q8C7gvMzdGxD7AvhQb8Ehja6ARlpMmwRZb2LCUJEmb27ixGF05dy685jV1p5EkSRp7fXspjZGWHd5DqbKG5S+BKRExC/gx8Crg3HaGkvo1UMOycazDPx2QJElj7Dvfgauugve/v/hwU5Ikqdf0nRI+YULxdYf3UKo0LCMz1wEvAj6fmS8FHtPeWFI/BpoS3jjW4Z8OSJKkMZQJ//mfsO++8IpX1J1G6ngRMT8iFkTEgmXLltUdR5LUKo3GZHMvpQt6KJUalhHxJOAVwPfLYxPbF0kagCMsJUlSVb/9bTG68t/+DSb6T1dpKJl5VmbOy8x5M2fOrDuOJKlVGo3J5l5KF/RQqjQs/w14B3BhZi6MiD2BS6pcPCKOiIibI2JRRJw6yHkvjoiMiHmVUqs3Ddaw3HprWLt2bPNIkqTO9ZnPwPTp8MpX1p1EkiSpPl06wnKwXcIByMxLgUsjYpuI2CYzbwPePNTrImIicAbwbGAJcEVEXJSZN/Q5bxpwEvD7kfwA6iFOCZckSVUsWQIXXFCMrpw6te40kiRJ9RmvIywjYv+IuApYCNwQEVdGRJU1LA8BFmXmbZn5IHAecHQ/530Q+Ahw/zByqxf194esYZttHGEpSZIKZ55Z7BB+wgl1J5EkSapXl46wrDIl/IvAyZm5e2buBpwCnF3hdbOAO5oeLymP/V1EHAzMyczvIw1l7VrYcsv+16GaOtWGpSRJgvvvh7POgn/6J9hjj7rTSJIk1eu++yACtthi07GtthoXDcupmfn3NSsz8xfAqOfWRMQE4BMUDdChznXHOhUNyW226f85G5aSJAngm9+EZcvgzUOuYCRJkjT+rVtXNCgjNh2bOnVcNCxvi4h3R8Tc8vYu4LYKr7sTmNP0eHZ5rGEa8FjgFxGxGDgUuKi/jXfcsU5A0ZAcaB0qG5aSOowftkk1yITPfhb22w+e8Yy600iSJNXvvvsevhfINtvAvffWk6eiKg3LVwMzge+Ut5nlsaFcAewdEXtExBbAscBFjSczc1VmzsjMuZk5F7gMOCozFwzzZ1CvGKph2eF/2CT1Fj9sk2rwu9/BlVfCm960+SgCSZKkXtUYYdmsCwZ9VdklfAXw5ojYDtiYmWuqXDgz10fEicCPgInAOZm5MCI+ACzIzIsGv4LUR5URlpm+QZEkqVd99rOw3XbwqlfVnUSSJKkzdOkIyyEblhHxBOAciincRMQq4NWZeeVQr83Mi4GL+xx7zwDnHl4hr3rZUA3LDRvgwQeLjXkkSVJv+ctf4Pzzi7UrB/r3giRJUq/pb4Rlo2HZwYO+qkwJ/xLwxqap2ycAX25rKqk/QzUsG+dIkqTec+aZxYeXJ5xQdxJJkqTOcd99/U8J37gRHnignkwVVGlYbsjMXzUeZOavgfXtiyQN4N57bVhKkqSHe+AB+OIX4fnPhz33rDuNJElS51i3rv8p4dDR08KHnBIOXBoRXwS+ASTwMoqdvQ8GyMw/tDGftIkjLCVJUn++9S1YurSYDi5JkqRN7rsPdthh82PNDcsZM8Y+UwVVGpYHlvfv7XP8cRQNzGe0NJE0kLVrN/2h6suGpSRJvSkTPvMZePSj4ZnPrDuNJElSZ+lvhGUX9FCq7BL+9LEIIg3JEZaSJKmv3/8eFiyAz3++YxeNlyRJqs3atV05JbzKGpZ/FxHfa1cQaVDr1xc7gNuwlCRJzT79adhuO3jVq+pOIkmS1HnuvRemTdv82HhrWAKz2pJCGkqjEWnDUpIkNfzxj8X6lfPnD7xsjCRJUq/KhDVrHt6wbPRQxlHD8qq2pJCGMlTDsvEmxYalJEm942Mfg0mT4C1vqTuJJElS57n/ftiw4eEf7HZBD2VYDcvMfHW7gkiDcoSlJElq9te/wpe/DMcfD7vsUncaSZKkzrNmTXHfhVPCh9x0JyKuo9gNvNkqYAHwH5m5vB3BpM1UbVh28B82SZLUQp/6FDz0ELztbXUnkSRJ6kyNHkkXTgkfsmEJ/ADYAHy9fHwssDXwV+Bc4J/akkxqNlTDsrHjlSMsJUka/1auLHYFf+lLYa+96k4jSZLUmRojLAeaEt54vgNVaVg+KzMPbnp8XUT8ITMPjohXtiuYtJlGI3KgBfUnTCiamR38h02SJLXIF75Q1Px///e6k0jjRkTMB+YD7LbbbjWnkSS1xEBTwidNKo6tWDH2mSqqsoblxIg4pPEgIp4ATCwfrm9LKqmvxjDlgUZYAkyfDqtWjUkcSZJUk/vuK6aDH3EEPO5xdaeRxo3MPCsz52XmvJkzZ9YdR5LUCgM1LAF23BHuuWds8wxDlRGWrwXOiYhtgABWA6+JiKnAf7UznPR3K1cW99OnD3zO9OmbzpMkSePTl78MS5fCqafWnUSSJKmzNQZ/9TdbdYcdYHnnbksz5AjLzLwiM/cHDgIOzMwDymNrM/Nbg702Io6IiJsjYlFEPOxflRFxckTcEBHXRsTPImL3Ef8kGt8aw5S3337gc2xYSuogETE/IhZExIJly5bVHUcaH9avh9NPh0MPhac+te40kiRJnW2wEZY77NDRIyyHbFhGxHYR8QngZ8DPIuLjEbFdhddNBM4AjgT2A46LiP36nHYVMC8zDwDOBz463B9APWLFCpg4sf8/ZA02LCV1EKfWSW3wzW/C4sXwjndARN1pJEmSOtt4blgC5wBrgGPK22rgyxVedwiwKDNvy8wHgfOAo5tPyMxLMnNd+fAyYHbV4OoxK1YUDcnB3pxMn97RC8ZKkqRRyIQPfxj22w+e//y600iSJHW+waaEj4M1LB+ZmS9uevz+iLi6wutmAXc0PV4CPHGQ818D/KC/J9yxTn9vWA5m++0dYSlJ0nj1/e/D9dfDV78KE6p85i5JktTj1qyByZNhyy0f/lxjhOXGjR35b6sqie6LiCc3HkTEYcB9rQwREa8E5gGn9/e80+rEihWDr18Jm3YJ37hxTCJJkqQx9OEPw267wbHH1p1EkiSpOyxfXjQm+7PDDkX/ZPXqsc1UUZURlm8Avtq0buUK4F8qvO5OYE7T49nlsc1ExLOA04CnZeYDFa6rXlS1YblxYzHkedttxySWJEkaA9ddB7/5DXzmM8UoAUmSJA1t6VLYeef+n9txx+L+nnuGntFagyq7hF+TmQcCBwAHZObjgGdUuPYVwN4RsUdEbAEcC1zUfEJEPA74InBUZi4ddnr1jqoNS3BauCRJ483++8OVV8JrXlN3EkmSpO5x992w0079P9cYedmh61hWnqSemaszszFO9OQK568HTgR+BNwIfCszF0bEByLiqPK004FtgG9HxNURcdEAl1Ovs2EpSVJvO/hg2HrrulNIkiR1j8FGWHZ4w7LKlPD+DLJV8yaZeTFwcZ9j72n6+lkj/P7qJZk2LCVJkiRJkoajygjL5cvHLs8wjLRhmS1NIQ1m7VpYv37ohmXjeRuWkiRJkiSpl61dC+vWDTzCcu5c+OEP4aCDxjJVZQM2LCNiDf03JgPYqm2JpL5WrCjuq46wbJwvSZIkSZLUi+6+u7gfaITlVlvBc54zdnmGacCGZWZOG8sg0oCG27B0hKUkSZIkSepljYblQCMsO1zlTXek2lRtWG67bXFvw1KSJEmSJPWypUuL+4FGWHa4ka5hKY2dRsOyMYJyIJMmwXnnwf77tz2SJEmSJElSx3roIXjEI7p2hKUNS3W+Jz8ZfvpTeNSjhj73ZS9rfx5JkiRJkqRO9pKXFLcuZcNSnW/GDHjmM+tOIUmSJEmSpDHgGpaSJEmSJEmSOoYNS0mSJEmSJEkdIzKz7gzDEhFrgJvrzjGEGcDf6g4xBDO2hhlbw4yj1+n5oDsyPiozp7XiQhExH5hfPnwscH0rrttGnf7fp9PzgRlbxYytYcbW6IaMLald1q22MGNrmLE1zNganZ6x0/NBxbrVjQ3LBZk5r+4cgzFja5ixNczYGp2esdPzQW9n7OWfvVU6PR+YsVXM2BpmbI1ezdirP3ermbE1zNgaZmyNTs/Y6fmgekanhEuSJEmSJEnqGDYsJUmSJEmSJHWMbmxYnlV3gArM2BpmbA0ztkanZ+z0fNDbGXv5Z2+VTs8HZmwVM7aGGVujVzP26s/damZsDTO2hhlbo9Mzdno+qJix69awlCRJkiRJkjR+deMIS0mSJEmSJEnjlA1LSZIkSZIkSR2jKxuWEfG+iLgzIq4ub8+tO9NAIuKUiMiImFF3lr4i4oMRcW35O/xxROxad6a+IuL0iLipzHlhREyvO1OziHhpRCyMiI0RMa/uPM0i4oiIuDkiFkXEqXXn6U9EnBMRSyPi+rqz9Cci5kTEJRFxQ/nf+aS6M/UVEVMi4vKIuKbM+P66Mw0kIiZGxFUR8b26s/QnIhZHxHXl34kLWnxt61YLWLdaw9o1cp1et8Da1Uq9XLfK61u7WsDa1RqdWrs6vW5B59cu61Zrjafa1ZUNy9InM/Og8nZx3WH6ExFzgH8E/lx3lgGcnpkHZOZBwPeA99Scpz8/AR6bmQcAtwDvqDlPX9cDLwJ+WXeQZhExETgDOBLYDzguIvarN1W/zgWOqDvEINYDp2TmfsChwAkd+Ht8AHhGZh4IHAQcERGH1htpQCcBN9YdYghPL+tKO/4hbN0aPetWa1i7Ru5cOrtugbWrlXq9boG1qxWsXa3RcbWrS+oWdH7tsm611ripXd3csOwGnwTeDnTkzkaZubrp4VQ6MGdm/jgz15cPLwNm15mnr8y8MTNvrjtHPw4BFmXmbZn5IHAecHTNmR4mM38J3FN3joFk5l2Z+Yfy6zUUf/HPqjfV5rJwb/lwcnnruD/LETEbeB7w33Vn0aCsW6PU6XULrF2j0el1C6xdrWLd6irWrlGydo1Yx9ct6PzaZd1qnfFWu7q5YXliOWT9nIjYvu4wfUXE0cCdmXlN3VkGExEfiog7gFfQmZ/2NXs18IO6Q3SJWcAdTY+X0GF/6XebiJgLPA74fc1RHqYc9n81sBT4SWZ2XEbgUxRvJjbWnGMwCfw4Iq6MiPltuL51qwWsW+OatavFrF2j8imsW2Dtaglr17hl3Wox69aofYpxVLsmjVGgYYuInwKP6Oep04AvAB+k+EE/CHyc4i/WMTVExndSTE2o1WAZM/P/MvM04LSIeAdwIvDeMQ3I0BnLc06jGCr+tbHMVn7vIfNpfIuIbYALgH/r8yl5R8jMDcBB5XpDF0bEYzOzY9aoiYjnA0sz88qIOLzmOIN5cmbeGRE7AT+JiJvKT6QrsW61hnWrNaxdsnaNXK/ULbB2tYq1qzWsXb3NujU647F2dWzDMjOfVeW8iDibYi2QMTdQxojYH9gDuCYioBhS/4eIOCQz/zqGESv/HimK0sXUUDyHyhgRxwPPB56ZmWM+7HoYv8NOcicwp+nx7PKYhikiJlMUzq9l5nfqzjOYzFwZEZdQrFHTMcUTOAw4KorF+qcA20bE/2bmK2vOtZnMvLO8XxoRF1JM86n8xs+61RrWrdawdvU2a9eo9UTdKl9r7WoBa1drdGHtsm61iHWrJcZd7erKKeERsUvTwxfSWf+TkJnXZeZOmTk3M+dSDA0/eKwL51AiYu+mh0cDN9WVZSARcQTFkOajMnNd3Xm6yBXA3hGxR0RsARwLXFRzpq4Txb9+vwTcmJmfqDtPfyJiZvkpHxGxFfBsOuzPcma+IzNnl38fHgv8vNMKZ0RMjYhpja8pRmu0rLZYt1rDujXuWbtawNo1etatv38Pa1cLWLvGNetWC1i3WmM81q6OHWE5hI9GxEEU0xMWA6+vNU33+nBEPIpifYM/AW+oOU9/PgdsSTFUGOCyzOyYnBHxQuCzwEzg+xFxdWY+p+ZYZOb6iDgR+BEwETgnMxfWHOthIuIbwOHAjIhYArw3M79Ub6rNHAa8CriuXK8E4J3ZWbtk7gJ8JYpdCicA38rMWkZAdLmdKaZ2QFEbv56ZP2zh9a1brWHdagFr18h1Qd0Ca1evaHfdAmtXq1i7WqATa1c31C3oitpl3eodw6pdUdNob0mSJEmSJEl6mK6cEi5JkiRJkiRpfLJhKUmSJEmSJKlj2LCUJEmSJEmS1DFsWEqSJEmSJEnqGDYsJUmSJEmSJHUMG5aSJEmSJEmSOoYNS6mPiLi3BdfYMSIuiYh7I+JzfZ57fERcFxGLIuIzERFNz30qIp5afr04ImZU/H6/HW3mQa59fN+focJr/i0itm5hhp9GxPatup4kjVfWsIdde9g1bJTfb/+IOHesvp8k9apurHeD5GjFNWZGxA9Hcw2p09iwlNrjfuDdwFv7ee4LwOuAvcvbEVAUTODQzPzlcL9ZZv7DyKO2xb8Bw2pYRsTEQZ7+H+CNowkkSaqs12vYiGXmdcDsiNit7iySpCGNab1rp8xcBtwVEYfVnUVqFRuWUgURcVBEXBYR10bEhY3RfhHxhPLY1RFxekRcD5CZazPz1xRFsPk6uwDbZuZlmZnAV4EXlE+/GHjYp2IRsVVE/CAiXjdIvnvL+8Mj4tKI+L+IuC0iPhwRr4iIy8tPCB9ZnnduRJwZEQsi4paIeP4Qv4JdI+KHEXFrRHy06fv+Y0T8LiL+EBHfjohtIuLNwK7AJRFxyUDnlccXR8RHIuIPwEsj4rgy5/UR8ZGm738RcNwQGSVJ/bCGDVjD+q055Uib0yNiYRQj/A+JiF+UmY4qz5kSEV8uX39VRDy96ft9Fzh2iEySpBbr5HoXEXMj4qaI+FpE3BgR58fmM9LeVL5Xui4i9i1fs0NE/L8y+2URcUB5/Gnlz3J1WYOmldf4f8ArRvTLkzqQDUupmq8C/56ZBwDXAe8tj38ZeH1mHgRsqHCdWcCSpsdLymMAhwFX9jl/G4o3Pt/IzLMrZj0QeAPwaOBVwD6ZeQjw38Cbms6bCxwCPA84MyKmDHLNg4CXAfsDL4uIOVFMW3gX8KzMPBhYAJycmZ8B/gI8PTOfPtB5TddeXh7/JfAR4Bnl93tCRLwAIDNXAFtG8YmmJGl4rGEPr2G7MkDNAaYCP8/MxwBrgP8Ang28EPhAec4JQGbm/hQfqH2lKcMC4CkVf15JUut0er17FPD5zHw0sJrNZ5D9rXxP9AU2jfh8P3BV+fO8s/z5KJ8/ofx5ngLcVx63/mhcsWEpDSEitgOmZ+al5aGvAE+NiOnAtMz8XXn866P8VrsAy/oc+z/gy5n51X7OH8gVmXlXZj4A/BH4cXn8Ooo3eA3fysyNmXkrcBuw7yDX/FlmrsrM+4EbgN2BQ4H9gN9ExNXAv5TH+xrqvG+W908AfpGZyzJzPfA14KlN5y2lGLkpSarIGgb0X8MGqzkPsmn0zHXApZn5UJ8MTwb+FyAzbwL+BOxTPme9kqQx1iX17o7M/E359f9S1JKG75T3V7J5rfkfgMz8ObBjRGwL/Ab4RDmzbXpZx8D6o3HGhqU0tu4EZjc9nl0eg+KTsb4jRH4DHBGxaZHnCh5o+npj0+ONwKSm57LP6/o+HuiaG8rrBPCTzDyovO2Xma/p57VDnbd2sB+myRQ2fXooSRp746mGDeahcgrgZhkys2+GgVivJKm7taveDVa7GrVqyDqVmR8GXgtsRTEopPGhnfVH44oNS2kImbkKWBERjeH1r6IYbbESWBMRTyyPD7leVWbeBayOiEPLgvbPFJ/IAdwI7NXnJe8BVgBnjO6n6NdLI2JCuSbYnsDNw3z9ZcBhEbEXQERMjYjG6JI1wLQK5zW7HHhaRMyIYgOe44BLy9cE8Ahg8TAzSlJPs4YNaMCaU9GvKNcJK2vabk0Z9gGuH2YeSdIodEm92y0inlR+/XLg10Oc31xrDqeYNr46Ih6Zmddl5keAK9g0y8D6o3HFhqX0cFtHxJKm28kU05hPj4hrKda6aqxh9Rrg7HKq81RgVeMiEbEY+ARwfHmd/cqn3kixFtciiuluPyiPfx84vJ88JwFbNW8U0CJ/pnjD9gPgDeVUucrKneiOB75R/l5+x6ZieRbww4i4ZIjzmq93F3AqcAlwDXBlZjb+YfB44LKm6Q6SpP5ZwyoYouZU8XlgQkRcR7G0yfHlNHaAp1P8PiRJ7dON9e5m4ISIuBHYnmK9ysG8D3h8+fN8uPz5AP4tig3jrgUeaspm/dG4EptmvEgarojYJjMbu5ueCuySmSeN4nq/Bp5ffhLYNhFxLvC9zDy/nd+nVSLi08BFmfmzurNI0nhhDWu9iNiSYqTmk/2QTZI6QyfUu4iYS1G7HjvS71vhe/wSOLrcsFTqelXW4ZE0sOdFxDso/iz9iWIk4WicQjGtbOUorzPeXG+zUpJazhrWersBp9qslKSOMu7rXUTMBD5hs1LjiSMspS4RETsC/TXtnpmZy1tw/ecAH+lz+PbMfOFory1J6m3WMElSL2h3vZN6iQ1LSZIkSZIkSR3DTXckSZIkSZIkdQwblpIkSZIkSZI6hg1LSZIkSZIkSR3DhqUkSZIkSZKkjmHDUpIkSZIkSVLHsGEpSZIkSZIkqWPYsJQkSZIkSZLUMWxYSpIkSZIkSeoYk+oOMFwzZszIuXPn1h1DkjQOXXnllX/LzJmtuFZEzAfmA0ydOvXx++67bysuK0nSZlpVu6xbkqSxULVuRWaORZ6WmTdvXi5YsKDuGJKkcSgirszMea2+rrVLktQu7ahd1i1JUrtUrVttmxIeEedExNKIuH6A5yMiPhMRiyLi2og4uF1ZJEmSJEmSJHWHdq5heS5wxCDPHwnsXd7mA19oYxZJkiRJkiRJXaBtDcvM/CVwzyCnHA18NQuXAdMjYpd25ZHUYpnwuc/BsmV1J5EkSZIkSeNInbuEzwLuaHq8pDz2MBExPyIWRMSCZTZHpM5w/fXwpjfBV75SdxJJkiRJkjSO1NmwrCwzz8rMeZk5b+bMlmzeKmm0rruuuL/55npzSJIkSZKkcaXOhuWdwJymx7PLY5K6wbXXFvc33VRvDkmSJEmSNK7U2bC8CPjncrfwQ4FVmXlXjXkkDYcjLCVJkiRJUhtMateFI+IbwOHAjIhYArwXmAyQmWcCFwPPBRYB64B/bVcWSW1w7bUQUWy6s3w57Lhj3YkkSZIkSdI40LaGZWYeN8TzCZzQru8vqY1WrIAlS+ApT4Ff/aoYZfkP/1B3KkmSJEmSNA5UmhIeEUdFxMfK2z+1O5SkDteYDn7MMcW908IlSZIkSVKLDNmwjIj/Ak4Cbihvb46I/2x3MEkdrNGwPOoo2GILN96RJEmSJEktU2VK+POAgzJzI0BEfAW4CnhnO4NJ6mB/+hNsuSXMmQN77eUIS0mSJEmS1DJVdwmf3vT1dm3IIamb3HNPsclOBOy7ryMsJUmSJElSy1QZYflfwFURcQkQwFOBU9uaSlJnu+ce2GGH4utHPQouuggeeggmT643lyRJkiRJ6npDNiwz8xsR8QvgCeWhf8/Mv7Y1laTO1tyw3HtvWL8e/vxneOQj680lSZIkSZK63oBTwiNi3/L+YGAXYEl527U8JqlXNTcsZ87cdEySpPFg8WK4//66U0iSetG6dcVgEKnHDbaG5cnl/cf7uX2szbkkdbLGGpawqXFpw1LaTETMj4gFEbFg2bJldceRVNX998NjHwuf+ETdSaQxZd2SOsSHPgQHHggbNtSdRKrVgFPCM3N++eWRmbnZR8wRMaWtqSR1tuYRljYspX5l5lnAWQDz5s3LmuNIqurGG2HtWrjiirqTSGPKuiV1iAULYOVK+OMfYZ996k4j1abKLuG/rXhMUi+4777iZsNSkjQeXXfd5veSJI2l668v7hcurDeHVLMBR1hGxCOAWcBWEfE4ih3CAbYFth6DbJI6UaMx2WhUbr/95sclSepmjUblbbcVIy2nTq03jySpd6xcCX/5S/H19dfDC19YaxypToPtEv4c4HhgNtC8iM8a4J1tzCSpk/VtWE6eDNOm2bCUJI0PjYZlZjE9fN68evNIknpH86jKxkhLqUcNtoblV4CvRMSLM/OCMcwkqZP1bVg2vrZhKUkaD66/Hg49FC67rGhe2rCUJI2VRpPygAOcEq6eN+Qalpl5QUQ8LyLeHhHvadyqXDwijoiImyNiUUSc2s/zu0XEJRFxVURcGxHPHckPIWkM2bCUJI1XK1bAnXfC0UfDlCmObpEkja2FC2GbbeC5z4Wbb4YHH6w7kVSbIRuWEXEm8DLgTRTrWL4U2L3C6yYCZwBHAvsBx0XEfn1Oexfwrcx8HHAs8PlhpZc09mxYSpLGq8Z08AMPhP32s2EpSRpbCxcW9Wf//WH9erjllroTSbWpskv4P2TmPwMrMvP9wJOAfSq87hBgUWbelpkPAucBR/c5Jyk28QHYDvhLtdiSatNoTDY22wEblpKk8eHmm4v7xptFdwqXJI2lG2+ExzymuIHTwtXTqjQs7y/v10XErsBDwC4VXjcLuKPp8ZLyWLP3Aa+MiCXAxRSjOCV1slWrYOLEYqpCgw1LSdJ48Le/Ffc77VQ0Le+6q6h7kiS128aNcPfdMGsW7LsvRBQNTKlHVWlYfjcipgOnA38AFgNfb9H3Pw44NzNnA88F/iciHpYpIuZHxIKIWLBs2bIWfWtJI7J6dbEreMSmY42GZWZ9uSRJGq3ly2GrrYrbLuXn80uX1ptJktQb1qwpmpY77ABbblncW4PUwwZtWJbNw59l5spyp/DdgX0zs8qmO3cCc5oezy6PNXsN8C2AzPwdMAWY0fdCmXlWZs7LzHkzZ86s8K0ltc3q1bDttpsf22GHYo2Ve++tJ5MkSa1wzz2w447F141/czZGXUqS1E599wqYMcMapJ42aMMyMzdSbJzTePxAZladF3MFsHdE7BERW1BsqnNRn3P+DDwTICIeTdGwdAil1MkGaliC08IlSd1t+fLN3yiCbxYlSWOjb8Ny5kxwhql6WJUp4T+LiBdHNM//HFpmrgdOBH4E3EixG/jCiPhARBxVnnYK8LqIuAb4BnB8pnNKpY7WX8OysQHPihVjn0eSpFZpHmHZaFj6ZlGSNBYcYSltZlKFc14PnAysj4j7gQAyM7cd/GWQmRdTbKbTfOw9TV/fABw2rMSS6rV69aY3cw3bbVfcuzGBJKmbLV9ebLYDTgmXJI2t/hqWl11WXx6pZkM2LDNz2lgEkdQl1qyBPfbY/Nj06cX9ypVjnUaSpNZpHmG59dYwZYoNS0nS2BhohGXm5hueSj2iypRwSdqkvynhjrCUJHW7zM3XsIwo3iw6JVySNBYaDcvGclszZhQbm65eXV8mqUY2LCUNz+rVMK3PwOtGw9IRlpKkbnXvvcUbw+ZlT2bOdISlJGls3HMPTJ0KW2xRPHbzN/U4G5aSqtuwoXhD5whLSdJ4s3x5cd8YYQmOsJQkjZ177tm8BrmWsnrcoA3LiJgYETeNVRhJHe7ee4v7vg3LyZOLtb5sWEp/FxHzI2JBRCxYZsND6nyNqXjNIyzdoVU9xLol1axvw9IRlupxgzYsM3MDcHNE7DZGeSR1ssb6KX0bllCMsnRKuPR3mXlWZs7LzHkzG5+QS+pcK1YU9421w8Ap4eop1i2pZvfcs3kNsmGpHjfkLuHA9sDCiLgcWNs4mJlHtS2VpM40WMNy+nRHWEqSulejhjWWOYHizeKqVfDgg5vWFJMkqR1Wr4Y99tj0uNGwdMSzelSVhuW7255CUncYaoSlDUtJUrfqr8Y1puatXAk77TTmkSRJPWT16s0/NJs2DSZO3DQDQOoxQ266k5mXAjcB08rbjeUxSb3GKeGSpPGqvxGWjal5jfUtJUlql1WrNn+fFVHUIRuW6lFDNiwj4hjgcuClwDHA7yPiJe0OJqkDNRqW06Y9/DmnhEuSulmjhvU3wtI3i5Kkdsos3mv1HRiy/fZ+aKaeVWVK+GnAEzJzKUBEzAR+CpzfzmCSOpAjLCVJ49Xq1bD11jCp6Z/HjRGWNiwlSe10332wYUP/DUtrkHrUkCMsgQmNZmVpecXXSRpv1qwp7vsbYekalpKkbrZq1ebTwcEp4ZKksTHQwJAddrAGqWdVGWH5w4j4EfCN8vHLgIvbF0lSxxqsYTl9OjzwANx/P0yZMqaxJEkatf4alk4JlySNhf7WUYbig7Nbbx37PFIHGLJhmZlvi4gXA4eVh87KzAvbG0tSR1qzBrbcEiZPfvhzjeK6apUNS0lS9+lv7bDp04t7R7dIktppoBGWTglXD6s0tTszL8jMk8tb5WZlRBwRETdHxKKIOHWAc46JiBsiYmFEfL3qtSXVYM2a/kdXwuYNS0mSuk1/IywnTSrePPpmUZLUToNNCV+xAjZuHPtMUs0GHGEZEb/OzCdHxBogm58CMjP72XVjs9dPBM4Ang0sAa6IiIsy84amc/YG3gEclpkrImKnUfwsktptsIZlYxSKG+9IkrrRqlUwe/bDj7tDqySp3QYbYdnYQbzxfkvqEQM2LDPzyeX9AN2JIR0CLMrM2wAi4jzgaOCGpnNeB5yRmSvK77X0YVeR1DmqjLC0YSlJ6kb9TQmHTaNbJElql8Ystf4allDUIRuW6jGDTgmPiIkRcdMIrz0LuKPp8ZLyWLN9gH0i4jcRcVlEHDFAjvkRsSAiFixbtmyEcSSNWpURlk4JlyR1o/6mhIMjLCVJ7dcYYTnQ5m/WIfWgQRuWmbkBuDkidmvT958E7A0cDhwHnB0R0/vJcVZmzsvMeTNnzmxTFElDsmEpSRqPNmyAe+/tv2HpCEtJUrs1GpZ932s1j7CUesyQu4QD2wMLI+JyYG3jYGYeNcTr7gTmND2eXR5rtgT4fWY+BNweEbdQNDCvqJBL0lhbswb22KP/51zDUpLUrdasKe77mxLuCEtJUrutXg1TpsAWW2x+vNGwtA6pB1VpWL57hNe+Atg7IvagaFQeC7y8zzn/j2Jk5ZcjYgbFFPHbRvj9JLXbYCMst9kGJkywYSlJ6j4DTcWDTSMsMyFibHNJknrD6tX9v89qTAl3hKV60JANy8y8NCJ2B/bOzJ9GxNbAxAqvWx8RJwI/Ks8/JzMXRsQHgAWZeVH53D9GxA3ABuBtmbl8ND+QpDYarGEZUbzRs2EpSeo2A+3OCsXolgcfhHXrYOrUsc0lSeoNA2385ghL9bAhG5YR8TpgPrAD8EiKjXPOBJ451Gsz82Lg4j7H3tP0dQInlzdJnSyzWN9roIYlFNPCXcNSAooN4yjqJ7vt1q6loCW1xGANy+YND2xYahyzbkk1WrOm/xq01Vaw5ZaOsFRPGnTTndIJwGHAaoDMvBXYqZ2hJHWgtWuLpuVQDUtHWEqAG8ZJXWWwhuWOOxb3jm7ROGfdkmo00AjLiOKDM2uQelCVhuUDmflg40FETAKyfZEkdaTGhgQ2LCVJ481Au7PC5iMsJUlqh4HWsAQblupZVRqWl0bEO4GtIuLZwLeB77Y3lqSOU6Vh6RqWkqRuVHVKuCRJ7TDQlHCwYameVaVheSqwDLgOeD1wcWae1tZUkjqPIywlSeOVDUtJUp0GmhIONizVs6o0LN+UmWdn5ksz8yWZeXZEnNT2ZJI6S9WGpZvuSJK6TaNhuc02D3/OhqUkqd3WrHFKuNRHlYblv/Rz7PgW55DU6ao2LNesgfXrxySSJEktsXp10aycOPHhzzV2aPXNoiSpHR58EO6/3xGWUh+TBnoiIo4DXg7sEREXNT21LeCfFqnXVG1YQvHGrzEiRZKkTjfYVDx3aJUktVPjfdZgDcv77ituW201drmkmg3YsAR+C9wFzAA+3nR8DXBtO0NJ6kDDaViuXGnDUpLUPQZrWEJR05YvH7s8kqTeMdT7rMb7qhUrbFiqpwzYsMzMPwF/iohnAfdl5saI2AfYl2IDHkm9pOou4eA6lpKk7lKlYekIS0lSOwy28RtsvpbyrruOTSapA1RZw/KXwJSImAX8GHgVcG47Q0nqQKtXF9Pi+tuQoKF5hKUkSd1iqIbljjvasJQktUeVKeFgHVLPqdKwjMxcB7wI+HxmvhR4THtjSeo4q1YVoysnDPLXhg1LSVI3coSlJKkujRGWQ00Jtw6px1RqWEbEk4BXAN8vj/WzhaKkcW2oN3Ngw1KS1J1sWEqS6jKcKeFSD6nSsPw34B3AhZm5MCL2BC5paypJnWf16k1rVA6k0bB0DUtJUjep0rBs7NAqSVIrOSVc6teQDcvMvDQzjwLOiIhtMvO2zHxzlYtHxBERcXNELIqIUwc578URkRExbxjZJY2lVauGHmHZeH7FivbnkSSpFTZuLBqWg20q17xDqyRJrTTUlPBp02DiRGuQes6QDcuI2D8irgIWAjdExJURMeQalhExETgDOBLYDzguIvbr57xpwEnA74cbXtIYqjIlfMIE2H57P/2TJHWPNWsgs6hfA2k0LJcvH5tMkqTesWpVsbnpQA3LiKIOWYPUY6pMCf8icHJm7p6ZuwGnAGdXeN0hwKJyROaDwHnA0f2c90HgI8D9FTNLqkOVKeFQ7KT6t7+1P48kSa3QWMaksaxJf2bOLO6XLWt7HElSj1m5shgYMtjmpjNnWoPUc6o0LKdm5t/XrMzMXwBTK7xuFnBH0+Ml5bG/i4iDgTmZ+X0kdbYqU8IBZszw0z9JUvdobBQ3WMNyp52K+6VL251GktRrVq0aemDITjtZg9RzqjQsb4uId0fE3PL2LuC20X7jiJgAfIJixOZQ586PiAURsWCZnypI9agyJRyKhqUjLCVrl9QtqjQsd965uL/77nankWpj3ZJqsnLl4DUIijpkDVKPqdKwfDUwE/hOeZtZHhvKncCcpsezy2MN04DHAr+IiMXAocBF/W28k5lnZea8zJw3szElR9LYWb8e1q1zSrg0DNYuqUtUaVhuv32x4YGjWzSOWbekmjjCUurXpKFOyMwVwJsjYjtgY2auqXjtK4C9I2IPikblscDLm667CpjReBwRvwDempkLqseXNCYaO9c5JVySNN40GpaDvVmcMKF4s+joFklSq61cCXPmDH7OzjsXjc3774cpU8YkllS3KruEPyEirgOuAa6LiGsi4vFDvS4z1wMnAj8CbgS+lZkLI+IDEXHUaINLGkPDaVjuuGMxGvO++9qbSZKkVqgywhIc3SJJao+qIyzBjXfUU4YcYQl8CXhjZv4KICKeDHwZOGCoF2bmxcDFfY69Z4BzD6+QRVIdGg3LKlPCZ5QDp5cvh9mz25dJkqRWaOwSPlSNc/0wSVI7VGlYNq+lPNRoTGmcqLKG5YZGsxIgM38NrG9fJEkdp/FmruqUcHAdS0lSd1i5EqZOhcmTBz9v550dYSlJaq3M4r1WlVH+YB1ST6kywvLSiPgi8A0ggZdRbJRzMEBm/qGN+SR1guFOCQfXsZQkdYcqu7PCpjUsMyGi3akkSb1g7VrYsGF4IyylHlGlYXlgef/ePscfR9HAfEZLE0nqPCOZEu4IS0lSN6jasNx552J95rVrYZtt2p1KktQLhrOOMjjCUj2lyi7hTx+LIJI62HCmhDdGWNqwlCR1g+GMsIRidIsNS0lSK1RdR3nq1OLmCEv1kCprWP5dRHyvXUEkdbDhTAnfYYfi3inhkqRuUGXtMNg0Hc/RLZKkVmmMsKwyk22nnaxB6inDalgCs9qSQlJnW70aJkwoPtUbyuTJRcF1hKUkqRusXFn9jSI4ukWS1DqNEZZVPzizBqmHDLdheVVbUkjqbCtXFqMrq24yMGOGIywlSd1hOGtYgqNbJEmt4whLaUDDalhm5qvbFURSB1u+fNNmOlXMmOEIS0lS59u4sXizuP32Q587c2Zx7+gWSVKrVF3DEhxhqZ4z5KY7EXEdxW7gzVYBC4D/yEyHUUnj3d/+tmkznSp23BH++tf25ZEkqRXuuQc2bNg03XswW2xRNDZ9syhJapVly4r7Ku+1dtqpOH/DBpg4sb25pA5QZYTlD4DvA68ob9+laFb+FTi3bckkdY6RjLB0SrgkqdM1ptZVaVgCzJkDf/5z+/JIknrL0qXFh2FbbDH0uXPmFDMD/vKX9ueSOsCQIyyBZ2XmwU2Pr4uIP2TmwRHxynYFk9RB/vY32H//6ufvuKNTwiVJna8xWrKxPuVQ9toLbryxfXkkSb1l2bLqH5rttVdxv2hR0byUxrkqIywnRsQhjQcR8QSgMf54fVtSSeoswx1hOXMmrF0L69a1L5MkSaM13BGWe+0Ff/xjMR1PkqTRWrp0ZA1LqQdUaVi+FvhSRNweEYuBLwGvjYipwH+1M5ykDnDffUXjcThrWDY+8XPanCSpkw13hOUjHwkPPgh33tm+TJKk3jGchuXs2TB5cvHBmdQDhmxYZuYVmbk/cBBwYGYeUB5bm5nfantCSfVqrEU5nBGWu+9e3P/pT63PI0lSqyxdChMmwA47VDu/MbrFN4uSpFYYTsNy4kTYc09HWKpnDNmwjIjtIuITwM+An0XExyNiuyoXj4gjIuLmiFgUEaf28/zJEXFDRFwbET+LiN2H/yNIaqtGw3I4IywbDUtHWKqHRcT8iFgQEQuWNXaAlNRZ7r67WMZkQpVJRzgdT+OadUsaY+vXF++1qjYsoahD1iD1iCr/OjsHWAMcU95WA18e6kURMRE4AzgS2A84LiL263PaVcC8zDwAOB/4aPXoksZEY/Oc4Yyw3HXX4hNAR1iqh2XmWZk5LzPnzZw5s+44kvqzdGn16eAAs2YVO7n6ZlHjkHVLGmPLl0Pm8BqWj3xkMco/s325pA5RpWH5yMx8b2beVt7eD+xZ4XWHAIvK1zwInAcc3XxCZl6SmY1dOS4DZg8nvKQxMJIRlpMmFWus2LCUJHWy4UzFg03T8ZwSLkkareFu/AbFCMt77930Wmkcq9KwvC8intx4EBGHAfdVeN0s4I6mx0vKYwN5DfCD/p5weoJUo5GMsATYbTcblpKkznb33cMbYQlOx5MktcZIG5ZgHVJPqNKwfANwRkQsLncJ/xzw+laGiIhXAvOA0/t73ukJUo0aIyyrbkjQsPvuNiwlSZ1tuCMsoZiOt2iR0/EkSaMzkoblIx9Z3DvSXz2gyi7h12TmgcABwAGZ+TjgGRWufScwp+nx7PLYZiLiWcBpwFGZ+UCl1JLGzt/+BttuW6zZNRy77w533lksJi1JUqdZu7a4Dbdhuddexevuvrs9uSRJvWEkDcu5c4uN4hxhqR5QcUtEyMzVmbm6fHhyhZdcAewdEXtExBbAscBFzSdExOOAL1I0K12EQepEy5cPb/3Kht13hw0b4C9/aX0mSZJGq/FGcSRTwsHRLZKk0Vm6tFj7f/r06q/ZYovifZY1SD1g0ghfF0OdkJnrI+JE4EfAROCczFwYER8AFmTmRRRTwLcBvh0RAH/OzKNGmElSO/ztb8NfvxKKQgrFtPDddmttJkmSRmvWLFi4cPgNy8Z0vEWL4LDDWp9LktQbjjgCZs4sRkwOR2NpEmmcG2nDstKiPZl5MXBxn2Pvafr6WSP8/pLGyvLlo29YPuUprc0kSdJobbEF7Lff8F83dy5873swb17LI0mSeshTnjKy90kf+hBMnNj6PFKHGbBhGRFr6L8xGcBWbUskqbMsXw777jv8180pl7B14x1J0ngyeTI873l1p5Ak9apDDqk7gTQmBmxYZua0sQwiqUPdcgs8MIL9sLbeupjiYMNSkiRJkiQNw0inhEvqFZMmFbeR2H13+POfW5tHkiRJkiSNa8Nc3VWShmH33R1hKUmSJEmShsURlpLa59nPHv7uq5IkSZIkqafZsJTUPq9/fd0JJEmSJElSl3FKuCRJkiRJkqSOYcNSkiRJkiRJUsewYSlJkiRJkiSpY0Rm1p1hWCJiDXBz3TmGMAP4W90hhmDG1jBja5hx9Do9H3RHxkdl5rRWXCgi5gPzy4ePBa5vxXXbqNP/+3R6PjBjq5ixNczYGt2QsSW1y7rVFmZsDTO2hhlbo9Mzdno+qFi3urFhuSAz59WdYzBmbA0ztoYZW6PTM3Z6PujtjL38s7dKp+cDM7aKGVvDjK3Rqxl79eduNTO2hhlbw4yt0ekZOz0fVM/olHBJkiRJkiRJHcOGpSRJkiRJkqSO0Y0Ny7PqDlCBGVvDjK1hxtbo9Iydng96O2Mv/+yt0un5wIytYsbWMGNr9GrGXv25W82MrWHG1jBja3R6xk7PBxUzdt0alpIkSZIkSZLGr24cYSlJkiRJkiRpnLJhKUmSJEmSJKljdGXDMiLeFxF3RsTV5e25dWcaSEScEhEZETPqztJXRHwwIq4tf4c/johd687UV0ScHhE3lTkvjIjpdWdqFhEvjYiFEbExIubVnadZRBwRETdHxKKIOLXuPP2JiHMiYmlEXF93lv5ExJyIuCQibij/O59Ud6a+ImJKRFweEdeUGd9fd6aBRMTEiLgqIr5Xd5b+RMTiiLiu/DtxQYuvbd1qAetWa1i7Rq7T6xZYu1qpl+tWeX1rVwtYu1qjU2tXp9ct6PzaZd1qrfFUu7qyYVn6ZGYeVN4urjtMfyJiDvCPwJ/rzjKA0zPzgMw8CPge8J6a8/TnJ8BjM/MA4BbgHTXn6et64EXAL+sO0iwiJgJnAEcC+wHHRcR+9abq17nAEXWHGMR64JTM3A84FDihA3+PDwDPyMwDgYOAIyLi0HojDegk4Ma6Qwzh6WVdacc/hK1bo2fdag1r18idS2fXLbB2tVKv1y2wdrWCtas1Oq52dUndgs6vXdat1ho3taubG5bd4JPA24GO3NkoM1c3PZxKB+bMzB9n5vry4WXA7Drz9JWZN2bmzXXn6MchwKLMvC0zHwTOA46uOdPDZOYvgXvqzjGQzLwrM/9Qfr2G4i/+WfWm2lwW7i0fTi5vHfdnOSJmA88D/rvuLBqUdWuUOr1ugbVrNDq9boG1q1WsW13F2jVK1q4R6/i6BZ1fu6xbrTPealc3NyxPLIesnxMR29cdpq+IOBq4MzOvqTvLYCLiQxFxB/AKOvPTvmavBn5Qd4guMQu4o+nxEjrsL/1uExFzgccBv685ysOUw/6vBpYCP8nMjssIfIrizcTGmnMMJoEfR8SVETG/Dde3brWAdWtcs3a1mLVrVD6FdQusXS1h7Rq3rFstZt0atU8xjmrXpDEKNGwR8VPgEf08dRrwBeCDFD/oB4GPU/zFOqaGyPhOiqkJtRosY2b+X2aeBpwWEe8ATgTeO6YBGTpjec5pFEPFvzaW2crvPWQ+jW8RsQ1wAfBvfT4l7wiZuQE4qFxv6MKIeGxmdswaNRHxfGBpZl4ZEYfXHGcwT87MOyNiJ+AnEXFT+Yl0Jdat1rButYa1S9aukeuVugXWrlaxdrWGtau3WbdGZzzWro5tWGbms6qcFxFnU6wFMuYGyhgR+wN7ANdEBBRD6v8QEYdk5l/HMGLl3yNFUbqYGornUBkj4njg+cAzM3PMh10P43fYSe4E5jQ9nl0e0zBFxGSKwvm1zPxO3XkGk5krI+ISijVqOqZ4AocBR0WxWP8UYNuI+N/MfGXNuTaTmXeW90sj4kKKaT6V3/hZt1rDutUa1q7eZu0atZ6oW+VrrV0tYO1qjS6sXdatFrFutcS4q11dOSU8InZpevhCOut/EjLzuszcKTPnZuZciqHhB4914RxKROzd9PBo4Ka6sgwkIo6gGNJ8VGauqztPF7kC2Dsi9oiILYBjgYtqztR1ovjX75eAGzPzE3Xn6U9EzCw/5SMitgKeTYf9Wc7Md2Tm7PLvw2OBn3da4YyIqRExrfE1xWiNltUW61ZrWLfGPWtXC1i7Rs+69ffvYe1qAWvXuGbdagHrVmuMx9rVsSMsh/DRiDiIYnrCYuD1tabpXh+OiEdRrG/wJ+ANNefpz+eALSmGCgNclpkdkzMiXgh8FpgJfD8irs7M59Qci8xcHxEnAj8CJgLnZObCmmM9TER8AzgcmBER/3979xpjV1WGcfz/cDFYaYEQIOU6XAoKRQYIlFhBCII1oFRAsWq1QBQwGhRBULkIMcEIwRvaGm0oJcGiIKByVVIKrdyltghCKIWkpiIiVAaBIH38sNe0u4c5ndPOaedM5/klJ7PPvqy99vkwb/Z612UJcJHt6YNbq1WMByYDC8t8JQDfdGetkjkauFrVKoUbAb+yPSg9IIa47aiGdkAVG6+1fXsby0/cao/ErTZI7Fp7QyBuQWLXcLGu4xYkdrVLYlcbdGLsGgpxC4ZE7ErcGj7WKHZpkHp7R0RERERERERERLzNkBwSHhERERERERERERumNFhGREREREREREREx0iDZURERERERERERHSMNFhGREREREREREREx0iDZURERERERERERHSMNFhGrCeSetpQxtaSZkvqkXRlw7EDJS2U9LSkH0lS7dgPJB22hveaKGnv2vdLJH2wn2tmSRqzJveJiIiIiIiIiKhLg2XE0PI6cAFwdh/HpgKfB8aUzwSoGjmBQ2zf03iBpI1Xc6+JwIoGS9sX2v5jP/WbCny9n3MiImItDaXk19rWVdIppQ4LJD0m6ThJP5E0X9Ljkl4r2/MlnViuuUnS/WX7Q7XjPZKeLNszJXU1XD+t4d7dkixpwtrUvcnz9JS/20i6vV3lRkQMFYld6y52SXq23Hd++Xvc2tS/2e+Q2BWDaZPBrkDEcCapG5gGjAAWAafYfknSQcB0YDnwB+DDtsfafhWYK2mPhnJGA6Ns9wa8mVQNjrcBJwC31859FrgOOAr4nqSRwBeAdwBPA5OBbuCjwAcknV/KuAD4ve3rJR0JXE71P+Qh4AzbbwD3AjMkbWL7f238qSIion16k19jy6euN/n1AHArVfLrtlry6yvrsmKSdgS+BRxge5mkzYFtbN9cjndRxaLu2jVbAgcCPZJ2s30HcEc5djdwtu2Ha9cvql/fYBIwt/xt6QWtvBjL9vLVnWf7BUlLJY23Pa+VsiMiYoXEruax6wjb/5K0F3AncHMLdU7sio6XHpYRg2smcK7t9wILgYvK/quA00pQequFcnYAltS+Lyn7AMYDjzSc/6LtA2zPAn5j+yDb+wFPAKfa/hPwW+Ac2922F/VeKGkzYAZwku19qRotzwAoAe9pYL9WHj4iIgau9Aq8v/TquFHSVmX/QWXffEmXSXoMwPartudSvfzVy1mR/LJtqhg1sRxuTH59t/QYWSDp8rJvV0n3lR4e3+mnzodLukfSLaUXyTRJGwHbAq8APaWuPbYX9/MTHA/8DpgFfLL/X6xpnQR8HJgCHFXiXbNzu0q9ZwKPATtJOkfSQ+U3ubjJpTcBn17bOkZEbCgSu9oTuxqMAl5qdjCxK4aaNFhGDBJJWwBb2p5Tdl0NHFaybSNt31f2XzvAW40GXmjYd11te6ykeyUtpApE+/RT3l7AYttPle9XA/VhFv8Eth9AfSMiYs2s1+RX6bHyMWCfcs/eF7wfAlNLMmtpC/c7GPgy1fQju1O9vP0FeB5YLOkqSR9poZxJwC/LZ1IL5+8q6VFJcyQdWtv/Pqr4tgi4Gzimn3LGAD+1vQ9VbBxTnqkbOFB9D0F8GDi0j/0REcNNYld7YhfA7NKwOwc4v59yErtiyEiDZcSG4e/AjrXvO5Z9AK8Bjb1EXq1tzwC+VIL0xX2cu6Y2K/eMiIh1bJCSX8uoerhMl3Q88N+yfzzVixfANS2U+aDtZ2y/Va57f9meAJwIPAV8X9K3mxUgaTuql625JZH2pqTGoYJ1S4Gdbe8PnAVcK2lUOTaJqqcL5W9/L5DP9U7FAhxdPo8CfwbeXerVKEm9iBj2ErvaGrugGhI+FtgXuLIMSW8msSuGjDRYRgwS28uAl2oZssnAHNsvA69IGlf29ztEwPZS4D+SDilD2j7LyrlLngD2aHoxjASWStqUVbv6v1KONXoS6NLKeTQnU2Xzeu1JNcQgIiKGlpaSX2WO4oOB64FjWXWuR6/B/RrPdSnfth+0fSlVDDxhNWV8AtiKqlfLs0AXq2lotP2G7RfL9iNU80fvqWoRuhOAC0s5PwYmqJrnuZl68k/ApWUalW7be9ie3sc1SepFRLTXsI1dfZy3iKqn596Nx2oSu2LISINlxPozQtKS2ucs4HPAZZIWUHXDv6Sceyrwc0nzgXdRZQSBFYvmXAFMKeX0BqQvAr+gmkNyEdWCOwC3AIevpl4XUE1QPQ/4W23/LOCcMvRg996dtl8HTgZ+XYaRL6daOKg3W/ia7X+0+qNERMTaG4zkV+m5sYXtW4GvsnLe4nm1+7Qy19XBZe6wjYCTqBaV217SAbVzuoHnVlPGJGCC7S7bXVQLGDR9VlWrnW5ctnej6knyDHAksMD2TqWsXYAbqIYPtuIO4JTeXi2SdpC0bR/nJakXEcNeYlfbYlfjedsCu/Zz77rEruhoWSU8Yj2x3SxBcEgf+/5a5lZB0nlU84b0ltPVpPyHefuKedi+V9Klkra0/XLj9banUq2s13jdPFbNzk2pHbsL2L+PanwK+Flf9YuIiLYYIak+V9cVVMmvaZJGUL3AnFyO9Sa/llP1hG9Mfo0C3iFpInC07cepkl8zgHdSJb7qya/TqBJjI4GbVS1KI6rhaQBnUg1TO5cWVigFHgKupHqZnA3cCOwEXC5pe6qhey8Ap/d1sapVU3cBeoe2YXuxpGWSxtl+oI/LDgMukfQmVcLtdNv/ljSp3L/uBqpF5Wb29yC275T0HuC+6n2ZHuAzVMPo6o6g+i0jIoaTxK6Vz9BFm2JX7fhsSW8BmwLn2X6+hedI7IqOp2ohrYjoJJJOAr5BlVR4Dphiu3HhnDUpbxxVz8cFbapis/ucDFxThlxERMQgkrS57Z6yfR4w2vaZAyhvLnBs6QEz0LodDpxt+9iBljWUSLoHOM5201VcIyKGs8SuzpPYFYMlPSwjOpDt61h1Je+BltdXpq7tbF+1Pu4TEREtOUbSKsmvAZb3NWBn4OUBljMsSdoGuCIvfBERq5XY1UESu2IwpYdlRERERKwTkvbl7auuvmF7XF/ndxpJWwN39XHoyN4FECIiYsOS2BXRGdJgGRERERERERERER0jq4RHREREREREREREx0iDZURERERERERERHSMNFhGREREREREREREx0iDZURERERERERERHSM/wPbzqOLLSsbVAAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# adapt x_labels..\n", "x_labels = [f'Log10({name})' for name in problem.x_names]\n", @@ -9120,26 +648,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = pypesto.visualize.profile_cis(\n", " result, confidence_level=0.95, show_bounds=True\n", @@ -9163,22 +678,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████| 10000/10000 [00:27<00:00, 368.56it/s]\n", - "Elapsed time: 30.99180300000002\n" - ] - } - ], + "outputs": [], "source": [ "import pypesto.sample as sample\n", "\n", @@ -9204,36 +710,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[-1.56903514, -5. , -2.20988782, ..., 0.58572263,\n", - " 0.81880994, 0.49856843],\n", - " [-1.56903514, -5. , -2.20988782, ..., 0.58572263,\n", - " 0.81880994, 0.49856843],\n", - " [-1.56903514, -5. , -2.20988782, ..., 0.58572263,\n", - " 0.81880994, 0.49856843],\n", - " ...,\n", - " [-1.42590748, -4.42706952, -2.00183415, ..., 0.73049356,\n", - " 0.85349596, 0.54844474],\n", - " [-1.48877016, -4.17264394, -2.22043926, ..., 0.61516694,\n", - " 0.93106418, 0.6043891 ],\n", - " [-1.48877016, -4.17264394, -2.22043926, ..., 0.61516694,\n", - " 0.93106418, 0.6043891 ]]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result.sample_result['trace_x']" ] @@ -9253,31 +736,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 3000\n" - ] - }, - { - "data": { - "text/plain": [ - "3000" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sample.geweke_test(result=result)\n", "result.sample_result['burn_in']" @@ -9285,32 +750,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Estimated chain autocorrelation: 454.31751144999856\n", - "Estimated effective sample size: 15.376083335131788\n" - ] - }, - { - "data": { - "text/plain": [ - "15.376083335131788" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sample.effective_sample_size(result=result)\n", "result.sample_result['effective_sample_size']" @@ -9329,34 +775,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "image/png": 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ej494rLOzc8zvBRQLmYWbkFe4yWTNq6PcCA3DEA2NSWSy5hWTE3mFm5BXuA2ZhZuQV7jNmBoao7nrrrt08803F/yGzijzKhiGMeLj++67T2vWrCn4NYFSI7NwE/IKN5m0eT1wOOQxDNbQmEQmbV4xKZFXuAl5hduQWbgJeYXbFLSGxtE89dRTY3r+lClT9PTTT+c/7urqUktLy4jnXHXVVVq+fPmIxzo7O1k8HGWLzMJNyCvcZLLm1XYceQxDhsGUU5PJZM0rJifyCjchr3AbMgs3Ia9wm+NuaIw24uJozjvvPH3ta19Tb2+vqqqq9Ktf/Up33HHHiOdEIhFFIpHjLQ0oGjILNyGvcJPJmlfHcSRD8njEouCTyGTNKyYn8go3Ia9wGzILNyGvcJvjbmi8drqo1zNlyhTddNNN+sAHPqBsNqv3ve99OvXUU4+3DAAAANfILQkuGWLKKQAAAAAACnXcDY1jcckll+iSSy4pxVsDAACUnJOfcko0NAAAAAAAKJCn1AUAAABUGvvAEA3DMOTYpa4GAAAAAAB3OO6GxljX0AAAAKh4jiNDhjyM0AAAAAAAoGAFTznV19enp59+Wh6PR+ecc45qa2slSZ/+9KcnrDgAAIDJyHYk48AIDRoaAAAAAAAUpqARGo8++qje9ra36d5779V3v/tdXXzxxXryySclSUuWLJnQAgEAACYbx3EONjRsGhoAAAAAABSioBEaq1ev1g9+8AOdeOKJkqQXXnhBn/3sZ7V+/foJLQ4AAGAyGh6UYRiS6GcAAAAAAFCQgkZohEKhfDNDkk455RQZhjFhRQEAAExmjuPIYxgyWEMDAAAAAICCFdTQWLJkib797W9raGhI6XRaP/7xjzV//nzFYjH19/dPcIkAAACTi+PkpptiDQ0AAAAAAApX0JRT999/vyzL0le/+tURj2/YsEGGYWjTpk0TUhwAAMBkZDuOZOSmnKKfAQAAAABAYQpqaLzwwgsTXQcAAEDFONDPkCFDDh0NAAAAAAAKUlBDI5VKaePGjYdNL7Vy5cqJqAkAAGBScxxHHo/BCA0AAAAAAMagoIbGqlWrNDAwoLa2tvxjhmHQ0AAAADgGtuPkRmgYhiybjgYAAAAAAIUoqKHR1dWlRx55ZKJrAQAAqAi27eQWBZck0dAAAAAAAKAQnkKetGDBAu3fv3+iawEAAKgItiOmnAIAAAAAYIwKGqHxjne8Q+985zu1YMEC+XwHv+T++++fsMIAAAAmK8dxZBi5KadoaAAAAAAAUJiCGhpf/vKXtWrVKs2YMWOi6wEAAJj0hqecknLraQAAAAAAgNdXUEOjpqZG11xzzUTXAgAAUBEs25HHMA6M0KChAQAAAABAIQpaQ+Ov/uqv9MADD6irq0v9/f35/wAAADB2tu3IY4g1NAAAAAAAGIOCRmjce++9ymQyuuOOO/KPGYahTZs2TVhhAAAAk5VlO/J6PTIk0c8AAAAAAKAwBTU0nnvuuYmuoyL0x1OKp1OKx031xlNqjIY0vSmsukio1KVhAj37cpdSqlEwaCh2yO++sSGk/d1J1deG5AsY6u5N5T83d1qdQqGC/jyBijA4mNb2zoH830ggYGggYao65NMJzWHV1ARLXSIwJpZty+MxDozQoKUBTGa27aije1DegKnuntyxYEM0pKYGn+JxW21NYVVXB0pdJjAmtu1oX19cPf3ZfKZra3yKVoU4v3WpoaGMtnXE87/PcI1PhqRk0uJ4G2Utmcxq696YqkKGkiknn+FgwFAm42jWlFryi5Loj6e0e38in8n6Op+6uzMKBLyqCwc1paFGHo9R6jJd6ahXTDds2KBly5bpnnvuGfXzH/zgByekqMmoP57SQDqlgF+qrfVJCqknlsp/noO+yStc7ZfPJ/n9Bx9zJA0k0jI8hnxBS/0xSwNDGYWr/PJ4DPWnEurec/CEt6bap1BISg5JyVTu8fpISJZpyefzsoPGpHXoAUBjNKSZ08PauSehhmhILc1BDQxYemVvnyLVVZo5NcLBAFzDshx5PYYk1tAAJgPbdtTZM6jeeEpDqayitQHJkaJR78gmRqNPqaxXkrSnI6VgwKf27T2qjwRkmlJ3f1KN0ZCCQa+m1lWrfzCr3nhSDZEqTW3ipBflwbYddcVjSmWlpsbcuW1vLCVDIQWDKfXHOb91m/54SkNWasRj2aylTMZRY4NPL+/uV03Ir9lTowoEvCWqEjhcPJ7SkJ1Sba1PAwMH97e1YZ8cR6oKSb1DSUnimgmKqj+eUkdPXE2NAUm5Bltfn6nYYEaNvpCytqnO/phq/SHV1rLPHKujNjR27twpSdq8eXNRipnMBtIp+f1SLJZVKHTwyrYjqX8od+DAQd8k5Ug9fWkFAz71DaQVDQclx5bPF1A2a2lvZ1pZy1ZtdUCJoVyntqcvIxke+byG+uJpDSTSitT6ZcijhnqfqqtqFKoy1NOjfGOssSGZP2FujIbUWBfS/l5OgOFe/fGUdnb0KxQKqC4cVG88LcOQpjSF5FiGXt07pKDfq+qQX1U1jjpjMdV4Q4qyLUWZs20nN+VUfoRGqSsCMFamaWvb3pi6Y0m11FerJ5bUYDKrb/z0OaWzloJ+r9bc/CZ195hKZ0w1RELqH0jLkDRtSpX6+y0FA14NDGUU9HtlWbmbX3KjeH3a25lWKpWV5FHfQFqOI3X3DeqUuc3y+QpaBhGYMF3xmKTchcJs9vDPp+yUUikfI85doj+eUiKT0sBARpJH06dUK5m01bE/12DNZqVpU4Lq6Uvrief36txF02hqoCwkk1klrJQ8hpRKZiUZCgZy+8iOfUOqrQmoscGnWNxUYnBA84N+9qEomv5kSvX1gfx1uub6Kvl9hgJ+rxzH0UDCVDTi06CVkpGQwmGuY4zFUY8wbrjhBknSnXfeecTnfOxjH9NXv/rV8a1qEorFTUlSQ4NfL2yOaWdHv84/8wSl05Ze7RzUUNRUyk6pxktnbrJZ99+b9dcX1uq7G57Pn+B+6u/OVCptyev1yms4qqkNKp22lLUcpdOmHMdR/8CQZHjUn0ipua5KQb9fN6/5g6677DTNm1Gr51+Oae369vxrrlq+WCfPj2r73pR+8MgmXXbhAs1pi+inG1/SqQumqL42oOpQQHOmRdmJo+z1x1PqSaQ0lLbU2RvXdzY8rxmtYV35toVKHbgw1BANaV/PkAIBr7q702poCCppp2TEpQhNDZQxy7YPNDNy/zFCA3AX07T1m2d361vrcs2LKy4+UafOa9CXv/+M0llLd350iSLRkNpfjunFbft1wRtmqqN7UKGgV5t39miO1aD+RFqN0YDCVX71DaQVCHjlsQ3t6ujX3v1+LToxqnRa2teVUFffkPb1Dqm5vkovv9qrE09o4FgOJZNIHLyL33aUn1K3IRrSvu4BdXR7tGhBVDs6Y1o4q7GElaIQ/fGUNu3o0dQpNdrdlVRVQLLsXJN2SkO1fD6Ptu9OqDEaUjQSVNDv05bdfTplTlOpSwe0t3dQNTVSOiNls7YceeXzeCRHMi1HtuNo376UpkwJ6fq7fq9rVyzWW848gX0oJtzgYFqhoLR7b1LRSFANkZCGklkFAz4l06aypi3HsdQdMzS7rVb9VkqBgJ9m8Rgc9y0T27dvH486Jr3e+PCBX0g7O/p1ypwm7eyI69vrD17kvnbFYi1aIGlANDUmkZXvPklTp9XptmuW5kdP1Nb61NefVSyRVkt9lXbsjes7Dx3MwqoVi/XoUzu1aWe/gn6vrrh4gRrrqjSjNayvP/gX3XbN0nwzQ5LSWUtr17frtmuWKp2x9XfvOklNdVUKBg2dPKdZz23ep7NOnqZv/PRp/c1FJ+otZ7axE0dZ270/IUkyLeWbGe86b7a+dP/TI7aZ01vC2tOVUEt9tTJZKZEwFQwmaWigrGVNO78NNozcBSEA7rFtbyzfzJCkYMAj05Yuu3C+3njmFG16JSYZPs2bGZZhSF/4f0+NOMbLZE2FQz4NJLL6//79TyNuTpk2pVZf/8lzmtp0hjq6B9XWUqVZXo/uuv/Z/PP+8W9O1/mnT2f0LUpiT8+golGvTEvatDWmtevaRxybNdcF1NNrKlTFRRk32L0/IdPKHUM/9fxeLV08Tf/6vadGbJdmTK3V4GBKW3cl1dZaq3Qm9fovDEww07SVyZpK9kj19blLm9VVPnX2DmrtuqdHZDhcG9Kd1y3Rp7/+lGa0RrRgRn2Jq8dkN5BNaiDhKBTwaMeeAT31/F4tWTRNa9ePvJ7RUOtTPG6qvt7HCLgxKvoVzWeeeUbvfe97tWzZMl111VXas2dPsUsoicZo7m7i3nhKSxZNl8/nzTczpNwF6W+ta9e+/VkNWhwgTCbpVFZbdsR023ee1Jd/8Izu+/kLenlbTHd87yl9+QfPaH9fKt/MkA40J9a166/Pn5v/+EePblYyZerSC+YpnbXUG0/ln59/nwOP/+jRzdrVmdCzL+3Xpq0xVQWkvzprpnZ0xHT+6W361rrntG1vrOg/B2AseuMp9cZTSqZNpbOWPvSeRfrWuvbDtpndfSnd/eM/6/bvPaUtO2PKZE2l0tbrvDpQWlnTlu/AhUhDhmw6GoCrdMeS+f1RU11INSG//u//+6N+8MuX1N9vau36djU2+jQ05By271q7rl3JtKU773ta8aGsZrSGD35ufbsMx6PzT29TbzyltevbZdkeBQOBEa/xtZ/8WR3dg6X55lHxBlOmYnFbsZiZb2ZIB4/NDI9PMhwlhzgec4Ph4+3EUEZ/ff7cw7dZ69vV1ZNUTzyruhq/MhlLAT9TiaH0tu2NyTRz0zVmMtLAkKmBwexh26W169uVTJpKZwwtntugnliyxJWjEnT3mEqnLTny6sGNm3XJm+cddlPyt9a1y+f3S4atvj5Tu7sGtXVPf2kLd5GiNzQ+8YlP6P/+3/+rDRs26JJLLtEXvvCFYpdQEtObwmpq8KkxGlLfQEqpjDXqBemu3iF195jqj9PUmCxsxzNiw/XX588d8fHwBdtDpbOWUhlrxMfJtKlUJtfJbYiGFPSP7NoG/V41REJKZy3ZTm545dr17aqL1Kg/kZZtSzJyr8VOHOWuIRpSYzSkqpBPQb9XvfH0qH8nyYyZ//fade0K+H3qH2D7ifJm2Y683oOHYLQzAHdpilblj8Pe+oYZ+t7PXsjvo4ZvOunpMY+47xo+D/jOQ8/r0gvmjfhcfyItj0f5Y7reeEp9r9mvpbOWegc4lkNppNKW0mnriPmODaTl2MZhuUV5ajhwvB2uDmh/X/KIx9tr17fL68tNldKfSJeoWuCg7lhSfQMpZbPS4KAp05L29w2NmuG+gdzNcpecP1eN0aoSVYxKkjt+S6tvIKXzT29TLHHkfaYcj/oGUrIdJ79GLl5fURsamUxGN954oxYuXChJOvHEE9XR0VHMEkpmeMHvhnqfGqIhRWsCo16QDga86o2n8tOtwP36B0ZuuFLpkc2sqpB31CyEDhlmFvR71T+QUlXAp6vefZKitT6tWr44/3XDQyktM7ewpMcwJOfgqI36SEiP/Xm35OSey04c5a6pwafqKo98HumaZYuOuM089O9k+EJQA9NNocxZlpPbTis35RSrggPuMmdaVNeuODW3Xzpws8iw4ZtOeuOp1913vfYGlqDfq7pwUAtm1OeP6RoiIdW/ZiraoN+rhlqO5VAadbVBxQYzR8x3JBxU30DqsNyiPDXU+eTzSMlMVqHgkc9L87MEZCw1RfndovSaolWqrw2OGNl/pAzX14bUEAkpkcxqzrRoiSpGJWmIhlQXDqghEpLHI0XDwdfdZ3oMQ41sXwtW1LGCgUBAy5YtkyTZtq01a9booosuOux58Xhc8Xh8xGOdnZ1FqXEiNVTXqndoQE0NPsXitlYtXzxiUecPL1uknz+2VR949ync0eIyR8tsXSS34Ro+2R1uYAx/vP43r+iaZYv0nQ0j19D4+WNbJeU2cle9+yRFagKKJVJ6+LFtmtpYrcUnRnXbNUvzGz/LzOiOe57VFRcvUHXIpwf/e0v+RPjJ9j26eMlMPfrUTl274lR24hXODdvYhupa9WpAJ0yv0eCgLUlatWLxiHmar1m2SA/99pX81wT9XkVrApreFC5V2ZgAbsjrWFm2La/3YEODGacmj8mYVxzO5/PoLWe2aUZrrQZTWW347cHjuob63E0nDdGQ/D7PYcd4Hz5k3xX0e1UV8OX/vWr5YjmGrROm5RYvXbV8sbweW4NJM3/sGPR79X+uPENTm2qO+/sgrzgWtWGvDCMor+fwfF+zbJEs01J9bUhVVeN7qYG8Toy6ULU8M6REwtK3H33+sOPt4W3W8Hmlz2toXhvrDxSCzE6sOdOiemFbV/4CcG88rYd/94o+vGyRvvuaayuWacrry11kZi3R0ZHX8VVb41MmayuVyurEGfWyLOuo+8ymRp/aYjWaO72u1KW7RkFHGX/+8591+umn5z9Op9O666679LnPfU7OEe4qfOSRR3TnnXeOeGzOnDm69957lclkdPPNN8s0Ta1ateqwr73vvvu0Zs2aMXwb7hAIeNWgXFMjGvGoJRnUZ/9+ifb3DSkY8Ornj23VxUtnqanRJ4munJscLbMejWxePfy7rSM+3tWZUDTs1+c+vFSxRFr10aDC1T7V1ZyojGWrJuRTVdArGYY8HkM3XH6G6ut9ymRzF8Jam6qVydgaTEuf+eDZ2t2V0IP/vUUDg1mtWr5YTY0+vfG0Ng2msvr4+8/SnGlRduIVzg3b2OHt5ZbdfWpsyO2q2qwq/fOHlqird0jhar8c29auztxotuFFtU5oqc2PiMPk4Ia8jpVlHzpCwzjisRTcZzLmFaPz+TxaMKNetu3opivP1Oof5hbt/n/rNumq95wkv18aHLQUqfHrxsvPUMa01Bit0r//8kVteTWW32+1NFbpk3/3BtXXhhSp9WlP56C6ew3dds1ShcM+xfpT2tGR0OUXL9CUhmrNmhpRW0vtuCwITl5xLBprquX3D0mSIrFcvpMZU1UBn7weR+mspebmkJpqjr/pdijyOjGqqwPKmrYUTund58/RjKnVun3VUnX3p2TI0PrfbtGuzoRWLV8sv89RNOrjXLJAZHZi+XwezW9rUDyT2x7FE2ldePZMbfzfnbrh8tOVzlhqaajWlGa/untS6uhOa/F8bnw7EvI6vhprQkqYKfX0maqPBtU/kFWkxjfqPnPa1JAsSywIPkaGU8BZ9Fvf+latXbtW8+fPV3t7uz7+8Y9r7ty5+sY3vqHt27dr9uzZBb/h4OCgPvrRj6qurk5f+cpXFAgEDnvOkTqDK1eu1MaNG9XW1lbw+5Wr/nhKKTulWNxWOmMpnsioPhpUY71P7S/HdPZJU7go5yJHy+y9P1qvqdOmKZEw86MpIrU+xeJZxRIZRcMBBQJe+XweJYbS8hpeyeNoIJFVbbVfXq9HHo8jySPTtNUXT6s+ElRjg0+mJfX3H3zdpkafunsOfjyjOawIOcJruG0bO7y9lKTBISmZzGW8paFapmmrfyCthmhQM1siqq4+fJ8Cd3NbXguxqzOu27/3lG68/Az9/PfbdMrcRr3n/LmlLgvjYDLmFa/Pth11dA+qu38otz6OYUuOR42NPpmmFIsd3G9ls7n91vCxXCxuqTeWVjDgVVXQJ8u2cl/b4FNjTa329yfVO5BUQ22VpjbVjEsjYxh5xbE69Nisp9dUXzytSDigYMCrcNijGl9o3M9lyevESiaziqUHNZCwVBv2ypHUc8h5ZTTikyNpWn2EhkaByGxxHLo9eu31tWjEp+2vJlRfG1IgYGje9IZx3Y9OJuR1/MXjKQ3ZKfX1m4onsqqLBPPHgcP7zGDQo7qq8d9nVoKCRmj8y7/8i6677jpddNFFWr9+vT71qU/p0ksvlaQxNTOk3KLgM2fO1O233y7DGH1DEolEFIlExvS6bpMLa0ghT0o9iaQMw1BvLCXHDtHMcKGjZfaE5ojaptRJU6SBgZQ6Y0nFBnKLqc2cFlYyldvp7u9NqiEalNcr1dT4pQOL6dXXhjS9YfTGRCZjaSDeL0OGvB5DDdW1aq2jo4ujc9s2ti4SUn9cSmRT+WZGXW1uyLshj86Y30wjYxJzW14LYTs6eDJliFXBJ5HJmFe8Po/H0PSWsKa3HLzz07YddcUHZdum/H6PouFg/livsS4gy5K27EioKVqlN5w45Yh35L32dccTecWxGj42G76I6MiRcWA0+UQ0MyTyOtGqqvyqqqpTrT+tHfsGcusw1obU2lSjfT1D+ammaGYUjswWR10kpIEBaWdXQjU1Xnk8vvz1NTkhzZgaVsDr05SG8b0pYLIhr+MvEgmp2gxIisuypP19SU1tqpZ0cN2MqXXV3IR8jApqaJx11lm64447dO211+qee+4ZMf3UWLz44ovauHGj5s2bl2+ItLS06Dvf+c4xvd5kUBehE1dJamtDqi1wgby2xtd/TiDg1UmzC3gi4HJ1kZDqFCro7wIod5Zlj5hyijU0gMnH4zHUWsfUFpi8hm/Qa60rdSUYTzU1QZ0yJzjisfmsmYEyV1sb0qICr7MAxeTzedRaV8e+cgIctaFxySWXjHyyz6ePfvSjampqkiQ9/PDDY3qzk08+WS+//PIYSwQAAJg8bMeR58ANjobEGhoAAAAAABToqA2Nf/7nfy5WHQAAABXh0EXBPYYhmyEaAAAAAAAU5KgNjXPOOadYdQAAAFQE23YOriNmsIQGAAAAAACFYkUnAACAInIcacSa4Ew5BQAAAABAQWhoAAAAFNGhIzQMppwCAAAAAKBgNDQAAACKyLYdeTzDDQ2mnAIAAAAAoFA0NAAAAIrIchwdXELDkMMIDQAAAAAACkJDAwAAoIhs25HHODhCg34GAAAAAACFoaEBAABQRLbz2jU07BJXBAAAAACAO9DQAAAAKKLcGhq5fzNCAwAAAACAwtHQAAAAKCLbPjhCw2MYsh06GgAAAAAAFIKGBgAAQBHZzmvW0GCIBgAAAAAABaGhAQAAUESHjtAwGKEBAAAAAEDBaGgAAAAUkW078uT6GYzQAAAAAABgDGhoAAAAFJHtvGYNDRoaAAAAAAAUhIYGAABAEdm2ZBw4AjM8TDkFAAAAAEChaGgAAAAUke048ujgCA3LoqEBAAAAAEAhaGgAAAAU0aGLgntYQwMAAAAAgILR0AAAACii3BoauX97PIYsGhoAAAAAABSEhgYAAEARWZYjj+eQRcFZQwMAAAAAgILQ0AAAACiiw0ZosIYGAAAAAAAFoaEBAABQRLbtyDO8hobHkGXbJa4IAAAAAAB3oKEBAABQRNahDQ3DkMkIDQAAAAAACkJDAwAAoIhs25HhOThCw2aEBgAAAAAABSlZQ+PFF1/UokWLSvX2AAAAJZEboZH7t9fDCA0AAAAAAApVkoZGMpnU7bffrmw2W4q3BwAAKBnLtkeuoUFDAwAAAACAgpSkoXHXXXfp6quvLsVbAwAAlNShU07lRmgw5RQAAAAAAIXwFfsNN27cqFQqpXe84x1HfE48Hlc8Hh/xWGdn50SXBhwzMgs3Ia9wk8mYV8ty5M03NDwyWUNj0piMecXkRV7hJuQVbkNm4SbkFW4zYQ2NRx55RHfeeeeIx+bMmaNEIqF77733qF973333ac2aNRNVGjDuyCzchLzCTSZjXs1DppzyeQ1lTRoak8VkzCsmL/IKNyGvcBsyCzchr3Abw3Gcok3c/OCDD2rt2rWqqamRJL300ktauHChHnjgAYXD4fzzjtQZXLlypTZu3Ki2trZilQwUhMzCTcgr3GQy5nXtuufkSHrjqdO0r3dID27crLWfvqjUZWEcTMa8YvIir3AT8gq3IbNwE/IKtynqlFOXXXaZLrvssvzHJ554ojZs2HDY8yKRiCKRSDFLA44LmYWbkFe4yWTMq2nb8vu8kiS/z6NM1ipxRRgvkzGvmLzIK9yEvMJtyCzchLzCbUqyKDgAAEClOnQNDb/PowxTTgEAAAAAUJCSNjRefvn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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# scatter plots\n", "ax = visualize.sampling_scatter(result)\n", @@ -9399,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -9433,42 +858,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: You are loading a problem.\n", - "This problem is not to be used without a separately created objective.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# read result\n", "result_loaded = store.read_result(result_file_name)\n", @@ -9500,6 +896,12 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Further topics\n", "\n", @@ -9508,13 +910,7 @@ "* Model Selection\n", "* Hierarchical Optimization of scaling/noise parameters\n", "* Categorial Data" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] } ], "metadata": { diff --git a/doc/example/hierarchical.ipynb b/doc/example/hierarchical.ipynb index 684433e9b..639ab9dd2 100644 --- a/doc/example/hierarchical.ipynb +++ b/doc/example/hierarchical.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -95,104 +95,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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observableNameobservableFormulanoiseFormulaobservableTransformationnoiseDistribution
observableId
pSTAT5A_relNaNobservableParameter2_pSTAT5A_rel + observableParameter1_pSTAT5A_rel * (100 * pApB + 200 * pApA * specC17) / (pApB + STAT5A * specC17 + 2 * pApA * specC17)noiseParameter1_pSTAT5A_rellinnormal
pSTAT5B_relNaNobservableParameter2_pSTAT5B_rel + observableParameter1_pSTAT5B_rel * -(100 * pApB - 200 * pBpB * (specC17 - 1)) / ((STAT5B * (specC17 - 1) - pApB) + 2 * pBpB * (specC17 - 1))noiseParameter1_pSTAT5B_rellinnormal
rSTAT5A_relNaNobservableParameter2_rSTAT5A_rel + observableParameter1_rSTAT5A_rel * (100 * pApB + 100 * STAT5A * specC17 + 200 * pApA * specC17) / (2 * pApB + STAT5A * specC17 + 2 * pApA * specC17 - STAT5B * (specC17 - 1) - 2 * pBpB * (specC17 - 1))noiseParameter1_rSTAT5A_rellinnormal
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" - ], - "text/plain": [ - " observableName \\\n", - "observableId \n", - "pSTAT5A_rel NaN \n", - "pSTAT5B_rel NaN \n", - "rSTAT5A_rel NaN \n", - "\n", - " observableFormula \\\n", - "observableId \n", - "pSTAT5A_rel observableParameter2_pSTAT5A_rel + observableParameter1_pSTAT5A_rel * (100 * pApB + 200 * pApA * specC17) / (pApB + STAT5A * specC17 + 2 * pApA * specC17) \n", - "pSTAT5B_rel observableParameter2_pSTAT5B_rel + observableParameter1_pSTAT5B_rel * -(100 * pApB - 200 * pBpB * (specC17 - 1)) / ((STAT5B * (specC17 - 1) - pApB) + 2 * pBpB * (specC17 - 1)) \n", - "rSTAT5A_rel observableParameter2_rSTAT5A_rel + observableParameter1_rSTAT5A_rel * (100 * pApB + 100 * STAT5A * specC17 + 200 * pApA * specC17) / (2 * pApB + STAT5A * specC17 + 2 * pApA * specC17 - STAT5B * (specC17 - 1) - 2 * pBpB * (specC17 - 1)) \n", - "\n", - " noiseFormula observableTransformation \\\n", - "observableId \n", - "pSTAT5A_rel noiseParameter1_pSTAT5A_rel lin \n", - "pSTAT5B_rel noiseParameter1_pSTAT5B_rel lin \n", - "rSTAT5A_rel noiseParameter1_rSTAT5A_rel lin \n", - "\n", - " noiseDistribution \n", - "observableId \n", - "pSTAT5A_rel normal \n", - "pSTAT5B_rel normal \n", - "rSTAT5A_rel normal " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from pandas import option_context\n", "\n", @@ -209,278 +114,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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parameterNameparameterScalelowerBoundupperBoundnominalValueestimateparameterType
parameterId
Epo_degradation_BaF3EPO_{degradation,BaF3}log100.00001100000.00.0269831None
k_exp_heterok_{exp,hetero}log100.00001100000.00.0000101None
k_exp_homok_{exp,homo}log100.00001100000.00.0061701None
k_imp_heterok_{imp,hetero}log100.00001100000.00.0163681None
k_imp_homok_{imp,homo}log100.00001100000.097749.3794021None
k_phosk_{phos}log100.00001100000.015766.5070201None
ratioratiolin-5.000005.00.6930000None
sd_pSTAT5A_rel\\sigma_{pSTAT5A,rel}lin0.00000inf3.8526121sigma
sd_pSTAT5B_rel\\sigma_{pSTAT5B,rel}lin0.00000inf6.5914781sigma
sd_rSTAT5A_rel\\sigma_{rSTAT5A,rel}lin0.00000inf3.1527131sigma
specC17specC17lin-5.000005.00.1070000None
offset_pSTAT5A_relNaNlin-100.00000100.00.0000001offset
offset_pSTAT5B_relNaNlin-100.00000100.00.0000001offset
offset_rSTAT5A_relNaNlin-100.00000100.00.0000001offset
scaling_pSTAT5A_relNaNlin0.00001100000.03.8526121scaling
scaling_pSTAT5B_relNaNlin0.00001100000.06.5914781scaling
scaling_rSTAT5A_relNaNlin0.00001100000.03.1527131scaling
\n", - "
" - ], - "text/plain": [ - " parameterName parameterScale lowerBound \\\n", - "parameterId \n", - "Epo_degradation_BaF3 EPO_{degradation,BaF3} log10 0.00001 \n", - "k_exp_hetero k_{exp,hetero} log10 0.00001 \n", - "k_exp_homo k_{exp,homo} log10 0.00001 \n", - "k_imp_hetero k_{imp,hetero} log10 0.00001 \n", - "k_imp_homo k_{imp,homo} log10 0.00001 \n", - "k_phos k_{phos} log10 0.00001 \n", - "ratio ratio lin -5.00000 \n", - "sd_pSTAT5A_rel \\sigma_{pSTAT5A,rel} lin 0.00000 \n", - "sd_pSTAT5B_rel \\sigma_{pSTAT5B,rel} lin 0.00000 \n", - "sd_rSTAT5A_rel \\sigma_{rSTAT5A,rel} lin 0.00000 \n", - "specC17 specC17 lin -5.00000 \n", - "offset_pSTAT5A_rel NaN lin -100.00000 \n", - "offset_pSTAT5B_rel NaN lin -100.00000 \n", - "offset_rSTAT5A_rel NaN lin -100.00000 \n", - "scaling_pSTAT5A_rel NaN lin 0.00001 \n", - "scaling_pSTAT5B_rel NaN lin 0.00001 \n", - "scaling_rSTAT5A_rel NaN lin 0.00001 \n", - "\n", - " upperBound nominalValue estimate parameterType \n", - "parameterId \n", - "Epo_degradation_BaF3 100000.0 0.026983 1 None \n", - "k_exp_hetero 100000.0 0.000010 1 None \n", - "k_exp_homo 100000.0 0.006170 1 None \n", - "k_imp_hetero 100000.0 0.016368 1 None \n", - "k_imp_homo 100000.0 97749.379402 1 None \n", - "k_phos 100000.0 15766.507020 1 None \n", - "ratio 5.0 0.693000 0 None \n", - "sd_pSTAT5A_rel inf 3.852612 1 sigma \n", - "sd_pSTAT5B_rel inf 6.591478 1 sigma \n", - "sd_rSTAT5A_rel inf 3.152713 1 sigma \n", - "specC17 5.0 0.107000 0 None \n", - "offset_pSTAT5A_rel 100.0 0.000000 1 offset \n", - "offset_pSTAT5B_rel 100.0 0.000000 1 offset \n", - "offset_rSTAT5A_rel 100.0 0.000000 1 offset \n", - "scaling_pSTAT5A_rel 100000.0 3.852612 1 scaling \n", - "scaling_pSTAT5B_rel 100000.0 6.591478 1 scaling \n", - "scaling_rSTAT5A_rel 100000.0 3.152713 1 scaling " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "petab_problem_hierarchical.parameter_df" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -537,44 +180,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/3 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Waterfall plot - analytical vs numerical inner solver\n", "pypesto.visualize.waterfall(\n", @@ -664,20 +230,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Time comparison - analytical vs numerical inner solver\n", "ax = plt.bar(x=[0, 1], height=[time_ana, time_num], color=['purple', 'green'])\n", @@ -696,34 +251,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/3 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Waterfall plot - hierarchical optimization with analytical inner solver vs standard optimization\n", "pypesto.visualize.waterfall(\n", @@ -762,20 +281,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Time comparison - hierarchical optimization with analytical inner solver vs standard optimization\n", "import matplotlib.pyplot as plt\n", @@ -796,35 +304,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/3 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Waterfall plot - compare all scenarios\n", "pypesto.visualize.waterfall(\n", @@ -872,20 +343,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Time comparison of all scenarios\n", "import matplotlib.pyplot as plt\n", diff --git a/doc/example/julia.ipynb b/doc/example/julia.ipynb index 29ecc5afe..48f8bcb6f 100644 --- a/doc/example/julia.ipynb +++ b/doc/example/julia.ipynb @@ -33,157 +33,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ce619530-f9c0-4c7f-ba8f-e1c5a55fda4f", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
# ODE model of SIR disease dynamics\n",
-       "\n",
-       "module SIR\n",
-       "\n",
-       "# Install dependencies\n",
-       "import Pkg\n",
-       "Pkg.add(["Catalyst", "OrdinaryDiffEq", "Zygote", "SciMLSensitivity"])\n",
-       "\n",
-       "# Define reaction network\n",
-       "using Catalyst: @reaction_network\n",
-       "sir_model = @reaction_network begin\n",
-       "    r1, S + I --> 2I\n",
-       "    r2, I --> R\n",
-       "end r1 r2\n",
-       "\n",
-       "# Ground truth parameter\n",
-       "p_true = [0.0001, 0.01]\n",
-       "# Initial state\n",
-       "u0 = [999; 1; 0]\n",
-       "# Time span\n",
-       "tspan = (0.0, 250.0)\n",
-       "\n",
-       "# Formulate as ODE problem\n",
-       "using OrdinaryDiffEq: ODEProblem, solve, Tsit5\n",
-       "prob = ODEProblem(sir_model, u0, tspan, p_true)\n",
-       "\n",
-       "# True trajectory\n",
-       "sol_true = solve(prob, Tsit5(), saveat=25)\n",
-       "\n",
-       "# Observed data\n",
-       "using Random: randn, MersenneTwister\n",
-       "sigma = 40.0\n",
-       "rng = MersenneTwister(1234)\n",
-       "data = sol_true + sigma * randn(rng, size(sol_true))\n",
-       "\n",
-       "using SciMLSensitivity: remake\n",
-       "\n",
-       "# Define log-likelihood\n",
-       "function fun(p)\n",
-       "    # untransform parameters\n",
-       "    p = 10.0 .^ p\n",
-       "    # simulate\n",
-       "    _prob = remake(prob, p=p)\n",
-       "    sol_sim = solve(_prob, Tsit5(), saveat=25)\n",
-       "    # calculate log-likelihood\n",
-       "    0.5 * (log(2 * pi * sigma^2) + sum((sol_sim .- data).^2) / sigma^2)\n",
-       "end\n",
-       "\n",
-       "# Define gradient and Hessian\n",
-       "using Zygote: gradient, hessian\n",
-       "\n",
-       "function grad(p)\n",
-       "    gradient(fun, p)[1]\n",
-       "end\n",
-       "\n",
-       "function hess(p)\n",
-       "    hessian(fun, p)[1]\n",
-       "end\n",
-       "\n",
-       "end  # module\n",
-       "
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from pypesto.objective.julia import display_source_ipython\n", "\n", @@ -200,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "96df9672-94ad-4671-9ff4-d6e426e321ce", "metadata": {}, "outputs": [], @@ -211,19 +64,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "724abb5f-3ce2-4ac5-89fe-2b48f89d8733", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1min 42s, sys: 3.78 s, total: 1min 46s\n", - "Wall time: 1min 48s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "\n", @@ -263,29 +107,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "8579d6f5-193e-444f-a8ff-44ae90293470", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "23.12228487889986\n", - "CPU times: user 1.36 s, sys: 12.5 ms, total: 1.38 s\n", - "Wall time: 1.37 s\n", - "23.12228487889986\n", - "CPU times: user 208 µs, sys: 11 µs, total: 219 µs\n", - "Wall time: 215 µs\n", - "[-38.6806348 19.9557434]\n", - "CPU times: user 1min 42s, sys: 2.21 s, total: 1min 44s\n", - "Wall time: 1min 43s\n", - "[-38.6806348 19.9557434]\n", - "CPU times: user 1.17 ms, sys: 0 ns, total: 1.17 ms\n", - "Wall time: 1.13 ms\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -307,21 +132,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "38c8c4aa-3faf-4f96-bf9d-271fb8a8d8b9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(obj.get_grad(x))" ] @@ -336,21 +150,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "9a43d18f-ccf8-4c3f-a30a-2ad824e1e87c", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-38.75164238, 19.9661208 ])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from pypesto import FD\n", "\n", @@ -369,23 +172,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "3b826499-e4c7-443e-9a59-d8d90e10d3b4", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sol_true = np.asarray(obj.get(\"sol_true\"))\n", "data = obj.get(\"data\")\n", @@ -410,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "d38e3540-9693-4b24-a734-cabdb5159d36", "metadata": {}, "outputs": [], @@ -436,26 +226,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "c2267d70-85fc-46cd-bb81-5bf7742c8d84", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 77.83it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.38 s, sys: 88.2 ms, total: 1.47 s\n", - "Wall time: 1.47 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "\n", @@ -477,30 +251,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "2b929224-6fa6-4f63-8892-37b2475b3738", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", - "2022-09-07 00:49:47,222 - pypesto.engine.multi_process - WARNING - Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", - "Performing parallel task execution on 8 processes.\n", - "2022-09-07 00:49:47,233 - pypesto.engine.multi_process - INFO - Performing parallel task execution on 8 processes.\n", - "100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 130.42it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 285 ms, sys: 177 ms, total: 462 ms\n", - "Wall time: 1.73 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "\n", @@ -512,23 +266,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "05888c10-1027-4580-aa0a-1076410579c6", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from pypesto import visualize\n", "\n", @@ -547,28 +288,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "b88b414b-598a-40f1-876e-ef1255a50c93", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████| 10000/10000 [00:08<00:00, 1234.51it/s]\n", - "Elapsed time: 8.135626907000017\n", - "2022-09-07 00:49:57,692 - pypesto.sample.sample - INFO - Elapsed time: 8.135626907000017\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.87 s, sys: 320 ms, total: 8.18 s\n", - "Wall time: 8.15 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "\n", @@ -585,31 +308,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "1fa22993-8100-4ec4-9b75-64ea745e77f1", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 0\n", - "2022-09-07 00:49:57,807 - pypesto.sample.diagnostics - INFO - Geweke burn-in index: 0\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "visualize.sampling_scatter(result, size=[13, 6]);" ] diff --git a/doc/example/model_selection.ipynb b/doc/example/model_selection.ipynb index 968a77221..4365b8f0f 100644 --- a/doc/example/model_selection.ipynb +++ b/doc/example/model_selection.ipynb @@ -80,90 +80,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
model_subspace_idpetab_yamlk1k2k3
M1_0example_modelSelection.yaml000
M1_1example_modelSelection.yaml0.20.1estimate
M1_2example_modelSelection.yaml0.2estimate0
M1_3example_modelSelection.yamlestimate0.10
M1_4example_modelSelection.yaml0.2estimateestimate
M1_5example_modelSelection.yamlestimate0.1estimate
M1_6example_modelSelection.yamlestimateestimate0
M1_7example_modelSelection.yamlestimateestimateestimate
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "from IPython.display import HTML, display\n", @@ -182,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -222,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -238,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "scrolled": false }, @@ -268,21 +187,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - "virtual_init | M1_0:649bcd9 | AIC | None | 3.698e+01 | None | True \n" - ] - } - ], + "outputs": [], "source": [ "# Reduce notebook runtime\n", "minimize_options = {\n", @@ -309,21 +218,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - "M1_0:649bcd9 | M1_1:9202ace | AIC | 3.698e+01 | -4.175e+00 | -4.115e+01 | True \n", - "M1_0:649bcd9 | M1_2:c773362 | AIC | 3.698e+01 | -4.275e+00 | -4.125e+01 | True \n", - "M1_0:649bcd9 | M1_3:e03f5dc | AIC | 3.698e+01 | -4.705e+00 | -4.168e+01 | True \n" - ] - } - ], + "outputs": [], "source": [ "best_model_2, _ = pypesto_select_problem_1.select(\n", " method=Method.FORWARD,\n", @@ -342,34 +239,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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fcHuY1aWy/nSpJAzbDHw0zu/MH+Ol277MGo6DMptNJY12F/DKKcZfSXLyglnDksTatWunXVFWerVrjWfGMEzSa0oefhz4fUkXAt8tjL2WJBz7zWzKM6stDspsKjPO0Uo6R3La7Wz/ZURE5Pb8Zs/RWjUMDw9P2HoRkkbc3d3trRfrRyq3d21JoRCzhtTZ2UlLS8uEoGxsbIze3l4HZQ1mxkYbEc8sVCFm9agYlA0NDTE4OAi8FJSNjIywfv36CSfzWn2qdMFCM/DjJMeMT/jbJyLuSrEus7pRDMra29snrCgbHh5mbGzMK8oaQCX30V5EckDjFpJ5ibMkjfoMcCoiOrIqcr48R2t5cfbs2QlBWdHatWtZs2aNg7Lak/p9tB8B/h/J8eJjJOeG7QQeA66prDazxjTdirLBwUGvKKtjlTTaHwPuKOzadQ5ojohHgP8M/PcsijOrV1NtvVgMyrz1Yv2ppNGK5EoWYAjYUPh+ANieZlFmjcAryhpHJWHYD4BLgb3A94H/Iuks8G/wxt9mc1IalPX393PmzBnAQVm9qeSK9gO8NPF7M8mdBw+QHGvzH9IsStK7Je2SdErSpyc992ZJeySNSXpAkvdZsJrX2trK9u3bzzujbO/evQwODvqMshpXdqONiL+IiHsL3+8t7Na1GlgXEQ+mXNch4A7gU6WDklYD9wK3kBynswu4J+XfbVYVDsrqVyVXtOeJiOen2M1r3iLi3oj4CsmBkKWuBnZHxBcj4iRwO3Bp4dYzs7rgoKz+zNhoJf1JOX+aK3GdpHelV9qUdgCPFx8U7oDoK4xPrumGwvTDrqGhoYzLMkvXTEHZwMCAg7IaM1sY1g88Iel7JIsVdpH8WX8SWElypM3rSE5d2A/8WmaVJtpJ7ngoNQwsn/zCiLgTuBOSBQsZ12WWuumCsmPHjnH8+HEHZTVkxivaiPgtklu3HgRuAB4muevgELCbZA51I/ArEXF5RPwg02phFJi8Aq0DGMn495pVjYOy2jfr7V0RMQR8EPigpJUkjbUFOAL0ZTFHO4PdwPXFB5LagG2FcbO6VQzK2tvbOXTo0PjWi4ODg4yOjnrrxZyrKAyLiKMR8XhEfDcierNqspKaJS0DmoAmScsKG9rcB1wi6ZrC87cCT0TEnizqMMubFStWOCirQbM2WkmXSLpf0nmbxkjqLDyX9sGMNwMnSE5u+KXC9zcXrq6vIbmn9yhwGXBtyr/bLNcclNWeWXfvkvS/gEMRcdM0z78f6I6If5VBfanw7l1Wr8bGxiYEZQCLFy92ULZwUtu963LgyzM8fx9wRTm/zMzS5aCsNpTTaDdy/sKBUs8D3emUY2aVKgZl3d3dXlGWU+U02qMkyf50LgSOpVKNmc2Zg7L8KqfRPgS8d4bn3wt8K41izGx+HJTlUzmN9neBqyTdJ+mywp0GnZJeK+krwJWF15hZDhRXlG3dupXFixePjx87doze3t7xE3lt4czaaCPiMeDnSUKxb5PMyT4P/F/gnwHviIhHM6zRzObAQVl+lLXxd0R8rbC5zE+TLMkV8BTwlxHhfx7NcsoryvKh7BMWIuIEya1cZlZjVqxYQWtrKwMDA+NTB8WgbMOGDXR2dla5wvo2a6OVdHU5b1TcFNzM8qkYlA0NDTE4OAgkQVl/fz8jIyN0dXXR1NRU5SrrUzlXtF8q4zVBsi+BmeXYTFsvjo2N0d3d7RVlGSgnDFs02xfJ3K2Z1YhiUFY6ZXD69GkHZRmZ81E2kjZIullSH/DNFGsyswXQ1NRET0+PV5QtgIoaraQmSVdL+jrJiQpvBz5BcieCmdWgmVaUDQ8PV7Gy+lFWo5X0I5I+RHKywoeBRwpPvSsi/ltE7MuqQDPL3nQryvr7+72iLAXl7Ef7t8B3Sc4Ie0dEbI2ImzOvzMwW1Ewryvr6+ryibB7KuaL9p8BdwO9HxEMZ12NmVeagLH3lNNofI7kN7GFJj0r6DUkvy7guM6siB2XpKuf2rkcj4t8BXcDvAT9Lcgz5IuBnCgc2mlkdclCWjrLvOoiIkxFxd0S8CbgY+BDwG8Czkr6RVYFmVl0OyuZvTvfRFk7A/U2gB3gH4L8jzOqYg7L5mfOCBYCIOBsR/zsi3pZWQWaWXw7K5mZejdbMGo+Dssq50ZrZnBSDspaWlvExB2VTc6M1szlbsmQJW7duZc2aNeNjDsrO50ZrZvMiiXXr1rFlyxYHZdNwozWzVLS1tU0blA0NDTV0UOZGa2apmS4oe+655xo6KHOjNbPUOSibyI3WzDLhoOwlbrRmlhkHZQk3WjPLXKMHZW60ZrYgGjkoc6M1swU1XVDW19dXt0FZTTZaSask3SfpuKRnJL2z2jWZWfmmCsrOnj1bt0FZTTZa4GMkWzOuA64D/lDSjuqWZGaVmC0oO3HiRBWrS1fNNVpJbcA1wC0RMRoRDwNfBd5V3crMbC6mC8r6+vrqJiiruUYLvBx4MSKeKhl7HJhwRSvpBkm7JO0aGhpa0ALNrDJNTU10d3ezYcOG84Ky/fv3c+bMmSpWN3+12GjbgRcmjQ0Dy0sHIuLOiNgZETtL54HMLJ8ksXLlyvOCsuPHj9f8irJabLSjQMeksQ5gpAq1mFnKZgrKDh48WJNBWS022qeAZkkXloxdCuyuUj1mlrLpgrKjR4/WZFBWc402Io4D9wLvl9Qm6XLgbcDd1a3MzNJWL0FZzTXagl8HWoBB4PPAjRHhK1qzOlQPQVlztQuYi4h4Hvi5atdhZgujGJS1trYyMDAwPnVQDMrWr18/4ao3b2r1itbMGtDSpUtnDMrOnTtXxeqm50ZrZjVlpqCst7c3l0GZG62Z1aRaCsrcaM2sZtVKUOZGa2Y1rRiUbdu2Lbcrytxozawu5Dkoc6M1s7qR16DMjdbM6k7ezihzozWzujRVUBYRVQnK3GjNrG7NFpS98MLkHVez4UZrZnVvuqDswIEDCxKUudGaWUOoZlDmRmtmDaUYlHV0vHR+QNZBmRutmTWcpqYmenp6pgzKsjhj0I3WzBrSVEFZc3Mzq1atSv131eR+tGZmaSkGZYODg7S1tdHcnH5bdKM1s4ZXDMqy4qkDM7OMudGamWXMjdbMLGNutGZmGXOjNTPLmButmVnG3GjNzDLmRmtmljE3WjOzjLnRmpllzI3WzCxjbrRmZhlzozUzy5gbrZlZxtxozcwy5kZrZpYxN1ozs4zlqtFKerekXZJOSfr0FM+/WdIeSWOSHpC0qQplmplVJFeNFjgE3AF8avITklYD9wK3AKuAXcA9C1qdmdkc5OrMsIi4F0DSTqB70tNXA7sj4ouF19wOHJF0UUTsWdBCzcwqkKtGO4sdwOPFBxFxXFJfYfy8RivpBuCGwsNRSf9Y4e9bDRyZY61mVpsq/dx/MyJ+erYX1VKjbQeGJo0NA8unenFE3AncOddfJmlXROyc68+bWe3J6nO/YHO0kh6UFNN8PVzGW4wCHZPGOoCR9Ks1M0vPgl3RRsQb5/kWu4Hriw8ktQHbCuNmZrmVq7sOJDVLWgY0AU2Slkkq/mNwH3CJpGsKr7kVeCLDIGzO0w5mVrMy+dwrIrJ43zkp3Elw26Th34qI2wvPXwl8FNgEfA/45YjYv4AlmplVLFeN1sysHuVq6sDMrB650ZqZZcyN1swsY3XdaCXtl3S6sE9C6fijhft3N0t6U2GDmmFJ+yt4782FnxsrbHRzZer/A8ysYhl/7h+QNCTpBUmPS3pbOT9X1422YB/wi8UHkl4JtJY8f5xkE5v/VOH7fh54FLgAuAn4kqQ18yvVzFKS1ef+PUBXRHSQLPH/jKSu2X6oERrt3cC/LHl8PXBX8UFEfD8i7gb2lvuGkl4OvAa4LSJORMSXgb8HrkmnZDObp9Q/94WfeyIiXiw+BBYDPbP9XCM02u8CHZIultQEXAt8Zp7vuQPYGxGly38fL4ybWfVl8bkHQNLXJJ0kuZf/QZItW2dUS5vKzEfxX7eHgCeBg/N8v3aSDW1KDQMb5vm+ZpaetD/3AETEP5e0GLgSuDgizs32M43UaL8FbKHkz4d58AY3ZvmX9ud+XEScAb4h6T2SeiPiqzO9vhGmDoiIZ0gmx99KckrDfO0Gtkoq3aLxUrzBjVluZPC5n0ozyeZWM2qIRlvwr4GfiIjjpYOSFhU2qVmcPNQySUtmeqOIeAp4DLit8Pq3A/8E+HI2pZvZHKX2uZd0kaS3SGqRtFjSLwGvJ5mamFGjTB0QEX3TPPV64IGSxydI/o974yxveS3waeAocAD4+YiYvDG5mVVRyp97AbcDrwDOAk8DvxARj8xWhzeVMTPLWCNNHZiZVUXDTB1UStIVwDemei4i2he4HDNbAFl97j11YGaWMU8dmJllzI3WzCxjbrRmZhlzozUzy9j/ByH5DoCowxU4AAAAAElFTkSuQmCC", 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pypesto.visualize.select as pvs\n", "\n", @@ -390,22 +262,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pvs.plot_calibrated_models_digraph(\n", " problem=pypesto_select_problem_1,\n", @@ -428,19 +289,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initial model:\n", - "model_id\tpetab_yaml\tk1\tk2\tk3\n", - "myModel\tmodel_selection/example_modelSelection.yaml\t0.1\testimate\testimate\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "from petab_select import Model\n", @@ -468,20 +319,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - ":myModel | M1_1:9202ace | AIC | inf | 3.897e+01 | -inf | True \n", - ":myModel | M1_2:c773362 | AIC | inf | 2.410e+01 | -inf | True \n" - ] - } - ], + "outputs": [], "source": [ "pypesto_select_problem_2.select(\n", " method=Method.BACKWARD,\n", @@ -493,20 +333,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "initial_model_label = {initial_model.get_hash(): initial_model.model_id}\n", "\n", @@ -547,31 +376,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - "virtual_init | M1_0:649bcd9 | BIC | None | 3.677e+01 | None | True \n", - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - "M1_0:649bcd9 | M1_1:9202ace | BIC | 3.677e+01 | -4.592e+00 | -4.136e+01 | True \n", - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - "M1_1:9202ace | M1_4:3d668e2 | BIC | -4.592e+00 | -2.899e+00 | 1.693e+00 | False \n", - "M1_1:9202ace | M1_5:d2503eb | BIC | -4.592e+00 | -3.330e+00 | 1.262e+00 | False \n", - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - "M1_1:9202ace | M1_7:afa580e | BIC | -4.592e+00 | -4.889e+00 | -2.973e-01 | True \n", - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n" - ] - } - ], + "outputs": [], "source": [ "petab_select_problem.model_space.reset_exclusions()\n", "pypesto_select_problem_3 = pypesto.select.Problem(\n", @@ -588,22 +395,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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XmjVrdMcdd5iOBACAS1FYUSDr16/XhQsXFBkZaTqKT+nRo4fS0tLUoUMHxcTEqF69eqYjAQDgMhRWFAiPYTWnd+/eSktLU1hYmNauXas6deqYjgQAgEtQWJFve/fu1ZYtWzRv3jzTUXxW3759lZaWpnbt2mndunWqUaOG6UgAADgdhRX5NnXqVD333HMKDAw0HcWnPf3000pLS1Pbtm0VFxenatWqmY4EAIBTUViRL7///ru++eYb7du3z3QUSHruueeUmpqqtm3bat26dapSpYrpSAAAOA2FFfkyY8YMPfroo7rllltMR8H/ef7557MnrevWrVPFihVNRwIAwCkorMiz5ORkzZw5Uxs2bDAdBX8zZsyYq5YHlC9f3nQkAAAKjMKKPPvkk0/UqlUrvpVuUS+//LJSU1Ozdw8oW7as6UgAABQIhRV5kpGRoalTp+qzzz4zHQU38I9//CO7tMbGxqp06dKmIwEAkG9snok8+e6771SuXDm1aNHCdBTcgM1m0+TJkxUWFqYOHTrowoULpiMBAJBvFFbkmsPh0Ntvv63Ro0ebjoJcsNlsevvtt9WyZUs98MAD+vPPP01HAgAgXyisyLWNGzfqzJkz6tq1q+koyCWbzaZ33nlHjRo1UseOHZWYmGg6EgAAeUZhRa5FRUVp5MiRKlSokOkoyAObzab3339f9evXV+fOnZWUlGQ6EgAAeUJhRa7s379fGzZsUN++fU1HQT74+fnpo48+Uo0aNdSlSxf99ddfpiMBAJBrFFbkyrRp0zRo0CAFBQWZjoJ88vPz08cff6wKFSrowQcf1OXLl01HAgAgVyisuKk//vhD8+bN0+DBg01HQQEVKlRIn376qYoXL65HHnlEqamppiMBAHBTFFbc1AcffKDu3burXLlypqPACfz9/TV37lzZ7Xb17NlTaWlppiMBAHBDFFbcUHJysj744AONHDnSdBQ4kd1u17x585SWlqbevXsrPT3ddCQAAK6Lwoob+uyzz3Tvvfeqbt26pqPAyQICAvTNN9/ozz//1BNPPKGMjAzTkQAAyBGFFdeV9RhWHhTgvYoUKaJvv/1Wp0+fVv/+/ZWZmWk6EgAA16Cw4roWL16sUqVKqVWrVqajwIUCAwMVHR2tI0eOaODAgZRWAIDlUFhxXVFRURo9erRsNpvpKHCxoKAgLVmyRHv37tXgwYPlcDhMRwIAIBuFFTnatGmTTp48qW7dupmOAjcpVqyYli1bph9//FHPP/88pRUAYBkUVuRoypQpGjFihPz9/U1HgRsVL15cK1as0IYNGzRmzBhKKwDAEiisuMahQ4e0bt069e/f33QUGFCyZEmtWrVKa9as0SuvvEJpBQAYx/gM15g2bZqeeeYZFS1a1HQUGFK6dGmtWbNGbdq0UUBAgF5//XXTkQAAPozCiqucOXNGc+fO1e7du01HgWFly5bVmjVrFBoaKrvdrldeecV0JACAj6Kw4ioffvihHnroIVWoUMF0FFhA+fLlFRsbq9atWysgIEBjxowxHQkA4IMorMh2+fJlzZgxQ7GxsaajwEIqVqyYXVrtdruef/5505EAAD6Gwopsn3/+uUJCQlS/fn3TUWAxVapUUWxsbPbygMGDB5uOBADwIRRWSJIyMzM1ZcoUzZw503QUWFS1atUUExOT/UWsp59+2nQkAICPoLBCkrR06VIVK1ZMrVu3Nh0FFlazZk3FxMRkT1r79u1rOhIAwAdQWCGJx7Ai92rXrq01a9aobdu28vf3V58+fUxHAgB4OQorFB8fryNHjuiRRx4xHQUeol69elq9erXCwsIUEBCgHj16mI4EAPBiFFbwGFbkyx133KEVK1YoPDxc/v7+euihh0xHAgB4KRqKj/v1118VExOj2bNnm44CD3TXXXdp2bJl6tixo+x2uyIjI01HAgB4IT/TAWDWtGnT9PTTTys4ONh0FHioRo0aacmSJXrqqae0fPly03EAAF6IwurDzp07py+++EJDhw41HQUerkmTJlq0aJGefPJJrVmzxnQcAICXobD6sJkzZ6pr166qVKmS6SjwAs2bN9eCBQv02GOPKS4uznQcAIAXobD6qJSUFE2fPl2jRo0yHQVe5L777tP8+fPVvXt3rV+/3nQcAICXoLD6qC+//FJ33323GjRoYDoKvEybNm305Zdf6qGHHtKmTZtMxwEAeAEKqw/KzMzMflAA4AodOnTQp59+qq5du2rr1q2m4wAAPByF1QetWLFChQsXVtu2bU1HgRfr1KmTZs+erYiICP3444+m4wAAPBj7sPqgqKgojRkzhsewwuW6dOmitLQ0dezYUatWrdKdd95pOhIAwANRWH1MQkKCDh48qO7du5uOAh/x8MMPKz09XeHh4YqJiVH9+vVNRwIAeBgKq4+JiorS888/L7vdbjoKfEjPnj2Vlpam9u3bKzY2VnXr1jUdCQDgQSisPuS3337TqlWr9NFHH5mOAh/Up08fpaWlKSwsTGvXrlXt2rVNRwIAeAgKqw9599139dRTT6l48eKmo8BH9evXT2lpaWrXrp3i4uJUo0YN05EAAB6Awuojzp8/r08//VQ7duwwHQU+buDAgVeV1ltvvdV0JACAxVFYfcSsWbMUERGhKlWqmI4CaPDgwUpNTVXbtm21bt06Va5c2XQkAICFUVh9QGpqqt577z0tW7bMdBQg24gRI5SWlqa2bdsqLi5OFStWNB0JAGBRFFYf8NVXX+mOO+7QXXfdZToKcJUXXnhBqamp2csDypUrZzoSAMCCKKxezuFwKCoqSlOnTjUdBcjRuHHjlJqaqrCwMMXGxqps2bKmIwEALIbC6uVWrVolPz8/hYWFmY4CXNf48eOz92mNiYlR6dKlTUcCAFiIn+kAcK2oqCiNHj2ax7DC0mw2m9544w21bdtWHTp00IULF0xHAgBYCIXVi/3000/as2ePevbsaToKcFM2m01RUVFq3ry5OnbsqD///NN0JACARVBYvVhUVJSGDx+ugIAA01GAXLHZbHrvvfd01113qVOnTkpMTDQdCQBgARRWL3Xs2DEtX75cAwcONB0FyBObzaYPPvhAdevWVUREhJKTk01HAgAYRmH1Uu+++6769u2rEiVKmI4C5Jmfn59mzZqlW2+9VV27dtVff/1lOhIAwCAKqxe6ePGi5syZo+HDh5uOAuRboUKFNGfOHJUtW1YPPfSQUlJSTEcCABhCYfVC//rXv/TAAw/wjHZ4vEKFCunzzz9X0aJF9cgjjyg1NdV0JACAARRWL5Oamqp3331Xo0aNMh0FcAp/f3999dVXKlSokB599FGlpaWZjgQAcDMKq5eZP3++brvtNjVq1Mh0FMBp7Ha75s2bp5SUFPXp00fp6emmIwEA3IjC6kWyHsM6ZswY01EApytcuLAWLFigCxcuqG/fvsrIyDAdCQDgJhRWL7JmzRplZGQoPDzcdBTAJYoUKaLvvvtOp06d0oABA5SZmWk6EgDADSisXiQqKkqjRo3iMazwaoGBgYqOjtahQ4f0zDPPUFoBwAdQWL3Ejh07tHPnTj322GOmowAuV7RoUS1dulS7d+/W0KFD5XA4TEcCALgQhdVLTJkyRUOHDlXhwoVNRwHcIjg4WMuWLdO2bds0YsQISisAeDEKqxc4fvy4Fi9erGeeecZ0FMCtSpQooRUrVmj9+vV68cUXKa0A4KUorF5g+vTpeuKJJ1SqVCnTUQC3K1WqlFatWqWVK1dq3LhxlFYA8EL+pgOgYP7880/Nnj1bCQkJpqMAxpQpU0Zr1qxRmzZtVLhwYb322mumIwEAnIjC6uE+/vhjtW/fXtWrVzcdBTDqlltuUUxMjEJDQ2W32/XSSy+ZjgQAcBIKqwdLS0vTO++8owULFpiOAlhC+fLlFRMTo9atW8tut2v06NGmIwEAnIDC6sG++eYb1ahRQyEhIaajAJZRqVIlxcbGKjQ0VAEBARo2bJjpSACAAqKweiiHw6G3335bEydONB0FsJyqVatetTxg0KBBpiMBAAqAwuqh1q5dq8uXL6tjx46mowCWVL169exJq91u14ABA0xHAgDkE4XVQ2U9htXPj53JgOupWbOmYmJi1KZNG9ntdj355JOmIwEA8oHC6oF27typH3/8UQsXLjQdBbC8OnXqaM2aNWrbtq3sdrt69eplOhIAII8orB5o6tSpGjJkiIoUKWI6CuAR6tWrp1WrVql9+/ay2+3q3r276UgAgDygsHqYU6dO6bvvvtOBAwdMRwE8SoMGDbRixQqFh4fLbrfrwQcfNB0JAJBLFFYPM336dPXu3VtlypQxHQXwOHfddZeWLl2qjh07yt/fXxEREaYjAQBygW/seJDExETNmjVLzz//vOkogMdq3LixFi9erP79+2vlypWm4wAAcoHC6kH+93//V23atFGtWrVMRwE8WrNmzfTdd9+pT58+iomJMR0HAHATFFYPkZ6ermnTpvGoScBJWrRooQULFujRRx/VunXrTMcBANwAhdVDLFiwQFWqVFGzZs1MRwG8xv33369///vfeuSRR7RhwwbTcQAA10Fh9QBZj2EdM2aM6SiA12nXrp2++OILdevWTVu2bDEdBwCQAwqrB/j+++916dIlvtEMuEh4eLjmzJmjLl26KCEhwXQcAMDfUFg9AI9hBVyvc+fOmjVrljp16qSffvrJdBwAwBXYh9Xi9uzZo/j4eM2fP990FMDrde3aVWlpaXrggQe0Zs0aNWjQwHQkAIAorJY3depUDR48WIGBgaajAD7hkUceUVpamjp06KCYmBjdfvvtpiMBgM+jsFrYf/7zH33zzTc8hhVws8cee0xpaWlq3769YmNjddttt5mOBAA+jcJqYTNmzNBjjz2msmXLmo4C+JwnnnhCaWlpateuneLi4nhgBwAYRGG1qKSkJM2cOVMbN240HQXwWU899dRVpbV69eqmIwGAT6KwWtScOXN03333qU6dOqajAD7t2WefVVpamtq2bat169apatWqpiMBgM+hsFpQRkaGpk6dqi+++MJ0FACShg4dml1a4+LiVLlyZdORAMCnUFgt6Ntvv1WFChXUokUL01EA/J+RI0cqNTU1e3lAhQoVTEcCAJ9BYbWYrMewjh071nQUAH8zduzYq0rrLbfcYjoSAPgECqvFbNiwQefOnVOXLl1MRwGQg9dee01paWkKCwtTbGysypQpYzoSAHg9nvVpMVFRURo5cqQKFSpkOgqA65gwYYIeeOABtW/fXufPnzcdBwC8HoXVQvbt26eNGzfqySefNB0FwA3YbDa9+eabat26tcLDw3Xx4kXTkQDAq1FYLWTatGkaNGiQgoKCTEcBcBM2m01Tp05V06ZN1bFjR126dMl0JADwWhRWizh9+rTmzZunwYMHm44CIJdsNpvee+89NWzYUJ07d1ZSUpLpSADglSisFjFjxgz16NFD5cqVMx0FQB74+fnpww8/VK1atRQZGank5GTTkQDA61BYLSA5OVkffvihRo4caToKgHzw8/PT7NmzVblyZT344IO6fPmy6UgA4FUorBbw6aefqnnz5qpbt67pKADyqVChQpozZ45Kly6thx9+WCkpKaYjAYDXoLAalvUY1tGjR5uOAqCA/P399fnnn6tIkSLq0aOHUlNTTUcCAK9AYTUsOjpapUuXVqtWrUxHAeAEdrtdX331lRwOh3r16qW0tDTTkQDA41FYDYuKitKYMWNks9lMRwHgJAEBAfr666+VnJysJ554Qunp6aYjAYBHo7AatHHjRp06dUrdunUzHQWAkxUuXFgLFy7U2bNn1b9/f2VkZJiOBAAei8Jq0JQpU3gMK+DFihQpou+++07Hjh3T008/rczMTNORAMAjUVgNOXjwoL7//nv169fPdBQALhQUFKTFixfrwIEDGjRokBwOh+lIAOBxKKyGTJ06Vc8884yKFi1qOgoAFytWrJiWLVumX375RcOGDaO0AkAeUVgN+OOPP/TVV19pyJAhpqMAcJPg4GAtX75cmzdv1qhRoyitAJAHFFYDPvzwQz388MOqUKGC6SgA3KhEiRJatWqV1q5dq5deeonSCgC55G86gK/566+/NGPGDK1du9Z0FAAGlCpVSmvWrFGbNm0UEBCgCRMmmI4EAJZHYXWzzz//XE2aNFH9+vVNRwFgSJkyZbJLq91u16uvvmo6EgBYGoXVjTIzMzVlyhR99NFHpqMAMKxcuXKKiYlRaGioAgIC9OKLL5qOBACWRWF1oyVLlig4OFitW7c2HQWABVSoUEExMTFq3bq1AgICNGLECNORAMCSKKxuxGNYAfxd5cqVFRsbq9DQUNntdnYPATzM7t27veKhIH5+fpZerkhhdZMtW7bo6NGjevjhh01HAWAxt956q2JjY9W6dWvZ7XY988wzpiMByCVvKKuS9e+DwuomU6ZM0YgRI+Tvz99yANeqXr36VZPW/v37m44EAJZBe3KDw4cPKzY2Vh9//LHpKAAsrFatWoqJiVHbtm1lt9v1+OOPm44EAJZAYXWDadOm6emnn1ZwcLDpKAAs7rbbbtPq1avVrl072e12Pfroo6YjAYBxFFYXO3v2rL744gvt2rXLdBQAHuL222/XypUr1b59e9ntdvn7++vFF1/UL7/8IrvdbjoeALgdhdXFZs6cqQcffFCVKlUyHQWAB2nYsKFWrFih1q1b6/Lly/Lz89P333+vdu3amY4GAG7nZzqAN7t8+bLef/99jRo1ynQUAB5oz549SklJUWpqqlJSUvTll1+ajgQARlBYXejLL7/U3XffrQYNGpiOAsADzZo1SzabTf7+/nI4HJo/f77lt54BAFegsLpI1mNYR48ebToKAA+1du1abd26VSNHjlSpUqWUlJSkpUuXmo4FAG5HYXWR5cuXq3Dhwmrbtq3pKAA8WIMGDfTWW2/p7NmzWrVqFf9MAeCT+NKVi0RFRWn06NE8hhWAU9hsNrVv357HQALwSUxYXWDbtm06dOiQevToYToKAC/jDWVV8p77AOAeFFYXiIqK0vPPP89+iQAAQJIUHh6ue+65R+fPn7/q/e7du6thw4Y6ceKE4uPj1b9/fzVv3lzh4eF5/oytW7eqYcOGeu+995wV2zIorE7222+/afXq1RowYIDpKAAAwEKqVKmiZcuWZb/ev3+/Ll++nP06MDBQ3bp108iRI/N87bS0NL311lu68847nZLVaiisTvbOO+/oqaeeUvHixU1HAQAAFhIREaHFixdnv46OjlZkZGT264YNGyoyMlJVqlTJ87U//fRTtWjRQtWrV3dGVMuhsDrR+fPn9dlnn2nYsGGmowAAAIu58847lZSUpMOHDysjI0PLly9XREREga978uRJfffdd3r22WedkNKa2CXAiT766CNFRETk67+MAACA94uIiFB0dLRCQkJUs2ZNlStXrsDX/J//+R8NGTJEQUFBTkhoTRRWJ0lJSdF7772n5cuXm44CAAAsKjIyUn379tWJEyfUpUuXAl8vLi5OycnJeuCBB5yQzroorE7y1VdfqUGDBrrrrrtMRwEAABZVqVIlVa5cWevXr9f48eMLfL0tW7Zo165dCg0NlSQlJibKz89PBw4c0PTp0wt8faugsDqBw+FQVFSUpk6dajoKAACwuAkTJujPP/9UUFCQ0tPTs9/PzMxUWlqa0tPT5XA4lJKSIj8/vxtukzlkyBA99dRT2a/ffPNN3XLLLV63npXC6gQrV66Un5+f2rdvbzoKAACwuKpVq+b4fkJCgvr375/9OiQkRCEhIZozZ851r1W0aFEVLVo0+3XhwoUVGBioEiVKOC+wBVBYneDtt9/mMawAAOC6Vq5cmeP7/v7++uWXXyRJlStXzv45vyZPnlygP29VbGtVQNu3b9e+ffv06KOPmo4CAADglZiwFtCUKVM0fPhwBQQEmI4CAADy4Pvvv9fjjz+uYcOG6emnn7bsQ38SEhI0aNCgHI/Fx8e7OY0ZTFgL4OjRo1q+fLkGDhxoOgoAXMOVzy6fPn26unXrprvvvlsffPCBs6MDbpGUlKQ//vhDr776qipUqKA+ffpox44dpmNdo3HjxoqPj8/xL1/BhLUAPv/8c/Xr18/rFjYD8B5Zzy7v3bu3pOs/u7xjx46aPXt2rq976623auTIkZo/f77TMwM34nA4lJSUpEuXLikxMVGXLl265ufcHjt//rz++uuv7Gt/+eWX2rNnjxISEgzeIXJCYS2AoUOHqkiRIqZjAMB1ZT27PKuwZj27PGt/xoYNG6phw4batGlTnq7btWtXSdLSpUudGxheJzMzU4mJifkqlDn9nJSUpCJFiqhYsWIKDg5WcHDwdX++5ZZbVKNGjRyPBQcH67ffflN4eLhsNpuKFSum999/X4888ohL/36kpqZq0qRJ2rx5sy5evKiqVatq+PDhuu+++645d/ny5frggw905swZBQQEqFWrVnrppZdUrFgxSdLYsWO1ZcsW/fXXXypbtqz69eunhx9+2KX5TaGwFoBV17oAQJY777xTS5Ys0eHDh1WtWjUtX75cn3/+uVdtKA7nysjIKFCh/Pt5ycnJCgoKum5pvPJ1hQoVVKdOnRsW0WLFiqlQoUJOuVe73a7AwECNHj1aI0aMcMsQKj09XRUqVNCcOXNUsWJFrV+/XqNHj9bChQtVuXLlq86955579Nlnn6lUqVJKTk7W+PHjNX36dL300kuSpAEDBmjChAkKCAjQ4cOH1b9/f9WrV0933HGHy+/D3SisAODlXPHsclhHWlpagX81fuXPKSkpKlq06E2nl8HBwapSpcpNi2jRokXl52fNr8xUqFBBZ8+edetnBgUF6bnnnst+3bp1a1WuXFm7d+++prBWqFDhqteFChXS0aNHs1/Xrl07+2ebzSabzaZjx45RWAEAnsfZzy5HwaSkpORpQnmzY2lpabkql8WKFVO1atVuem5QUBD7irvRmTNndOTIEdWqVSvH49u3b9fgwYOVmJiowMBAvfPOO1cdnzRpkhYtWqTLly/r9ttv1/333++G1O5HYQUAL+fsZ5f7kqzHYzprennp0iU5HI5cF8zy5cvf9LwiRYpQMD1UWlqaxo4dqy5duqhmzZo5ntOoUSNt2rRJv//+uxYsWKBKlSpddXzcuHF66aWX9PPPP2vr1q03fIyrJ6OwAoAPcOazy6X//os2MzNTmZmZSk9PV0pKivz9/Z22tjC/HA6H/vrrL6dNLxMTE+Xn53fTtZdZP1eqVOmm5xUuXJiCCWVmZurll1+W3W7Xyy+/fNPzy5cvr5YtW+qFF164ZneOQoUKqVGjRlqyZInmz5+f/SVLb0JhBQAf4Mxnl0vS66+/rujo6OzX//rXvzRx4kQ9+OCDTsmbZfny5dqzZ0+evvQTEBBww6lk1s+lSpXSrbfeetMpJw+GgbM5HA699tprOnv2rD744INcT0UzMjJ07NixfB/3ZJYvrLt371ZmZqbpGAXm5+en+vXrm44BwIe48tnlkydPdsszy3fv3q1jx47luEVRTkW0WLFiXvsrUXiPiRMn6tdff9W//vWvG+5MsGTJEjVu3FgVK1bUyZMn9d5776lZs2aSpLNnzyo+Pl6tW7dW4cKFtXnzZi1fvlxvvfWWu27DrSxfWL2hrErecx8A4E6jRo0yHQFwqpMnT+rrr79WQECAQkNDs99/7bXX1LhxY3Xt2lWLFi1SxYoVdfjwYU2bNk2XLl1ScHCw7rvvPj3//POS/rsrwLx58zRx4kRlZmaqYsWKeuGFF9SmTRszN+ZiNofDcd2DISEhjm3btrkxzrV27txp9POdqUGDBqYjAPBw7vpnojueXc4/E+EN6CnOY7PZEhwOR0hOxyw/YQUAuF/Ws8sBwAqsuZMvAAAA8H8orADgAy5evKjhw4eradOm6tChg5YuXZrjeQ6HQ1OnTlWrVq3UqlUrTZ06VVcuHdu7d6969OihJk2aqEePHtq7d6+7bgGAD6OwAoAPmDx5sux2u+Li4vTmm29q0qRJOnjw4DXnff3111q7dq2++eYbLViwQOvWrdPXX38t6b97rw4bNkwRERHasGGDunbtqmHDhiktLc3dtwPAx1BYAcDLJScna/Xq1RoyZIiCgoLUqFEjhYaGavHixdecGx0drSeeeEIVKlRQ+fLl9eSTT2rRokWSpK1btyojI0OPP/64AgIC1Lt3bzkcDm3ZssXdtwTAx1BYAcDLHTlyRP7+/qpevXr2e3Xr1tWhQ4euOffQoUOqW7fuVedlTWIPHjyoOnXqXPWUpttuuy3H6wCAM1FYAcDLJScnq2jRole9V6xYMSUlJeV4brFixa46Lzk5WQ6HQ8nJyQoODs7VdQDAmSisAODlgoKCrimVSUlJ15TYnM5NTExUUFCQbDabgoKClJiYmKvrAIAzUVgBwMtVq1ZN6enpOnLkSPZ7+/btU61ata45t1atWtq3b1/26/3796t27dqSpNq1a2v//v1X7Rqwf//+HK8DAM5EYQUALxcUFKSwsDDNmDFDycnJ+vHHH7V27VpFRkZec26XLl302Wef6ffff9fp06f16aefqmvXrpKkJk2aqFChQvryyy+VmpqquXPnSlL2s80BwFUorADgA8aNG6fLly8rNDRUL7zwgsaNG6fatWsrISFBTZs2zT6ve/fuCg0N1UMPPaRu3brp/vvvV/fu3SVJdrtd7777rqKjo9WiRQt99913evfdd2W3203dFgAfYbvyVzt/FxIS4ti2bZsb41yLZ/QCwP/HPxMBa+H/k85js9kSHA5HSE7HmLACAADA0iisAAAA+eTn5x1Vyur34W86AAAAgKeqX7++6Qg+wdp1GgAAAD6PwgoAAABLy3Nh7dixoz799FNXZMnRBx98oLFjx97wnFOnTqlp06bKyMi46fVyc27Tpk117Nixm17rxIkTatiwodLT0296rqu8/vrr6tOnT67ODQ0N1ezZs12cCAAAwLnyXFiXL1+uJ5988rrHP/nkE7Vq1apAofKqYsWKio+PV6FChfJ8br9+/bRgwYKrzomPj1fVqlVdkhUAAAB549QlASYnjQAAAPBOuS6sw4cP17Bhw676tfInn3yili1basSIESpTpox69uypZ599Vps2bVKxYsVUsmRJSdf+KvrvU9jhw4eratWqKl68uBo3bqz169fn6Sb+/qv5fv36afr06Xr88cfVrFkzDRw4UOfPn7/m3Pfee0/bt2/XG2+8oaZNm2ry5MmSpIYNG+ro0aOSpO+//17du3fXvffeq7CwMH3wwQd5ypaV57333lOLFi1UrFgxRUZG6uzZs+rdu7eKFy+uJk2a6Lfffss+f+PGjWrSpIlKlCihJk2aaOPGjdnHfv31V7Vu3VrBwcFq3769zpw5c9Vnbd68WS1atFDJkiV11113KS4uLs95AQAArOSGhTVrnWdGRobmz5+vXr16XXPOli1bVLNmTf3+++/64osvNHPmTDVv3lyJiYm6cOFCrkI0adJEP/30k86dO6devXqpe/fuunz5ct7v5grLli3TxIkTtW7dOqWlpemTTz655pxhw4apUaNGevnllxUfH69XXnnlmnMCAwM1efJkbdy4UTNmzNC8efMUExOT5zwrVqzQ559/rhMnTujQoUNq3ry5+vXrp3Pnzun222/X+PHjJUnnzp1T586dNWzYMJ09e1YjR45U586ddfbsWUlSr1691LhxY505c0avvvrqVeuJT5w4oc6dO2vcuHE6d+6coqKi9PDDD+uPP/7Ic14AAACruGFhzSqcsbGxCgoK0r333nvNOZUqVdLQoUPl7++vwMDAfIXo06ePypQpI39/f40aNUopKSnat29fvq6V5cEHH1T16tVVpEgRhYeH5/t6TZo00W233SY/Pz/VrVtXnTp1Un4eV/vggw+qVq1aKlGihDp27KhatWopLCxM/v7+6t69u3788UdJ0tKlS1WnTh09/vjj8vf312OPPaZ69epp8eLFOnr0qLZu3aqJEyeqcOHCuv/++xUZGZn9GV988YU6deqkTp06yc/PT+3bt1dISIiWLVuWr3sHAACwghsW1nPnzkmS5s6dm+N0VZJTvpwUFRWl22+/XSVKlFDJkiV18eLFa37VnVdly5bN/rlIkSJKTk7O13V27Nih/v376/7771fz5s01f/78XE+Or1SmTJnsnwMDA1W+fPmrXicmJkqSTp48qWrVql31Z6tVq6YTJ07o5MmTKlWqlIoWLXrVsSxHjhzR119/rZIlS2b/9cMPP+jUqVN5zgsAAGAVNyysly5d0vHjx/Xtt99et7DabLYbvpakokWLXlUY//Of/2T/vH79ev3zn//U/Pnzdf78eV24cEElSpSQw+HI043kV055r/Tiiy8qNDRUq1ev1qZNm9SjRw+XZqtUqZKOHDly1XtHjx5V5cqVVbFiRZ0/f15JSUlXHctStWpVPf7447pw4UL2X0lJSTfdFgyA57D64xNzy1vuA4B73PDRrMHBwerXr59q1Kih22+/PVcXLF++vI4fP67U1FQFBARIku6++24tXLhQAwYM0MmTJ/Xxxx9nTxgvXbokf39/3XLLLUpPT9ebb76pP//8s4C3lXtlypTR8ePHr3s8OTlZJUqUUOHChfXLL79o2bJlat68ucvydOrUSUOHDtXcuXPVo0cPLViwQLt371ZERITKli2rkJAQ/eMf/9Abb7yh+Ph4LV68WF26dJH036UVTZo00cqVKxUWFqa0tDRt3rxZtWvXVpUqVVyWGYD78BhIAL7ohv+JW7p0aa1Zs+a609WctG3bVnfccYcqVKiQ/Wv5ESNGKCAgQOXLl9eTTz6p3r17Z58fHh6uBx54QLfddpuqVaumIkWKuHUP1N69e2v16tVq0aKF/ud//uea46+88opmzJihZs2aaebMmerQoYNL85QpU0ZLlizRlClTVKZMGf3zn//UkiVLsv9ezp07V1u2bFHp0qU1fvx4PfHEE9l/tmrVqlq0aJHeeOMN3XLLLapatarefvttZWZmujQzAACAK9lu9OvtkJAQR36+YORMO3fuNPr5ztSgQQPTEQAAACzJZrMlOByOkJyOsYgIAAAAlnbDNaxWsmTJEk2YMOGa9ytVqqTvvvvO/YH+pmnTpjm+/+GHH6px48ZuTgPAlzkcjpt+oRQAPInHFNaIiAhFRESYjnFd8fHxpiMAgA4dOqTevXtr8+bNpqMAgNOwJAAAvMjkyZMVHh5uOgYAOJXHTFgBADd26NAhRUdH68CBAy77jMzMTF24cEGlS5d22WcAwN8xYQUALzF58mQNHjxYpUqVctln7N+/X6GhoS67PgDkhAkrAHiBrOnqwYMHXfo5tWvX1q+//qpz584xZQXgNkxYAcALTJ48WUOGDFHJkiVd+jn+/v5q2rSpNm3a5NLPAYArMWEFAA938OBBt0xXs7Rs2VIbNmxQ586d3fJ5AMCEFQA8nLumq1latWqlH374wS2fBQASE1YA8GgHDx7U4sWL3TZdlaR7771X27dvV2pqqgICAtz2uQB8FxNWAPBg7p6uSlLx4sVVu3Ztbd++3W2fCcC3MWEFAA9lYrqaJWtZwL333uv2zwbge5iwAoCHmjx5soYOHerW6WqWrC9eAYA7MGEFAA9kcroq/bewDh8+XA6HQzabzUgGAL6DCSsAeKBJkyYZm65K0q233qoiRYoYK8wAfAsTVgDwMAcPHtSSJUuMl8WWLVvqhx9+UJ06dYzmAOD9mLACgIcxPV3NwjpWAO7ChBUAPIhVpqvSf3cKmDFjhukYAHyA5Sesfn6Wj5gr3nIfAMyaNGmShg0bZny6KkkNGzbUyZMndebMGdNRAHg5y09Y69evbzoCAFjCwYMHtXTpUh04cMB0FElSoUKF1KxZM23cuFFdunQxHQeAF2PsBwAeYuLEiZZYu3ol1rECcAfLT1gBANKBAwe0dOlSS6xdvVKrVq302muvmY4BwMsxYQUAD2CltatXatasmX766SddvnzZdBQAXowJKwBY3IEDB7Rs2TLLTVclqVixYqpXr54SEhLUsmVL03EAeCkmrABgcVnT1RIlSpiOkqNWrVrphx9+MB0DgBdjwgoAFmbl6WqWli1b6osvvjAdA4AXY8IKABY2ceJES09Xpf8W1o0bN8rhcJiOAsBLUVgBwKL279+v5cuXa9iwYaaj3FDlypUVHBysffv2mY4CwEtRWAHAoqy+dvVKLVu2ZB0rAJehsAKABXnKdDULDxAA4EoUVgCwoEmTJmn48OEeMV2V/rtTAIUVgKtQWAHAYrKmq0OHDjUdJdfuuOMOnT59Wr///rvpKAC8EIUVACzG06arkuTn56fmzZtr48aNpqMA8EIUVgCwkH379nncdDULywIAuAqFFQAsxBOnq1nYKQCAq1BYAcAi9u3bpxUrVnjMzgB/17RpU/3yyy/666+/TEcB4GUorABgEZMmTdLzzz+v4sWLm46SL0FBQWrQoIG2bt1qOgoAL0NhBQALyJqueuLa1SuxHysAV6CwAoAFePp0NQvrWAG4AoUVAAzzlumq9N/CumnTJmVmZpqOAsCLUFgBwLCJEyd6xXRVkipUqKDSpUtrz549pqMA8CIUVgAwaO/evVq5cqVXTFezsCwAgLNRWAHAIG9Zu3olHiAAwNkorABgyN69e7Vq1Sqvmq5K7BQAwPkorABgiDdOVyWpXr16On/+vE6dOmU6CgAvQWEFAAOypqtDhgwxHcXp/Pz8mLICcCoKKwAY4E07A+SEwgrAmSisAOBm3jxdzcJOAQCcicIKAG42ceJEjRgxwmunq5LUpEkT7d69W0lJSaajAPACFFYAcKO9e/dq9erVXrczwN8VKVJEd911l+Lj401HAeAFKKwA4EZZ09Xg4GDTUVyOdawAnIXCCgBukjVd9ea1q1dq1aoV61gBOAWFFQDcZMKECT4zXZWkFi1aaPPmzcrIyDAdBYCHo7ACgBvs2bNHa9as8ZnpqiTdcsstKl++vHbt2mU6CgAPR2EFADfwpbWrV2JZAABnoLACgIv54nQ1C1+8AuAMFFYAcLGJEydq5MiRPjddlSisAJyDwgoALpQ1XR08eLDpKEbcdtttSkpK0vHjx01HAeDBKKwA4EITJkzw2emqJNlsNqasAAqMwgoALrJ7927FxMT47HQ1C4UVQEFRWAHARXx57eqV2CkAQEFRWAHABXbv3q3Y2Fif3Bng7xo1aqT9+/fr0qVLpqMA8FAUVgBwgazparFixUxHMa5w4cK65557tGXLFtNRAHgoCisAOFnWdNXX165eiWUBAAqCwgoATsZ09Vp88QpAQVBYAcCJdu3axXQ1By1atNCWLVuUnp5uOgoAD0RhBQAnYrqas9KlS6tq1arasWOH6SgAPBCFFQCcZNeuXVq7di3T1etgWQCA/KKwAoCTTJw4UaNGjWK6eh0UVgD5RWEFACfImq4+99xzpqNYVtZOAQ6Hw3QUAB6GwgoATsB09eZq1qyp9PR0HT161HQUAB6GwgoABcR0NXdsNhvLAgDkC4UVAApowoQJTFdziQcIAMgPCisAFMDOnTsVFxfHdDWXmLACyA8KKwAUwMSJEzV69Gimq7l0zz336PDhw7p48aLpKAA8CIUVAPJp586dWrduHdPVPLDb7QoJCdGmTZtMRwHgQSisAJBPWTsDFC1a1HQUj8KyAAB5RWEFgHxg7Wr+UVgB5BWFFQDyYcKECRo9ejTT1Xxo3ry5tm7dqrS0NNNRAHgICisA5NHOnTv1/fffM13Np5IlS6pGjRr66aefTEcB4CEorACQR0xXC45lAQDygsIKAHmQNV0dNGiQ6SgejQcIAMgLCisA5AHTVefImrA6HA7TUQB4AAorAOQS01XnqVatmvz8/PTrr7+ajgLAA1BYASCXxo8fz3TVSWw2G8sCAOQahRUAcuGXX37R+vXrma46EV+8ApBbFFYAyIUJEyZozJgxTFediMIKILcorABwE1nT1WeffdZ0FK9y11136ejRozp37pzpKAAsjsIKADfBdNU1/P391bRpU23atMl0FAAWR2EFgBtguupaLAsAkBsUVgC4gfHjxzNddSF2CgCQG/6mAwCAVe3YsUM//PCDPv30U9NRvNa9996r7du3KzU1VQEBAabjALAoJqwAcB2sXXW94OBg1alTR9u3bzcdBYCFUVgBIAc7duzQhg0b2HfVDVgWAOBmKKwAkIOs6WpQUJDpKF6PL14BuBkKKwD8TdZ0lZ0B3COrsDocDtNRAFgUhRUA/obpqnuVK1dOfn5+euGFF9S1a1cdP37cdCQAFkNhBYAr/Pzzz0xX3Wjq1KkKDg7W2bNnNW3aNC1evFh2u910LAAWQ2EFgCswXXWv8PBwBQQEKD09XRkZGapQoYLKly9vOhYAi6GwAsD/+fnnn7Vx40amq250xx136KuvvlLhwoUlSW3atDGcCIAVUVgB4P9MmDBBL7zwAtNVN4uMjNSrr74qSWrRooXhNACsiCddAYD+/3T1888/Nx3FJ7388svasWOHOnfubDoKAAuisAKAmK6aZrPZNG/ePNMxAFgUhRWAz/vpp5+YrgKAhbGGFYDPY7oKANbGhBWAT/vpp5+0efNmffnll6ajAACugwkrAJ+WNV0NDAw0HQUAcB1MWAH4LKarAOAZmLAC8FlMVwHAMzBhBeCTmK4CgOdgwgrAJ40fP57pKgB4CCasAHzOjz/+qC1btmju3LmmowAAcoHCCsDnTJgwQS+++CLTVSfbvXu3MjMzTccoMD8/P9WvX990DABXoLAC8ClMV13HG8qq5D33AXgT1rAC8ClMVwHA8zBhBeAzmK4CgGdiwgrAZ4wfP57pKgB4ICasAHzCjz/+qK1bt+qrr74yHQUAkEdMWAH4BKarAOC5mLAC8HpMVwHAszFhBeD1mK4CgGdjwgrAqzFdBQDPx4QVgFd7/fXXma4CgIdjwgrAa23fvl3btm3Tv//9b9NRAAAFwIQVgNcaP368xo4dy3QVADwcE1YAXilrujpv3jzTUfA34eHhOn36tGJjY1WqVKns97t37669e/dqxYoVOnHihGbOnKk9e/aoePHiWrlyZa6vffbsWfn5/Xcec/fdd2vWrFkuuQ8A7kNhBeCVsqarRYoUMR0FOahSpYqWLVum3r17S5L279+vy5cvZx8PDAxUt27d1LFjR82ePTtP154+fbqaN2/u1LwAzGJJAACvkzVdffrpp01HwXVERERo8eLF2a+jo6MVGRmZ/bphw4aKjIxUlSpVTMQDYDEUVgBeh+mq9d15551KSkrS4cOHlZGRoeXLlysiIsIp1x47dqzuv/9+DRw4UPv27XPKNQGYxZIAAF4lISFBCQkJrF31ABEREYqOjlZISIhq1qypcuXKFfiab775pm6//XY5HA59+eWXeuaZZxQdHa3ixYs7ITEAU5iwAvAqTFc9R2RkpJYtW6ZFixapS5cuTrnmPffcoyJFiigwMFADBgxQcHCwtm/f7pRrAzCHwgrAayQkJGj79u0aMGCA6SjIhUqVKqly5cpav3692rVr55LPsNlscjgcLrk2APdhSQAAr8F01fNMmDBBf/75p4KCgpSenp79fmZmptLS0pSeni6Hw6GUlBT5+fnJbrdf91qnTp3Sf/7zHzVo0ECZmZmaO3euLly4oHvuuccdtwLAhSisALxC1nR1/vz5pqMgD6pWrZrj+wkJCerfv3/265CQEIWEhGjOnDnXvVZSUpImTpyo48ePKyAgQPXq1dOHH36okiVLOjs2ADez3ehXJSEhIY5t27a5MQ4A5E+XLl3UoUMHDRkyxHQUn7Vz507TEZymQYMGpiMAPsdmsyU4HI6QnI4xYQXg8bZt28Z0FQC8GIUVgMcbP368XnrpJdau+oCEhAQNGjQox2Px8fFuTgPAXSisADzatm3b9OOPP+rrr782HQVu0LhxY4op4IPY1gqAR2O6CgDej8IKwGNlTVefeuop01GQT3PnzlXPnj3VqFEjvfLKK9c9LzU1VW+99Zbatm2rFi1aaNKkSUpLS8s+fvHiRQ0fPlxNmzZVhw4dtHTpUnfEB+AmFFYAHovpqucrV66cBg4cqG7dut3wvI8//li7d+/Wt99+qyVLlmjPnj2aNWtW9vHJkyfLbrcrLi5Ob775piZNmqSDBw+6Oj4AN6GwAvBIW7duZbrqBcLCwtSuXTuVKFHihufFxcWpV69eKlGihEqXLq1evXrp22+/lSQlJydr9erVGjJkiIKCgtSoUSOFhoZq8eLF7rgFAG5AYQXgkZiu+p4r9w13OBz6/fffdenSJR05ckT+/v6qXr169vG6devq0KFDBlICcAUKKwCPs3XrVv38888aMGCA6Shwk5YtW+rLL7/UuXPndObMGc2dO1eSdPnyZSUnJ6to0aJXnV+sWDElJSWZiArABdjWCoDHyZquFi5c2HQUuMnAgQN16dIlde/eXXa7XY888oj27NmjMmXK6MyZM9eU06SkpGtKLADPxYQVgEfJmq6ydtW3FClSRK+88opiYmK0YsUKlShRQvXr15efn5+qVaum9PR0HTlyJPv8ffv2qVatWgYTA3AmCisAj8J01bukp6crJSVFmZmZyszMVEpKitLT06857/fff9fp06flcDj0888/66OPPtLgwYMlSUFBQQoLC9OMGTOUnJysH3/8UWvXrlVkZKS7bweAi7AkAIDHiI+P188//6wFCxaYjgInmTVrlj788MPs10uWLNGgQYPUrVs3de3aVYsWLVLFihV17NgxvfLKKzp37pwqVKig559/Xi1atMj+c+PGjdOrr76q0NBQlShRQuPGjVPt2rVN3BIAF7Bd+a3LvwsJCXFs27bNjXEA4Po6d+6szp0767nnnjMdBTnYuXOn6QhO06BBA9MRAJ9js9kSHA5HSE7HmLAC8Ajx8fH65ZdftHDhQtNRAABuxhpWAB6BtasA4LuYsAKwPKarAODbmLACsDymqwDg25iwArC0LVu2MF0FAB/HhBWApTFdBQAwYQVgWVu2bNHOnTv17bffmo4CADCICSsAyxo/frxefvllpqsA4OOYsAKwJKarAIAsTFgBWBLTVQBAFiasACyH6SoA4EpMWAFYzuuvv850FQCQjQkrAEvZvHmzdu/ere+++850FACARTBhBWAprF31XH5+3vGvFG+5D8CbMGEFYBlZ09VFixaZjoJ8qF+/vukIALwU/xkJwDKypqsBAQGmowAALIQJKwBLYLoKALgeJqwALCFrZwCmqwCAv2PCCsC4TZs2ac+ePYqOjjYdBQBgQUxYARg3fvx4vfLKK0xXAQA5orACMCprutq3b1/TUQAAFkVhBWAU01UAwM1QWAEYw3QVAJAbFFYAxrz++utMVwEAN0VhBWDExo0btXfvXqarAICborACMIK1qwCA3KKwAnC7jRs3at++fUxXAQC5QmEF4HZMVwEAeUFhBeBWWdPVJ5980nQUAICHoLACcCt2BgAA5BWFFYDbbNiwQfv372e6CgDIEworALdh7SoAID8orADcYsOGDTpw4ADTVQBAnlFYAbgF01UAQH5RWAG4XNZ09YknnjAdBQDggSisAFyOnQEAAAVBYQXgUj/88IMOHjzIdBUAkG8UVgAuxdpVAEBBUVgBuEzWdJWdAQAABUFhBeAy48eP17hx42S3201HAQB4MAorAJdg7SoAwFkorABcgukqAMBZKKwAnI7pKgDAmSisAJzu9ddfZ7oKAHAaCisAp1q/fr0OHz7MdBUA4DQUVgBOxdpVAICzUVgBOE3WdPXxxx83HQUA4EUorACchukqAMAVKKwAnILpKgDAVSisAJyCnQEAAK5CYQVQYN9//71+++03pqsAAJegsAIoMNauAgBcicIKoECypqt9+vQxHQUA4KUorAAKhOkqAMDVKKwA8o3pKgDAHSisAPKNnQEAAO5AYQWQL+vWrdORI0eYrgIAXI7CCiBfxo8fr1dffZXpKgDA5SisAPJs3bp1Onr0KNNVAIBbUFgB5FnWzgD+/v6mowAAfACFFUCeMF0FALgbhRVAnmTtDMB0FQDgLhRWALkWFxenY8eOMV0FALgVhRVArrF2FQBgAoUVQK4wXQUAmEJhBZArWfuuMl0FALgbhRXATWVNV3v37m06CgDAB1FYAdzU66+/znQVAGAMhRXADcXFxen48eNMVwEAxlBYAdwQ01UAgGkUVgDXFRcXpxMnTjBdBQAYRWEFcF1MVwEAVkBhBZCjrOlqr169TEcBAPg4CiuAazgcDv3jH/9gugoAsAQKK4BrxMXF6eTJk0xXAQCWQGEFcBWHw8HaVQCApVBYAVwlLi5Op06dYroKALAMCiuAbExXAQBWRGEFkC1ruvrYY4+ZjgIAQDYKKwBJ7AwAALAuCisASdLatWv1n//8h+kqAMByKKwAWLsKALA0CisApqsAAEujsAI+Lmu6+tprrzFdBQBYEoUV8HFr167V77//rkcffdR0FAAAckRhBXwYOwMAADwBhRXwYbGxsTp9+jTTVQCApVFYAR/FzgAAAE9BYQV8FNNVAICnoLACPoidAQAAnoTCCvggpqsAAE9CYQV8TNbOAK+99poKFSpkOg4AADdFYQV8TExMjP744w+mqwAAj0FhBXzIlWtXma4CADwFhRXwIUxXAQCeiMIK+AimqwAAT0VhBXxETEyMzpw5w3QVAOBxKKyAD2C6CgDwZBRWwAesWbNGZ86cUc+ePU1HAQAgzyisgJdjugoA8HQUVsDLrVmzRmfPnmW6CgDwWBRWwIsxXQUAeAN/0wEAOF9CQoLKly+vPXv26Ny5c0xXAQAejcIKeKGBAwdqx44dKl26NNNVAIDHY0kA4IUyMjKUnp6u06dPa/To0Xr99ddNRwIAIN8orICX8/PzU82aNU3HAAAg31gSAHihS5cuSZKqVKmipUuX6s477zScCACA/KOwAl4oOTlZTZs21Zo1axQcHGw6DgAABUJhBbzQ0aNHZbfbTccAAMApWMMKeCHKKgDAm1BYAQAAYGkUVgAAAFgahRUAAACWRmEFAACApVFYAQAAYGlsawXk0e7du5WZmWk6RoH5+fmpfv36pmMAAHBTTFiBPPKGsip5z30AALwfhRUAAACWRmEFAACApVFYAQAAYGkUVgAAAFgahRUAAACWRmEFAACApVFYAQAAYGkUVgAAAFgahRUAAACWRmEFAACApVFYAQAAYGkUVgAAAFiav+kAgDcLDw/X6dOnFRsbq1KlSmW/3717d+3du1crVqzQiRMnNHPmTO3Zs0fFixfXypUrb3rdU6dOqWvXrle999dff2n06NF68sknnX4fAACYxIQVcLEqVapo2bJl2a/379+vy5cvZ78ODAxUt27dNHLkyFxfs2LFioqPj8/+a+HChfLz81NYWJhTswMAYAUUVsDFIiIitHjx4uzX0dHRioyMzH7dsGFDRUZGqkqVKvn+jMWLF6tx48aqXLlygbICAGBFFFbAxe68804lJSXp8OHDysjI0PLlyxUREeG06zscDkVHR6tLly5OuyYAAFbCGlbADSIiIhQdHa2QkBDVrFlT5cqVc9q1t2/frrNnz6pDhw5OuyYAAFZCYQXcIDIyUn379tWJEyecPgmNjo5W+/btFRQU5NTrAgBgFSwJANygUqVKqly5stavX6927do57bqXL1/WqlWrWA4AAPBqTFgBN5kwYYL+/PNPBQUFKT09Pfv9zMxMpaWlKT09XQ6HQykpKfLz85Pdbr/pNWNiYlS8eHE1bdrUldEBADCKwgq4SdWqVXN8PyEhQf37989+HRISopCQEM2ZM+em14yOjlZERIRsNpvTcgIAYDU2h8Nx3YMhISGObdu2uTEOYH07d+40HcFpGjRoYDoCAACSJJvNluBwOEJyOsYaVgAAAFgaSwIAi0pISNCgQYNyPBYfH+/mNAAAmENhBSyqcePGFFMAAMSSAAAAAFgcE1bADebOnatFixbpwIED6tixoyZPnpzjeQcOHFBUVJR2796tCxcu6Jdffrnq+N+3r0pJSVHPnj318ssvuyw7AACmUVgBNyhXrpwGDhyojRs36vLly9c9z9/fX+Hh4erZs6eGDx9+zfErlwgkJycrNDSUR7ICALwehRVwg7CwMEnSrl27blhYa9SooRo1aujo0aM3vebq1atVunRpNW7c2Gk5AQCwItawAh4qOjpaXbp04aEBAACvR2EFPNDJkye1bds2denSxXQUAABcjsIKeKDFixfrnnvuUZUqVUxHAQDA5SisgAdavHixunbtajoGAABuwZeuADdIT09XRkaGMjMzlZmZqZSUFBUqVEj+/lf/X9DhcCg1NVVpaWmS/rttlc1mU0BAQPY5P/30k06fPs3uAAAAn8GEFXCDWbNmKSQkRB9//LGWLFmikJAQzZo1S6dOnVLTpk116tQpSf9dmxoSEqIHH3xQkhQSEqLIyMirrrVo0SK1a9dORYsWdfdtAABghM3hcFz3YEhIiGPbtm1ujANY386dO01HcJoGDRqYjgAAgCTJZrMlOByOkJyOMWEFAACApVFYAQAAYGkUVgAAAFgahRUAAACWRmEFAACApVFYAQAAYGkUVgAAAFgahRUAAACWRmEFAACApVFYAQAAYGkUVgAAAFgahRUAAACWRmEF8sjPzzv+b+Mt9wEA8H7+pgMAnqZ+/fqmIwAA4FMYsQAAAMDSKKwAAACwNAorAAAALI3CCgAAAEujsAIAAMDSKKwAAACwNAorAAAALI3CCgAAAEujsAIAAMDSKKwAAACwNAorAAAALI3CCgAAAEujsAIAAMDSKKwAAACwNAorAAAALI3CCgAAAEujsAIAAMDSKKwAAACwNAorAAAALI3CCgAAAEujsAIAAMDSKKwAAACwNAorAAAALI3CCgAAAEujsAIAAMDSKKwAAACwNAorAAAALI3CCgAAAEujsAIAAMDSKKwAAACwNJvD4bj+QZvtD0lH3BcHAAAAPqqaw+G4JacDNyysAAAAgGksCQAAAIClUVgBAABgaRRWAAAAWBqFFQAAAJZGYQUAAICl/T/sTYLmlfIe+gAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pvs.plot_calibrated_models_digraph(\n", " problem=pypesto_select_problem_3,\n", @@ -642,31 +425,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - "virtual_init | M1_0:649bcd9 | AICc | None | 3.798e+01 | None | True \n", - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - "M1_0:649bcd9 | M1_1:9202ace | AICc | 3.798e+01 | -1.754e-01 | -3.815e+01 | True \n", - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - "M1_1:9202ace | M1_4:3d668e2 | AICc | -1.754e-01 | 9.725e+00 | 9.901e+00 | False \n", - "M1_1:9202ace | M1_5:d2503eb | AICc | -1.754e-01 | 9.295e+00 | 9.470e+00 | False \n", - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - "M1_1:9202ace | M1_7:afa580e | AICc | -1.754e-01 | 3.595e+01 | 3.612e+01 | False \n", - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n" - ] - } - ], + "outputs": [], "source": [ "# Repeat with AICc and criterion_threshold == 10\n", "petab_select_problem.model_space.reset_exclusions()\n", @@ -685,22 +446,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "pvs.plot_calibrated_models_digraph(\n", " problem=pypesto_select_problem_4,\n", @@ -747,26 +484,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - ":myModel1 | M1_1:9202ace | AIC | inf | 3.897e+01 | -inf | True \n", - ":myModel1 | M1_2:c773362 | AIC | inf | -4.275e+00 | -inf | True \n", - ":myModel1 | M1_3:e03f5dc | AIC | inf | -4.705e+00 | -inf | True \n", - "--------------------New Selection--------------------\n", - "model0 | model | crit | model0_crit | model_crit | crit_diff | accept \n", - ":myModel2 | M1_7:afa580e | AIC | inf | 4.011e+01 | -inf | True \n" - ] - } - ], + "outputs": [], "source": [ "petab_select_problem.model_space.reset_exclusions()\n", "pypesto_select_problem_5 = pypesto.select.Problem(\n", @@ -806,20 +528,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "initial_model_labels = {\n", " initial_model.get_hash(): initial_model.model_id\n", diff --git a/doc/example/nonlinear_monotone.ipynb b/doc/example/nonlinear_monotone.ipynb index da8d6e6d9..85b2d0e45 100644 --- a/doc/example/nonlinear_monotone.ipynb +++ b/doc/example/nonlinear_monotone.ipynb @@ -45,7 +45,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -82,17 +82,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Visualization table not available. Skipping.\n" - ] - } - ], + "outputs": [], "source": [ "petab_folder = './example_nonlinear_monotone/'\n", "yaml_file = 'example_nonlinear_monotone.yaml'\n", @@ -112,347 +104,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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observableIdpreequilibrationConditionIdsimulationConditionIdmeasurementtimeobservableParametersnoiseParametersobservableTransformationnoiseDistributionmeasurementType
0ActivityNaNInhibitor_07.6824035NaN1linnormalnonlinear_monotone
1ActivityNaNInhibitor_37.8761075NaN1linnormalnonlinear_monotone
2ActivityNaNInhibitor_108.3145875NaN1linnormalnonlinear_monotone
3ActivityNaNInhibitor_259.1309155NaN1linnormalnonlinear_monotone
4ActivityNaNInhibitor_358.0784945NaN1linnormalnonlinear_monotone
5ActivityNaNInhibitor_505.4521165NaN1linnormalnonlinear_monotone
6ActivityNaNInhibitor_752.6987465NaN1linnormalnonlinear_monotone
7ActivityNaNInhibitor_1001.6731545NaN1linnormalnonlinear_monotone
8ActivityNaNInhibitor_3000.3928865NaN1linnormalnonlinear_monotone
9YbarNaNInhibitor_00.0000005NaN1linnormalnonlinear_monotone
10YbarNaNInhibitor_30.7444115NaN1linnormalnonlinear_monotone
11YbarNaNInhibitor_102.3105245NaN1linnormalnonlinear_monotone
12YbarNaNInhibitor_254.2380565NaN1linnormalnonlinear_monotone
13YbarNaNInhibitor_354.6428595NaN1linnormalnonlinear_monotone
14YbarNaNInhibitor_504.8328795NaN1linnormalnonlinear_monotone
15YbarNaNInhibitor_754.8986845NaN1linnormalnonlinear_monotone
16YbarNaNInhibitor_1004.9137915NaN1linnormalnonlinear_monotone
17YbarNaNInhibitor_3004.9290085NaN1linnormalnonlinear_monotone
\n", - "
" - ], - "text/plain": [ - " observableId preequilibrationConditionId simulationConditionId \\\n", - "0 Activity NaN Inhibitor_0 \n", - "1 Activity NaN Inhibitor_3 \n", - "2 Activity NaN Inhibitor_10 \n", - "3 Activity NaN Inhibitor_25 \n", - "4 Activity NaN Inhibitor_35 \n", - "5 Activity NaN Inhibitor_50 \n", - "6 Activity NaN Inhibitor_75 \n", - "7 Activity NaN Inhibitor_100 \n", - "8 Activity NaN Inhibitor_300 \n", - "9 Ybar NaN Inhibitor_0 \n", - "10 Ybar NaN Inhibitor_3 \n", - "11 Ybar NaN Inhibitor_10 \n", - "12 Ybar NaN Inhibitor_25 \n", - "13 Ybar NaN Inhibitor_35 \n", - "14 Ybar NaN Inhibitor_50 \n", - "15 Ybar NaN Inhibitor_75 \n", - "16 Ybar NaN Inhibitor_100 \n", - "17 Ybar NaN Inhibitor_300 \n", - "\n", - " measurement time observableParameters noiseParameters \\\n", - "0 7.682403 5 NaN 1 \n", - "1 7.876107 5 NaN 1 \n", - "2 8.314587 5 NaN 1 \n", - "3 9.130915 5 NaN 1 \n", - "4 8.078494 5 NaN 1 \n", - "5 5.452116 5 NaN 1 \n", - "6 2.698746 5 NaN 1 \n", - "7 1.673154 5 NaN 1 \n", - "8 0.392886 5 NaN 1 \n", - "9 0.000000 5 NaN 1 \n", - "10 0.744411 5 NaN 1 \n", - "11 2.310524 5 NaN 1 \n", - "12 4.238056 5 NaN 1 \n", - "13 4.642859 5 NaN 1 \n", - "14 4.832879 5 NaN 1 \n", - "15 4.898684 5 NaN 1 \n", - "16 4.913791 5 NaN 1 \n", - "17 4.929008 5 NaN 1 \n", - "\n", - " observableTransformation noiseDistribution measurementType \n", - "0 lin normal nonlinear_monotone \n", - "1 lin normal nonlinear_monotone \n", - "2 lin normal nonlinear_monotone \n", - "3 lin normal nonlinear_monotone \n", - "4 lin normal nonlinear_monotone \n", - "5 lin normal nonlinear_monotone \n", - "6 lin normal nonlinear_monotone \n", - "7 lin normal nonlinear_monotone \n", - "8 lin normal nonlinear_monotone \n", - "9 lin normal nonlinear_monotone \n", - "10 lin normal nonlinear_monotone \n", - "11 lin normal nonlinear_monotone \n", - "12 lin normal nonlinear_monotone \n", - "13 lin normal nonlinear_monotone \n", - "14 lin normal nonlinear_monotone \n", - "15 lin normal nonlinear_monotone \n", - "16 lin normal nonlinear_monotone \n", - "17 lin normal nonlinear_monotone " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from pandas import option_context\n", "\n", @@ -470,133 +124,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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parameterIdparameterNameparameterScalelowerBoundupperBoundnominalValueestimateparameterType
0K1K1lin-5.000005.00.040NaN
1K2K2lin-5.000005.020.000NaN
2K3K3log100.10000100000.04000.001NaN
3K5K5log100.00001100000.00.101NaN
4sd_Activity\\sigma_{activity}lin0.00000inf1.001sigma
5sd_Ybar\\sigma_{ybar}lin0.00000inf1.001sigma
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" - ], - "text/plain": [ - " parameterId parameterName parameterScale lowerBound upperBound \\\n", - "0 K1 K1 lin -5.00000 5.0 \n", - "1 K2 K2 lin -5.00000 5.0 \n", - "2 K3 K3 log10 0.10000 100000.0 \n", - "3 K5 K5 log10 0.00001 100000.0 \n", - "4 sd_Activity \\sigma_{activity} lin 0.00000 inf \n", - "5 sd_Ybar \\sigma_{ybar} lin 0.00000 inf \n", - "\n", - " nominalValue estimate parameterType \n", - "0 0.04 0 NaN \n", - "1 20.00 0 NaN \n", - "2 4000.00 1 NaN \n", - "3 0.10 1 NaN \n", - "4 1.00 1 sigma \n", - "5 1.00 1 sigma " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "\n", @@ -660,376 +190,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "amici model will be re-imported due to version mismatch: Cannot use model `Raf_Mitra_NatCom2018OptimalScaling_3CatQual` in /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/Raf_Mitra_NatCom2018OptimalScaling_3CatQual, generated with amici==0.15.0, together with amici==0.18.1 which is currently installed. To use this model, install amici==0.15.0 or re-import the model with the amici version currently installed.\n", - "Compiling amici model to folder /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.\n", - "2023-11-30 13:29:31.502 - amici.petab_import - INFO - Importing model ...\n", - "2023-11-30 13:29:31.504 - amici.petab_import - INFO - Validating PEtab problem ...\n", - "Visualization table not available. Skipping.\n", - "2023-11-30 13:29:31.515 - amici.petab_import - INFO - Model name is 'Raf_Mitra_NatCom2018OptimalScaling_3CatQual'.\n", - "Writing model code to '/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual'.\n", - "2023-11-30 13:29:31.515 - amici.petab_import - INFO - Species: 6\n", - "2023-11-30 13:29:31.516 - amici.petab_import - INFO - Global parameters: 9\n", - "2023-11-30 13:29:31.516 - amici.petab_import - INFO - Reactions: 6\n", - "2023-11-30 13:29:31.528 - amici.petab_import - INFO - Observables: 2\n", - "2023-11-30 13:29:31.528 - amici.petab_import - INFO - Sigmas: 2\n", - "2023-11-30 13:29:31.530 - amici.petab_import - DEBUG - Adding output parameters to model: ['noiseParameter1_Activity', 'noiseParameter1_Ybar']\n", - "2023-11-30 13:29:31.530 - amici.petab_import - DEBUG - Adding initial assignments for dict_keys([])\n", - "2023-11-30 13:29:31.533 - amici.petab_import - DEBUG - Condition table: (9, 2)\n", - "2023-11-30 13:29:31.533 - amici.petab_import - DEBUG - Fixed parameters are ['K1', 'K2', 'init_I']\n", - "2023-11-30 13:29:31.533 - amici.petab_import - INFO - Overall fixed parameters: 3\n", - "2023-11-30 13:29:31.534 - amici.petab_import - INFO - Variable parameters: 14\n", - "2023-11-30 13:29:31.538 - amici.sbml_import - DEBUG - Finished processing SBML annotations ++ (3.04E-04s)\n", - "2023-11-30 13:29:31.547 - amici.sbml_import - DEBUG - Finished gathering local SBML symbols ++ (6.41E-03s)\n", - "2023-11-30 13:29:31.552 - amici.sbml_import - DEBUG - Finished processing SBML parameters ++ (2.55E-03s)\n", - "2023-11-30 13:29:31.555 - amici.sbml_import - DEBUG - Finished processing SBML compartments ++ (9.72E-05s)\n", - "2023-11-30 13:29:31.560 - amici.sbml_import - DEBUG - Finished processing SBML species initials +++ (6.81E-04s)\n", - "2023-11-30 13:29:31.562 - amici.sbml_import - DEBUG - Finished processing SBML rate rules +++ (2.23E-05s)\n", - "2023-11-30 13:29:31.563 - amici.sbml_import - DEBUG - Finished processing SBML species ++ (4.52E-03s)\n", - "2023-11-30 13:29:31.568 - amici.sbml_import - DEBUG - Finished processing SBML reactions ++ (2.97E-03s)\n", - "2023-11-30 13:29:31.572 - amici.sbml_import - DEBUG - Finished processing SBML rules ++ (1.79E-03s)\n", - "2023-11-30 13:29:31.575 - amici.sbml_import - DEBUG - Finished processing SBML initial assignments++ (4.14E-05s)\n", - "2023-11-30 13:29:31.577 - amici.sbml_import - DEBUG - Finished processing SBML species references ++ (8.37E-05s)\n", - "2023-11-30 13:29:31.579 - amici.sbml_import - DEBUG - Finished processing SBML events ++ (3.80E-05s)\n", - "2023-11-30 13:29:31.579 - amici.sbml_import - DEBUG - Finished importing SBML + (4.27E-02s)\n", - "2023-11-30 13:29:31.593 - amici.sbml_import - DEBUG - Finished processing SBML observables + (1.05E-02s)\n", - "2023-11-30 13:29:31.596 - amici.sbml_import - DEBUG - Finished processing SBML event observables + (7.50E-07s)\n", - "2023-11-30 13:29:31.607 - amici.de_export - DEBUG - Finished running smart_multiply ++ (5.98E-04s)\n", - "2023-11-30 13:29:31.613 - amici.de_export - DEBUG - Finished importing SbmlImporter + (8.07E-03s)\n", - "2023-11-30 13:29:31.626 - amici.de_export - DEBUG - Finished simplifying Jy ++++ (6.19E-03s)\n", - "2023-11-30 13:29:31.626 - amici.de_export - DEBUG - Finished computing Jy +++ (8.08E-03s)\n", - "2023-11-30 13:29:31.632 - amici.de_export - DEBUG - Finished simplifying y ++++ (1.90E-03s)\n", - "2023-11-30 13:29:31.632 - amici.de_export - DEBUG - Finished computing y +++ (3.30E-03s)\n", - "2023-11-30 13:29:31.637 - amici.de_export - DEBUG - Finished simplifying sigmay ++++ (5.40E-05s)\n", - "2023-11-30 13:29:31.637 - amici.de_export - DEBUG - Finished computing sigmay +++ (1.84E-03s)\n", - "2023-11-30 13:29:31.646 - amici.de_export - DEBUG - Finished writing Jy.cpp ++ (2.87E-02s)\n", - "2023-11-30 13:29:31.659 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (7.08E-03s)\n", - "2023-11-30 13:29:31.665 - amici.de_export - DEBUG - Finished simplifying dJydsigma ++++ (2.58E-03s)\n", - "2023-11-30 13:29:31.666 - amici.de_export - DEBUG - Finished computing dJydsigma +++ (1.50E-02s)\n", - "2023-11-30 13:29:31.668 - amici.de_export - DEBUG - Finished writing dJydsigma.cpp ++ (1.85E-02s)\n", - "2023-11-30 13:29:31.677 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (4.44E-03s)\n", - "2023-11-30 13:29:31.683 - amici.de_export - DEBUG - Finished simplifying dJydy ++++ (3.41E-03s)\n", - "2023-11-30 13:29:31.684 - amici.de_export - DEBUG - Finished computing dJydy +++ (1.28E-02s)\n", - "2023-11-30 13:29:31.687 - amici.de_export - DEBUG - Finished writing dJydy.cpp ++ (1.69E-02s)\n", - "2023-11-30 13:29:31.692 - amici.de_export - DEBUG - Finished simplifying Jz ++++ (2.88E-05s)\n", - "2023-11-30 13:29:31.693 - amici.de_export - DEBUG - Finished computing Jz +++ (1.43E-03s)\n", - "2023-11-30 13:29:31.695 - amici.de_export - DEBUG - Finished computing z +++ (5.49E-05s)\n", - "2023-11-30 13:29:31.698 - amici.de_export - DEBUG - Finished simplifying sigmaz ++++ (3.06E-05s)\n", - "2023-11-30 13:29:31.698 - amici.de_export - DEBUG - Finished computing sigmaz +++ (1.34E-03s)\n", - "2023-11-30 13:29:31.698 - amici.de_export - DEBUG - Finished writing Jz.cpp ++ (8.52E-03s)\n", - "2023-11-30 13:29:31.705 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.39E-05s)\n", - "2023-11-30 13:29:31.707 - amici.de_export - DEBUG - Finished simplifying dJzdsigma ++++ (3.51E-05s)\n", - "2023-11-30 13:29:31.707 - amici.de_export - DEBUG - Finished computing dJzdsigma +++ (4.18E-03s)\n", - "2023-11-30 13:29:31.708 - amici.de_export - DEBUG - Finished writing dJzdsigma.cpp ++ (5.97E-03s)\n", - "2023-11-30 13:29:31.713 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.34E-05s)\n", - "2023-11-30 13:29:31.716 - amici.de_export - DEBUG - Finished simplifying dJzdz ++++ (4.99E-05s)\n", - "2023-11-30 13:29:31.716 - amici.de_export - DEBUG - Finished computing dJzdz +++ (5.14E-03s)\n", - "2023-11-30 13:29:31.716 - amici.de_export - DEBUG - Finished writing dJzdz.cpp ++ (6.83E-03s)\n", - "2023-11-30 13:29:31.721 - amici.de_export - DEBUG - Finished simplifying Jrz ++++ (2.90E-05s)\n", - "2023-11-30 13:29:31.722 - amici.de_export - DEBUG - Finished computing Jrz +++ (1.46E-03s)\n", - "2023-11-30 13:29:31.724 - amici.de_export - DEBUG - Finished computing rz +++ (5.85E-05s)\n", - "2023-11-30 13:29:31.725 - amici.de_export - DEBUG - Finished writing Jrz.cpp ++ (5.55E-03s)\n", - "2023-11-30 13:29:31.731 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (2.75E-05s)\n", - "2023-11-30 13:29:31.732 - amici.de_export - DEBUG - Finished simplifying dJrzdsigma ++++ (3.32E-05s)\n", - "2023-11-30 13:29:31.733 - amici.de_export - DEBUG - Finished computing dJrzdsigma +++ (3.18E-03s)\n", - "2023-11-30 13:29:31.733 - amici.de_export - DEBUG - Finished writing dJrzdsigma.cpp ++ (5.23E-03s)\n", - "2023-11-30 13:29:31.737 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (2.24E-05s)\n", - "2023-11-30 13:29:31.739 - amici.de_export - DEBUG - Finished simplifying dJrzdz ++++ (3.80E-05s)\n", - "2023-11-30 13:29:31.739 - amici.de_export - DEBUG - Finished computing dJrzdz +++ (3.61E-03s)\n", - "2023-11-30 13:29:31.739 - amici.de_export - DEBUG - Finished writing dJrzdz.cpp ++ (4.87E-03s)\n", - "2023-11-30 13:29:31.746 - amici.de_export - DEBUG - Finished simplifying root ++++ (4.95E-05s)\n", - "2023-11-30 13:29:31.746 - amici.de_export - DEBUG - Finished computing root +++ (2.29E-03s)\n", - "2023-11-30 13:29:31.746 - amici.de_export - DEBUG - Finished writing root.cpp ++ (3.86E-03s)\n", - "2023-11-30 13:29:31.761 - amici.de_export - DEBUG - Finished simplifying w +++++ (7.46E-03s)\n", - "2023-11-30 13:29:31.762 - amici.de_export - DEBUG - Finished computing w ++++ (9.77E-03s)\n", - "2023-11-30 13:29:31.770 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (4.99E-03s)\n", - "2023-11-30 13:29:31.774 - amici.de_export - DEBUG - Finished simplifying dwdp ++++ (1.90E-03s)\n", - "2023-11-30 13:29:31.774 - amici.de_export - DEBUG - Finished computing dwdp +++ (2.40E-02s)\n", - "2023-11-30 13:29:31.779 - amici.de_export - DEBUG - Finished simplifying spl ++++ (3.88E-05s)\n", - "2023-11-30 13:29:31.779 - amici.de_export - DEBUG - Finished computing spl +++ (1.72E-03s)\n", - "2023-11-30 13:29:31.784 - amici.de_export - DEBUG - Finished simplifying sspl ++++ (5.66E-05s)\n", - "2023-11-30 13:29:31.784 - amici.de_export - DEBUG - Finished computing sspl +++ (1.90E-03s)\n", - "2023-11-30 13:29:31.787 - amici.de_export - DEBUG - Finished writing dwdp.cpp ++ (3.78E-02s)\n", - "2023-11-30 13:29:31.803 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.19E-02s)\n", - "2023-11-30 13:29:31.810 - amici.de_export - DEBUG - Finished simplifying dwdx ++++ (3.73E-03s)\n", - "2023-11-30 13:29:31.810 - amici.de_export - DEBUG - Finished computing dwdx +++ (2.01E-02s)\n", - "2023-11-30 13:29:31.815 - amici.de_export - DEBUG - Finished writing dwdx.cpp ++ (2.57E-02s)\n", - "2023-11-30 13:29:31.818 - amici.de_export - DEBUG - Finished writing create_splines.cpp ++ (2.07E-04s)\n", - "2023-11-30 13:29:31.825 - amici.de_export - DEBUG - Finished simplifying spline_values +++++ (3.98E-05s)\n", - "2023-11-30 13:29:31.825 - amici.de_export - DEBUG - Finished computing spline_values ++++ (1.95E-03s)\n", - "2023-11-30 13:29:31.828 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (4.18E-05s)\n", - "2023-11-30 13:29:31.830 - amici.de_export - DEBUG - Finished simplifying dspline_valuesdp ++++ (4.07E-05s)\n", - "2023-11-30 13:29:31.830 - amici.de_export - DEBUG - Finished computing dspline_valuesdp +++ (8.33E-03s)\n", - "2023-11-30 13:29:31.831 - amici.de_export - DEBUG - Finished writing dspline_valuesdp.cpp ++ (1.03E-02s)\n", - "2023-11-30 13:29:31.839 - amici.de_export - DEBUG - Finished simplifying spline_slopes +++++ (4.92E-05s)\n", - "2023-11-30 13:29:31.839 - amici.de_export - DEBUG - Finished computing spline_slopes ++++ (2.08E-03s)\n", - "2023-11-30 13:29:31.842 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (4.28E-05s)\n", - "2023-11-30 13:29:31.844 - amici.de_export - DEBUG - Finished simplifying dspline_slopesdp ++++ (3.13E-05s)\n", - "2023-11-30 13:29:31.844 - amici.de_export - DEBUG - Finished computing dspline_slopesdp +++ (8.22E-03s)\n", - "2023-11-30 13:29:31.844 - amici.de_export - DEBUG - Finished writing dspline_slopesdp.cpp ++ (1.05E-02s)\n", - "2023-11-30 13:29:31.854 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (2.23E-03s)\n", - "2023-11-30 13:29:31.858 - amici.de_export - DEBUG - Finished simplifying dwdw ++++ (9.72E-04s)\n", - "2023-11-30 13:29:31.859 - amici.de_export - DEBUG - Finished computing dwdw +++ (9.75E-03s)\n", - "2023-11-30 13:29:31.861 - amici.de_export - DEBUG - Finished writing dwdw.cpp ++ (1.35E-02s)\n", - "2023-11-30 13:29:31.870 - amici.de_export - DEBUG - Finished simplifying xdot +++++ (1.36E-03s)\n", - "2023-11-30 13:29:31.871 - amici.de_export - DEBUG - Finished computing xdot ++++ (3.52E-03s)\n", - "2023-11-30 13:29:31.877 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.59E-03s)\n", - "2023-11-30 13:29:31.880 - amici.de_export - DEBUG - Finished simplifying dxdotdw ++++ (9.25E-05s)\n", - "2023-11-30 13:29:31.881 - amici.de_export - DEBUG - Finished computing dxdotdw +++ (1.47E-02s)\n", - "2023-11-30 13:29:31.885 - amici.de_export - DEBUG - Finished writing dxdotdw.cpp ++ (2.05E-02s)\n", - "2023-11-30 13:29:31.891 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (2.16E-04s)\n", - "2023-11-30 13:29:31.894 - amici.de_export - DEBUG - Finished simplifying dxdotdx_explicit ++++ (4.32E-05s)\n", - "2023-11-30 13:29:31.894 - amici.de_export - DEBUG - Finished computing dxdotdx_explicit +++ (4.73E-03s)\n", - "2023-11-30 13:29:31.895 - amici.de_export - DEBUG - Finished writing dxdotdx_explicit.cpp ++ (6.42E-03s)\n", - "2023-11-30 13:29:31.902 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.58E-04s)\n", - "2023-11-30 13:29:31.905 - amici.de_export - DEBUG - Finished simplifying dxdotdp_explicit ++++ (3.41E-05s)\n", - "2023-11-30 13:29:31.905 - amici.de_export - DEBUG - Finished computing dxdotdp_explicit +++ (4.89E-03s)\n", - "2023-11-30 13:29:31.906 - amici.de_export - DEBUG - Finished writing dxdotdp_explicit.cpp ++ (7.09E-03s)\n", - "2023-11-30 13:29:31.914 - amici.de_export - DEBUG - Finished running smart_jacobian +++++ (9.13E-04s)\n", - "2023-11-30 13:29:31.918 - amici.de_export - DEBUG - Finished simplifying dydx +++++ (5.35E-04s)\n", - "2023-11-30 13:29:31.918 - amici.de_export - DEBUG - Finished computing dydx ++++ (6.63E-03s)\n", - "2023-11-30 13:29:31.924 - amici.de_export - DEBUG - Finished running smart_jacobian +++++ (1.50E-03s)\n", - "2023-11-30 13:29:31.927 - amici.de_export - DEBUG - Finished running smart_multiply +++++ (2.36E-04s)\n", - "2023-11-30 13:29:31.931 - amici.de_export - DEBUG - Finished simplifying dydw +++++ (1.63E-03s)\n", - "2023-11-30 13:29:31.932 - amici.de_export - DEBUG - Finished computing dydw ++++ (1.07E-02s)\n", - "2023-11-30 13:29:31.935 - amici.de_export - DEBUG - Finished running smart_multiply ++++ (4.72E-04s)\n", - "2023-11-30 13:29:31.941 - amici.de_export - DEBUG - Finished simplifying dydx ++++ (4.36E-03s)\n", - "2023-11-30 13:29:31.942 - amici.de_export - DEBUG - Finished computing dydx +++ (3.14E-02s)\n", - "2023-11-30 13:29:31.944 - amici.de_export - DEBUG - Finished writing dydx.cpp ++ (3.54E-02s)\n", - "2023-11-30 13:29:31.952 - amici.de_export - DEBUG - Finished running smart_jacobian +++++ (1.57E-04s)\n", - "2023-11-30 13:29:31.954 - amici.de_export - DEBUG - Finished simplifying dydp +++++ (5.82E-05s)\n", - "2023-11-30 13:29:31.955 - amici.de_export - DEBUG - Finished computing dydp ++++ (4.57E-03s)\n", - "2023-11-30 13:29:31.958 - amici.de_export - DEBUG - Finished running smart_multiply ++++ (2.93E-04s)\n", - "2023-11-30 13:29:31.961 - amici.de_export - DEBUG - Finished simplifying dydp ++++ (4.39E-05s)\n", - "2023-11-30 13:29:31.961 - amici.de_export - DEBUG - Finished computing dydp +++ (1.20E-02s)\n", - "2023-11-30 13:29:31.961 - amici.de_export - DEBUG - Finished writing dydp.cpp ++ (1.37E-02s)\n", - "2023-11-30 13:29:31.966 - amici.de_export - DEBUG - Finished computing dzdx +++ (7.24E-05s)\n", - "2023-11-30 13:29:31.966 - amici.de_export - DEBUG - Finished writing dzdx.cpp ++ (1.95E-03s)\n", - "2023-11-30 13:29:31.971 - amici.de_export - DEBUG - Finished computing dzdp +++ (8.34E-05s)\n", - "2023-11-30 13:29:31.971 - amici.de_export - DEBUG - Finished writing dzdp.cpp ++ (2.13E-03s)\n", - "2023-11-30 13:29:31.975 - amici.de_export - DEBUG - Finished computing drzdx +++ (6.45E-05s)\n", - "2023-11-30 13:29:31.976 - amici.de_export - DEBUG - Finished writing drzdx.cpp ++ (1.90E-03s)\n", - "2023-11-30 13:29:31.980 - amici.de_export - DEBUG - Finished computing drzdp +++ (6.54E-05s)\n", - "2023-11-30 13:29:31.980 - amici.de_export - DEBUG - Finished writing drzdp.cpp ++ (2.21E-03s)\n", - "2023-11-30 13:29:31.987 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (8.94E-05s)\n", - "2023-11-30 13:29:31.989 - amici.de_export - DEBUG - Finished simplifying dsigmaydy ++++ (2.78E-05s)\n", - "2023-11-30 13:29:31.989 - amici.de_export - DEBUG - Finished computing dsigmaydy +++ (4.42E-03s)\n", - "2023-11-30 13:29:31.990 - amici.de_export - DEBUG - Finished writing dsigmaydy.cpp ++ (6.36E-03s)\n", - "2023-11-30 13:29:31.996 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.25E-04s)\n", - "2023-11-30 13:29:32.000 - amici.de_export - DEBUG - Finished simplifying dsigmaydp ++++ (9.25E-05s)\n", - "2023-11-30 13:29:32.000 - amici.de_export - DEBUG - Finished computing dsigmaydp +++ (6.41E-03s)\n", - "2023-11-30 13:29:32.001 - amici.de_export - DEBUG - Finished writing dsigmaydp.cpp ++ (9.60E-03s)\n", - "2023-11-30 13:29:32.005 - amici.de_export - DEBUG - Finished writing sigmay.cpp ++ (3.41E-04s)\n", - "2023-11-30 13:29:32.011 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.32E-05s)\n", - "2023-11-30 13:29:32.013 - amici.de_export - DEBUG - Finished simplifying dsigmazdp ++++ (4.46E-05s)\n", - "2023-11-30 13:29:32.013 - amici.de_export - DEBUG - Finished computing dsigmazdp +++ (4.42E-03s)\n", - "2023-11-30 13:29:32.014 - amici.de_export - DEBUG - Finished writing dsigmazdp.cpp ++ (6.28E-03s)\n", - "2023-11-30 13:29:32.016 - amici.de_export - DEBUG - Finished writing sigmaz.cpp ++ (1.95E-05s)\n", - "2023-11-30 13:29:32.020 - amici.de_export - DEBUG - Finished computing stau +++ (8.78E-05s)\n", - "2023-11-30 13:29:32.020 - amici.de_export - DEBUG - Finished writing stau.cpp ++ (2.19E-03s)\n", - "2023-11-30 13:29:32.026 - amici.de_export - DEBUG - Finished computing deltax +++ (7.33E-05s)\n", - "2023-11-30 13:29:32.026 - amici.de_export - DEBUG - Finished writing deltax.cpp ++ (2.55E-03s)\n", - "2023-11-30 13:29:32.031 - amici.de_export - DEBUG - Finished computing deltasx +++ (9.33E-05s)\n", - "2023-11-30 13:29:32.032 - amici.de_export - DEBUG - Finished writing deltasx.cpp ++ (2.24E-03s)\n", - "2023-11-30 13:29:32.038 - amici.de_export - DEBUG - Finished writing w.cpp ++ (3.20E-03s)\n", - "2023-11-30 13:29:32.044 - amici.de_export - DEBUG - Finished simplifying x0 ++++ (6.70E-05s)\n", - "2023-11-30 13:29:32.044 - amici.de_export - DEBUG - Finished computing x0 +++ (1.83E-03s)\n", - "2023-11-30 13:29:32.045 - amici.de_export - DEBUG - Finished writing x0.cpp ++ (4.53E-03s)\n", - "2023-11-30 13:29:32.050 - amici.de_export - DEBUG - Finished simplifying x0_fixedParameters ++++ (3.67E-05s)\n", - "2023-11-30 13:29:32.050 - amici.de_export - DEBUG - Finished computing x0_fixedParameters +++ (1.66E-03s)\n", - "2023-11-30 13:29:32.051 - amici.de_export - DEBUG - Finished writing x0_fixedParameters.cpp ++ (3.47E-03s)\n", - "2023-11-30 13:29:32.057 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (2.47E-04s)\n", - "2023-11-30 13:29:32.060 - amici.de_export - DEBUG - Finished simplifying sx0 ++++ (7.41E-05s)\n", - "2023-11-30 13:29:32.061 - amici.de_export - DEBUG - Finished computing sx0 +++ (5.41E-03s)\n", - "2023-11-30 13:29:32.061 - amici.de_export - DEBUG - Finished writing sx0.cpp ++ (7.22E-03s)\n", - "2023-11-30 13:29:32.067 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.38E-04s)\n", - "2023-11-30 13:29:32.070 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (7.53E-05s)\n", - "2023-11-30 13:29:32.072 - amici.de_export - DEBUG - Finished simplifying sx0_fixedParameters ++++ (4.96E-05s)\n", - "2023-11-30 13:29:32.073 - amici.de_export - DEBUG - Finished computing sx0_fixedParameters +++ (6.93E-03s)\n", - "2023-11-30 13:29:32.074 - amici.de_export - DEBUG - Finished writing sx0_fixedParameters.cpp ++ (9.50E-03s)\n", - "2023-11-30 13:29:32.079 - amici.de_export - DEBUG - Finished writing xdot.cpp ++ (1.48E-03s)\n", - "2023-11-30 13:29:32.083 - amici.de_export - DEBUG - Finished writing y.cpp ++ (6.84E-04s)\n", - "2023-11-30 13:29:32.088 - amici.de_export - DEBUG - Finished simplifying x_rdata ++++ (6.71E-05s)\n", - "2023-11-30 13:29:32.088 - amici.de_export - DEBUG - Finished computing x_rdata +++ (2.08E-03s)\n", - "2023-11-30 13:29:32.089 - amici.de_export - DEBUG - Finished writing x_rdata.cpp ++ (4.21E-03s)\n", - "2023-11-30 13:29:32.095 - amici.de_export - DEBUG - Finished simplifying total_cl ++++ (4.85E-05s)\n", - "2023-11-30 13:29:32.096 - amici.de_export - DEBUG - Finished computing total_cl +++ (2.09E-03s)\n", - "2023-11-30 13:29:32.096 - amici.de_export - DEBUG - Finished writing total_cl.cpp ++ (4.03E-03s)\n", - "2023-11-30 13:29:32.101 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (4.51E-05s)\n", - "2023-11-30 13:29:32.104 - amici.de_export - DEBUG - Finished simplifying dtotal_cldp ++++ (4.72E-05s)\n", - "2023-11-30 13:29:32.105 - amici.de_export - DEBUG - Finished computing dtotal_cldp +++ (5.05E-03s)\n", - "2023-11-30 13:29:32.105 - amici.de_export - DEBUG - Finished writing dtotal_cldp.cpp ++ (7.06E-03s)\n", - "2023-11-30 13:29:32.112 - amici.de_export - DEBUG - Finished simplifying dtotal_cldx_rdata ++++ (3.78E-05s)\n", - "2023-11-30 13:29:32.112 - amici.de_export - DEBUG - Finished computing dtotal_cldx_rdata +++ (1.84E-03s)\n", - "2023-11-30 13:29:32.112 - amici.de_export - DEBUG - Finished writing dtotal_cldx_rdata.cpp ++ (3.45E-03s)\n", - "2023-11-30 13:29:32.119 - amici.de_export - DEBUG - Finished simplifying x_solver ++++ (7.78E-05s)\n", - "2023-11-30 13:29:32.119 - amici.de_export - DEBUG - Finished computing x_solver +++ (2.07E-03s)\n", - "2023-11-30 13:29:32.121 - amici.de_export - DEBUG - Finished writing x_solver.cpp ++ (4.99E-03s)\n", - "2023-11-30 13:29:32.126 - amici.de_export - DEBUG - Finished simplifying dx_rdatadx_solver ++++ (1.63E-04s)\n", - "2023-11-30 13:29:32.127 - amici.de_export - DEBUG - Finished computing dx_rdatadx_solver +++ (2.67E-03s)\n", - "2023-11-30 13:29:32.128 - amici.de_export - DEBUG - Finished writing dx_rdatadx_solver.cpp ++ (4.59E-03s)\n", - "2023-11-30 13:29:32.135 - amici.de_export - DEBUG - Finished simplifying dx_rdatadp ++++ (2.07E-04s)\n", - "2023-11-30 13:29:32.135 - amici.de_export - DEBUG - Finished computing dx_rdatadp +++ (2.23E-03s)\n", - "2023-11-30 13:29:32.136 - amici.de_export - DEBUG - Finished writing dx_rdatadp.cpp ++ (4.62E-03s)\n", - "2023-11-30 13:29:32.144 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.60E-05s)\n", - "2023-11-30 13:29:32.147 - amici.de_export - DEBUG - Finished simplifying dx_rdatadtcl ++++ (4.37E-05s)\n", - "2023-11-30 13:29:32.147 - amici.de_export - DEBUG - Finished computing dx_rdatadtcl +++ (4.94E-03s)\n", - "2023-11-30 13:29:32.147 - amici.de_export - DEBUG - Finished writing dx_rdatadtcl.cpp ++ (8.01E-03s)\n", - "2023-11-30 13:29:32.150 - amici.de_export - DEBUG - Finished writing z.cpp ++ (2.04E-05s)\n", - "2023-11-30 13:29:32.152 - amici.de_export - DEBUG - Finished writing rz.cpp ++ (1.95E-05s)\n", - "2023-11-30 13:29:32.159 - amici.de_export - DEBUG - Finished generating cpp code + (5.43E-01s)\n", - "2023-11-30 13:29:48.664 - amici.de_export - DEBUG - Finished compiling cpp code + (1.65E+01s)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "running AmiciInstall\n", - "running build_ext\n", - "running AmiciBuildCMakeExtension\n", - "------------------------------ model_ext ------------------------------\n", - "\n", - "==> Configuring:\n", - "$ cmake -S /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -B /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/build_model_ext -GNinja -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX:PATH=/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -DCMAKE_MAKE_PROGRAM=/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/bin/ninja -DCMAKE_VERBOSE_MAKEFILE=ON -DCMAKE_MODULE_PATH=/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/cmake/SuiteSparse;/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib64/cmake/SuiteSparse -DKLU_ROOT=/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici -DAMICI_PYTHON_BUILD_EXT_ONLY=ON\n", - "\n", - "==> Building:\n", - "$ cmake --build /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/build_model_ext --config Release\n", - "\n", - "==> Installing:\n", - "$ cmake --install /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/build_model_ext\n", - "\n", - "-- The C compiler identification is AppleClang 14.0.3.14030022\n", - "-- The CXX compiler identification is AppleClang 14.0.3.14030022\n", - "-- Detecting C compiler ABI info\n", - "-- Detecting C compiler ABI info - done\n", - "-- Check for working C compiler: /Library/Developer/CommandLineTools/usr/bin/cc - skipped\n", - "-- Detecting C compile features\n", - "-- Detecting C compile features - done\n", - "-- Detecting CXX compiler ABI info\n", - "-- Detecting CXX compiler ABI info - done\n", - "-- Check for working CXX compiler: /Library/Developer/CommandLineTools/usr/bin/c++ - skipped\n", - "-- Detecting CXX compile features\n", - "-- Detecting CXX compile features - done\n", - "-- Performing Test -Wall\n", - "-- Performing Test -Wall - Failed\n", - "-- Performing Test -Wno-unused-function\n", - "-- Performing Test -Wno-unused-function - Failed\n", - "-- Performing Test -Wno-unused-variable\n", - "-- Performing Test -Wno-unused-variable - Failed\n", - "-- Found OpenMP_C: -Xclang -fopenmp (found version \"5.0\") \n", - "-- Found OpenMP_CXX: -Xclang -fopenmp (found version \"5.0\") \n", - "-- Found OpenMP: TRUE (found version \"5.0\") \n", - "-- original library suffixes: .tbd;.dylib;.so;.a\n", - "-- revised for static search: .a;.tbd;.dylib;.so;.a\n", - "-- major: #define SUITESPARSE_MAIN_VERSION 7\n", - "-- minor: #define SUITESPARSE_SUB_VERSION 0\n", - "-- patch: #define SUITESPARSE_SUBSUB_VERSION 1\n", - "-- Found SuiteSparse_config: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libsuitesparseconfig.dylib (found version \"7.0.1\") \n", - "-- result: \n", - "-- SuiteSparse_config version: 7.0.1\n", - "-- SuiteSparse_config include: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include\n", - "-- SuiteSparse_config library: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libsuitesparseconfig.dylib\n", - "-- SuiteSparse_config static: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libsuitesparseconfig.a\n", - "-- major: #define AMD_MAIN_VERSION 3\n", - "-- minor: #define AMD_SUB_VERSION 0\n", - "-- patch: #define AMD_SUBSUB_VERSION 3\n", - "-- Found AMD: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libamd.dylib (found version \"3.0.3\") \n", - "-- AMD version: 3.0.3\n", - "-- AMD include: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include\n", - "-- AMD library: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libamd.dylib\n", - "-- AMD static: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libamd.a\n", - "-- major: #define BTF_MAIN_VERSION 2\n", - "-- minor: #define BTF_SUB_VERSION 0\n", - "-- patch: #define BTF_SUBSUB_VERSION 3\n", - "-- Found BTF: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libbtf.dylib (found version \"2.0.3\") \n", - "-- BTF version: 2.0.3\n", - "-- BTF include: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include\n", - "-- BTF library: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libbtf.dylib\n", - "-- BTF static: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libbtf.a\n", - "-- major: #define COLAMD_MAIN_VERSION 3\n", - "-- minor: #define COLAMD_SUB_VERSION 0\n", - "-- patch: #define COLAMD_SUBSUB_VERSION 3\n", - "-- Found COLAMD: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libcolamd.dylib (found version \"3.0.3\") \n", - "-- COLAMD version: 3.0.3\n", - "-- COLAMD include: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include\n", - "-- COLAMD library: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libcolamd.dylib\n", - "-- COLAMD static: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libcolamd.a\n", - "-- major: #define KLU_MAIN_VERSION 2\n", - "-- minor: #define KLU_SUB_VERSION 0\n", - "-- patch: #define KLU_SUBSUB_VERSION 3\n", - "-- Found KLU: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libklu.dylib (found version \"2.0.3\") \n", - "-- KLU version: 2.0.3\n", - "-- KLU include: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include\n", - "-- KLU library: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libklu.dylib\n", - "-- KLU static: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libklu.a\n", - "-- Found HDF5: /opt/homebrew/Cellar/hdf5/1.12.2_2/lib/libhdf5.dylib;/opt/homebrew/opt/libaec/lib/libsz.dylib;/Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk/usr/lib/libz.tbd;/Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk/usr/lib/libdl.tbd;/Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk/usr/lib/libm.tbd;/opt/homebrew/Cellar/hdf5/1.12.2_2/lib/libhdf5_cpp.dylib;/opt/homebrew/Cellar/hdf5/1.12.2_2/lib/libhdf5.dylib;/opt/homebrew/opt/libaec/lib/libsz.dylib;/Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk/usr/lib/libz.tbd;/Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk/usr/lib/libdl.tbd;/Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk/usr/lib/libm.tbd (found version \"1.12.2\") found components: C HL CXX \n", - "-- Found AMICI /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/cmake/Amici\n", - "-- Could NOT find Boost (missing: Boost_INCLUDE_DIR) \n", - "-- Found SWIG: /opt/homebrew/bin/swig (found version \"4.1.1\") \n", - "-- Found Python3: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/bin/python3.10 (found version \"3.10.10\") found components: Interpreter Development Development.Module Development.Embed \n", - "-- Python extension suffix is .cpython-310-darwin.so\n", - "-- Configuring done (6.1s)\n", - "-- Generating done (0.0s)\n", - "-- Build files have been written to: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/build_model_ext\n", - "[1/35] /Library/Developer/CommandLineTools/usr/bin/c++ -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dsigmaydp.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dsigmaydp.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dsigmaydp.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/dsigmaydp.cpp\n", - "[2/35] /Library/Developer/CommandLineTools/usr/bin/c++ -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/w.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/w.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/w.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/w.cpp\n", - "[3/35] /Library/Developer/CommandLineTools/usr/bin/c++ -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/x0_fixedParameters.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/x0_fixedParameters.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/x0_fixedParameters.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/x0_fixedParameters.cpp\n", - "[4/35] /Library/Developer/CommandLineTools/usr/bin/c++ -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dxdotdw.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dxdotdw.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dxdotdw.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/dxdotdw.cpp\n", - "[5/35] /Library/Developer/CommandLineTools/usr/bin/c++ -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dwdw_rowvals.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dwdw_rowvals.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dwdw_rowvals.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/dwdw_rowvals.cpp\n", - "[6/35] /Library/Developer/CommandLineTools/usr/bin/c++ -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.cpp\n", - "[7/35] /Library/Developer/CommandLineTools/usr/bin/c++ -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dwdp_colptrs.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dwdp_colptrs.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dwdp_colptrs.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/dwdp_colptrs.cpp\n", - "[8/35] /Library/Developer/CommandLineTools/usr/bin/c++ -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dxdotdw_rowvals.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dxdotdw_rowvals.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dxdotdw_rowvals.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/dxdotdw_rowvals.cpp\n", - "[9/35] /Library/Developer/CommandLineTools/usr/bin/c++ -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dJydy.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dJydy.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/dJydy.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/dJydy.cpp\n", - "[10/35] /Library/Developer/CommandLineTools/usr/bin/c++ 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/opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/x_solver.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/x_solver.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/x_solver.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/x_solver.cpp\n", - "[31/35] /Library/Developer/CommandLineTools/usr/bin/c++ -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -isystem /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/y.cpp.o -MF CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/y.cpp.o.d -o CMakeFiles/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/y.cpp.o -c /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/y.cpp\n", - "[32/35] : && /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/cmake/data/bin/cmake -E rm -f libRaf_Mitra_NatCom2018OptimalScaling_3CatQual.a && /Library/Developer/CommandLineTools/usr/bin/ar qc 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/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/cmake/data/bin/cmake -E make_directory /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/build_model_ext/swig/CMakeFiles/_Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/build_model_ext/swig /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/build_model_ext/swig/CMakeFiles/_Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir && /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/cmake/data/bin/cmake -E env SWIG_LIB=/opt/homebrew/Cellar/swig/4.1.1/share/swig/4.1.1 /opt/homebrew/bin/swig -python -I/opt/homebrew/opt/python@3.10/Frameworks/Python.framework/Versions/3.10/include/python3.10 -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/swig/.. -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/include/../swig -I/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual -I/opt/homebrew/include -I/opt/homebrew/Cellar/hdf5/1.12.2_2/include -I/opt/homebrew/opt/libaec/include -outdir /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/build_model_ext/swig -c++ 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/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/share/amici/swig -isystem /opt/homebrew/include -isystem /opt/homebrew/Cellar/hdf5/1.12.2_2/include -isystem /opt/homebrew/opt/libaec/include -O3 -DNDEBUG -std=gnu++17 -arch arm64 -isysroot /Library/Developer/CommandLineTools/SDKs/MacOSX13.3.sdk -fPIC -Xclang -fopenmp -MD -MT swig/CMakeFiles/_Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/CMakeFiles/_Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/Raf_Mitra_NatCom2018OptimalScaling_3CatQualPYTHON_wrap.cxx.o -MF swig/CMakeFiles/_Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/CMakeFiles/_Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/Raf_Mitra_NatCom2018OptimalScaling_3CatQualPYTHON_wrap.cxx.o.d -o swig/CMakeFiles/_Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/CMakeFiles/_Raf_Mitra_NatCom2018OptimalScaling_3CatQual.dir/Raf_Mitra_NatCom2018OptimalScaling_3CatQualPYTHON_wrap.cxx.o -c 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/Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libcolamd.a /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libbtf.a /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libamd.a /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libsuitesparseconfig.a /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libsundials_sunnonlinsolnewton.a /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libsundials_sunnonlinsolfixedpoint.a /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libsundials_cvodes.a /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/lib/python3.10/site-packages/amici/lib/libsundials_idas.a -lm -Xlinker -framework -Xlinker Accelerate /opt/homebrew/lib/libomp.dylib /opt/homebrew/Cellar/hdf5/1.12.2_2/lib/libhdf5_hl_cpp.dylib /opt/homebrew/Cellar/hdf5/1.12.2_2/lib/libhdf5_hl.dylib /opt/homebrew/Cellar/hdf5/1.12.2_2/lib/libhdf5_cpp.dylib /opt/homebrew/Cellar/hdf5/1.12.2_2/lib/libhdf5.dylib && :\n", - "-- Install configuration: \"Release\"\n", - "-- Installing: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/./Raf_Mitra_NatCom2018OptimalScaling_3CatQual.py\n", - "-- Installing: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/./_Raf_Mitra_NatCom2018OptimalScaling_3CatQual.cpython-310-darwin.so\n", - "-- Installing: /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/Raf_Mitra_NatCom2018OptimalScaling_3CatQual/lib/libRaf_Mitra_NatCom2018OptimalScaling_3CatQual.a\n", - "------------------------------ model_ext ------------------------------\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-11-30 13:29:48.903 - amici.petab_import - INFO - Finished Importing PEtab model (1.74E+01s)\n", - "2023-11-30 13:29:48.906 - amici.petab_import - INFO - Successfully loaded model Raf_Mitra_NatCom2018OptimalScaling_3CatQual from /Users/zebo/Documents/PhD/Projects/pypesto_amici_dev/dev_venv/pyPESTO/doc/example/amici_models/Raf_Mitra_NatCom2018OptimalScaling_3CatQual.\n" - ] - } - ], + "outputs": [], "source": [ "objective = importer.create_objective()" ] @@ -1044,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1078,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1111,26 +274,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/10 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from pypesto.visualize import parameters, waterfall\n", "\n", @@ -1192,20 +319,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plot_splines_from_pypesto_result(result, figsize=(10, 8))\n", "plt.show()" @@ -1221,20 +337,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "visualize_optimized_model_fit(\n", " petab_problem,\n", @@ -1262,44 +367,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using minimal_diff_on options: {'min_diff_factor': 0.5}\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using minimal_diff_off options: {'min_diff_factor': 0.0}\n" - ] - }, - { - "data": { - "image/png": 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observableIdpreequilibrationConditionIdsimulationConditionIdmeasurementtimeobservableParametersnoiseParametersobservableTransformationnoiseDistributionmeasurementTypemeasurementCategory
0ActivityNaNInhibitor_005NaN1linnormalordinal2
1ActivityNaNInhibitor_305NaN1linnormalordinal2
2ActivityNaNInhibitor_1005NaN1linnormalordinal3
3ActivityNaNInhibitor_2505NaN1linnormalordinal3
4ActivityNaNInhibitor_3505NaN1linnormalordinal2
5ActivityNaNInhibitor_5005NaN1linnormalordinal1
6ActivityNaNInhibitor_7505NaN1linnormalordinal1
7ActivityNaNInhibitor_10005NaN1linnormalordinal1
8ActivityNaNInhibitor_30005NaN1linnormalordinal1
9YbarNaNInhibitor_005NaN1linnormalordinal1
10YbarNaNInhibitor_305NaN1linnormalordinal1
11YbarNaNInhibitor_1005NaN1linnormalordinal1
12YbarNaNInhibitor_2505NaN1linnormalordinal2
13YbarNaNInhibitor_3505NaN1linnormalordinal2
14YbarNaNInhibitor_5005NaN1linnormalordinal3
15YbarNaNInhibitor_7505NaN1linnormalordinal3
16YbarNaNInhibitor_10005NaN1linnormalordinal3
17YbarNaNInhibitor_30005NaN1linnormalordinal3
\n", - "
" - ], - "text/plain": [ - " observableId preequilibrationConditionId simulationConditionId \\\n", - "0 Activity NaN Inhibitor_0 \n", - "1 Activity NaN Inhibitor_3 \n", - "2 Activity NaN Inhibitor_10 \n", - "3 Activity NaN Inhibitor_25 \n", - "4 Activity NaN Inhibitor_35 \n", - "5 Activity NaN Inhibitor_50 \n", - "6 Activity NaN Inhibitor_75 \n", - "7 Activity NaN Inhibitor_100 \n", - "8 Activity NaN Inhibitor_300 \n", - "9 Ybar NaN Inhibitor_0 \n", - "10 Ybar NaN Inhibitor_3 \n", - "11 Ybar NaN Inhibitor_10 \n", - "12 Ybar NaN Inhibitor_25 \n", - "13 Ybar NaN Inhibitor_35 \n", - "14 Ybar NaN Inhibitor_50 \n", - "15 Ybar NaN Inhibitor_75 \n", - "16 Ybar NaN Inhibitor_100 \n", - "17 Ybar NaN Inhibitor_300 \n", - "\n", - " measurement time observableParameters noiseParameters \\\n", - "0 0 5 NaN 1 \n", - "1 0 5 NaN 1 \n", - "2 0 5 NaN 1 \n", - "3 0 5 NaN 1 \n", - "4 0 5 NaN 1 \n", - "5 0 5 NaN 1 \n", - "6 0 5 NaN 1 \n", - "7 0 5 NaN 1 \n", - "8 0 5 NaN 1 \n", - "9 0 5 NaN 1 \n", - "10 0 5 NaN 1 \n", - "11 0 5 NaN 1 \n", - "12 0 5 NaN 1 \n", - "13 0 5 NaN 1 \n", - "14 0 5 NaN 1 \n", - "15 0 5 NaN 1 \n", - "16 0 5 NaN 1 \n", - "17 0 5 NaN 1 \n", - "\n", - " observableTransformation noiseDistribution measurementType \\\n", - "0 lin normal ordinal \n", - "1 lin normal ordinal \n", - "2 lin normal ordinal \n", - "3 lin normal ordinal \n", - "4 lin normal ordinal \n", - "5 lin normal ordinal \n", - "6 lin normal ordinal \n", - "7 lin normal ordinal \n", - "8 lin normal ordinal \n", - "9 lin normal ordinal \n", - "10 lin normal ordinal \n", - "11 lin normal ordinal \n", - "12 lin normal ordinal \n", - "13 lin normal ordinal \n", - "14 lin normal ordinal \n", - "15 lin normal ordinal \n", - "16 lin normal ordinal \n", - "17 lin normal ordinal \n", - "\n", - " measurementCategory \n", - "0 2 \n", - "1 2 \n", - "2 3 \n", - "3 3 \n", - "4 2 \n", - "5 1 \n", - "6 1 \n", - "7 1 \n", - "8 1 \n", - "9 1 \n", - "10 1 \n", - "11 1 \n", - "12 2 \n", - "13 2 \n", - "14 3 \n", - "15 3 \n", - "16 3 \n", - "17 3 " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from pandas import option_context\n", "\n", @@ -525,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -542,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -574,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -618,19 +241,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 10/10 [00:09<00:00, 1.06it/s]\n" - ] - } - ], + "outputs": [], "source": [ "np.random.seed(n_starts)\n", "problem.objective.calculator.inner_calculators[\n", @@ -659,17 +274,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 10/10 [00:09<00:00, 1.04it/s]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "np.random.seed(n_starts)\n", "problem.objective.calculator.inner_calculators[\n", @@ -698,17 +305,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 10/10 [00:09<00:00, 1.04it/s]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "np.random.seed(n_starts)\n", "problem.objective.calculator.inner_calculators[\n", @@ -744,19 +343,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean computation time for standard approach: 0.9591359615325927\n", - "Mean computation time for reduced approach: 0.9587480783462524\n", - "Mean computation time for reduced reparameterized approach: 0.9444998025894165\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "time_standard = res_standard.optimize_result.get_for_key('time')\n", "print(f\"Mean computation time for standard approach: {np.mean(time_standard)}\")\n", @@ -782,30 +371,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from pypesto.visualize import waterfall\n", "\n", @@ -824,26 +392,9 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0.0019888825676075647], [0.001988882370072136], [0.001988882910838056]]\n", - "[[0.0005749912862480574], [0.000574991288188709], [0.0005749912568270656]]\n", - "[[0.0019873258450258152], [0.00198732584677828], [0.001987325844380883]]\n", - "[[0.000575277818265601], [0.0005752778595524745], [0.0005752778031689591]]\n", - "[[0.001988829061937092], [0.0019888290614633627], [0.0019888291358973434]]\n", - "[[0.0005764328010595188], [0.0005764329969969911], [0.0005764329960499568]]\n", - "[[0.0005747398568712971], [0.0005747398648398072], [0.0005747398437695331]]\n", - "[[0.0005735607865200714], [0.0005737543524337683], [0.0005767971102241927]]\n", - "[[0.0005735594279397531], [0.0005735611260351738], [0.0005741377033402549]]\n", - "[[0.0005735572044450639], [0.000573961371293838], [0.0005736263264256104]]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "for start_id in range(10):\n", " print(\n", @@ -872,20 +423,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "plot_categories_from_pypesto_result(res_standard, figsize=(10, 10))\n", "plt.show()" diff --git a/doc/example/petab_import.ipynb b/doc/example/petab_import.ipynb index 9039dc094..c2c4d2398 100644 --- a/doc/example/petab_import.ipynb +++ b/doc/example/petab_import.ipynb @@ -17,24 +17,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-10-19T09:12:27.344220Z", "start_time": "2023-10-19T09:12:11.884260Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3\u001b[0m\r\n", - "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n" - ] - } - ], + "outputs": [], "source": [ "# install if not done yet\n", "# !apt install libatlas-base-dev swig\n", @@ -44,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-10-19T09:12:33.778201Z", @@ -90,7 +80,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-10-19T09:12:37.201025Z", @@ -136,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-10-19T10:40:50.415797Z", @@ -144,21 +134,7 @@ }, "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model parameters: ['Epo_degradation_BaF3', 'k_exp_hetero', 'k_exp_homo', 'k_imp_hetero', 'k_imp_homo', 'k_phos', 'noiseParameter1_pSTAT5A_rel', 'noiseParameter1_pSTAT5B_rel', 'noiseParameter1_rSTAT5A_rel'] \n", - "\n", - "Model const parameters: ['ratio', 'specC17'] \n", - "\n", - "Model outputs: ['pSTAT5A_rel', 'pSTAT5B_rel', 'rSTAT5A_rel'] \n", - "\n", - "Model states: ['STAT5A', 'STAT5B', 'pApB', 'pApA', 'pBpB', 'nucpApA', 'nucpApB', 'nucpBpB'] \n" - ] - } - ], + "outputs": [], "source": [ "importer = pypesto.petab.PetabImporter(petab_problem)\n", "\n", @@ -194,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-10-19T10:40:55.952163Z", @@ -220,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "scrolled": true }, @@ -248,19 +224,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'fval': 138.22199566457704, 'grad': array([ 2.20546436e-02, 5.53227499e-02, 5.78876640e-03, 5.42272184e-03,\n", - " -4.51595808e-05, 7.91009669e-03, 0.00000000e+00, 1.07876837e-02,\n", - " 2.40388572e-02, 1.91925085e-02, 0.00000000e+00]), 'rdatas': [ >)>]}\n" - ] - } - ], + "outputs": [], "source": [ "ret = obj(\n", " petab_problem.x_nominal_scaled,\n", @@ -280,7 +246,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -296,17 +262,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[6, 10] [0, 1, 2, 3, 4, 5, 7, 8, 9]\n" - ] - } - ], + "outputs": [], "source": [ "print(problem.x_fixed_indices, problem.x_free_indices)" ] @@ -320,19 +278,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(138.22199566457704, array([ 2.20546436e-02, 5.53227499e-02, 5.78876640e-03, 5.42272184e-03,\n", - " -4.51595808e-05, 7.91009669e-03, 1.07876837e-02, 2.40388572e-02,\n", - " 1.91925085e-02]))\n" - ] - } - ], + "outputs": [], "source": [ "objective = problem.objective\n", "ret = objective(petab_problem.x_nominal_free_scaled, sensi_orders=(0, 1))\n", @@ -341,19 +289,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fd: [ 0.02993985 0.05897443 -0.00149735 -0.00281785 -0.00925273 0.01197046\n", - " 0.01078638 0.02403756 0.01919121]\n", - "l2 difference: 0.017256061672716528\n" - ] - } - ], + "outputs": [], "source": [ "eps = 1e-4\n", "\n", @@ -392,22 +330,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine will use up to 8 processes (= CPU count).\n", - " 0%| | 0/10 [00:00" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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hRx99lHPPPZd77rkHRVH45je/yY033jioGSi33norf/vb37j88stJpVIcddRR3HLLLXZrsKqqKh5//HHOP/98Lr30UioqKjjmmGPYe++92X///Ut6jOHYR6nq6ur473//y89+9jMeeughbrvtNqqqqpgxYwbXXXedvZ3T6eQvf/kLF198MaeddhrpdJo777yTiRMnMm/ePF577TWuuuoqbr31ViKRCPX19cyZM4dTTz11wGMYN24cRx11FM899xx33303DoeDrbfemvvvv5/vfOc7w/p8hRBCCCHEhkMxR+KtSEIIIYQQQgghNkl33XUXJ5xwAm+++SazZ88e7cMRQgghhBBioyczaoQQQgghhBBCCCGEEEIIIUaJBDVCCCGEEEIIIYQQQgghhBCjRIIaIYQQQgghhBBCCCGEEEKIUSIzaoQQQgghhBBCCCGEEEIIIUaJVNQIIYQQQgghhBBCCCGEEEKMEglqhBBCCCGEEEIIIYQQQgghRoljtA9AiA2RYRisWrWKYDCIoiijfThCCCGEEEIIIYQQQgghNjKmadLV1UVjYyOqWrxuRoIaIQpYtWoV48aNG+3DEEIIIYQQQgghhBBCCLGRW758OWPHji16uwQ1QhQQDAYB6wsoFAqN8tEIIYQQQgghhBBCCCGE2NiEw2HGjRtnn28uRoIaIQrItDsLhUIS1AghhBBCCCGEEEIIIYQYsoHGaxRviiaEEEIIIYQQQgghhBBCCCFGlAQ1QgghhBBCCCGEEEIIIYQQo0SCGiGEEEIIIYQQQgghhBBCiFEiQY0QQgghhBBCCCGEEEIIIcQokaBGCCGEEEIIIYQQQgghhBBilEhQI4QQQgghhBBCCCGEEEIIMUokqBFCCCGEEEIIIYQQQgghhBglEtQIIYQQQgghhBBCCCGEEEKMEsdoH8CG5vjjj6ejo4N//etfo30oRSmKwsMPP8whhxyyQexHbHpM06S1tZVEIoHb7aaqqgpFUUb7sIQQQoh1l0zCbbfBF1/ApElwxhngcgGQ1tP830f/R1O4iYZQA/tN3w+H1v+vy8lkisdue5pVX6yhcVIdB5+xPy6Xc308k43S+vwdQ36fGT2mqUPyLTCaQa0B12wURRvtwxJigydfO0IIIcTma1SDmuOPP56//OUveev3339/nnrqqVE4ok3TggUL+Ne//sWiRYty1jc1NVFRUbHejmPevHm89NJL9vXa2lr23HNPbrjhBsaPH1/yfhYsWMCVV16Zt/6ZZ55hn3324aGHHuKaa67h888/J5VKMWXKFM4//3yOPfbYYXkem7qmpiY+/PBDVq1axVNPPcUBBxxAY2MjM2bMoKGhYbQPTwghhBi6Cy+EX/0KdL133QUXwHnncc93tmXBywtYFVuFgYGKSuPTjSzYYwHHzDmm4O7uuPBu/vnrxzF0o3fdj+/mOz86iFN+Kb939JX5HSMej9vrPB7PiPyOsT4fS+Qy409jhq8GY3XvSrUeQpegePYfvQMTYgMnXztCCCE2RU1NTfz+97/n1FNPld/DBzDqrc8OOOAAmpqacpa///3vo31YI0rXdQzDGHjDEVZfX4/b7V6vj3nyySfT1NTEqlWreOSRR1i+fDnHHFP45Ed/ZsyYkfd5s+eeewJQWVnJJZdcwmuvvcb//vc/TjjhBE444QSefvrp4X46m5ympibefvtt4vE4bW1t/P3vf6etrY14PM7bb79NU1PTaB+iEEIIMTQXXgjXX58b0gDoOv/+1/Wc+fiJrIiuwKk68ak+nKqTFdEVnP7M6dzzxj15u7vjwrt54IZHc0IaAEM3eOCGR7njwrtH8tlsdLJ/x8g2Er9jrM/HErnM+NOYHWfnnmgGMNZgdpyNGZffx4UoRL52hBBCbKqampq48sor5XfwEox6UON2u6mvr89ZMlUeiqJw++23c+CBB+L1etlyyy158MEHc+7//vvvs9dee+H1eqmqquKUU04hEomU9Ni6rnPeeedRXl5OVVUVF154IaZp5mxjGAbXXnstEydOxOv1st122+Udw6OPPsqUKVPweDzMnz+fv/zlLyiKQkdHBwB33XUX5eXlPProo0yfPh23282yZct488032XfffamurqasrIy5c+fyzjvv5Ox78eLF7Lnnnng8HqZPn84zzzyT9zwuuugittpqK3w+H1tuuSWXXXYZqVTKfuwrr7yS9957D0VRUBSFu+66y359s1u8DfRaHn/88RxyyCHccMMNNDQ0UFVVxQ9/+EP7sUrh8/mor6+noaGBnXfemTPPPDPnOeu6zkknnWS/3lOnTuXmm2/O24/D4cj7vHH1tC2ZN28e3/72t5k2bRqTJk3inHPOYdttt+WVV14p+Tg3R6Zp8uGHH/a7zYcffpj3NSKEEEJs8JJJq5KmAF2BS/aClJHCq3rRFA1FUXAoDnyaj5SRYsHLC0jr6azdpfjnrx/v9yH/+evHSSZL/x1pU7Y+f8eQ32dGj2nqVjUAhV5ba50ZvsZq7SSEsMnXjhBCCCFgI5hRc9lll/GLX/yCm2++mbvvvpsjjzyS999/n2nTptHd3c3+++/PLrvswptvvsnatWv5wQ9+wJlnnmmHEf258cYbueuuu/jzn//MtGnTuPHGG3n44YfZa6+97G2uvfZa7rnnHn73u98xZcoU/vOf/3DMMcdQU1PD3Llz+eqrrzjssMM455xz+MEPfsC7777LBRdckPdY0WiU6667jj/+8Y9UVVVRW1vLl19+yXHHHcdvfvMbTNPkxhtv5Otf/zqLFy8mGAxiGAaHHnoodXV1vPHGG3R2dnLuuefm7TsYDHLXXXfR2NjI+++/z8knn0wwGOTCCy/kiCOO4IMPPuCpp57i2WefBaCsrCxvH6W+li+88AINDQ288MILfP755xxxxBHMnDmTk08+eeD/zD7a2tq4//77mTNnjr3OMAzGjh3LAw88QFVVFa+++iqnnHIKDQ0NHH744YN+DNM0ef755/n000+57rrrim6XSCRIJBL29XA4POjH2ti1trayatUq2traAPjiiy9yPoJVrfTqq68SDAZxuVz24nQ6c647HA7pAS+EEGKDkNJTpH97E96eSppmHzwwA1p81vJxNXxQB5gQT3XgTyv40yqmooCqgAqr00t58aYL2Gfbb8D48Tz+0Ht5lTR9GbrBY7c9zXfOPWg9PMvRZRgGiUSCZDJJMpm0L2c+RiKRvOqWvuLxOK2trVRXV5f8uKZpkkqliMVixGIxotGoXQk83I8lSpB8K78aIIcJRpO1nXtOP9sJsekxTRPMbjBarPkzRgvozZhGC6Q+kq8dIYQQm5RM9yPAfoN+9hv1GxoapA1aAaMe1Dz++OMEAoGcdT/96U/56U9/CsB3v/tdfvCDHwBw1VVX8cwzz/Cb3/yG2267jXvvvZd4PM5f//pX/H4/ALfeeisHH3ww1113HXV1df0+9k033cTFF1/MoYceCsDvfve7nPZYiUSCa665hmeffZZddtkFgC233JJXXnmF3//+98ydO5ff//73TJ06leuvvx6AqVOn8sEHH3D11VfnPFYqleK2225ju+22s9dlB0IAd9xxB+Xl5bz00kscdNBBPPvss3zyySc8/fTTNDY2AnDNNddw4IEH5tzv0ksvtS9PmDCBCy64gPvuu48LL7wQr9dLIBCwK1CKKfW1rKio4NZbb0XTNLbeemu+8Y1v8Nxzz5Uc1Nx222388Y9/xDRNotEoW221Vc5r7nQ6c+bPTJw4kddee437778/J6h5//33cz5vpk+fzn//+1/7emdnJ2PGjCGRSKBpGrfddhv77rtv0eO69tprC8692ZwkEgmeeuqpvNaDt956q335qKOO4uijj6a9vb3ffSmKUjTEkXBHCCHEujBNk2gqiolJwGX9LrAmsoZ737+XlmgLLdEWmqPN9sfOeCfnr96S83vu3+6Fn+/Zu7+4A4yeHz9Kz/4VXSfzE8kFJNwQ+ePN8IlV5Xso8G1ARyGNShKVOA6iOOjGSRcuOnFTdXc3KF/A2LEwfjw0NkJVFazn1rODZRiGHboUCl76hjLpdHrgnZYg+00z0BvERKNRO4jJDmVisdiQH7vvY4lhYDQP73ZCbARMMwlGm/V5rTfbIYxpNIPekhvMmLF1ezD52hFCCLGR+P3vf593njX73PEVV1zBggUL1vNRbfhGPaiZP38+t99+e866yspK+3ImIMm+vmjRIgA+/vhjtttuOztYANhtt90wDINPP/2036Cms7OTpqamnGoOh8PB7Nmz7VYIn3/+OdFoNO8EfzKZZPvttwfg008/Zccdd8y5faeddsp7PJfLxbbbbpuzbs2aNVx66aW8+OKLrF27Fl3XiUajLFu2zH5+48aNs0OaQq8HwD/+8Q9uueUWvvjiCyKRCOl0mlAoVPS5F1Lqazljxgw0TbO3aWho4P333y/5cb73ve9xySWX2M//mmuuYb/99uPtt98mGAwC8Nvf/pY///nPLFu2jFgsRjKZZObMmTn7mTp1Ko8++qh9ve+snWAwyKJFi4hEIjz33HOcd955bLnllsybN6/gcV188cWcd9559vVwOMy4ceNKfl6bArfbzQEHHMBOO+1EMpnkoosuAuC0005j6tSpgPW1OWHCBFwul32CJpVK5ZzM0XUd0zTzqpQGUkq40/d2CXeEEGLTYJgGHfEONEWjzGNV/q6JrOHORXfS3N1MS6ylN4TpbiaejnP+Ludz/q5W/NKV7OL6V6/P2Z9pmJimiWEYfGZ227fVR+A7H0F1FGqi0OKF63YHlw5OrLZnhmaCaaIYJmnVRDOhqttqQJP5qaMADkwc6HjQCVGgzdk7S+GdJ/PXKwo4nVZg4/NBIAChEFRUWEFObS00NMCYMVbIM2aMtb6iwrrfIJmmWTR0KXR5MG1te59S789xt9udczmZTPLVV18NuI+Wlhba2tpyghi970yhAlwuF16vF5/PB1BS/+v1Padxs6DWlLSZabQhv72JDZlpGmB29AQtPSGL0YKpN+dVxGB2DG7nih/U6p6lBrQaMGIQf3Dg+5b4NSaEEEKMtlNPPZVvfvObxGIxdt99d8B6I3jmvLZU0xQ26kGN3+9n8uTJo30YBWXms/z73/9mzJgxObcN9o87r9ebd0L5uOOOo7W1lZtvvpnx48fjdrvZZZddSCaTJe/3tdde43vf+x5XXnkl+++/P2VlZdx3333ceOONgzq+Ujn7nBxQFAXD6L/1R7aysjL7/3vy5Mn86U9/oqGhgX/84x/84Ac/4L777uOCCy7gxhtvZJdddiEYDHL99dfzxhtv5OzH5XL1+3mjqqp9+8yZM/n444+59tpriwY1brd7s/+DvaqqisbGRiorK3NahkycONF+LT0eDzNmzOg3HNF1vWiI03fJ3D4c4U5/1TrZi6ZpEu4IIcR6kNSTtEZbcTvcVHqtN+E0dTVxx9t35FS8tEZbaY21ohs65+1yHhfsarWQjSQj3PLGLUX33xZro7u7m3A4TGdrJ3vV7IXX9OI1vZQ5ygg6gpQ5yihzlOHTHRjqESiGQSgBv8nKTnQFHpoGi6sgabox0iaG0TMTABPdmcbfGuTKlXsTKPMx/Wtj2GOXRl654X6qzShVxKggTjkJQiQJkMJHCi9pyvxOlGQS+gYfpmnNzUkmoasL1qwp/YVVVUyXC7xeTK8XIxDACAZJh0KkKipIlJcTr6ggVlVFpKyMmNdLt8tFOhDAzHqzTSkKhS7F1jmdzryfr6ZpEo/HiUajrFixYsAAaPny5QXXu91ufD4fXq/XDmSyP2a/icg0TZ577rl+2595PB6qqqoG8UqIkrhmg1oPxhoKz9ro0fVzjNQHKMELUTRpPyfWH9OIFmg91px1PXNbKzCYaj1Hb/ii1dghjNLnOmoViurPu7dp6pjJV/r52lGsry3X7CE+cyGEEGL9yrQ26+7ufcPczJkz2WGHHUbxqDZ8ox7UDOT111/n+9//fs71TDXLtGnTuOuuu+ju7rYrQRYuXIiqqnYFQDFlZWU0NDTwxhtvsOeeVv+LdDrN22+/bX/STJ8+HbfbzbJly5g7d27B/UydOpUnnngiZ92bb75Z0nNbuHAht912G1//+tcB64/TlpYW+/Zp06axfPlympqa7KTx9ddfz9nHq6++yvjx4+0qFYClS5fmbONyuQZ8N+K6vJbrIvOHdSwWsx9z11135YwzzrC3yZ6RMlSZ3umiOEVRmDFjBm+//XbRbQYKacD6P82cSClVdrhTKMgptBiGMaRwR1XVktuxSbgjhBC9TNMkkozQGmvF5/RR668FYFXXKm7976125UtztxXChBPWvLfs8CWaivL7t39f9DEiyYh9uS5Qx4nbn0i1r5pKTyU+fHh0D860E0fCQbI7yQsvvGBvf2TlkfZlj8dDKBQiFAoRDAatSuPzzkO54Ya8x1RNuPp5OOJQlZiWQEVDQcFUTQynjgMHJzWczJXNlxGqDNr3W045D9zwaN7+Mr57wTc55ZfHZl48iEahpQWWLYPly2HFCmhqgrVrMZubMdvaoKMDIhFr20QCJZVC6Tv03jBQ4nGIx1Ha21F7VruKHknWXXtCHsPjwfD5MIJBjPJyqKzErKtDqa9H3WILtHHjcNbVoVRWWlU8ZWVQIOTJ/H7V1dVVsDVZPB4v+Q09gUCAsrKynAAms2iDCJiG6/cZMXiKokHoEsyOs+lpJJh9q3XdtQskX4P4vzATz0LgXPAdjaJs8H+Wig2UaaasYCVT4ZKpfrFbj/WuI6u6siRKeZ+gpRol53oNaNWglKEo6oC7K/owA37tgBL6qbWdEEIIITZZo/4bcSKRYPXq3MF5DofDHu75wAMPMHv2bHbffXf+9re/8d///pc//elPgNVG64orruC4445jwYIFNDc3c9ZZZ3HssccOOJ8G4JxzzuEXv/gFU6ZMYeutt+ZXv/oVHR0d9u3BYJALLriAH/3oRxiGwe67705nZycLFy4kFApx3HHHceqpp/KrX/2Kiy66iJNOOolFixZx1113AQz4B+CUKVO4++67mT17NuFwmB//+Mc5J7f32WcfttpqK4477jiuv/56wuFwTiCT2ceyZcu477772HHHHfn3v//Nww8/nLPNhAkT+Oqrr1i0aBFjx44lGAzmVY+s62tZqmg0av9/r1mzhquuugqPx8N+++1nP5+//vWvPP3000ycOJG7776bN998k4kTJ5b8GNdeey2zZ89m0qRJJBIJnnjiCe6+++68FnsiX0NDA7NmzeKtt96y11VUVNiVNCNVmjic4U5/IY9hGPZJpeEKd4qFPBLuCCE2Brqh0x5vpyXaQpm7jIag9X1+ZXglN752Y97Ml0Ta+t75o51/xI93+zEA8XScuxbdVXD/DtVBPN1b2VAfqOeMHc+gyltFjb+Gal81NT7rY6W3EofqIBqNEg6HCYfDHBI6hHA4TLQlau8j1fMPrO/PmSAm8zEUCuFy5ccWHx5yEt1PL2LW+8+hZZ0EM4DI4q2Y8lANX+zzIbHKbtBMnJqDRt84FuyxgGPmHJO3v0wI889fP46h94YRqqbynR8dxInXHEV3d3dua7FUikRlJclAgMSWW+bcZvYNZABMEzWRwBWJ4OzsxNfcjKelBU9bG+72djxdXdZt3d04o1HUeBw1kUBJJvMDHkA1DIjH0eJxKxQqkQngdGK43RheLymvl5TPR8LvJ1FWZlXxVFaSqKwkFQySDAQwAwHUQADT78fTE7woikJnZ2fOXJmR+B0j8/vMhx9+mFNZM9K/zwhQPPtD+S2Y4atzh6Or9daJZs/+mMlFmOErIf0hZtfPIfYAhK5AkWoB0cM0TTA7+4QvzZj29ezWY/3PzsyjeO3QJRO0KH2uW7dXoiilRODDo5SvHSGEEGJj1t/sdGEZ9aDmqaeeyvtjaerUqXzyyScAXHnlldx3332cccYZNDQ08Pe//53p06cD4PP5ePrppznnnHPYcccd8fl8fOc73+FXv/pVSY99/vnn09TUxHHHHYeqqpx44ol8+9vfprOz097mqquuoqamhmuvvZYvv/yS8vJydthhB376058CVluoBx98kPPPP5+bb76ZXXbZhUsuuYTTTz99wFZaf/rTnzjllFPYYYcdGDduHNdccw0XXHCBfbuqqjz88MOcdNJJ7LTTTkyYMIFbbrmFAw44wN7mm9/8Jj/60Y8488wzSSQSfOMb3+Cyyy7LGcj0ne98h4ceeoj58+fT0dHBnXfeyfHHH59zLOv6WpbqD3/4A3/4wx8AKwDYdttteeKJJ+yqnVNPPZV3332XI444AkVROOqoozjjjDN48skC/d2L6O7u5owzzmDFihV4vV623npr7rnnHo444ohhfS6bqoaGBubPn29f32+//dhiiy02uNBhYwh3Sm3Hlrldwh0hxHBIpBN2wFLprWRcmTVzbXnncn7xyi+sdmOxVpq7m2mLtWGYVshw7s7ncuFuFwKQMlLc98F9Bffvd/nRzd5K3fpAPT/a+UdU+6rtJRPClLnLcr6v+V1+Lt3zUsCqZO7q6iIcDrN2zVo+D39OV1dX0eHwbrfbDmIyi9/vR1ULv4v54zc+46k/v8B7L3zA6iVr0dMGUI6Db3Mwn9NIN83uMj7eYV/mHLIzL5wwn0Cln//76P9oCjfREGpgv+n74dCsX5d1Xbd/FmR+Rux1yi7sfPR2vPi311i7tIWyuiBfO3ArDAyeeuqpQfyvWRwOR0ltxjIfiz13AGIxaGuD1lZobraqeFauhFWrYO1aaG3FbGvD7Oiw2q/FYiiJBEqBChgFIJVCS6XQIhEGMyXHVBSUrFk8ZihEKhgkXVGBUluLZ8IElK++gspKa6mo6F2CQWuezxA0NDRQX19Pa2sriUQCt9tNVVWV/JxdDxTP/uDeB5JvWSfU1RpwzbarARTXTKh6EGL3Y3b9CtKfYrYdjek5RNqhbeJMM5YTvFhzX7KrXjJVMC1QaO5XURqoVX2CluoCrceqQfFvsN8HBvraEUIIITZmEtQMTDELvn1vw6AoCg8//DCHHHLIaB/KoFx99dX87ne/K9pnW2z4wuEwZWVldHZ2Wu1SNjPd3d0EAgHAmtWUaYe3uTFNc0gzdwYztylb33CnlIBnMO1ghBAbJ9M06Up22eFLrb+WCeUTAFjWuYyfvfQzu+KlJdpCV6LLvu/Zc87mJ7v/BIAlHUvY9U+75u1fURQqPBUcP/N4u0VZLBXjD+/8wa54qfJV2ZUvXmfpAXnm+GOxmF0lk1mi0WjB7VVVJRAI5IUyhapksn3y5uc8/efneff5D1j91Vr0dH7bV2/Aw4SvbcHOB81iv+Pm4q/w5QQvmct9P2bmqQ2Wpmklhy4j8T1d1/WclmR9W5MVegOCXcHT1YWzqwtPZyfBcBh/Zyeezk7cnZ04w2G0SAQlEkGJRlFiMRjC6zMgRQGvF/x+CIWgvByqqqCmBurrraVQwFNRYd1nAz0ZK3qZRhtm141WVQ2AEkCRdmgbFdNMg9HWJ2jpqX7JDl6MZjAjA+8wm1KWNfelUOuxntuU8nVqPSaEEEKIkSHnFy2lnmeW336HwW233caOO+5IVVUVCxcu5Prrr+fMM88c7cMSYsg0TbNnJ23OQYCiKDgcDhwOBz6fr6T79A13Spm5k0ql7MqdeDze7wDkvgqFOwMFPJvz/6kQGwrd0GmLtdnhSmOwkUmVkwArULnshcvsWS8t0RaSetK+b3b4YpgGTyx+Im//Ts1Jta8al9YbbtQH6rli7hU5FS/ZLceyeZ1ezp5z9qCfV3aVTDgcti8PV5VMts/e+ZKn//w87zz7Pk1frUFP5QcFbp+L+sm1TNt9EjO/MQN30Gl/7339ndcG/fwy33NLDV4cjpH9VTudTvcbxCSTyQH3kV2hmpkNkz0jxu12l/bu81gM2tt7q3ja2mDNGquCJ6uKh/Z26Oy0q3go8rkB9M72iUatqqDB0DS7ioeyMivgqaqCujprKRbwVFZa4ZBYLxS1EqXsakzv4T3t0D7Iaod2OYprx9E+xM2S1XqsqydoKdR6LLsKpo3cmSoDcecEL6g1VhVV3/BFrV6vrceEEEIIMfzk/OLgbNIVNZnErpAnn3ySPfbYY4hHlutHP/oR//jHP2hra2OLLbbg2GOP5eKLLx7xP843JC+//DIHHnhg0dsjkUG+e2qUbe4VNWL9Gkq4U3SeQQk0TRvSzB0hRP/i6bg1z6UnYBlXNo6tq7cGrPDlwmcutCtf2mJtOV/DZ+10FhfvcTEASzuWssufdsnbf8AVoNpXzVHbHMVZc86yH/O+D+7LmfVS468h6AqOaGuX7CqZ7GCmu7vwoOZCVTKFZuZlMwwj53veZ+98wYt/W8hHCxfTsqytp5VZLodbo2qLcibuOJbp+0whWN3/O7YURSk5dHG73eu9RWUqlSoYwGQ+plIDtwZyOBx54Uvmus/nw+l0jm4boHjcCnWyl9bW3pBnzRorpGlrs+bqdHVBdzeU8NyHzOHoreKpqOit4hko4KmogAFaH4viTFOH2ANWhY3Z04ra862edmg1o3twmwjTjOdWuBgtmH2Dl0wrMgYOenupPa3HqnODFjt4qe0NYjbg1mNCCCGEECOh1PPMG3RQs64+//zzoreNGTNmULMtRP9isRgrV64sevvkyZPX49GsOwlqxIauv3Cnv5BnXcKdUit2MrdJuCM2dqZpEk6ErfClJ2CZWD6RGbUzAPiq/SvOeeocu+olksx9U8KZO53JT/ewZtot61zGzn/cOed2RVGo9FZS46vhu9O/y+k7ng5YM2Ye/uRhqrxVOZUvHodnPTzrfLqu5wUypVTJBINBO5QJBAIoipLXYqxYm7FEIsGqxWv56JnPWLaoiY6mLowiwUzluHImzh7L9H0nU1YbzAtb+gteHA7HqJ0wNE0zL4jpG8gUe42zOZ3OfitiRj2IGSnxeG8FT3YVTybkaWrKr+Lp7oYSqozWictlzdYpK7PCm+pqqK3tDXL6hjzZ152Dmf6z6bLaof2qpx2a2dMO7RzwfU/aoRVgmjoY7XlzXkyjOacVGUaLVSUzGEooJ3hBremZ+1KdUxGDWiFzVIQQQgghipCgRoh1IEGN2BSZpkk6nS6pWif7Ngl3xKYkbaRpjbbSGmu1K18mV05mu/rtAPiy/UtO//fpNHc30xprJaXnvmv/hzv+kEv2vASA5Z3LmfPHOTm3OzUnNb4aavw1HLr1oZw862QAknqSf3/2bzt0ybQc09QN53PeNE3i8XjeLJliVTKKohAIBPD5fHg8Hvtr2DCMnNAlO4TpT+uydj78v89ZtmgV7avCBYMZp9tB3ZY1bDN3KnOP3oUxkxpyApgNKZQwTZNkMtlvEFPK3BuXyzVgECMGIZGwgptMsJO9tLRYIc+aNdbl1tbeNm0F5vkMO48nv4qnlICnvNyqAtrEmMn37HZoADimomwm7dCs1mORrCqXnuqXPtetpRUYzHxElxWu9Gk3lj/7pRpFGZ03CAghhBBCbEpkRo0QYsi6u7upra0FYO3atZvtsK9NjaIoOJ1OnE5nyf+nfcOdgWbtZIc7mSHWsVis5GPsG+4MFPI4nU4JdwTRVDSn5VhLtIWtq7dmVuMsAL5o+4KTHj2J5mgz7bH2vPufseMZdlDj0ly8v+b9nNtD7hBVvipqfDU0Bhvt9fWBev5w8B+o8ddQ46uhyldVtOWYS3Px7WnfHs6nvU50Xc+rkOmvSkZVVZxOJ6qq2l/fqVSKrq4uuroG9w5tp9OJ2+2msynCe098xOI3ltKyrJV0Mj+0cHldbDF1DDseMJMDT96bhol1Q3q+I8E0TRKJRL+tyQxj4JOnbre7aGsyr9e7WbXSXS/cbqivt5bBSCYLt2jLvrx6tdWmLVPFEw5blT+lisetZe3awR0bWPN4MsFNdXXxgKfvurIya57PBkhxbQdVD/S0Q/sVpD/FbPsepuebKMGLNsp2aKaZ7BO0ZNqPtfSpiGkGBhMOKqBWZgUtVhCjZFe9ZKpglJFtjSmEEEIIkSHnFwdH/vITQhQUjUZH+xDEBmC4wp1SKniGGu44HI6SqnWyl1IGlYvRY5gGnfFOu91YZtmmdht2GrMTAJ+3fc6xDx9Lc3cz0VT+96szdjzDDmo8Dg+ftX5m36YqKlW+KruyZXzZePu2On8df/32X+2qmCpvFW5H4ZkTTs3JN7b6xnA+9WGV+VoMh8N0dHQQDoeJRCLEYrGShsxny1TJFNL3a6xvm7HMx+ZlrTz715d55//+x/JPV5FK5M8YcXmcjJ3ayI4HbM+BP9iLMZMahvTch0OmwqhYRUwsFispiPF4PP1WxEjYvJFwuYYe8BRq0ZZ9vaXFCniyq3gG8XMQgGjUWvppRVyQolit2jIBTqkBT0WFVf0zwj9PFUUD35Hg2b+nHdr9EH8UM/E8BM4G3zGj3g7NNA0wO0BfW6D1WEtuMJOZvVMqJZBT4WK3HssOXtRqUCtH/XUQQgghhChEzi+WTn6bE0IIMayGM9wZKOgBSKfTpNPpQYc7pbZjk3BneKT0VE67sUwIM7N+JruO2xWAxa2LOfzBw2mNtpI28is7Tp99uh3U+Jw+lnYstW9zO9zU+Kx5LjX+GiZVTLJvq/XXcv9377du89VQ7ikv2nLMqTnZZ8t9hvOpDytd1/NmumR/7O7utq+X0larkMzXR3+zXbIvF/vaWPlFE0/e+jxvPb2I5Z+sJBnPD2acbifjpjYya7/tOPCkvRg3dcyQjnkoDMPIC2L6Xi6l9WOhKpjMR4/HI0HM5s7lgro6axmMVKp4i7bsdZmQJ1PFM9g/hE3TqvwJh2Hp0oG3z6aqvbN4ioU5xdYFg1ZIVCJFrUApuwrT913MzgWQ/gCz6xqI/RNGoB2a1XqsO6fqBb0Z0243lj37pRUYzPdbZ07wYs9+0bKqYezWYzJTVQghhBBicyFBjRBCiFE31HAnE96UOm+nb7gzGNknrwcKeTK3b8rhjmmadKe6cypeMiHM7MbZ7DF+DwA+a/2Mb933LTrjhd9FfNrs0+ygJuAKsCayxr4t5A5R46+xA5atq7e2b6v11/LIkY/Yt/ud/qKtXJyak9232H24nvqw0nU9J2wpdnldwxdFUezPYa/Xi9/vx+fzFQxghhosNH21hif/9BxvPrmIZZ+sJBnLr9pxuh2MmdLQE8zszfhpY4f0WKUwDKNg+JK5HI/HBwxiFEXJC2Gyr3s8nk3661yMIqcTamutZTAyAU+xYCf7eqaCp7UVisyi6pdhWI/Vnt9SckCaNnCgUyDgUSonQeX9EH+wQDu0C1G0/l8vq/VYW1bQsrZ39ovekhvMmIOsalIrs4KXatCyql96rqNWg1ImrceEEEIIIUQeCWqEEEJslBRFsU8wl6rUcKdvyAO94c5gynb7hjsDBT2jHe4YpkF7rN2ueGmNttqVLzuN2Ym9Ju4FwKctn3Lg3w4kni48e+HUWafaQU3IHbJDGk3VqPL2thyr8dXwtdqv2fer8dfw1DFP2ZUxTq34kHSH6mDHMRveQGnDMAoGLMXWDTYwHIimafj9fgKBAGVlZVRUVFBWVjYiVR1rlzXz5J+e541/v8Oyj1eQKBDMOFwOxkyutytmJszYYtgeP7tVYqHWZPESZoOoqjpgRYycUBUblXUJeDo6Bg53Mktzs/VxkDOqbLpuBUUtLYO+q+J09gQ3ZRCKQqgDyu6A8rswq6ZjVk1CqVAwQ2kIJaEsCmXdEOgET3iQD+bvDV7y5r5khS9qFYpS/GeWEEIIIYQQA5GgRgghxGZjXcOdUufupFJWi6ehhDuZyqLhCneSejKv4iWz7DpuV/adtC8AHzd/zH737IduFK7YOGXWKXZQU+4pt0Maj8NjV7VkwpcdGnaw71fjq+GF416gxm+1HFOV4sfqUB1sW7dtya/V+mAYBqlUquTgJfN/PxiZirLsFnu6rucEhYXuEwgECAaDhEIhe3G73SMWLDSvaOXJPz7LG0+8y9KPVpCI5s+scbgcNE6qY9a+23LAiXux5bYThvx4mZaGxSpiis3Myaaqak4lTN8gZiRfLyE2Kk4n1NRYy2Ck01bAM1Cwk309M4dnqFIpWLsWZe3aAje+jMLLABT6yjbdCpSpUO6Cci9UBKA81BP8VENVHVQ2QOU4qByHUj2mZx5PBbgLzysTQgghhBBiOEhQI4QQQvRjuMOdYgFP5gR/KpUilUoVDXdM0yRmxOhKd9GZ7iScDtNtdtNldNFtdDOrZhbzxszD5XKxLLaM4547zj4RrShKzklpE9MOaiq9lXZIU+4pt2e91PhqqPJWsfPYne37VfuqefWkV6nx1eBz+vo90a2pGlOrp5b82o000zRLajGWuTzU4KXYPBeXy4XD4bBnzcTjcSKRCOFwmEgkUnB/TqczJ4wJhUIEAoERn33Ssqqtp2LmbZZ+uJx4d34wojk1GifVsf3eVjAzZfuJJe8/lUr1G8QUC6lyHl/TCgYxmcsul0uCGCFGksMB1dXWUgLTTFkzXZKroXUJtC6F1pWYbaugZS20taC0dUB7F7THod2ADh06DGhLo4QHnhvVHyVhwlod1saAGNBW+p293sHN4cle55RqGyGEEEII0T8JaoQQeVRVZe7cufZlIcTgDDbcMUyD1mgrTeEmVneuZnXXatZG1rI2spaWaAuzqmaxW/VupFIpPmv/jHPePqfobI10Ms1kczIAHakO0uk0KiohR4iQFiLkCFHmLKPcVU6gLcDChQutuSQOjb/P/zu1gVr8Hn/Byp0MTdWYUD5hnV+n4ZAdipUSvJRy8r+Q/oKXvh+dTieKomCaJolEgnA4bC9dXV1EIpGC/3+KouD3+/NCmfVV9dG2up0n//w8rz/2Fks+WEG8O791mObUaJhYx/Z7bcMBJ85nq9mTC+4r8//SX2uyUkIwh8PRbxCTea2FEKPHNE0wO625LnqzPefFzLmemf3SDvR8/3MAdT1L9v5wABU91zw97cWsFmMmlShhP3R6oN0BnYoV4rQnUdo6MVubYO1r0PKlFfC0m9DphPbughU2gxKLWcuqVYO/r99f8hyenHXl5VYYJoQQQgixEZLzi4Mjv/UJIfJ4vV5efPHF0T4MITZqaSPNmsgae95L3/Zj+2y5D9+e9m3Amvmy91/3LrqvMTVjmDlzJgATuifget+F3+Wn2ltNpaeSCk8FFS5r2aZyG7au3JpkMkk8Eecftf/AZbhIp9J2tU629j5DoLsoPm+gWOu1/ubwDPYkummapNPpkkOXZDI54ED4Ys+lUOhSKHgppSpD13UikQhr1qyhq6vLDmaKBUOjVSWTrX1tB0/9+Xlee+xtlry/jFikQDDj0KifWMvM+duw/wnzmDZnK6A3iOno6ChaEVPK/B2n09lvazKnvAtdiFFjmjHQMyHLWjBaMPVM4NLzMXM7g6k+1ECtsme+ZEIYJWfmS89HxZ///bei8F6ht92ZmXofM7wAUu/33DAZzB+hdE0ovUVbWxu0t8MQfsbk6e62lhUrBn/fYHDwAU9FBZSVwXr8mSKEEEII0ZecXxwcCWqEEEKIEqX0FMvDy/PmvWSCmP0n7c93Z3wXgM/bPmevv+xVdF8V3go7qKnx16AoChWeCrvlWJW3ihqfNftlVuMs+37Vvmq+OPsLvE7vkJ5DZubKYObt9G3L1t3dXfLjORwOu92XpmlommafdDNNE8MwMAyDdLo3SDIMY9DPy+FwlFTtklnW5d088Xjcro7JBDLFqmQAAoGAHcZkZsqMxpD6jpYwT//5eV579C2+fH8psa5CwYxK3YRatps3g/lH78aE7cbmhC///e9/7cu6XnieUTaXy1UwgMlcdsg7xYVYr0wzDUZbn6Clp/olO3gxmsEs3I6xKKXMClfssKUapU8YY32sQOlnXtlwUJxfg8oHIPYgZtcNYH6OyQ8xaw5G2fJCFG1OaTsyDGsGz2DCndbW4Qt4ALq6rGXp0sHdT1GssGagQKdQ6BMKgbzrVQghhBBivVLMobwNVYhNXDgcpqysjM7OTkKh0GgfjhBiBCX1JF+0fZETuNghTKyFb0z5BkducyRgVb7M/8v8ovs6cfsT+flePwewWpbdMYtqX7UVvvSELpll+/rtmTPWOlFkmia6qeNQN8yT1n3DnXg8TjQaJR6Pk0gk7OqWdDpNOp3GMIwhVbn0paqqHe5kKnfcbjdut9s+2e/3+0fshL9hGPb8mOxloCqZTBiTubw+q2Syhdu6ePrOF3j1kTf58n9LiYZjeduomkr12Aom7ziB7b++DQ3TauwgppTALPN/USyIGa3nLsTmxGo91pUTvGC0YBrNOa3IrI9t2K3HSuLuU+VSg6JV51a99NyuKKXPclufTKMds+smiN0HmFaVTuAs8B2LooxQ1Z5hQGdnacFO9rr2duu+Q5V5A8C6/gxWVavt2mCreCorIRDoPQ4hhBBCCFHyeWYJaoQoYHMParq7u5kwYQIAS5Yswe/3j+4BCTFI8XScj5s/titeMiFMc3czrbFWDt7qYI7d7lgAPmv9jHl3zSu6rxNmnsDVe18NQGu0lV3+tEte+JKpgNm2blu7+iXz43VjmJ+h63pJbcYyH0uppOhLURS7qkZRFPt1ya6oGUolTcZg2rEVasvWd5ZMKVUy2YHMaFXJZIt0RHj6rhdZ+PB/+fK9pXSHo3nbKKpCWW2Axm3qmLbXJBqn1/ZbXeTxeHLCl75hjAQxQowc00zkBS1m3+AlE8wwmPlbak/rsercoKVP8IJaA0pgo/g5VgqrHdqVkPqftcIxBSV4OYq7xOqa9cEwIBwurWqnb9izrgGPolgBz7qeHnA4ckOeUgKezDqfT0IeIYQQYhMi5xctpZ5n3jDfuiuEGHUtLS2jfQhC5IilYry35j274qU11kpzd7NdBfPtrb/NCdufAMDyzuV8495vFN3XlMop9uUaXw1Vvqq8ipfM9Rm1M+xtq3xVfHbWZyUd72ie2DIMIydkKTbbJbsSZrBUVS1ptkvmcikVL9nH3V87tuzbMseeWT/Y56AoSr8VQJqm2YFMWVkZZWVlo1olk2EYBq1rWnnqrhd447F3WPbhyoKtzBQFAjUBxm5Ty9Z7TWLsNvV2MKMoCh6Pp2hFjNfrlYGPQgwz09TBaM+b82LmhC/WPBjM4jPDClKCvQFLT7ux3rkvfVuPbX4hq9UO7X6I/ROz63pIL8ZsPxbTcxBK8CIUrW60D7G3kqW8HCZNKv1+mYCnlKqdvtcNY+gBjar2tkjTdWsf6TS0tFjLYDmdQwt4KirAO7SWsEIIIYQYWXJ+sXQS1AghhBg13clu3lr1Vk7bsezLh007jJNnnQzAivAKDv3HoUX3tU3tNvblGn8NjcFGavw94Yu32q58qfZVM616mr1thbeC909/f+Se5DDItB4rpdolE2IMlqIoBUOWQqFLJngZ7jBKVVU8Hg8ej6fk+/QNd7JDnFgsRnd3N/F43A51sgOZUip4dF2no6ODjo4OFEUpqVonexnq66TrOrFYzF6i0Sjtze28/q93+OD5z1jzeQuJ7vxgSlEgUO1nzIw6pu09ma3mTLTbw/UNYjwejwQxQgwDq/VYJCtoyWo9lnXdWlqBwVQ+OEGt7QleetuNKTntyKpBq0ZRSv/eublSFBV83wXPvj3t0P4O8ccxEy9A4EzwfX/k2qGNpOyAZ8stS7+fYVjzb0qdvZN9Xdet+w+lkkdVQdN6q3jSaetjKgVr11rLYLndg5vDk73O7R784wkhhBBCDDMJaoQQQgyrSDLCq8tfzZn10hztrYA5YsYRnL7j6QA0RZo46p9HFd3XDvU72Jdr/bVMrJjY226sTwXMVlVb2duWe8p565S3Ru5JriPTNO3gZaBql0QiMaTgBSi52sXtdo9I8LI+ZCp7MrNzwuEwXV1dhMNhEolEwfs4HA4CgQA+nw+Px4PT6UTTtJwWcH0XXdcxTXPQlTuZAKxvkON0WicCTdNE13XS6bQdMEWjUetxokk+efErPn9tKWu/aCURKRzMhGqDTNphPLseOpvt9/0agUDADmI2xv9TITYUppnsE7Rk2o+15FXEQH5FW3EKqJV5c16UPsELai0oQfk6HgGKWo5StgDTd1hPO7T3MLuug9g/IXjFhtUObSSpKpSVWcvEiaXfzzRzK3gG06ItnV63gMfpzK3iSaWs40kkYPVqaxksr3doAU9FhXU8QgghhBDDQIIaIYQQAwonwry45EVao612tUt2BczR2xzNWXPOAmBt91qO/9fxRfe1tHOpfbnWX8u0mml21Uum4iXTjmxy5WR72zJPGQtPXDhiz3FdZIKXUqpdMpeHIrtSY6Dgpe8Mlk1FIpGwg5jsWTLFqmP8fr89QybTwszr9Q76tdF1vd9WbIVatWXCnUwgN5BkPM2nL37B4leX0vxFK/GuwsFMWV2IqTtNYv5Ru7HT13fA4/FstEGbEOubaRpgdmSFL2t7Wo9lVb1kbjM7B7dzxZ8TvNitx7JakVnhTCWKIn+GbQgU5zZQ+Y+edmg3QPrzDa8d2oZIUYYe8GQqeAZbxZMJeEr4eZpHVcHlyq3iSaUg8/tYLGYtq1YNft9+f2mBTt/r5eXWPB8hhBBCiB7ym4EQQmxGTNO0T+aGE2Ge/vxpq9qlJ4DJDl+O+doxnL/r+QC0Rls57fHTiu53RXiFfbnaV83M+pnU+Guo8vbOfsmEMBPKJ9jbhtwhnvv+cyPzZNeBaZqk0+mSql0y64rNOOlPpsJioNAls25zOhFvGAbd3d05gcxAVTLZYUzmcimzcUqhaRqaptlt2VKplN2SLBaL2bNuANLpNLquD7jPdFLns/98xWevLGHN4hbiXQWemwL+Sh+N02qZOnciE3cca7cri9HFSy+9ZG2WVbnTXyu27EXTtM3qc0psuqzWY905VS/ozT1zX7KqXozmntZjA3999nJmVbj0thtT+rQiQ61GUX0j9RTFCNpk26FtiBQFQiFr6RksXBLThEhk8OFOa2tvwBMfTNVbD1W12qJlqnhMs7eKJ/P7SHe3tSxfPvj9h0KDm8OTWcrKrNBJCCGEEJsUCWqEEGIjZpomhmmgqdYfax3xDh779DE7bMmufGmONnPizBP58W4/Bqzw5Zynzim67zXda+zL1b5q5oydk9duLHN9i7It7G1D7hBPfO+JEXrGQ5NpL1VqtUsymSxpfklfDodjUMGLzAexJJPJvECmvyoZn89nhzGZZShVMsVkKqSyg5jMx8zldDo94H6cTqc9D8bn86EpGu89/TFvP/k/vli0lHBL/qBwRVGoqC9n6k5bssshs9hmr62LtmTLVPAMtnIn+7EGM29Hwh2xvlmtx9r6BC091S992pFhxga3c6Wip+qlOit8qelTEVMNSpl8zm8m+m+HdjmKe+fRPsTNl6JAMGgt48eXfr/sgGcwLdpaW60wxjB6q20GQ9OsgCe7iscweqt4Mj+rw2FrWbq0//31laloGmzAU1lpvYby+6cQQgixQZKgRgiRR1VVZs+ebV8W65dhGqSNNC7NBUB7rJ1/fvzP3pkvsawQpruZU2edykW7XwRYQc1Fz15UdN9ru3uHs9b4a9hz/J45FS9V3iq78mVMcIy9bdAd5OEjHh6hZzw02cFLKQHMUIIXTdNKDl0yJ7FFcUOpksmukBmuKpnMrJm+4Uv25VIqYlwuV04Qk/3R6/ViGiYv/uM1/u/+Z/nkzc/pbA7n70SByvpypu88lb2O3p1dvjV70M9voCCn0GIYxpDCHVVV+63UKXSbhDsiW2/rsew5Ly2YfYMXvdnabjAUf07wglaNklXx0hvMVEmFhCiqtx3aQ5hd1/e0Q/s+pucbPe3Q6kf7EEWp1iXg6e4efIu2TMCj6xCNWstgqKo1L8ftttqiZVfxZAKeRMJa19FhLV99NfjHKC8fXMCTWRcIWK+pEEIIUSI5vzg4ijmUXi1CbOLC4TBlZWV0dnYSCoVG+3DEJiCpJ0kbaXxOqy1KW6yNv7//97yKl9ZoK62xVn644w/5ye4/AWBpx1J2+dMuRfd99NeO5ob9bgCgO9nNGU+c0Ru+9KmAaQg2EHJvmJ/T2SebB6p2SSQSJZ1I70tV1ZKCl8xHCV6GLrtKJjNTpqura71UyWQCiP4qYkoJ7txud14Qk325b6CSTKZ4+YHXePEfr/LJfz+nY22B+RYKVNaVM23nrZh35K7sfuicYWvPNhjFwp3+Qp6hhJ1gfd2V2o4tc7uEOxsf04gWaT2WFbwYLT2tx1KD2LMD1KqsOS/VvbNftN5WZFbrMf9IPT2xmTKNDszITRD9O2CC4keRdmiiGNO0wpmhtGgb4vxCwApffD7weKw2bX2reBIJqypoXR4DrPAoE/IMJuCpqLCOT36uCyGE2EyVep5ZghohCpCgRgzENE0iyQgmph18tERb+Muiv9Aaa7UrX5q7rRAmnAhz5k5n8tM9fgrAss5l7PzH4i00ssOXaCrKj576UW+7MX9vCFPlq6LWX2sHQBsSwzCKBi+FAphSWkn1lTkBPFC1S+byaJwQ39RlV8lkAplwOEy8SC94TdPyApnBVsmYpkk8Hi8axMRisZKDmEKVMJnLAwV1qVSaV/75Oi/ct5BP3lhM+5rCg8cr6srYes4U5h+xG7t9Zw4u18Z3ci/TPrC/Kp1CAc9whDuDmbkjhpdpprJaj2VVv/RpRWa1Huse3M6V8vygpU8rMrRqUMqtGSJCjCIz9WFPO7RF1grHZBRphyaGS3bAU2oVT2bdIKpi86gq+P1WFY/LVbiKJx63qotSgwnYC3A6+2/J1l/o4/Wu22MLIYQQo0yCGiHWgQQ1myfd0GmPt6MpGhXeCgCau5v5wzt/KDjzJZFODCp8OWqbo7hx/xsBiKViXPTsRXbYkt1+rNpXTZW3Cqe2YZ3MNQyDVCpVUrVL5oTtYGVmZpRS7ZIJXuRd9+vPUKtkstuX+Xy+Af/PDMMgHo/nhS/Z10v59SUTvBRqTebxeAZ9Yj+dTrPwof/y/H2v8Mnri2lb0wEFDqO8NsTWO05h7pG7sud3d9kog5nhkAl3Sm3Hlrldwp2RZZommJ19qlx6ql+ywhgrhGmn4Cd5UZ6e8CUz5yW79VimIqamp/WYa6SeohAjwjQNiD2M2fVLMNutldIOTYwm07QqZYbSom1dA55AILeKJ9POxjAgnbb2H41CV5cV+qwLt3toVTwVFdZ9hRBCiFEmQY0Q62BzD2qi0SjTp08H4KOPPsLn2/CqNUqVSCdoibbg0lzU+GsAa07Lb//72952Yz0VMG2xNgzT4Ic7/pBL9rwEgOWdy5nzxzlF93/stsdy3b7XARBPx7ns+cvssCW79ViNv4Yy94Y1kDgzML2UapdEIjGk4AUoGLoUC16cTucG9RptrkzTLDhLZqAqmexAJhgM4nQWDigMw8gLX7JDmHg8PmAQoyhKwSAmc9nj8axzD9x0Os1rj7zJ839fyEevfUbb6vaC56zLakJM3XEScw/flXmH74rLIyegh6pQuDNQyDPc4U4pM3c2RKYZ66ly6a10sea+ZM1+ydw+qNZjalaVS2+7MUWtyauIQfHL93CxyTONzqx2aIa0QxMbp1is9Kqd7Oux2NAfU1WteUHZVTya1lvFk073VvFEItayriGP1zu0gKeiwgqghBBCrJNN6fziupCgZiMzb948Zs6cyU033TTahzIoL774IvPnz6e9vZ3y8vLRPpxhs7kHNeGuMGUzysAPzz7yLPMmz0NTN4wTU6Zp0pXsoiXags/poz5gvYNxTWQNv3791zkVLy3RFroSXQCcseMZXLrnpQCsCK9gpz/sVHD/iqJw/HbHc/XeVwNW+PLz//zcrnxxp90EtSD1oXq2GrMVPteG80MmE7yUUu2S+TgU2ScsB5r3IsHL8DNNk9bWVhKJBG63m6qqqgFf4/7uk0wmc1qWlVIlkx3IFKqS0XV9wCBmIKqq4vF4irYm83g8RZ93MpnisdueZtUXa2icVMfBZ+xfUlWLYRi89uhbPHfvy3z06qe0NXUUDIzKqoNsNXsSex62C3sdvbsEM6NsKOFOMpks8H9rUBn8ErczTCIVoq1rSyA/7NM0bUgzd4b23NI9rceyg5as2S/ZwYwZGdzOlbKc4AW1mmjcRzIdQnXUESrfEkWrBbUcRdkwfgcQYkNipj7qaYf2rrVCm4QSuhzF3TtX0DR1SL5lfY2qNeCaLV9PYuOWXcEzmCqedQ14QiGrisfrza/i0XUr5InHrcfp6oJw2Ap/1kUgMLSAp7zcaiM3FLoOL78MTU3Q0AB77GGFWevThnAMQohNRnd3N4FAAIBIJILfv3nOlCz1PLM06xcbBEVRePjhhznkkENG+1A2e08sfoKLn7kYjgEU+NYD32JK1RSu3vtqvj7l6yPymLqh0xZroyXaQtAdZGxoLACrI6u57pXrcma9tERbSOpWwJAdvqSNNH99768F9+/UnKT03ncP1/hqOGPHM/LajVX7qqn0VuJQe781ehwefr7Xz2lqauLDDz8kHo+TJs0KVtDyRQszZsygoaFhRF4X0zRJp9MlVbsUP/E4sMyJxFLnvEjwMnqyPw8zPB5Pv5+Hhe6jaRqBQIBkMkmsyB/OmqblBTKZKpl0Om2HL83NzXmhTKKEdhqqqhacC5P56Ha7h/S5dseFd/PPXz+OofcGTXf8+G6+86ODOOWXx+ZsaxgGb/z7HZ7728t8+OontK5sL/g1FKoKMmXWlux52M7sdfTueHyeQR+XGDmKouBwOHA4HCW/Qyvz/TUT5JB4Br9xE5rSbG+TTFeypPV7rOnYNud7bHYQWarscMflcuJxpfB6uvG6IridXbgcYZxaJw61A01pQzFbUcwWK6QZVOsxF2i1OXNeFC2r4sWugqlCUXrbweR/nzDxeFYyY0Y5DQ1yckaIQhTndKj8O8T+hRn5JehfYLYfh+k5ECV4MaTewwxfDcbq3jup9RC6BMWz/+gduBDrwuuFMWOsZTBiMWhvH1y409ZmtU8zDOjosJbBUBQoK7OqeAIBK+DJVPEoSm4VTyxmzeIJh60Feqt6li8f3OOCFSwNNuB55RW45BJYubJ3P2PHws03w6GHDv4YhuKhh+Ccc2DFitE7BiHEJmv16tVMmjRptA9jgyZBjdikpFKpoq12xMCeWPwExzx0DPF0HNKAAQ6fg8/aPuOYh47hnkPvKTmsiafjVnVLdzMV3gomlE8AoKmriZ+99DOr3VhP5UtbrM0+OXr67NO5bO5lABimwT8+/EfB/QdcgZwTqjX+Gs7b5byCLceCrmDOCV+3w20HPKVoamri7bffzn+O8Thvv/02s2bNKimsyX7Xd6nBy1Da+TgcjpKqXTLr1rU9lFg/BvN5mEqlCIfDrFixguUF/rjUdZ3Ozt7B916vNyeQ8Xq9KIpiz4mJRqO0tLTYJ6dLqcbSNK1gEJO5PBKh3x0X3s0DNzyat97QDR644VFM02Tm/G149p7/8OHCT2hZ0VYwmAlWBpiyw0T2OGwX9v7e7nj9MsR2U6MoCk6nE6fTiVd9GTN+KSi5nwsuRztb1d3K1Km3oHj2zwnPC1XspJIRu9WYShsqbWhKO25HGJezC7czjNvZhdvZhaam8w/KBAp0eDFNhbQRIm2Uo5uVGFRhqlUoag2qoxbVWYfDVY/T1YDqGHyLzeH6GSfE5khRVPAdCp69MSM3Q/ReiD+JGX8OKPCz0liD2XE2lN8iYY3YvHi91tLYOLj7xeMDt2MrtK672wpihhLwgFUVU1bWW8XjdlshT+ZnbHYVT3e3Feh0dlrVPNAb+CxdOvjHzrZiBXznO7DvvrDDDv2HPsFgb5XRUDz0EBx2WH4l0sqV1voHH5SwRgixTiSoGZgENRuof//73xx99NHcdtttfO973+t32z/+8Y/ceOONfPXVV0yYMIGzzz6bM844A4ATTzyRt956izfffBO3200ymWTOnDl87Wtf469//StLlixh4sSJ/P3vf+eWW27hnXfeYfLkyfz2t79l7ty5JR/v22+/zUUXXcRHH33EzJkzufPOO5k6dap9+yOPPMKVV17JRx99RGNjI8cddxyXXHIJDoeDCRMmAPDtb38bgPHjx7NkyZIB7wfWiZbbbruNJ598kueee44f//jHLFiwgNtvv50bbriB5cuXM3HiRC699FKOPTb3ndQil27oXPLcJST0BCFXiJhhvVPYoTrwOr2Ek2EufvZiJldMpi3eRo2vhkmV1jfYVV2ruPT5S+2Kl5ZoC5FkbwuW02afxuVzL7evP/LpI3mPrygKld7KnBZrNb4aLt794t7wJavyxePIfUe7S3Nxwa4XDOtrAla48uGHH/a7zQcffIDH48k5eVcsiBlK8KJpWsnVLhvy/AQxdKV8Hr733nssX76crq6ukt/pr2kakyZNIpVKEY1GWbNmDUuWLClpHpHD4ShYCZO5vL7b3iWTKf7568f73ebBGx/jwRsfy1sfqPAzZYct2f3Qndjn2Ln4AhLMbC5MU7fe8V6wasUEFMzwVZjqGBSzHYfRjMNswau1gLsZnD0fjRYww4N6bN30k9bLSOplJFJB4skg8WSAaNxHPBkgkQqRSAVJpf2Y9Pd9vRv4AviiT+XOwDN3nE7ngN9bPvzwQ+rr66WaUoh+KGoZSuhyTO9hmJ1XQvrdIltmvq9cA+59pA2aEAPxeKxwZygBT6aCZzBVPN3d1v2HWsGTaX0WCOTP4oHcKp5otLeKZ+XK/lu1PfOMtfRHVa3HLlax019Vj9drVdIUOgbTtJ7buefCt74lbdCEEGIESVCzAbr33ns57bTTuPfeeznooIP63fZvf/sbl19+Obfeeivbb7897777LieffDJ+v5/jjjuOW265he22246f/OQn/PrXv+aSSy6ho6ODW2+9NWc/P/7xj7npppuYPn06v/rVrzj44IP56quvqKqqKumYL7nkEm688UZqamo47bTTOPHEE1m4cCEAL7/8Mt///ve55ZZb2GOPPfjiiy845ZRTALjiiit48803qa2t5c477+SAAw6wTzIPdL+MBQsW8Itf/IKbbroJh8PBww8/zDnnnMNNN93EPvvsw+OPP84JJ5zA2LFjmT9/fsHjTyQSOa16wuHBnWzZFLyx8g0+b/scFy7S6bTVlt8LnclOzJSJYRq8v/Z9dv3zrrg0F6fOOpUr5ln/DwoKT33+VN4+nZqTGl8NXkfvSc9qXzUL5i3IqXrJtBzrOwfHqTk5a85ZI/q8B9La2jrgPI1EImF/vpdCVdUBwxa3221fl+BFlPJ5mE6nWbt27aD2q+s6n332WcHbnE5nTviSCWAy6za06sXHbns6p91ZfwLlfiZvP4Hdvj2Hfb8/F39ow5k1Jdaz5Fu5bYnymGCshbZDS2xA5uxpL5bbbkzRslqPqdWgVeNUPDiBvrFgocqdgWbupFKpIbdlG0g8Huejjz6irKzMbjGXWTRNsy9LkCOE1Q7NDJ4L7cf1s5UJRhNm9D5w7wlaNYoibxAQYlh5PNZ8lcFWhCYSVsBTStVO9vVIxAo02tutZSQcdpgV/rS19T5OZonFrBZxmeMZLFW17l+MaVot4F5+GebNG/JTEEJsPpqammhqasr5u2TRokV4vdbvPA0NDVK1X4BiDmWggRh28+bNY+bMmUyZMoVLLrmERx55pKSKlsmTJ3PVVVdx1FFH2et+/vOf88QTT/Dqq68C8NprrzF37lx+8pOfcO211/LCCy+w++67A9gVNb/4xS+46KKLAOtk38SJEznrrLO48MIL+338F198kfnz5/Pss8+y9957A/DEE0/wjW98g1gshsfjYZ999mHvvffm4osvtu93zz33cOGFF7Jq1Sqg8IyaUu937rnn8utf/9reZrfddmPGjBnccccd9rrDDz+c7u5u/v3vfxd8HgsWLODKK6/MWz/QkKdNyb8++ReH33c4qUjPO+ldQN/zsApUeCvsapZpNdOoD9RT46vhjZVvcOkel1Ljr6HGV4PP6aPWX7vRn7hZuXIl775b7F2RvZxOpz1XY6B2Y5qmbfSvi1i/Sv08XBeKoqCqas4J2L6LqqoDrhtoG1VVR+Tz/zdn/YlHf5sfGPd14A/25rw7Thv2xxcbJzP2OGbnecO0Nxdo9aAEQQ2A0rP0XFby1gdB8WdtE0RRhhaAmqZJKpUqGuQUC3qGUya0yQ5vCgU6pa6Tn5NiYzWk7yuKH9SqnkC396M1Y6oqa+5UNYq6eQ7hFWKDlkzmBzurVsF778GiRfC//1kVNOvi3nsh67xPjkwFUWZpa4OvvoL334cPPrCWSKTwfYfrGIQQIkux86wZV1xxBQsWLFh/BzTKwuEwZWVlA55nloqaDciDDz7I2rVrWbhwITvuuOOA23d3d/PFF19w0kkncfLJJ9vr0+k0ZWVl9vVddtmFCy64gKuuuoqLLrrIDmmy7bLLLvZlh8PB7Nmz+fjjj0s+9m233da+nElE165dyxZbbMF7773HwoULufrqq+1tdF23Zx8UG/xb6v1mz56dc7+PP/7YrrzJ2G233bj55puLHv/FF1/Meef1/kEVDocZN27cQE97k1Lrr8XtcuOv9KMpGu0d7Rimgd/rx+l0YhgG3Xo3PqcPw7TebfNx88d83Gx9nkyrmcY3tvqGvb95d81jeXg5DYEGGoON1AfqaQg00BBsYEL5BOZNmDcaT3PQ3G73wBsBs2bNorq6eoSPRmyuSv08rKurw+l02t8r2wfxjr7Mu/F1Xc+pMBwJpYY+g1lXMaa0UH389LEj+tzERkatKW07546gOMGMgBGxPpoRMLNPuiRBX1Z0F6W8M8rEnRXmZIIdf1awE0DJCnZ6Qx4/TjWI0xvA5wuUFPiYpsmaNWt46623Bty2oqICTdNIp9P2ous66XTanvOU+f4xXCT4ERutUr+vqNVgdAEJMLtB7877HlKwKaPiLRDqVKOoVT3VfFnBjhKQz30hRoJpWmHI0qWwbFnvkn19dX8Vuz0UxWo/1to68LaZd57rOqxda7VLW7Gi92P25ZUrSw+GfD7rGFasKP0YhBBiAKeeeirf/OY3icVifPe736WpqYnf/va37LzzzgBSTVOEBDUbkO2335533nmHP//5z8yePXvAX6ojPe+I+MMf/sCcOXNybstulWQYBgsXLkTTND7//PPhP3DIaYGTOe7MLI5IJMKVV17JoQUGz3k8nrx1GaXez+9f93eVZdpMbc7mjJnD5MrJfNb2GSFniMqKSlpaWvCWeXE4HISTYbat25b//uC/RNNRmrqaWB1ZTVOkiVVdqyj3lOfsb3VkNbFUjC/bv+TL9i9zbptWMy0nqDnqn0cRTUXtUKch0EB9oJ7GYCNjQmOoD9Svh1egsKqqKjweT79tpzweT8ltAoUYilI/D7N/dpimyXPPPdfvfZxOJ1tssQVdXV0DzrbJbsfndDrtWWGZk7OGYdiXC63LLuDNrCtlFk6pAls5UVQF0yh+OlxRFcpneHnllVcGFQKVuq2cDNsIuWaDWg/GGgqfElVArUep/GvBWRKmqVsnWM2IdcI1E+AYETC7wOzGtIOdrtyQx+jqva8d+CTASADFT9iUHvgE86p6+lb41Ab9jK9bSiyuktK9pHU3ad1jLYYH09TweDzsuuuuBT+/TdPEMIy88KZQoDPQegl+xCaj1O8rNc8DqvV9wGjpWVrBaMHUey/nfDSjYMZAX2EtWQp/b3BhqtWg5QY7SibIyQ52lJB8XguRkUxaYUff8CX7cilBiNcL48fDFlv0LtnXx4wBhwMmTOh/To3XCxdfbG3T1GTNuSlFZSWMHWs9TrGP5eVW27P+jkFRrO332KO0xxVCbPayW5s9/vjjzJo1i5133pkddthhlI9swyZBzQZk0qRJ3HjjjcybNw9N0/LmyPRVV1dHY2MjX375Jd/73veKbnf99dfzySef8NJLL7H//vtz5513csIJJ+Rs8/rrr7PnnnsCVkXO22+/zZlnnrnuTwrYYYcd+PTTT5k8eXLRbTLvAB/s/QqZNm0aCxcu5LjjentDL1y4kOnTpw/uwDczmqpx9d5Xc8xDxxBOhnHhAiBtpIkmo7g1Nz/f6+c4NAchLUTIHWJq9dSi+1t02iJWR1azqmuVHeqs6lpFU6SJsaHcd7S/0/QOXYmugvuZXjOdZ7//rH390ucvBbCrczIf6wP1dku24aQoCjNmzODtt98uus2MGTPkD1sxoobyeVjKfbbddtucd7Kk02m6uroIh8OEw2H7cjqdzpvlBVZ4EwqFCIVCBINBQqEQgUAAVVXzHisT2gwU6Ay0vu86+7pbZ9a3t+Gtf75f9Plu/63pxBIxYonhm9+RTVXVAVvBDVclUaHXWAyeomgQugSz42xAIfdUp/X1pIR+WnTgt6JooISAEBQZJ1bKTwfTTGcFPoWCnW7MnCCobyiUWTKf25nAp6X4Y/Z8nNFPAbGuO0ENYrb8EjMv8LGCIFUJ4FIDuNQgaH7wZFcEhXpCodL+5Cgl+Ck1ENoYgp/s9fJ7xKZj0N9XMl9bTOizVT7TiOaHN0Yzpt6aF/ZgdgNJMFZZS/Z+Cu7diZkJbbKCHTvUyb5NKUdR5OeQ2EiZJnR0FA5fMtebmoqHJtnq6vLDl+zrVVVWyAEQDvdWvCxZAq+80nvd6ez/8WIxeP313uuqCvX1uaFL3wBmzBgr4CmFpsHNN1tzcBQl91gyx3/TTdZ2QgghRowENRuYrbbaihdeeIF58+bhcDi46aab+t3+yiuv5Oyzz6asrIwDDjiARCLBW2+9RXt7O+eddx7vvvsul19+OQ8++CC77bYbv/rVrzjnnHOYO3cuW265pb2f3/72t0yZMoVp06bx61//mvb2dk488cRheU6XX345Bx10EFtssQWHHXYYqqry3nvv8cEHH/Dzn/8cgAkTJvDcc8+x22674Xa7qaioKOl+hfz4xz/m8MMPZ/vtt2efffbhscce46GHHuLZZ58teh9h+fqUr3PPofdwyXOX8Hnb5zgDTtKkmVo1lZ/v9XO+PuXrJe/L4/AwoXwCE8on9LudaZrce+i9rOpalVOh0xRpoqmriS3KtsjZ/oGPHiga6uw8dmceOuIh+/pdi+7C4/DkhDlBV3DQJ0My7wRoamrKfY4eDzNmzJCSTbFeNDQ0MGvWLD788MOcKpn+Pg8Hex+Hw0FFRQUVFRX2OtM0icVidniTCXC6u7tJJBI0NzfT3Nxsb6+qKoFAwA5wMovL5RrxcGHffffl4MAxxKO5gZKqKRx0xn5874pDBxUClbLOyBq8ahiGfZJ5pCmKMmxzgwZaN1JzhTYUimd/KL8FM3w1GFltStR662SqZ/+RPwbFAUoZUDY8gU92kNMnzDGNviFQhFSijWSyHU2J4dASODRrfo2mpYA20IsPJi6pwkfx9gY8ir9AtU8QpScEsoMfZwBc2aFQZcmBT+/rsfEFP8MV/mzKX7Mbg5H6vqKoPlC3AHJ/Py70v22accipzLEum5kwR88KdswwkLKO1VgNWT/GCn+NOzDVyj4BTk/7tT4t2VDLi4bdQoyIdLq3GqZYEFPKvBa3u3AVTOb62LHg8VjVKC0tuW3H3n47vyVZV+G/Yfvl88G++8Jee+WGMPX1ViXOcDr0UHjwQTjnnNw2aGPHWiFNgU4nQghRioaGBq644go5d1YCxTRLeZuAGGnz5s1j5syZdjDz8ccfM2/ePI455hhuvPHGfu977733cv311/PRRx/h9/v52te+xrnnnsuBBx7IrFmz2H333fn9739vb/+tb32LlpYW/vOf/7B8+XImTpzIvffey0033cSiRYuYPHkyt956K/Pnzx/wuF988UXmz59Pe3s75eXlACxatIjtt9+er776igkTJgDw9NNP87Of/Yx3330Xp9PJ1ltvzQ9+8AN7ts5jjz3Geeedx5IlSxgzZgxLliwp6X6KovDwww9zyCGH5BzX7bffzg033GA/v0svvZRjjz12wOeTUeqQp01VV6SLbb++LWl3mr/c9hfmTpqLpo7+H1iGafDX9/5KU1eTFeT0hDlNkSZiqRj7bLkPf/32X+3tt/rNVkSSub+E+11+6gP17Dp2V67b9zp7/SvLXqHcU05DoIFKb2XeCY433niD5uZmJkyYQEVFBW63m6qqKjkRItY70zRpbW0lkUiU/Hk4lPsMpL/qm0Kyq28yi9/vH9bwZs2yZo6ZcAYA+x43F2/AS+OkOg4+Y39crqENaR9I5kTwcIQ+pawbLcMR/JS6zWh9XzVNHZJvgdFszZhwzd6sTi7mfJ9waVRWulHs1myZYKe3zZuZ1+YtkrVNz/YUb704JNmBT4HqHpSAHfjkbhPMuuwfdOCT/RqNVPAz3PqGOOva8k1+3xmajeX7imkmegOdrOocM7tyJxP6mB2D3LsKamXeXB1Fq84KczK3VQz561NsRsLh/mfDrFxphScDqanpP4ipqbFCn6am/Pkv2R9XrbJapZWirKz/VmT19fDRR9Z8m4YGq9XY+q5i0XV4+WXreY/WMQghNhnRaNSew/7mm28WnVO+qSv1PLMENZu5JUuWMHHiRN59911mzpw52oezwdjcg5ru7m4CgQBgzQoajjlAI8k0TcKJMPF0nLpAHWC1bPvJsz+hKdLbdq0z3mnfZ99J+/KXQ/5iX5/ymyl0J7sBcGrOnNZq29dvz7jmcaTTafbYYw8SjgSV3kocqvwhKUS2QtU34XCYaJEe3v1V3wzFr0/9PU/84VkcLgdPxO7d5E4s9g2FBtUabght5kbLcLWHK2XdpvY5sqExzVR+hU/OnJ5IT+DTd35Pn8vDHvj4sqp7eoKcPsGOFfj484Kg3u3963zCXYIfsTEyzSQYbX2CneasUCcr2DE7KK3uLkMBtaJPeJPVfi1n1k4VijIyb8IQo0jXrYCgvyCms3Pg/TiducFL3zBm3DirvVex8CXzcc2a0lqgKQrU1hZuQZbdiqznb2whhNhcbGznF0dKqeeZ5SyjEGKjpygKZZ4yyiiz1zlUBzfsd0POdrFUzA5tvM7efr2JdIJJFZNYHVlNc7SZlJ5iWecylnUuA6Cju4OjfUejaRqBQIBZv51FPB2n1l/bOyenJ9iZXjOdeRPmrZfnLcSGRlEUfD4fPp+P+vp6e33f6ptMBU46nbavZxtq9c2r//ovANN32WqTPAmYaXemrYd3NZqmiWmaw1YVNND17JPLmRZyqVRqxJ9nJrAZqVlCfVvIbW4UxQlKOajlxbcpYT+mmewJfIqFOYUqfLK3z1T49LRFNKPWAlAkkyytpZuvQPXO4Cp8VNWP2+3G7XaX8IgDvU7DF/xklozM12rfWWVDJcHPxktRXKDVW0v2+gLbmma6J9TJnatjZrddsz+2AUbP9rntFot9PZpKOdiVOT2VOtmVO1m3KcrQ3gQihlkkUjh8yVxescIKawZSWVl8Lsy4cVZQk6mEyQQvr74KDzzQe72jo7Rjdjp7g5ZiIUxDAwzxjUZCCCFEhgQ1G7hAP++4ePLJJ9ljjz1G9PFPO+007rnnnoK3HXPMMfzud78b0ccXYjh5nV4mVkxkYsXEnPVuh5unjnkKgJSeYk33mt72al1NOOIO6IDy8nJieoyEnsAwDVZHVrM6spp3edfe176T9rWDGtM02flPOxNyh2gMNlLvr+8NdoINjC8bz/jy8evr6QsxaorNvolGo3kBTjQaLTr7JhgMEgqF7I/Z1TervlhNR7MV+Bx+wTfX7xPcBCmKgqIoqKqK0zny71jOhDPD2S6u0DyhYnOF1ofsoG2kw6HM/9+mQlFcoLisd9oX26aE/ViBT3bIk1/tY+a0cSscCkFPixs78Fm7joGPP6t6p2+FT0/1TsE2btmhkA9F6f0cGIngZ7BBT6F1GRL8bB4UxQFarbVkry+wrWnqYLT3CXB62q/pfUOdVkC3KnbSHcDnvfspciymEsqZp9NbqZM1T6cn2FGUdf/62SwZhtWyq7/ZMO3tA+/H4bACkEItycaOtQKR9vbc6pd334XHH++9Hi+xEjMQ6L8V2dixUF0Nm+GbLYQQQqx/0vpsA/f5558XvW3MmDF4vd6itw+HtWvX5r3TOSMUClFbW1vwto2dtD6T0sRsixYtYsWKFUyZMoWpU6eiGzot0Ra7rVpTVxOrulaxOrKabeu25eRZ1gylcCLM1rduXXS/2e3XTNPk+EeOp9JTmRPmZD5WeCrkpIHYLBSqvgmHw0VbcXk8HkKhEP+69hnefGQRTo+TxyP3bJbVC6I02ZVCIz1faH2FQH1lQraRmiWUvW405wqNlvzAJ/tyV1ZLt55gx57t02eeDyXONChVJuzpbz5PwTZugdxtlJH5/lks+FmX8GekSPCzYTNNwwpp9NacYMc0mnPn6RgtPdU5g6zQVAK57de0rPZrfWftqJtRr/1otHg7smXLYPlyKKUatry8cDuyujorhEkkrMCnUCuypqbS5s+AFbD014ps7FjYDP/WF0KI9UnOL1qk9dkmYvLkyaP6+LW1tZtsGCNEqdrarPYLmWoATdWoC9TZ83CK8Tl9PPf95+wQpynSlHN5ckXv13dXsotnvnim6L4OnHwgf/rWnwDrRMcvXvkFdYE66gP1NAQaaAw2Uu2rRlNl0KPYuPVXfZPdNi1TfROPx4nH4/zvuY8AaNi6mqeeeqrf6huxeVMUxT6BOtL6zhUa6XAo+3HX56yhkZ4llH19QzjhbVX4VFoD0ottU8J+Cgc+/VX49KkAsit8ek6Mmt3WwpphqPDJb+OWXcljV/hk355X4ZMb+GRXlY1kxc9wBD9S8bNhUxQ162twSu/6AtuapglmZ9Y8nZ72a9nzdLJvo+frUo+AvqR3P0WOxVR8uVU5mVZrWp9qHbWqJwjdQP/vDAOam/ufDdPSMvB+NM0KQbKDmHHjoKrKCmEMI7ca5quv4OWXreutraUdq6ZBY2P/rcgaG8HjWbfXRAghhFjPJKgRQoh+JBIJexB69onjUjhUB9NqpjGtZlpJ2950wE29FTqRVXb7tdZoa04oFE6E+c1/f5O3D03VqPPXccjWh3DpnpcC1h+nj376qBXoBBuoD9Tj0uRktdi4KIqC3+/H7/fT0NBgr0+lUnR1dfHJW5+RiFjvTN/p8JkYhkFnZyedfYbNZqpv+s6+2WBPmoiN3vqeK5Td3m0kZgllr8suyl+foVB2eDPcwVDf9SP9vWFkAp+ugsGP2bfyp+/8HjPCyAQ+hVq1+bMCn74VPX22LxD42K+NBD9DCnr6rt/Ugx9FUXpnZTl63yRVPNTp6hPg9LRf6ztTR28B4lb7Qz0K+vLcfRU8Gg9mToCTNVdHq84Ne5TA8P6/xONWxUuxIGb5cquSZSDBYG4VzNixUFHRO58lGs2thnnrLetjd3dpx+n1Fm9BlrlcW2uFNUIIIcQmRoIaIUQeRVEYP368fXlz1t7TRzkYDI7onAaf08fhMw4veFtST5JI9/7hZJgGJ+9wsjVDp6dKZ233WnRDZ1XXKqKpqL1tOBHm9H+fnrO/al+1HdzsM3Efjt3uWMD64/TL9i+pD9Tjd22e5ahi4+J0OqmsrOT5O18DwO1zc8pFJ+RU32QqcLKrb9auXWvvI3v2TWYJBoNSfSM2OuszFAJGrFKo0O3ZoVBmrlCqlPY66yi7vdtIVw2tS7vGYQ18+lT00Gdmj2l2gdFdtAKoYODTT5eigQMfpbfCJ69VmxUEKQVn9gRKDnzsR9qMg5/hqPLZFIIfK9QJgRoCtuxdX2BbK9Tpzpmng9GKqTf3mafT89GMAnHQV1hL9r4KHo0Ls6ftWm6ok9uSDbUaCKG0tvY/Gybrd59+XgCrEiUTxIwZY7UGc7ut25JJq+pl5Upr+eADWLUKSm1DWFnZfxXM2LFWW7SN9PNHCCFEPjm/ODgS1Agh8vh8PpYsWTLah7FB6Nv2bDS4NFdOFUyFt4Ir51+Zs01mbs6qrlWE3L39LqOpKDuP3ZnVkdWs6lpFUk/SEm2hJdrCB2s/YGxwrL1tOBFmjzv3ACDkDtkVOJnWajs27sjcCXMB7JNm8oNWbAjeemoRADPnzxiw+qZvgKPrulTfCDEEqqqiquqIvokhIxPOrI8WctlzhTKPuz5khwQjMUsoe1EUpeD3MkVxWSd+qSp+nAM8D+v3g2Thip2+FT55M366+gQ+acDM2qbIY5b2CmMWCnBy2rkVm9+TqQAK9gQ+pf0c2JiCn8x1CX5KZ4U6PZ8rjO9dX2R704jmhzdGi9WCrc+sHevzPwnGKmtJGNCUhpVpzJVpWJFCWZmGFWlYmYKVaYgP/JVg+nwwfjzKFltAQwOUlVkhjKpas2W6uqwZMCtWwPPPlxbuWC+Gtb/+QpgxY8C3Gc3zEUIIAcj5xcGSoEYIIfqRqagZzaCmFMXm5jQEG3joiIcA6w/89ni73VKtqauJKVW9fb1bY60E3UG6El2EE2HCiTCftnxq337CzBPsoKYz0cn2v9/eDnGyA536QD1bV2/NxIqJ6+GZi83dp299QXenVUV29CXfKbpdpvqmsrL3Hed9Z99kllgsVnL1TSgUWi8nqoXYnGVCofU1V2ikZwllr8t+3L4nz0fScFQF9X97EE0rR9UKzxUqLfBJ9Du/Jz/w6RsK9Q18unq2KfKYJb1yalaFTybY8ecEO0qxNm55FT6DCx8k+Nm4gx9F9YG6BbBF7nrTtGa2LOutgjGXfAHLvuhZtwrWtFnbDcCs06DRATUOCCmYHhVUUNJA1IA2HZq+wHz1M5SuEttVulzFW5BlPtbXw3r4/iyEEEJs6uSnqRBCFJF5pz1s+EFNKRRFodJbSaW3khm1M/Ju37JiSz4981O6El2sjqy2q3CaIk2sjqxml3G72Ns2dTWRSCdY0rGEJR1L8vZ1wswTuHrvqwHojHdyzMPH0BhopCHYQEOgIedjnb8OpyYnusXQ/P0aK4j0Br1M33mrQd13uKpvvF6v3TJNqm+E2LgpirJeAiHInSu0LrOESl2XbbTmCg1mRlDvOg1Nq0TTavK3c+Tet+/33dzAp0/Fjh3sdPcEPoXavnX3XkYHjKzAp6ng8y098BlshU/fy0FQvEP+WTMSwU/m82q4wp+MjTr4SaWsNmHFZsMsW5Y3v6Xg3rxeGDcO6uqsShifDzQNU09Bdwe0t8Kq1fBhM0oyUXw/WcyQCvUOaOhdzHqHFfbUO6DBCdU1WfNzqkCtRNH8VhWOmgI1DIoLzEoURU4vCSGEEOtCfpIKIfLEYjH23HNPAP7zn//g9XpH+YhGRzgcxjAMXC4Xfv/mM7Ml6A4SdAdzqm36mlI1hddOes2uzMn5GGliq6reE+arulbx9qq3eZu3C+4rO9QJJ8Jc98p19gyd7FDH69w8Pw9F/9559n8AzNp322HbZ7Hqm+7ubju0ya6+ySxr1qyxt9c0jWAwmFeBI9U3QoiM9TlXKBMKjeQsoex1G9JcofwQyIOm+dG0xtJCJJfW83+VRlPiaGoUTY2imPnVO8UrfLp7gyA78AlbyzpX+GTP58mf55Nb4ePPCnyygqB1CHwyMiGnw+EY1uCnWKAzlPAnY9iCH9PE0d1NoL0df3Mz/tZWvM3NeJub8axZg2vNGpzNzaVVw9TUWCFMRQWKz2dVqBgGxGLQ0QFr1sDixfDZZzn3K/q/VleXV/liNtZBYwgaPFYw44v1tl/Lm6vTjlWJ1grpVqC3yr7ws1EwlfKeUCczVyczU6fvrJ0qFEV+HxJCiM2BnF8cHAlqhBB5DMPgrbfesi9vrrLn08g743M5VAfjy8czvnz8gNs2Bhu54+A7WB1ZTVNXE6u6VrG6e7Ud7DQEeysZVoRXcOeiOwvup8xTxumzT+fsOWcD0J3s5pFPH8kJc0LukPxfbUY+WPgxsUgcgO9dWrzt2XBQFIVAIEAgEMhZn0ql8ipvwuEwuq7T0dFBR0dHzvaZ6pvsChypvhFCjLTsUGh9zRUaTAg01GqivnOERmeukNXmrZRZQtZ1E1VJoWlJNCWJpibQ1DiaEkNVrBBIVSJoSheqEkZTwqgUqAbCYPgCH61PhY+/T5DTt8LHX6C1WxDwDNvPs+zgZzhkBz8lhzzxONqaNWgrV+JYuRLn6tW4Vq/GvWYNnjVr8DY344zFBnxsXdNIVFSQCgZJezygaWAYqKkUzu5uXOEwruZmaG4e+Hk4HKRqa9Hr69Hr6zEaGzEbGzHHjEEZOxZl3Di0sWPRvN68ip9i/zOF1ptmGoy2vLk6pt5SYNZOG9bnYjuk24HFvfsp9jyUciu0yQp2lKyApzfYqbbmZwkhhNgoyfnFwZGgRgghithY5tNs6Mo8ZRy01UEFbzNNk7TR+w7HMncZ58w5h6ZIT6DT034tmorSGe9EU3rfdby0cykX/N8FOfvzOr12cHPEjCM4bPphACTSCT5t/ZSGQANVvipURR2BZyrWt79f8zAAgXIfk2eOzkwkp9NJVVUVVVW9w7ezq2+yw5uBqm+yK2+CwaBU3wghNlqZuULrQ3bLrZGeLzSyc4WcQFnPkisTCuXOCFLQNBNNNdE0A01Jo6ppNDWNploBkKokUNUYmhpDU7rRlG5UpRtN6bICIKWzJyhKWdurEVRFp1DWMqjAp0h1D2oApWA7tz5t3oYx8Ml+DfOCn3AYmpoKtyNbutRqWVbCSSUzFMKorMT0+TCcTus+8ThqJILa2oqWTOJraYGWln73k/Z4iFdVEauuJl5V1bv0XI9VVZEsK7PajhViGNZxL12as3qwrd1y14/B4RhvrXNqBVu9maYORkdPcNMb4phGC+h9Q51WQAezA/QO0L/o3U+x11cJ9Vbp9AQ7ippbuZMJfRTF0+9rLIQQQmzIJKgRQogCTNO0g5rs9kdieCmKkjOfZkxoDBftflHONqZp0pW05uaUuXtPXigo7L3l3tYcna4mOuIdxFIxvmz/ki/bv2T+hPn2tp+3fc4B9xwAgFNzUh+ot9qrBRpoDDayz5b7sOu4XQHQDR3DNGRuzkbgvZc+BGCnA3cY5SPJlV1909jYaK/vW32TPftmoOqbTHgj1TdCCJFruCsv+lNortBIhkPZj1t6IKQB3p5laDQNKwBSDVTVsMIfLY2qJHNCICsAiqKp3ahqEk1JoWkpq2JITVrVQWpnTxBkrVPty9Z2hX+kOXoCn57qngLVO1bg488Lguw2b6YXmtpQli8vHMQsW2a1FCvlxaiqgkAAXD2VHYkEdHVBezvoOko4jBYO97+f6uq8VmT2x7FjMRsbwe/Hoet402lcuo5vmFu9DZf+g54QmlaBw7F14e1cKg6tG4fWiUPpQDHbUMzW3PZrelawQ8qqGNPDoH8FPR0Ui4c6/qwWaz0VOTkzdrIreHzD9poIIYQQw0GCGiGEKCAajZJIJFAUhbKy/Hc2ivVHURRC7hAhdyhn/bSaadz97bvt6/F03K7AaepqYpvabezbIskIdYE61navJaWnWN65nOWdy+3bq33VdlDzccvH7H/P/tT6a+0wJxPoNAQb2L5+eyZWjE71huj19jPvkYgmATh6hNueDZdSqm8ySzwel+obIYTYwIzGXKF1bQ1Xapu5bLoOuq5ghT4aVrXPyFDVFJqS7glxknago6mprOupnu1SaGonmtqCFo/iXtOBa00nztVhHKsjOJsiaE1R1KY46uoESrqE2TA+FwS94HYBKqTSEImjdHX3vhhr11pLIZoGDQ2FA5jMx8ZG8PRf6aFgnZxxDNPP8yG1ehuV4EfB4ajH4RhbIPjRcDvjuBxduJ1dOB1duLQwDq0TTWlHUzrQaEehzQp8SFrzoPRu0HurioqHOr688Aa1qkCwUw2KvFFGCCHEyJOgRgghCshU05SVla2XP8bFuvM4PEwon8CE8gl5t80ZO4d3T32XlJ6iOdpsz8ppijSxOrKaHRt3tLdt6mrCNE3WRNawJrKG93gvZ1+X7nkpZ+x4BgAfN3/MmU+eaYU4gQbqA/U5l8eGxuJ3+Uf0eW+u/vHLRwAIVQUZP23sKB/N0BWrvkkmk3bLtFKqb3w+X16A4/P55KSCEEJspNZ3KNS3hdy6VAUNdN00e0+dG4YTAycpPasCyDBwd3TgbW7GsbYNd3Mz3j6Lq6trwOdlKAq614vudIKioOpptHgcLdUbMijRJPS88SPv/m4H6Vo/6To/eq0foyGIUR/CbCjDbKxAGVOFUleF5vahaV40hw/N4UfVAqhapgpIBaUbTAVwrbefyxvEjJ9hDX48PUtNsSPEocVxO7rwemJ43d14XN14nBHczjAuZxdOLYxTC+NQO1CVJJhR0KOgL++zp8KPb2qFqnIyc3ayblOC8vuXEEKIIZGgRgghCmhrawOk7dmmxqk5aQw20hhsZBazCm6z95Z7895p79khTqZCZ3VkNU2RJraq2sredkV4BR83f8zHzR8X3Ndle17G6TueDsBX7V9x8xs35wY6QetypbdS5uYM0gevfALALgcX/n/c2LlcrrzqG8MwiEajBatvotEo0Wg0r/omU3GTHeCsj/ZAQgghNh6KoqAoCqqqrpcKTSMSwVi6FGPJEsyemSrK8uUoy5ejLl+OumoVSio18H6cTgyXC1NVUXQdLR5HyZopo5omajRasB4oGQjkzYGJ9ZkJkwoEKNKbzbK2Z8mTQFG67fZuWna7N9XomS1kjZrRtN5ATtUcODQXquZE09xoDg+a5kXVvGgOP5rmR3MGrdt67pOZGTPSwcBIBz/DEv7oXtK6l+7EgI+OpiZwO7twOyN2xY7b2YXLEbE+OrtwO6zbHVoCiIO+wlpy9lTouTkxlApMpQqUKtCqULQaVK0GxVFrzdfRMqFOmYQ6QgghbPKXuhCioOrq6tE+hFGVqaipqKgY5SMR65uqqNT4a6jx17Bt3bb9bjurcRZ/O/RvvYFOpDfQWdW1ioZgg73t522fc/+H9xfcj1Nz8vP5P+fY7Y4FYFXXKp5Y/ETOHJ0afw0OVX5sA7z62JukEtYJnO9ddtgoH836o6pq0eqb7Kqb7Oqb9vZ2+/tZhlTfCCGEGDGmabUJy54Fkz0bZulS1JYWSnp7isdjtRbTdYjH825WUynUvoGOokB9vd16zBwzBqOxEaOhAaOhgXR9Pem6OnS326ruMQxrLoyu49J1AjkVQGn0dAJdT/YsKQw9jW6k0XUDQzesNnEG6IaCYfRWP5mmg7TuYF1mBVkDWVJA//NvFHpmCWkGmmqiaqCpCpqmWmGOqlnhj8MKgFTNjaZ5cDhcqKqaE/r0vd53naqqw/L7wvoIfkoNelLpNPGkTjqau02GpibsAMcKc7oKhDzWZacjjqKk0FgL5loryTGwZ+tAbrhjmBppPUTKKEM3ytGpwDArMJRKK+TJasWmOapwOF05s3+G6/9DCCFG0uZ+fnEw5IyPECKP3++nubl5tA9j1KRSKbp62ilIUCP6U+mtZP7E+UVvz27tMalyEj/d46dW27WIVaXTFGmiubuZlJ7KmcHzwdoPuPyFy3P2pSoqtf5aGoINnL3T2ew/eX8A2mPtfNr6qV2p43a4h/lZbngevOExAMrrymiYWDfKRzP6XC4X1dXVOb8AG4Zhz77JbqE2UPVN39k3Un0jhBAiRzwOy5cXD2KWLYPEgCUNVgDjcFghTLG2V33DGZer+ByYzMf6esiqCsqetAMwkr8l9Z0rlGn7lk6n0NNRdL0bQ4+ip2Poegw9ncDQE+h6qmfJCoAME90AQwfdUHsWB4bhRO9Z6Im7TDK3FzsyA0j0LJF1fp6apmSFN46eZeCQp791hdYPJoQYreAnkk7TmdJJxzK3R1HMNjTa7Tk6DrUTp9aZVakT6Zm5E0NVdFyOdly0D3hMRkolGQ2QSAeJpAIkUkGS6SApvYy0UYZuVqCb5RhUglqBw9Eb6uTO/im+ToIfIcRw29zPLw6W/PUthBB9ZN597vP58Aww+FOI/mT/obNlxZacudOZeduk9BRrutdQ5i6z11V4Kjh4q4NZFVnF6shq1kTWkDbSrI6sZnVkNfF074mLN1e9yfH/Ot6+XumtpCHYQEPAWg6fcTizGq32YIl0gpSRIuAKjMCzXT8Mw+Dj1z8DYI9vzxnlo9lwqapKMBgkGAzmrB9K9U3f8Eaqb4QQYhNlmtDamh+8ZF/PCvn7pWlgGNY+C9F1a8kIhXrDlmJBTHV1/63IRln/c4WqCqwbHNM0wOwGM4Kpd2EYEYx0F3o60hMEZUKghLWkkxhGsicA0jF03QqAdHoDH9OJbrhyAiDrsivrshOT3uek61aIkUrpQOH5PsOlbzXPUIOgUtZl/24z0sFPLJ2mKxXF1FusxWgBowXFbEOlDZWekEfpwKF14tQiqIqBxxXG4+q/wsp6PIVk2m+HOYlUgEQ0SCQdJJHqXZLpIMmU3/7/zXwODxTolBr+SPAjhBCDI0GNEEL0IW3PxPrk1JyMDY3NWbfjmB3ZccyO9nXd0GmJttht1WbWz7RvM0yDCeUT7ACnLdZGW6yND9d+CMCu43a1g5oXl7zICY+cQMAVyAlzMpf3HL8n48vHj/yTXgcvPfAa6ZR1YufoSw4d5aPZ+AxUfZO9JBIJu/pm9erV9vYOhyOvdZpU3wghxEYgmYQVKwpXwWSux2ID70dRigcwGdkhTG1t/1UwY8ZAnzcWiHyKooISBIIoWkNp7eMKME0dzCiYETC6rI9mBIwImNZ102jvCYW6wIhg6N3oehwjHbdCICNttYLrCXT6BjuZdYbZfwCUv50L0+wNhQzDwDAMUiXMLFpXqqoOuhXcUCqHXC6r5RyUA40DHJXFNFNgtPYsVrhjpJsx9WZM3Qp5MFpRaUMxO1EUE7czgtsZAZoG2LdCMu3rCXSCJFNW1Y51OUgiHSAW671smqX/vlco+BlMhU/f9RL8CCE2dfIXtRAiTywW48ADDwTgySefxOtdl97KG59MUFNZWTnKRyKERVM16gJ11AXq2I7tcm47YPIBHDD5AEzTpDPRabdUy3z8Wt3X7G3XdlsTbyPJCItbF7O4dXHOvn530O/soOa5L5/j0hcuzQlzGoONdou1yZWTCbrX/0mVh2/+NwBVjRVUj1n3d6eK3OqbMWPG2Ouzq28ySyQSIZ1Ol1R9EwqF8Hq98ge1EEKsD6YJ7e3F25EtXQpN/Z+wHdRjgdW6rLGx/xCmoQHcm35b1o2Jomh24IPWUHibPtdV8k8eWYFPd1bIE7GDnUz4Y2Zdxui0g6DsbTCjOfs1TLVAhU+fkCcr2MkPgDzophfd8GKYbnTDnXWbo6eFnNozV6j3mWZCoXSxVnzDKLv6qpS5QL23udG0LVDVCfn3cao924OmdKEp7ahKO6rZimK2YurNdqDT+7ENRTFwO7txO7sJelcPeOy6ESBllJHSy0ilQyTSVogTT/qJJf3EEj5iCT/JVBDDdNhVRMP1uhUKdST4EWLDtbmfXxwsCWqEEHkMw+Cll16yL29ODMOQihqxUVIUhXJPOeWecqbVTCu4zbHbHcuh0w61K3NWdVmt1TJzcyZVTLK3Xda5jKUdS1nasbTgvn530O/45tRvAvD6itf50zt/sitzGoON1AfqaQhaoY5Lcw3LczQMg8/e+gKAeYfvOiz7FMUNV/VNpuJGqm+EEGIdpFKwcmXxlmRLl0J39/A9nt8/cBVMbS2oQ63rEBs7K/AJASEo1O2N/MCnkNzApwvNjKCZEZx2hU93VuDT1ScUaum9b5/ApxSmqeSFP73Bjx/dDGKYQXTTby2GD8PwopsedMNtVRSZrp7wx4FuaOi6FQDpOugGOfOKeh/XHNYAo38ONK0RTRtXIPhRUVUDTU2hqUlUJYGmxtCUKKrS1RP4hK3Qhw40NY6mplDVFJrajltbi8+ZQvWnrPVKKqc7oUkQU6nEoAKDCnSzgnQm5NFDJNIhUukg8WSAVForOg9I76nQM02TVCo1bBVWEvwIMfI25/OLQyF/JQshRJbMrIZMax8hNjV+l59JlZOYVDmp3+0O2foQZtTOsEKcTKATabIDnjHB3sqLj5s/5t+L/110X3/65p84cIr1LpoP137Ic189R32gPqdCx+/yD3jsz/zlRfS09cvdkRd/u5SnK4ZZseqbRCJhz7zpW33T1tZGW1tbzn78fn9e+zSpvhFCbNY6O4u3I1u6FFatGrjdWKmqqgYOYcrKNuh5MGLTMXyBT7pPhU9+9U5uhU8XihnBkVfhU0L7v0HxgBrAJIhBCN0s6wl/QuhmAN0MYBg+DNOHbnh7gh83Rk8lkG460HUNwzBzQp/M5WLrshVaV5gC+HqW6gG2LcREVdNoahJNSfYEOqneIMi+vAZVXdGzXQq3msKrKWguN5rmQXP4UDU/DmcA1RFC08qszxG1HEMpwzQ9Oc83O+DpG/IUWi/BjxBiQyVBjRBCZMmuppFfisTmrMJbwU5jdipp213G7cJV86/Kabm2OrKaVV2rSOpJavw19rb/XflffvHKL/L2EXKHaAg2cO3e17Lz2J0Bq6pncetiK9AJNvCv3z4FQO0W1ZTXlA3DsxTDxe1243a786pvIpEI4XA4J8RJJBJ0d3fT3d0t1TdCiM1DOm21HSsUxHz1lXV5OKphVNVqNVYofMlcbmwEaTsiNkGK4gClDCgbnsAne35PwcAnt82btX13gcAnDkYchRY0sg5NyTqgIsebe/BeUAI9ix/UYO91NQBKEEW1rpsEMBQ/huFH7wmADNPb0ypOGTDk6btuoNtNO0RWMHqqklIM/Cas0qWBtp6ll6roaJputXtTFTRNRdMcqJoTTXOhaR68Xm9eG7lCAYhpmnlL9vPMtMWT4EcIMZLkL18hhMiSede3tD0TonRbV2/N1tVb5603TZP2eDtBV2912qTKSRwx4wg7zGmKNNGV6CKcCBNOhHGovb+aPPvls1z6/KX29fY9wrhnupncOIkznziTH+74Q7vNWzgRJpaKUe2rRlNL+WtXjDRVVe3AJVsikbBDm0yA09XV1W/1TXblTTAYlOobIcSGpaurcBXMF19YH9euhXVt9+HxDFwFU1dnzY0RQgyZHfioxd8UVFrgk+oJfPq2bett82bmhEDZFUDdvdsT79lhrCf8aS7+mFmXVQrPFrIDH0cAXIGssKc3+MkEPr0hUACU8qzLfut16pEdZgw2BLKWOHo6hq7HMfQkejqFbqTRdaOnkkhBN1QMw4FhZj2uqWGk+/7ebwLJniVSwv9UabLnCmUWp9NpX1YUJWcpxDAMTNPM+dj39Uin03Z7qPUV/Ax1nQQ/Qgw/+S1OCCGyyHwaIYaPoihUeitz1u05fk/2HL9nzrpIMmLPyskOfPxOf2/7tdYmDE0nVh5lZWg5D328ghNmnmBv++BHD3Lp85fiUB3UBeqs1mqBRntuzsFTD6Yx2DiyT1iUxO12U1NTQ01Nb6VVdvVNdoCTXX3TlDUEO1N9kx3ehEIhNE1COiHEMNN1WL06vwpm8eLelmTRwc/GyBEKWWHLuHHFQ5jKSmlFJsRGRFGcPeFGefFtSthPb+DTVbC6ByPSE/j0rfDpc7lQ4FOkG1opTRZNxZdV3RNAU4JoSgBnT3UPWgDF2XO7EswKebJDIb/V+m4QDD2CnlqLnm5BT7VhpDtIpzrQ010Yehd6OoquR9HTcSsA6Zk51Dt7qGcWUc9Mot7bMpfd1namA8PoPbb1OVdIURRUVc1bskOgzMdMNVN2JZBhGHlLZpv1FfwMJfyR4EcICWqEEMIWj8eJxawS9fLy8tE9GCE2IwFXgMmVk5lcOTln/RHbHMER2xwBwEnbn8OnSxYT3NrHKX/+Hk2RJras2NLetjPeiaqopI00K8MrWRleydu8bd8+u3G2HdTc+e6d3PTGTTQEGuwgJ3O5PlDPdnXbEXTLjKr1qZTqm1Jn32QHOKFQCI/HI3/0CSGK6+7ObUe2eDF8+iksWWKFMG1tQ6+GURQrYBk7FiZMyG1Blvk4Zgz4h7NFkBBiUzJ8gU+yT4VP3zCnUIVPgYogEj07jFoLDFPgk129kxvsKH0uO9QgDnc5eMf2G/iYZgz0FjBawWjpWVoxjWbQV9vXMVr+n73zjpOkLND/t3JVp5nZvEtclpwkIyKyCCeIyHl6ZwKzKMchcvzEAB4mUE+C4CmCeqJivOP0jiCoRBFFMgKC5LhhNkzo7uqu+P7+qK4OM90zPWlndvf9fj716UpdXT2hw/vU8zzJcxx1f4U41olEQ+RpiDo2kZhHzDwi0UskCkkHUZyr9Q3ZRMKuiT8aUTS+66jxuI0+ok1BswjU7jNzu1i4dP1MCD9Tdf2k66XwI9kckULNHGflypXst99+XHrppbN9KhPi9ttv56ijjmJgYEAOeG+mZDKZ2T6FTU464FcoFDAMY5bPRiKRpPiez0uPrMaJs7z7+LfzD3v8w6h9/vWwf+Vjh36MdeV1rCquqseqpZ052/VsV9/3leIrrCuvY115HX9Z+5dRx7ruXddx4LIDAfjfJ/6X//7rf7eIOUtziaCzLL+MglWQXwBmkG7cN+nk+35b941hGC29N6kDR7pvJJKtgDhOYsdeeCGZnngimZ59NhFh1q+HyiSLw3UdFixIOl+WL28IMc0izNKlID9TSiSSOYCimKCYoHZOjuhe8GkWeUa7fURLjFt7USiJJqNJ8OmfouCTrbl3cm37e1CyieCjLkDRloPV6vARGCBclHigLt4o8QaUeD1avB6iDQ1hRwx1cUbNaKDOA3U+qAvqt4rWmBfKPGIWJGJPJLqKjBtveaz1zcRTjeacIM3fnRr9RjMr/ExH148UfibP1ji+OFmkUCORSEaRzWYpT0eh6WaEEIJVq1YB4DgOQgj5JiyRzDK+H3Dd5b/h99fcjYiTD/H/eNYJHffXVT0RUvJLxzzuxw75GG/Z/S11Ead+W5vfprBNfd9H+x/ltudu63is6999PQcsPQCAO56/gz++9EeW5peyLL+MJbklLM0tZX5mPqqiTuSpd00UR/z5lT/TX+5nUXYRh25z6Bbf0TNR900QBG3dN7lcbpSAMxn3jRCCDRs24HkelmUxf/58+f4hkaT4Plx+edLVsmIFnHYamOb0Hb9SgZdeSoSXRx5piDCvvALr1sHw8OTcMJYF8+c3RJidd4btt2+IMNtum2xXZ+a1XSKRSOYqieAzLxEeOu3TxXFGCz6lUe4d0Szs1Lt9RvT51AWfcjJNi+DTxuGjbw/KnjUxyKntHIMSgvBrkXI10SoeBLEhcfOIQSCCeF0yjXE+Sa+Qiq72tQg6mPNR1AWgpUJPum1e4riaIKkrpp2gE4YBkfcEcThEJPJEyg61nqCIKAqJ/FeIogpxbBPRV+sRGlswahZj0sffFEy38AO0iDnprWEY9b6iiQg/W9pFY52+E2UyGV544QU8z6NSqZDJZOR3pTGQQo1EImnL6tWrufLKK/noRz/K0qVjD3pu7qxevZrHHnuMajXJ7V27di233HILe+211xb/3CWSucp3Pnk1//P164mj1gG2q79wDR/52numdOweu4ceu4e9F+097r5v2+Nt7Dxv5xYxZ1VxFauLqxmsDrb03vz+hd/z7fu+PeoYhmawJLeEq//hanadvyuQCEDPDTxXd+gsyi7C0Cb2RevXT/2ac285l2cGniESEZqisaJvBRccfQHH73L8hI61JTBR902pVKJUKk3JfTPy/QPAtm35/iGRAHzyk3DJJUnHS8onPgFnnQVf+9r49xciEVuefBIefhj++tdEhHnppcQlMzSUCEETJZNJRJYlSxIXzG67JSJScxTZCCFYIpFIJNPLzAo+Yzl8RjiA6g6f2mB+XfBZ21Hw6e4JpoLPTqA6gAGKVntWAkQE+CCqNZGndi7ENUfPhtbn2en5K72gNYs3C1FSgadF2JmX/MyhHnGmqmpLkoio/gYxfAFoayD96KsuQSmcm2wfvgDiNY0Hr21T7GPH/FGkPTndOH0muy5dnml3UNpT5HnetBxPVdW6WycVb0aKPKkQZJpmy/JcEn46fSdatmwZq1atYtWqVdx0000cd9xxLFu2TH5XGgMp1Gxm3HDDDbz73e/m8ssv56STThpz3+9973tcfPHFPPfcc+y4446cccYZnHbaaQB88IMf5L777uPee+/Fsix83+fQQw9ln3324Uc/+hHPP/88y5cv52c/+xnf+MY3eOCBB9h555351re+xZFHHtn1+d5///186lOf4q9//Sv77bcfV111Fbvttlt9+7e//W0uuugiXnrpJZYvX85nP/tZ3vOexgCcoihcccUVXHfdddx6663ssMMOfP/732fhwoV8+MMf5t577+VVr3oVV199NStWrOj6uJLxWb16NV/4whc48cQTt+gX0NWrV3P//fePWl+tVrn//vs58MADt+jnL5HMRb7zyav574uubbstXT9VsaZb9li4B3ss3KPttkpQwdKt+vKrt301XuS1iDr95X6CKOCloZcoWI2Bv189/qsWUUdRFBZlF9Vj1b501Jfqzp41pTVUwypLckuwdRtIRJqTf3kyXuSR0TNoqkYURzy58UlO/uXJ/PitP94qxZqRtHPfCCHq7ptisdi1+6ZZuCkUCgwMDPDAAw+Mekz5/iGRkIg0F144en0UNdZ/8Yvw2GPw0EOJCPP00w0RZmAgcctM5KpbRUm6XubNS0SYHXaAXXeF3XdPBJlttkkcMpY17qEkEolEsnkwM4JPsa3wI0Y6f0b294gSowUfYEragQnoNZGndjAR0ugKGoRwEHi68Vw6PUelp+HSqYk4Sk3gEdGLUP7O6DvFaxGDH2t/wHgtYvAM6P3GmGKNqqqoqoquz/wQdLNTaDqFoDAMR22bDmdQKmJNF80iXPOUCjntHD6p8JOKQelkGEZXcW9jjak9++yzQFIz8LOf/YxDDjmEefPmye9KY6CITeU5k0yK5o6an/70p5x66qn89Kc/5YQTOke/APzkJz/h7LPP5pvf/Cb7778/Dz74IKeccgqXXHIJ73vf+yiVSrzqVa/ixBNP5Otf/zpnn30211xzDQ8//DCFQqEu1Gy77bZceuml7LnnnlxyySX84he/4LnnnmP+/PljPn7aUXPooYfy7//+7yxcuJBTTz2VKIq46667APjVr37FO97xDi699FKOOeYYrr/+ej75yU/yu9/9jqOOOgpIXmS22WYbLrnkEvbbbz8+9alP8dBDD7HTTjvxyU9+ku23354PfvCD9Pb2cuONN3Z93PEYHh6mp6eHoaGhUdEqWwPVapVjjjmGu+66iz/+8Y8cdthhs31KM4IQgltuuaVF9R+JbdscffTR0popkWwifD/gzdmTRzlpmlFUhQtv+RzzFvdSWJAn25tF1+emdTyIAta561hdXM1+S/arx5L95wP/ybVPXsvq4mrWltcSRK2W/Ac/+iCLc4sB+NIdX6qLOr12L0tyS3hozUMU/SKO7pAxMvVoNSEEw/4wu83fjXtPuXeLj0GbTqIoqrtvmgUcfzJX7CPfPyRbMb6fuFamswRZVRMRprc3EWG23x522QX23jsRY7bbDhYtklFkEolEIplVhPBHOXoY0dkjRBHickcHUIvgM+dREmfNwltRlK3re4cQo7uEuhV9Rk7tnEHplEbVzSbp95lUuGkWgSqVypjn5/s+n/3sZ3n88ce58MIL2X333YGt77tSt+PMUqiZ46RCzS677MK5557L//3f/3XlaNl555350pe+xLve9a76uvPPP59f//rX/PGPfwTgT3/6E0ceeSSf/vSn+cpXvsJtt93Ga1/7WoC6UPPVr36VT33qU0Bi8Vu+fDkf+9jH+OQnPznm46dCzc0338zRRx8NwK9//Wve9KY3UalUsG2bww8/nL322ovvfKeh2r/97W+nXC5zww03AMmLwWc/+1m+9KUvAXD33Xdz2GGH8Z//+Z988IMfBODnP/85H/jAB6jUikC7Oe5IPM9rsS4ODw+z3XbbbXVCzerVq1m9ejWVSqX+t/DNb36zLtQsXbp0i1K8169fz9133z3ufosXL2b+/PlkMpn6tCmuBpFItkb+59LrueKsH07qvoqioKgKqqai6SqarqEbOrqpY1g6pm1g2iaWY2Fnk8nJ2dg5m0zBIVvIkO3JkOvNkuvLkp+XIz8vR2FeDidnY9rmjHyQjEXMBncDq0urWVNaw6riKt6z73vqIsvnbvscP37kx1SC5H3Oj3w2VjbWn/PCzMKWDpwgCvAij5tOvonXbPeaaT/frYE4jus5ykNDQ/T397Nhw4YJX/H26le/mgULFszQWUokc4j+frj2Wrj9drj5Zli7tvv7aho4DvT1JWLL9tsnUWR77QX77gs77QQ9PYljRiKRSCSSLZxkmNZv79gZ6fAZ1fFTHCH4hJvknJW+q1GsQzfJY22NNItC7YSftIsnnUaKQGEYttxvpAg0E2JQmlLg+359XPnUU0+tpyzNmzeP448/fqv5rtStUCNHGjcDrrnmGvr7+7nrrrs4+OCDx92/XC7zzDPP8KEPfYhTTjmlvj4MQ3p6eurLhx12GJ/4xCf40pe+xKc+9an6wHwzzU4KXdc56KCDePzxx7s+93333bc+nw7w9/f3s/322/P444/zkY98pGX/ww8/nMsuu6zjMRYvTq4u3meffVrWVatVhoeHKRQKXR+3ma985St84Qtf6Pp5balceeWVo34Op59+en3+c5/7HJ///Oc38VnNHN3miq5du5a1IwYcDMNoEW4ymQyO49Rvt7RiOIlkU7HqmQkM7o1ACIGIBHEUE07OBDE+Ss2+r6lomopmaOiGhm4aiRhkmZiOURODTOysjZNLpkzBIVPIkOvJkO3NkOvLke/LUpifx8k77JRZwR59e6Abeosg9IWjvsDnV36eol9kVXEV//3Yf3P+nedjqRYxcYtI4wYuhmYQiYj+cv8M/RA2b8IwpFqttkyVSqVlebpyp6frOBLJnCGO4Y9/hBtvhLvvhr/9LRFpJlPU+0//BD/4QeK+kUgkEolEAqTuBQs0C+icZtPp8gUhIog3IqJ+iFdB+HJyG62FeF2tA2cwEXWmVMTTRLxueo4jaYuiKJushyaOY4IgwPf9+tRJCCqXy5RKpbbHuemmm/jZz37Wsu6KK66oz7/rXe+qX9gvaSCFms2A/fffnwceeIDvf//7HHTQQeNezZv+k3z3u9/l0ENbFe3mf+o4jrnrrrvQNI2nn36amaC5mCw974lejdruGNNx3GY+85nPcNZZZ9WXU0fN1sZHP/pRTjzxxDEdNVsSVpcZ5enzdl0X13Xrb05DQ0MMDQ21vY9t2y3iTfNk2/ZWY++USCbKshWLu9rv1Evex4mnHUtxY4mhDUVKG0sUB8qUBsqUBsuUh13coQrusEul5FEpVai6Hl7Zw6v4+FWfwAvwvZDQDwmDkCiIiKM4meIOVxQJ6vuEAJVpe+otKKqCpqmomoZmqOiGjmHqGJbBwDbrESshiEM0dIpuCUVRiNSIiuKCAgYGL/zhFe5+5n7yfVmyPVmsjImdsbAyFpZjos3RuLjJIoQgCIKO4ks6BV0OKCuKgm3b2LaN4zj1edu28X2fRx99dNxjdPs+I5HMSdavh+uvh9tugwcfhBdegOHhzvurauKKyWSSrpnxeM1rpEgjkUgkEkmXCBHURJZ+iNZBvA5RF1/W1db1J/tMRIBRcjX3zRRQF07t/pI5g6qqWJbV1feYsVJqjjvuOA455JAxHTXyu9JopFCzGbBixQouvvhiVq5ciaZpfPOb3xxz/8WLF7Ns2TKeffZZTjrppI77XXjhhTzxxBPccccdHHvssVx11VV84AMfaNnn7rvv5nWvex2QXIF6//33tzgspsIee+zBXXfdxfve9776urvuuos999xzkx+32xehLZ002qxcLtfX7bfffhxwwAGzeFYzx/z587Fte9yOmgMOOKBFWAmCgEqlUhduXNdtWY6iqD4g2A5FUUYJOM3Lpjkz8UoSyebAm087lu+cffWYHTWqpvLm047FMA3mLelj3pK+GTmXOI7xKj7lIZfixlJ9Kg2WKQ+WKdWEILdYoVKsUC17VMtVqmUfr+rhVwICL5kSMSgiDCPiMBF6xrKXi1gQxhEEEYx4KREvCqy9Hdz5JTQvRkmvp1MEWCB0QRAHXPi/l7DLzfug+8boBwBQaBKDEmdQKgaZtolpG4mok0mdQRaZvIOTd8j2NGLiUiHIziYikJ0xMR2zLgip09AZIYTA87yOAky6rtuLNjRNayvANK8b67VYCMHTTz897vvHeJ1+EsmcII7h3nvhhhvgT39KXDJr1yZdM52wbVi2DPbcEw4/HN785iSqDLrrqNE0OO206X0eEolEIpFshgjhQdTfJLj0I5rmE/FlHcQDQLfxVAqo8xMBRV0I2iJQF6LU5xeBVtuGhlh3FMRrJ3D85sdZAuZBE7yfZEtgrDG1efPmMW/evJZty5cvZ+eddwbkd6VOSKFmM2HXXXfltttuY+XKlei6zqWXXjrm/l/4whc444wz6Onp4bjjjsPzPO677z4GBgY466yzePDBBznvvPO45pprOPzww7nkkkv4+Mc/zpFHHslOO+1UP863vvUtdtllF/bYYw++/vWvMzAwUO+GmSpnn302b3/729l///055phjuO666/jlL3/JzTffPCePK9nyUBSFvfbai/vvv7/jPnvttdeogTrDMDAMo22upBAC3/dHiTfNgo4Qor7cDk3TxhRymh1lEsmWhmkavO1fT+C/L7q24z5v+9cTMM2Z/z9QVRUna+NkbRYsmzftxxdC4Fd9PNenWq5SGqpQ3FikNFCmOFCiNJA4g8pDiRBUKVWplqpUy1Xsl1V+N/9aIjtCD3WIQSigCBUlBDXQWLf7KorLBtjt1/vTs6rN+QuIwpgojAm8mSssVVQFTdeSziCzSQyyDEwnEXVM28Cw9WRydHRLR7c1NEutT7qtY1gauqmjWxq6lXQP6ZaOZqgtrtuxBBjbttF1fUqC+GTfPySSWWdgIBFkbrklcck8/3zikukkHCtK4pJZvhwOOgiOOQaOOw5yuc6PYZpw1llw4YWd9znrrGQ/iUQikUi2UERcbhFfiGsCTIsosw5E+5SO9migLqiJL4sT8SUVXJrFF3UBijKBId/CuYjBM0gC1SYgBgFK4RwUZcty6ku6o5vvRJ2Q35XaI4WazYjddtuNW2+9te6sufjiizvu++EPf5hMJsOFF17I2WefTTabZZ999uHMM8+kWq1y8skn8/73v583v/nNAHzkIx/hhhtu4D3veQ+///3v68f56le/yle/+lUeeughdt55Z6699tppK3p6y1vewmWXXcZFF13Exz/+cZYvX85VV13FypUr5+Rxt1aWLFky26cwoyxdupQDDzyQxx57rEXpt22bvfbaa8Jxb4qi1B1afX2jr/IXQlCtVju6carVKlEUUSqVOmZ9pv047WLVZD+OZEvgI197DwD/8/XrW5w1qqbytn89ob59c0dRFCzHwnIsCvPzLJrg/X/91K8595ZzeWbgGSIRoSkae83bk/Nffz6Ls4v5l1//Cy8Ovcgruz7BP73qTN61w0lUytUkHm6gTHGgTHmoTHmoFhNXrOCWKlRLiTPIq/h4lcQZ5Hs+QT0mLiKqOYO6KZ0UsUju54d47fXpaUHVVXS9JuSYOqZlYNgGlm1i1mLfRnYGNTuDnJxddwGlziArY7XM60bjo/N0v39IJNNKHCdCzA03JJ0yjz8Oa9aM7ZKxLFi6FPbYo+GSaeqKnBBf+1pye8klrc4aTUtEmnS7RCKRSCSbEUKIpNsl7m8RXEQ6X48fWweiPP4B65h110siwixEUUc4X9RFoPahKFN3q49EsY+F3m8ghi+AeE1jg7oEzAOg+uvRd1KXJCKNfey0n49k82Hp0qVst912vDQi9ta2bZYtW8ZTTz1VX9fX1ye/K42DIrr5hi3Z6nj++edZvnw5Dz74IPvtt99sn84mZ3h4mJ6eHoaGhtq6JrZ0yuUyudqVkqVSiWw2O8tnNPMIIdiwYQOe52FZFvPnz58VdT+KIiqVSkc3jj/WAEsNy7I6unFs256WGCKJZFPg+wHXXf4bVj2zlmUrFvPm047dJE6azYkojvjzK3+mv9zPouwiDt3mUDQ1EWuLXpHP3vpZ/vuv/833//77HLfzcdP2uKl7sFwqMzQwTHGwyPBAMpWHywwPJo4gd8jFK/t4bkBQCfDT22pAUA0J/YjQi4iCiCiIicO43hUUhTGiU1fQLKEoSlNMnIFh6Ri2gW4mDh8na5PryeHkbTI5m0whkwhCvRmy+Uw9Eq6dCNQcGycFd8mEGB5uuGQeeCBxyQwOju2S6e1NXDIHHACvfz286U0wE595fR8uvxyeeQZWrEjizqSTRiKRSCRzDCFiEIMN8SVaB/HamgOm5n5Ju2Hwuj+wkhkRP7aoFj82wgWj9MwJd4EQEfj3Jc9XXQjmQSiKhqj8EjF0LhCBthPkz0axVkonjQSAhx56iJdffpltttmGRYsWtYypPfPMM/W4s+eff57tt99+Tvytb2q6HWeWQo2kLVKo2bqFGsncJQzDjm6cSqVCGIZj3j/tx+nkxrEsa6t805RItmQeWvMQ+y3Zr768uriaJbklHf/X4ziu98F06oSZSB+Mruvj9sEYhtH2fNKeoGrZw3O9ltuq61EecpPOoCGX8mAZt1jFHXaplKpUylW82n6e6xNUfXwvIPBCAj9MxKFwAkWrmxBVU9FNvRYTp7d0BiXOIAs7Z5PJO2QKyZQtZBKHVsas7zfWvGnLPrTNDiHgL3+B66+Hu+5KXDKrV4M3xoCRZcHixYlL5jWvgRNOgP32A3nRhkQikUi2AoSIIN7QEFlqbhdRn0/XrwfG/i7dglKoO18Sp0saQbaoxRmjqGNEhW5mCO8uxODHQJRA2wml77so+nazfVqSOcAdd9xBsVjkoIMOGpXKs2bNGu677z76+vo4/PDDZ+kMZ59ux5ll9NlmSm6MXOgbb7yRI444YkYf/9RTT+XHP/5x220nn3wyV1xxxYw+vkSytaLrOoVCoWM/ThAEYwo5cRzXlzds2DDqGKqqtnXiyH4ciWTzpVmkWTW0iqOvPppDlxzKpw74FEZstIgvlUoFb6xB3xGYptlVH8xkae4JmgmEEARekAg/5SrVWl9Q2htUdX0qpQrucJXyYInSkEtlOI2Jq3UGuYl45Fd8vEpAUPUJ/EQMCoNw4p2sQBzF+BUfvzK+i3Iq6IaGbiXOINMysRyj4e7JWklMXN4hW3Bwck4iENW2W7U4OWscQUg3ptYHtNVSKsGvf524ZO6/H557LumXGcsl09MDO+6YCDFHH524ZNrEsEokEolEsrkjhJ+IKy3iS38jdqzugtkAdHdxEQBKX0Noqd0qTfPpekWZmc+mcxnFOhzm/QwxcApEzyI2vh36voNi7DPbpyaZRdLofoCenp5ZPpvNH+mo2Ux5+umnO27bZpttcBxnRh+/v7+f4eHhttsKhQKLFk00aX9uIR01ki2RtB+nU6xapVIZ9xhpSXcnMUfG9Ugks4MQgjAMOzpg0nV/WPcHrnjxCmJi5hnz+Oh2H2X33O6jjqcoyijxZaQAY1mW/J/vgsAPWoSfarna1iFUKVUTZ9Bw2hnkUil7VEsVqmWfqltN+oKqfhId5weEftTS4zQXUVSl5gwyMG0jiX9zap1BObveG5QpODhZe4TQM1oQajvvmGj6Zvy3+NhjcN11iUvmsccSl0xT79EoTDNxyey+e+KSeeMb4eCDpUtGIpFIJJs9QlRae16idYi66yWNH+tPYsq6RgV1QZMDJnG+KE1umESImY+iyHjO8RDRGsTARyB8AhQHpefrKPbrZ/u0JLPE4OAgf/jDHzBNk7/7u78bdYGWdNQkyOgziWQKbO1CTbVa5T3vScq6r776amx767taZGskjuNRDpxmMafbfpxOsWqO48h+HIlkEqR9MGMJMNVqlSjqLsbrucpzXPHyFfR7/aiKysm7nMyp+51KPpNvEWGkC2LzIAqjuqsnFX4q7aLiajFwbtHFHa7iDpWplKq4pQqVkld3EvkVH6/qE1SDekTc5oCmqzUxyMR0DCynJvbUnUGJM6u96DN2b1A6b9rG1N7HXBduugluvhnuuw+efTZxyYwVI9jTA9tvD/vv3+iSWbBg8ucgkUgkEskmRggBotwUM5YILiLtg4nXNbphRHECRzYSAaYuviyuiS8LWxwxiQCzGV/QMQcRcQkx+HHw7wRUlMK/oWROmu3TkswCL7zwAo888ggLFy7k0EMPHbX9+eef5+STT8YwDG688catdnxRCjUSyRTY2oWacrlcj9crlUpks9lZPiPJXCAMw45uHNd1u+rHsW27oxtHDgxLtkbiOG7b/zJSlOn245phGB0dMOlkGAbloMy/3fpv/OKxXwCw/9L9+dbx32LH3h1n8NlKNkfSnqBmwae5J6i9O6iSOITSvqBipUUs8mqxbkGtM2hzwrB0jOaYOMesu4KcnI2VtbAdk0XeRnZ55TG2Wf00vetfxhneiBb4tHuXEwC6gVi4ELH7bqiHHYZy/PFw2GHSJSORSCSSOUsiwAw1RJaaECOa5uvOGDF+ekMDu8X5glaLH2vuf9EWgtKLosj3ydlCiAAx/DmoXJOsyH4YJfcJ+TvZyvjLX/7Ciy++yIoVK9hjjz1GbX/22WdZsWIFsHWPL0qhRiKZAlKokUKNZGKk/ThjCTnjFY+rqjpKvGle7lQ2LpHMVdIosrEEmIn0wViWNaYA4zjOhKPIrvvbdZz9u7MZ9ob5wH4f4IKjL5jo05RIpkRLT9AoF9Do3qBGjFwSF+cWK23FIL/i12PiRDxzX3d0EXIg/RzEWnZlgGWUyOPT6T9RAC46/WR4hl4eYgF3s4yhpqx7RVWSmDg7iYmzMhZOzsLO2thZqxEN5zQ7f8buCho5bzlJf5BEIpFIJCMRIoZ444i+l/7WCLL0lgn06SnZpqixNH6sufulJsYoefm9bzNBCAHlbyNKlyYr7Dei9HwNRbFm9bwkm44777yToaEhDjjgAJYtWzZquxRqErodZ5afziUSiUQyZRRFwTRNTNNsWyAnhMDzvI6xatVqlTiOKZfLlMvlto+h63pbJ066biqF5RLJREiFyU59MOkUBEFXx1NVtW0fTLMgY1nWjEQHvnm3N3PgsgO56I8Xce7rzp3240sk46EoShJXZpsU5udn5DHa9QSNEn46OYRcj0q5ijtcIbt+NbuveZwVxRfZxtvIvKiMRdTWJQMQojCIxcvkeZx53MsSHmU+YpwrTUUs8Fwfz/WZSADMRFF1Fcs26+KN0yQEtYg7zti9Qe0EIdMxsTMmpmPKLiuJRCKZIwgRQry+JYJsdPxYP8QbgAlEnyo9TTFjSeSY0hI/lmxT1MyMPTfJ7KAoCuROA20ZYuhcqN6Y/E31XY6i9s326UlmmDiOKRaTT6vtxoEkE0eOakkkEolkxmkuJp83b96o7Wn8Uyc3jud5hGHI8PAww8PDbR/DNM2OsWqyH0fSLamoOF4fzHgOsRRN0zq6X9J50zRn9arBZfllXHLsJfXlWMSc9ZuzeMde7+Cw7Q6btfOSSKYLwzQwTINcb5dX8Pl+0iPz29/Co/fC00/Dhg3QoQdKAORyRMu2Idh1D0r7H8zAwUdQzvbWBZ++ssfrXI9DRjiGvEpDQKrU3EHVUrLsucn2mYqHi8M4cSOVqjNy/BTD0rGcmvMnO1roScSg0b1BbferC0etApFpz+7rqEQikcwmQvht4sf6W7tf4nWJS4ZuXaYKqPNG9L3UBJjU+VKbl+4JieK8BdTFiMHTIbgfseGd0PddFH372T41yQxSKpWI47h+Ua1k6kihRiKRSCSzjqqqdVGlHVEUdXTjVCoVgiDA931832dwcLDtMZr7cUYKOrZtywGerYAoisbtg/E8r+s+GNM0xxRg0j6YzY2f/OUn/Ndj/8V///W/Of3g0/nEaz6BoW1+z0Mi6YoXXoBrr4U77oBHHoFXXoEOzk4AdB0WLIBdd4VDD4XjjkN53etA19FJvlw5wMJpPMWRPUEjO4La9QQl22qOIbda31YpVamUPaqlat0x5LkTiK2ZBIEXEnghpcExfq5TRFGUuounEQfXZr6NINS8XzsRqHleN3T5eUEikWwyROw2iS+J4CJaxJeaM0YMTeCoGqjzW/te1IWNCLL6uvkoivz8J+kexToM5v0cMfBhiJ5DbHw79F6JYr5qtk9NMkMMDSWvPYVCQX4+miakUCORSCSSOY+maeTzefL59rE4QRB0dOOk/TjpQPzGjRtH3b+5H6ddtJrsx5n7pFFkY/XB+H73g5HjCTC2bW+xcT5v3eOtPLz2YX76yE/5j3v+g9+/+HsuP/5ylvctn+1Tk0gmTxjCrbfCb34D99wDTz0F69d3dMkAkM3CdtvBPvvAypVwwgmw/aa/MlRVVZysjZO1p1cBqjFmT1Cb3qDmnqBq2cOrjL5PpVQTh2rLcdSdC3Eqz8Fzk3NgBgPjVE3t3PszQhBqFYqSOLiOLqER81vq+4tEIql1eohiS/xYEkG2trX7Je4HMRGB26iJL42+F0Vd3NT9krpg+lAU+RojmRkUYxeY/1+IgY9C+FfExvdA7yUo9jGzfWqSGSAVamTs2fShiG4vG5VItiK6LXnaUimXy+RyOWDrLvuSbBkIIfB9v6OQU6lUxnVQaJrW1omTTrIfZ+ZIf39jCTDVapUw7C4aaGQfTDsBZqb6YDY3bnjyBj7xu08wVB0iY2Q4//Xn84693iFFS8nc5+WXGy6Zv/wFXnppbJeMpiUumV12gUMOgTe8AY46Ckxz053zFk4YhF0IPyP7g5rEonb9QeWkP6ha9gj9mYmHmy0MUx+zA8hqJwg547iJRsybtiHf6ySSaSQRYAYabpcoEVtE03xdnMHr/sCK0xIzNjp+rCbOKD3yM5pkziDiEmLw4+DfCSgo+c+iZN8z26clmWb++Mc/snHjRvbbbz+23Xbbtvs8++yzrFixAti6xxe7HWeWQo1E0oatXagRQuC6LgCZTEZ+4JNs0TT344yMVUv7ccbDNM22vTjprbwytj1xHON5XkfxZaJ9MLqutxVemgUZ6Y6aGKuLq/n4TR/nDy/+AYDTDj6Nz77us7N8VhJJjTCE3/8ebroJ/vxnePLJxCUzlnCbycC22yYumSOPTFwyy6VbbHMnCqN6308n4aedUJTGv1XbCEfN9/MqMxsPN1s0xJ1WR0+LIOQ0HEEdHURjzBumjIuTbN4IEUG8YVTfi4hqwktdiFkPBN0fWMmPiBpL48daRRmUrPwfkmyWCBEihj8Plf9KVmQ+iJL/JIoiLxLYEhBCcNNNNxFFEUceeWTH9JPVq1dz11130dvby9FHH73Vvp51O84sLwGWSCSjUBRlq1W5JVsf3fTjtOvFSeeb+3FS6+9I0n6cdrFqW2o/TtoH006ASdd1I4KlWJY1bhyZdDZNP0vzS/n5P/6cK+67gkv+dAlv2f0ts31Kkq2VVavg+uvh9tvh4YfhxRehVOq8v6bB/PmwYgUcfDAceywcc4x0yWyhaLpGJu+QyTszcvw4jvGrQUO46RAN5zX1Bo3sCRopII3cfzaun/QqfiJCbZi5x1BVpX0fULM45JgjHEHju4lGzmu6vChGMjGE8BMBpklwEXXnS7MLZgMwgfhGpbcmvixqiiBb1Bo/pi1EUWbm9UoimSsoig6FL4G2LaJ0CbjfR0SroPdrKIo926cnmSLlcpkoilBVdczxQ0VR6t/dt8Rxj+lGjmhIJBKJRDIGmqaRy+XqcYAjCYKgrRMnXddcYN8ORVE6unEymQymac6pDzRCCMIwHFOAqVarBEF3VxSmH9zGiyOT8Syzh6qonHbwabxr73fR5/TV1//ppT9x0LKDMDRZNCuZRuIY/vAHuPFGuPvuxCXT3z+2S8ZxEpfM3nvDa18Lb35zEmMmkUwTqpp009gZa2Z7gjoKPm3EoJoQ1KknaORxonCMPqYZJI4FlVKVSqn956DpQje09oJQrRuoLu44zULPOG6iUVFzpvw8shkgRLXF+ULUjxjRB0PUn8SUdY0K6vxRfS9K3Q2TCjELUBR5QYBEkqIoCuROBW0bxNCnwbsJsbEf+i5HUefN9ulJpkB6kWqhUJDvjdOIFGokEskoPM/jox/9KABXXnkllmXN8hlJJHMXwzAwDKOtfXVkP047Z04aNZjGDY4k7cdp58ZxHAfDmL5BciFEPYpsrE6YaKzy7RHn3k0fzFwSoiSdaRZpHln7CO/8n3ey+4Ldufz4y1kxb8Usnplks6W/H667Dm67reGSGR7uvL+qJi6ZnXZKXDJ/93dJn4wtr8qUbN4oioJpm5i2SWFe++iQqdKpJ2ikuNO2J2ikENTmfoE3gcinGXl+EeGQS3mo/eep6cK0jbqAY9ZEoFGijzNSABotCI01b1gyprUdIi6NiB/rrzlg1jW6X+J+EMUJHFUHdUFL34sywvmSrJ+XuAMkEsmkUJw3g7oIMfgvEDyA2PBO6Pseir79bJ+aZJKkQk1PT8+Y+3mex9e//nUsy+JXv/qVHF8cB9lRI5G0YWvvqCmXy3X3wNZc9iWRzDRCiHo/Tjsxp5MLpxnDMNo6cUb246RdPGMJMNVqtevoFcMwxhRgHMdB12Uu/ZbKHc/fwT/f8M8MVgdxDIcvrvwi797n3fL3LWlPHCfumF//Orl94olEpBnLeec4sGwZ7LVX4pI58UTYbbdNd84SiWRCRFGE5/pNYk6bnqB2/T8jeoJGx8il8XETKF+fBtL3M4GATTxioihKixOoYwdQG0Gom94gO2NiOia6Mfuf04QQIIaaYsaSyDERr4N4basLRkxEhLOaRJY0fmwhqIsbjhhtESi9si9DItmEiPBpxMZTIH4FlD6UvitRzP1m+7Qkk+Duu+9m/fr17Lvvvmy/fWfB7dlnn2XFiuSivq15fLHbcWYp1EgkbZBCjRRqJJK5QNqP0ylazffHLzeuDzRM4O0+7YPpJMDYtl0XgCRbL2tKazjjxjP4w4t/AOCNO7+Ri95wUYvzRrIVsnFj4pK59VZ46CF44YXEJdPpNUhVoa8vcckcdBAcfTS88Y3QoTdMIpFsnTT3BI3V99Mx/q1NT5A34jaON93QiKIqLZ/REsFikz08AKqmdo55GyEItQo9HfZrmjdtHcuuYNmDaMr6uuDSNoKM8T/P1lGyNbdLTWhRFzbFjy1sdMMo+VkXoSQSSXtEtA4x8BEIHwMslN6LUew3zPZpSSaAEILf/va3BEHAEUccMaarRgo1Cd2OM0vvpkQikUgkcxRVVTFNkziOieO4vk7XdUzTrIs4Y0WRjSfQpM4Yx3HI5XLk83lyuRyO48hYMsmYLMkt4ef/+HOuvO9KvnrXV7nx6Rt5YM0DfOO4b3DEDkfM9ulJZpo4hvvvhxtugD/+MXHJrF0LYwnItg1Ll8KeeyYumRNOSHplJBKJZBxaeoJmACEEgR+2FX4qbZ0+jZ6gxPEzvoAUBo3PayIWiVtnhlBUBVVVIP0cJ5LnGEdxfZ84inGLFdxiZcbOA8AwYywnxnJE7TbGduLENWQvS9ZnNOyMjZVxsDJZrGweO1vAyvZiZ+dhZedjZRdi53qaeoYaApFpGbIjQSLZTFC0hTDvx4ihfwXvdsTgxyB/Dkr2fbN9apIuqVQqBEGAoigdu3wlk0MKNRKJRCKRzAJp7Nl4cWSpQDMeuq6Pcr+k/TVCCMIwxPf9ujunUqkQxzFBEBAEAcVikf7+/pZjqqraMVYtk8lMaz+OZPNEVVT++eB/5rXbv5bTfn0az2x8hsfWPSaFmi2NwcFEkLnlFnjwQXj+eRga6uySUZTEJbN8ORxwABxzDBx/PMgvchKJZI6iKAqmZWBaxoz2BI0UfsZyAXk1IahdT9DobqHWniARC6IZdAgpqkBVBUqTFgSJhi9ihThurAx8lcBXKQ11c+QQGKpNEyMVcEb2/rS4gByrjYuocR+zjQjUPG+Ysx8XJ5FsCShqFnovRwx/CSo/QxQvQESvoOQ/haLI5Ia5TtpPk8/nZdLGNCOFGolEIpFIppkoisYVYDzP6zqOzDTNMftgmkWZbmnuxxkZrZY6deI4plQqUSqV2h7DMIy2vTjpvPzQtvWwz+J9+O3Jv+Unj/yED+7/wfr6KI7QVPl3sNkQx0lc2fXXw5/+BI8/DmvWgDdGN4RlwZIliUvm8MPhTW+CffdNIs0kEolEUkc3dPQenWzPzMS+jOoJcj0qxSG8cj/VUj/V4ga88iCV8iBeeZhqqYTnuokrqBJQdVWqropXSW6rFRXPValWlNr6xvu5iBWieOYEC0VRUHUVVVVrMXHpA0MsBCKKiaIY0SRGeRUfr+LDhhk7LVRVaSvgjBSHbGe0UGQ6ZseouZHzmi4/O0m2fBRFh8LnQdsWUboQ3B8golXQexGKYs/26UnGYHh4GGDMyDPJ5JBCjUQikUgkEyAIgjEFmGq12lV3DNTKYsfpg7Esa0YED0VRcBwHx3Habo/juEXAGSnm+L5fd+OkH9RGYllWRzeObdsyomILwzEcPnzAh+vLbuDylp+/hZP3PZn37PseeQXqXGN4GG68EW6+GR54AJ57LnHOjOWS6e2FHXdMXDJHH52IMlthl59EIpHMJkmfTQnqfS/9EK9Difqx43XYxjp68v2Q7YcF5Qkc2aj1vCxMel60hSjqovo6oSzE83qpVjL4lXDMvp+2DqHKSBdQ554gIQRREBHROd53qmi6hqarKKpaj4lTAIFAxII4EsRRRBTG9Yur4lhQKVWplKozdl4AuqGN2QE0ShDqxk00sl/IMeVnccmsoygK5E4BbSli6FPg/Rax8b3QdwWKOm+2T0/SgdRRI4Wa6UcKNRKJRCKRkHwh9H1/TAFmvD6YZlRVHVOAsW17TnfAqKpKNpvtWPYXhuEo8aZZ0AnDEM/z8DyPgYGBUfdXFAXbtju6cebyz0bSHT995Kc82v8on77509z2/G1c/IaLmefIL1yzwl/+krhk7roL/vpXWL16bJeMaSYumT32gMMOSwSZAw6QLhmJRCKZQRIBZqAmvqyrCzEiXgdRf12QIVoHTEAoUJxEbFEXgpYIL0pdkFncEGeU3jE/eymAY4IzM8lwLT1BIwWctj1Bo5ZH9wSNPE5zT1AURkThzAtBmq4lriBVbXEGpZ1BURQ3zqV2rUQYRIRDLuUhd8bOD8C0jdECzkhxyDGb3EAdHERjzBuWIT/TS8ZFcU4AbTFi4DQIHkJseDv0fQ9F33G2T03ShlSoKcgLtqYdKdRIJJJRZDKZeldFJpOZ5bORSKZOHMd4njemAON5Xtd9MIZhtI0faxZlDGPL/lKi6zr5fJ58fvS3dSEEQRB0dOOksWppxNqGDaMzKlRVHTNWbUv/+W4JfHD/DxKLmAvuvIDfPP0bHlz9IN944zd43Q6vm+1T23IpleCmmxKXzP33w7PPwsDA2C6ZQiFxyey/f+KSOf54mCcFNYlEIpkuhIgg3lgTWdbWRJh1iCY3TF2cIRj3eHWUXIv4grooKelucsGgLgIlt1l8ZmruCWKG3oba9QS16/sZ1RPk+q1CUAfByK82fn8NIWgCv9MJoGoquqG1dQYBIGrOoFgQRRFxGBOGEVGTWOVXA/xqQHFj+5jj6UBRlHGdPpaT9Ae17tfZTdRuXjfk8ObmjmIeDPN/jhg4BaIXa2LNFSjmAbN9apIm0hh36E6ocRyHH//4x/T09MjxxS5QRLcB+RLJVsTw8DA9PT0MDQ1JhVgimeNEUdRRfGnug+mWNIpsrE4YXZdfBKaCEALP89o6cdL58dB1vWOsmuM48nc0h3is/zFO+/VpPLXhKQA+euBH+cwRn8HUzFk+s82cv/41ccnceWcyv2oVVMe4wto0YfFi2G23hkvm4IOlS0YikUgmiRABxOtrbpdUfOlvccMk8xtgIhFeSm+T+LJwRATZokYsmdI+vlYye0RRhF/x64LPqPi3ESJR4vapzVfaiUTN+ybH21QoqoJh6mipGKSpqFrSGQRKYrGKRc0ZJIjjxBkUBhGhHxJH3V0AN11outbW0WM6Zqu444wUesZwE42YNx1TdnBuAkS0HjHwEQgfBUyU3otR7GNn+7QkNfr7+7nnnnvI5XKsXLly3P3XrFnDfffdR19fH4cffvjMn+AcpdtxZjmKIZFIJJI5SerKGEuAqVarBEF3V6mlUVtjCTCyN2XT0Py7mNfmyv04jqlWqx1j1TzPIwxDhoeHO/bjmKbZ0Y3jOI78PW9C9lq0FzeddBNf+v2X+MFDP+DK+6/Ej3wuOPqC2T61zQPXhd/+Fn73O7jvPnjmmcQlM5YDsFCAHXZIXDIrV8Kb3wwLFmyyU5ZIJJLNGSG8JvGlH6L+JH6s7nxJt22cwFEVUOePiCBLHTCNThjUhSiKvJBhc0XTNJycg5ObGRFNCIFX8TvHv5Xbxb817dPhfs33r/cExSJxCFVnxhGkKKCbBoapoRl6EhOnaSiagqIodReYEAIhQMRxEhXXJAYFXlA3DUdhhDtcwR0e/4KvqWBYRl3AaUTCtXMJdRaExps3bXOzcMHNFIq2AOb9GDF0Fni3IgbPgPynIfP+rfrnMleQ/TQzixRqJBLJKDzP46yzzgLgkksuwbKsWT4jyZZG6qgYS4CpVqtd98FomjamAOM4Dqa5dX/g3ZxQVbUuqrQjiqIxY9WCIMD3fXzfZ3BwsO0xHMfp6MaxbVv+rUwzjuHw5aO/zFE7HsX5d57PGYeeMdunNDd56im49trEJfPoo4lLZiyHmWHAokWJS+bVr05iyw47TLpkJBKJpA0iLtWdL6kQ09L/kjpgRPuLQNqjg7qgETOmLhwhvqTOmPkoihx+kUwNRVGwawP6PTNw/cVYPUEje3/aCj6VkTFynXuChIDACwi8mRGCAHRTx7B0dENPYuIMDVVTUdXEGaQoSj0eNo6TqLh6Z1AqBvkBgRfWj5mec3GgPGPnDSRxcG16f1rEHaed0DPOfdKouYyFYepz9juPomag91uI4pfA/Smi+BWIXob8OSiKdDXNJhPtp/E8j29/+9tYlsVBBx0kxxfHQUafSSRt2Nqjz8rlMrlcDoBSqdSxTFwiaUfqhhhLgKlWq3T79pP2wXQSYNIosrn6IVOy6Wnux2kn6IzXRdTcj9NOzJH9OFMjFjGq0hASfvjQDzlh1xOYn5k/i2e1ialWE4fM734H996buGQ2bBjbJZPPJy6ZffeFo46CE06AJUs23TlLJBLJHEQIkQgrIwQXMSp+bB2IiQysmiPixxahjHC+JMJMH4oixXGJpFvSnqDqOH0/I50+I3uCRgpI7XqCZhrN0DAtHcMya0JQzRnU3BlE+p2h0RkUhxFRFBMGIaEf1cWfMJhAROI0oKpK25i3URFyTjvRp81+6bxjtuyr6ZMXVoQQ4H4fUfz3ZIV1NErvJTL6cRa55ZZbqFQqvPrVr2ZBF479Z599lhUrVgBb9/iijD6TSCQSybQThuGYAkylUsH3/a6PZ1nWmAKMbdsyB1gyYQzDoKenp60dO3VzjXTipGJOpVIhjmPK5TLlcvsBHV3XR0WpNQs5sh9nbJpFmhuevIHP3PIZLrn7Ei477jJW7rhy9k5spnjuOfi//4Pf/z5xybzyShJn1gldh4ULGy6ZY4+F1742WS+RSCRbCULEEA80YsZqkWOiyQ1TF2fo/rMnSqbufEldMEp9fiFoi5NbpSAvypBIZgDd0NF7dLI9MzNY29IT1KbnZ2RPUHI7WghqKxi5Hp7r1y84jIKIShBRKc1Md5CqqRi2gWkZGJaBbmrohp70Bek1Z1AaE6fUYuJSMSiKiMLRYpBX8RFxw0VUKVWplMboOJwGdEMbtwNopCDUEIAs7MyumPq/YsU/wHLuxsq/B3vR57Hzi1tcQjLaeubxfb/eJyujz2YG+Y1vDrBy5Ur2228/Lr300gnd7/3vfz+Dg4P87//+74ycVzf84Ac/4Mwzz+wYLSORSCaOEIINGzbgeR6WZTF//vwZ/6LY3AfTSYCpVquEYTj+wUgcCe36X5oFGMuy5IcpySanuR+nr69v1PaR/TgjBZ20H6dYLFIsFts+hmmaHWPVMpnMjPzdz8brxnSwU99O7LZgN/62/m+8+3/ezSkHnMI5R5yDpVtEccSfX/kz/eV+FmUXceg2h6KpMyDc+j5cfnnialmxAk47DcxJ9AP4PtxyS9Inc8898PTTiUtmrAjHXA622w5e9aqkS+aEE2CbbSb9VCQSiWQshIjAvy8RONSFYB60ySNkhAgh3tAkvqxNIsjqzpfUBbMe6O5zJwBKoUV8SSLIFtHog6n1v6i5GXtuEolk9tkUPUF+1a8LPx1dQC3xbyN6gjoISCN7guIoxit7eOWZEYIUBUwn6cQxLAPD1DFMHc3Q0HStJgYpKKpa8wUptc4gQRyN6AwKQgIv6Q3yKz5epSGgh0FEOORSHhrjQqWu2L5p/gujtpq2hpUxsDKZEUJPkzjktO8NSvdr9A61dwwZ1vQmK0RRxKN3PsGG1QPMX9rH3kfsPmcvVBVC8NJLLwHJBbfdXpwYhY3vQn+4/m6OfttK9Cm4rLZ0pFCzGXPZZZd1HR00l3n++edZvnw5Dz74IPvtt99sn45kBGvWrKnbFCUzz+rVq3nssceoVhtXtdi2zV577cXSpUsndUwhREfhpXkaLw4qRdf1cftgZDSUZHOlm36cdr046XxzP06a3zsS27Y7unEm048zE68bm4o9Fu7BjSfdyJfu+BJXPXQV333gu/zhpT/wjj3fwbfv+zbPDDxDJCI0RWNF3wouOPoCjt/l+Ok7gU9+Ei65pFVM+cQn4Kyz4Gtf63y/F16A666DO+6ARx6Bl1+GDg4sIHHDLFgAu+wChx4Kxx0HRx4pXTISiWSTIaq/QQxfAPGaxkp1CRTORbGPnfrxhd/ifGmIL/2tDph4AzCB77BKX01oaUSOKS3iS7JeUewpPweJRCIZD0VRsBwLy5nZnqCO8W8jen8qTUJQu56gdsdp7gnyXB/PnYArcYKYtoFpm5iOgWkb6GbNGaTraEbiClK1VmcQgporaIQY5IcEXoWgsgHfi/GqGoHX+N7kVyP8akRx48w5hBRFGbcDyG7qAWrtFGoVhf52/7Nc+62bGFrX6EWbt6yPf77k/ax8+2tm7DlMhpHfNz3P45Zbbhn3++a1/3kT3/301fXlL77zIq5ceDXv/8o7OfFDx834eW+OyG+HmzHSZjaaIAgwDGO2T2OLQgo1m47Vq1dz//33j1pfrVa5//77OfDAA0e9CUZR1FUfTLeYptlVH4xEsrWiaRq5XK7e4zWSIAg6xqq5rtvyP7tx48ZR91cUZUw3jmmaLULOZF435hq2bnPB0Rfw+uWv519/8688uPpBbn/+dnRVp2AW0FSNKI54cuOTnPzLk/nxW388PWLNJz8JF144en0UNdZ/+ctw223wm9/An/+cuGTWrRvbJZPNJi6ZffZpuGS2377z/hKJRDLDiOpvEINnMEogidcm63u/0VGsEaLSEF9qgosY2f0S9YMYnMAZqaAuaHLA1CLIRnTCoM5HUSbhcJRIJJLNFEVRMK0k7izfNzMOwJE9QZ36ftr2B1WanUJ+WwGpuSfIrwbJ8uB0PgO1NoFuxFiZGMuKMS2BYQoMS6AbAs3eEd2a3yQGUf8eJQQtzqAwiIjCRkyc7yXnnT7/OIpr9xN1UWwm2LhqgAve+XW+ctJl2NnOfUCjBCFntGA0VtRc6h7qxr0z2e+b1/7nTfzHKf9JJBruWJ8qxfVl/uOU/wSQYk0b5GjbHOSGG27g3e9+N5dffjknnXRSx/1GRp+tXLmSffbZB03T+OEPf4hpmpx//vm8+93v5vTTT+eaa65h8eLF/Md//AdvfOMbAbj99ts56qijuP766/nMZz7Dk08+yX777cf3vvc99t57767P+Te/+Q1nnnkmL730Eq997Wu56qqrWv5Rv/e973HxxRfz3HPPseOOO3LGGWdw2mmnAbB8+XIA9t9/fwCOPPJIbr/99nHvlzpxfv7zn3P55Zfz5z//mSuuuIL3vve9nH/++XznO99h3bp17LHHHnz1q1/luOPkC4Bk7iKE4LHHHhtzn4cffpj+/n48z6uLMkHQXVmioijj9sFYljVnbbYSyeaCYRgYhtG2IFAIge/7HWPVKpUKQoj6cjs0TasLN47j8Morr4x5Po899hhLlizZLBxuR+90NL89+bfsefmexCIma2QxtOTiC1VTKagFhv1hPnvrZzl2xbFTi0Hz/cRJMxYXXtheyEnRtMQls/POcMghSZfMUUdNLjZNIpFIZgghosRJ09bFkqwTQ/+GCF9sRJI1u2BEaQKPZiQCTJPbRakLMQsbzhh13iaPXJNIJBJJwkz3BMVx3CIEjSn8jHQMua2OoRZBqE1PUBiohEMq7X3t/bVp8mi6hp2tCSS2gemYGJaOYZloRtIZpKedQU3OIIEAQa0zKCYKE2dQFCZC0NMPPU8UdL7wK45i3OEK7nBlSuc/HoZljO4KahaEHIsNg+tRdQXd0tEtHcPSknkzmX/pgTUc/OqDm4QlE93UuerTPxvzsX9wzs85/n1/J2PQRiCFmjnGT3/6U0499VR++tOfcsIJJ0z4/j/84Q/55Cc/yT333MMvfvEL/vmf/5lf/epX/MM//APnnHMOX//613nPe97Diy++2BLrcvbZZ3PZZZexZMkSzjnnHN785jfz5JNPduVOcV2Xiy66iKuvvhpVVTn55JP5xCc+wU9+8hMAfvKTn3DeeefxzW9+k/33358HH3yQU045hWw2y/ve9z7uueceDjnkEG6++Wb22msvzNoAx3j3S/n0pz/NxRdfzP77749t21x22WVcfPHFXHnlley///58//vf58QTT+Sxxx5jl112afscPM/D8xqK+PDwcNv9tnRWr17N6tWr6+VgAA899BCOk+S7Ll26dM5fmb25smHDhnGdL2EY1jNBO5Fe7Z/NZltu04LzzWGwViLZUkkFU8uy2vbjpDGFndw41WqVKIrG7McZSXqlU09PD6ZpYhgGpmnWJ8Mw5pRA++zgs4RxSI/Vg603Ymw2VDYkmdgIHul/hDf+5I3s2LsjGSPDzvN25vRDTq/v+39P/B+RiMgYGbJGloyRqU95K888Z17SSTOWK6YTigJLl8KBByZ9NvPmwfz5yWSa8OSTybpcLnHWzKGfrUQi2XIRIoB4EOKBZBID9XkRPN4ad9b2AINQGkOYxm5xvqA1x48tajhjlF4URfYPSiQSydaMqqpNPUHdJQEJ4ScXBsTFptvm+TJCFCEuIuIifullqsOP4VVUqhU1uXVrU23eq6h44iQ8f8kIB9D43UKpgyYKo00imLTjUz88neX77lAXvTzXGzXvuX5NvErPvSFoVZv28SrJrV8JCLzGxb6BlywXB8aIcO6CX/KbUes8UcGjSkzjO1eRQVSRfD/y+iv88YY/87q/n1sxb7ONFGrmEN/61rc499xzue666zjyyCMndYxXvepVfPaznwXgM5/5DF/96ldZsGABp5xyCgDnnXce3/72t/nLX/7Cq1/96vr9Pve5z/F3f/d3QCL2bLvttvzqV7/i7W9/+7iPGQQBV1xxRT0e6/TTT+eLX/xiy7Evvvhi3vrWtwKJg+avf/0rV155Je973/tYuHAhAPPnz2fJkiVd3y/lzDPPrO8DcNFFF/GpT32Kd77znQD8+7//O7fddhuXXnop3/rWt9o+h6985St84Qujy8i2Nq688spRP4fTT28Mfn3uc5/j85///CY+q62DZqFwKkRRxNDQUMduDE3T0LRaJuyI+ZG3E9kmBSCJZOqksWeO4zB//vxR29N+nFS4Wbt2Lf39418ltmbNGtas6TxIp2naKPGm3XzzsqZpM/J/31/ur4sszURxhEDUr567b9V9PNr/KAAHb3Nwi1Dz+Ts+z9rS2rbH32PhHtzy3lvgmWcA+Pt3wtocZH3IBK3TdsPw6T807nvdruAagkywiuyjq8g82LhfzofF7b7fKEoi1hhGIuRYFtg2ZDKJkJPNQqGQTL29yTR/fuLUWbAAenoS0ScVfnK55L6qHAiVSLZUhIhADDVEl6ZJtBFikuXuxPsxMfYHY78R8WMLQV0MSk5+1pNIJBLJKISIQbjJ+1AqsogixKW60CLi0eta54tA9z05CmAZYM0H6HzhlRAQOtvhRa+nUqq2TNW2y5X68vDGEoP9Q8m0dojADzs+zkzxyupVLNinh8iMiPUYJRthRKBGGoZvoJdjNDdGLYXoFQOzquB4GkHVIqiGBNWQsBoSeGFtOSCohviVgOqwR2W4SmWoSqXoTag6rlte5lme4/GWdX/jofr8cvZg3cvrp/+BN3OkUDNHuOaaa+jv7+euu+7i4IMPnvRx9t133/q8pmnMnz+fffbZp75u8eLFAKMGdg477LD6/Lx589htt914/PHWf6hOZDKZlg6TpUuX1o9fLpd55pln+NCHPlQXiyBxBozVsTOR+x100EH1+eHhYVatWsXhhx/ess/hhx/Oww8/3PHxPvOZz3DWWWe1HGe77bbruP+Wykc/+lFOPPFEAB544AFOOeUUvvvd73LAAQcASDfNDGJZVlf7LV++HNu2iaKIMAxbbtutS29T0v18f3oLA1VVnbTQk86326bKwUiJpM7IfpxsNtuVULNs2TI0TcP3fYIgwPf9+rwQokUA6hZVVTuKOJ2WuxF1F2UXoSlJJ42qNf7/5znzEAiCKMCPfM589ZlsV9gON3BZmF3YcozXbPsa1lfW4wYubuBS9svJbVAma9QiHmqfW17qgTUd4r93Xw+fLu+fOGiKRS561YM8lakk3/ogDbcGYJthuPe7jfu+8x/hqXmQDQSZICQThGT9CpkAFrhwwa2NfW/YBQachuiTDcAJGgLQdu1MvpqWCD+mmQg/jpMIOLkc5POJ8NPTA319DddPPj9a9GledpxEWJJIJNOGEKI2ODVSWBlAxBvbijGIISY3YqKA0gtqX+skKlC9fvx7585CsQ6dxONKJBKJZHNECG+0YNIipJQQbZwtraJMmekY5Y9j8CoqFTdHxc1RdbNU3AwVN0PVtahWTCplk6qrUynrVIrDVIb+SrWsUimrVFytMV9OHDWVskYU/hj48ZTPrx2qpmA6JqZjYDgGhq23TJqloRkaWnM0mgrFdWWeuO3ZcY9/81V3ctuP70oEl7rYkgguURDPyHMa+fwMO3leuq0nkW/prWNgWLX1ts68hX0U+go4WQsn57Dq2TX86j8qLGQZkDhpHud+9uBA8vQCYGGzcNsFM/48NjekUDNH2H///XnggQf4/ve/z0EHHTTpK5ZGRpUpitKyLj1uHE/fP3W7x0yveC2Vkkzj7373uxx6aOsH/7GiViZyv2x26rmaaRTN1k67aLMDDjigLtRIZo758+dj2/aY8We2bbPnnntO+PVBiCQXtZOI043Q02lbShzH0y7+QDIYPJ3On/RWCkCSLYFuXzf233//tq8bQgjCMKwLN+2EnOZt6bo4jpPs6RGxoeORfiYZy7mzg74DOxR24NnBZymohfp566qe9PfELrsv2J1zjzi3Y0fNt97U3j2bPmcATjsNPvEJfnZNRMkE10imctN8IVDg3rvrnTOvufkzbDf8UkMACsp1IainsC1cfhMUizA8zJpr38rq4vPJt87mSQiWegYXhPsl+5bLXPHaF7l/vt8i/KTkPfjbNxvLH/h7eGApZIKITFAhE1TIBkNkgmTfy25q7PubFbA633AINQtBmQCWDyRXJLaQCj+p6ycVcvL5hvDT19cq8owUfkaKQKYpBSDJFkEiurhtHS2ijRBTnx/jat8xUQqjRRelD2Xkuvq2QtveFyEihH8fxGtpP5imgLoEzIPabJNIJBLJXCNxXpZbo8LqjpVknWjjbBntYumu73YkYUBdCEnEE5VK2ayJKhkqrk3FtamWTSquTsXVqZY1KmWFahkqZaiUI6qliEo5pFLyqZY7jSW4takdoxMIOqFbWl10MGwD06n1rdgauqmj6SqqoaFqSk1QUVq/PwlBHIukcyaMiaKY0IsaAoqXCCjlQTdxsVRDQn+S7/811vxt3bj7KKrS9JwMzNqtVe+dMZMpa+Fkbez0Nmfj5Gwy+QyZnIOTd8gWMmTyDtlCllxPFss2EUJwyy23jPt98+ijj275eYVhxJ0//zPF9bXIgdrHjzy9FJQkAjy/MMtr3iQvEBmJFGrmCCtWrODiiy9m5cqVaJrGN7/5zfHvNI3cfffdbL/99gAMDAzw5JNPsscee0z5uIsXL2bZsmU8++yznHTSSW33STtpmq/67+Z+7SgUCixbtoy77rqrJT7urrvu4pBDDpnks9j68H2fSy+9FKDrsnrJ1FAUhb322ov777+/4z577bXXpERcRVHqosV0kgpA0yX6NG9LB1PTAeHp/jtUFGVanT/NDiAZDSLZVEz1dSMVTgzD6Pqih9SB007IGUvkiaIIIUR9eSze1vs2Lh64mAF3AEuz0NCIifFiD1Mz+cCOH+CJx58Y08nT6fWu/rMwTTjrLHa7cIxOhrM/URdpAL5yzFfG/wHVhIwfvOd/GPaGW9w8laCCG7gYmgFfO7l+l1f//gLmb3yqLgAl4k8J1yuRVx14+KpE1BkaYv1j57Ku8uwo8QchyAZw2VPbgetCpcIPDyxy+3advyC+fAkotS9Npx8Pty6HrO+TCXyywXBLDNw3bgSzdqibd4Lne1vFn+ZpxQDozdcDKUrD8ZPGvaXCT3O021hun3bzXfQoSiRjIUS11uuycbToMjJarC66TPKiFCVbE1N6m8SVeaNFFyWd70FRpudvXFE0KJyLGDyDRJ5tFmuS10SlcE5bkUcikUgk00fyHbfZxdIkpMTl+joxlrhSd7F083jgVxUqrloTSpodJw6Vcq42b1Mp21Rci6prJI6V+v4KlbKgWhZUyhGeGxF43Vz4HdamCaKAaRsYjt5wc1gaum1gGCqqrqEZKoqm4lglerKrEIoGqMSxRhSrxJFKFKqU3R583yAKIkI/aor/CnAHK4nI4oUzEvs18jkZtp48r5qQEgURG14a7HiXw//pYHY9eCecvJMIKjmnJqJkaoJKcms51oxehDrZ75u6rvH+r7yT/zjlP4lFzIs8BYCg8bfz/i+/E12Xnz1GIoWaOcSuu+7KbbfdxsqVK9F1vT5Qvin44he/yPz581m8eDHnnnsuCxYs4C1vecu0HPsLX/gCZ5xxBj09PRx33HF4nsd9993HwMAAZ511FosWLcJxHG666Sa23XZbbNump6dn3Pt14uyzz+Zzn/scK1asYL/99uOqq67ioYce4ic/+cm0PJ+tgSAIuPrqqwFYsEBaETcVS5cu5cADD+Sxxx5ruWLBtm322muvORc91ywAmU2DmdPBWA6gqYhAqZtQCEEQBDMiRE6n86fZASQFIEk7NvXrRipy6vrEPkJGUdS1sPO6zOvQNI2rXryKNd4aPDxUVJZZy3jPNu9hebCc5557bszHa+7d6di5c/bZ5KtVrG99C6XZaaxpcNZZ8LWvTeZHBMCOvTt2ve+5rzu3632veO2rGPKG2sa6CSHgwkaH36v//A2yax+h7A3jVoqUK8O4fgnXdxFRiPrTL8PwMAwPMzj8AwZ5jsE4BhFDLBoOHyH41m0WVDyIY67ZE67drfM5PvFNKNSMVp8+Bn61uyATuDihS9Zf3+Lqueg66K392d6xA/x1YWPbSBfQLhvAqP2aBKDoemvcW3O0WzeiTzsRKJuFCf5tS+YGQgQ10WW0o2WU2yXdLjpdoTseJqjzRjlalBahZcQ2ZXad+4p9LPR+AzF8AcRNnWXqkkSksY+dvZOTSCSSzYDExVJidOF9OxdLLSKsxe1SW9fBxRJFiUulEd+l1ZwqNWElFU7cLJVyHs9VccsG1bKB6xq17alrRaHiJuKKmFSYTncii6ar9civ1LGimxq6oaEaKpquoaQOFaV2wVTtegEhau6UKCaOBFEYE/mJoJIKKH4loDzgEnoRIu6kpqQD/XFtaqY7QStFt3RMO4n1Mh2jHmtmZUzs1J2StbAzFnY2caQ4ORs7a5OpCSrZfKbuTsnmM2R7sjhZu62Ycucv/8zlZ17F+pc31Nct3G4+//z1D3DEW+eO02Sy3zdP/NBxAHzv01ezZv2LABhY5Bdmef+X31nfLmlFfhOZY+y2227ceuutdWfNxRdfvEke96tf/Sof//jHeeqpp9hvv/247rrrpm3g9cMf/jCZTIYLL7yQs88+m2w2yz777MOZZ54JJIOa3/jGN/jiF7/IeeedxxFHHMHtt98+7v06ccYZZzA0NMT/+3//j/7+fvbcc0+uvfZadtlll2l5PlsbS5Ysme1T2KpYunQpS5YsYcOGDXieh2VZzJ8/f6sbpFdVddrFH2CUA2i6ouCa4yTDMCQMwwlFQnXDdDp/mue3tr+tLZHN4XUj/buzbbur/V/P6/l0+GnueuEuVg+vps/sY5++fYjC0W6eKfXuvOENsHIly2+8keyaNXjbbsuat74VM5fDvP/+MXt3UjfSpvw5b1PYhm3Ypqt9zzj0jK6Pe0n5vQxWB1vEn3SqhlW0r30gEW2qVQ6+5wp4+R4q1RKuV6TslWpikYsbVcmc/SEolmFwkOHcHRTzqyg2OX+aI94u+m3jHG7YFX68b4cTBO75Lmxb6+s5/3Vw1f4hmWCYrD/cIv44AXzlBlhW61a/a7skLq5ZAHKa9t9tPTi1sYhYAcU0UXJTFH1GLmcyICM3uyYZDBtq62gR7aLF4oFkEGxS6B0cLR0ixpQ+UJw59fraLYp9LFjHgH8fxOtAXQjmQdJJI5FItmgSF0u1ybnSLK40nC0NF0sqsDTHhpXrLhYhIPCVRFBx1bqY0uxYaQgsWn2/SjlPtdzT5FTRm3pVFPzqdL2vCNpZRNLeFN2qOVRMvSGoaGqToJKIKQpNtYxxkqQRh4IojIiCRFRJ4768sk9pQziGmDJ9aKZWd9wkMV+pmKJiOSpWxsLO9WBlrJqIkkR9ObmaMyXvJHFfeacp5iuDk3OmPYFkPI5466G85u8P4tE7n2DD6gHmL+1j7yN23+Tn0Q2T/b554oeO48h/fA29vT8F4Lyff4Kj37ZSOmnGQBFCTOg/KQxDvvzlL/PBD36QbbfddqbOS7KJuP322znqqKMYGBigt7d3tk9nzjA8PExPTw9DQ0MUCoXZPp1NTrlcrpdVl0qlaekBkki2ZNKuj+kWgZojIWeKTkLPVF1Bm+MglmTzZyq9O5Ohm96dkcuGYWw1PVkDlQEGq4P1Pp+GEFSmXBrgpG2PxyhXoFjkF0//H39Yfz+uV8L1y4nwE1Zwoypu7HHbU6+hb9CDoSE+vePT/GjHoba9PgB3fw+2H0rmz38dXH5w53O8+UewZy0C/NJXw8WHJSKOE7R2+mR9+MLtsPPGZN97l8GdO7Tv/0kFoHwtJStUQbUd1FQAmoro07zsOHO+/yfpdSm2dbSIEZFjjW1DTC6HRBkRLdZGdFH6Wt0wSk6+X0kkEskcRYiwTfxXaxRYs4tFxLV1cWNfr1Ku9aKkgkpSPF+fr4srWus+YxbUz9z7hqIq6LaGaRnoloZm6mg1d4pmqKiqiqopjQ4VRQEhko9EUSqoxERhTOiHDYdKrUclDme+hF4ztNbeFMfAckzMjJH0pjhmTUixsLPN7pSGmOLkHLL5DJlChmzeSW4LWTnAvxkixxcTuh1nnrCjRtd1LrzwQt773vdO6QQlEolEItlSaO76mE5SZ8B0OX+ab1PS/cbrDZkoqqpO2vkzliC0tQxwSybHXO3dGUl6juOJOt307sxl+pw++py+rvZ9x4EH8o4uj3uuV+R0bygRfbwS5eIGKsMbKQ+vxy0NsOANB4AbwPAwr1p/F+8sP4brlxPhJ6xQjqu4sU8Zn9y2S0BPxCLXKBGpMGwl00g+84fG/J+2g4te0/kcf/kLePXLyfzV+8K5R1dwggqZoL9F/HFCOPd38Kq1yb4PLYHfrmjf/5MNYPf1jbi4QAOyWYxMfnqcP+myZbUVgBLRxW3raBkVL9a8nUledKAU2jpa2jpd1D5QCtIdIpFIJHOA5P2igoiGiKMicTREHA0TR8N1IUU0OVsUUSQKSlTLLr5bwXOreK6H54YjYr9SEUVrca60uFrqfSx9VN35CDFzoopqqJiWnrhTLA3NSEWVpEtFVRVUtSamUBNThEgElSgRU9L+lNAPG4KKm0wziaqr7cWUtIg+a2PXCumbxZRMzsbOOWTSIvqCk/SnFDLkClmyhQyGKXsEJZLJMqnos9e//vXccccd7LjjjtN8OpJmUsWxHTfeeCNHHHHEjJ/DG9/4Ru68886228455xzOOeecGT8HiUQi2Vpp7gOxrOnLtxdCjNkDNBURKCWO42kXfyARgKbT+dPcAyTZOtkUvTvpctqLlc67bvcdGV317oxY3lLjDfNWnryV72rfN/Ne3tzlcf+1WuKDG1fhDq6jPLQOd3gDbnEj5dIAbnmQ7c7ZAUohDA+zd/Vx3hM8QzlMIt/KcRVXBLgEuEpIT7VxxapbG6+oGMm0YcTjlpuSPh9akjh7OvGD/4U3PJPM/99ucMYbyxhReZSgkwng/90Kh7+U7Pv4AvjlHq0OoeZ9d1sPC2t/jr6hEOYNbEtHyeiQVSArwIkhC2TVZJ2jItL5rAqZEfPNy9kc2PNHOF7mjRZd6tFjPSiKHOiRSCSS6ST9DtDc35nON5Y94mgYEY3sYUkiwBRRIvSKeBWXwC3ju1X8ShXf9fGrPkElxKtEVF2lyZnSPhqsWYAJPBWwa9P0k8R+GRimhpb2qOgqqqbWO1TSAhUhQMRJd0riTokaDhUvJPAioiAiDmKqgQ9M/3ceSNw1hq1jOkaToGImQkp9SjtTLOycXYv5qt3W4r3SEvok8itDrieLZU9/zLhEIpk6kxJq3vjGN/LpT3+aRx55hAMPPHDUFYsnnnjitJzc1s5DDz3Ucds223SXTz4eK1euZKz0u+9973sd893nzZs3LecgkUgkkk2Loih10WI6af7yN91RcOl7VRzHxHFcH/CeLtLB+uly/jQ7gLbEgXLJxHt3gPrf7lgCz5R7d5pQVbUrt85s9u7MJRw7h7NsV1i267j7vr42deTyEEolGB7mlMENvGNgTSL+FDcmU3kQtzxI2R1i1zf1wlAAxSK78TIfWL26Jv54uMLH1WLKRiL49DX9+lMBKNBgSIOhEX+KxSZ9//GF8K1DOp/uZTfBPz2WzN+xneB9/+CjCL9F/Em7fU6/pyEWPdeXdAuNcv/UhKDdNjT6ggJLp9KbI2Pl0DPT1AGUzcIERVaJRCKZSzQ76DuLJ1EX+4SI2AVRRBFlVKWMiMoE1SJhtUhQqRBWXULPI6x6hF4iqvjVgKAS4VUi/EqM59KmyL41LiyOR35OmD6BRdUVDFNLulTSHhVNS8QUTWn5jJK6U6I07iuICIOoVkqfxH6lBNXErdL9p6guUah3phi2kfSmZMxavFdDTLEyZr14vt6bUhNR0hL6TN4h25OtR35ZtikvJpNItjIm3FEDjPlCoSjKJsnUl0hmEtlRIzMkJRLJ3GIsB9BERaDm+cl2k0yE6XT+NDuAttbB9K2NifbupMvT0bszlnNna+3d2RQIESU9LWl0mLcOhlbD0GrE8DoYWgfDGwmHN1IeHqBcLOKWK7hujFuNKHsxrhdzyGqFJQMCijEPWxG/3F5QURMHj2s0prIBn7sDVj6fPP7/7g6nvanz+X39JnhHTdS5eSd47z/UNjS/JtW+Yp5/m8IHH0jm/7wN/MM7k81mNFrU+fAD8LbHk+2v5OHbB7d3/2R92GUj7DiY7BtkLIq9GTJ2HsvJoWSnQQTKZkH+TUskWyXNFx6NJ5p0L6J03l8IgaJEKHGZ2C8j/DKR7xJ5ldqUCiteIjZUAvxqiF+J8CsRXkXgu4Kqy6goMK8ys3GQuqlgWIk7RTO0JO5LU1HUVkdvUkYviMOIKEoElSiICYOamDLzHfT1mK/UoWJlzMSdkgoq2YYzxaoLKYkzJe1MyeQcsj2tzhQ7Y8nPQBLJGMjxxYQZ66gBNsmghkQimT0cx+HRRx+tz0skEslso6oqpjn9Fv2RDqDpioJr/qwUhiFhGOJ53rSe+3Q6f5rnpQA0t5hK785IEWeu9O6k85tj785ESXL6i639LbUOFxFvHL0+HkhEmpGjVjowvzY1reoBesgDeUAZES3Wh6jdvqo24WehbEFJh7IKxRClWITjijA8DMUiJw4PcUxxI24t8s2tDCeTV8L1Suxd9EEvQRiy3RCcdi81x49oiD81MWhxsfE8miPefC2ZBpsuwB5wSPpxDINVy+D7B5Qb4k/ztYVC8Mm74My7k8WnMx5Hv8sDBlDFaHfPyffC+x9K9t3gwIWHd+4A2nkj7FrLpouyDusXZMmYWTJ2Hi2Xn3oHkOO07f+RSCRj0+w6mag4Mpn7jHcuoRfiV0KCqk/sucS+S+RX6uJK6Pk1cSUgqPo1gaXJtVIReK6oxYMlAksYTGSwX6tNXaKAaalopoJuJDHCiqai1C/8UWpxX4I4pqU/JQqjjmJK6AtCPwSmp09FtzQMy8DMGDWXStqbYmJnG86U5s4UO5v0pqRF9EnEl0MmnyFbSCYn52wVnzkkkrmIHF+cGFP2iVer1QlFPkgkkrmPqqrstddes30aEolEMuOoqlqPh5pOUhfEdItAzYMH3QwmTIaJCD0T2SYFoE1Hc+/ORL4QjRR3xhJ1tsbenUR0cetCS7O4ItoIMfV5Jvl/qhRa+1tqHS6jel3q2wooyjgDUQ6JujMGKpCrTR0RAqpVdisW+WxN4KH5Np3fobHuqOEhnn90OIl8qwzjVocp+yVcr0xZF4lA4nngeSxaA2fc3RB/ykarC2jb4capuE0v37ECJTOZUkFk48IsLCuArrOuN+RHB6xtPIfm5yMEp94H592RrFqtVTjkHyvAegCssOHqyQTwtgfgY/ck+5YN+NxRo11C6f7LB2Cf/uSc4myG1YszZM0kBs7I5lGmKgJZlhSAJJuU1HUyVUGk2+XJXiwcR3Et8iqoxXw15gOvSlwXVrxaJJhPWBNW/ErNtVKN8d1EXKm64LlMoaBeYTyBRdNBNxQ0Q6n1pygoilr7H1cQKIhYIGKIopg4FEmHShAj4g7WFAF+NYYqJO9Jk48Q1gwN02kqobc79KY0daakooqTFtDn7borJVtInCmZvBRTJJItETm+ODEmJdREUcSXv/xlrrjiCtauXcuTTz7JTjvtxL/927+x44478qEPfWi6z1MikUgkEolks6HZBTGdNF9ROl1RcOltSrrfRB0V46Gq6qSdP+P1AEmmh82xd2esSLbxeneEqEI8CCOcLaKNENMQXSb5f6Fka2JKb5O4Mm+06KKk8z0oyvS+fkwripK4QxwHFi3q7i6AWZt6mzcIAa7bIvbsMDzMp0eKPs23uxdhWbLtwGKRl34xhFsp4npFXD0VdARlE3YYLMFgCYC+DfD/7mx1/TSLQGmcGkBVB0WAUJLn65kKngkbFQUUhQ3lPqguBWDIDvjp/k81nk96W5ve9Qhc/NtkXSkoc/BbysA6APQ4EXPSDqA3PgLn3pkcIlbgrGPb9/9kguR8D1wNaBpks7y42Maxc2TsPI6dR83lp9YBNANOVsnMkb6ubgrHyXRfKCKEIAriJkGlJqZUg5a4r6AaEFZDIt8n8mvCiufX7hfgV0I8N6pNMcG0fpQZ/T6iGaDrCqoOqqrAKIcKNYeKSFwqY4kpNaIQolBARQCTE6g0Xa3HfBmOUY/6St0pjZgvs96ZYtcjvhJ3Sr2EPp8hW8iSLSQuFcOcw+9NEolEspkzKaHmggsu4Ic//CFf+9rXOOWUU+rr9957by699FIp1Egkmzm+7/PlL38ZgHPOOWdG4oYkEolEMnGanRKWZY1/hy5Jr4ztJOJMRQRKSa+ITR0Y00UqAE2X8yddJ3uAukNVVSzLmtDf41R6d+I4xvO8CUYJCkwjwNA9TN3F1IoY+hCmNoShu5h6GUMvY9YmQ3cxdBdV6TRAZoI6b5SjRWkRWkZsU6bv/3WLQ1EavTBLlkzqEBpJ+Fs+jhPRp4PAs3h4mP83UvQpFmGgtn+1CEuTdTtvLPHyJeDpNdEndffUBJ4lpXWwMRFbshZ8SmkVfypNHUC7bKydqGVRWWBjUCLQkuceKgrDFgzXXm/WKwtB2wWEwMXnv/a7L7lvk/ADQBxzwpPwneuAKCIuDnPYh4cRSn/95+I0uXqOfAL+/ebGz+z/vQE00V4A2nYYDn8JMAzI5Xh+sYVl58jYOTJOASOTn1oHkD7lYI/NAiHEuEXx0yGiNPepbJLnFYu6S8WviSiRHyeTFxF4IaEXEVZDgmrUcLRUQ4KKh1/18F0Pv+LjuT6eG1Ith8TRzBWVKEoiqqiaUnOoKDXdRQEBsQARJYJKFI4vpqREAURBuu/EhBVVU1o6U0zHTAQVx8TMmNhNMV9WKqTk7LpLJe1NSaK+MnUhJdeTxbTk93eJRDI3kOOLE0MRQkz43XDnnXfmyiuv5Oijjyafz/Pwww+z00478cQTT3DYYYcxMDAwE+cqkWwyui152lKRZV8SiUQimQ6aC3mnOwpuEh9hJ0Qqik2X86fZASQFoLERIkp6WppdLtEAUTiI7xXx/TJ+4BH4Pr4fEQQKfqgThBn8MIsfZuvzUTz5iGZdjzENBdPQMUwT07QxzRyG6WBZ1lbbu7PVEEVQLnd29Yzl+Bm5rU0kYKC2ijmpq6ev2ujJqejw/f0Z1f1TNsC1NQ7b4HDms4sgk6GSs9nndY/ianEjBq3pteb44hK+99L+EEWIMGC7V91KjGgVgOIYhOB1L8DPr2mc6+6nw3CT1mhE4ISJqHPIK3DF9Y1tnzom6R8a2f+TCWBJCY55liSqLZfjuSUWmpMlYyUCkJMpoGQnGf+WzUIXDsvm96Xpdpi0u89skV7EICJRF1FCLyT0YiI/IvQigkpA4IU1ASURUzw3caZ4FR/fTYUUn2rZw3OrtdvpveBi1LlrNUFFVVBU6n/HDYeKQERiRoWdFEVVMKyGK8VM474cEyubOFTsrFWP+ko7U9KYr4YzJelNSTpTsmQLGSzblJ8HJBLJFo8cX0zodpx5UpeyvPLKK+y8886j1s/EVZISiUQikUgkks0TRVHqAsZ0Xz01lgOo3fxYIlDzfHpFshCipYNlOplO50/qsJqrAlDS61IcHSEmBhAjIsca24Zo11qskVSsODrJt5hR9TvKiGixPiLmEUTzCaJ5BFEBP8jhhxmC0MIPdXxf1B07zbcAYagShuBWIqBSm8a+IC39W59IPNts9u5IxkDToFBIpqkShlAqtYg4xvAwRrFIYQyBxxke5l+KRdjYtK1arR00Akq1Kfl3ePruJC6tqjeJO7XbnP88bHgeSPY5b23T9oyBmzVxHQPX0djb6YU37pZE29k2RuHXGGpIUHOZBbVpGNjYuwQWrQTfB9/n2uU3MKQGo8QfhGC/NTWhptZF9Pa3witNP15FJAJQ1oc9XmoVi/7tKBhwWp0/2Vpk3AIX3vwkRJZFnMnw9CKDyLKwdAdDdzCNDFgOoWURWBaR4xDaNpFtJ7dOsq2+vmk5msb+n2bHZjqfToqiJF0jXrOIkrpRatFf9cnHdwM812sRUhpTlWqpSqVUJQxmVixKRZW6Q0Ukr94irhXTT0JMiWvOlrbt9e1QSCK+7GYhZWRvSq0zJS2hz9rYOYtMzsHJO8ltziFbcMgWkr6UXE8Wy7FkxKpEIpFINhmTEmr23HNP7rzzTnbYYYeW9ddccw3777//tJyYRCKRSCQSiUTSCVVVZ8Q6P9IBNF1RcM2RNKlwNLH4rvGZTudP87xSv5pZgHDrQsuoXpcRQkyj12WSA4VKYXSMmNI3utelvq2AorQ6WlRgomn6qUjXHL82m7077bZ16t2RzFF0HXp7k2mqBMGYrh61WCQzPExmDMePVizykb8MJcdKDkprufhG4Nn60iPpXupod48VPgsbGvt+6mCTobxJKWNQdnRKtkbZUihbCtvlCrz0j3sTaRqRqsK8m9H1ClWl8RoRAMNC0JPt4bkTDkavVNCqVW7Y90FW2T5KkwNIARCC5QOJUKN5Hprncfqb4fGFrT82M0rEnR0G4cafNNaf/zp4uTA6Ai4bQE8V/vFxhdhxiB2HZxab+Fkbx8hi6Q6mnkUzMoSGQ6hb+LqJp5p4ikFV0alg4AoNN1QohyrDARQ9wXAlZtgNqbo+lVJDVJlJp6iiJO4QRYV614qouVO6jPlqx0REFd3Sm2K+msSUmqDS7ExJe1NSV0pSQp8IKplC4k7JFbLJfM6RYopEIpFItggmJdScd955vO997+OVV14hjmN++ctf8re//Y0f/ehHXH/99eMfQCKRSCQSiUQimYOoqlofOJ9O0l6WqYhAndalTDRqR1V8TN1t6Wcx9RKmUR6xzq1vU9Vw/AO3e/5kEDW3i6L2oWjzQJ03WnSpd7z0oCizU1isKEpdGOmWTd+7w5hCTqdtcjBz86W5hySybSLDIOrtnVJsV1ypQLGIUiyilssopRJqqYRWLqO7Lnql0vbWqlTIpuv8CrHmotZee95/rw90anBfBTxRX7o3fW5KEvOWCkAVHVA8tnNvI8pkiLJZzszNY0Nep+xoifPHUmuTwoJt8qz74nEohoFqGJjVq8jG66gIn4gYIQTVWFAVgmw2w9oTVkIpeb6/2/NB/laoJMJPXQBKRIcFLvzTXwWa66K5LuccA3dv2/qM1FrPz3wX/vSfjfUXvQaeWDC6/2d+ANsFcNLD4KFTQedvfSpD8zWUQIdARwl0RGjiC4MKOlU0XKFTrc1XavdLtrXehkr7/3EhQESippt3FlU0U8O0dQxHb3GnWI6JlbWxMlaLO8XJpe4Uu+5KyRSSAvo08ivfm8XJOTIaUiKRSCSScZhURw3AnXfeyRe/+EUefvhhSqUSBxxwAOeddx5veMMbpvscJZJNjuyokRmSEolEIpFsDtSLqsMqYbABEW0gDjcg4gFElESLKWIQhUEUhlAZQlOGk0mdnKMninX8MFfrgsniB9mmbpjGfPO6WIwWXdL4n+mKgmvuAdocSH93Yzl12gk+YTg5sQyS2L1uI9lk787YtOs66bb0fTK9KDPdy9WJ5gjLdrFdqqqiqSp6GGJUq+iuW7/VKxU010UvlxOxo1xGLZdRS6WGMFQsQqmEUizWHT9K3H0h+1gIINBg2IANhs4GU8dVNJaut3BJRJBbdw3oz0LRVCgbUDaS24oh0AKNI3+7grgm3dz0D0/Qv7RIZMREerJWIfGn5Csql1++LTYhNiGfe/sGHtnOr29PxR+FpOPnhUsb5/m+t8DvVow+f7vWA/TgFWDUfiT/cQjct2x0/08qCL37LwqoibvnqYU6Q1kDU3Ow9Sy2mce2cli5AmqhgNbTg9ZbwJzfh7mgD3P+PPTenvYdQPqkrvGVSCQSyVaOHF9M6HacedJCjUSyJSOFGvlCKpFIJBLJbCFElPS0tOlvEe2ixeKBpAdmUugdHC29CHqJRIGYHsI4TxjnCcIcUWwQho2B5Ik4haYiMnRL80ByN0JPt1Fwc6UHaKS4041zZypdS9307oxcnq3endShtCmK4uNpEhMmQ7uOk7YCygT3abfcTvgMg5BKLa6rObqr03KlVKVa7rxPtVSl6taEYyGwiMgQkiEYdesQkq0tp/NOfZ90v8a+0y3bBihUMBhSNQZ0g42GxqChMaxpLNiQwUXHRefBFR7r+gSurVK1FTxHoWorBLZA0VX+/vnXkLVUcobgmu3/xJO5tXhqiK+GUBOAAAyh8OgTb8YMPPTA4yM7PMhv+zaM7gCq8dylYNWMlWe8Ea7Zc/Rz0OJE2PnTf8K8Wkrjdw6E23dsjX7LNE0n/c0kbySizfOLTNb3mWTMHBk7T8YpkM304GR6UHP59kJP83y6nM0mXVASiUQi2WKR44sJ3Y4zT+qyiJ122ol7772X+fPnt6wfHBzkgAMO4Nlnn+1wT4lEIpFIJBKJZOsh6XUpjhZdxAAi3thWjEEM0XWJcgsK1OLFRk5KixAzr0mYyY05oD7dQ2jNLoSpRsGN3JZef5YO1k9FnGhH6iyYLudPswNoIqJGelzbtru+z2z27hiGga7r6LqOYRijRIDm599OaJnI8lxwnYwnkExVUJnI34sQAq/S2oNS7iioVBLBpOxRKY8twgT+DAquioKHjofOAN3/jbc9VE30ydZEnFZhJySjhOT1iJwWkdNCsmpIVgnIiGQfJ/axowArTCYAA4GBTyGG7fxKx5S39z4z3tn9DSwL8nk+9FgB8ttBoYDI5/B7cpQLDm7eppKz6X3tjlAoQD7PKcrRvEEtJjFxhsDVYlw1ohy4VCpFzDO/AOUylEoUHrmMbdbdTTl0caMqfhyAEERCUBQC56R/hGIVSiX+uvQhbl/Q3yr+NP0/vfVxn/zGjbBxI9/dGa7at/2zcobh1v+AHYaS5av3het2Gx0BlwpB73rKZr6eh2yWFxaZrJpnkDVzZKwcWaeHTKZAJtuLkS2MFno6LTsObCbOSolEIpFImpmUUPP888+3zb/2PI9XXnllyiclkUhmF9u2ueeee+rzEolEIpFIUtHFbetoEW2EmPo8A//blQAA/i5JREFU3ffGtKAURosuSt/oXpf6tgKKMrevTm4e0J5IB0w3xHHcldCTznfbB5Q6J9IemplwBU2n86d5Ph3Qb+7d6VYMCcOwRbhJl5t/Rs37CyFaxLLJ9O5MJxN1j0zGcTKW62SiRGHU4jqplr2xHSrpfHlsV8tmH6ChkBTQ2xqGrWE6KqatYjoKlqNgZcB2BJYjsLMxdkbgZBXsjIqT1cjkDTI5AydvksnZZApZcoUs2UIeK1NA1Qqg5EDN125zNQF7xOtTFCUCyPBwPaKtZX7k7VjbXDc5pucl0/r1zU8XqzbNa/PjOLw2jcJxIJ+Hwu9rtwXOz+c5v3BwXeQJ81ncgo2bs3AzBvZrdoaeHsjneY//PK8J+nF1gRtVcQOXsl/G9YqUy4Pk/+VTUA2hVKL38e+xQ/8duGGFcljBjb26sFOJY+x/PBGGIyiVeGrRo/xh6ZpW8afpb/KNT1WZv64K69bxi23g0r3a/xkYw3D9FbBPf7J8zZ7w031GC0CpCPT2Zx2WKYm489ICgxfm6zhWjqydb7iAsr3Y2V6UZsFnLBHItmEOuColEolkc0KOL06MCQk11157bX3+N7/5DT09PfXlKIq45ZZb2HHHHaft5CQSyeygaRoHH3zwbJ+GRCKRSCQzihBViAdhhLNFtIsWq4sunUqyx0HJ1sSU3iZxZd5o0aUePdaDoozudZF0RlXVaRd/gFEOoKk6f9Lb5uisVACaTWFjU6AoSosbpFncmSyapo2KXrMsa8yItqn07gghCPxwbPGkZblSF1Q6iSlusULgzXws4Exi2DpGKqbYKqajYjlKTVChJqQIbEdgZ2LsTISdicjkQpxsiJMJyGQhk9fI5g2yBYNM3sbJOqh6AZQ8KFmUuqCSr61riCvJcmZmYvc0LRE8piMWO0wEj2kRfarV5JiVSjL193d8WB0o1KaRHFibAMhkGs+1JvqQ/0R9+exCH2cX3lvfJnI5qjmbctbAtTXmL94Zevogl+Md6x9n/w1PUg7KDfEnKONWi7juEH2nnAqhAaUSPU/9nBVrftMiAIUigjgmEAL7hDfAkAalEs/P/xt3b/dyq/jT9FryuhcqLFtdgbVrub4PvrRHmycdgzIMv/g+vPbFZNUNu8AVBzUcPy0uoFDhrS9k2ClKBKBVCyz+tgAyVo6MlSfrFBIRKNuLk+3FyPeM7fxJ501TCkASiWSLRY4vTowJddSkVwopijLqA7VhGOy4445cfPHFnHDCCdN7lhLJJmZr76iRSCQSiWRzQ4igJrqMdrSMcruk24U7yUczW+PDmuPFlPZuF0WxpvPpSmaYNPJrOjtOxtpnc0NRlLq7pJ3LJI04S2PPTNMc0w2kKEo9rm46e3dELAi8kKAaElQCgmqIXw2IvBgRCmJfEAUxkR8TeiFhNSL0QvxKiF8J8Cs+fiXAK3u4TcJKHM1eP81U0E2tIaY4iTvFshVMh5o7RWBnwMrE2E5cE1Ji7EwipDjZkGwuIJPzyWQ9srlkcjIhmp6tiSfZRDBJBRUlD2oWZdS63Kh5KU5PgiCYnMDTbp0/yQsRxiKXayP6jLgdY1uQc3BtjbIBC7OLMLTkb+SpDU/x+PrHqQSVRAAKyrh+mbI7hOsO8a87n8yyOAvlMj977jq+u+q6egRcOapSjf26uHPdqiM5cIMF5TJX9j7DF3Z6saMA9NP/gZXP1+b3gU+8ofNT/+618KankvnbdoSvHd7q+nHSCLhI5S0vZdnTSwSgtfMsHl4syJhZMnaerF1IIuCcAtlsH1auFyWfH18EMuT/k0QikcwVZqSjJr3qa/ny5dx7770sWLBgamcpkUjmJL7vc9lllwHw8Y9/fEauTpVIJBKJpBNCRElPSxuBRbSLFosHkh6YSaF3cLR0iBhT+kBx5kSp/NZEc7fNRDtMxtun3f6z2XUy1aL4dh0mqXOleWqOOOvWFdR8vHTf6SIKYyI/IvLjRECpzYdeVBdSgmpIWA3xa8KL5/p4rke1nNx6ZZ9KuYpX9vErPkF183OpaIaaRH05ekvUVyqkWI7AcmoRX5m47k5xsokzJZMNcbKJmJLNemSyPtl8lUzOQ9faCEyKM8KZ0uxYqQko47pY5GvirGEYMG9eMk0Vz5s+0SeNiCyVkmnVqsk9PaAH6FHVhuiTz7NLocAubQWeHihsB+Vn6+vetdvbeNfBH2js4zjECCpBhXJQptfuBS35vnvs4PNsv+5x3MBtCEBeCbcmAG1/0ltAmQelEvmXb2Hvl3+JG1aSKfYoxx6RiEAInNe9GvbIQanE2sKLPLzt860dQHVi9l5TZM9nk88x9+4KH3lNmx+Gn0wX/xze9Wiy6p5t4JyjW90/qRsoE2u86ZUsB1Z6IZtlfZ/FPUvjRACycg0BKNNDJtNLJteHli+MHf+WzSbuMolEIukSOb44MSbkqGlHtVqVGXOSLY6t3VFTLpfJ5XIAlEolstnsLJ+RRCKRSDZXkl6XYltHixgROdbYNgRM5iOqMiJarI3oovS1umGUnBxgnCTNg/Uz7TiZTddJJ+fIVAWVTkXxc5VULAuCgEq5SmmojDvsUq5N7rCLW6pQKVZxa8X0addKOnmuh+f6yXzZx3d9Ai8RX+Jo8+lSUTUVw9GTuC+rdmsb6C3Leq1bRcXMKEmPSiYik4nqQkouVyWbcyn0FMn3DNObHyLjFFHVbhw7WhtnysielW5cLJOqrZVIOiNEIvpMJdKteT6eZgebpk3I1TPmNstqiS0TQhDEAW7gkjEymDUBaHVxNY+te6whANX6f9zyEGV3gHctPZY9tMVQKnH7qj9y4Sv/hRtUEhdQXKUce3giBCH49qoD+Pu1vVAq8evMy3x4n+dbO4CauPC3cNIjyfzvd4B3/mPnH8t5d8Cp9yXzjy2Ef3nT6P6fTABZofOGNVleW0zi7YZ6LO5YFuBYWbJWPhGBMj2JAJTtJZedl8TAjdUBlMnAHH7/k0gkk0eOLybMiKMmJY5jLrjgAq644grWrl3Lk08+yU477cS//du/seOOO/KhD31o0icukUgkEolEIpmbJKKL29bRMiperHk7kxxkVwptHS1tnS5qHygFFGXrvdIzHUifbsdJp+V4ugfPJsB0CyRj3WdzFvKiKKqX0nfqR+lUUO8OVygPlXGLlVrPiodXSZwqk9JRNzGKqjSEFEcfVUhv1F0qSt2hYmeiuiMlm/PIZj2yeZdsvoplq3UhRqgOQWQShFn8MEsQZvCb5oMwSxiPfzFjtTZtKAJNpkBdizANME0Fw9AwTQPTMDEsB9PIYto5DCOLZVnT0rsjkUwrigK2nUyLFk3tWEIk3TvTIfoUi8nxoggGB5NpqhhGi3ijFAqY+TzmCIFnaT7P0hbxZ9tkfknTutpV5it5HSv59KiHiuKISlhJxJ+aAHSIu56f9j/aKgBVh3HLg7juEHu99TVgbg+lEpm1D3Lwyz+hHLi4YYVK5FGOq7jCJxYx2f32gUULoFRiwFzDk4ueaRv/BiFLBod47SNDADy7BE599cjfG1BOpjPvhk/elax+vhdOeuvoDqBsABkMjurP8neD8yCXo9zjcNM2FbJmLukBqsXAOdkkAq6Qm4+V6+0c/5bLJX+Dm/F7uEQi2fqYlFBz/vnn88Mf/pCvfe1rnHLKKfX1e++9N5deeqkUaiQSiUQikUg2A4So1npdNo4WXUZGi9VFl0lm2CvZmpjS2ySuzBstutSjx3q2iL6C5oipqYoj3ewzW7RzncyE40TTtFGF9FsCEy2or5YbJfSloTLlIZfycKW+zXM9/EpAGMzt/htFAd1KxA8zY2I6BlbGxHRMrEwyb2V07IyWTDkVO6PgZJPYLycbkskFZLIemaxLJuuSzQyTzQ9imUMoogiM32XTLbFQCSOHILQJI5swtgnD5DaIbMLIIYys+j5emCMMHYLIIYwtosgkFgYw9pXjYaQRRuBWIRnxrGUeUQLWdfhZKui63tILlE6WZWFZFrZtt6xP/58kkjmLoiRui0wGliyZ2rHiGFx3apFu6W2plBwzCGDjxmSaKpY1pqtHy+fJjdi2oFBgZaEA+fnJuiX5ZHubfpqDWMn/8a+j1gsh8CIPVVHrAtA+1SH+q/+Rejxco/9nELc8yIEn7g/WTlAuY69/nNe89MO6AFSOqkkMnPAIRER2z90gtxjKZYbUdTy34OkOAlBAoTTI3z0wCMDaPvjYISN/hySidhE++CCcf2uyen0GTnh3Q/xxwlYB6PCNOd6yfgFkswT5DL/cvkzGyJCx8mTsfNL/4xTIZHsp5OaTKywYuwPINKUAJJFIZoRJCTU/+tGP+M53vsPRRx/NqaeeWl//qle9iieeeGLaTk4ikUgkEolE0h1CBDXRZbSjZZTbJd0u3Ek+mtkaH9YcL6b0tXW7KIo1nU930jT3c0ynw6RT78lccZ3MdGzX1jTYG8cxnuvVY73GE1VSMaU0UK45VKpUSpUk/qvmUAm8EBHPXZuKbmrolo5pGxg1McXOWTUhxcTKJPNOzsbOWNg5EyejYmc1snlwMgInH5PJRIlTJe+RyZax7RKqUoK4BKJUi0ms3Yry1E565I9TyY7oXmmNAlPq/Sx5ULNN2/P1eQ0LXVEwa//f3fT6TGTbVF4vhBAEQUAQTEyYSnuR0v9lXdfrk2EYGIaBZVmYplkXegzDqL8G6Lq+1b0GSDZT0p6bWgTPlIjjRKzpVuAZS/Rxa5/FPC+Z1q+f+vnZdteRbkqhgD1iW0+hwGsLeyfL47j19uD1XMO/tN0WxmHixtYS4WiFX+JX/Y8l4k/gJgJQeYBKeYhyeZDD3rQ72LtCqYQ28BxHvPj9ZL+wghtXcSMPV3i4BGR2WQ7aNlAqURIDvDjvqY4CkPngAG95agCAoQz860gBKKhNw/BPj8FlNyWrPQ0O+3Ab90+okMXgoMEsJ61dBLkcIpvhpzsM45jZxAVk58g4aQdQD725BfQUFo3dAdRGYJNIJFsXkxJqXnnlFXbeeedR69PMYolEIpFIJBLJ5BEiSnpa2ggsol20WDyQDGxOCr2Do6VDxJjSN+3F0c1F8TPtOJlt18lE47cmG+G1uUd2TRdhEHYvpgyWKQ4UKQ2WKQ9XqBSTvpWqWxNUqgFhde66VDRDRTf1uqBiZgysjIWdq00ZEztrY2ctrIyViCpZm0zexsk5ZHIOds4im1PI5iGTj8nmAjKZKqpSqYsoQhSbBJXBRGCJm9eV6NrF4nWzk9FaZF8XVxrrlGYBpi6uNIsy2WmNRUz/x4xpHlQTQowp5rQTe4IgIAxDfN9ve99UnB7rMdN9p/pdvvn1qJ3g0yzspLft1jVvm8u9TZKtGFVtiB1TJQwnJvqMta1aTY5ZrSZTf//Uzy+TmXSPj968LZcjZ+Y4dNtDu3rYHYBf8JG224QQRCICNRnSXBp6XN/cAeQVccuDlIsbcd1BXnXccvjiHsnPeXAVR7/0vWS/0MWNqkkEXOzj4pPZcRkcvQOUSpS9Qdb01BxALUJ64nQM/+pz0t8SASjQ4OyDR5yoV5sG4A3PwA/+t7HpwI+AGY3o/4lUMhjsU8zw0VeW1IWcn203iGo5ZK0kBi7rFMg4PWSzvRSy81jQs3TsDiAZjSmRbDZMSqjZc889ufPOO9lhhx1a1l9zzTXsv//+03JiEolEIpFIJFsCSa9Lsa2jRYyIHGtsG2JyJRDKiGixNqKL0tfqhlFybQf04zgmTMWOKCbyU6GjQhSVplVEmQuuk01RFC+Fk84IIfCr/rj9KcXBMsUNRYoDJUpDLuUht9afUsUre3iVgKDq4/sh4Rx0qaiaimZqGKaGYRuYjoFZc6U4OQsn72Dn7ERUydk4WTsRVHLJbSbnkClkktu8Q7aQJVvI4ORsdD1qCCZ18aTU5FApIeJizU3XvL12K0qjXSwhMDyFSpoWQWXkfJOLZVTJfUOUmStuvE2Boih1UWM6aRZjUhHH87z65Pt+fQrDsC7+TLSTaroEn2aahZ/JCD2d1kkBSDJn0HXo7U2mqRIEU+vxab71a1G3rptMa9ZM/fxyua4Eno7bUhdQNouuNoYzLd3igKUHdHUKC4Cr+UDH7bGIQUleH/JRwE3rH2/q/ynilgdwS0kE3C7HLILP7AulEuHwBo596QrKgUslrFAOK/UIOFf4ZJctgJXLoVwmKBdZ3fNkG/dPDHgUn/H46OMD9bXnfBy85tHbSm3aCIe9BP/zX41NR3wAKkbDAZQJIBurZDDZ1XU464VldRHnF9sOEdlmIv5YeRw7RybTQzbTS09uPkt6tukc/5bJJIKlRCKZViYl1Jx33nm8733v45VXXiGOY375y1/yt7/9jR/96Edcf/31032OEolEIpFIJHOCRHRx2zpaRsWLNW9nklfgK4UWJ4tQehFKDzE9RKKHWBSIRIEwzhNGecLYIY6VLsQRnyhaRRS91FFQGesK7JlEUZQxxY/pLI2XrpPJ0amgPl1XHiwzuGGY4sYSxY1FSkMubupQKVfxyj5exSPwAgIvIvRDojnkUlFUpe5OMay0hN7EyprY2cSdkik4OHkHpyag2FkLJ5sIKolw4pDNZ8jkHTL5DNmeDNlCFsMY/fVLiDARSFpEluYosEFESyxYs/hSBL8EG0oIwmn8KZijBJNRLpY260a7WOQgzlwg7a/RdR3LmrzwFcdxXdAJgqBF7GkWfNIItlTsmQqpSDTdyR3pz2Qqok8nB5B8X5HMGoYB8+Yl01TxvM7CzkTFn7D2/lQqNfp9pkIaYzcV0Se9dZyWvhm16X3L0Az2XbxvV6eUAa7i5I7bhRD1x9FEzC3r/5YIQEEZ1x2iXNqIWxqkXB5gu6N64eP7QbmMKBY55uXLcf3UAVTBjRIBqCIC8gsL8LoVyc+1XGZV39NU1LiNAFTlkFeqnPVYQwD68qmwLtu0i1ubgD3Xwc0/ahzh2JNhba5J/PEhIzSywmAHz+HfntuuLuRcs80Q5YxOxsiStfNknDxZu4dMtod8dh7b9+3YOf5txO9DItnaUMQkv4XfeeedfPGLX+Thhx+mVCpxwAEHcN555/GGN7xhus9RItnkDA8P09PTw9DQEIXpsDNvZkRRxJ133gnAEUccgSatshKJZAtFiGqt12XjaNFlZLRYXXTxJ/VYsXCImoSVKM4TRLlkCnP4YRY/zOIFGTw/g+fbRE2iy2y7TjaF40S6TqafwA/aOlTKgy5DG4YZWp8IKsMDJdzh1KGSiC5eLfIrqIYEXkAURIRhhIjmgEtFAc3Q0A2tpT/FdBqCipO3yRQcsj0ZMgUHO9twpTg5m0xdSHHI9mTJ5jNkCxkM0+jq7zARbisjulVG9qykLpZWZ0uri2WyXVEdfjBjuVjUPIqST/paWrpaWkUWRTGn8ZwkWzNpd06za2es5XR+KhcLtBNL0ojNmSa92GAsoac5Gm4850+zA0i+P0o2S4RIRJ+puHua56f7/1jTJifwtFtnWbMuMjy14alE/PHLlN1BKqUB3OIAZXeQBbHN32cPrIs6Z73yfTb4g7hBBTeqUI6quMKjLHz2Kmf5rwd3ru+7/7HPsdaORghACXusg1uaRJ0jPgDPdNAKtx2Ge77bWP6Hd8CT82vdP3UHkE4GnUWRzYVP7lgXcn65bIiNGaUeAZex82ScHjKZArnsPHaZt3Pn+Lc58LvZWpHjiwndjjNPWqiRzDwrV65kv/3249JLL53Q/d7//vczODjI//7v/87IeXXDD37wA84880wGBwdn7RymwtYu1EgkkskjhGDDhg14nodlWcyfP3+z+2ItRAT+fRCvA3UhmAdNa7b/TCFEgIgGENEG4nADcbQREW1siRdTxGAyMYTKEKpSndRjRbGOXxNXgjCLH2TrQksQNuab18Vi+qJsRrpOxhI/piqoTGVwyPcDrvvWb3j2r88zf9s+jj/laBYvXbzZ/U9sCoQQVF2vRVApD5UZXF9kaN0QwxuKFAeKFAfKlFOHSpOg4lcDgmpAUHOnREFEFMVTyKyaBpRa1JeuJmKK2XCnmI6BlbXqcV+poJLryybiSU1McVIhpZBJnCm1qC/LNqf0d5S4WJq6VeqOllRoKSYulhbxpUlcSUWXybrl2mKNEFdGiCgtLpbmiLBmUSYjXSySzZ60q2csIafd8lSEmGaBpPm9r/l1Ju37SV0+cRzXXUObQgSaivNHVVVc1yUMQ2zbZsGCBfXt8j15fLaEz/dbBEJApTI10af5drqHQ3V93Ni2rreZc+uCiecHn0/i3/wy5fIAbnEjbs0FlIs03po9qO6Y+tyan/OKtw43dJMIuMjDjT1cfLapGFx/3671fY885iWeKoRtfxfLinDfdxrLb3o3PLi0/fkVPHjim43lk94K9y1r6gAKUwHIoCBMrnh8RV3IuXbJEKuyMRmz5gCy8mQyBbKZXjKZHvaYtxtKrWdJZDL1eXK5xMUmaUsUR/z5lT/TX1zDoqdWcWi5D23ZNnDEEVtlb1K348yTij5rplQqjfpQIge2Z5fLLrts1uJKJFsOq1ev5sorr+SjH/0oS5d2eDeUSCQtrF69mscee4xqtTH4b9s2e+2112bzfySqv0EMXwBxUw61ugQK56LYx07qmOlgxsRK332Ih2uiyiCKSIQVTR1CU4bRlCKaOoyhldDVEoZewtAqACiAVpvaMuJ7dRyrIwSW3LiiSxSbow7UVvywNHKZmSmKn+t855NXc80l17V0hPz8S//HwW/bh9Mv/fBm8z/RjiiMGmLKsMvQuiEG+ocY3jDM0PrmuC+X8nCFak1Q8SuJoOJXA0I/JAwioiAmjuJZ7VJRNSURVAwF3VTRTQPDthIxJWPWiuctnEIimOR6E0El11sTVUZ0pqQOFduxpv1vtRE/OJiU2sflRgRYTTwRHZwtrS6WyjSeldrGudIqoiht+lla95UuFokkpbmrJ5vNjn+HGlEUjencaSf6hLVIpjAM6/Pdous6pmmSyWQwTXOUSyZ9v24WfNK+oFTgGXk7cl3zWMtkzrEbptP503y7pQgZW8Ln+y0GRUm6UTIZWLJkaseK46R7Zzr6fNI4tzCEjRuTaaqY5tQi3dL5fH5axIQde3fset8vjBEBN5KfF1dT9IuUq8VE+LnlJtyrv49bGkRPX/7mz4d3v5ujtn+Z7bxVVIJaB1BUpRwnHUB5XYFDd4VyGUolhvOvULQCii1pnyEQkvcq8MAD9bU/exvc0VdbqNamoWRREfDyJY0jfPTNcPNOjQ6gbKiQEXoyKSbf+esKzGwBcjluXDzMM7kgEYBqHUBZp5D0AGX72HPebuj5HsjliLMZ1Fy+4QDSpzxcP6v8+qlfc+4t5/LMur8ReVXUWJDdCBfdCidXtoXLLoO3vnW2T3NOMqnf/HPPPcfpp5/O7bff3vJmJYRAUZQp59BKpkZPT89sn4JkMycIAi677DL+/d//neOPP15+AJVIumD16tXcf//9o9ZXq1Xuv/9+DjzwwDnzv5ReDTpSIFH8m8mEnwZaJQgRrUEMfIzV7mcY9g4dJaqMJ8IIEaNrVUy9hKmXMfUyRu22edlJl80yhu6iKBMfuBZCwQ8zdYEliPKEcS7pbxF5orhQ63VJel6E0oui5lDVJrHE1FBtFVvTyHYpqMhIkla+88mr+e+Lrh21XsSCe/77L1waXsmZ35z5CwGaC+oH1w8zsGaQgf7BetxXcaBEabCMO1zBLaaCStKfkjhUwnp/ShRGxFFMPEuxX4qqJIKKrqEbKpqR9KeYtUJ6K2MmDpW8XRdKUkGlZ0Ehif+q9aVk8g5Z+yEy8edwMlVUtfk5JX/HSu83Ji3OdkKIYEREWJOjJRVZOrlYmu8zrS4We5Rg0upiyTUV3jeLLE37Khn5/y+RzAE0TcNxHBzH6fo+I3t3uo1ng8kJJ5qmYRgGpmnWJ9u26/Mjt+m6Xh9jaSfmjCX6hGGI67oUi8Uxzyk9znSjquqkOn+66QHaVGxOn+8lEyTtucnlpn6sOE7EmukQfdxaHKrvw/r1yTRVbHv6RJ9pdkIszS9lKbX/oV/+Ek7/+mh3zcaN8M1v8olrrul6cP8qdz3D3jButYhb3Eh5eD1uaYByaQBRrcDxh9ZFndetv5EFlZeSDqCwQjmq4MZJB5AQEcrBu9Uj4MrZNVR1n6oOG4DEuh7Upgr6fQ/U3ez/dwJc2/v/2XvzMMeqOv//de5NbvZKKrVXbzRN09AL+yqiuGADiqO4sozrODrqqOOIfgW/g/jFcWXEEVH8IYiOgIigouIGog6yI1uzNPRCL1Vd1ZWq7Ou99/z+uEkqqUqqUtVV1dt5Pc95ktzl3JNUJbk57/t+f3Dq/+TKbbxEEM9/C0LlRO2LXwe3rQafWSMA2S4CuPBjcPXGw4h4HFHnD90png7lCLgD+L1BAt42/L5yC0RY17EaIxSBYBDT70UPhREVAWgBPj9/88JvuOj2iygUsvjTBXQbkhoMdcCHz4Po7Ts4561vhRn8PQ8mZiXUXHTRRUgpuf766+npUfEVC8Wvf/1rLrjgAq655houvPDCpttNjD4744wzWLduHbquc+ONN2IYBldccQUXXHABH/3oR7ntttvo6enhW9/6FmeffTYA9957L6961av41a9+xWc/+1k2btzIMcccw3XXXcfatWtbHvPvfvc7PvGJT7B9+3Ze/vKXc8MNN1RPZGzb5oorruB73/seu3fv5sgjj+TLX/4yZ511FgBbt25l+fLl/OQnP+Fb3/oWjzzyCGvXruXHP/4xiUSCf/mXf+G5557j9NNP54c//CFdXV0t9auYnmKxyFe+8hWAOS/cqVAciEgp2bBhw5TbbNiwgd7e3qbfmY1cJ9M9bmWbZvs0GAGvOupL4J4c3yuEc77c7voOjz8bRdfMGoHFEV8CrgyGt0aEcdeLMJqYXSSIZQewZJsjqhDGFhEkEdAiINpBjyK0doQeRdM70F0R3C4Dz37iOjkQKRZL/Owbv5pym7//4hmeeP8Tde8Jy7JIjaaJDY4yNpxgbDhOciRFctQRUxxBJUsulS8XpK+vn2IWrRpBpVyLYKE1FeFMTGm6QHfrTg0VQ68RVBwxxRvwODVSynFfofYgoWiAUDREuDNEMBysxn1VCtO75vDKOikt5O53gd3IVSIBgUz+J3heixB62cWSmSCupOuiwhq6WOxypFhlGbOLGmyMVuNYaRQFFizXYqmvzzLR+SKEiqxQKA5mNE3D6/Xi9Xpb3mdP6u5UzsVqL3idDiFEUyGn8tjn89U9dpevoL/77run7Nvj8XDqqafWxbi1IgJNta5CRQSbayoXysyV86c2Hq6WuTi/VxwkaNq4oLGnmGZz0afVOj6V28rnTD7vtOHhPR+f379nkW6V+8FgvWhgWfDxjzeOo5PS+UH6iU/AP/xDS2JRp7+TTn+n82AaLfVfeE/LT/+afIJEIUE2myCTjJFLjZJJjZLNxMlnk2h3nlwVdV4WuwdffgvZUkUAypO1C2RkgSwl/OsOh7QjFmWDuynpRUo6JD0wUQDSHnkUCs4Yfn8m/PgonOuWMuVWw8Pfg0Vlff6Lr4TvHe8IQIEi+C0Nv9QdBxAG/7VpBYvcHRAI8OfODA+1pQgYAXyeAH5viIAv7AhA/jDrOlcTCHdBIEDJ70EEQ7jaIuDzVScQLNvi0rsvpWAWaEsXEeWfYzkbKEDJA597NazfJNFn8Pc8mJjVL74nnniCRx99lFWrVs31eBRNuOmmm/jQhz7ETTfdxBve8IYZ73/jjTfy6U9/moceeoif/OQn/Mu//At33HEHb37zm7nkkkv4xje+wT/+4z+ybds2/H5/db+LL76Yb37zm/T29nLJJZdw7rnnsnHjxuqJ31Rks1m+/vWv86Mf/QhN07jooov41Kc+xY9//GPAiWi78sorufbaazn22GO5/vrreeMb38iGDRtYuXJltZ/LLruMq666iqVLl/K+972PCy64gFAoxDe/+U38fj9vf/vb+Y//+A++853vzKjfWgqFAoVCofo4mUzO+DU+EBgcHGRwcJBcbnzi5vHHH69eldbX16euGFIoGhCLxab9wZ3P5/nLX/6CpmkNBZS9FVlZqXXSEXoRn5GYYjvweeKcdfz/QROzvOpS+EGLgoiA1l5ujtBCbROV+2Hcwo2aRt2/uPOa32FbUwtz0pb81xuv5xviBqRtz3lE+HQITaBpAs2lobvGBRXD68btdeOpEVT8bb4JgkqQSFcb4a4wbdEQgbJ7xR/y43bv+zEFUlrI/B/q4w0nbwX2IHL45UiKjtjCHNZfEL6yoNKkoH1TF0ugvF8IhE9NjCkUir1CrXDSKrV1d2Yi8ti2jZRy0u/VVtB1fVqnTKFQIJ/P09nZOaO+m1Fxbc9E2GlVGKqcK1cubprrCwqFEHUijpSypfP7WCw2Z6+fQoHLBZGI0/aUUmnqGj0zcfxURNds1mm7pjqPbJFgcFy4Adixo/m2UsL27fDXv8IZZ+z5sWdJ2Bsm7A1DmGkFoHfxPt7VYr9fK6b5j3ySTDZONhlzHECpMbLpMbLZBP5bj4NMDtJpTon/DXIvkC1lqxFwWStPVhbJUiJ4xHKIO9tmAzGkKJJ1Q9YNzvm8TUUAsh97DMrTn395BXznRMb1oXT9GO++EY4sG7yuPgW+dhq4LQiUygKQrVN0azzdUSBY0rBtSUEHV83XkF6C56Pww0Vw1vbt9O3lv+e+yKx+TZ544ols375dCTULxLe//W0uvfRS7rzzTl75ylfOqo+jjz6az33ucwB89rOf5ctf/jKdnZ184AMfAKgKHU8++SSnnHJKdb/LLruMM888E3DEnsWLF3PHHXfw9re/fdpjlkolvvvd77JixQoAPvrRj/KFL3yhuv7rX/86n/nMZ3jnO98JwFe+8hX+9Kc/cdVVV/Htb3+7ut2nPvUp1q93ojc+/vGPc/7553P33Xdz2mmnAfD+97+fH/zgBzPut5YvfelLXH755dM+pwOda6+9dtLr8NGPfrR6/7LLLuPzn//8Ao9Kodj3afWH83TRE3tKJdN94hWVU91WriAUhVL1JG0qmoo0og3cq8G1GqEvrhdeyk0IT+N9FQcUA5uGWtpO2hI5F5YXAbpLx/C58Aa9BNv9hLvb6FwUpWdZF0uOWMSha5fRtajDEVMM9wE5we+4XpJgDYE9XL2V9lDNsmGwR2g5OkzGJizQJ8d+1blZyi6WhkXuK/cDysWiUCgOOmrr7syE6eruNHLuVOLYWo0ze/jhhwkEAtXYuInN4/G0/L1ZuQBIn+MrpGtje+fK+VO5XxGAKk6pmQpAW7duxbIs2tvbZyTeKRTzjtsN0ajT9pRCwRFtZhvpVnu/EhmZTo/X92mVwcE9fy77IEEjSNAIQls/TFN+6S18gLe02O/lZp5P5ZNk0qPkEjGy6VEyyVhZAIrTedoayJYgnebE+CPk88+TKWUdF5CVJ2PnydpFMhQJrVgEYWfbTGAUKFHSIa5DvCwA5V1gCchrNkk/2ALHCVT+k2dsQMD7AnAZ8PkD9O+5J8xKqLnuuuv40Ic+xM6dO1m7du2kk42jjjpqTgangNtuu43h4WHuu+8+TjzxxFn3U/s30XWdjo4O1q1bV13W09MDwPAEK+Spp55avR+NRlm1ahXPPvtsS8f0+/1VkQYcN0al/2QyycDAQFVsqXDaaafxxBNPNB17ZZwTxz6bfmv57Gc/yyc/+cnq42QyyZIlS1p6ngcSH/zgB3njG99ILpfj5S9/OQBXX3119f9AuWkUisZ4PK0JEO3t7dWrG5tFlO2Js0ZKWf2xPlOioU2csifXX8gkFB+A4gNYhLDpQYpepNYHWh+4+hF6P5prEbq7F01XE7UHKv0relraTnNraEI4dV9sOfuYMglWySJXssglC4wNJNi+oclJv3AmkTRdQ3NpuFwaLsOp9eL2OnVevAEP3mClzotT46WtHEkW7g4T7Y7Q0R+loy+K17cw4qOUubLIMlS9lXXiS3k5rV5tLWjpBQ99HuE5ZVxkwXtAilwKhUKxr7IndXeGh4d58sknp93esiySyWTTVIlKPJzP58Pv9+P1evH7/dVxeb3eORdmJlIrAM21GFLrAKoVckZHR3n++een3X/Xrl3sKrsL/H4/kUiESCRCe3s7bW1t8/7aKBQLgsfjtD11j0npiD4TRZy//Q0uuWT6/dWc1Izwurx4g166gt3TCkBn8UFaLRjxGavERwvl+j/x3ewe28FP/v4jfjzwO6BIQXd+begSfIybcwKa8y9wdQbnWOrvOYlZCTW7d+9m06ZNvPe9760uE0IgpawWulPMDcceeyyPPfYY119/PSeccMKsfxxPFNMqV/TUPgaa1C2YHY2OOZsJyEbjnLhsT8ft8Xhanmg9kKlEm2Uy4yGXxxxzDMcdd9xeHJVCse/T0dGB1+udMh7B6/Xyspe9bNrP8YpgM1WtmZnWpZlq3wqjqUPJFcN43YlJNWrAOaHKFyPc/9xH8HmSeI0xfMYYPiNed9/tyqGRQiMF8kXnwn0Lxz5deY5SI1cMky+1ky9FKZodFK0OilYnpt2JaXeDFqpmkFcKx1buz/SxpurVLCjnfng937v4R1PGnwlN8G93vJf1Z6+ve08UCiVGd40SGxhjbNcYY7sTJGMpEiMpp0ZNIksmmSWXLteoyRYp5kuUCiZm0SzXqLGxLbvsMJlwYOkImpbt1LNx/i1nFiUz+cmApgmErqHrGrrbqUnj8rgxasQfX6hSkyZAqCz+tHUGiHQKol2Szp487Z1JXPrIuPhSEWLkDCJZRQT0btB6yrfdCL0HtG4o30oRgZEznWM0FGwEaL0I/zsQQk0wKRQKxf5ERVhZsmQJGzdunPb89KSTTiKXy5HP58lms+RyuWrL5/PYtk02myWbzRKLTXRaOng8nqaOHJ/Ph9u977pZNU1rKP5Eo1FeeumlKV8/t9tNd3c3iUSCdDpdfZ0GBgYAZ66ira2tTrwJBAL77GuhUMw7QoDX67Tu7vHlL385XHMN7NzZuE6NELB4MZx++sKNVdEUt+4m4o8Sydok/vhLXr/tc6StHG5A9zu/LtqK4DWd+xWhxnLDkTF4107QlyxRf88GzEqoed/73sexxx7LzTffTE9Pj/qSmUdWrFjBlVdeyRlnnIGu61x99dULevwHHniApUuXAjA2NsbGjRs58sgj97jftrY2+vv7ue++++ri3O677z5OOumkfa5fhUKhmAohBGvWrOHRRx9tus2aNWta+r5cSFFBSllX2FbmgfynyqXEa7dzbtPiYyw/7NQ60SdlWcRzFlbaeYydRtd249Z2Y+gxDH0EwzWK1z2K1xjD646jaTY+zxg+zxiwueHYSqaXfDFCrthOrthOPhchW76fK0YolMJI2foEcu2VmDMVeWYjEB3M50aG4eYt//YGfvr1Xzbd5th/WM3Rxx496XXyeNz0Leuhb1lrrpxWyGVy7N4ZY3TXGGPDCcaGEiRjSVJjaVKjGbLJLLlknmw6RyFTpJgviz95E7PkiD+2ZWNbEmk3KnAKtiXBsrCwIFciN3mrGSFEuY6OHkJ3taG7BW5D4PZqGF4dj9+FN2DgCxn4Q36CkRChaDuhjk7au6O094Rp747QuaiDcGcb2oSreQUg2y5Fxj/GZHeN8zcRbZcokUahUCj2Y1o9P21ra6OtSRF027bJ5/N14k1ty2az2LZdraETj8cb9uNyuaYUcrzefc+x2crrd9RRR1VTJ0qlEvF4vNrGxsYoFoskEgkSiQQvvfQS4LwWFeGm0rxe74I8J4Vin0XX4ZvfhLe+1TkRrhVrKp8NV12lCs/vA+za9SIP/eIa3viL5+APfyBsmhzzNtgZgvNzK2g/8RX8a+EOCq4cerqAZgMa4Aa3CVfcA7oU6u/ZhFkJNS+99BK//OUvOeyww+Z6PIoGHH744fzpT3/ijDPOwOVycdVVVy3Ysb/whS/Q0dFBT08Pl156KZ2dnbzpTW+ak74vvvhiLrvsMlasWMExxxzDDTfcwOOPP86Pf/zjfbLfg5Xe3mn8kQqFAnAcaccffzwbNmyou/LO6/WyZs2afTI6UAjhxEBpmuNU9L4Rmfcgk1+sKzQu9D5E2yX0eNezJ9PnUkpsq4RpDiHNAaS5E2kNgL0LYe9Ck7vQ5BCaSOB25XG7dhHyNy5UKaWgZLVTMKPkS1HyFRGnECFbCJPJhymWPFQmnStFfCu57fNNRbBpJvrMtTi0r01u/PNX/xGA2/7rzjpxQ2iCE9+yjo9e9U8L9p7wBXwsPXwxSw9fPCf9WWaSTHwbIzu2MjIwQGJ4hPjuBMlYmuRYnky8SDZpkk1DPiPI56CYg1IBSkWJWZTYpsSyJNKSDS8alBKkJbEtiTnzJMOGCCEQuhP7prs1XG4XbuNU3B4Tw2vj9YPXL/GFXATalxGMbiEU/bET+9bVRrQ3QrS3nc7+KMFwQLnUFAqFYj9gT89PNU3D7/fj9/sbrq/UdZnoxKltlbo5qVSqab1GIUQ1Xm1iq8Ss7Y0IsZm8fm63m66uLrq6ugDntcnlcnXiTTwexzRNRkZGGBkZqe7r8/nqhJtwOIzLNavpOoVi/+W88+C22+CjH62vRbN4sTOpf955e21oBzvFbIrf3fZlfvL3/+FefRvChlP+At0mcNxxfO/ENxF+x7sR5Qv92194K5fefSmbxPNYhTzYEmLwjXvgnPwSuO0q9fdswqw++V/96lfzxBNPKKFmAVm1ahX33HNP1Vlz5ZVXLshxv/zlL/Pxj3+cF154gWOOOYY777xzzjJhP/axj5FIJPj3f/93hoeHWb16Nb/85S9ZuXLlPtnvwYTH4+FXv/oVAMuWLdvLo1Eo9h/6+vro7e0lFotRKBTweDx0dHTsc5PoUyG868HzWig+AvZu0LrAOGFOrqwXQqC7DHTXEqB5HTBpZ8EeBGsQrAFk+dZZNgDWIEKUMFyjGK5RQs0uQhR+pNbnNNGDRQ82PZiyG9PupGR1YEt9VrFxjbarjeGc+Hi+aSQMzZd7qFVh6J+/+o+854p3cue3f8fmZ7bSsbidcz7wGnr69k03tpTFSZFjsloTZhjKNWGEzBAEgr1wyHTXMmgd5cixchSZ1o2oxpKV48i0KLYtScRSjOyMMTo0Rnw4URV/UmNpMvEMmWSOfCrvxL5VIt/yJUrVyDcL25RI224i/kikKbFNG7MABZopQCbwfLlNjdAEmibQXE7km8ut4/a4y/V+3Hj8Hnzlej/+sI9QJEioI0hbNESkO0y0t51oT4SuxR34Aq3XX1AoFApF68zn+akQAsMwMAyDSCTScBvLspq6cSpunYqokcs196QahjGlK8cwjHk5v5jt6yeEqIpc/f39gHN+mEql6oSbVCpVfe6D5clpIQShUKhOvAmFQvvk+ZNCMaecdx68/vVONBrAz34G//APynmxN7AsNvz6B9xy79X8zHySuGFD+c9wcjJA7FPvpvv8j8GqVUQm7HrOynNYv2I9D+58kMH4Trb88m4ON4Oc852z4Ywz1N9zCoScRdGQ733ve1xxxRW8733vY926dZNqkbzxjW+cswEq9g733nsvr3rVqxgbG2t6wnUgk0wmCYfDJBKJpjZwhUKhUOwdpLTBjtWJN1UxxxoEewDs0RZ6Eo4QpfeB3g9aH0LvH3+s94Fob/lHcSVGbk9qCM2kHtFs6r7NFfMVGddoXcX5NVuktJz/l0q9F3sIWRFkrNo6MGOtdyqCdTVf0LsRVfGlUhumEyHmtuDxTCiVSowNJYgNjhIbHCW+O0liJEkyliI9liGTzJBJ5Orr/eSK1Xo/ZsnCLtf7se0G9X7mA+FMTmm6hl4RfwxH/DG8bgy/gS/gwRus1PvxE4wEqq6fSGcb0d52OhZFifa04/HuvddfoVAoFK0hpZwyXi2Xy7XkiNY0rerA8Xq9VSdObbzavugGNU1zUmRaoTC5fp6u65Mi03w+dZGD4gCluxt274YnnoCjjtrbozl4kBIeeghuvpk7HryRj5wWr67qLbh5e/Bk3nHOxSx/+bnjkXSKlmh1nnlWQs1UX25CiLrixIr9EyXUKKFGoVAo9mekzFcdOViD5Yi1GjHHGoCmjoJavHXCjdD7QasVc3oRwjPfT6chFdfObESe2QhEe1sYmizkaBjuAh53Aq87gcedwNDHcLvGMLQ4Ln0Ul4ihizGEaNXdZJRFlu6q6CJq3DCVdUILzOvz3VfJ54vEdo4wumuM0aE48eEEiViK1Gia9FiadCJLNpUjnyqQzxQo5IqO6ydfwixamOa4+CPlwok/muaIP5pLw+XWcRkuR/zxOa4fb8CDL+SIP8FwgGB7gLaOEJHONiJdYaJ97XT2RYn0qCgahUKhWGgq8bW1TpyJQk4jYaMRzeLVKgLPvvIZ3ygyrdE8m8fjIRKJ0N7eXhVv9pXnoFDsEevWwdNPw+9/D2eeubdHc8BjbXiKv9zyFVx/vIfTH3CcfQkPnPQhjVcZq3jHqR/gFW/4CLpbXQQ1W1qdZ57VJ/hCRnkoJhMMBpuuu+uuuzj99NPnfQxnn302f/3rXxuuu+SSS7jkkkvmfQyK+aNUKlVr+lx44YWTXHMKhUKxryOEF1zLnUalUs04UkrHdWMP1Is5FUeONQj2CJAHa4vTaDyvLLVOR7jRasScqkunH7TovERVaJqGpmkL9oO8VRFoLhxFUubK4ksSrzuJ13CEGK+RwOtOlsWZJLpeamnsUgoKpRD5UphCsY18KVxzv41CKUy+GKZk+RGiWYycia4Poesjc+og2p9iTLxeg0Ur+lm0on9O+pNSkk5mGR0cZXRojNFdceK7E6RiaRKjKTJjGTLJLNlkjny6QCFbI/4ULMySE/tmm47rp7Ye0vhBwLYktmVB0aJIa/8zUyEECM0RfirOH7dnXPzx+j14gx58QZ8j/rQHCEWCjvjTHSbS1UZHXzudizppiwb3ySu8FQqFYl9BCIHb7cbtdjed3LIsq6krpxKxZts2+XyefD7P2FhjF63b7Z4yXs3j8SzI93bleJUaOFLKhpFphUKBoaEhhoaGqvs2ikxT3zOK/Y6eHkeoqfnfVswxL73E1puu4Za//5Cftu9iMATHHgqnP+mHf/gHwhdcwBOvfgVe/55dvK7mF2fGrBw1ir3Liy++2HTdokWLFsT+unPnzqb5sdFolGg0Ou9jmE8OdkdNJpOpCoLpdJpA4OC8clihUBzcODVLdpUj1iaIOVZZ4CE/bT+OS6N/yog1IZoV2jmwkLJUjq0bKkePTagDU7kvky33ackQluzAtKOYdpSS1U7RilA02ymYYQrFMPlSENsWU4pLe4uK4LandYQmrmu2/f4kDM0Gy7JIJzLs3hFjdNcY8d0JxoYT5ci3susnniWXzpPL5ClknMi3YsHELMe+2aaNZdlISy6Yk0xoAqEJdF1Dc2u43C7cHpcT+eYz8PgNfEEvvpCPQJufYLufUDRIuCNEuDtCtDtCtDdC56IO/CGfmpRTKBSKGqSUFAqFKePVSqXphXxN0ya5cmoj1rxeL/oC1V4wTZNEIlEn3jSao9E0rWFk2oF+PqDYz7nwQrjpJrjySvjkJ/f2aA4chofJ3Po//OpP1/IT90YeWDy+KuIKct6i13LZu2/A3RaZs0Oq+UWHeXXUgPNC//nPf2bbtm0Ui/XRIR/72Mdm262iBQ477LC9PQQWLVq0t4egUCgUCsW8IoQBrqVOqyyrWe9EN8XrhBtZ68ixBsDeDRTB2uq0yr4TjiVFe52YI2odOXpfud7Jvjvx6rwWYzU1X5zb8VowFSFmhNbzrrwTar50I/TaCDJnuVt42dPrsqSUc+4Kmup+rTBUibBrJX9/LqiIN7N1BM1024WeCNJ1nXC0jXC0DY5aPid9mqZJfHeC2MAYo8NjjA3FSexOkRx1Yt8yCcf1k0vlnMi3bNFx/pSFH6tkOc4fSyJtm0bajyw7gmzThgIUWopmnBqhCTRdoOk6ulsrR76Niz/egAdf0Is/5MMf8RMq1/tp6wwS6YoQ7Yk4sW/9Ubz+g0NMVigUByZCCLxeL16vl/b29obb1MarNYpYq7hystks2Wy26bE8Hs+Urhy32z0n340ul4uOjg46Ojqqy/L5/KTINNM0GR0dZXR0vH6jYRiTItPUVe6KfYrubudWOWr2nGQSfv5zR/j64x/5+OstfrPSWaUZBmeEj+Edr/kY6499G4auos32NrMSav7+979zzjnnkM1myWQyRKNRRkZG8Pv9dHd3K6FGoVAoFArFAY8QAkQ7aO3gXu0sm7CN48oZqquPIytunMoymXVEDnMMzA3OfpOO5kbqvVVHTp2Yo/c5Lp15qpsi7XSd+DLJBVNZ3nKklAu0rrpaMKJaE2a8NgwiuGCT/EKIqriwEEgpp42Mm0mk3HTCUa0rpCIMLRRzJfq0su98CUMul4vOvg46+zqm37gFpJQUi0XGhhKMDMQYHYqT2J10Yt9G06THMqQTWXLJLLl0ud5PtlCOfDMpFUyscr0fy7KdyLcm4o9lS6ySTSkP0FoNh6YI572i6eORby5DdyLfvOP1frwhL4E2x/kTigYJdQQJR9to74kQ7W0n2ufcGoaaFFQoFPsOLpeLUChEKBRquL4SndbMkZPNZrFtm0KhQKFQIB6PN+xH13X8fj9er7fOjVPrypntd5nX66W3t5fe3l7A+b7JZDLE43HGxsaIx+Mkk0mKxSLDw8MMDw9X9w0EAnXiTVtbm3JnKvYePT3OrRJqZkc+D7/5DUO33sBPt/+WNz9lsijlrHpz6TCeCZd450nv420v+wB9ob69O1ZFHbMSav7t3/6Nc889l+9+97uEw2EeeOAB3G43F110ER//+MfneowKhUKhUCgU+yWOK2cJsGR8Wc16x4mSrIo4WAPIGlHHEXTKIoi13WmVfSccS4pIfZxajajjiDldCKHXHLtY43RxBBdZJ8iUb2Wm9SesRescMGg9iKr7pSzEaNF92h20EAghFrTYb0UYmuuaQs3WNRKGWomUmQvm2hU01b6zncASQuDxeOhd2k3v0u45ed5SSnKZHKO7xojtGnNi34aTJGMpUmMpUmMZsokc2VSOXCpPPltwIt9yjvhjFS3MkoVtlZ0/soH4I8v/S7bjECI3B39TAZrmiD+ay4l8cxku3F4XHq+BJ2DgCXjwB30Ewj4CET/Bcr2fcKcj/nT0ttPRFyXS3bZgYqtCoTj40DQNv9+P3+9vuF5KSalUaujGqbRisYhlWaRSKVKpVMN+Ku6fRm6cirDT6medEIJgMEgwGGTxYifjyLIskslkVbiJx+PVi7AzmQw7d+6sPt+2trY68cbv96vINMXCoISamWOa8Kc/Ubr5x/z+sVv5yaE57lkO9mIodnbxyeM+Auefz1mHreAcceDHIe+vzKpGTSQS4cEHH2TVqlVEIhHuv/9+jjzySB588EHe/e5389xzz83HWBWKBUPVqFEZkgqFQrGv4NR1Ga4RbwYnR6zJxj/26xGAAWiABTOJVhLBSZFjolZ80bvLQpCyyyuoi3jbU9Gnlcd7CyHEnLuCptp2oa9sllKSjKcZHRwlNjjG2HCcxEiSxEgl8i1DJpEjl8w59X6yRYrZIsVCqVzvx6o6f+xytNtCIAQIXas6f1yGI/4YXheG1xF+vEEP/qCXQNhPIBIg1B4k3NlGuDNEtKedaF870d522qJBdUW5QqGYMyzLaurGqbh1WpmiMwxjyng1wzBmNAlbLBbrhJt4PN7wAgu3212NSquIN4ahzv0U88BvfgOvfz0ceyw89tjeHs2+i5TwwANw8808+/ubuLk/xu1HwqgP0DTw+Tip/0T++VWf4pzDX79XhqjmFx3mtUaN2+2unrB2d3ezbds2jjzySMLhMNu3b59mb4VCoVAoFApFqwjhBn0RUusHVxKsYUTV8VJ2wZg7y8LNbqduTsM6MJLpo48M0MKgdZbdOYeAawXCtbwcu9aNEAvnBFHsnyykqCClnNIxNB8CUe2xF1Isqo3oWwiBSAhBuD1EuD3E8tXL5uQ5mKZJcjRFbGCU0aE4Y0NjxHc7wk9qLE06niabzDuun3SeQq5c7ydfwixWxB8b27KRFefPBKQEadrYpo1ZgEJmbur9CE2g6+ORby7DheEzMHxuvH6n3o8v5CMQ9hNsDxBqDxDuDNPe1UZ7d5iO/g6ife34Q6qIt0JxsKLretXh0ggp5ZTxarlcDtM0KRaLFItFEolEw340Tas6cBpFrHm93rrvacMw6OnpoafsYpBSks1mJ0WmlUoldu/eze7du6v7+v3+OvGmrU25GxVzgHLUTM3TTzs1Z265BbZsIeeCcz8MWUOAz0dPuJ+3nfge3rnufA5tP3Rvj1YxA2b1S/vYY4/l4YcfZuXKlbzyla/kP/7jPxgZGeFHP/oRa9eunesxKhQKhUKhUBzQSJkvx5ANVSPHZE1NGCo1Yci32KPmiC0Vt4voAi0AuEFIkAUn0syKlWvlDIJMAEWwdzvNfHZ8fDX9Sq2nQcRa//gyEVKTkIoFQwhRdbYsRCFkKWVToWc+YuQmCkOmaWKa5rw/TxivLzRV9NtsxCCXV6fvsB4Wr+qvWz+bz43KazI2HCc26ES+jQ0nSIw4sW+Zcr2fbDJHLpUr1/spUsyXKBVKmAULs2Q6rh/L+ds2q/cjbYlt2pQKc/P6C02g6QJN19HdGm7DhctTdv34neYP+fCFvATDAYLRcedPe3eE9u4InYuiRHsjeHyeORmTQqHYuwghqmJKMyrxas0i1gqFArZtV6PMmtEsXq0i8AQCAQKBAIsWLQIct2wymawTbzKZDNlslmw2y8DAQPU5TIxMCwQC6txQMTMqQs3wMNi24w452NmyBW65BeumH/O/qQ38ZRl8bguIQADfm97EW1cXGO1t4x3rLuCVh7wSl6YurtsfmVX02SOPPEIqleJVr3oVw8PDvOtd7+Jvf/sbK1eu5Prrr+foo4+ej7EqFAvGwR59Zpomd9xxBwBvfvObFzRHX6FQKA4kpDTBHqmr+yJrasJUb2Wy9U5FuC6CbLwOTHdNHZjOGTtfpJ0Ge1c5Ym3AiVerxK3ZA2DtAlqYoBSBsvvGEW5EVdSpLOtxXEIKhWJaJgpDcykINdquVhhaaGYjCM1WMJpKGJJSUigUiA2OERscJT6cYGzIEX9SY2kn9i2ZJZfMk8vkyafL9X7ypbLzx6nhY5nO6yntBvV+5gNRjuMrR77pbh234cLtcZw/Hn+53k/Ihz/k1PsJtQdpi4YIl10/0V4n8i3SE8Yw1Oe0QrE/YlnWtK6cVj7r3W73lPFqHo+HUqlEIpGoE2+KxckuRpfLVXXdVAQcj0cJzIopKBTA63Xux2IQje7d8ewthobg1lvh5pvZ+uz93LoGbl0DAyHA4+Guw7/A0W/9CAQCSCn3SUFUzS86tDrPPGOhRkrJ9u3b6e7uxlt50ygUBxgHu1CjUCgUiqlxCm2P1btd6lww5Vt7hNZn6Lw1NV+cWzFBkEHvRoi9c/4lpVUWncoCjj2ILIs6ldo5yLEWehLl5zjuwhFaX91jRHif/KGhUBzo1Ma5zVdNodr7s7hmcM6YyxpC0+0rpSSXyTG6K87IQIyxoTiJWIrkSIpkLOW4fhJZMskc+UyeQqZIIVukVChRKpiYRROrZGNbFra1cPV+EKBpFfFHd8Qfjwu3x43hc+Pxe/BWxJ+wj0A4QFs0QCgaItIVJtoTccSfvnYiXW2q3o9CsQ9QEaOnEnIa1aeZiKZpDV05mqZRLBbJZrNVB04jYcjn89WJN5FIREWmKeppb4d4HJ55Bo48cm+PZuFIJOCOO+Cmm8j++Y/8+jDJT9bC35YAhgE+H+FwN+eteRv/fPw/sywyN/G0ivll3oQa27bxer1s2LCBlStX7vFAFYp9ESXUKBQKxcGL4yypjxyTdeJL+T7T/4h10EHrqhFhuhF14kt5+QEQGSZlbly0sQYcIceuceZYA7T0ugl/1ZGD3uvEq+l95Yi18jKhitcqFPs7FRfPfNYUqn28N5lLV1Cl5VI54sNJErEUqZEUiVia9GiadDxDeixDphL5li6QzxYo5hzXT6lgll0/jvOnEu22EAgBQteqzh+X4XKcP143Hp+BJ2DgDXjxh7wEIn4CbQHaOpzIt7aONqK9ETp6o0T7IgQjASX+KBTzgGmak8SbbDZLPp+v3raCx+PB6/VWo0lN0ySfzzfcXwhBKBSqE25Cof3/3FixBxxxBDz/PPzpT3DGGXt7NPNLLge//jXcfLNzW3Dqit69HP7xHW7w+RA+P6889FW8c+07Wb9iPR6XcqXtT7Q6zzxjv5GmaaxcuZJYLKaEGoXiAEVZExUKxYGIlOX6KzW1X2RNTZjqrWye5z0JLVqOGqt1wfTUu2C0KEIcHBNJQvjAdajTgIk/raW0wR4tR6kNTohYK4s6dgxkFqxNTqOxJ0lqXTXiTd+4mFOJWRPt6se9QrGPUxEcFuJcU0rZsmNorgSjWuZdLIpCMOomSDtCRGclCEkpKWQKpGIZErtTZMaypMfKbp94lmwqTz6VJ5cuu35yRYq5IqWCWSf+2JZEWjaNLgmVEqRpY5s2ZgEKmckxSTNFaAKhCSfyzaXjMnTH9eN1Y/gMvAEPvqAXXznyLRh2xJ+2jrLzp7edjt52on0R/CH/Ho9HodjfcblchEIhQqFQw/W2bU8br2ZZFoVCgUJ5wrkRlRjKSsxnMpkkmUyybdu26vpKnZtKm6p+j+IAo6fHEWqGhvb2SOYH04S774abboI77mDITvGz1eA7Et6bPwIuuIBXvuNtnPz3z/LKZa/k7WveTn+of2+Pesao+cWZMasaNXfeeSdf/epX+c53vsPatWvnY1wKxV7lYHfUZDIZgsEgAOl0mkAgsJdHpFAoFM1xJv9jdS6YSRFk1lCLsVxlRKC+5oveM8EF0w1al3J1zANS5p16ODX1cerq5VgDQPMf/eN46uPVakSdynIh1JVoCoVifqitLzQfsXGN1u0tKoJbo1pAAPlkgcxYlkw8RzaeJ5vIkk3myacK5JJ58plCOe6tUK71Y9ZEvlnYpo1t2dgLVe8HR/zRys4fl6HjcuvVyLeq+BPy4g/5CEYCBCOVyLc2It1hoj3tdPS1E+2NYHjVuYLi4EJKSalUqjpxGgk5jWrZtEJFRIpEInR2dtLR0aEmfg9U3v52+OlP4ZvfhI99bG+PZm6wbbj/fsc5c+utlGK7+eOhcMtauOcwHcvvpad9MQ//61O49AOjVpyaX3SYN0cNwLve9S6y2SxHH300hmFMUrRHR0dn061CoVAoFApFFacOTKpGcBmqccHU1oYZoaUi9wC4J9eBqRNkusvLgvP51BRTIIQXXIc4rbKsZv14faDx+jiyRtRxbncDBbC2OI1mrpyOmoi1sphTG7N2ELmhFArF3CKEqAoXC0GtMDSfdYVqxacKlQg702z+Xay1QajNR2ipD2jfo+dqlSxKWZNsokA+URZ8UgXySUfwyWcKFNNFCtlSWfhxxB+zaGKWrLL447xeson4I22JZTvblvKtRp1OQU29H03XcLl1XLX1fnwevEHH+RNo8xOM+AlGg7RFQ0Q6w0S6w3T0RmjvbSfSE8btVhPTin0XIQSGYWAYBuFwuOE2lmU1FHAq0Wq5XK5hHTPTNBkbG2NsbIwtW5xzPE3T8Hg8+P1+wuEwkUgEv9+Pz+fDMAzlsN5f6elxbvd3R42U8NRTjnPmllvgpZd4vgNuXge3rdMZjXjA5wPD4IT+Ezh/7fkLdU2CYh9kVt/uV1111RwPQ6FQKBQKxcGE45qoFVuGkHWPy03mWuxRgNZZ43apRJB117tgVBzWfo8QAkTUiZ1zO87uyRFrxXFXjj1YL+ZYA46gI3NlJ1YMzKed/SYdzUDqfeMuHK0SsebUyUHrQ2gqJkehUOx99oYwNF+RcY3W1U7Y6m4dPazjDXuAuUk/MIsm2bE82USOXLJAPpl3btNFCpkixUzRqfGTMynmSpgFE7NgUZro+rGa1PuRYFsS27IAi2JursQfDU0X6C4d3dDr6/34DbxBx/Xjb3OcP6H28ci39u4wHX3ttPdGCHe2qXo/igVF13WCwWD1SvuJSCknxatlMhlSqVTVkVP5XLBtu7pNLBar60cIgc/nqwo3ldtK83q96n9/X6W727ndX4WazZsd58xNN8Ezz4wvDwa58aJD+EH3AHg8dAe6edvqt/HOte9kRXTF3huvYp9gVkLNu9/97rkeh0KhUCgUigMAKU3H4VKJHLOHa2LIaoQYmWi9UxGuii/1dWBql3UihLq6VOEghAGupU6rLKtZ77hyEnVxatKeEK9mDwNFsF5yWmXfCceSIjIhYq2/xqXTV47IUxMACoXiwEIIsaBxQxXXznzWFPIH/Vj9zuNZJMRPopgtkk3kySby5BI58skiuVSBQqoS9VaimC1SzJmU8uPij1WysEr2eL0feyrxx8a2wCxakN3jISOEQOgCXdccQcyt4/bUiz++oBd/mw9/m59gJEBbR4hwNFSNfGvviRDtixAI+9UEuGLWVASWqWrSlEol4vE4sViMeDxOJpMhn8/XvX+llGSzWbLZ5m8Qr9dbJ95MbG73gRFBtd9RcdQMD+/dccyEwUG49VZHoHnwQWwB9y2Bm8/VeF/wdE5480fg9a/n/NQmhh/8Ju9c+07OOOQMXJr6Hatw2OP/hHw+Pylb8mCs6aFQKBQKxYHMeNzUcF3tFznBFePEkLU6ueGdUPOlB1EnyPSA3uUUqFco5hDHlRMBLQLu1c6yCdtIWSr/n1cEnJpaOZVlMgMyDmYcTOdKucn//W6k3lNXH2c8Yq3s0tEOzqxmhUKhaJVKHZyFEoca1ReaTwdRq2MqZk0yY1lyCcfxk0vkKaQd508xU3b/VJw/+ZIj/FQj3+yy80cibZtGWpSUEmlKbNOmVGg1VnZqhCac2DeXhu7ScRku3B4XhtftuH4CXrxBD/42P4FwWfyJBol0hQl3tRHtcWr9tPdG8AfVOaFiHLfbTVdXF11dXdVlUkoymUw1Hm10dJR0Oj1lP/l8nnw+z9hY43qWbrd7SiHH4/Eox/58sL9En8XjcPvtjnPmT38C22ZbGG49TfCTU0LsDAvw+fCsPZYTznobAOv867jujdft3XEr9klmdZaTyWT4zGc+w6233jrJVgi0fKKhUCgUCoVi7yPtTJ34Uu+Cqbml1ZgOHbSuOhFG1IovlfsipH7UKPZZhHCDazGweHxZzfpqDaWa+jh18WrWoCNeUgJrh9PKb6HJrpzweLya3oeoEXUcMacLIRYmzkihUCgU48LQQlCpLzRXNYVaEYwq2KZNPl0gE8+TS+bIxwvk0gXyqQKFVJFCtkAxW6KQdcQfs2BSKpiYxXHnj23ZSMvGnrLej8QybUqYQGGPXzNRV+9Hw2W4cHlcjuvHZ+AJeKqRb4Fq5FuQcEeY9u42on1Roj1h2nsiGF5jj8ej2HcQQlQj1ZYsWQI4c5TJZJKxsTHi8TjxeLyhw0YIgdvtxuVyIaWkVCphmialUolSqUQymWx4TE3TGrpy/H5/dflCxVIeUOzLQk02C7/6leOc+c1voFjE1OAXq+CWV0a5r8906s5oGm2eNs478jzOX3v+3h61Yj9gVkLNpz/9af70pz/xne98h3/8x3/k29/+Njt37uTaa6/ly1/+8lyPUaFQKBQKxSyQsugUVa9xwUh7qEZ8KbtgZKb1TkV7NX5s3AXTUxdL5hRgVz9GFAc2jiunzamQ7T7CWTZhGycKcLg+Yq0s6jiPB0EmnRg2MwHmc85+k46mI7XeejGnGq1Wduloofl+ygqFQqGYB2rrCy1ExFJFGJprV5Bpmti2TSFXID2WIT2aJT2WIZcokEvmKaSLFDLl2LdMiVKuRDFvYuZNzGKN+GPa2KZ0BCA5lfjjbF/Kwx6LP8L5O2i6hu7S0GvEH6MS+xbwVGPfnHo/Ado62oh0R+jsbSfaEyHaHyXS3bagsYCK6dF1nfb2dtrb26vLCoVCVbSptFKpRLFYrEsNcrlchEIh/H4/brcbIQSFQoF8Pk82myWfz2Pb9rTxah6PZ9p4NXUB2wRqhRopYW+/PqUS/PGPjnPm5z+HWqfW6tVoF5zPV8O3sb0wjBBeTl96OuevPZ+zDjsLj8uz14at2L+Y1bfHnXfeyQ9/+EPOOOMM3vve93L66adz2GGHsWzZMn784x9z4YUXzvU4FQrFAmIYBjfccEP1vkKh2LeQ0gZ7tMbxMjTugKlzxoy23qkI1Istek+NC6Zy2+XU/lAoFC0hhGvcFVNZNmEbaaerjhyswbIrp+LIGQRrF2CCvdNpTV05wZq6OLXxauVbrdtxCSkUCoXioKZWGFoIJgpDjQSfZgJRxc2Qy+RI7E6RHEmTiqVJj2XIJnLkUnlyyTz5dMX5U6SUrzh/Kq4fR/yRpuP6aVbvR8px8YcczIX4o2kCURF/XDouQ6+KP4avHP0W9OAL+Qi0+Qi0OzV/2qIh2nvKNX96I0S62nC73ei6jqZp1Vs1sT97PB4PPT099JTFgEotm1rXTTKZxDTNaoxaBb/fTyQSobe3l0gkgmEYFAoFcrlcw2ZZFoVCoSoONULX9aoDx+/3TxJyvF7vwff37u52bvN5SKVgb5TZsG247z7HOXPrrVBOldrth9teF+V3J3fw0/Nuxn30cWhC8JEnlhLLxnj7mrezqG3Rwo93H0TNL84MIWdRJS8YDPLMM8+wdOlSFi9ezO23385JJ53Eli1bWLdu3bT5jwrFvk4ymSQcDpNIJFTNJYVCsWBUo5RqBBisYWRdBNmw45Kh1dxwdxMBprY2TDdCC87nU1MoFLNESqvsjBuvjzMpYk3GW+hJK38G1NTK0SaIOaLt4JsEUCgUCsUBhZRySkGoIv4kx5LEh5KMDsVJxlKkRzOkRjNkk1myybwT/5YpUMiV6/6UnT9W0Yl8s0yrXO+nifgzDwgBQtPQ9HLNH7eOy63j8ui4PW7cPjcevxuP34Mv5MHX5sMf9hKsCEAdIafuT2+YQDiAy+Wqina1IlCjx0KIg+4cwbIsUqlUnXiTyUxOIhBC0NbWRiQSob29nUgkQiAQQAhRjVDL5XJks9mGQs7Eut+NEEI0jFerRKwdsPFqwSBkMrBxI6xcuTDHlBKeeMJxztxyC2zfDkBJg7uPDXPLmX3cHR7Fcjuv9/937v/H6w9//cKMTbHf0uo886wcNYceeihbtmxh6dKlHHHEEdx6662cdNJJ3HnnnUQikdmOWaFQKBSKAxYp8+NRY1aTOjD2MMhciz0K0DrrRBgxQXxx6sC0H3Q/qhSKAwkhdNB7ncaxzrIJ2zh1pgbr49XKoo4j5uwCSmDvcloTVw4igKyNU6tz5PQ5Qq9y1SkUCoViH0YIgcvlmj5+bOncHM+yLEqlEvl8nvjuOLsHxogPxxkbipOMpUmNpcnEs2QSWfKpPPly/FsxV6JYKGHW1PyxTbss/tg0uqRaSpx6QBZQtGi9fmRzhCbKNX8EuktHd5cFII/LEYC8bgyfC8Nv4A0YeIIeAmEfvjZHAAp1BGjrChHqCBEI+RqKPFMJQM0eL1SNqKnQdZ1IJFI3z1ksFkkkEnXiTWVZIpHgpZdeAsDtdhMOh6vCTSQSIRwONzyOZVlN3TiVJqWs3m+GYRhTxqsZhrH//S7s6YHNm534s/kWal580XHO3HQTPPdcdfFAb4Dr3rGS2xYlGNHyQALQOb7/eN655p2cvuz0+R2X4qBiVo6ab3zjG+i6zsc+9jH++Mc/cu6551ZV4v/6r//i4x//+HyMVaFYMA52R41pmvzud78DYP369SpjV6GYAqcGxUhdzRc5QZDBGnJqULSKaKup99LIBdMDWoeKMVIoFC3hxCWO1ESs1Yg5lVs5Nn1HCNC6mkSslZeJyP43CaBQKBQKxT5ExQmUz+dJjKSIDYwyumuMxEiS+EjScQCNZcgksmSTOfLpAoVswYl+K5hl8cd0nD+W7Yg7duN6P/NBRfjRdG3c+WPURL/53Lh9LgyfgSfgxhP04A0aeEMefGEvvjYPgXY/oY4AHp9nj0WfVh1Ds6EioMTj8ap4k0gksG170rY+n68q2rS3txMOh1tywUgpyefzUwo5pjl92oKmaQ2dOLXxavuCQFbHy14G998PP/sZnHfe3Pc/MAA/+Ykj0Dz8cHWx9BiI178BLriA508+jFf95GwAugJdvG3123jHmnewsmOBHD77OWp+0WFeHDW2bfO1r32NX/7ylxSLRQYGBrjssst47rnnePTRRznssMM46qij9njwCoVi71IoFHjDG94AQDqdPmg/SBUHN04MWXyCC6YiwtS6YEaAySfijfFMqPlS64LpKd92IYRvHp+ZQqE42BBCc4RevRs42lk2YRspc47zxqqplWNPEHMojrv/So87+006mtdx5ZSFG6H3g1bjzNF7EUIVVFUoFAqFohkVR4nb7SYUCrF4ef/0O01DJfotm80zNjjK6FCc+HCCseE4ydE0qdEM6bE02WSObKos/mQKFHIlSvkSpULJcf6YNc4f2Vj8kbbEsiVWqdXfSNMgyuKPpqG5xp0/FeHHqfvjwu136v54Am48AQ/ekIE35Ag//ogXX9iJgtNck8WIymveisjTbLuKg0bTNAqFAtlsllQqRTqdros9GxwcdJ6WEIRCoTrxJhgMThKNhBBVMaUZlXi1Sstms+Tz+epxC4UCtm2TyWQaxrdVaBavVmlu9wJfKFiuIcTQ0Nz1OTbmCD833QT33kvFvmZrgr+9+QRueVkI92Gr+MYbrwFgFfDhEz/MyYtO5oxDzsCtq4slZ4KaX5wZM3p1vvjFL/L5z3+e1772tfh8Pr75zW8yPDzM9ddfz7Jly+ZrjAqFQqFQzCnSztaJL04MWUWQqakFw/R5wQ66E0M2sQ5MVYApT5Cq+g8KhWIfRQgfuJY7jUZCjgR7tOrIqYo5VZfOYFm4zoO1xWk0vnhXap1VR05VzKlGrPWDFlWflQqFQqFQzCEVUcEwDCKRNpYfuWf9SSmxbRvTNEkn04zuijO6a4yxoQSJkSSp0TTJ0RSZeLZO/Clmi+PiT6Xmj2lhW3a15s/kg4G0yrWHSlBquVbnFAjQNIHQNXRdQ3NrTr2fWvHH58bwuzD8jvDjqbh+2jx425waQIF2H96QMSsnipSSZDJJMplk27Zt1eVutxvDMPB4PHi9XgzDaNk95PP5CAaDk4Ql27andeVUtsnn84yNNXZau91uvF4vfr+/elsr5Hg8nrk9h5sroSaTgTvvdJwzd90FpfHYwO2vOp5bz17CTwJb2ZHbARKMzZu4vJCkzeM4Hz73is/t2fEVihaZkVDzwx/+kGuuuYYPfvCDAPzxj3/k9a9/Pdddd92+Z49TKBQKxUGHlMVyvM947Rc5MYLMHgaZbr1T0V4TOebcilrxRespTyoegMUbFQqFoowQAvQOp7nXOcsmbCNloc6Vgz0wLuZUbsmX4yJHgKec/SYdzVN25YzXx6kTc/Q+hPDO7xNWKBQKhULRFCFEVQjwdHno6OqAdXvWp5QS0zQd8SeRYWRnjNGhMRK7U4ztTpCKOcJPKp4hl8yRS+fJpfMUsyWK+SKlvBP55jh/bGzLRtrNxR/bkmBZWFjQapnQKRBCIPTx2LeJkW9uj8uJfCs7f4yAgTfowds27vwJtPvwhX0Y/sKUzpeZMpXIEwgEqk6eigBXid+r/D1KpVK1HlOpVCKVSjV9DRo5cSrCjs/naynurcqeCDXFIvz+944484tfOGJNhXXruOedJ/Pd/gH+N/Z3YABy0OZp401HvIl3rn0nISM082MqFHvIjISabdu2cc4551Qfv/a1r0UIwcDAAIsXL57zwSkUCoVCAZX6CqM1YktNHZia2jDYo613KgJ1EWST6sCU76vC2QqFQtEaQnjAtcxplWU1652IlLE64WZSxJo9DBTA2uq0yr4TjiW1aF2kmqgRdZzbDifyTaFQKBQKxX6BEAK3243b7cbn89HV27nHfVZcP6VSicRIktFdY8R2jZHYnSQxkiI1miY1liaTyFbFn3ymSDFbdMSfwrj4U4l8s227ceSblEhTYpsAFlCavNEMEZqo1vypRL7pho7LcOH2Vlw/hiP8+N14g86tJ2g4rp+wF3/Yi7/dj+F1USrt+ZimQkpJNpslm8023aYiErlcLlwuV9VBZBgGXq8Xt9uNy+VC0zRCXi9tQHH7drLxeEOxqc44YNvw1786sWa33Qaj4/MD8pBlyAsuQDv/Ali7lucevob//cuvAXj50pdz/trzOXvl2Xhd6mIgxd5jRkKNaZp4vfX/sG63e97f6Ip9jzPOOINjjjmGq666ar/qW6FQTI2UFhQfAXu3UzDaOGFenSLOpF16QuTYELJWfLGGnfG0bHF317td9O6yAFPvghFacN6el0KhUCgmI4QAEQUtCu41zrIJ20hZLLtyxiPVxiPWyoKOzJbF+1Ewn3b2m3Q093itHK23Xswpx64JzT8vz3Ohv0sVCoVCoVA0RtO0qggQCAToX9a3R/3VRr7l83kSIyliO0cZG46TGHHEHyfyLUcmniGXypHPFJyaP7liud6PWa33Y5k20rKx7eb1fqTtiD9mwdqjsQNOvR9Rdvy4ys1w3D66oWN4XRi+stOnHPfmC3rxtnnwhjz427x4w95y/JuB5tKq9Y9apeLYaWUuuW90lOMB+7772HnxxWw5+2wwxi+ktKTFC5mNmAMbWfn4i5z126cI7I5V1xejUV5Y/3J+emqEX4tnuOCwLtZrGtpzz3Gc9zjes+o9nHvouSwOLUbTNLZv2U6pVMK2bTweDy7NxQM/+zvDW0foP6yXN37kbDweVaNmJkg5/o89MjKC3+9XEcdTMCOhRkrJe97zHjye8QKg+XyeD33oQwQCgeqy22+/fe5GqFAo9iq7du1ixYoVe3sYigVA5n+HTH4R7F3jC7VeaLsU4V0/8/5koU58qa8DU44is4dAtuozF6B1TIgg657kikFE1FXUCoVCsZ8ihAGupU6rLKtZ7wj8yXEHjjWArHXkWANlV04JrG1Oq+w74VhSROrj1GodOXofaF0zFljm+rtUoVAoFArFvkNd5JvHQzgcZumKPUsYklJWY8ay2TzxXWPEhpyaP8mRFMmY4/zJJLJkEjmyyaxT8ydToJQrUcyXqrV+rJI9Xu9HNhB/ZPl4toVVqogrhT0af6XejyP+6NXIN7fXjeF1O8JPwMDj9+AJGHgCTq0fx/njuH+MoIEn5Hbq/bic3/JH3HADK+64AwBvPM6a667jyOuvZ/Ob3sRz730vT23+LT8c/DE7tAQ2En0xrDgPLr/Pw5q+0/jVyxfzq/adPJ76G3bKBuDnG3/OSnNldegv42XENseIEZv0tP56wyP8/RfP1EXn/X+f/h+Oe9MazvinkxvGyLVSR2im2+7Posbg4CCPPPJI9fHvf/97li9fzpo1a+jr2zPR9EBlRkLNu9/97knLLrroojkbjEKh2PdQQs3Bgcz/Dhn/GJPO5OwhZ3nkv6sTTFKaYMfqBBdpTYggs4ZBxlsfgGibHDlWI8g4QkwHQqirVxQKheJgxnHlhEELg9uphDzZlVMqfyfViDk1Dh2snSAzzveUGQfzWWe/SUdzIfXesnhTiVgbd+Sg99W5M2fyXapQKBQKhUIBzrlNJQbM6/USjUY4dHXr+0spSafTxOPxakskEtX1xbxJLp4jm8hj5WysvE0xU6KYMUnH04ztjjt1fnKlsuPHwixYWEXTqfVTadPU+7EtC7M4B64f4J/kk6xg46TlwrZZcfvtPLbhV3ztTUUKOvhLoNtQcOk82w1vfYtFm3gCW/wdkRIIITgyfCTrF63n1YteTdgbrjqjLMsim80Sj8frjvPXGx7hsTs2TH6qtuTR259GSsnp7z1hTp7rdNRGvO2pONTKvnMlDA0ODvLoo49SKIwLgWNjY/T19fHoo49y/PHHK7GmATMSam644Yb5GodiP+fXv/41F1xwAddccw0XXnhh0+3e8573EI/HOfbYY7n66qspFApccMEF/Pd//zdGjX3Rtm0+/elPc91112EYBh/60If4/Oc/X12/bds2/vVf/5W7774bTdM466yz+Na3vkVPudDYE088wSc+8QkeeeQRhBCsXLmSa6+9lhNOWJgP0v0dwzD4zGc+w1e+8hXcbjUxfqAjpeVc/dvIa11eJuOfQurfBbm7XADabrF3z+TIsTrxpdu5Ynme4mcUCoVCcfAhhBv0RU6rLJuwjbRTZSHHqZEjax051qBz0QEmWDucVk7nmOzKaSsLN31QeqjBFpW9BDL5n+B5rYpBUygUCoVCMWcIIQiFQoRCIZYsWQKAZVkkEok68WaqujHN8Hq9vOY1rwGoChumaZIcTTK6K87oUJyxoTjJWIpkLE26XO8nm8qTSzqun2KuSKEsAplFx8ljmZbj+mkg/rikzVvLIs3E8zcBWAK+dIYj0ngLgrzQKaEjTYE0JaVAkVF7FP9QiJ5nFtPz9BJ8oyH+Kp7kf/WnHfePS8Pldmr96IaGy+PEv7n9blwenU1/28ZUPP7LZ/j0tR9D00U1+s2yrOprNPH+TNZZllUXF1aJilsoWhWBplu3YYMjdLlcLt7ylrfws5/9DF0fPwfesGEDvb29+7VjaD6YkVCjUDTipptu4kMf+hA33XQTb3jDG6bd/u6778br9XLvvfeydetW3vve99LR0cEXv/jF6jY33ngjn/zkJ3nwwQe5//77ec973sNpp53GmWeeiW3b/MM//APBYJA///nPmKbJRz7yEd7xjndw7733AnDhhRdy7LHH8p3vfAdd13n88cenFBwKhUKdyptMJmf/guzHDA4OMjg4CMBhhx0GwJNPPonL5XxU9PX1KcX7QKT4SH1ES0MKYE2+oqQeD7gOAdehoB+KcC13Jq9EAETQudUCgEd9GSsUCoViryK0EGirwL3KeTxhveMe3T0hYm2CmCOTTjOTwPPTHFE6glDycvC8DPQloC9GaOH5eHoKhUKhUCj2c2oj0Won8SttJsvdbjeGYVAsFmc0hnw+TywWo7OzszoJbxgGfr+f3sW9s3timQw88wzymWewX3gBuXUr7NiBNThIKhUjUUzwdw+sHQZvuUztb1bCr1dCzA8vtsOTPc5lMFmXBCyMnAthg0DgKujYLskRvzmW9m1dNa8nSNN2LjktWhSZfb1125Lc+z9/4y2fmH4OdDZU/vatiDp7IghVHjcShva0Hv3o6Cijo6MA1XnErVu3VsWaaDRa/d9SjKOEGsUe8e1vf5tLL72UO++8k1e+8pUt7WMYBtdffz1+v581a9bwhS98gYsvvpj/9//+H5rmZFEeddRRXHbZZQCsXLmSq6++mrvvvpszzzyTu+++m6eeeootW7ZUrxb44Q9/yJo1a3j44Yc58cQT2bZtGxdffDFHHHFEtY+p+NKXvsTll18+25fhgOHaa6+d9Dp84AMfqN6/7LLL6pxNigMEe/ccdVQA83mn0fiaYgcXUgTGhRsxoZWXiVpxp7o+OGkfdWWyQqFQKOYaIVzluLPxC1Qmu3LSzoUO1gAy/1vI3TZ9x7lbkLlbxvsQoapog74Y4Rq/j74YITxTdKZQKBQKhWJvIaWcExGl2bqFdFFMRe1FzU2xbdi8GZ55Bp5/HrZsQW7fRnr3ALHMbmKlJDGR4+WbSvjL8/8/Ww23rYaYD2JHQOxYKNX8tL/nRjhixLm/sQPucBJvybtAlk/KhBAIKdC8At3UsW0JUiApIiM2YodoHNc2BwxsGpqXfqE+Dm8hqAg2sxF5Gj3OZrPcdNNN3HzzzXXHufrqq6v3zz///KpbSzGOEmoUs+a2225jeHiY++67jxNPPLHl/Y4++mj8/vGYo1NPPZV0Os327dtZtmwZ4Ag1tfT19TE8PAzAs88+y5IlS6oiDcDq1auJRCI8++yznHjiiXzyk5/kn/7pn/jRj37Ea1/7Wt72trdNWWfls5/9LJ/85Cerj5PJZF3/Bwsf/OAHeeMb34hlWdx66618/etfr4uMU26aAxSta/ptANquQLgOdXL97Uw53z8NMoOsPq5pdcvSICtWaxNkwmlTnHu2ejolha+p2FMVc7Rgg22Ck/fBq9w+CoVCoWgJoQVBOwxchwEeZCtCjfsUIOfEqdkxkCkwn3EaDaLVtO6yaLOkRsgpizlat7pYQaFQKBSKJkycSJ6NWDLV8oUUUipuFpfLVRc3NbE1W99oeTKZrCv03ghXMkng0Ufh179GbnqRzLZNxGLbGUnvZrQYZ4Qcb3imRCjnvBY/PBr+5yhHfBk5DEqr6vu7+0Y4siy+7GwT/OUQQAgQAonAFgJ/EXpSJQo1pziveMlx13RmYTAAnz0TpKljWk5yjo3ExrHf2JoNtkAb1RuKNEIT6G4dw+vC8Bu4vDq+kAd/uw9/u5/EriSbH9g+7d+kf0XPtNvsL2iaVr1wfi4YGRnhrLPO4qSTTsK2bf73f/+XO+64g4985CPV9J5oNIrHoy5ImogSahSz5thjj+Wxxx7j+uuv54QTTpjTyc2JMWVCiBl9CX7+85/nggsu4Ne//jV33XUXl112GbfccgtvfvObG27v8XjUBwTj0WaZTIavf/3rAKxZs4bjjjtuL49MMa8YJ4DWW87jbySPCNB6Eb63NJ0QauXdL6XtiDVlcWeymJMBO41sKvaMb4PMMF4wIOc0Rpofu4XxOeizdPsEG4pEQqivWYVCoTgoaPW7NHpD9btU2tnxGjjWDqS1vebxduc70x52WukxZ5+6Pt1IfVFVyBH6YnCNizqIsLr4QKFQKBT7LI2ElEaCiGma2LY9aV2z5XtLSJlKRJmJgDJx+VwWeAegWITnnsO7YQNL//Br0rtfIpveTbqUIGlniGt53vV3m3DWQgDfPQGuO84RXwqLgEX13R3zEhyZc+6PeeHpbqrCixQCj6kRzLvx5D1cHzmCTKmb7QUvo1sSdKbiuLOG03Ie3FkDjyn4FbdTKxscN+g0AFPA94+DpzpAtyQajtAjBCDANmyi+Q7OfeU59C7tZsnKRSw7YgmLD++nLRqqG7uUkrvvvpt8Pl9dZhZNrnn7TVM6cTRd49wPr5/tX+CAp6Ojg/7+fqLRKPl8njvuuAOApUuXVoUar9dLR0fH3hzmPomaQVLMmhUrVnDllVdyxhlnoOt6nYVtKp544glyuRw+nw+ABx54gGAw2LKD5cgjj2T79u1s3769us8zzzxDPB5n9erV1e0OP/xwDj/8cP7t3/6N888/nxtuuKGpUKNQHMwIoUPbpcj4x3Akl9oTEueEULRdssdX7QqhOYIGwam3a7E/KYuO6NPUzVN2+zQRhCbtA4A1XnNgLtw+eCcLPpMEoCBiouDTSCQSPjXhplAoFPsos/kuFZoftMPBfXjNVg5SSpBjYG53RBtrB7Ii4Fg7nBo5lMDa6jQafDeJILI2Vk1fUhZyKrFq3jl9DRQKhUJxYFErpEwllszUtVJZX1sXY77ZU7FkquVzLqTMBtuGgQGyTzzCyAuPE9v2PCMj2xlNDTFSjBOTWT52v00kWQTb5r9OhW+dBIUoEJ3c3Ws3QqQciFHQYaCsb0gEhiUI51z48y6MrMGP3EvJurrYbAaIbSzStzuDO+upii+6OX7u83j1noV/OIh/uGZuQDgCiBbQ+YW1mjfnn0Ey4fwI0CV8htfy4dAjFANF/C4/uqZj2RZZM0tID/GDd13POSvPmfZlE0KwZs0aHn300eoyl+Hi2H9YzWN3NK/R+5Z/ewOG0bwO9sFOo9d1ImvWrNn775t9ECXUKPaIww8/nD/96U+cccYZuFwurrrqqmn3KRaLvP/97+dzn/scW7du5bLLLuOjH/1oyza71772taxbt44LL7yQq666CtM0+fCHP8wrX/lKTjjhBHK5HBdffDFvfetbWb58OTt27ODhhx/mLW95yx4+24OT3t5ZFohT7FcI73qI/Dcy+UUnb7+C1utMLHn3vatFhDBAREFrcGZZu10LfY27faaIbrMz9W6fRoLPRLcPebDzQGzq47fyhNHG3T6NxJyJbp8G9Xzq3T7qxFKhUCjmkrn8LhVCON9xRhQ42llWs15KE6xdzd049ojz3WU+6zSmilWrdeRUYtV6VKyaQqFQ7ONUhJS5qIfSaPlCCinzJaLsM0LKLMnHR9j9+N+IbXyckR3PE9u9jVhqmFhhjJjM8H//LOiIF8A0ueIVcE2lKoEGdJdbmbc+ApHyxYguCwrlGWHDEkSygrasji/nxp0x+GmplzydbKWNnc9Ilr5Uwp1znC+6WT+V/HDljgB/3MAfd8QXTXcixlwBF4bfjSdg4Gvz4I/4CER9tHWH6FzcTu/ybvqW9xAKh/D5fPh8PrxeL/bnP4/2zW+CZVWPJXQdPvlJzv/qVwm/8BsuvftSNo1twjItdKGzqmMVV7z6ipZEmgp9fX0cf/zxbNiwoeqsOf29Tvz/33/xTJ2zRtM13vJvb+Cfv/qPLfd/sFJ5XR966KHqsvb2drxeL2vWrFGlFZqghBrFHrNq1SruueeeqrPmyiuvnHL717zmNaxcuZJXvOIVFAoFzj///BkVqBdC8Itf/IJ//dd/5RWveAWapnHWWWfxrW99C3CulIjFYrzrXe9iaGiIzs5OzjvvPC6//PI9eZoHLUqoOXgQ3vXgeS0UHwF7t1O7xjjhoJiomR+3T7PotkZun4mCz8TaPhKwnVoGMuUcxGpy7FbHiGfKeLeKu6dxfZ+J+/n32x8/CoVCMZcs1HepEC7HHeNa7DyesF7KHFg7q46cejfOjvJ3zXSxav31bpzaaDURUZ/7CoVCMQ1TxXLNRZ2UhRJShBDzJqLs70LKbIilhtmx4X5GNj5ObMdGYiPbiaWGiBXjxOwMV97joiuWh2KRr75C8t0TanYOlFuZf/ordDilWYiU07s8JrTnHPEllNXx5Vy4sx7uKnRxO1FeIsTmpwxWPA/unIFW0hHlMwkbKAD/C9WTC08avFmB7tJw+Vy4fS5HdAl58EV8BNp9hDoDhLoDhHtDhHuChNpDeL3equgy8b7X60XXpzk3uvJK+NKX4JprYNMmWLECPvxhMAwAzll5DutXrOfBnQ8ynBmmO9DNyYtORtdmfs7V19dHb28vsViMfD5PoVDgmG8eg+tbLu77ySMMbB6if0UP5354vXLSzIC+vj7Wrl1bffy6172OpUuXHlTv95ki5EJK5IqDnve85z3E43F+/vOf7+2hTEkymSQcDpNIJGhra9vbw1lwMpkMwaAzYZ1OpwkEAtPsoVAo5gvH7ZObwu3jCDqyTtyZIu6N4jyMUgPhnzLeDa3W7TNdDSBjHsaoUCgUCqiJVbN2gLmjRsipiDkDUC7I2xQRqDpxHCGnfN+1BPRFCOFbkOeiUCgUs0VK2bTY/GxcKo2W700hZaZiiRJS9oxtiW1s2vKYEzu2fSMjI9uIpYfL4kuW63/vo2d3FgoF/t9pFt85sXlfv/8RrB127n/7RPjaadCRhWgWwjnNEV+ybtxZg9XPRsmmw2wnxGa3n6Tw1gkvjRACNJeOy9Bxe10YAQNv0MAX9hJo9xHsDBDqKosufSH8bd6afcUkwWXiY4/HM6eF6RX7N5s3b2bFihXAwT2/2Oo8s3LUKBQKhUKxD+O4fSZcOtVouxb7k7LUXPCpc/s0E3wmun1sp8m002AO3D5Gk3i3endP624f9UNBoVAoKlRj1bQouI9yltWsl9ICe6jsxplYG2eH48KRGTCfdxqNYtW6JseqVaLVtN6Dwq2rUCj2jKmElLkQUfamkDKXIorL5VKT4vPEs7ufZcPOvzOyZQOxnS8Si20viy8JYmS5/a4wfUMZyOW48dTSZPHFKDdgOJehp1zzpTcNfSmI5hzxJZLVCOY0/Fk3rqzB4+kIfyPEDkJsfjTI4Q8HkeXvzUS5VXhGgObWcLkd0aXD73ZElzYv/nYfwai/TnQJRH1N/19cLldD4aV2mdvtVqKdQjGPKKFGMadUXBiNuOuuuxZwJAqFQqFohBBuEBEgAlPMk7VW20dOdvtMEnMyyIm1fJrUAHKM9gBFkEWwxqY+fmvPGFnr9mkg+LTu9gkqt49CoTjgEUIHvd9pnNwgVi0/LtqYE904O8qf6budVvq7s09dDy6k3jfZjVMRckS7mgRSKPYDmgkpcyWi7C0hZbZiyVTLlZCy9yhaRVyaC6184dajA4/y6MDDjAxuJjbwIiOx7YxmRhgpJYiR457f9rB4IA2ZDD87KT9e86WWcvLV7mSWvqRz/5A4rBmGjqr4Igjm9Kr4sikR4gmC7CTI9sdC9D8WJi9cjAKjNV1ruuBBt44r4sLwufEE3SwJeQlExp0ubT1Bwj0hgp0+XEZr07qGYUwpwHi9XlwuNUWsUOxt1LtQMac8/vjjTdctWrSI008/feEGo5g1brebr371q9X7CoVC0Qjnqmw/4Ae6mm/XYn9Sms2FnJr4Ntk0Am5CzR9sQI6vm+rYrY4Rd2OxZ4LAI8pxb83dPkHl9lEoFPslQnjBdZjTPBPdOBJkYly0MXcga9041k6gVF6/3dln0gECyEZuHL0cq6b5F+iZKhT7N42ElLkSUSrLFwohxJQCyp7WS1FCyv5DySoRy8Xo9Hfi0pwpzb+89Bfu23YfI/EBYrs2ExvdSSw7QsxMkpIFHvr9MhYPpJHJFL85IcN3Tmh+5h8bfonFMef+qhE4/SUndqwjB+1ZQTCr4c+5MLIedo36eYEggwTY/mSIjifbiOElVj6/F1q5rotHxx124wkaeIMe/BEvR7b7CXUFae9rI7ooTHt/GLfPXa1rZNt2S6+HEGKS+NLIFaP+xxV7C5fLxXvf+158Pp+aX2wBVaNGoWjAwV6jRqFQKPZ3nNObfPNaPZNq+0zn9snPz0CnjW4LILSKsNNkm4pohKGuQlcoFPs0TqzacI0bZ3u9G8cenr4TrWPcgaMvRtTcR+9DCHUtomL/YKKQMpdOlIUWUjRNmzKWa6JY0mx5M2FFTTIfuJSsEqO5UUayIxzecThu3ZnIveuFu7h7y93EMrsZ2b2NWHyA0fwYSTMDlsX9f17Joq1xSCT40vEZvnNCc2HjNz+GY3Y59391OPxmZVl8yUIkJ2jLCvxZN0bOwJP0kbD8DBFgB0Feoo2dhDA1zRFdDBcurwuP34036MEXcSLGQh1+2rqCRBe307U0SltnCCEEQojq+9w0TUqlUssijK7r0wowHo9Hnf8r9ml27drFI488Qnt7O6eddtreHs5eQ9WoUSgUCoVCcdDi/GDxge4DOptv12J/jtsn29i5Myu3T3nyZA7dPuBCVgSdKdw8YkpHUGW5X9WQUCgUc44Tq9bnNOPEBrFqhRr3zQ6kWevG2Q4yBXbMaaXHnX3qetDLsWq1bpwl48KOFlUTWoqWqRVSGokisxFQJq5fKBoJKXMZ8aWEFEWFivBSEV9OXnwyhu5EB9/2zG385oXfEMvFGEkMEkvsIllIgmmCaXLPHw9jybYEeirJIydkuOmExu8R3YbkC0+zrKztn7odCjp0Zp3YsWgW2nIawawLd9bAKni4Bx/D+BnYGMS7McSzepisJ4DbW44YCxn4FnkItPsIlEWXjp4Ahy9up6O/vRoNpmkamqZN+nwoFovk85ULuyTJZHLK18ntdjcVXypN1YNRKA4+lFCjUCgmYVkWjz32GADHHXccuq4m6xQKxcGNEC4QbcDULsuWa/tQmFTLp5EANMnx0ygSTubKPZsg406b4kK9lmPehK/eydNAAJra7VMjGqGu9lMoFNMjhAdcK5zG5M9UaSfqHDjSrHHjWDtwYtUq9x9oEKvmr4lVm+jGWex8pin2GyoTpXMholSihiauXyhmIqTMphC9ElIUs8W0TUZzo8SyMUdgyY5w9mFn43F5APjB4z/gjufucNZnRkhkYshSCVlyxJc77zqU5dtTGNkUz5yY5rcnTn5faRKiOZCbNxAYcZa9ejMEC07kWGcW2st1XwI5Hb3gJiW93I+XIXwMbQni2t7GBm+UMX8EI+TF1+3FH/ESiDq1XcI9QSL9bRzaFWBt0I9hGHg8HjweD263u+49UvlMME2TQqFAPp8nHo+3/Jp5PJ6mAkxlmZpjURwsWJbFxo0baWtr45RTTlH/+9Ogos8UigYc7NFnmUyGYDAIQDqdJhBQP1oVCoViX0VKq8btk24a9yabCj4T3T7mPIzSNWW8W2WZqAg8EwWfiQKRcvsoFIoJSGmXY9W2T3Dj1MaqTfPTV4vW1MNZXOPIqcSqqWz1mSClnNN6KI3aQjFRSJkrEaX2Cn2FYiGYKLxUbs9fez4+tw+Aax6+hpufvplYNkY8N4ZVLCGLJcf5Ypnc8vNDOGwggy+f5pqT01x7Uv17sSK8dGThe3fCylFn+aN98HS3I7x05BznS3sWvAWdvHSTwiCOl5jmY8QVYtgTZlewi9GObvT2Nkd06XBqu4R7Q0T6Q3j8Bpqm1QkvlfuGYeByOden17pfKuJLPp8nl8thmq2d+2qa1tD5UivAeDwe9X5WKGrYvHkzK1Y4F+AczPOLKvpMoVAoFAqF4iBACB1ECAhNvV0LfTnX7xQb1PPJTHIATV3fp7Jvttyz6RQYl4m5cfvgnRzf1lJ9nwb74FVuH4XiAEAIDfRep9EsVm3nFLFqSbBHnVZ60tmnrofaWLWJbpwloHXsd58ljYSUuRRR9paQMpciSuX+/va3VRw8WLbFWH6MkexInfgykh3hIyd9BL/bD8DX//Z1rv/79YxmRrFMG9s0oVQCy0JYFh0/+RKrhrIEzSxDp6R58WSz+jnqqggvZfHlkKGNLC0ne739WThuaFx86chCWx4sqZNHJ43BM5qXuO5nNN2GsCO8EO3mocMXI/u6CPWECPcGifS14W0bFzmibje9hsGJNcLLRBEGHAGmVCpVxZdcLkc2m2V0dJR8Pt9yPRiXyzVtPRjDUPUgFQrF/KKEGoVCoVAoFAoFUKnt4wHhca4sn2rbFvqT0i67fSbGu9UIOvY0bp9a0YhSuec82HlgZOrjtzBG0JGzcvs0jnxThcwVin0TJ1btUKfRKFYtWR+rZm2HqpizEyjWxKo1+HwRPqS+qIkbZzFCC854zBOFlD0pKt8s6muhqAgpcxnnpYQUxYFIRXipiC21rpdYNsalr7iUoBHEtm0uuetSbnjqekzTwrZsbMsG00RYFpq06HvrdzliJE/EyhJ/WYaxU0pogC6hPV8WV8oCy7HxF1laLrHynqdh/Uvj6yN5xyVjolFAJyvcbHZ5SRoBkrSB3cHmZb08vXQp1uHLaVsSJdjlr3OWCCHwGAaHeDysmiC4TBRhdF2nVCpVXS8VASaZTFYfFwoFWg0IMgyjpXowCoVCsbdRvyQVCoVCoVAoFPOCEJojaDD1BGWr02tSFhvHu01wAMmG8W7pyfsAYDlX0svkHLl9PC26fYL1gk8jkUj41OSjQrFACK0NtNXgXu08rlk3Hqs27sBx6uOUBR17CCFzYL7oNCZ/ZpiyjZLVQ9HqpmB1Uyh1kSt2OK0QwbTEJHFloYWU+RJRlJCiONhJFpLsSu9qKr587XVfQy+42PniLi6//zLuiv26LLw4dZiwbTTbQpM2S8+/lTWxIlGZQzsthXZKEU+t8FJ2tXRm4dWZzfSXU70+8gT843PO+vacI9ZIwEZQEjo5zc1Oj5esN0A6GMHu6GTb0Yt48fDl2Eevgmhk0vPSdR2Px0O7YdBbFlsmul5q68AIIRoKMGNjY3XLisViS6+rEGLaejAV4UehUCj2B5RQo1AoFAqFQqHYLxDCABGdY7dPMzdPA7dPw0i4iW6fAtgFIDb18Vt5wmhI4W8u5lTdPjWiT5NtnO3U1aKKg4uKI2W2jpPGy4NY1uFY1oqqkKIJE68xht8Tw+8ZxWc4t35PDJ9nFMOVRRdJdFcSr+uFBuMU5IsRsoUo2WIHuUKUbKGDbCFKrthBoRRC1/dcLJlquRJSFIqZMZga5KXES9XIsZHsCKO5UUayI+zO7OYLq79IaluW4a3DfHfXNdwn/oK0HeFF2hKkRLNtNGxWvvtujhoz6STH0pclME4tECkLL10TxJfzcpvpKZ9EfPpR+OTfHceLq0bXtQFT6BR1NzG/l5w/SD4cwezrYefyxWw+ZCmppUvJdXfDhHoqtSJLdILY0qz+Czift8VisakDprKs1UjESj2YZgJMpR6M+uxSKBQHEkqoUSgUCoVCoVAcdMyP26dZdFtNbZ8Jjxtt49T2ca5zdZalnYM0mdto3e1jTHb71Ik5wca1fRo6fvxqckSxx0gp51hEqV++UI4UW7oomL2YchGZ0mRBxO0q4DNieN0jeN3DGPpuDH0Yt7YLlxhCiCI+zxg+zxgdbGpwBC/oi8DlxKmJ2to4+pJZxaopFIrJvBB7gY2xjcRysToBZiA2yK74Li72X0JuZ4GRnaPc7voJj7c/irQlUjoNKdGQ6Ngc++ENrBsz6SbHulPjvHhcns4cdGVlnQDTmYU35TdRuQRl9UPwhQfAPeHjSwKW0DB1N6mgl2IwRKmjnWJPF7HubrL9/aSWLCG9eDG211vdT9O0Se6WkGHQ2UCEcbvddXFlFWzbrqsBM9EBk8vlyOfzLUeRud3uhvFjtaJMxYGjUCgUBxNKqFEoFAqFQqFQKPYQx+1jgNY+9XYt9OW4fXJTu31kBmnXP24a90YlQqToFEpndOrjt/KEEVO7fcruHjHhcfMaQEZLR1UsLLZtz5uIstDRXhMdJXMV9VXpa7YTik6s2ki1Ns54fZxyzJo9COTB2uQ0Jr9HpYg4wo2rUh9nvDYOer96fykOKmxpIxDV9+TfB//O08NPVyPHdo4OMDgyyFByiNHcKP8y/AnygyUSu5P8adHveH7phrLoArL8bnPEF8njN1zKUaMmx5IlduIYxXV5OrOSrpykuyzAdJbFl1dnN9JWHtOp98NX7588VglIIbB0F/mQDzMYpBgJk49GyXV1kentJbNoEallyyi2j59fuN3uSbVdAh5PnQOmcutyuab8fLIsi1wuRyqVmiS81NaDaZVKFNlUNWFqnTgKhUKhGEd9OioUikm43W4uu+yy6n2FQqFQKBQLh+P2CQCBqbdrsT8pS00En4m1fZoJPhPdPjbODFZNrZ89dvu4y8LNRCdP/WNRFXeCk8SeerfP5CuCD0SaCSkTRZFK4fiZiit7Q0iZSiSZqbhSWbYnQsp8I4QGerfTON5ZVrNeyiJYg+O1cSqCjlm+lXGnmXEwn3b2qTuChtR6qkJOvRtnMWhdB837RbF/IqUkVUwRNIJo5f/V/932vzwy8AiD8UG2D+1g19gudqdHiBfHSNkpLnzonzGHLdKJDI+s+RsvHfkCSFn33hCAjs2uW37AUaMmPWQIHjNKh1akKyvpydp05xgXYLJwXOqZ6jfzqx+G/3y4yZgB2+WiGPBg+v0U29ooRCLkOzvJ9vQ44suSJWT6+qAspEwUXjweD4Fy/ZeJyxu5Xhq9bqVSaUoBJp/PUyqVpu0LnHowU4kvlXowrYxNoVAcPLhcLs4///yqU04xNUK26k1UKA4ikskk4XCYRCJBW1vb9DsoFAqFQqFQHAQ4VxlPcPvUiTsVt096im3GawBB61fpto4A4Z9S8Gnd7RPcIzdCrZAylVgyUwFlbwkpc1FUvtHyfVlI2deRdnqyG6d8H3MHkJ+mB48Tq9ZQyFmC0EIL8TQUBxEV4SWWjbEssqwqvtz57K+4e8Mf2T68k6HkMKPZGHEzQYYUpjR5/S/fgT0ChWyBZ059jB1HbWnYv4bkoh+s5eiYSR8ZnloT44nDMnTnJL1ZuyzASLrK4stho+BpoWyKBKSmYRsGps9HMRikGImMu1/6+sgsXkxq6VLMYBBd1ycJLM3uzzTmS0pZjSJrJsDkcrmWvyN0XZ+2HoxhGOpzWqFQzJhdu3bxyCOP0N7ezmmnnba3h7PXaHWeWTlqFAqFQqFQKBQKRUsIURZB8ANdzbdrsT8pzamFnNraPk1rANWIQo3cPs2O3eIYbenCxoct/djSi2X7sGwvlu3BtLyYtgfTMiiZzm2xZFAyDQolN6ZplNd7sCwvpm0A83O18VwVlW+0XAkp+y5CC4J2JLiPdB7XrJNSlmPVdlTFHFm5b1Zi1QpgbXZasVGsWnjcfeOaGKu2SMWqKarCy2hulJHsCMf2HotAMLIjxo33/5A/7/gLI6ndjBbGSNoJsiKDKS2klLzyxtcjEjpWyeKFM55i4NitDY8hAPfQBo6KWSwizeZtI2wUgr6sTV/Ooq8swHTlnNovXZlH0cv7vmUDsKHJ2AGEwHK7ML1ezECAYlsb+WjUcb/09pLu7ye1dCn5ri6McgH7ibVdPIZB24Tluq43Pug0WJZVVw+mkQBTKBRmVA9mKgGmEkWmPuMVCoVi76OEGoVCMQnbtnn22WcBOPLII5V9WaFQKBQKxbwghAtEGAhDgzmtiiOl6iyRFpbdzHFiYls5pJ1G2qmyY8eJaxNkEWQQMosmctXm0gq49AK6ni/fz+PSC7i0ArpeKo/RRCeFLlLOoGY391bFsj1loceLJX1I6cPGX71F+JEEEBWXjxZCaAGEFkTTQ05ztaHrIXTdh76HNVIUBy5CCNC7nMaxzrKa9VKWyrFq2xu4cbaDHAOZADPhxKoVJgo5YjxWTV+CcE2MVetWsWr7IVJK0sU0sVyMWDbGSHaEEyMnseuFYQa3DHPr5p/wSOYh4qU4KZkiq2ewsJxoMQmnfO9M3GlHwNv0qqfZ2UR80Ys6vtIu1hVMFpNi19Y4O0su+rMWi3I2/VmL3qxFT1bSmQOP9eD4zs+XW7PnANi6juXxOO6XUMhxv3R0ONFjvb2kli0jt3Qp7lCoqeMl5PGwpKbmy55+zpqmOaUAk8/nKRaL03dUZqL40sgVM1vBSKFQKOYC27Z56aWXGBsb49RTT1Xzi9Ogos8UigYc7NFnmUyGYDAIQDqdJhCYOiNfoVAoFArFwYeUEinlnBSVb7Z8oX6qCCEaOEvAY5i4XSXcriJuvYjb5Qg7br2ArhfQtbzTRA5N5KsCkCMMOeIQMouQGZoW8tkjXFPGu9XV9mm6TWWZHyHUhJ5iHCdWbedkN05FzJG5aXowxmPV9CUIV60bZwlCO/h+Z+0NpJRkShlGsiPEsjF2xXfxwpZNHFM4nti2MYa3xfhN7pc85XmcjEiTdWWxhYWUVOu6nPLd12JkvQBsOmMDO4+bHDumF124cwan3nYMq+MmS0iRXDpCojvNopzFkpxFf86kP2PSm7Xwma27L6EsEmoaltuN5fVSCgYphMNO9FhnJ9m+PjKLFpE/9FBEX19VcGkmwlRcL3MhclfqwUwlwOTzeUzTbKk/TdOmFWBUPRiFQrE/sHnzZlasWAEc3POLKvpMoVAoFAqFQqE4iJFStlxsfrbL96aQsqdxXgsZ7eW8ToVJtXzG49zGH8uJ8W4TI+FkpmaC3HQcDzLhpL41O36r4xS+8bo9DQQfRI3Tp2F9n+D4PniUy2c/x4lVWwXuVc7jmnVOrFpsXLSxttc7cqxBoAjWFqfRKFatrc6B4zhyltTEqnkW5Hnub0gpyZayxHIxdiV3sWnLZjZv38q2Xds5PnYy8YEkY8MJ7m37Axu7nyHvzmFpVlV0qXDyta/Fk6mIL88yeNxA+QBO00s67qyBO+dB6CaHyDjLRJqXv1DCHA2zNGexJG+yJGOyKF0iVMqh2WkE94wfZFu5TfV8ANvlwvJ6Mf1+iqEQhfZ2ch0djvjS309xxQqsQw7BEwg0FF+CNcvmWrywbXvaejD5fL7lejAul2vaejAzrVmjUCgUigMDJdQoFAqFQqFQKBR7gWZCymwKy+9rQspcF53f368adibcvKB7gY6pt22hPyktkNkJ9Xwm1vepqe3TqL5PXW2f8lXeMlcWgUaaH7vF5ww6spmTp0YAEg3dPRMFoIBy++xjOLFqnU7jGGdZzXonVm1XczeOHQOZBPMZp9FAyKnGqtVGqy1x2gEWq5YtZdmd3s2mlzbzwpZNbN35EgOxAYaSuzn+xVNIjWRIj6Z5cOVf2XrYRizNavhefPbabVXxZeiMYdKLy5GNEnSzLLxkPbizBgKbXleBFe4MZ2wxcMX6WZ63WJYtsSRTIJov4DYzaGYc5G+dv68EdpTbFEhAahq2YTjiSzBIMRwm39FBrrub/KJF5Jcvxzz8cFzRaJ3LpSK4dJbvz6doYVnWlAJMpR5MqxiG0VI9GIVCoVAoGqG+IRQKhUKhUCgUigY0ElLmUkRpNQJlLqgVUuZaRKk4UhQLhxA6iBAQmnq7FvpyxLziZPGmFbdPg22Q2XLPljMRL5Nz4/bBO0HMmez2qXf8BBuLRFoA8Kqr1ecZIdzgWuI0Jv8vSjtTjlWruHHGnTlOrFoW7CGnlR519qnrwY3UF427cfTFzrEqwo4I79W/ca6UY8vAVp7buJHNL21h+9B2BuNDjGRGiBfHOOahU8gnCuTSeR4/9QEGjnipcUcCsvfIqviS681jak6M4rjw4rhejLwHb8BDt8/HSk+Ws3Z3E/yzjxV5k+WZIp25LN5CBlchiVYogLXN+buUgK3TPycJIAS2241Zcb+0tTniS2cnxUWLKC5fjrlqFSxahMfnm+R+iZbvz3fdlEos53T1YEqlUkv9CSGmrQezEM9LoVAoFAc2SqhRKBQKhUKhUOyXTBRS5lpEsaz5qCnSGCHElELJTASWigOldrkSUhTNcCazPSA8oEWn3raF/qS0y26fifFuNYKOPRO3T2UiNQ92HohNffwWxui4ffytu32qDp/GIpEQ6mf1TBFaALTDwX2487hmnZQS5BiY22scObWxagNACaytTqPB312EkLVuHH0x1ESrzTRWLZ/N8/Szz/L0C8+wdcdWdo4MMJwcZjQ3SsJKkCbFUb87BTNjUiqYPHvmYwytbmA78TstuLULT9oHgJZ2Jvc1U8Odc9wuRt6Dt+DFZ/rpXtRJtzfKIb4i79BcdDxxKCvzRfqyafzZJO50Ei2VQORyUHoRUXFSTuN6qb7egHS5sCrul1CIQiRCobOTYn8/peXLKR12GPZhh+EJhyfFjkXLtwsljEkpKRaL09aDafU7XNf1lurBKHFXoVAoFPONOqNUKBQKhUKhUMwLtUJKI0HENE1s2560rtnyRuLKQqFp2pSxXK2IJVMJLkpIURwoCKE5ggbBqbdrsT8piw3i3SYLOnI6wacq/IDj9kk5bU7cPp4a4SY4ScgZd/sEp90G4TvoJ4SFECCiYESBo51lNeulNMuxak3cOPaI87c1n3Ua9X9L04Shnb0Mbu/liZc0nt9tsiteYncuS7yYJSUzZLQMeXeWo3/6MmTB6eD5sx6vF1/KokuFxbmVeLKO+OLOGmiW5oguOQ9GwYuv6MNvBwjRxpoTjqQ32kN/WKdXX8misRRL8mnaUmO4RoaRu3cj4qOIzHZE4Umw7ZbfM5XnKzUN2+3G8nophkIU29oodnZS6uujtHQppRUrsI84Aldf36TYsUDZHbLQ/4u2bTes/zJRlGk11tPtdk8pwKh6MAqFQqHYl1BCjUKhUCgUCsVBSkVIaSaizNSd0mj9QtFISJnLiC8lpCgUewchDGfSfk7dPo3cPE3cPg0j4dIT3D4FsAvA6NTHb+UJo427fRrU6hl3+9S6exrEu1XdPu6Wjro/IYTLcce4FmPbJzG6K87Ai4Ps2jLM0LYYT+16jM2pjYzmR0iYSVIiQ04vUPAUKHqLHPeDV6BbLkDy/PpHGVqzw9EVG2iLeVcOT94RXzwpL56UF6PgwVvw4TP9BGSQsCtM1BPlxHeezOL+fnp7w/TrCZbk4wRHBpBbtiB374ChIcTYACL1PCL/V4Rpzkh8AZBCYLtcWB4PJb+fUlsbpY4OSr29lJYswVqxAmvVKrRDD8UTCNS5XwKGsVe/yypRZFPVhJlJPRiPxzOlAOPz+VQUmUKhUCj2K5RQo1AoJuF2u/nUpz5Vva9QKBSKvUMzIWUuI74WiolCyp7UQ1FCikKhmA3z4/Zp4ORp6vZpIvhUa/tIwC5vlwaGoMnHdOtuH6NGvGkU3RacUNtnKsePf96dB+l4mh0v7GLbph2MbYuze8coo7vGeDa/gR1iG0k7WXW7FDwFSr4CJX+Rk797JrrpTMo/v/5xR3xpQslfQk85UyGBRJC2WBvekkHAMghKjXaXi06fRm9I4+T/M8yypRaLluXo1rIYW/zYm3TsrQZyoIQYGkKMbkWkcmjZOxClEkg5c/eLrjvRYz4fZihEKRrF7O7GXLQI85BDsFetgtWrMaLROveLfx9wg0gpKZVKTcWXyvJW67JpmtawHkytIOPxeNT3vkKhUOwHuFwu3vzmN1cdjIqpUUKNQqGYhGEYfO1rX9vbw1AoFIp9HinlnNZD2VeElLkUUSoRYGpCRaFQHGg4bh8DtPapt2uhL8ftk5va7SMzyAYOoPF9aiLiKJZ7LoIsgjU29fFbe8Y1bp9G9X0cB1Cltk+x4GFgq83AliI7t2bZOhijNKCT2J0jGcvxgv95hoKDZF0ZCkaeordAyV+g5CtiekxO+++z0E1nymJa8cVXwJ0N4jZcdGa60UYgIAO06WE6vFG6Qt30d/RxSP8yXnbPqSxftQx/0AfxOPLppzE3bMB+4QXkli3w0na0kWFEPImWyaIVSnXRY618m0kAIbDdbkyvFzMYwIxEHPGlrx/7kEOwV65Erl6Ne+lSPD5fVXzx7UMuECklhUJh2nowtj1FhmANuq5Pcr9MfLyQ9W4UCoVCMb8YhsH73vc+2tvbMQxjbw9nn0cJNQqFQqFQKA5YJgopcymiVOqoLBStCCl7slxNiigUCsXew3H7BIDA1Nu12J+UpQaCTyO3TyPBZ7IDyDQlu3e6GNwKu7abDO5Ms304y66UjXtHG+mEm1xKY/PiQWI9YxS8RUq+Yll4KWF6nJi40352FnrJmYZ48XXPs2vt9ubPIVIiUPLgC7gwZT/tcQ9hdxudvgi9kW4W9yzi0CWHcsTha1j95WMxXB5nR9PEeu45zKeewn7uOeTmzYjnXkAM34c2OoqWTqPl88hSCVF+TVu9xlcC0uXC8hhYfj9mOIAZDWL1+LEWGdjL3XC4gXaYwBPIYrhSGK4sbiEBExgG3QW6AbqG0Iugj4C+GFxLQHhbHMmeY1nWtPVgCoVCy/VgDMNoqR6MQqFQKBSKxiihZi/xnve8h3g8zs9//vO9PZSDAvV6zwzbttm2bRsAS5cuVVdBKxQtIKUkFotRKBTweDx0dHSoie8WaFVIme3yvSGkzIeIooQUhUKh2PcpFkvcec3vGNg0RP+KHs798HoMw5mYtmyLB3c+yHBmmO5ANycvOhldmz/nhBBuEBEgAhMOY9s28eEEO1/cxeDmYXZv283Qzt3sHBlgOGkRy2UJvNiPlcryutxzPLNujIeXmWzyeyj6SpT8RUd46QP64LTfjYsvu48bY9eayeKLI4gI/IuzdAmDYMQiHPETL/bQFRAsimgs7YaVS2wOXSzp9um0/fvz1e8+KcEc1Ck9m0BujMPmFxD/W0IbLqKP5hGpIlauiFY0QUp0Jj3tpkhAahq2243l92O1tWF1dGD19GAvXoy9fDniyCPR1q7F6OvD4/Hg0nXcE76XpZQg42DtAGsHsrSd2GiCQjKFx7WNqP8RhCiCtc1pNHAyiQBSX+wIN/oSRPnWEXIWI4SvpedUiSKbqiZMsVicviNACDFtPRiv16vqwSgUCoViEpZlMTQ0RCqVYuXKlXR1danftVOghJoDhC1btnDppZdy7733Mjo6SmdnJ8cffzxf+cpXeOCBB3jve9877f6HHHII999/Py9/+cs566yz+PWvfw04IseNN97YdN9ly5axdevWhtutX7+e3/72t5P2+eAHP8h1113HLbfcwtve9rZZPGPFfJLL5Vi+fDkAL774IitWrNjLI1Io9m0GBwfZsGED+Xy+uszr9bJmzRr6+vr24sj2nFohZa5FlL0hpMyXiKKEFIVCoTi4+d6nf8TPvvErbGv8e+17F/+It/zbG1j8gQ4uvftSNo1twpIWutBZ0b6CL77mi5yz8pw9PnYmmWXnxgEGNw8z9NJuRnbGGB6MMTQ2xEh2hHgxTkomiW7owSpa2LZk+4kvMnroMMVK1FigVGfm+d6Di7mo+CI68KlueOkwZ/LARMfCqYuioeEtellyQj99oT7au8OsXXwoyfYxFnUvYsXS5aw4ZAW94R46/Z20edrQvq6V3T5ZbCtNcXQXpaefRm54Hh5/CX45iD44gh5LYCbS6JkcWqGIZll4AM8MXhcpBLZLx/YaWEEvVsSD1enD7vMgl3rgUB39CA3X8hKGkUPTbDQq7po8iBEQOdAGQDzhOJ6KATADSOE0MamWjxP/NjjSyzPPaeTzpfJoTsLrvYjVR/bT15UGcwfS2g7W9qqwgz3sOJjM553GZCHHFp0U5WHkzUPIlxZTKHWSL4XJF7zkixr5fIF8Pj+zejBGDq9rB14j7jR3Aq+RxBt6Bf7Of8bj8ahzHIVCoVDMmMHBQR555BH+6Z/+CYDvfe97LF++/ICYJ5kvlFBzAFAqlTjzzDNZtWoVt99+O319fezYsYO77rqLeDzOO97xDs4666zq9ueddx5r167lC1/4QnVZV1cXAN///vf513/9V77//e8zMDBAf38/3/zmN/nyl79c3bavr48bbrih2mftlTNnnXUWN9xwQ/WxxzP5VDqbzXLLLbfw6U9/muuvv36PhJpisagyDueZXbt2KaFGoZiCwcFBHn300UnL8/k8jz76KMcff/y8noRUhJT5EFEWWkiZy8LySkhRKBQKxULxvU//iJ9+/ZeTltuWzXd+fi0b/Y+DIfG7/OiajmVbbBzdyEW3X8T/nPc/dWJNsVBk4MUhBjfvYtfW3ezeHmN0cIzY0Ci7UyOMFmIkrSQZkSHvytLz2FJEORBt62nPMbJykKK/iLmkBEvqx/Oyp9bjsssyRCRLYtGos6cQaAiE0DAKHpZmi6x3v4hWNly86TlYNwQdWejMWXRe8F46Pv9Vwt4wmhh33lfOSQq5HOamTVhPPgl334XYuhVt506yw8Po8TiudBotl0MzTbxS0ppHpHwMQOo6tseDHQhghcPYXZ3YvV3IpX2Iw/rRjlyCe20vrpCNRhpNZtAnRrs1qgFkZ4DKRTclxx0j4zDFqVCjULBdY+t4bNN7yo/Gzz3y+RyP/X0Txx1+D72dI+PijutwcB+LLT0UCpAv2OUmyechX9TJFwzyxRCFUhhbTpzGyZVbPS6Xjdej4fN68fra8PrCk1wxeu4biNz3mzy7J6GYQ3g/0/wFUCgUCoWiAZV5klKpVF02NjZGX1/fgsyT7K8ooWYOue2227j88st58cUX8fv9HHvssfziF7/A6/Vy8cUXc/3116PrOu9///tbznkFOOOMM1i7di0AP/rRj3C73fzLv/wLX/jCFxBCsGHDBjZt2sTdd9/NsmXLAMflctppp1X78PnGT38Nw8Dv99Pb21t3nHQ6zU9+8hMeeeQRdu3axQ9+8AMuueQSwuEw4XC4bttIJDJpf3CEmUbLa/npT3/K6tWr+T//5//Q39/P9u3bWbJkyZT7VKhEmJ144ol8+9vfxuPxsGXLFrZv386///u/8/vf/x5N0zj99NP55je/ySGHHNJSvwqFQjEbpJRs2LBhym02bNhAR0cHtm3PSWH5fU1ImQsRpVJsXgkpCoVCodjfKBZL/Owbv2q4TgrJi69+mnwpT5vWRqloUrRK2LaNtCHpSvHOb1/Iut+fSF7LkTfylHwFlt5/eFV8efHVT7P7iJ2Y3SVkg6/Jzqf7cRUd8aXkL5KLZhzhRWhoQsNn+wgRIuyK8Pb/+0ZWLltB3/IedkeGSOoJugJddPo7ifqiPHrfoxRTac5529sQ9rjE8PJtToOyMPG17xPbbZEYHEQfGkIfHcWVTKLncmjFIn7bbrmWTrVPIbANA9vvxw6FsDs6kL29yCVLECtWoK9ejevoo9EXLUJoGjMNZm69to8JMtukVk+6Ku7IJvV/pJ3lme1vaXJUAUie2nQK6fSfKJhh8sUI+WKYfClMoRSClp6ZjcedxuNO4TMSeNwJvMYoPiNevp/A607g0idGm3lA7wa9H+QSyC+G3PVTHyp7A3bw39A0dXGkQqFQKFqj1XmS3t5eNQcwASXUzBGDg4Ocf/75fPWrX+XNb34zqVSKv/71r0gpufLKK/nBD37A9ddfz5FHHsmVV17JHXfcwatf/eqW+7/xxht5//vfz0MPPcQjjzzCP//zP7N06VI+8IEP0NXVhaZp3HbbbXziE5+YdTbsrbfeyhFHHMGqVau46KKL+MQnPsFnP/vZGb1p7r33Xrq7u2lvb+fVr341V1xxBR0dHXXbfP/73+eiiy4iHA5z9tln84Mf/ID/+3//b8vHuPvuu2lra+MPf/gD4DiK1q9fz6mnnspf//pXXC4XV1xxBWeddRZPPvlkS46bQqFAoVCoPk4mky2P50BicHCQwcFBcrnxK7Ief/zxqtDX19enFG+FooZYLFYXd9aIfD7P73//+wUZT6tCyUwElspjJaQoFAqFQjGZO6/5XV3cWS2JRTFy0QxaSSdbGD+/Nj0lbMMCIBWJ89Ab/oxmj0/QL3r0UFwlNy6Xju7VsPwWmtDRNIHf9hPS2mj3ROkMdPKZn/87q49cRdeSTjaObmQ0N0qnv5MOXwcRb2SKOjhH1D0aGRmhWCiy/K670Ka4CEQASElnTYpCMyQg3W5srxcZDGK3t2N3d8PixYhDD0U/8khca9ciDj8cDGNGdWXmCyFcINqAtqm3a7J8ZGSEfPGBKfcsWQE2DryhyfFtvEYer5HB606V48hieN0jeN278boTeNxJNM1q6fnUUyhHrW0HHmxxHxuyN0HwPbM4nkKhUCgORmKxGAMDA4yOjtbVQ9uyZUt1jjYajRKLxejs7Nxbw9wnUULNHDE4OIhpmpx33nlVV8u6desAuOqqq/jsZz/LeeedB8B3v/tdfve7382o/yVLlvCNb3wDIQSrVq3iqaee4hvf+AYf+MAHWLRoEf/93//Npz/9aS6//HJOOOEEXvWqV3HhhRdy6KGHtnyMioACToRZIpHgz3/+M2eccUZL+5911lmcd955LF++nE2bNnHJJZdw9tlnc//991fFoxdeeIEHHniA22+/HYCLLrqIT37yk3zuc59reQIwEAhw3XXXVd/c//M//4Nt21x33XXVPm644QYikQj33nsvr3vd66bt80tf+hKXX355S8c/kLn22msnvQ4f/ehHq/cvu+wyPv/5zy/wqBSKfZdagXdv4Ha7MQwDwzBwu90NhZdKgftmjxstV6KMQqFQKBStMbBpqOm6YqCAFBLNdr5ThRAIAZoQSCGQ5eCsTm8nh/gOoautm0Wd/Vy+6zK6I90AbEtsI1vK0unvpN3bPoXwAkd0HtF03XRUzmn8g4MtbW/ruiO8RCLQ1YXs70csW4Z2+OG41qyBNWsQ7e0IWvOIHCi0em4YjUaJRqPVKLJKHJlhGE3PwaS0atw+6cbRbbVun1rHj50qx7ilnD4o0Di4rQHWtta2UygUCoUC57vwt7/9LTfffHPd8u9+97vV++effz6vec1rFnpo+zxKqJkjjj76aF7zmtewbt061q9fz+te9zre+ta3omkag4ODnHzyydVtXS4XJ5xwwoziz0455ZS6E7ZTTz2VK6+8Esuy0HWdj3zkI7zrXe/i3nvv5YEHHuCnP/0p//mf/8kvf/lLzjzzzGn7f/7553nooYe44447qmN8xzvewfe///2WhZp3vvOd1fvr1q3jqKOOYsWKFdx7773VN9/111/P+vXrq4rpOeecw/vf/37uueeelt+g69atq3PJPPHEE7z44ouEQqG67fL5PJs2bWqpz89+9rN88pOfrD5OJpMtx7EdSHzwgx/kjW98I7lcjpe//OUAXH311Zx66qkAyk2jUEygUR2uRixZsgSv11sXVzYxvmxixFll2VTfFaVSiVKpRCaTmaunVKVVUWemolCjbZQopFAoFIr9lf4VPU3XGRkPQgqkJgkGA3gDznmDLW3yZh635qZgFfjZRT/lZUte1rCPpeGl8zLuiVTOabItnu9rX/862ic+MY8j2j9p9dzw8MMPn/FVxELoIEJAaOrtWuhLSonMfB/SX51+Y31h/gcVCoVCcWDg8Xg466yzOOmkkygWi3zmM06tsw996EOsWrUKcC5YaPU782BCCTVzhK7r/OEPf+Bvf/sbv//97/nWt77FpZdeWo3nWghCoRDnnnsu5557LldccQXr16/niiuuaEmo+f73v49pmvT391eXSSnxeDxcffXVk2rUtMKhhx5KZ2cnL774Iq95zWuwLIsbb7yRXbt24XKN/+tZlsX111/fslATCATqHqfTaY4//nh+/OMfT9q2q6urpT49Ho/6gGA82qx20veYY47huOOO24ujUij2XTo6OvB6vVPGn3m9Xo466qhZixEzEXWmWz7dvrWiUGVZbfG/+aJVUWcmApAShRQKhUKxEJz74fV87+IfNYw/C+/swDcaINuRxvCPX2iWLCSrQs2a7jWcvOjkSfsuNJVzmi1nn82R3/8+QsqGE/4SELoOH/7wQg9xv6DVc8OJ8eALjRAC6X8XpL8O/397dx7eVJm3cfybk6TpBoUW2hTZFBBkFxBUHDcUXGBGQEdFURBBVNxQUdRBQdyQzcFBGQXccHlHcRmXcRQHFxRQFBhBZagCKmmhLaV72uSc94/Y2NItXVPo/bmuXj056++EkrTPned5qGq+QwOixzVWWSIicgRISEigXbt2xMfHl3k/PProo+natSvQNN4LmyIFNfXIZrMxdOhQhg4dyqxZs+jUqRNr1qwhOTmZDRs2cOqppwLg8/nYtGlTjRq/N2woO4bs+vXr6datW6Xz0dhsNnr06MHnn39e7bl9Ph/PPfccCxYsKDdM2AUXXMBLL73E1KlTQ661xC+//EJGRkawF8a7775LTk4O33zzTZm6v/32WyZOnEhWVhatWrWq8XUGDBjAK6+8QmJiIi1bVj2WsITG4XBw0UUX8Y9//KPWcx6JNAc2m41evXqxadOmSvfp1atXncIBwzAwDAOn01nrc4SqdGhTl0AolJAoXKFQQwRCCoVERJqviAgnY28ZyT/mv1Vum82y0fWj3nw7ZiMZWem0ahmYM8Zus2NhUWwWc3nfy6sczqyxlP6dxhsfT1RGRiCUKbVP8PH06RDCPKDNUWP8blhfDCMCM3oi5C+vfKfoiRiG/q1FRCR0pd8L7XY7Q4cOZd26dRjG74OhNpX3wqZGQU092bBhA2vWrGH48OEkJiayYcMG9u/fz3HHHcdNN93Eww8/TLdu3ejRowcLFy4kKyurRuffs2cP06dP55prruHrr79myZIlLFiwAAhM9n7vvfcyfvx4evbsSUREBB9//DErVqwIdi+ryttvv82BAweYNGlSuZ4zY8eOZfny5dUGNbm5ucyePZuxY8fidrtJSUlhxowZdO3alREjRgCBXjvnn38+/fr1K3Nsz549ueWWW1i1ahXXX399TZ4WAC677DIeffRR/vSnPzFnzhzat2/P7t27Wb16NTNmzKB9+/Y1Pmdz53K5eOyxx+jZs2dwziURqVhycjIDBw5k27ZtZT4tEhkZSa9evQ6rIQPDEQrVNRA6dH1F+5QOhUzTxDTNsIRCDREIKRQSEQm/KfPGA/DaorfL9Kwx7AYDI06A1bDzzG/Jsg4SEe3EbthpE90Gv+nnuS3PcXnfy0mMSQxX+UFJSUn0WLWKqIyMCrfb7PZASDMvhOGymrHKfjd0uVz07t27Sf1uaLS8A7PgH2BlH7olENK0rL49QURE5FAl74Vbt25lypQpdOzYkcTExMOynaQxKaipJy1btuSTTz5h8eLFZGdn06lTJxYsWMC5557L2Wefjcfj4corr8QwDK666ipGjx7NwYMHQz7/FVdcQUFBAYMHD8Zut3PTTTcxZcoUANq3b0/nzp2ZPXs2u3btwmazBR/fcsst1Z57+fLlnHXWWRUObzZ27FjmzZvH1q1b6du3b6XnsNvtbN26lWeffZasrCzatWvH8OHDuf/++3G5XKSlpfHOO+/w4osvljvWMAxGjx7N8uXLaxXUREdH88knn3DHHXcwZswYcnJyOOqooxg2bJh62NRBcnIy9913X7jLEDksJCcn43a7ycjIwOv14nK5SEhIUON5FUpCocZQEs7UtjdQTYaSawqhUEMFQgqFREQqN2XeeCbMvYR/Ln2fvSlptOuSxKjrRhAR4WT5Xat46ZHXOXhUBra2FvcsvY0z+p/GH1/+Iz+k/8DUt6fyfxf9Hw4jvH+eH3zjDbq8/DIAvuOOI+2dd4hbtYqY1FRsXbsGhjtTT5qQlP7dcPPmzRQWFtK9e/cm1zBl+vb/HtJEDAd7YmBOmuhx6kkjIiJ1UvKet2nTJiZPnkyfPn3UTlINm1WTGe0lLE4//XT69+/P4sWLw11Ks5GdnU1cXBwHDx5slmGPZVmkp6cD0KZNG72IioiEqLJQqCGGkjPNqsaUbzglIU5tQp6a7qv3HxE5Urzx+Hv87cYVADhdThZ/Ogf7MTbOXXUuuUW5XHfCddxz6j3hKzA9HbN9ewyvF390NPa0NIiNDV89R5AdO3awY8cOkpKSOOGEE8JdThnmwbuh4B+AAxK/bbQP0YiISPPg8Xj46KOPiIuL4/zzz2+2f9+F2s6sHjUiUk5+fj6JiYHhF3Jzc4mJiQlzRSIih4eSnkIOR8P/imVZVqMEQiXfSzRmSFRVKFTfPYea6x8NItI4Lph2LgnJrbn/4oUUe4u54aS7mfPmHSwcsZAp/5zC018/zcT+Ezmq5VGNX5xpYp14IobXi2Wzkf3GG7RWSFNv3G43O3bsYP/+/RQXFzfKELMhK/xX4HvEYIU0IiJS7woKCrj88ssBtS+GQkFNmO3Zs4eePXtWun379u2NWE34xVbxB8F7773HH/7wh0asRkREpOmy2Ww4HI6whEL1PbdQUw6FGmIoOYVCIs3TH8aeyIK1s5kxbDa+Yj9/GfUw05+eyt1/uJtTOp4SnpAGYPx4bCkpWMCOyZM59qyzwlPHEapFixbExMSQl5fHvn37OOqoMP07H8L0rgcrJ/Agtvoh00VERKRhKagJs3bt2rF58+Yqt69du7bR6gm3qp6LpvILrYiISHMTrlCoIQKhphIKNUYgpFBIpOnpc8pxPLl5PtefcCfefC8LJj3BhDkX0++efuEp6Nln4bd5RPcNHEjRtGl63ahnNpuN5ORkdu7cicfjaTp/1+b+NfDd1gojIkw/fyIiIhKkoCbMHA4HXbt2DXcZTYaeCxERkeYtHKFQXYaHC3XfikIhn8/X4PcYaihUH8PLadgckdB0Oq49z+1cwuQ+08nOyOWZWa+QsfcANy6dzNa0rbyz4x3uPOXOhg9MfvgBJk0CoDA+ni9nzeLEJjbZ/ZGiJKjZt28fPp+vUd7jqmKaPij+JvAgcmRYaxEREZEABTUiIiIi0iyVhEKNwbKsRgmEwhkK2Wy2akOd+uo5pFBIDnfx7tY8/9NSpvSZTtrudP755L/Zk/Yzr5+2ioLiAjrGdeSyvpc1XAFFRXDyyeD3YzmdfDZ/PhGRkSQkJDTcNZuxli1bEh0dTX5+Pvv37yc53IFY/rOAP7Dc4sawliIiIiIBCmpERERERBpY6RCjoVUWCtV3IHRoKGRZFj6fLyyhUEMOJadQSBpKdGwUz/xvCdMGzyRl8y62vP4d7e1d+N+Qb7nro7vok9SHvkl9G+biw4ZBZiYAux5+mMK2bemUnKxhzxqIzWbD7Xbz448/4vF4mkBQsyrw3d4Fw2gV1lJEREQkQEGNiIiIiMgRJNyhUEPOLVT6uuEIheoybFwo4ZFCoebH4XCw9KtHuPv8h/jq/c04X21JdGQceX2zmPzPybx/+fu0imxVvxedNQs++wwAc+pUdvTqBcXF4Q8PjnDJycn8+OOPpKWl4ff7G+U1uiKmLxXMXwIPYiaFpQYREREpT0GNiJTjcDi48sorg8siIiIiFTlcQqHa9Bwqfd3GDoVC7RFUl7mFFAo1HYZh8NB7dzN/0lLeX/kfOvxfd7bGfMGeY/Zww3s38OwFz2LY6unf66OP4P77A8t9+pAxZw7FGzYQERFBfHx8/VxDKtSqVSsiIyMpLCwkPT2dpKSk8BSSu/C3BSdEjglPDSIi0izY7XbOPPNMXC6X2hdDoGdIRMpxuVw888wz4S5DREREJOhwCIVqO5Rc6es2RiAEVYdC9RkI2e12bDabhtQKwW3Lr6PNUfGsmvsax77Wny2Xr+MD6wP+uuGv3HzizXW/QHo6nH9+YDk2Fj7/HM+PPwLgdrsV3jWwkuHPdu3ahcfjCV9Q4/0g8D3iJP2bi4hIg3K5XNxyyy20bt0al8sV7nKaPAU1IiIiIiIipYQzFGrIQCicoVB9DxtXWVB0uIdCE+ZcQkK7eP56/VN0+aA3/xuxhbX//Zgbh9xYt141pgknngiFhWCzwUcfYUZHk5qaCqBhzxpJcnIyu3btIi0tDdM0Gz0oMb2fgpUXeBB7S6NeW0RERKqmoEZEyrEsi/z8fACio6MP6z92RURERJqycIVCoYY9dRlervR1D13XkOoyHFxNjjUMo0F+Tx41dTjxya2ZM/ZRnAUR5O92siH5G04aObD2Jx0/HlJSAsuPPgonnEBmejpFRUU4nU4SEhLqp3ipUnx8PC6XC6/XS3p6OomJiY1bQO7jge9GPEZEr8a9toiINDuWZVFYWEhBQQGWZal9sRoKakSknPz8fGJjYwHIzc0lJiYmzBWJiIiISF2FIxSqS++fmhxbWrhCoZoMB1ddSNT/rJ48vOYv3DXiAXx+P7P+9DA3PTGZc68eht2o4b/fs8/Ciy8Gls85B269FYC9e/cCGvasMZUMf7Z79248Hk+jBjWmWQTFWwIPIv/UaNcVEZHmq6CggIsuughQ+2IoFNSIiIiIiIhIvSodCjmdzga9lmVZZXrt1FePoMqCotIaOhQa99goXrrlbQp9Xq57/Xo6bWvPvaPuDjkQcu3eTaerrsIG+BMTSXvqKexpaRiGgcfjAQK9PAoKCsqcR594bTjJycns3r278Yc/y18BmIANYm9onGuKiIhIyBTUiIiIiIiIyGGrZF4awzDCFgrVJOipyfByrY+KY8Lfx/DYg0+R1usX0viFxSueYOq4idUXW1TE8AkTsJkmpt3Ofx5+mMLNm8vttmXLlnLratpDqLZDyTXHUCg+Ph6n00lRURGZmZm0adOmcS6c/3Lgu+NYDCO2ca4pIiIiIVNQIyIiIiIiIhKCxgyFgGBgM/y8szljwjlsP3YLH7V9n6Sn2vHg3++pcni5TpdfTkRODhaQMncuLXr2JPq3bXl5efh8vuBcO36/H8uygtctOUdxcXGD32Nt5geq7fByTSEUMgwDt9vNzz//jMfjaZSgxvT9DGZgqDtirm7w64mIiEjNKagRERERERERaYJKgpRW8a1Y/+rH9LqhHz+32M3qhJcw/uTgic8exeGo4M/6WbPgm28AsE2dSrc77wxusiyLDz74AIBBgwYF50kpHfjUZW6hUHoNhSsUaohAqDahUHJyMj///DOpqan07t274QOk3EW/LUSAa1TDXktERERqRUGNiIiIiIiISBPninCxYdE6et/Xl8z4TN5LfJMJ3b08tXUhUTGRv++4di3MnRtY7tMHnniizHkyMjIoKirC6XSW6c1REgo1Zk+hugZCNQ2FSnoghSMUKv24JMjxer18+eWXxMTE1KnnULVBj3dN4LvrlMabE0dERERqREGNiIiIiIiIyGEgITqBt29+i7OeOJv9x3r4+teNXH70dTz134XEJ7WCzEw47zywLIiNhc8/L3cOj8cDgNvtDlujfUko1BiqGh6uvucWqk0otG/fvjrf46EhUOkgJz5mC90SC7As+N+vo/Dv3VannkNNYfg4ERGRI5GCGhEpx263c+GFFwaXRURERESkaRjYbiALxsxn1lv3Ep0ZS3Z6Dld2ncYTXz5M+1FnQEEB2Gzw0UeBsKYUy7JITU0FAsNvNQcloVCFQ8TVs8pCoYrWZWVlsWfPHhwOBx07dqyyl1FFIZFpmuWuW1EodGzbfwDgLW7Bzl0W8FOd7rEkxKnJkHG1HUpOoZCIyOHNMAyGDh2K0+lU+2IIFNSISDmRkZH84x//CHcZIiIiIiJSgYn9J/Kn7n9iQ7tvWDR1GYV5Xn7odQrtzd2BHR59FE44odxxmZmZeL3ecsOeSf2oSSh01FFH8euvv+Lz+UhOTqZ169Y1upZlWSEEQgW0Mn4BoJBzOPbYY2vUc6h0eFTi0JCoIVUVCtX33EIKhURE6l9kZCR33nknrVu3JjIysvoDmjkFNSIiIiIiIiKHEZvNRkJ0AudNPovW7la8Pf5qTs0JhDSZx59I/K23Vnjc3r17AUhKStJcJWFmt9tJSkpi7969pKam1jiosdlsOByOKkMhM+evkGcBNlq1v5t4I7pWtR4aCjXkUHJNLRSq70BIoZCIiFRGQY2IiIiIiIjIYSqnxRZeHb+F2M0w5bNILvmmHTc88T5/vHZEmf2a47BnTZ3b7Wbv3r14PB569OhR/w34Bb+NkuA4DqOWIQ2EFgrVl9KhUEMEQk01FGqIQEihkIjI4UVBjYiUk5eXR+xv41nn5uYSExMT5opERERERKScoiIKb7+FnNNgyRBYe2AAbDdYcv3TZPyaycS5lwZ3LRn2zOFw0LZt2zAWLSUSExMxDIP8/Hyys7OJi4urt3Obvl1gpgUexFxTb+dtaOEIhRo6EAp3KBRKL6G6BkUl1xERKS0/P59Ro0YBal8MhYIaERERERERkcPRsGH88ascvmwBy4e15qfJ++m6pD3FP5q8+OBq0vdmcvuK6wHweDxAoBeHGlSbBofDQWJiIqmpqaSmptZrUEPOwt8WIjGizq2/8x5BSkKhxmBZVoMFQqGEQj6fr8Hv0WazVRvq1FfPIb2GiciRSEGNiIiIiIiIyOFm1iz47LPAYvfJbD7Gw6a9m/Dek07yw8eStiOdfz+zlsy9B5j7zsxgUKNhz5oWt9tNamoqHo+H7t2719+JvWsD312n1d85pdZKhxgNrbJQqL4DoUNDIcuy8Pl8YQmFGnJuIYVCItJYFNSIiIiIiIiIHE7WroW5cwPLvXvjfOLv/D3Hw9nPn813Gd/R77F+tL6vG99v+B9f/XsLU4+/jVFzziAiMoI2bdqEtXQpKykpCZvNRm5uLjk5ObRo0aLO5zQL3gMKAw9a3FLn88nhJdyhUEMOJVf6uuEIhWoyFFxthpdTKCTSvCmoERERERERETlcZGbCeeeBZUFsLHz+OQDJLZJ5cuSTXPLqJbz87cs8/dzTtL49ji/e+opd3/7CM9es5vbV1zZK462Ezul00rZtW/bt24fH46mXoIa8ZYHvRhKG45i6n0+kEk0xFKqvuYVKX7ephkJ1mW9IoZBI06OgJkwmTJhAVlYWb7zxRrhLaRbuu+8+3njjDTZv3hzuUkRERERERGrHNGHIECgoAJsN1qyBUg37p3Q8hRlDZ7Avbx9nH3M2571xHo9d93fefvIDcvbnMW/UUp7e1o2E5Pgw3oQcKjk5ORjUHHvssXU6l2nmg++7wIOoi+qhOpGm4XAIhWrbm6j0dRs7FKptIFSToMhms2Gz2Rr8nkQOdwpqjhA//fQTd999N2vXriUzM5M2bdowcOBAHnnkEdavX8/EiROrPb5z58588cUXnHLKKZxzzjm88847QCBUevbZZys9tlOnTuzatavC/UaMGMG//vWv4OPSL8x2u5127dpx4YUX8tBDD+FyuWpz6yIiIiIiIkeuoiJYuhRSUgK9Z3buDKyfNw8GDy63+w2Dbwj+3WVZFuffeiabizfx9Wf/JSvPxZXHTuNvGx4huaubfy59n70pabTrksSo60YQEeFszDuT3yQlJQGQk5NDSkoKcXFxJCQk1Khh0zSLIP9FKHgDsAIrY6bUf7EizUA4Q6FQwp66DC9X+rqNEQhB4Pms72HjKguPFAr9zrIsMjIy8Hq9uFyuGr+v1FcNJdLT04mOjta/TxUU1BwBiouLOfvss+nevTurV68mOTmZX375hffee4+srCwuvvhizjnnnOD+Y8aMoXfv3syZMye4rm3btgAsX76cG264geXLl7N3717atWvHY489xsMPPxzcNzk5mZUrVwbPWfqN65xzzmHlypXBxxWFLyXHFhcXs2XLFiZOnEhMTAz3339/je+9qKiIiIiIGh8nVbPb7Zx55pns2bOHffv2cfTRR4e7JBERERGR5mfGDFi4EEo1rgHQuTPcdluFh5Q0gHg8Hp7+9GkeT3mcnI45+C72YRZZRGXGcMGYn2m7MxnL/L0B5e+3P8/YW0YyZd74hrobqURGRgaGYWCaJt99F+gNExkZSa9evUhOTq72eDP7EchfCZhlN+Q+Bi3vaICKRaS+hCsUCjXsqctQcqWve+i6hlSX4eBqcqxhGE02dPB4PGzbto3CwsLgupq8r9RXDdu3b6dv377s37+fDz/8kE6dOjVqDYcbBTX16NVXX2X27Nns3LmT6Ohojj/+eN58800iIyO5/fbbWbFiBXa7nUmTJpVJFKtz+umn07t3bwCef/55nE4n1157LXPmzMFms7Ft2zZSUlJYs2YNnTp1AgK9XIYOHRo8R1RUVHA5IiKC6Oho3G53mevk5ubyyiuv8NVXX5GamsozzzzDXXfdRVxcHHFxcWX2bdWqVbnjIRDMVLS+smM7dOjAn/70J77++uuQnouSIcymTZvGAw88wO7duzFNk6ysLG677TbefPNNvF4vgwYNYtGiRfTr1y+k80pZkZGRPProowwcOJADBw4oqBERERERaWwzZsCjj1a8bdeuwPZ58yrc7PF4eHLNkzz444P4LB+RtkhinbEU2YrJT8jl2ws20Hv1YNrs/L2hxPSb/GP+WwAKaxqRx+Nh06ZN5dYXFhayadMmBg4cWGWDViCkWV7xxvzlmIChsEZECE8oVJfePzU5trRwhUJ1GSKuumNrEgrV9X2lPpTUYLPZmDhxIrfccgs5OTmNWsPhSEFNPfF4PFx66aXMmzeP0aNHk5OTw6effoplWSxYsIBnnnmGFStWcNxxx7FgwQJef/11zjzzzJDP/+yzzzJp0iQ2btzIV199xZQpU+jYsSOTJ0+mbdu2GIbBq6++ys0331zrF9z/+7//o0ePHnTv3p3LL7+cm2++mZkzZ9YoHV67di2JiYm0bt2aM888k7lz55KQkFDp/jt27OCjjz5iwoQJIV9j586dvPbaa6xevTp4rxdddBFRUVG89957xMXFsWzZMoYNG8aOHTuIj9f4yyIiIiIichgpKgr0pKnKwoUwdy4cMsKAZVls/XYrz+99PrjOa3lxWk4iHE6Kcorxu3yknLmNhBQ3Nqvs33uvLXqbCXMv0TBojcCyLLZt21blPtu2bcPtdlf4d3lguLOVFRxVSv5KzNhbMAyNRCEijad0KOR0Nuz7iWVZZXrt1KZHUE16DpUWjlCoul4+qampVZ6nqveVujJNE6/Xy3//+9+w1XA4U1BTTzweDz6fjzFjxgR7tfTp0weAxYsXM3PmTMaMGQPAk08+yfvvv1+j83fo0IFFixZhs9no3r07//3vf1m0aBGTJ0/mqKOO4q9//SszZsxg9uzZDBo0iDPOOIPLLruMY445JuRrLF++nMsvvxwIDGF28OBBPv74Y04//fSQjj/nnHMYM2YMRx99NCkpKdx1112ce+65fPHFF2XCo0svvRS73Y7P58Pr9TJy5EhmzpwZcp1FRUU899xzweHaPvvsMzZu3Mi+ffuCQ63Nnz+fN954g1dffZUpU6ofl9fr9eL1eoOPs7OzQ67nSOLxePB4PADBXk6lezslJycr8RYRERERaWhLl5Yf7uxQfn9gv5tvLrM6IyODrRlbSfWmEmWPosBfgN/y4zW94AMbNoxiO/nxuRw8KoNWv7Qpc7zpN/nn0vcZe/PIer4pOVR6enqZYWkqUlhYSEZGBm3atCm/Mf9Fyg13Vo4Z2C92Qm3LFBFp0krmpTEMI2yhUH33HCpZV3pEpvoKhap8XzlESfBSVFREUVFRmeVDH3u93uC8R5mZmWRmZgKQkpJS5jtAfHx8yDU0Jwpq6km/fv0YNmwYffr0YcSIEQwfPpwLL7wQwzDweDwMGTIkuK/D4WDQoEE1Gv7sxBNPLJMynnTSSSxYsAC/34/dbuf666/niiuuYO3ataxfv55//OMfPPjgg7z11lucffbZ1Z7/hx9+YOPGjbz++uvBGi+++GKWL18eclBzySWXBJf79OlD37596dKlC2vXrmXYsGHBbYsWLeKss87C7/ezc+dOpk+fzvjx43n55ZdDuk6nTp2CIQ3Ali1byM3NLddzp6CgoMyLQFUeeughZs+eHdK+R7Jly5aVex4mT54cXL733nu57777GrkqEREREZFmJsS/Yyraz+v1kuXLwsSk0FeIHz82bEQYEZj+wN+gNtOGabMoivGWOx5gb0parUuXqvn9ftLT00lNTWXv3r0hHVP6Q4VlT7YnxIuGuJ+IiFSpoUIhy7IoLi6msLCQwsJCCgoKKCwsJD8/n7y8PPLy8iguLq636x04cADLsioNYUqWS4KXmvrXv/7FSy+9VGbd448/Hly+9NJLy7QVS4CCmnpit9v54IMP+Pzzz/n3v//NkiVLuPvuu/nggw8arYYWLVowatQoRo0axdy5cxkxYgRz584NKahZvnw5Pp+Pdu3aBddZloXL5eLxxx8vN0dNKI455hjatGnDzp07y/znc7vddO3aFYDu3buTk5PDpZdeyty5c4PrqxITE1PmcW5uLsnJyaxdu7bcvq1atQqp1pkzZzJ9+vTg4+zsbDp06BDSsUeSa665hj/+8Y8UFBRwyimnAIEX0pNOOglAvWlERERERBpDly613s/lchFrxOIzffjxY2AQY4/BaTgptgcaeSzDwmbZiMhzVXjadl2Sal26lOfz+di/fz8ej4d9+/bVuOGrZOSI0kwzG7wfhXYCe8caXU9EROpP6RCmJIApvVzyPdTeMk6nk8jIyOCXw+HA4XBgGAYFBQXs2VN9OP/DDz/U6B4iIiJwuVxEREQEv0o/Lll2uVxkZ2eTmZnJ4MGDKSoq4o47AvOkTZ06le7duwOBHjUVvbc1dwpq6pHNZmPo0KEMHTqUWbNm0alTJ9asWUNycjIbNmzg1FNPBQK/pG3atIkBAwaEfO4NGzaUebx+/Xq6detW6Xw0NpuNHj168Pnnn1d7bp/Px3PPPceCBQsYPnx4mW0XXHABL730ElOnTg251hK//PILGRkZ1Tbul9xDQUFBja8BMGDAAFJTU3E4HHTu3LlW53C5XHqB4PehzfLy8oLr+vfvX6OfVRERERERqaPrroPbbqt6+DO7PbDfIRISEji+zfHE/BRDti+baCMapxH41K/D5aAwvwjT6ScmowVxv1Y8n+jg8/T7f10VFRWxb98+PB4P+/fvxzR/H6LM5XLhdrtxu91s2bKlyuHPIiMjy40eYea9AjlzgFA+XW1A9Lha3oWIiFSlJIQ5NHQ5NIgp/R5QFYfDQWRkJE6nE4fDEZx7BggOh1ZcXExRUREHDhyodY+XikKWypadTmeN5pJJSEigXbt2xMfHl3l/O/roo4Mf0K/ovU0U1NSbDRs2sGbNGoYPH05iYiIbNmxg//79HHfccdx00008/PDDdOvWjR49erBw4UKysrJqdP49e/Ywffp0rrnmGr7++muWLFnCggULANi8eTP33nsv48ePp2fPnkRERPDxxx+zYsWKYGpZlbfffpsDBw4wadKkcj1nxo4dy/Lly6sNanJzc5k9ezZjx47F7XaTkpLCjBkz6Nq1KyNGjCizb1ZWFqmpqZimyf/+9z/mzJnDsccey3HHHVej56TEWWedxUknncQFF1zAvHnzOPbYY9m7dy/vvPMOo0ePZtCgQbU6r4iIiIiISFhERMD06fDoo5XvM316YL9D2Gw2+vXpx437bmThTwsptooxLAM7dvz4saJNjCKDLh/1wmZV3PAype+tPPLvv9D31J71dUfNgtfrJTU1ldTUVNLT08sMdx4dHY3b7SY5OZlWrVoFG7169erFpk2bKj1nr169gvuaZiZkXgW+7b9tNcDRG3xbKy8qeiKGUf7nREREqmZZFkVFRRX2fim9XJMQpnTvlxKmaeLz+SguLsbn85Gbm1ujOm02W5lwxe/3c+DAgUr3HzBgQJkRleqbzWar0Xub/E5BTT1p2bIln3zyCYsXLyY7O5tOnTqxYMECzj33XM4++2w8Hg9XXnklhmFw1VVXMXr0aA4ePBjy+a+44goKCgoYPHgwdrudm266iSlTpgDQvn17OnfuzOzZs9m1axc2my34+JZbbqn23MuXL+ess86qcHizsWPHMm/ePLZu3Urfvn0rPYfdbmfr1q08++yzZGVl0a5dO4YPH879999frqfKxIkTgcB/XLfbzamnnsqDDz6Iw1G7H0ebzca7777L3XffzcSJE9m/f3/wvElJ6rJfV263O9wliIiIiIg0P6XmOS3Dbg+ENPPmlVmdmpvKsq+Wcfepd5OcnMx1w64j8tNIlu9aTqo3FS9eDAw6xnZgwHcncvDHYix+DxIMu8Hgc/uz8d1v8BX5uO2M+7jlqWs49yqNIV+V/Pz8YDhTMnFyiRYtWgTDmRYtWlTYKJWcnMzAgQPZtm1bmU8eR0ZG0qtXr+AIFWbes5DzCPDbp6eNjhC/HMPRCTP7EchfCZRuLDQCIU3L6j+8KSLS3JTMz1LZMGQlX6GGMIZhYLfbg6/zlmXh9/vLHO/z+ULqAXNo8FLdckU9XjweT7XvKw2p5L3tq6++Cq5r3bp1o9ZwOLJZNZnRXsLi9NNPp3///ixevDjcpTQb2dnZxMXFcfDgQVq2bBnuchpdXl4esbGxQKC31KHzAomIiIiISAM74QT46itwu+GOOyAlJTAnzXXXletJsytrFxe/ejE/H/yZqYOmMuu0WUCgoWjf/n2s/2U9B30HOSbpGCIzIkndm4rltzi4vZADe7Np1yWJUdeNICLCyQ9f7WT6afdSVFAEwEW3jWLKvCsa/fabstzcXFJTU/F4POU+gBkXF0dycjJutzv4N1UoLMsiIyMDr9eLy+UiISEBm82G6UuDrKvA97/f9jQgdhpG7LQyx5tmEeS/CP49gTlposepJ42INEslIUxFvV9KL4faJF46BKlNM3pJ8BJK6FKbocYqU9n7SmPKzc2lRYsWAOzatYuOHTs2y540obYzq0eNiIiIiIiISFNSWAhffx1YnjgRbr650l1/SP+BS167hLTcNDq36sxVx18V3Gaz2UhKTOJPiX8KrjM7mGws2kh6ejqJA+MYfeN5ZT6Y1X1QV57b+TjX9L+Ng/uz+cf8f7Lnu1+Z8+YdZYZqaU4syyI7OzsYzhw6LE18fHwwnImKiqrVNWw2G23atCmzzsx9EnIfA36bq8jeBVqvxHCUH/XAMCIgdkKtri0icriwLKvK8KXke31fszSbzVYuXKkqhHE4HGEJJyp6XwlHDSXatGnTLEOamlBQE2Z79uyhZ8/Kx/3dvn17pduORL169WL37t0Vblu2bBmXXXZZI1fUPBmGwWmnnRZcFhERERGRRrRgAZgm2Gxw112V7rY5dTPjXhtHVmEWx7U9jpfGvkRiTGKVpzYMg4EDB/LFF1+QnZ3Nxo0bOfnkk8sMWZ2Q3JoXdz/B9YPvZNe3P7Phna+Z0vdWln71CBGRzaOXhmVZHDhwIDisWX5+fnBbSeOX2+3G7XaXG+67rkzfr3BgIvh3/bbGDrG3YsReXa/XERFpSkzTDA5HVlBQQEFBAfn5+eTn51NQUIDX66W4uLjer2sYRo2GGgtX8HI4UvtizWjoszDz+Xzs2rWr0u2dO3eu9dwth6Pdu3dX+qKblJQU7C7X0Jr70GciIiIiIhJGnTvD7t3Qty9s2VLhLut/Wc8Vr19BblEuxycfz6oxq2gV2SrkSxQWFrJu3ToKCgpo1aoVJ510Ena7vcw+pmly35hH+eKtwBjzLdu0YNk382lzVHxt76xJM02TzMxMPB4PqampeL3e4DbDMEhMTMTtdpOUlITT6WyYGnIeg7wnCM434+gBrZZjONo2yPVERBqDaZrB4CU3N5e8vLxgGFNUVERxcTF+v79erlUSvITa60XBizS0UNuZFdSIVEBBjYiIiIiIhMUvv0CHDoHlZ56BK68st0teUR6Dnx7MgYIDDO04lJV/WklsROjzoZTIzc1l3bp1FBcXk5SUxMCBAyv8xOvTd77AK/PeBCAi0smCtbPpMbhbja/XFPn9ftLT0/F4PKSlpZX54KDD4SApKQm3203btm0b9EOUpm8XZE4E89ff1jihxUyMmMsb7JoiInVhWRY+n4/CwkJyc3PJzc0Nhi9er5eioiJ8Ph9+v79Wc7uUsNlsOBwOIiIiiIyMJDIyssqeL3a7XcGLNCkKakTqQEGNiIiIiIiExcSJgYAmIgIKCqCSoUI+3vUxz299nsfPe5xIR2StL5eZmcn69esxTZOOHTvSp0+fChu4/v3sf5g/6Qks08Jm2LjzuRs4c9wfan3dcPL5fOzbt4/U1FTS0tLKfIo7IiKCpKQkkpOTSUhIKNfLqCGY2Q9B/jPAb80zzr7Q+mkMo1WDX1tEpERJ8FJUVERRUVFwGLKSoccKCwuDvV98Ph+madb6Wna7HYfDgdPpJCIigqioKKKiooiOjlbwIkccBTUiddDcg5q8vDw6d+4MwK5du8pMLioiIiIiIg2oVSs4eBBGjIB//avMpmxvNi1d9f/3icfjYdOmTQB0796dbt0q7i3z7brvmHHWHIq9PgAu/8uFXDn74nqvpyEUFRWRlpZGamoq+/fvL9PAGBkZidvtJjk5mdatWzfaOPpm8Q44MAnMtN/WREDL+zCiL2yU64vIka0keCnp3VISvpReLj33i8/nq1PPF8MwsNvtOJ1OnE4nkZGRwfAlNjaWmJgYBS/NjNoXAxTUiNSBgpo8YmMDQyfk5uY22xdSEREREZFG9emncOqpgeWNG+GEE4Kbnv76aZZsXMLrF7/OMa2PqfdL//TTT2zbtg2Afv360aFk+LVDpO3Zz7UDZpCTmQvAHy48kVn/d2u911MfCgsLSU1NJTU1lYyMjDINkNHR0SQnJ+N2u2nVqlWjNhqapgk590HBK/zei2YQtP47hlHzIexEpHkIJXg5dLm+mn1LwpfSvV9iY2ODAUxERITCFylH7YsBobYzN59Z6kVERERERESasvvuC3yPjw+GNJZlsXj9Yh79/FEA3tnxDjcMuaHeL3300UdTWFhISkoKW7duxeVykZiYWG6/pI5teXHPE1w7YAa/7PDw6avruXbA7Ty2/kEiIpz1XldN5efnk5qaisfj4cCBA2W2tWjRIhjOtGjRIiyNimbRfyFrCpgZv62JhLgHMaJGNnotIhJelmVRXFwccuhSn8FLCZvNFpz7paT3S0xMTPBxZGQkTqdTIYxII1BQIyIiIiIiIhJuphnoUQNw0UVAoBHv/k/u58mvngRgxtAZTBs8rcFK6NGjB4WFhfz6669s2rSJk046iVatWpXbLzI6kuXbF3PXeQ+y6d9b2Ll5F+M7X8eTmx+ldWL5/RtaTk5OMJzJzs4us61Vq1bBcCacn+Q1TROyZ0Lh67+vjDgZWi3FMKLDVpeI1J/SwUvpcKUxg5fSDMMI9n4pCV1KvhTCiDQ9GvpMpAIa+kxdE0VEREREGtXf/w7XXBNY9njwJ7blzg/vZNV/VwEw54w5XD3g6gYvwzRNNm7cSHp6Oi6Xi6FDhxIdXXmQ8LebV/LGX98FwBUdwWPr5tKl39ENWqNlWRw8eDA4rFlubm6Z7QkJCbjdbtxuN1FRUQ1aSyhM71eQdS1YBwMrbFHQcgFG1FnhLUxEqlRZ8FLZcm2Dl5KgpCbHOhyOMoFLRcsOh0MhjISV2hcDNEeNSB0oqNELqYiIiIhIo+rVC7Zvh2OOoXjH99z43o28+cObGDaD+cPnc0nvSxqtlOLiYr744guys7OJiYlh6NChREREVLr/P5/8N0uufxrLsjDsBn/5v+mcMnpIvdZkWRYHDhzA4/GQmppKQUFBcJthGLRp0wa3201SUhIul6ter11bpumD7Nug8N3fV7rOgLglGEblz6eINIyS4KUkXKmq10tdghfDMLDb7cGQxDRN/H5/yOdyOp0V9n4pHcQ4neEfalKkOmpfDNAcNSIiIiIiIiKHg6ws+O67wPK111JsFvNLzi847U4eP/dxRnUf1ajlOJ1OBg8ezLp168jLy2Pjxo2cdNJJ2O32CvcfNXU47Y91c9e5D+Ir9jN77HwmPTiOS+4cXac6TNMkIyMDj8dDWloaXq83uM1ut5OYmIjb7SYxMbHJNVqa3s8h6wawcgIrbLHQagmGa2h4CxM5gliWFdLwYqUf14bD4cDhcGC32zEMA5vNhmVZmKaJz+ejuLg4MLzhb0zTLPO4tJIQpqKeMCXfHQ4114o0R+pRI1KB5t6jpqCggFNPPRWATz75pEkMFyAiIiIicsS69VZYuBDsdigoAKeTg4UH+XbftwztGL6G/ZycHD7//HOKi4tJSkpi0KBBVQ6j8+tOD9cPupO87HwAzhp/Knc8e0ONrun3+9m/fz+pqamkpaVRXFwc3OZwOEhKSiI5OZm2bdtWGhyFk2kWwcEbwfvR7ytd50DcQgxDja8iVSkdvIQSutQleHG5XERERAQDmJLwxbIsfD4fPp+PoqIiCgsLa9QTpqLeL6WXFcJIc6L2xQANfSZSB809qBERERERkUbkdpN1MI33z+3Gxat3hLuaMjIzM1m/fj2madKpUyd69+5dZViTl53P1P63k7prHwDHndiNhZ/MqbJxsri4mH379pGamsq+ffvw+/3BbREREbjdbpKTk0lISMAwjPq7uXpmFn4EB6eDFQiqsMVBq79huAaHtzCRMAkleDk0hKkNp9NJRERE8KskhLHb7cHwpXTvl8LCwuBXZT1fDhUREVHpMGQly00xPBaR8FNQI1IHCmpERERERKRRbNvGviG9uXQsfNc9nofPX8QV/a4Id1VleDweNm3aBED37t3p1q1blfubpsntZ85m6yfbAWjbIYFlm+fTonVscJ+ioiJSU1NJTU0lPT29TGNpVFQUbrcbt9tNfHx8k58M2zTzIWsaFH32+8rIC6Dlw006WBKpKdM0y8zxUl0AU7pHXE2UBC8lgcuhAUzJnFklvV+8Xi+FhYUUFBTUKoRxuVwV9n4pvawQRkRqS0GNSB0oqBERERERkcbw65/P5c8t/sVP8TYSu/Tl5QtfpkebHuEuq5yffvqJbdu2AdCvXz86dOhQ7TGLrlnGu099CEBUi0jmr72XiNYOPB4PmZmZZYYTiomJITk5GbfbTVxcXJMPZ0qYBe/CwTuBwsAKIx5a/R0jom9Y6xIJhWmalQ4p1tDBS2XLTqeT4uLiYOhyaPhS8jjU5sySEKaynjAul0shjIg0KAU1InXQ3IOa/Px8evbsCcD27duJjo4Oc0UiIiIiIkeeHw/8yJ9ndmVvrEUHezyvzNxE51adw11Wpb777jtSUlKw2WwMHjyYtm3bVnvMS4+sZsXMlwAw7DZG3X0mnQe1B6Bly5bBcCY2NvawCWcATDMPDkyG4q9+W2ODqD9Di9nqRSNhU1XwUtFyXYKXUEKXkq+S/xOmaVbY+6V0KOP1ekMOYUoCl8qCmMjISP1/FAkjtS8GKKgRqYPmHtTk5eURGxsYliA3N5eYmJgwVyQiIiIicmTZvn87l6w4j/T9u+maCa/c+AnJ/f8Q7rKqZFkWmzdv5tdff8Vut3PyyScTFxdXbp+cnJzgsGbZ2dn89OXP/PPB/2D5A80Pf757JJfNvOiwbbAxC1bDwVnAb/NpGInQ+mkMZ9PrCSWHt9LBS0m4UlWvl9oGL4cGK1WFME6ns8LwoySEqSyAKSwsrHEIU9EwZCXfXS6XQhiRJk7tiwGhtjNXPpufiIiIiIiIiNS7zIJMxv7fWA5m7aX3Pnjp0yQSVjTtkAbAZrPRr18/CgsLycjIYOPGjQwdOpSoqCgOHjyIx+MhNTWVvLy8MseccO7xDDxlAA+NWUJhnpf/e+Bt8tK93PzElDDeTc2ZZtZvvWi2/LbGBtFXQuydajCWkJQEL6HM8VJfwUsoPV+q681mmiaFhYXk5eVV2RMmFDabrcI5YBTCiEhzp6BGREREREREpBHFR8Vzw4BreX/dTTy/GlrePCHcJYXMMAwGDRrE559/Tk5ODp988gl2u71MI61hGLRt2xa3201SUlJw4u9Vu3tyTb/bSP81k3eWfcDP3//Ko2vuPSwaZM28FyHnAeC3hnOjHcSvwHAcE9a6JLxKepGEOtSYz+er1XUqGlKsqsc1GUbQ7/eX6QlTURBT0xCmop4wpeeEOZyGORQRaSwKakREREREREQagd/0YzcCk1ZftzaPya+A07LB3XeHubLQmKZJeno6qampFBYWAuDz+fD5fBiGQVJSEsnJySQmJuJwlG9uaBnfgud/+hu3/GEW32/4H1s/3s6EY2/gyW8eJbpF0xwGzfRnwIGrwPfdb2sMiJmM0eLWsNYlDcPv91fau6Uxg5eS5ZKhxmobbPj9/kqHIStZLioqCulchmFUOyeMQhgRkdpTUCMiIiIiIiLSwF7/7nWWf7OcF8e+SEtXS/j733GaQO9e0KJFuMurlN/vZ9++faSmppKWllamYdrhcOD3+7Esi7Zt2zJgwIBqG2kdDgdLvniQh8f/lTWrPsXz4z7GdbyWpZseod0x7oa+nRoxc1dA7qOAP7DC3glar8BwdAhrXRK6yoKXypZrE7zYbLaQhhqrj+Dl0HurqPdL6eWahjBVzQlT0546IiJSMwpqRERERERERBrQ81ue5841d2JZFs9teY5p7cfArl2BjbfcEtbaKlJcXExaWhqpqans27cP0zSD21wuF263G7fbTUJCAgcOHGDDhg2kpaXx7bff0rt375Aac+98/kY69mzPyrtfIu9gPpOOu5mH/nUP/c/o3ZC3FhLTlxroRePf+dsaA2Jvwoi9Nqx1ye/BS6hzvDRk8FKyXF/BS2k+n6/anjChzl9jGEaFvV9Kr2uIexARkZpRUCMi5dhsNnr27BlcFhERERGR2ln65VLmfjIXgAn9J3DdCdfB1ZMDGyMiYMKE8BVXitfrJS0tDY/HQ3p6OpZlBbdFRUWRnJyM2+2mdevWZf5GSEhIoH///nz99dfs3r2bqKgounbtGtI1x80cQ4fu7Zh78SJ8xX5mnDWHm56czPmTz673+wuVmbsUcv8K/BZO2btB6+UYjqbV2+dIUTI/SnVDjJUs+/3+Gl+jdPBSXejSUMFLaSUhzKEBTOkgJtQQxm63VzoMWcmyQhgRCRe1L9aMzSr925eIAJCdnU1cXBwHDx6kZcuW4S5HREREREQOM5ZlMW/dPB7b8BgANwy+gTtPuTPQUNG6NWRlwfDh8P77YauxoKCA1NRUPB4PmZmZZbbFxsYGw5mWLVtW28Dy008/sW3bNgD69+9P+/btQ67jf9/8xC2n3IO3IDBM05ibz+fahRNqdjN1ZPp+hgMTwb/ntzV2iL0dI/aqRq3jcFc6eKmqp0vJ47oEL9UNMdZYwUtpxcXF1faECbWXj91ur7YnjMPhUOOniEgTF2o7s4IakQooqBERERERkdoyLZNZ/5nFim9WAHDXH+5i2uBpgY3r1sEppwSWP/8cTjqpUWvLzc0lNTWV1NRUsrKyymyLi4sLhjOxsbE1Pvf27dv58ccfsdlsDB48mLZt24Z87IG0LK7pfxsH0g4CcMI5/Zn79kwMw6hxHTVl5iyAvKcI9qJx9Az0orEnNPi1mzqfz1ejOV4aIng5dDkc4YRlWfh8vmrnhAk1hHE4HGXCl4p6xTidzga+KxERaQwKakTqQEGNiIiIiIjU1r68fYx4YQRpuWk8OOxBJvSf8PvGs8+GDz+EVq3gwIEGr8WyLHJycvB4PKSmppKTk1Nme3x8fHDOmejo6Dpf65tvvmHv3r3Y7XZOPvlk4uLiQj6+qKiYG4bM5MctuwHo0L0dSzc9QmR0ZJ3qqozpS4HMSWDu/W2NE1rcgxFzaYNcL9wsy6rRHC9er7fM/EShMgyj2sCl9ONw9wqxLCvYE6aqICbUEMrpdFYavpR8KYQREWk+FNSI1EFzD2ry8/M54YQTAPjyyy/r/AebiIiIiEhz80P6D3yX/h0X9Ljg95WmCZGRUFwMV18NTz3VINe2LIusrKxgOJOfnx/cZrPZaNOmTTCccblc9Xptv9/Pxo0bycjIwOVyMXTo0Br/PTH7wvl8tnoDAC3iY3nym0dJ7NCm3mo0TRNyHoSC54HfmkSc/aH10xjG4fP3X0nwEsoQYw0dvJReDnfwUlpJCFNRAFN6XU1DmEMDmNJBjMOh6aBFREDtiyUU1BxGJkyYQFZWFm+88Ua4SzlinX766fTv35/FixeHtH9zD2ry8vKCQx3k5uYSExMT5opERERERJoWv+lnw68b2Je3j8SYRPom9uX7jO8Z1G5Q+Z2LimDpUnjrLfjPfwLr9u6F5ORqr2NZFhkZGXi9XlwuFwkJCRU2gpumSWZmZnBYs8LCwuA2wzBo27YtycnJJCYmEhERUev7DkVxcTGff/45OTk5xMTEcPLJJ5OTk1PtPZT2zKyXWTX3NQCcLiePrplFr5N7UFRUzD+Xvs/elDTadUli1HUjiIgo3zvBNIsg/8XAnDP2jhA9DsOIwCz+Hg5cDeY+ACwiOOi/FdM1KqS6GlJ1wUtFy3UJXkIdasxut4f0vIT6s1pfLMuiqKio0p4wJd9DfY4iIiKq7AkTFRWF3W5vsPsRETnSqH0xINR2ZsX8R4gtW7bwl7/8hfXr15OdnY3b7WbIkCEsWbKEpUuXMnv27CqPL8nrXnrpJS6//HKmTp3K3/72NyAQcnz88ceVHnvaaaexdu3aCve75pprePLJJ8sdM2LECD788EPWr18fTFalaUpNTaVLly7hLkNEREREpMl493/vcveau0k5kILf8mPYDAybQaQ9ktUXr2Zox6G/7zxjBixcCId+Yn/RIpg3r8rreDwetm3bViZ0iYyMpFevXiQnJ+P3+0lPTyc1NZW0tDSKioqC+zkcDhITE3G73SQmJjbqp/ydTieDBw9m3bp15OXl8eGHH1L6M6Kl76EyE+ZcQvvu7Zh35eMUe4u55dRZnDCiP1/9ewum//eG97/f/jxjbxnJlHnjg+vM7EcgfyXBOWcAch/GdHQD3w7AwrIgK78rG364CtOMBNaHVFdNlMxrUpM5XmobvFQ2rFhdgpeaqO5ntaZKQpiKer+UXq5JCHNo6HLoskIYEZGGo/bF6imoOcwVFRVx8OBBhg0bxsiRI3n//fdp1aoVu3bt4q233iIvL4/bbruNqVOnBo854YQTmDJlCpMnTy53vuXLlzNjxgyWLVvGggULiIyMZPXq1cFf+H/++WcGDx7Mhx9+SK9evQDKfBpr8uTJzJkzJ/i4oi5te/bs4fPPP2fatGmsWLGiTkFNUVFRg38arLnTC6mIiIiIyO/e/d+7XL76crx+L9GOaAybwYHCAxSbxeTacvly75e/BzUzZsCjj1Z8opL1lYQ1Ho+HTZs2lVtfWFjIpk2baN26NTk5OWUmL3c6ncEhzdq0aRPWhueoqCiOOeYYtm/fzqEDeZTcw8CBA6tsxD/rslM5qksSt545m+LCYja+9025fUy/yT/mvwXAlHnjfwtplldwNhN8PwBgWS6+SfkzqVnH16iu0sFLdUOM1WfwUt1yQwQvNVHdz+qhz6dlWXi93kqHIatpCONyuaqdE0YhjIhIeKl9sXoKahrIq6++yuzZs9m5cyfR0dEcf/zxvPnmm0RGRnL77bezYsUK7HY7kyZNKvdLa1VOP/10evfujcPh4IUXXqBPnz7cdNNNHDx4kKeffjr4Kamjjz6aM844I3hcSTczALvdTosWLXC73WXO/dNPP/H555/z2muv8Z///IfVq1czbtw44uPjg/uUfDomISGh3PEQCGYqWl/aypUrGTlyJNdeey0nnngiCxcuJCoqqtb3/5///Idvv/2W22+/nU8//ZSYmBiGDx/OokWLaNOm/sYxFhERERGR5s1v+rl7zd14/V5aRrTEwuJA4QH8lh+7zY7T7uS5Lc9xw+AbsPv8gZ40VVm4EObOhUM+fGZZFtu2bavy0AMHDgCBRurk5GTcbjfx8fEYhlGne6wvlmXx448/VrnPtm3bcLvdVYYMx53Ynae/XcSVXadVea7XFr3NFXPGEpG/straPt72APmFlT9PW7ZsISMjo1wQU1xcXKvgxW63Vxu6lH58OM1xEsrP6ubNm9m7d2+ZECbUdhCXy1Vh75fS65rKz7yIiEhdHD7v/ocRj8fDpZdeyrx58xg9ejQ5OTl8+umnWJbFggULeOaZZ1ixYgXHHXccCxYs4PXXX+fMM88M+fzPPvss1157LevWrQMCv6D7fD5ef/11Lrzwwlp/kmblypWcf/75xMXFcfnll7N8+XLGjRtXo3OsWrWKF154AbfbzahRo/jLX/5SpleNZVmsXLmSv/3tb/To0YOuXbvy6quvMn78+CrOWtah95+VlcWZZ57J1VdfzaJFiygoKOCOO+7gz3/+Mx999FFI5/R6vXi93uDj7OzskOs5kng8HjweDwUFBcF1mzdvDgZpycnJ9TYMgIiIiIjI4WbDrxtIOZBCtCMam82G1+fFZ/owbAbxUfGYpsnOzJ1s+HUDJ7+2sfxwZ4fy+wNz19x8c5nVGRkZZYaQqkzv3r3p1KlTk5m4vbRQ7qGwsJCMjIxqP2D3xVtfVns902/y9mMPM+bK6oOUxJafsqvwtEq3+3w+du3aVen2UIKXkuWSocaOVKH8O/v9fjweT7n1VQ1DFhUVhcvlUggjInIYUvti7SioaQAejwefz8eYMWPo1KkTAH369AFg8eLFzJw5kzFjxgDw5JNP8v7779fo/N26dWPeId3j77rrLsaNG8fUqVMZPHgwZ555JldccQVJSUkhndM0TZ555hmWLFkCwCWXXMKtt97KTz/9xNFHHx3SOcaNG0enTp1o164dW7du5Y477uCHH35g9erVwX0+/PBD8vPzGTFiBEAwEKpJUHPo/c+dO5fjjz+eBx98MLhuxYoVdOjQgR07dnDsscdWe86HHnqo2nl8moNly5aVex6mTfv9k2v33nsv9913XyNXJSIiIiLSNOzL2xfoPWMEGt4jHZFYlkWEIwK7zY7NsOH3+dmXtw9SUkI7aQX7lf4QWVWcTmeTDGkg9HsIZb+9KWkhncsT4n7RrvRq90lMTCQhIaHCXi9HcvBSU6H+Ox911FEkJSUFgxiFMCIiRy61L9aOgpoG0K9fP4YNG0afPn0YMWIEw4cP58ILL8QwDDweD0OGDAnu63A4GDRoUI2GPxs4cGC5dQ888ADTp0/no48+YsOGDTz55JM8+OCDfPLJJ8GQqCoffPABeXl5nHfeeQC0adOGs88+mxUrVnD//feHVNeUKVOCy3369CE5OZlhw4aRkpISHINwxYoVXHzxxcGu3Jdeeim33357mX2qc+j9b9myhf/85z9lhncrkZKSElJQM3PmTKZPnx58nJ2dTYcOHUKq50hyzTXX8Mc//hGAr7/+msmTJ/PUU08xYMAAAKXdIiIiItKsJcYkYrfZ8Zt+DHugkTnK+fswzn4zMARaYkwihDoOewX7uVyukA4Ndb9wqM97aNcltA8gJoe4X763+iGyjznmGA2lHYJQ/507dOig51NEpJlQ+2Lt6OMLDcBut/PBBx/w3nvv0bNnT5YsWUL37t2r7DpdEzExMRWuT0hI4KKLLmL+/Pl89913tGvXjvnz54d0zuXLl5OZmUlUVBQOhwOHw8G7777Ls88+W6sxeIFgILVz504AMjMzef3111m6dGnwGkcddRQ+n48VK1aEfN5D7z83N5dRo0axefPmMl//+9//OPXUU0M6p8vlomXLlmW+mqPk5GQGDBgQ/ALKPNYLqYiIiIg0Z0OOGkKX1l3I9+WX+7CdZVnk+/LpGt+VIUcNgeuug+p6Xtjtgf0OkZCQQGRkZJWHRkZGkpCQUON7aCz1eQ+jrhsRDMYqY9gNRt50J9U3cxikZg+rl7rkyPhZFRGR+qX2xdpRUNNAbDYbQ4cOZfbs2XzzzTdERESwZs0akpOT2bBhQ3A/n8/Hpk2b6v36ERERdOnShby8vGr3zcjI4M033+Tll18uE3R88803HDhwgH//+9+1qmHz5s3A7ynpqlWraN++PVu2bClznZJ5e/zVjd9ciQEDBrBt2zY6d+5M165dy3xVFmqJiIiIiIjUlN2w88CwB3DZXWQXZVPsL8a0TIr9xWQXZeOyu5h75tzA0GgREVCq136Fpk8P7HcIm81Gr169qjy0V69eTXbYM6jfe4iIcDL2lpFV7jP2lpFERkZD9MSqTxY9kV69+tVLXXJk/KyKiIg0BQpqGsCGDRt48MEH+eqrr9izZw+rV69m//79HHfccdx00008/PDDvPHGG3z//fdcd911ZGVl1el6b7/9Npdffjlvv/02O3bs4IcffmD+/Pm8++67/OlPf6r2+Oeff56EhAT+/Oc/07t37+BXv379OO+881i+fHm150hJSeH+++9n06ZN7Nq1i7feeosrrriCU089lb59+wKBXjsXXnhhmWv07t2bSZMmkZ6ezr/+9a9a3f/1119PZmYml156KV9++SUpKSm8//77TJw4sdbhjwQCtnvvvVcpt4iIiIhIKed1O48XxrzAsfHH4vV7ySnKwev30j2hOy+MeYHzup33+87z5sHtt5fvWWO3B9YfMvdoacnJyQwcOLBcb4XIyEgGDhx4WPyeXp/3MGXeeC667Y/letYYdoOLbvsjU+YF5j01Wt4B0ZMo39xhQPQkjJZ3HBHPbVOi51NERCqj9sXQaY6aBtCyZUs++eQTFi9eTHZ2Np06dWLBggWce+65nH322Xg8Hq688koMw+Cqq65i9OjRHDx4sNbX69mzJ9HR0dx66638/PPPuFwuunXrxtNPP8348eOrPX7FihWMHj26wk+4jB07lvHjx5Oenl7leLIRERF8+OGHLF68mLy8PDp06MDYsWO55557ANi0aRNbtmzhqaeeKndsXFwcw4YNY/ny5Zx//vk1uPOAdu3asW7dOu644w6GDx+O1+ulU6dOnHPOOZqcsA6Sk5M1sZeIiIiISAXO63YeI7qMYMOvG9iXt4/EmESGHDUk0JPmUPPmwdy5sHQppKQE5qS57roKe9IcKjk5GbfbTUZGBl6vF5fLRUJCwmHVO6E+72HKvPFMmHsJ/1z6PntT0mjXJYlR140gIsJZZj+j5R2YsbdA/ovg3wP2jhA9DsP4/Tk/Ep7bpkTPp4iIVETti6GzWTWZxV6kmcjOziYuLo6DBw822/lqRERERERERERERKT2Qm1nVncDERERERERERERERGRMFFQ04Ts2bOH2NjYSr/27NkT7hIbVHO/fxERERERERERERFpfjRHTRPSrl07Nm/eXOX2I1lzv38RERERERERERERaX4U1DQhDoeDrl27hruMsGnu9y8iIiIiIiIiIiIizY+GPhMREREREREREREREQkTBTUiIiIiIiIiIiIiIiJhoqBGREREREREREREREQkTBTUiIiIiIiIiIiIiIiIhIkj3AWINEWWZQGQnZ0d5kpERERERERERERE5HBU0r5c0t5cGQU1IhXIyckBoEOHDmGuREREREREREREREQOZzk5OcTFxVW63WZVF+WINEOmabJ3715atGiBzWYLdzkiYZWdnU2HDh34+eefadmyZbjLEZEw0uuBiJSm1wQRKU2vCSJSQq8HIr+zLIucnBzatWuHYVQ+E4161IhUwDAM2rdvH+4yRJqUli1b6hcsEQH0eiAiZek1QURK02uCiJTQ64FIQFU9aUpUHuGIiIiIiIiIiIiIiIhIg1JQIyIiIiIiIiIiIiIiEiYKakREpEoul4t7770Xl8sV7lJEJMz0eiAipek1QURK02uCiJTQ64FIzdksy7LCXYSIiIiIiIiIiIiIiEhzpB41IiIiIiIiIiIiIiIiYaKgRkREREREREREREREJEwU1IiIiIiIiIiIiIiIiISJghoREREREREREREREZEwUVAjIiI15vV66d+/Pzabjc2bN4e7HBEJg127djFp0iSOPvpooqKi6NKlC/feey9FRUXhLk1EGsnf/vY3OnfuTGRkJEOGDGHjxo3hLklEGtlDDz3ECSecQIsWLUhMTOSCCy7ghx9+CHdZItJEPPzww9hsNm6++eZwlyLS5CmoERGRGpsxYwbt2rULdxkiEkbff/89pmmybNkytm3bxqJFi3jyySe56667wl2aiDSCV155henTp3Pvvffy9ddf069fP0aMGMG+ffvCXZqINKKPP/6Y66+/nvXr1/PBBx9QXFzM8OHDycvLC3dpIhJmX375JcuWLaNv377hLkXksGCzLMsKdxEiInL4eO+995g+fTqvvfYavXr14ptvvqF///7hLktEmoBHH32UJ554gh9//DHcpYhIAxsyZAgnnHACjz/+OACmadKhQwduuOEG7rzzzjBXJyLhsn//fhITE/n444859dRTw12OiIRJbm4uAwYMYOnSpcydO5f+/fuzePHicJcl0qSpR42IiIQsLS2NyZMn8/zzzxMdHR3uckSkiTl48CDx8fHhLkNEGlhRURGbNm3irLPOCq4zDIOzzjqLL774IoyViUi4HTx4EEC/D4g0c9dffz3nn39+md8VRKRqjnAXICIihwfLspgwYQJTp05l0KBB7Nq1K9wliUgTsnPnTpYsWcL8+fPDXYqINLD09HT8fj9JSUll1iclJfH999+HqSoRCTfTNLn55psZOnQovXv3Dnc5IhImL7/8Ml9//TVffvlluEsROayoR42ISDN35513YrPZqvz6/vvvWbJkCTk5OcycOTPcJYtIAwr1NaG0X3/9lXPOOYeLLrqIyZMnh6lyERERCafrr7+eb7/9lpdffjncpYhImPz888/cdNNNrFq1isjIyHCXI3JY0Rw1IiLN3P79+8nIyKhyn2OOOYY///nP/POf/8RmswXX+/1+7HY7l112Gc8++2xDlyoijSDU14SIiAgA9u7dy+mnn86JJ57IM888g2Hoc0AiR7qioiKio6N59dVXueCCC4Lrr7zySrKysnjzzTfDV5yIhMW0adN48803+eSTTzj66KPDXY6IhMkbb7zB6NGjsdvtwXV+vx+bzYZhGHi93jLbROR3CmpERCQke/bsITs7O/h47969jBgxgldffZUhQ4bQvn37MFYnIuHw66+/csYZZzBw4EBeeOEF/dEl0owMGTKEwYMHs2TJEiAw5FHHjh2ZNm0ad955Z5irE5HGYlkWN9xwA6+//jpr166lW7du4S5JRMIoJyeH3bt3l1k3ceJEevTowR133KFhEUWqoDlqREQkJB07dizzODY2FoAuXboopBFphn799VdOP/10OnXqxPz589m/f39wm9vtDmNlItIYpk+fzpVXXsmgQYMYPHgwixcvJi8vj4kTJ4a7NBFpRNdffz0vvvgib775Ji1atCA1NRWAuLg4oqKiwlydiDS2Fi1alAtjYmJiSEhIUEgjUg0FNSIiIiJSYx988AE7d+5k586d5cJaddgWOfJdfPHF7N+/n1mzZpGamkr//v3517/+RVJSUrhLE5FG9MQTTwBw+umnl1m/cuVKJkyY0PgFiYiIHKY09JmIiIiIiIiIiIiIiEiYaLZXERERERERERERERGRMFFQIyIiIiIiIiIiIiIiEiYKakRERERERERERERERMJEQY2IiIiIiIiIiIiIiEiYKKgREREREREREREREREJEwU1IiIiIiIiIiIiIiIiYaKgRkREREREREREREREJEwU1IiIiIiIiMgRxWaz8cYbb4S7DBERERGRkCioERERERERaSATJkzAZrNhs9mIiIiga9euzJkzB5/PF+7Sak0hiIiIiIhI/XKEuwAREREREZEj2TnnnMPKlSvxer28++67XH/99TidTmbOnFnjc/n9fmw2G4Zx+H/mrri4GKfTGe4yRERERETC7vD/7V5ERERERKQJc7lcuN1uOnXqxLXXXstZZ53FW2+9BcDChQvp06cPMTExdOjQgeuuu47c3Nzgsc888wytWrXirbfeomfPnrhcLvbs2cOXX37J2WefTZs2bYiLi+O0007j66+/LnNdm83GsmXLGDlyJNHR0Rx33HF88cUX7Ny5k9NPP52YmBhOPvlkUlJSyhz35ptvMmDAACIjIznmmGOYPXt2sAdQ586dARg9ejQ2my34uLrjSup54okn+OMf/0hMTAwPPPBAuefqrrvuYsiQIeXW9+vXjzlz5gCEdO+lrV27FpvNRlZWVnDd5s2bsdls7Nq1K7jus88+4w9/+ANRUVF06NCBG2+8kby8vErPKyIiIiJSXxTUiIiIiIiINKKoqCiKiooAMAyDv/71r2zbto1nn32Wjz76iBkzZpTZPz8/n0ceeYSnn36abdu2kZiYSE5ODldeeSWfffYZ69evp1u3bpx33nnk5OSUOfb+++/niiuuYPPmzfTo0YNx48ZxzTXXMHPmTL766issy2LatGnB/T/99FOuuOIKbrrpJrZv386yZct45plngqHKl19+CcDKlSvxeDzBx9UdV+K+++5j9OjR/Pe//+Wqq64q99xcdtllbNy4sUx4tG3bNrZu3cq4ceMAQr73mkhJSeGcc85h7NixbN26lVdeeYXPPvuszHMjIiIiItJQbJZlWeEuQkRERERE5Eg0YcIEsrKyeOONN7AsizVr1jBy5EhuuOEGHn300XL7v/rqq0ydOpX09HQg0KNm4sSJbN68mX79+lV6HdM0adWqFS+++CIjR44EAj1Y7rnnHu6//34A1q9fz0knncTy5cuDIcnLL7/MxIkTKSgoAOCss85i2LBhZYZle+GFF5gxYwZ79+4Nnvf111/nggsuCO4T6nE333wzixYtqvI569+/P2PHjuUvf/kLEOhl89FHH7F+/foa3XtJjWvXruWMM87gwIEDtGrVCgj0qDn++OP56aef6Ny5M1dffTV2u51ly5YFz/vZZ59x2mmnkZeXR2RkZJU1i4iIiIjUheaoERERERERaUBvv/02sbGxFBcXY5om48aN47777gPgww8/5KGHHuL7778nOzsbn89HYWEh+fn5REdHAxAREUHfvn3LnDMtLY177rmHtWvXsm/fPvx+P/n5+ezZs6fMfqWPS0pKAqBPnz5l1hUWFpKdnU3Lli3ZsmUL69atK9MTxu/3l6vpUKEeN2jQoGqfr8suu4wVK1bwl7/8BcuyeOmll5g+fXqN770mtmzZwtatW1m1alVwnWVZmKbJTz/9xHHHHVfrc4uIiIiIVEdBjYiIiIiISAM644wzeOKJJ4iIiKBdu3Y4HIE/w3bt2sXIkSO59tpreeCBB4iPj+ezzz5j0qRJFBUVBcONqKgobDZbmXNeeeWVZGRk8Nhjj9GpUydcLhcnnXRScEi1Ek6nM7hcco6K1pmmCUBubi6zZ89mzJgx5e6jql4loR4XExNT6TlKXHrppdxxxx18/fXXFBQU8PPPP3PxxRcHt4d67yUMIzDid+nBJIqLi8vVf80113DjjTeWO75jx47V1iwiIiIiUhcKakRERERERBpQTEwMXbt2Lbd+06ZNmKbJggULgmHC//3f/4V0znXr1rF06VLOO+88AH7++efgcGl1MWDAAH744YcK6y3hdDrx+/01Pi5U7du357TTTmPVqlUUFBRw9tlnk5iYGNxe03tv27YtAB6Ph9atWwOBoc8OrX/79u31Ur+IiIiISE0pqBEREREREQmDrl27UlxczJIlSxg1ahTr1q3jySefDOnYbt268fzzzzNo0CCys7O5/fbbiYqKqnNNs2bNYuTIkXTs2JELL7wQwzDYsmUL3377LXPnzgWgc+fOrFmzhqFDh+JyuWjdunVIx9XEZZddxr333ktRUVG5OW1qeu9du3alQ4cO3HfffTzwwAPs2LGDBQsWlNnnjjvu4MQTT2TatGlcffXVxMTEsH37dj744AMef/zxGtcvIiIiIlITRrgLEBERERERaY769evHwoULeeSRR+jduzerVq3ioYceCunY5cuXc+DAAQYMGMD48eO58cYby/Q6qa0RI0bw9ttv8+9//5sTTjiBE088kUWLFtGpU6fgPgsWLOCDDz6gQ4cOHH/88SEfVxMXXnghGRkZ5Ofnc8EFF5TZVtN7dzqdvPTSS3z//ff07duXRx55pFx41LdvXz7++GN27NjBH/7wB44//nhmzZpFu3btalW/iIiIiEhN2KzSA/WKiIiIiIiIiIiIiIhIo1GPGhERERERERERERERkTBRUCMiIiIiIiIiIiIiIhImCmpERERERERERERERETCREGNiIiIiIiIiIiIiIhImCioERERERERERERERERCRMFNSIiIiIiIiIiIiIiImGioEZERERERERERERERCRMFNSIiIiIiIiIiIiIiIiEiYIaERERERERERERERGRMFFQIyIiIiIiIiIiIiIiEiYKakRERERERERERERERMJEQY2IiIiIiIiIiIiIiEiY/D/i3+uA5VBbKgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ref = visualize.create_references(\n", " x=petab_problem.x_nominal_scaled, fval=obj(petab_problem.x_nominal_scaled)\n", @@ -552,36 +428,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'plot1': ,\n", - " 'plot2': ,\n", - " 'plot3': }" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# we need to explicitely import the method\n", "from pypesto.visualize.model_fit import visualize_optimized_model_fit\n", diff --git a/doc/example/prior_definition.ipynb b/doc/example/prior_definition.ipynb index 7025b17d8..e52f6fb27 100644 --- a/doc/example/prior_definition.ipynb +++ b/doc/example/prior_definition.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -146,7 +146,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -173,17 +173,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 41.26it/s]\n" - ] - } - ], + "outputs": [], "source": [ "import pypesto.optimize as optimize\n", "\n", @@ -199,34 +191,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import pypesto.visualize as visualize\n", "\n", diff --git a/doc/example/sampler_study.ipynb b/doc/example/sampler_study.ipynb index 828df431f..b974fa715 100644 --- a/doc/example/sampler_study.ipynb +++ b/doc/example/sampler_study.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -72,39 +72,18 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:01<00:00, 7.77it/s]\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "result = optimize.minimize(problem, n_starts=10, filename=None)" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "visualize.waterfall(result, size=(4, 4));" ] @@ -118,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -136,26 +115,9 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5000/5000 [00:26<00:00, 187.98it/s]\n", - "Elapsed time: 32.125099131\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 27.9 s, sys: 4.26 s, total: 32.2 s\n", - "Wall time: 26.6 s\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "%%time\n", "result = sample.sample(\n", @@ -172,27 +134,9 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 0\n" - ] - }, - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sample.geweke_test(result)" ] @@ -206,22 +150,9 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ax = visualize.sampling_parameter_traces(result, use_problem_bounds=False)" ] @@ -235,20 +166,9 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ax = visualize.sampling_fval_traces(result)" ] @@ -262,20 +182,9 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "ax = visualize.sampling_scatter(result, size=[13, 6])" ] @@ -289,40 +198,9 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "for i_chain in range(len(result.sample_result.betas)):\n", " visualize.sampling_1d_marginals(\n", @@ -353,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -389,20 +267,9 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "xs = np.linspace(-4, 5, 100)\n", "ys = [density(x) for x in xs]\n", @@ -426,18 +293,9 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [00:02<00:00, 3521.27it/s]\n", - "Elapsed time: 3.027658070000001\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sampler = sample.MetropolisSampler({\"std\": 0.5})\n", "result = sample.sample(\n", @@ -447,37 +305,9 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 0\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sample.geweke_test(result)\n", "ax = visualize.sampling_1d_marginals(result)\n", @@ -493,18 +323,9 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [00:02<00:00, 3686.03it/s]\n", - "Elapsed time: 2.8828427840000046\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sampler = sample.MetropolisSampler({\"std\": 1})\n", "result = sample.sample(\n", @@ -514,37 +335,9 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 2500\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sample.geweke_test(result)\n", "ax = visualize.sampling_1d_marginals(result)\n", @@ -575,18 +368,9 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [00:12<00:00, 805.62it/s]\n", - "Elapsed time: 13.030148679\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sampler = sample.ParallelTemperingSampler(\n", " internal_sampler=sample.MetropolisSampler(), betas=[1, 1e-1, 1e-2]\n", @@ -598,47 +382,9 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 500\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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hnLpU2aGaOvoeZx8/MlHbrtF0NruDv35xjm0f5aPy9uLfJg7knmHR7QrrnvRFItqvowN82vtv1RPITA3dQEd/o2q02K75bM+/+v5MGV9+X0x8rxB+OrI3gf49ZzZsq83e7tkg7kmOJlYXzP/de4wt/5vHR4cvct/wmJv2LY1M1EogCdEBEkjdgLnB2qHfqNozgODE+Qq+/L6YfjGh/GJeMueLTDd9jydptNjIPVve7uMT+4Qx+yf9OX25kuxjJez+9Ax3xIczdkiULAYoRCeRQOqBLpaY+OybQuKignlgTBwq7+sv6SB+pFAoSOwTTt/oUL7ON5B7poxzRVXcNah5WQuZqFWI2yMPWfQwNfVNfPL1ZSJC/ZgyNr7N0G1xcz4qJfckR/PQ5ER0YQF8+X0xOz8+xSV9z7rLFKKzybdRD2J3ODhw5BJ2u4MpY+Ploc/bFB7iR+p9/Zg2Lh67w8HfvrzA3748T2VNg6tLE6Jbkia7HiTvfAX6inom3RmLOtjX1eV4BIVCQd/oUOJ0weSeLefrfAP/feAUQxM0JHWDh4GFcCfyK3IPUd9g4Z/H9cRoghgYJ1+UnU2p9GJEopZHpySRFB/O92fK+P83HWLf4YvY7B7zZIUQTiWB1EMcOaHHYrXzE5lxwKkC/FRMGBXLv00cQK+IQN7e/T3/X/rnnCno2HNbQvRE0mTXA5jqGjl50cjgfs0zbwvn04QF8OwjI/n2dBn//fEpnn3zCybfFces8Qn4+lx7VKM8SNv5ZFaE7kUCqQfIOVmKQqFgZJLO1aX0KE1WO95KL+bfP5DDx0s4cOQyh4/p+enI3sRFBbc5Xh6k7XwdfYZP/hu4ljTZeThTXRMnLxoZ1C+CoB40E4M78fVR8tORvZn9k/4olQqyvjzPJ19fpqGx/b+5C9ETSCB5uOPnynEAIwZqbnqscK5oTRAPTRrI6CQtZy5Xsn3/SU5dqrzm2mFC9ETSZOfBrDY7eReN9IsOlWYIN+Gt9GLMkF4kxKr5e04hn3x9mfyLRn46sudOBtwRtzKv49WaLDbKq82Y6pqorbdQU99Ek8WGze7AbnfwxbdFaML8CQn0ISzYlxhNEP17q4lU+3f2pYhrkEDyYGcKqmhssjG0f6SrSxH/IiLUnzkTEsg7X8Hh4yX85eNT1NQ3sWj6IHxUMpXT9XS0Tygi1I/j5ysoLqulrNJMVW3rdbz8fJT4+Xqj9FKg9FLgcEBRWS019U1YflgdGSBS7c/Q/hHceYeOAXFhLbO9yyCIziWB5MGOnysnPMSPaE2gq0sR1+ClUDCkfyR9Y0I59H0xf/3HeXJOGvh56hDuHKTr9OH5PWHEmd3hQF9ex9miai6VmDDVNQEQ6OeNNjyAgXFhaML8UQf5EuivajNbyZVlTBwOB01WO5WmBgzGeorKavnHd0X8PacQdbAvwwZouCM+nDvv0HW7vyN3JoHkoYymBkorzdw7LFqeO3JzgX4qHhjThxn39OW/PznNK/95hMS4MB6dmsSwAZpO++/nqSPOHA4H5VVm8i8aOVdUTX2DFaWXglhd8+TBPt5K1MG+Hfp7VCgU+KqUREUEEhURyLABGixWG+eLqsk9W87n3xTy3elSfLy9mDymjxOvrmeRQPJQpy9XogASYtWuLkW005D+kbwzsjcHvy7gvz8+xZp3D5PUJ4zp9/Zj3NBe0pT3L8yNVk5friT/opGK6gaUXgrie4XQv3cofaJC8FEpb2kxyetReStJ7BPOwLgwLulryM4t5q1d35F/0cj/mTUUP1/5Or1d8jfogRwOB6cvV9FbF0SgrNXTrXgrvUgZ24f7R/dm/z8v8dcvzvMf23N4x9ebMYOjGJGoZVDfcHThAT3yztdmt3OxxMTJi0YuFJuwOxxow/z5yYgYEmLV+Pk4/ytNoWgOvlhtEAWGWj46fIFzhdWseWyMDH64TRJIHuhCsYma+ibGDJYHYbsrlbeSGff2Y9q4vuSeLeOLb4v453E9n31TCECAnzcRof6EBvkQGuSLj7cXDsBhb/7Stlibf5qstubOeQegaL6rUHl7EeTvQ3CAipBAH8JD/AgJ9HHrgLtQXM2nRwv4+9ECquua8PNRMjQhgjviw4kIdU0IKJVezJmQwOhBOl7bdpR/f+sL1j11L70ipc/2Vjk1kLKysti0aRMWi4XFixezcOHCax73/PPPM2bMGObMmQNAcXExK1eupKKigr59+7JhwwYCA+U/cnt9c6oUpVfzLNSie/PyUjB8oJbhA7Usm+/gQnE1ZwqquFhiorKmgeraJi4WV2OxOfBSNP/27qVQ4KPyQuXthcpbib+vNwqgrsFCQ6MVU62dSyUmrLYfn3/yUXmhUQegDfP/od/kxsuzdwWDsZ5D3xfz95wCLpaY8FYqGNo/El1EAPG9QtxmLa/Rd+j43bJ7Wb0pm9V/OMT6p+5Fe5Pl7cW1OS2QDAYD6enp7NmzBx8fHxYsWMCYMWNISEhodcxLL73E4cOHGTNmTMv23/zmNzzyyCNMnz6dt99+m3feeYeVK1c6q1SP4nA4OHa2nFhdsPQ5eBgvLwX9e6vp31sN3NozOSfOVwDN/07MjVZMdU1UVDdQVmWmrLKe78+U8+3pMgCyvrzA4L4RJPYJIyk+jL7RoXgrnRcCDU1Wzlyu4ptTpXyVp+eyvgZoHvn2xJxk7hseQ0NjxwZmdJW+0aG88vjdrN50iFf+8wivP32f9CndAqf9jWVnZzN27FjUajUAKSkp7Nu3j2XLlrUck5WVxcSJE1uOAbBYLHz99de8/fbbAMyZM4dHH320TSCZTCZMptYrdOr1eudcTDdSXmWmsqaR4TIzg8fr6Ki5xKvWZ1IoFAT4qQjwUxEV8WPrg9Vmp6zSjN5YR2OTjWPnyvn82+ZmQh+VkgGxapL6hBEfHUpURAC9IgI73NxnszuoqmmgqKyWwtJaCvQ1nLxcyYWiamx2B0ovBYP7RTD5wT6MGRzVqgnMnadb6t9bzXNpd/KbzYd5a9d3rHx0lFs3g7ojpwVSaWkpGs2PX4parZbc3NxWxyxZsgSAnJyclm2VlZUEBQXh7d1cmkajwWAwtDn/1q1bycjIcEbp3dqFYhMKIL5XiKtLEd2Qt9KLXpGB9IoMZGSiFk2YP+VVDZy8ZOTkJSOnLlaS+cW5Vs19vj5KggN8CAls7pdSeStReinwVnphtTX3ZTVabNQ3WKiqaaS6tpGrl4jy81HSv7eaORMSuCM+nDv6dt95F0cmanl06h386cN8hg/U8IAMCe8QpwXStebnas9vC+1936JFi5g9e3arbXq9/rr9VD3FhZJq+saEEiCj60QnUCgUaML80YTFcN/wGAAsVhsl5XXoK+opqaijvKp5Kp6a+ubpeOoarNhsdqy25rsdX5US1Q99VANiw1AH+xIe7EuMNoje2mAiQv267Z2E1Wan1Fjfatt9w2M4ckLPHzOP00cX3GbJl3+dzkj8yGmBpNPpOHr0aMvr0tJStFrtTd8XHh5ObW0tNpsNpVJJWVnZNd8XEhJCSIjcBVytpr6J8qoG7kmOdnUp4hZc68vtRlz1xabyVhIXFUJclPz/12ixkXu2vM32O+/Qca6wiozd3zP9nr6t9iXK0vbX5bRAGjduHBs3bsRoNOLv78+BAwd45ZVXbvo+lUrF6NGj+fDDD0lNTWXv3r2MHz/eWWV6lAJDcyfwoH4RmGqbXFyN6Kjrfbldj7O/2DoakF5eYLff/LirdcfpidojNMiXOwdFcfhYCQWGGmJ1bde/Em059Q5pxYoVpKWlYbFYmDdvHsnJySxdupTly5czdOjQ6773pZdeYtWqVWzatIlevXrxxhtvOKtMj1JgqCHQz5uo8AAJJHHbbiUgOzorQnJC5G3N3u3OkhMiOX6unOxjJfybNqjbNkt2JaeOS0xNTSU1NbXVts2bN7c5bv369a1ex8TEsG3bNmeW5nHsDgcFpbX07RUi//BFt+Fud4Wd6cpSI598dZnTBVUkxnWf2l3FPZ4sE7etrNJMY5NNmgaEcCMDY9VEhPqRc9IgCzG2gwSSh7jSf9RbG+TiSoQQVygUCkYM1FJpauRSSY2ry3F7EkgeosBQQ6TaT4Z7C+FmEmLVBAWo3HKGCXcjgeQBrDY7BmM9MRpprhPC3Si9FAwfoKGkog5DB0Yt9kQSSB6g1FiPze4gRlaGFcItJcWH4630Iu9ChatLcWsSSB6guLwOQKa9F8JN+f4wD+Dpy1U0NLnvfHyuJoHkAYrLaokI9euSxcmEELdmUN9wrDY7OSelL+l6JJC6OZvdQUlFPdFydySEW9OFBxAR6sc/j5W4uhS3JYHUzZVV1mO12YnWyHBvIdyZQqEgMS6MS/oaqmsbXV2OW5JA6uZKfug/kjskIdxfQqwagDMFVS6tw11JIHVz+op6QgJ95PkjIbqB4AAf+kWHSCBdhwRSN+ZwONAb61qt+CmEcG8jk7QYTQ1UVJtdXYrbkUDqxmrqLdQ3WIkKD3B1KUKIdho+sHl9t7OF1S6uxP20K5CefvppsrOznV2L6CB9RXP/UVSEBJIQ3UVIoA+9IgK4WGJydSlup12B9MADD/DOO++QkpLCe++9R1VVlZPLEu1hMNbjrfQiItTf1aUIITogPjqU8iozNfWybtnV2hVIqamp/PnPf+add96hoqKC+fPns3LlSnJzc51dn7gBfUUd2jB/vLxk/SMhupP4Xs3Lv1+Su6RW2t2HZLfbuXTpEhcvXsRqtRIREcGvf/1rXn/9dWfWJ67DarNTXmWWAQ1CdENhwb6EBvlwoVgC6WrtmmsmPT2dPXv2EBsbyyOPPMKbb76JSqWivr6eCRMmsHLlSmfXKf5FeZUZu6P56W8hRPeiUCiI7xXCsXMVNFls+KiUri7JLbTrDsloNLJ582Z27NjBjBkzUKman3kJCAjgP/7jP677vqysLKZNm8bkyZPZvn17m/35+fnMnTuXlJQUVq9ejdXaPOlgYWEhCxcuZObMmfzsZz+jqKjoVq7No5VXNQ8ZjVRL/5EQ3VGfqBDsdgfFZXWuLsVttCuQbDYbSUlJrbY9/fTTANx7773XfI/BYCA9PZ0dO3aQmZnJzp07OXv2bKtjVq5cyZo1a9i/fz8Oh4Ndu3YB8OabbzJ9+nQyMzN54IEHSE9P7/CFebqyKjO+KiXBAfJArBDdUa/IQLyVCi6XykqyV9ywye6ll17CYDCQk5OD0Whs2W61Wjl//vwNT5ydnc3YsWNRq9UApKSksG/fPpYtWwZAUVERDQ0NDB8+HIA5c+bw1ltv8cgjj2C326mtrQXAbDbj5+d3q9fnscqrzESq/VEoZECDEN2Rt9KL6MggCg0SSFfcMJDmzZvHmTNnOHXqFCkpKS3blUolI0aMuOGJS0tL0Wg0La+1Wm2rUXn/ul+j0WAwGAB45plnWLBgAdu2bcNisbBz58425zeZTJhMrTsE9Xr9DWvyFDa7g4rqBob2j3R1KUKI2xCrC+ZQbjE19U0EB/i4uhyXu2EgDR06lKFDh3LPPfeg0+k6dGKHw9Fm29W/zd9o//PPP8/LL7/MpEmT2L9/P8uWLeOvf/1rq/dv3bqVjIyMDtXkKapqGrDZHUSGSf+REN1ZrK55lv4CQw2D+ka4uBrXu2EgPfPMM7z55pssWbLkmvuzsrKu+16dTsfRo0dbXpeWlqLValvtLy8vb3ldVlaGVqvFaDRy/vx5Jk2aBDQ39b300ktUVlYSHh7ecvyiRYuYPXt2q8/U6/UsXLjwRpfkEcp+GNCgkQENQnRr4SF+BPh5U2ColUDiJoG0dOlSANasWdPhE48bN46NGzdiNBrx9/fnwIEDvPLKKy37Y2Ji8PX1JScnh1GjRrF3717Gjx9PWFgYvr6+HD16lNGjR5OTk0NgYGCrMAIICQkhJCSkw3V5grJKM95KBepgX1eXIoS4DQqFgt7aYAoMNTgcjh7fJ3zDQBoyZAgAd911FwUFBcTGxnLgwAFOnz7NokWLbnhinU7HihUrSEtLw2KxMG/ePJKTk1m6dCnLly9n6NChbNiwgRdffJG6ujoGDRpEWloaCoWCjIwMXnnlFRoaGggMDGTjxo2dd8UeoLzKTESoP149/B+vEJ4gRhPI6cuVVNU0EhbSswdwtevB2F/96ldAczPZyy+/zH333cfq1at56623bvi+1NRUUlNTW23bvHlzy5+TkpLYvXt3m/clJyfz/vvvt6e0HsfhcFBeZWZgXJirSxFCdIIrqz0XldX2+EBq13NIx48f59e//jUff/wxs2fPZt26dfKwqouY6ppostrlgVghPERooA+Bft4Ul8sDsu0KJIfDgZeXF4cOHWLs2LFA8/NBouvJgAYhPItCoSBaE0RRWe01Rx/3JO0KpLi4OJYuXUphYSF33XUXzz77LImJic6uTVxDeZUZLwWEh/bsW3shPEmMJoj6BivVtT17OYp29SGtW7eOjz/+mFGjRqFSqRg9ejSzZs1ycmniWsoqzYSF+OGtlMV+hfAU0ZrmWfuLymp79OjZdn2rBQQEMHr0aEwmEydOnCA5OfmmUwcJ5yirMktznRAeRh3ki5+PsmUV6J6qXXdIr7/+On/+85+JiPjxwS2FQsHBgwedVphoq85swdxolQENQngYhUJBVEQgemO9q0txqXYF0kcffcSBAwc6PH2Q6FwyoEEIzxUVEcDFEhMNjVb8fNv11exx2tVk16tXLwkjNyBrIAnhua6s/tyT75LaFcN33303r732GhMnTmy1FMTgwYOdVphoq6zKTGiQj6wuKYQH0ob5o1CAvqKO+F49c1q0dgXSnj17ANi3b1/LNulD6nrlVWY0MsO3EB5J5a0kUu2PvkLukG7o008/dXYd4iYam2yY6poY1Df85gcLIbqlqPAA8i9WYrc78PLqeXNVtqsPqa6ujpdffplFixZRVVXFr371K+rqevbwxK4m/UdCeL6oiECsNjsVpgZXl+IS7QqktWvXEhwcTEVFBb6+vtTW1rZMuCq6hoywE8LztQxs6KHPI7UrkPLz81mxYgXe3t74+/uzYcMG8vPznV2buEp5lZlAP28C/FSuLkUI4STBASoC/Lx7bD9SuwLJy6v1YTabrc024VxlVWZprhPCw7U8ICt3SNd355138vrrr9PQ0MA//vEPli1bxpgxY5xdm/hBk8VGZU2DNNcJ0QNEhQdgqmuivsHi6lK6XLsC6d///d8JCAggODiY3//+9yQlJfHcc885uzbxg8LSWhwOiJQh30J4vJ78gOxNh31//PHHvPfee5w6dQo/Pz8SExMZOXIkvr49d0barnbZUAPIgAYhegJNmD9eCgX68jr6RYe6upwudcNA2rt3L++88w7Lly8nKSkJhULBsWPH+O1vf0tjYyMPPPBAV9XZo13Wm/BVKQkO8HF1KUIIJ/NWeqEJ88cgd0itbdu2jS1bthAdHd2yrX///gwbNowXXnjhpoGUlZXFpk2bsFgsLF68mIULF7ban5+fz4svvkhtbS2jR4/mN7/5Dd7e3pSWlvLiiy9SWlqKn58fGzZsoHfv3rdxmd3bZX0NkWp/FIqe96CcED2RLjyAvAtG7PaetYLsDfuQLBZLqzC6om/fvjQ2Nt7wxAaDgfT0dHbs2EFmZiY7d+7k7NmzrY5ZuXIla9asYf/+/TgcDnbt2gXAc889x4QJE9i7dy8zZ85kw4YNHb0uj2Gz2Sksq5URdkL0INrwAKw2O5U1PesB2RsGklJ5/Uk8b7b2e3Z2NmPHjkWtVhMQEEBKSkqrufCKiopoaGhg+PDhAMyZM4d9+/ZhNBo5efIkCxYsAGDu3Ln88pe/bHN+k8lEYWFhqx+9Xn/DmrqjwtJaLFa79B8J0YPowgIAelyzndMW3SgtLUWj0bS81mq15ObmXne/RqPBYDBQUFBAdHQ0r776KkeOHCE6Opo1a9a0Of/WrVvJyMhwVvlu41xRNSBTBgnRk4QG+eCrUkogXe3UqVOMHDmyzXaHw0FTU9MNT3ytO6ir+0Cut99qtZKXl8fTTz/N6tWref/991m1ahXbtm1rdeyiRYuYPXt2q216vb5NP1V3d76oGpW3F2HBMqpRiJ5CoVCgDe95AxtuGEgff/zxLZ9Yp9Nx9OjRltelpaVotdpW+8vLy1tel5WVodVq0Wg0BAYGMmHCBABmzJjB2rVr25w/JCSEkBDPXzPkfFE1sdrgHjnzrxA9mS48kJyTBhqbbK4upcvcsA8pJibmhj83Mm7cOA4fPozRaMRsNnPgwAHGjx/f6ty+vr7k5OQAzUPMx48fT1xcHDqdjs8//xyAv//97z12IUCHw8H5oiriooJdXYoQoovpwgJwOOCyweTqUrqM0yak0+l0rFixgrS0NGbNmsWMGTNITk5m6dKlHDt2DIANGzawbt06pk6ditlsJi0tDYCMjAz++Mc/MmPGDP70pz/x6quvOqtMt2Yw1lPXYCVOJ4EkRE+jDW/uNz5f3HMCyWmDGgBSU1NJTU1ttW3z5s0tf05KSmL37t1t3tevX782fUY90ZUBDXFRwVTW3HiYvRDCswT4qQgOUHHhh++BnkCm7HZj54uq8fJS0Fsb5OpShBAuoA0P4EKxBJJwA80DGoJQeV//eTAhhOfShQVQXt1AVQ9pIZFAcmPnCqvo31vt6jKEEC6iC29+QPZMQaWLK+kaEkhuqtLUQGVNI/1ietZsv0KIH2nC/FEo4PTlKleX0iUkkNzUlQENEkhC9FwqbyUxmiBOX5Y7JOFC568EUg9bD0UI0Vrf6FBOX6686fyhnkACyU2dL6omKiKAQH+Vq0sRQrhQ3+gQas0WSirqXF2K00kgualzRVX0j1G7ugwhhItdaSXpCf1IEkhuqM5sQV9RL/1HQgiiNYH4+ig50wP6kSSQ3ND5YhnQIIRopvTyIqG3mlMSSMIVrgxo6C+BJIQABsSqOV9UjcVqd3UpTiWB5IbOF1UTFuxLWIifq0sRQriBgXFhWKx2LpV49kSrEkhu6HxRtTTXCSFaJMaFAXDaw2dskEByM40WG5cNNRJIQogWmjB/1EG+Hv+ArASSm7lQXI3d7mBAbJirSxFCuAmFQsGAOLUEkuhaZwuqgOZOTCGEuGJgXBiFpbXUmS2uLsVpJJDczJmCKtTBvkSEyoAGIcSPBsaG4XDA2cIqV5fiNBJIbuZsYRUJvdUoFApXlyKEcCMD4tQAHt1s59RAysrKYtq0aUyePJnt27e32Z+fn8/cuXNJSUlh9erVWK3WVvvz8vIYMmSIM0t0K+ZGK4WGGmmuE0K0ERzgQ3RkoATSrTAYDKSnp7Njxw4yMzPZuXMnZ8+ebXXMypUrWbNmDfv378fhcLBr166WfWazmZdffhmLxXPbS//V+aJq7A7pPxJCXNvAuDCPntPOaYGUnZ3N2LFjUavVBAQEkJKSwr59+1r2FxUV0dDQwPDhwwGYM2dOq/3r169n8eLF1z2/yWSisLCw1Y9er3fW5XSJMz8MaEiQVWKFENcwIE6N0dRARbXZ1aU4hbezTlxaWopGo2l5rdVqyc3Nve5+jUaDwWAA4ODBgzQ0NDBlypTrnn/r1q1kZGQ4oXLXOVtQRWSon8zQIIS4poFXHpC9XMndQ/1dXE3nc1ogXWsxqas76q+3v6ysjE2bNrFly5Ybnn/RokXMnj271Ta9Xs/ChQtvrWA3cLawkgRprhNCXEe/6FC8lQpOX67i7qHRri6n0zktkHQ6HUePHm15XVpailarbbW/vLy85XVZWRlarZbPPvuMqqqqVsEyc+ZMtm/fTlBQUMu2kJAQQkJCnFV+l6szWygqq2PC6FhXlyKEcFM+KiXxP6wg64mc1oc0btw4Dh8+jNFoxGw2c+DAAcaPH9+yPyYmBl9fX3JycgDYu3cv48ePZ/78+XzyySdkZmaSmZkJQGZmZqsw8kRXni2QGRqEEDcyMFbNmYIqbHbPW9LcaYGk0+lYsWIFaWlpzJo1ixkzZpCcnMzSpUs5duwYABs2bGDdunVMnToVs9lMWlqas8pxe2dlQIMQoh0GxoVhbrRSVFrj6lI6ndOa7ABSU1NJTU1ttW3z5s0tf05KSmL37t03PMepU6ecUpu7OVNYhS48gJBAH1eXIoRwYz8ObKgiLspzui1AZmpwG2cLqmRAgxDipmI0QQT4eXvkUhQSSG7AVNeEwVjPAGmuE0LchJeXggGxnjnztwSSG2jpP5I7JCFEOwyMC+NisYmGRuvND+5GJJDcwMlLRrwUMmWQEKJ9BvWNwGZ3cMrD7pIkkNxA/gUj8b1CCfBTuboUIUQ3kBQfjkIBeReMri6lU0kguVjzbzlGkuLl+SMhRPsE+avoExVC3oUKV5fSqSSQXOyy3oS50cYd8eGuLkUI0Y0M7hfBqUtGbDa7q0vpNBJILpZ/sfmWO0kCSQjRAYP6hmNutHGh2OTqUjqNBJKL5V80Ehbsiy48wNWlCCG6kUF9IwA8qtlOAsnF8i8Yf+iglCXLhRDtF6n2RxsewPHzEkiiE5RVmjEY61t+0xFCiI5I7h/JsbPl2D1kolUJJBc6fr55+Y3khEgXVyKE6I6SB0RSa7Zwobja1aV0CgkkFzp2tpxAfxV9ennWBIlCiK5x5ZfZY+fKb3Jk9yCB5ELHz1UwpF8ESi/pPxJCdFxEqD8xmkC+PyOBJG5DWaWZkoo6hvSX5johxK0bmqDhxPkKj3geSQLJRa70Hw3tLwMahBC3LjkhEnOjlTM/rDrdnUkguUjumeb+o/joUFeXIoToxpITIlEo4NuTpa4u5bZJILmAw+Hgm1OlDB+gkf4jIcRtCQ3yZWBsGDkSSOJWXNbXYDQ1MCJR6+pShBAeYGSSltMFlZjqmlxdym1xaiBlZWUxbdo0Jk+ezPbt29vsz8/PZ+7cuaSkpLB69Wqs1ubFpnJycpg7dy4zZ85k0aJFFBUVObPMLnflN5mREkhCiE4wKkmLwwHfnured0lOCySDwUB6ejo7duwgMzOTnTt3cvbs2VbHrFy5kjVr1rB//34cDge7du1q2f7b3/6WzMxMUlNTWbt2rbPKdIlvThmIiwpGE+bv6lKEEB4gITaM4AAfck4aXF3KbXFaIGVnZzN27FjUajUBAQGkpKSwb9++lv1FRUU0NDQwfPhwAObMmcO+fftoamrimWeeISkpCYDExERKSkranN9kMlFYWNjqR6/XO+tyOk1Do5UT541ydySE6DRKLwUjEjV8c6oUWzeeRsjbWScuLS1Fo9G0vNZqteTm5l53v0ajwWAw4OPjw8yZMwGw2+1kZGQwadKkNuffunUrGRkZzirfab4/U4bVZmdUkgSSEKLzjB3Siy++LeLkRSOD+3XPx0mcFkgOR9uUvnpG65vtb2pqYtWqVVitVh5//PE2xy5atIjZs2e32qbX61m4cOHtlO102cdKCPRXMbifPBArhOg8o5K0qLy9yD5W3G0DyWlNdjqdjvLyH6ezKC0tRavVXnd/WVlZy/66ujqWLFmC1Wpl06ZNqFSqNucPCQmhd+/erX6ioqKcdTmdwmqz83WenrsG6VB5ywBHIUTnCfBTMXyghsPHSq75C3934LRvxXHjxnH48GGMRiNms5kDBw4wfvz4lv0xMTH4+vqSk5MDwN69e1v2r1y5kj59+vDmm2/i4+PjrBK73IlzFdTUW7h7aLSrSxFCeKBxQ3tRVmnmXGH3nP3baU12Op2OFStWkJaWhsViYd68eSQnJ7N06VKWL1/O0KFD2bBhAy+++CJ1dXUMGjSItLQ08vLyOHjwIAkJCcyaNQto7n/avHmzs0rtMtnHivH1UTIiUXPzg4UQooPuHBSFl5eCQ7nFJMSqXV1OhzktkABSU1NJTU1tte3qYElKSmL37t2t9g8aNIhTp045syyXsNnsHD5WwqgkLX4+Tv1rF0L0UKFBvgwfqOGzbwr52dQ78OpmM8FIR0YX+fZ0GZU1jfx0ZG9XlyKE8GATR8dSXmVumcC5O5FA6iKfHi0gOMCH0Xe498ALIUT3NmZILwL8vPn0aIGrS+kwCaQuUGu28M/jJfxkRIyMrhNCOJWvSsk9ydFk5xbT0Gh1dTkdIt+OXeAf3xVhsdq5/85YV5cihOgBJt4Zh7nRxuffFrq6lA6RQHIyh8PB3748T9/oEBJ6q11djhCiBxjUN5x+0aFk/eN8t3omSQLJyb47XcZlfQ0P3te/1UwUQgjhLAqFgtT7+nJJX0Pu2e4zuEECyckyvziHOtiXn4yMcXUpQogeZPyI3oQE+vDXL867upR2k0ByokslJnJOljJtXF9U3kpXlyOE6EF8VEpm3NOXr/L0nC2scnU57SKB5ETbPsonwM+b6ff0dXUpQoge6MHx/QnyV7F930lXl9IuEkhOknehgiMn9MyZkEBIoOfMxyeE6D4C/VXMmZDA0XwD+ReMri7npiSQnMBud7Dlb3mEBfsy877+ri5HCNGDpd7bj7BgX97dm4vNZnd1OTckgeQEH2VfIP+ikbRpg/DzlXnrhBCu4+frzf+ZPZRzhdVkfXnB1eXckARSJzMY69nyv3mMTNQyUR6EFUK4gXuSoxl9h44/78unqKzW1eVclwRSJ2q02Fj/p69RKBT8Yv4wee5ICOEWFAoFT80dho+3kle3fIXZTacUkkDqJHa7g7d2fsu5wir+feEotGEBri5JCCFaaML8ee5noyg01JD+l2/csj9JAqkT2OwOMt7/ji++LeJnU+/grsEyo7cQwv0MH6jl5w8O4fCxEt5ww1CSHvfbVGe28ObObzl8rIQFkxOZd/+ATv+Mx377caefUwjRPSiAv/7HzE4738zx/bFY7Wz93zxq6y08u3CU2zyaIoF0G749VUrG7u8przKzZOYQZo6XId5CiM7ljKlR590/gCB/Fe9+cIxfpn/G47OGctfgKJf3ezu1yS4rK4tp06YxefJktm/f3mZ/fn4+c+fOJSUlhdWrV2O1Nne0FRcXs3DhQqZMmcKTTz5JXV2dM8vsEJvdwdF8A2vezeZX//cwXgr43S/ulTASQnQrU+6O53fL7sXPx5u1//UVqzdl81WeHpvddbODKxxOmpvcYDDw8MMPs2fPHnx8fFiwYAFvvPEGCQkJLcfMmDGDtWvXMnz4cF544QWGDBnCI488wuOPP86DDz7I9OnTefvtt6mvr2flypU3/czCwkImTpzIwYMH6d27c5YKb2i0ctlQw4ViE6cuGTmab6CyphF1kC9zJiQw417nz1OX+mymU88vhHBvWZ3YZPevrDY7Hx66wJ7PzlJR3UBokA+jknQMjAsjoXco8dGh+Kq6Zi5OpzXZZWdnM3bsWNRqNQApKSns27ePZcuWAVBUVERDQwPDhw8HYM6cObz11lvMnz+fr7/+mrfffrtl+6OPPtomkEwmEyaTqdW2oqIiAPR6/S3Xve/wRfIvGDHVN2Gqa8JU18iVyPb39WZwvwhmj+vNiEQt3koFBn3JLX9We1nq3X/KDyGE8xQWOnehvZH9fBkWP4jvTpeRc9LAl1/nsf8LCwAKBQQH+BAa5ENIkC/Tx/VlYFzYbX1eVFQU3t5t48dpgVRaWopGo2l5rdVqyc3Nve5+jUaDwWCgsrKSoKCglmKvbP9XW7duJSMj45qfvXDhws66jDbygPeddnYhhGhr4qfrXV1Ci79tvv1zXK8Vy2mBdK2WwKs7zK63/2bvu2LRokXMnj271bampiYKCgqIj49HqfSM5R70ej0LFy5k+/btREV55nByuUbP4OnX6OnXB113jdc7t9MCSafTcfTo0ZbXpaWlaLXaVvvLy39cybCsrAytVkt4eDi1tbXYbDaUSmXL9n8VEhJCSEhIm+39+vXr5CtxD1FRUZ3WL+au5Bo9g6dfo6dfH7juGp02ym7cuHEcPnwYo9GI2WzmwIEDjB8/vmV/TEwMvr6+5OTkALB3717Gjx+PSqVi9OjRfPjhh622CyGE8GxOCySdTseKFStIS0tj1qxZzJgxg+TkZJYuXcqxY8cA2LBhA+vWrWPq1KmYzWbS0tIAeOmll9i1axfTpk3j6NGj/PKXv3RWmUIIIdyEUx+MTU1NJTU1tdW2zZt/7BFLSkpi9+7dbd4XExPDtm3bnFmaEEIINyNz2bm5kJAQli1bds3+Mk8h1+gZPP0aPf36wPXX6LQHY4UQQoiOkDskIYQQbkECSQghhFuQQOpG8vLyGDJkiKvLcIqcnBzmzp3LzJkzWbRoUcs0UJ7gZpMMe4KMjAymT5/O9OnTee2111xdjtP87ne/Y9WqVa4uw2k+/fRT5syZw5QpU1i7dm3XF+AQ3UJ9fb3joYcecgwcONDVpTjFhAkTHPn5+Q6Hw+F4//33HU888YSLK+ocer3eMWHCBEdlZaWjrq7OkZqa6jhz5oyry+pUhw4dcjz00EOOxsZGR1NTkyMtLc1x4MABV5fV6bKzsx1jxoxxPP/8864uxSkuX77suPfeex0lJSWOpqYmx8MPP+z47LPPurQGuUPqJtavX8/ixYtdXYZTNDU18cwzz5CUlARAYmIiJSXOn7S2K1w9yXBAQEDLJMOeRKPRsGrVKnx8fFCpVPTv35/i4mJXl9WpqqqqSE9P54knnnB1KU7z8ccfM23aNKKiolCpVKSnpzNs2LAurUECqRs4ePAgDQ0NTJkyxdWlOIWPjw8zZzZPr2+328nIyGDSpEkurqpzXGuS4WtNFtydDRgwoGXW/osXL/Lhhx/yk5/8xLVFdbJf/epXrFixwqOHfF+6dAmbzcZjjz3Ggw8+yI4dOwgNDe3SGmTFWDfy0UcfsW7dulbb+vXrR21tLVu2bHFNUZ3sete4ZcsWmpqaWLVqFVarlccff9xFFXYuRzsnC/YEZ86c4fHHH+f5558nPj7e1eV0mvfff59evXpx9913s2fPHleX4zQ2m42jR4+ybds2AgICeOqpp/jggw+YM2dOl9UggeRGpk6dytSpU1tte//993n33XdbLakxc+ZMtm/fTlBQUFeXeNuudY0AdXV1PPnkk6jVajZt2oRKpXJBdZ3vZpMMe4qcnByWL1/OCy+8wPTp011dTqf68MMPKSsrY+bMmVRXV1NfX8+rr77KCy+84OrSOlVkZCR333034eHhAEycOJHc3NwuDSQZ1NDNeOqghieffNLx4osvOux2u6tL6VRXBjVUVFQ46uvrHQ8++KDj+++/d3VZnaq4uNgxZswYR3Z2tqtLcbr/+Z//8dhBDd99950jJSXFUV1d7bBarY7HH3/csWvXri6tQe6QhMvl5eVx8OBBEhISmDVrFtDc13L1vIfd1dWTDFssFubNm0dycrKry+pU7733Ho2Njaxf/+MicgsWLODhhx92YVWio4YNG8aSJUt45JFHsFgs3HPPPcydO7dLa5Cpg4QQQrgFGWUnhBDCLUggCSGEcAsSSEIIIdyCBJIQQgi3IIEkhBDCLUggCSGEcAsSSEIIIdyCBJIQbuiDDz5g4sSJ1NXVUV9fz9SpU9m7d6+ryxLCqeTBWCHc1LPPPktwcDBNTU0olUpeeeUVV5ckhFNJIAnhpmpra5k5cyZ+fn7s2bMHX19fV5ckhFNJk50QbqqiooLGxkZMJhOlpaWuLkcIp5M7JCHckMViYcGCBSxYsAC73c7u3bvZsWOHxyzLIcS1yB2SEG7ojTfeQKPRMH/+fB566CHUajXp6emuLksIp5I7JCGEEG5B7pCEEEK4BQkkIYQQbkECSQghhFuQQBJCCOEWJJCEEEK4BQkkIYQQbkECSQghhFuQQBJCCOEW/h8XZFpuK1VxKQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sample.geweke_test(result)\n", "for i_chain in range(len(result.sample_result.betas)):\n", @@ -670,18 +416,9 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [00:03<00:00, 2607.40it/s]\n", - "Elapsed time: 3.8705950479999984\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sampler = sample.AdaptiveMetropolisSampler()\n", "result = sample.sample(\n", @@ -691,27 +428,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 2500\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sample.geweke_test(result)\n", "ax = visualize.sampling_1d_marginals(result)" @@ -733,18 +452,9 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [00:13<00:00, 765.33it/s]\n", - "Elapsed time: 13.167537605999996\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sampler = sample.AdaptiveParallelTemperingSampler(\n", " internal_sampler=sample.AdaptiveMetropolisSampler(), n_chains=3\n", @@ -756,47 +466,9 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 0\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sample.geweke_test(result)\n", "for i_chain in range(len(result.sample_result.betas)):\n", @@ -807,20 +479,9 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1.0000000e+00, 2.1910497e-01, 2.0000000e-05])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "result.sample_result.betas" ] @@ -834,70 +495,9 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sequential sampling (1 chains in 1 job)\n", - "Slice: [x]\n" - ] - }, - { - "data": { - "text/html": [ - "\n", - "\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n", - " \n", - " 100.00% [11000/11000 00:27<00:00 Sampling chain 0, 0 divergences]\n", - "
\n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Sampling 1 chain for 1_000 tune and 10_000 draw iterations (1_000 + 10_000 draws total) took 28 seconds.\n", - "Elapsed time: 28.322158023\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from pypesto.sample.pymc import PymcSampler\n", "\n", @@ -909,27 +509,9 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 0\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sample.geweke_test(result)\n", "for i_chain in range(len(result.sample_result.betas)):\n", @@ -954,18 +536,9 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [00:27<00:00, 360.39it/s]\n", - "Elapsed time: 28.11863592600001\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sampler = sample.EmceeSampler(nwalkers=10, run_args={\"progress\": True})\n", "result = sample.sample(\n", @@ -975,47 +548,18 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 100000)" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "np.array([sampler.sampler.get_log_prob(flat=True)]).shape" ] }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 0\n" - ] - }, - { - "data": { - "image/png": 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Tk7F69Wo4HA4sX74cMTExSEpKwpo1axAdHY0//OEPeOaZZ2C32xEaGoqXX35ZrHKISEKWWjvsrU6E9mMgAUBYiB/OX7ShvrH1hjs/kHyI2u07ISEBCQkJ3dalpaW5/jx58mR88MEHYpZARDJwtqwOABAa3L+hMTSkM+CKyuowZgS7f7s7dmogItEVldVB7a3EEJ2mX49rCPKFSqlAYWldvx6XpMFAIiLRFZXVI3SIX7eRtv1BpVTCNESLIgaSR2AgEZGoGu0OVFQ1Iiykf+8fdQkN1qKksgEOZ7sox6eBw0AiIlEVltRCAGASadCBcYgW7R0CzlX07PRC7oWBRESiKro0oMEQ5CvK8U1BnUFXUFIryvFp4DCQiEhUhaV1MAb5QqMWZ1Cvn683Av3VDCQPwEAiIlEVldVh1NCbb6h6JQqFAqOGBqKgpE6096CBwUAiItHUNbSiqtaOiDBx23+NCtOhvKoRjZywz60xkIhINF33j0aKHUiXzsCKSnnZzp0xkIhINIWldVAogBGmAFHfpyvweNnOvTGQiEg0RaV1CDcGwNdH1C5l8PP1xjCDHwc2uDkGEhGJpqisFmOG6wfkvcaMCEJBSS1nkHVjDCQiEkVNvR1WWysiw/Wiv5ezvQNDg/1Q29CK/Au1sFibYbE2o6GZs8m6E3HPo4lo0OpqeDoQZ0itjna0OjpbB+XklWD0sM73nBplRIBWLfr7U//gGRIR9auG5jZYrM04WlAFpUIBf19vV1iIKUTvC6VCAYu1WfT3InGIGkhZWVlYuHAh5s+fj4yMjB7bU1NTMWfOHCxZsgRLlizpdR8ici/2FicO5VtwrKgaQTofnCiugbO9Q/T39VIpEaLXoNJqF/29SByiXbKrrKxESkoKduzYAbVajZUrV2LGjBmIjIx07XPixAn87ne/w5QpU8Qqg4gkIAgCLLXNonZo6I1xiBb5FzoHNvT3VBckPtHOkHJzcxEbGwu9Xg+tVov4+Hjs3r272z4nTpxAWloaEhIS8Pzzz6O1tVWscohoADU0O9DS1g6jSA1Vr8QUpIXD2YHaBv4scUeiBZLFYoHBYHAtG41GVFZWupabmpowfvx4PPXUU9i5cydsNhveeOONHsex2WwoKyvr9jKbzWKVTUT9wFLbeR/HKNKUE1fS1VG8qpaX7dyRaJfsensW4PJTaD8/P6SlpbmWH3zwQaxfvx7JycndviY9PR2pqalilUlEIqiqbYZSoUBwP09Zfi1BARp4qRSoqm1GVETQgL433TzRAslkMiEvL8+1bLFYYDQaXcsVFRXIzc3F8uXLAXQGmJdXz3ISExOxbNmybuvMZjNWrVolUuVEdLOq61owJFADlWpgB/IqlQqEBPrCUsczJHck2qclLi4O+/fvh9Vqhd1uR3Z2NmbPnu3artFo8Morr6C0tBSCICAjIwPz58/vcRydTofw8PBur9DQULHKJqKbJAgCqursCNEP7NlRF0OQL6rr7OzY4IZECySTyYTk5GSsXr0aS5cuxeLFixETE4OkpCQcP34cQ4YMwfPPP49f/OIXuPvuuyEIAn7605+KVQ4RDZD6xjbYW50w6Ad2QEMXw6WBDXWNHNjgbkTt1JCQkICEhIRu6y6/bxQfH4/4+HgxSyCiAVZa2QAACAmUKJD0HNjgrtipgYj6VUlXIEl0hhSk00ClVDCQ3FCfAumXv/wlcnNzxa6FiDxASWUDdH5qqL1Vkry/SqlAcKAvqurYQsjd9CmQ7rrrLrzxxhuIj4/H22+/jbq6OpHLIiJ3VWK2SXb/qIsxyBdVtXZ0cGCDW+lTICUkJODdd9/FG2+8gZqaGqxYsQJr167FsWPHxK6PiNxIc4sDllq7ZJfruhiCfNHm7OBlOzfT53tIHR0duHDhAs6fPw+n04ng4GBs2LABr7zyipj1EZEbOX/RBkC6+0ddus7QLphtktZB16dPo+y6mqQOHz4cP/rRj/Daa6/B29sbzc3NmDNnDtauXSt2nUTkBorL6wFIH0hDAjVQKhW4cJGB5E76FEhWqxVpaWkYN25ct/VarRa//e1vRSmMiNxPcXk9/LXe8NNIO/enSqlESKAG580NktZB16dPl+za29t7hNEvf/lLAMCsWbP6vyoickvnKuoxwhQgi6kfQvS+KDHb2LHBjVz115j/+Z//QWVlJQ4ePAir1epa73Q6UVxcLHpxROQ+nO0dOH+xAfNuHy51KQAAY5AWp85ZUWltRmiwn9TlUB9cNZCWL1+OwsJC5Ofnd+uooFKpOKkeEXVTZmmEs70Dw00BUpcC4LupKIrK6hhIbuKqgRQdHY3o6Gj84Ac/gMlkGqiaiMgNdQ1oGBEaAHON9A+lBl/q2HC2rB6zJg+Tuhzqg6sG0uOPP47XXnsNP//5z3vdnpWVJUpRROR+isvrofZWIXSInywCSaVSYpjBH0VldVKXQn101UBKSkoCADz77LMDUgwRua9zFfUYGRYApVL6AQ1dIsJ0OFJggSAIshhoQVd31VF2kyZNAgBMnz4dYWFhmD59Ourq6vDtt99i/PjxA1IgEcmfIAgoLq/HqKGBUpfSTURoABqaO7tHkPz1adj3c889h7S0NJw9exbPP/88ysvL8fTTT4tdGxG5iapaOxrtDoweJrdA0gEAzvKynVvoUyCdOHECGzZswJ49e7Bs2TJs2rQJ5eXlYtdGRG6iuKJzQMMomQXScJM/VEoF7yO5iT4FkiAIUCqV+PrrrxEbGwsAsNuvfQqclZWFhQsXYv78+cjIyLjifp9//jnuvPPOPpZMRHJTXF4PpQIYGaaTupRuvL1UGBEagLNl9VKXQn3Qp/4eI0aMQFJSEsrKyjB9+nQ88cQTiIqKuurXVFZWunrgqdVqrFy5EjNmzEBkZGS3/aqrq/Gb3/zmxv8GRCS54vJ6DDX4Q6P2gg1tUpfTzehhehw4bebABjfQpzOkTZs2YfHixXjnnXfg7e2NadOmYePGjVf9mtzcXMTGxkKv10Or1SI+Ph67d+/usd8zzzyDxx577IrHsdlsKCsr6/Yym819KZuIBsi5inrcIrPLdV0iwwNR39iG6roWqUuha+jTGZJWq8W0adNQX1+PkydPIiYmBsXFxZg4ceIVv8ZiscBgMLiWjUZjj/mT/vrXv2LChAmYPHnyFY+Tnp6O1NTUvpRJRBJoaG6DpdaOhXHyDKTRw/UAOjs2dHVvIHnqUyC98sorePfddxEcHOxap1AokJOTc8Wv6a2h4eWnywUFBcjOzsaWLVuuesaTmJiIZcuWdVtnNpuxatWqvpRORCI7J9MBDV1GhumgVHSOtJsZHSZ1OXQVfQqkTz75BNnZ2dfVPshkMiEvL8+1bLFYYDQaXcu7d+9GVVUV7r33XjgcDlgsFvzoRz/C1q1bux1Hp9NBp5PXjVIi+k5Xy6BbZPYMUheN2gvDTQE4W86BDXLXp3tIYWFh193LLi4uDvv374fVaoXdbkd2djZmz57t2r5mzRp8+umnyMzMxObNm2E0GnuEERHJX3F5PYboNNAH+EhdSg/O9g5YrM0YZvRHQUktKmuaYLE2o6FZXgMvqFOfzpBmzpyJl19+GXPnzoVGo3Gtv9o9JJPJhOTkZKxevRoOhwPLly9HTEwMkpKSsGbNGkRHR9989UQkuXMVNtkOaGh1tONYUTWUCgVsTW346mgF/H29MTXKiACtWury6Hv6FEg7duwAgG6j5K51DwkAEhISkJCQ0G1dWlpaj/3Cw8Oxd+/evpRCRDLS5mhHSWUDpk8MlbqUqzIGaQEAVbXN8PeVZ3hSHwOJYUFEvSkxN6CjQ5Dt/aMuIXoNFOhscSS3fnv0nT7dQ2pqasLzzz+PxMRE1NXV4bnnnkNTU5PYtRGRzH3XMkjeA4+8vVTQ63xQVccmq3LWp0D69a9/jYCAANTU1MDHxweNjY147rnnxK6NiGSsobkNJ8/WQKNWQalQwGJthsXajFZHu9Sl9cqg16KqVvp5mujK+hRIp0+fRnJyMry8vODr64tXX30Vp0+fFrs2IpIxe4sTp8/XIChAgyMFVTiUb8GhfAuc7R1Sl9YrY5AvmlqcaGpxSF0KXUGfAkmp7L5be3t7j3VENLh0CAKq61sQ4ibdDwz6zjqrODeSbPVpUMPtt9+OV155BS0tLfjqq6/w7rvvYsaMGWLXRkQyVlXbDIezAyGBmmvvLAMhrkDiZTu56tNpzq9+9StotVoEBATg97//PcaNG4cnn3xS7NqISMZKKhsBfHfmIXdqbxX0ARzYIGfXPEPas2cP3n77beTn50Oj0SAqKgpTp06Fj4/8nsomooFTYrZBqQCG6NzjDAnoDM+L1RwhLFdXDaRdu3bhjTfewJo1azBu3DgoFAocP34cL774IlpbW3HXXXcNVJ1EJDOllQ0I0mmgUrnP/WRDkC8KS+tga2qDcYhW6nLoe64aSO+88w62bNmCoUOHutaNHj0akydPxvr16xlIRINYibkBoSF+UpdxXYz6zhC6YLYh8tK0FCQfV/3VxuFwdAujLqNGjUJra6toRRGRvNXaWlDf1OY294+6dI0IvHDRJnEl1JurBpJKpbritt7mOyKiweFcRecP9JBA9wokH28VAv3VuGBukLoU6oX7XPwlItk4W14HAAjWu8+Ahi4GvRbneYYkS1e9h5Sfn4+pU6f2WC8IAtraOJ8I0WB1rsKGkEANNOo+PcooK4YgXxSV1aG+sRWB/hwtLCdX/TTt2bNnoOogIjdSXF6H4aYAqcu4IcZL95HOltdjapTxGnvTQLpqIA0bNmyg6iAiN9Hc4kBFdRNuHy/vOZCupKtjw9myOgaSzIh6DykrKwsLFy7E/PnzkZGR0WP7nj17kJCQgEWLFmHdunW8DEjkBorK6iAIwMih8p5y4ko0ai8Y9L44W1YvdSn0PaIFUmVlJVJSUrB161ZkZmZi27ZtKCoqcm1vbm7G888/j7/85S/4+OOP0draip07d4pVDhH1k8KSOgDAyDD3DCQAiAjToaisTuoy6HtEC6Tc3FzExsZCr9dDq9UiPj6+2xToWq0We/fuRUhICJqbm1FTUwOdrucH3GazoaysrNvLbDaLVTYRXUNhWR2MQ7QI0KqlLuWGRYQGoNLajIZmXpWRE9GGyFgsFhgMBtey0WjEsWPHuu3j7e2NL774Ak8++SSMRiNmzZrV4zjp6elITU0Vq0wiuk6FpXUY4+ZdDrrO7gpLeR9JTkQ7Q+rtwVmFQtFj3R133IFvvvkGc+bMwYYNG3psT0xMRE5OTrdXb/ejiEh89Y2tsFibMdbNA2nU0EAoFED+hVqpS6HLiBZIJpMJ1dXVrmWLxQKj8bvfROrq6rBv3z7XckJCAvLz83scR6fTITw8vNsrNNQ9R/cQubuu+y5jhgdJW8hN8vXxwghTAM5csEpdCl1GtECKi4vD/v37YbVaYbfbkZ2djdmzZ7u2C4KAtWvXoqKiAgDwySef9PoQLhHJR2FpHRQKYHR4oNSl3LRxI4cg/0ItOjrYBk0uRD1DSk5OxurVq7F06VIsXrwYMTExSEpKwvHjxxEUFIQXXngBDz30EO655x6cP38ea9euFascIuoHhSV1GGbwh1bjLXUpN21cRBCa7A6UVzVKXQpdImrfj4SEBCQkJHRbl5aW5vrzvHnzMG/ePDFLIKJ+VFRWi8ljDNfe0Q1ERQwBAJw5b3XbrhOehs1ViahPaurtsNpaPWYeoWEGf/j7euMMBzbIBgOJiPqk4NIDsWPdfEBDF6VSgaiIIA5skBEGEhH1SVFZHVRKBUYNc/8BDc72DliszRhuDECpuQHnK+r5kKwMMJCIqE8KS2oREaqDj/eVJ+50F62OdhzKt6BDECAAyP6mBPYWp9RlDXoMJCK6JkEQUFRW5zH3j7qYhmgBAGZrk8SVEMBAIqI+6Oz75nD7lkHfp/ZWYYhOg8qaZqlLITCQiKgPulrsjB3hGQMaLhcarIXZ2oSOXtqd0cBiIBHRNZ05b4WvjwoRoZ73vE5osB/aHB0wV/OyndQYSER0TacvWDF2RBBUKs/7kRF66T7S2XJO2Cc1z/t0EVG/src6ca7ChnGXOht4Gn2AD3zUKhSW1kldyqDHQCKiqyos7WxAOm6kZwaSQqFAWLAfCkvZsUFqDCQiuqqjBVUAgGCdBhZrs+vV6miXuLL+M8zgD0utHTX1dqlLGdREba5KRO7v9PlaBOl8kF/S/QwiKsJzRtwNNfgBAE6crcEdU8Mlrmbw4hkSEV1RR4eAs+V1CB3iJ3UpogoJ9IVGrcKJ4hqpSxnUeIZEV9XQ3NZrSxVfjRcCtGoJKqKBdMFsQ3OL03UG4amUSgXGDNfjxNnqa+9MomEg0VXZW5w4lG/psX5qlJGBNAicvHTGMDTEX+JKxDd2RBD+/s8i1Da0IChAI3U5g5Kol+yysrKwcOFCzJ8/HxkZGT22f/bZZ1iyZAnuuecePPLII6iv53MARHJy4mwNhug00Pl5/i8fXffEThTxsp1URAukyspKpKSkYOvWrcjMzMS2bdtQVFTk2t7Y2IgNGzZg8+bN+PDDDxEVFYXXX39drHLokobmtm4jpbpebL1P3ycIAk4W13jU4IWrGRmmg1bjhaNFVVKXMmiJdskuNzcXsbGx0Ov1AID4+Hjs3r0bjz32GADA4XBgw4YNMJlMAICoqChkZWX1OI7NZoPNZuu2zmw2i1W2x7vSJbiYyJBe7xV50tBeuj5llkbUNbZirIc1VL0SlVKJ6NEhOFrIQJKKaIFksVhgMBhcy0ajEceOHXMtBwUFYd68eQCAlpYWbN68GT/+8Y97HCc9PR2pqalilUmXtDrase9oBUoqG2Ctt8NH7QVDkC/iZ0RIXRpJpOv+UVREEMqrBkeft8ljDPjmpBnmmiaEBnv2QA45Ei2QhF465yoUih7rGhoa8Mgjj2DcuHFYtmxZj+2JiYk91pvNZqxatar/ih3kWtqc+CCnEF8dKYcAwNtLCYezAwCQd7oSMyeFYbjJ85pq0tUdP1uNoAAfGIO0gyaQbh3b+Uv00cIqBpIERAskk8mEvLw817LFYoHRaOy2j8Viwc9+9jPExsZi/fr1vR5Hp9NBp9OJVeagV2trQda+YjTaHZg0OhjRkSHQ+/ugQwDOVdTjSEEVsr4qxg8mD8XkMYZrH5A8QkeHgKOFVZgSZez1F0lPFW70xxCdBkcLqxEfO1LqcgYd0QY1xMXFYf/+/bBarbDb7cjOzsbs2bNd29vb2/Hwww9jwYIFePrppwfVh14uLNZm/P3zIjjbBfy/+6di9pRwBAVooFAooFIqEBmux69WTcXIoTrsO1qB43xGY9C4YLahvrENtw6yX0IUCgVuHWvA0cIqdHRwfqSBJuoZUnJyMlavXg2Hw4Hly5cjJiYGSUlJWLNmDcxmM06dOoX29nZ8+umnAIBJkybhxRdfFKskukx9Yys++voc1F4qLJl9CyLCdK5J2C7no/bC3TNH4pPc8/jqSDkC/dQYEcozVk935FL/ulvHGtDePrh+ME+JMmJvXimKyuo8ckJCORP1wdiEhAQkJCR0W5eWlgYAiI6OxpkzZ8R8e7qCVkc7Pvr6HDoEAQn/NgqB/j5X3V+pUGD+9BHY8XkRsr8twf13RQ1QpSSVIwVVGG7yR3CgLyzWwTW999QoIxSKzvunDKSBxV52g4wgCPj8YCnqG1uxcObIPj+RrvZWIX5GBJzODnxxqKzXQSvkGRzOdpworsGtY43X3tkD6fzUGDsiCAfPVEpdyqDDQBpkvjpSjqKyesyYGIqhhutrBxOk02DGxFCcq7Dhm5N8FsxTnT5vRZujHZMjQ6QuRTLTxptQWFqH+sZWqUsZVBhIg4jF2oy/7SnAMIM/pkbd2G+/k8caYAzyxfs5hbC39nyQltxf3mkLvFRKxAyyAQ2Xu22cEYKAXh8iJ/EwkAYJQRCQ+v4RQAHcOW34DY9qVCoUmDV5GOoaW/H3vYX9WyTJwoFTZkwaHQxfn8Hbe3n0MD30AT74llcCBhQDaZD4/FAZDhdUYfmcMTfdKDMsxA8zJoZi5+dFqKrlDJue5GJ1E8osjbh9gknqUgacs73D1duxus6O6NEhyDtdCauNn/GBwkAaBFpandjy0SmMHaHHv9/WP7Nh/nBOJDoEAe/nFPTL8UgeDpzuPCO4fXyoxJUMvFZHOw7lW1wvnZ8aLW3tOHyGve0GCgNpENjxeRGsthb8/J5oKPvpAeSQQF/MnxGBPd9eQOUgGxbsyfJOVWKYwR9hIWybM9zoD28vJe8jDSAGkoerrrPj7/8swqzJQzF+1JB+PfZ/zB0LQIHtn/EsyRM02R04frZmUF6u641KpUREaACOFFahnV0bBgQDycO988lpCIKAnyye2O/HDtH74u6ZEfjsQAkuVg+O5pue7MApM5ztHfhBzFCpS5GNW4YFwtbUhjPnrVKXMigwkDxYYWkt9uaV4p5/uwWmIdp+PXbXDeA5U4dDpVQg/eNTnOjPzX1xuBxBAT7QB/h0m7xxMM+JFRGqg7eXEl8dKZe6lEFh8I7r9HCCIGDzzuPQ+akx57bhrvYv/fXDpdXRjmNFnc1WJ4wKRu6xCowM0+HOacMRoPX86a49TXOLA0cKqjBh1BBXH7sug2XG2N6ovVWYPMaAr49WIGnJJKhU/B1eTPzueqjc4xdx5kItpkYZcfq81TVyyNne0e/vNTXKAJVKiQOn2WrFXeWdroSzvQOjhwVKXYrszJgQirrGVhwtYrd7sTGQPJDD2Y4tH53EMIM/xo/s34EMvdFqvDFpdDAKS2phruG9JHe072gFAv3UHF3Xi+jIYGg1XvjycJnUpXg8BpIHyvrqHMw1zbhv3lgolQMzz9SUsQaoVAp89PW5AXk/6j+2pjYcOGXGjEmhnJesF95eKsyMDsP+4xfR0sZ2WWJiIHmY+sZWbPssH9PGmzDxluABe1+txhuTbgnBv05cREVV44C9L928rw6XwdkuIC6ao+uuZO60EWhucWL/8YtSl+LRRA2krKwsLFy4EPPnz0dGRsYV93vqqaewY8cOMUsZNLZ+egYtbe14MKH/h3lfy5QoA7xVSmzjc0luJSevFKOG6jDcFCB1KbI18ZZghAZrseebEqlL8WiiBVJlZSVSUlKwdetWZGZmYtu2bSgqKuqxz8MPP4zdu3eLVcagUmK2Yfe/LmDhzJGS/HDRarzx77eF4/NDZaio5lmSOyitbEBhaR3unDZc6lJkTalUYN70ETh+tprP3IlItEDKzc1FbGws9Ho9tFot4uPjewRPVlYW5s6diwULFohVxqDy56yT8FWrsFLCGV3vjh0JL6UC2/bwLMkd7N5/Hl4qBe6Y0j89Dj3Z3GkjoFQAe769IHUpHku0QLJYLDAYvptPxWg0orKy+7Dgn//851ixYsVVj2Oz2VBWVtbtZTazJfz3HTpjwcEzFqy8K+qaU5KLKdDfBwviRvEsyQ20tDqRc6AEcdFDEaTr28zBg1HXQ+AdHQKiR4fg039dQLmlkQ+Bi0C0B2N7m+L6RkbwpKenIzU1tT9K8lh1DS14a+cxGIN8cfv40H5/CPZ63TsnEp/knsP2zwrwXyunSlIDXdvnh8rQ1OLEwh+MkroUWbv8IfARoTocLarGB3sLcP9d4/gQeD8TLZBMJhPy8vJcyxaLBUbj9c9SmpiYiGXLlnVbZzabsWrVqpuu0VNk/+sCKqqbsGDmSBw/+93De1I8Yd/14O3sKeHYm1eKebePgDFIC1+NF//xyoggCPj463MYGabDhH5uuuvJhpv8ERTgg2NF1Vg5nw1X+5tol+zi4uKwf/9+WK1W2O12ZGdnY/bs2dd9HJ1Oh/Dw8G6v0NDBN1fLlTTZHdj15VkMDfHDqKE6qctxzSkTbvSHQgH85aNTOJRvgb2Fz2/IyeGCKpy/aEPCv93CZ4+ug0KhQHRkCCy1dpwtr5e6HI8jWiCZTCYkJydj9erVWLp0KRYvXoyYmBgkJSXh+PHjYr3toPO3PflobHZg1uShsvrB4ufrjcljDCgoqXVdQiT5eD+nACGBGsy5jaPrrldURBB81Cr8I5cPgfc3UZurJiQkICEhodu6tLS0Hvu99NJLYpbhsS5ctOHDr4ox69ZhMAT1bzfv/nDbuM4+el8fq0B8bITU5dAlp87V4MTZGiQtmQRvLz4bf73UXipMjgzBt6cqca6iHqOGsv9ff+Gn0U0JgoA3dxyDn8YL986JlLqcXqm9VZg+wYSK6iYczuc00HLx7ien4a/1xpSxRk4zcYOiI0OgUavwfk6h1KV4FAaSm/rnwTKcLK5B4qIJsh4sMGFUMIJ0Pnh/bwEczv7vNE7X51C+BcfP1mBypAEnz9W4usCL1QneU2nUXrhz2nDsO1qOcxW8l9RfGEhuqNHuwF+yTiJqRBDmT5f3pTClUoEfRA+FpdbOa+4S6+gQsOWjkwgJ1CB69MD1OfRU8bEjodV4I/3jU1KX4jEYSG4o45PTsDW14uF7Ywasm/fNGBEagAmjhuC97HzU2lqkLmfQyjlQgnMVNvxwTiQnmusH/r7e+I+5Y3DwjAXHinhJuj/wU+lmThbX4OPcc1gYNwqR4Xqpy+kThUKBVfHj0NrWjrTME1KXMyjVNbTiz1knMWHUENw+gY9N9JfFs26BIcgXabtO8JJnP2AguRF7qxO//9shmIZosXrRBKnLuS6hwX64b/5YfHWkHN+eYuungZaWeRwtbU48tuJWKGX0eIC7U3urkLRkEs5ftOHDL89KXY7bYyC5kT/uOIbKmmasXjgBDU1tbjc66t45YxARGoDU7UdQ39gqdTmDxpeHy/Dl4XKsmDuWU0yIIHZSGGZMDEXGp/mcMfkmMZDcxBeHyrA3rxRTogxosjvccnSUt5cST6y6DQ3NDry+/Uiv/Q6pf5lrmpD6/lGMiwjCf8wbK3U5HkmhUOA/l0XDS6XAbzMOot1N/j3KEQPJDZRWNuAPHxxBZHggpk8Mk7qcG9LVMdlP440fzonENyfNyNh9hh2TRWRvdWLTlgNQKhVY+8A0eHEgQ7/q+kxbrM2AADxw9zicuVCLLRx1d8NE7dRAN6++sRX/+6d/wcfbCw8ti8H5izapS7ohl3dMDgnUYNRQHbbnFCDc6I9/Z/uaftfeIWDTlm9x/qINa+67FQAk7wLvaS7/TAOAt5cK4yKCkPnFWUwYFYyZ0e75y6OU+CuTjNlbnfj1n79Bra0Fzzw4HUM8ZM4ahUKBebePgD7AB2/uOIYLbhqyctXeIeC1vx3C4YIq/NutQ+FwdrjlJV53dMfUcIwcqsOrGQdRUFIrdTluh4EkUy2tTvzvn/6FgtI6PLHqNkRFeNYUAWpvFRb/YBTUXio8+1YuJ/PrJw5nB3639SD+ebAMS+8YjUmjQ6QuaVDxUinxyxW3Qh/ggw1p+1FUVid1SW6FgSRD9Y2tePatXJw+V4P/d/9UxMUMlbokUej8fPDEj6bC2S5gXeo+nOU/3pvS9bn58nA5EhdNQMKsW6QuaVAK9PfBiw/HwdfHC8+8+TWOX3ZZj66OgSQzRWV1eOK1L1FcXo8nV9+OO6aGS12SqIYa/PHSoz+ASqXEf7+xD/uOlktdkls6lG/B47/7HIUltfjVqtuw/M4xUpc0qIUG+2HTo7MQpNPgmbdyseuLs+jo4KjSa1EIbjj2tqysDHPnzkVOTg7Cw+XzA3vrp2fwo/hx3ZaPF1Vj06OzcN/6j7Ft4yIse/JDqL1U2LZxEbZ+egaZX5xFcysnr+uLSbcE40RxDbxUCoy7dAmzuLwe2zYukriyG9f1mfn+Z+e//7APxeX18PP1RlWdHfffFYXPvi1BVZ1dwmqpvxj0vgDg+n/7tz356PpJnPXbJUh4ItP15/vWf4wld4zGe9n50Pp4uT7v3//M9Oa//7APmx6dJd5fpJ/xDKkfvZed32P5RHENALhCx9kuuP78XnY+w+g6dH0vne0CThTX4ERxjdt//7o+M9//7HT93boC6L3sfIaRB6mqs3f7f3u104LmVqfr83H55/37n5nedP2bcReiBlJWVhYWLlyI+fPnIyMjo8f206dP495770V8fDyefvppOJ3u/cPlelRU8SY+EdHlRAukyspKpKSkYOvWrcjMzMS2bdtQVFTUbZ+1a9fi2WefxaeffgpBELB9+3axypFUV5ucy3tdPfRSjlTleBw3vOp8RYIg8GHhQe7jfcWuP9c2DK7u+KLdQ9q5cycOHDiAjRs3AgD+8Ic/QBAEPPbYYwCA8vJyJCYm4rPPPgMA5OXl4f/+7//w17/+tdtxbDYbbLbuz6mUl5dj9erVyMjIQGjojXUuPn+xHvnnayEAnS9BgCAAAgRAADo6VwIAOjo3XLbvdz8EBaFznplWhxNfHi7HlLEGtDo60NLmRHH5lSfu6rpvQDfPS6VEgJ8aOq03/LVq+HiroFarOv/rpYK3txIKAAoFXI1FFUoFFApAAYVrvQLf/T8Guv8/FrrWCt99Bi6/znL5ZwffO45r+6XPmMPZjjZHOxxOAd+cvIgJo4Jx6lwNggM1aGh2oI0PrtJVjB4WCG8vFc5csOK2cSaovZXw9lLC20vV+TlWdM5DplAo8EnuOSyeNapzvVLh2q74XoPd77fbvdJWHx8lZkYPhY+36qb+DqGhofDy6tmXQbRODRaLBQaDwbVsNBpx7NixK243GAyorKzscZz09HSkpqb2+h6rVq3qx4r7x7m9fdvvxT7uR57v3Pf+S3Q1l39O+vLz5nUZ/qy50oA00QKptxOvy1P5Wtu7JCYmYtmyZd3WtbW1obS0FCNHjoRKdXNJ7e7MZjNWrVp1U2eLgx2/hzeP38ObM9i+f1f6O4oWSCaTCXl5ea5li8UCo9HYbXt19XcPjFVVVXXb3kWn00Gn0/VYf8stfOjvcqGhobIaAu+O+D28efwe3pzB/v0TbVBDXFwc9u/fD6vVCrvdjuzsbMyePdu1fdiwYfDx8cHBgwcBALt27eq2nYiIBhfRAslkMiE5ORmrV6/G0qVLsXjxYsTExCApKQnHjx8HALz66qvYtGkTFixYALvdjtWrV4tVDhERyZyo008kJCQgISGh27q0tDTXn8eNG4cPPvhAzBKIiMhNsFODm9PpdHjsscd6vc9GfcPv4c3j9/Dm8PvXyS172RERkefhGRIREckCA4mIiGSBgeQBDh48iHvvvRdLlixBYmIiyss5p1BfXasBMF1bamoqFi1ahEWLFuHll1+Wuhy39Zvf/Abr1q2TugxJMZA8wNq1a/Hiiy8iMzMTCQkJ+PWvfy11SW6hLw2A6epyc3Oxb98+7Ny5E7t27cLJkyexZ88eqctyO/v378fOnTulLkNyDCQ319bWhscffxzjxnVO1BUVFYWLFy9KXJV7yM3NRWxsLPR6PbRaLeLj47F7926py3IrBoMB69atg1qthre3N0aPHo2Kigqpy3IrdXV1SElJwcMPPyx1KZJjILk5tVqNJUuWAAA6OjqQmpqKefPmSVyVe+itAXBvDX7pysaMGYNbb70VAHD+/Hn84x//wB133CFtUW7mueeeQ3Jy8qAf8g2I/GAs9a9PPvkEmzZt6rbulltuwZYtW9DW1oZ169bB6XTioYcekqhC99LXBr90bYWFhXjooYfw1FNPYeTIkVKX4zbef/99hIWFYebMmdixY4fU5UiOgeRGFixYgAULFvRY39TUhF/84hfQ6/V488034e3tLUF17udaDYCpbw4ePIg1a9Zg/fr1WLRokdTluJV//OMfqKqqwpIlS1BfX4/m5mZs3LgR69evl7o0SfDBWA/wyCOPIDg4GM8//zx/w78OlZWVuP/++/HBBx/A19cXK1euxAsvvICYmBipS3MbFy9exLJly5CSkoKZM2dKXY5b27FjB7799lu89NJLUpciGZ4hublTp04hJycHkZGRWLp0KYDOeyGX9wyk3l3eANjhcGD58uUMo+v09ttvo7W1tdsP0ZUrV+L++++XsCpyVzxDIiIiWeAoOyIikgUGEhERyQIDiYiIZIGBREREssBAIiIiWWAgERGRLDCQiIhIFhhIRDKxc+dOzJ07F01NTWhubsaCBQuwa9cuqcsiGjB8MJZIRp544gkEBASgra0NKpUKL7zwgtQlEQ0YBhKRjDQ2NmLJkiXQaDTYsWMHfHx8pC6JaMDwkh2RjNTU1KC1tRU2mw0Wi0XqcogGFM+QiGTC4XBg5cqVWLlyJTo6OvDBBx9g69atnE6EBg2eIRHJxO9+9zsYDAasWLEC9913H/R6PVJSUqQui2jA8AyJiIhkgWdIREQkCwwkIiKSBQYSERHJAgOJiIhkgYFERESywEAiIiJZYCAREZEsMJCIiEgW/j8+a8LrnLjczQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sample.geweke_test(result)\n", "for i_chain in range(len(result.sample_result.betas)):\n", @@ -1040,18 +584,9 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "12509it [00:38, 321.78it/s, batch: 9 | bound: 3 | nc: 1 | ncall: 41584 | eff(%): 30.057 | loglstar: -1.671 < -0.471 < -0.561 | logz: -2.176 +/- 0.028 | stop: 0.969] \n", - "Elapsed time: 39.37040828100001\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sampler = sample.DynestySampler()\n", "result = sample.sample(\n", @@ -1071,40 +606,9 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "visualize.sampling_1d_marginals(result)\n", "visualize.sampling_fval_traces(result, full_trace=True)" @@ -1119,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1135,40 +639,9 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dummy_sampler = sample.DynestySampler()\n", "dummy_sampler.restore_internal_sampler(\"dynesty.dill\")\n", @@ -1188,40 +661,9 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "dummy_result.sample_result = dummy_sampler.get_samples()\n", "visualize.sampling_1d_marginals(dummy_result)\n", @@ -1266,7 +708,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1286,18 +728,9 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10000/10000 [00:31<00:00, 320.68it/s]\n", - "Elapsed time: 31.302212088999994\n" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sampler = sample.AdaptiveParallelTemperingSampler(\n", " internal_sampler=sample.AdaptiveMetropolisSampler(), n_chains=10\n", @@ -1309,37 +742,9 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 500\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "sample.geweke_test(result)\n", "ax = visualize.sampling_scatter(result)\n", diff --git a/doc/example/sampling_diagnostics.ipynb b/doc/example/sampling_diagnostics.ipynb index fbdd2732f..5bdad7458 100644 --- a/doc/example/sampling_diagnostics.ipynb +++ b/doc/example/sampling_diagnostics.ipynb @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:28:30.546369Z", @@ -46,22 +46,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:33:33.768292Z", "start_time": "2023-06-26T07:33:30.535312Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Visualization table not available. Skipping.\n" - ] - } - ], + "outputs": [], "source": [ "import logging\n", "\n", @@ -99,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:34:35.117499Z", @@ -122,22 +114,14 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:44:20.629679Z", "start_time": "2023-06-26T07:43:02.209978Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Elapsed time: 77.57845200000003\n" - ] - } - ], + "outputs": [], "source": [ "%%capture\n", "result = sample.sample(\n", @@ -153,23 +137,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:37:18.376334Z", "start_time": "2023-06-26T07:37:17.333572Z" } }, - "outputs": [ - { - "data": { - "text/plain": "
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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = visualize.sampling_parameter_traces(\n", " result, use_problem_bounds=False, size=(12, 5)\n", @@ -185,31 +160,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:37:22.016036Z", "start_time": "2023-06-26T07:37:21.380827Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 500\n", - "Geweke burn-in index: 500\n" - ] - }, - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sample.geweke_test(result=result)\n", "ax = visualize.sampling_parameter_traces(\n", @@ -219,23 +177,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:37:25.353191Z", "start_time": "2023-06-26T07:37:24.097221Z" } }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = visualize.sampling_parameter_traces(\n", " result, use_problem_bounds=False, full_trace=True, size=(12, 5)\n", @@ -251,32 +200,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:37:28.830563Z", "start_time": "2023-06-26T07:37:28.805396Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Estimated chain autocorrelation: 8.33281897868538\n", - "Estimated chain autocorrelation: 8.33281897868538\n", - "Estimated effective sample size: 1018.0203882341143\n", - "Estimated effective sample size: 1018.0203882341143\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective sample size per computation time: 5.99\n" - ] - } - ], + "outputs": [], "source": [ "sample.effective_sample_size(result=result)\n", "ess = result.sample_result.effective_sample_size\n", @@ -287,23 +218,14 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:37:30.874974Z", "start_time": "2023-06-26T07:37:30.610636Z" } }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "alpha = [99, 95, 90]\n", "ax = visualize.sampling_parameter_cis(result, alpha=alpha, size=(10, 5))" @@ -318,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:37:43.265325Z", @@ -382,7 +304,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:37:48.270529Z", @@ -419,31 +341,14 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:38:00.338142Z", "start_time": "2023-06-26T07:37:52.885968Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine will use up to 8 processes (= CPU count).\n", - " 0%| | 0/8 [00:00", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from pypesto.C import CONDITION, OUTPUT\n", "\n", @@ -492,23 +388,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:38:06.930290Z", "start_time": "2023-06-26T07:38:06.061992Z" } }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = visualize.sampling_prediction_trajectories(\n", " ensemble_prediction,\n", @@ -537,22 +424,14 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:38:19.022901Z", "start_time": "2023-06-26T07:38:13.471020Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 10/10 [00:05<00:00, 1.80it/s]\n" - ] - } - ], + "outputs": [], "source": [ "res = optimize.minimize(problem, n_starts=10, filename=None)" ] @@ -566,22 +445,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:40:56.975257Z", "start_time": "2023-06-26T07:39:47.907190Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Elapsed time: 70.66135700000001\n" - ] - } - ], + "outputs": [], "source": [ "%%capture\n", "res = sample.sample(\n", @@ -600,23 +471,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:41:07.398824Z", "start_time": "2023-06-26T07:41:06.448954Z" } }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = visualize.sampling_parameter_traces(\n", " res, use_problem_bounds=False, size=(12, 5)\n", @@ -632,31 +494,14 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:41:11.826906Z", "start_time": "2023-06-26T07:41:11.174104Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 0\n", - "Geweke burn-in index: 0\n" - ] - }, - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sample.geweke_test(result=res)\n", "ax = visualize.sampling_parameter_traces(\n", @@ -666,32 +511,14 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:41:16.161400Z", "start_time": "2023-06-26T07:41:15.930905Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Estimated chain autocorrelation: 9.392601973748596\n", - "Estimated chain autocorrelation: 9.392601973748596\n", - "Estimated effective sample size: 962.3191598468054\n", - "Estimated effective sample size: 962.3191598468054\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Effective sample size per computation time: 13.62\n" - ] - } - ], + "outputs": [], "source": [ "sample.effective_sample_size(result=res)\n", "ess = res.sample_result.effective_sample_size\n", @@ -702,23 +529,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:41:18.244703Z", "start_time": "2023-06-26T07:41:17.884791Z" } }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "percentiles = [99, 95, 90]\n", "ax = visualize.sampling_parameter_cis(res, alpha=percentiles, size=(10, 5))" @@ -726,30 +544,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:42:28.807853Z", "start_time": "2023-06-26T07:42:20.364810Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/8 [00:00", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = visualize.sampling_prediction_trajectories(\n", " ensemble_prediction,\n", @@ -798,23 +591,14 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:42:34.826288Z", "start_time": "2023-06-26T07:42:34.207270Z" } }, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = visualize.sampling_prediction_trajectories(\n", " ensemble_prediction,\n", @@ -842,38 +626,14 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-06-26T07:42:49.563714Z", "start_time": "2023-06-26T07:42:38.258389Z" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/8 [00:00", - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a custom objective with new output timepoints.\n", "timepoints = [np.linspace(0, 10, 100), np.linspace(0, 20, 200)]\n", diff --git a/doc/example/store.ipynb b/doc/example/store.ipynb index 1ab8d3889..62696b653 100644 --- a/doc/example/store.ipynb +++ b/doc/example/store.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -163,75 +163,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/15 [00:00\n", - "* message: Converged according to fval difference \n", - "* number of evaluations: 73\n", - "* time taken to optimize: 1.219s\n", - "* startpoint: [-2.27612603 -3.97878893 -4.75536979 1.01507509 4.52085869 -3.49099606\n", - " -1.43819985 -0.57852243 0.30743517]\n", - "* endpoint: [-1.50314486 -3.3582361 4.99984874 -1.77793181 -1.75877103 3.98195654\n", - " 0.63197992 0.78930862 0.73484354]\n", - "* final objective value: 147.54396431144596\n", - "* final gradient value: [-1.24210833e-02 -1.07239871e-03 -4.96546443e-06 -1.15883105e-02\n", - " -3.68401402e-03 -6.12209422e-03 -3.14630353e-03 8.91043641e-04\n", - " 1.91794317e-03]\n", - "* final hessian value: [[ 2.41991876e+03 2.18595132e+01 -1.18703033e-04 1.80814818e+03\n", - " 6.87980199e+02 -9.06169344e+02 1.80781358e+01 -9.17931272e+00\n", - " -8.87022249e+00]\n", - " [ 2.18595132e+01 1.00911607e+00 -2.10294398e-06 1.35925382e+01\n", - " 8.93547021e-01 -9.00820125e+00 -4.45106795e-02 -2.96987292e-01\n", - " 3.43967261e-01]\n", - " [-1.18703033e-04 -2.10294398e-06 1.06019527e-11 -8.28728104e-05\n", - " -2.49854392e-05 6.41691062e-05 2.77361513e-06 -5.69473921e-08\n", - " 8.71673663e-06]\n", - " [ 1.80814818e+03 1.35925382e+01 -8.28728104e-05 1.37880908e+03\n", - " 5.22003515e+02 -6.38361857e+02 2.39978066e+01 -8.16598344e+00\n", - " -1.58051401e+01]\n", - " [ 6.87980199e+02 8.93547021e-01 -2.49854392e-05 5.22003515e+02\n", - " 2.98487654e+02 -2.95580650e+02 -1.05293443e+01 -4.04534691e+00\n", - " 1.45831740e+01]\n", - " [-9.06169344e+02 -9.00820125e+00 6.41691062e-05 -6.38361857e+02\n", - " -2.95580650e+02 8.97338305e+02 1.04739037e+01 -3.74496175e+01\n", - " 2.69898104e+01]\n", - " [ 1.80781358e+01 -4.45106795e-02 2.77361513e-06 2.39978066e+01\n", - " -1.05293443e+01 1.04739037e+01 8.48376144e+01 0.00000000e+00\n", - " 0.00000000e+00]\n", - " [-9.17931272e+00 -2.96987292e-01 -5.69473921e-08 -8.16598344e+00\n", - " -4.04534691e+00 -3.74496175e+01 0.00000000e+00 8.48283181e+01\n", - " 0.00000000e+00]\n", - " [-8.87022249e+00 3.43967261e-01 8.71673663e-06 -1.58051401e+01\n", - " 1.45831740e+01 2.69898104e+01 0.00000000e+00 0.00000000e+00\n", - " 8.48259535e+01]]\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(Markdown(result.summary()))" ] @@ -355,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -366,116 +223,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/9 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# waterfall plot original\n", "ax = visualize.waterfall(result)\n", @@ -757,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -768,18 +470,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# waterfall plot loaded\n", "ax = visualize.waterfall(result_loaded)\n", @@ -799,7 +490,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -810,18 +501,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# profile plot original\n", "ax = visualize.profiles(result)" @@ -829,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -840,18 +520,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# profile plot loaded\n", "ax = visualize.profiles(result_loaded)" @@ -870,7 +539,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -881,18 +550,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# sampling plot original\n", "ax = visualize.sampling_fval_traces(result)" @@ -900,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -911,18 +569,7 @@ }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# sampling plot loaded\n", "ax = visualize.sampling_fval_traces(result_loaded)" @@ -985,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -996,56 +643,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/15 [00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "print(\"History type: \", type(result.optimize_result.list[0].history))\n", "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", @@ -1137,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -1147,70 +727,7 @@ "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/15 [00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "print(\"History type: \", type(result.optimize_result.list[0].history))\n", "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", @@ -1311,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -1321,64 +820,7 @@ "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/15 [00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "print(\"History type: \", type(result.optimize_result.list[0].history))\n", "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", @@ -1451,7 +875,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "collapsed": false, "jupyter": { @@ -1462,28 +886,7 @@ }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: You are loading a problem.\n", - "This problem is not to be used without a separately created objective.\n", - "Loading the profiling result failed. It is highly likely that no profiling result exists within /tmp/tmp6a3yx147.hdf5.\n", - "Loading the sampling result failed. It is highly likely that no sampling result exists within /tmp/tmp6a3yx147.hdf5.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# load the history\n", "result_loaded_w_history = pypesto.store.read_result(fn_hdf5)\n", diff --git a/doc/example/synthetic_data.ipynb b/doc/example/synthetic_data.ipynb index 45b553563..7a7e3c927 100644 --- a/doc/example/synthetic_data.ipynb +++ b/doc/example/synthetic_data.ipynb @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +90,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -115,21 +115,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Visualization table not available. Skipping.\n" - ] - } - ], + "outputs": [], "source": [ "pypesto_importer_original = pypesto.petab.PetabImporter(petab_problem_original)\n", "pypesto_problem_original = pypesto_importer_original.create_problem()" @@ -148,21 +140,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:15<00:00, 6.64it/s]\n" - ] - } - ], + "outputs": [], "source": [ "pypesto_result_original = pypesto.optimize.minimize(\n", " pypesto_problem_original, n_starts=100\n", @@ -184,47 +168,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameters are estimated to be (linear scale):\n", - "k1: 0.7755613316064977\n", - "k2: 0.5442527266619289\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "x_mle_unscaled_original = get_x_mle(\n", " pypesto_result_original.optimize_result,\n", @@ -279,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -310,18 +260,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Visualization table not available. Skipping.\n", - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:11<00:00, 8.60it/s]\n" - ] - } - ], + "outputs": [], "source": [ "pypesto_importer_synthetic = pypesto.petab.PetabImporter(\n", " petab_problem_synthetic\n", @@ -341,43 +282,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameters are estimated to be (linear scale):\n", - "k1: 1.4998440660530643\n", - "k2: 2.5016180667422243\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "x_mle_unscaled_synthetic = get_x_mle(\n", " pypesto_result_synthetic.optimize_result,\n", diff --git a/doc/example/workflow_comparison.ipynb b/doc/example/workflow_comparison.ipynb index 9461a59c6..4e1ef440d 100644 --- a/doc/example/workflow_comparison.ipynb +++ b/doc/example/workflow_comparison.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-07-13T09:24:44.842827Z", @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-07-13T09:24:47.602789Z", @@ -121,228 +121,7 @@ "outputs_hidden": false } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-08-25 15:26:01.472 - amici.petab_import - INFO - Importing model ...\n", - "2023-08-25 15:26:01.473 - amici.petab_import - INFO - Validating PEtab problem ...\n", - "2023-08-25 15:26:01.558 - amici.petab_import - INFO - Model name is 'FullModel'.\n", - "Writing model code to '/Users/pauljonasjost/Documents/GitHub_Folders/pyPESTO/doc/example/amici_models/FullModel'.\n", - "2023-08-25 15:26:01.560 - amici.petab_import - INFO - Species: 8\n", - "2023-08-25 15:26:01.562 - amici.petab_import - INFO - Global parameters: 15\n", - "2023-08-25 15:26:01.563 - amici.petab_import - INFO - Reactions: 9\n", - "2023-08-25 15:26:01.618 - amici.petab_import - INFO - Observables: 3\n", - "2023-08-25 15:26:01.620 - amici.petab_import - INFO - Sigmas: 3\n", - "2023-08-25 15:26:01.632 - amici.petab_import - DEBUG - Adding output parameters to model: ['noiseParameter1_pSTAT5A_rel', 'noiseParameter1_pSTAT5B_rel', 'noiseParameter1_rSTAT5A_rel']\n", - "2023-08-25 15:26:01.634 - amici.petab_import - DEBUG - Adding initial assignments for dict_keys([])\n", - "2023-08-25 15:26:01.657 - amici.petab_import - DEBUG - Condition table: (1, 1)\n", - "2023-08-25 15:26:01.658 - amici.petab_import - DEBUG - Fixed parameters are ['ratio', 'specC17']\n", - "2023-08-25 15:26:01.660 - amici.petab_import - INFO - Overall fixed parameters: 2\n", - "2023-08-25 15:26:01.661 - amici.petab_import - INFO - Variable parameters: 16\n", - "2023-08-25 15:26:01.684 - amici.sbml_import - DEBUG - Finished processing SBML annotations ++ (1.34E-04s)\n", - "2023-08-25 15:26:01.717 - amici.sbml_import - DEBUG - Finished gathering local SBML symbols ++ (2.14E-02s)\n", - "2023-08-25 15:26:01.752 - amici.sbml_import - DEBUG - Finished processing SBML parameters ++ (2.24E-02s)\n", - "2023-08-25 15:26:01.765 - amici.sbml_import - DEBUG - Finished processing SBML compartments ++ (4.04E-04s)\n", - "2023-08-25 15:26:01.788 - amici.sbml_import - DEBUG - Finished processing SBML species initials +++ (6.57E-03s)\n", - "2023-08-25 15:26:01.799 - amici.sbml_import - DEBUG - Finished processing SBML rate rules +++ (7.80E-05s)\n", - "2023-08-25 15:26:01.801 - amici.sbml_import - DEBUG - Finished processing SBML species ++ (2.70E-02s)\n", - "2023-08-25 15:26:01.818 - amici.sbml_import - DEBUG - Finished processing SBML reactions ++ (5.84E-03s)\n", - "2023-08-25 15:26:01.838 - amici.sbml_import - DEBUG - Finished processing SBML rules ++ (9.97E-03s)\n", - "2023-08-25 15:26:01.849 - amici.sbml_import - DEBUG - Finished processing SBML events ++ (8.28E-05s)\n", - "2023-08-25 15:26:01.859 - amici.sbml_import - DEBUG - Finished processing SBML initial assignments++ (1.03E-04s)\n", - "2023-08-25 15:26:01.870 - amici.sbml_import - DEBUG - Finished processing SBML species references ++ (5.32E-04s)\n", - "2023-08-25 15:26:01.871 - amici.sbml_import - DEBUG - Finished importing SBML + (1.95E-01s)\n", - "2023-08-25 15:26:01.926 - amici.sbml_import - DEBUG - Finished processing SBML observables + (4.47E-02s)\n", - "2023-08-25 15:26:01.938 - amici.sbml_import - DEBUG - Finished processing SBML event observables + (2.83E-06s)\n", - "2023-08-25 15:26:02.017 - amici.de_export - DEBUG - Finished running smart_multiply ++ (2.33E-03s)\n", - "2023-08-25 15:26:02.122 - amici.de_export - DEBUG - Finished simplifying xdot +++ (8.01E-03s)\n", - "2023-08-25 15:26:02.123 - amici.de_export - DEBUG - Finished computing xdot ++ (1.85E-02s)\n", - "2023-08-25 15:26:02.147 - amici.de_export - DEBUG - Finished simplifying x0 +++ (1.72E-03s)\n", - "2023-08-25 15:26:02.149 - amici.de_export - DEBUG - Finished computing x0 ++ (1.35E-02s)\n", - "2023-08-25 15:26:02.152 - amici.de_export - DEBUG - Finished importing SbmlImporter + (1.45E-01s)\n", - "2023-08-25 15:26:02.334 - amici.de_export - DEBUG - Finished simplifying Jy ++++ (1.42E-01s)\n", - "2023-08-25 15:26:02.335 - amici.de_export - DEBUG - Finished computing Jy +++ (1.51E-01s)\n", - "2023-08-25 15:26:02.404 - amici.de_export - DEBUG - Finished simplifying y ++++ (4.67E-02s)\n", - "2023-08-25 15:26:02.405 - amici.de_export - DEBUG - Finished computing y +++ (5.86E-02s)\n", - "2023-08-25 15:26:02.425 - amici.de_export - DEBUG - Finished simplifying sigmay ++++ (1.38E-04s)\n", - "2023-08-25 15:26:02.426 - amici.de_export - DEBUG - Finished computing sigmay +++ (8.85E-03s)\n", - "2023-08-25 15:26:02.458 - amici.de_export - DEBUG - Finished writing Jy.cpp ++ (2.84E-01s)\n", - "2023-08-25 15:26:02.523 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.62E-02s)\n", - "2023-08-25 15:26:02.547 - amici.de_export - DEBUG - Finished simplifying dJydsigma ++++ (1.39E-02s)\n", - "2023-08-25 15:26:02.548 - amici.de_export - DEBUG - Finished computing dJydsigma +++ (7.16E-02s)\n", - "2023-08-25 15:26:02.558 - amici.de_export - DEBUG - Finished writing dJydsigma.cpp ++ (8.93E-02s)\n", - "2023-08-25 15:26:02.601 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.81E-02s)\n", - "2023-08-25 15:26:02.629 - amici.de_export - DEBUG - Finished simplifying dJydy ++++ (1.72E-02s)\n", - "2023-08-25 15:26:02.630 - amici.de_export - DEBUG - Finished computing dJydy +++ (5.57E-02s)\n", - "2023-08-25 15:26:02.641 - amici.de_export - DEBUG - Finished writing dJydy.cpp ++ (7.41E-02s)\n", - "2023-08-25 15:26:02.669 - amici.de_export - DEBUG - Finished simplifying Jz ++++ (9.60E-05s)\n", - "2023-08-25 15:26:02.670 - amici.de_export - DEBUG - Finished computing Jz +++ (8.37E-03s)\n", - "2023-08-25 15:26:02.682 - amici.de_export - DEBUG - Finished computing z +++ (1.74E-04s)\n", - "2023-08-25 15:26:02.701 - amici.de_export - DEBUG - Finished simplifying sigmaz ++++ (1.33E-04s)\n", - "2023-08-25 15:26:02.701 - amici.de_export - DEBUG - Finished computing sigmaz +++ (7.93E-03s)\n", - "2023-08-25 15:26:02.702 - amici.de_export - DEBUG - Finished writing Jz.cpp ++ (4.87E-02s)\n", - "2023-08-25 15:26:02.729 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (7.59E-05s)\n", - "2023-08-25 15:26:02.739 - amici.de_export - DEBUG - Finished simplifying dJzdsigma ++++ (2.09E-04s)\n", - "2023-08-25 15:26:02.740 - amici.de_export - DEBUG - Finished computing dJzdsigma +++ (1.98E-02s)\n", - "2023-08-25 15:26:02.742 - amici.de_export - DEBUG - Finished writing dJzdsigma.cpp ++ (2.80E-02s)\n", - "2023-08-25 15:26:02.769 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (6.99E-05s)\n", - "2023-08-25 15:26:02.778 - amici.de_export - DEBUG - Finished simplifying dJzdz ++++ (9.28E-05s)\n", - "2023-08-25 15:26:02.779 - amici.de_export - DEBUG - Finished computing dJzdz +++ (1.78E-02s)\n", - "2023-08-25 15:26:02.780 - amici.de_export - DEBUG - Finished writing dJzdz.cpp ++ (2.73E-02s)\n", - "2023-08-25 15:26:02.807 - amici.de_export - DEBUG - Finished simplifying Jrz ++++ (1.01E-04s)\n", - "2023-08-25 15:26:02.808 - amici.de_export - DEBUG - Finished computing Jrz +++ (7.65E-03s)\n", - "2023-08-25 15:26:02.818 - amici.de_export - DEBUG - Finished computing rz +++ (2.55E-04s)\n", - "2023-08-25 15:26:02.819 - amici.de_export - DEBUG - Finished writing Jrz.cpp ++ (2.59E-02s)\n", - "2023-08-25 15:26:02.845 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (9.20E-05s)\n", - "2023-08-25 15:26:02.856 - amici.de_export - DEBUG - Finished simplifying dJrzdsigma ++++ (9.63E-05s)\n", - "2023-08-25 15:26:02.857 - amici.de_export - DEBUG - Finished computing dJrzdsigma +++ (2.05E-02s)\n", - "2023-08-25 15:26:02.858 - amici.de_export - DEBUG - Finished writing dJrzdsigma.cpp ++ (2.91E-02s)\n", - "2023-08-25 15:26:02.886 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (6.81E-05s)\n", - "2023-08-25 15:26:02.899 - amici.de_export - DEBUG - Finished simplifying dJrzdz ++++ (1.23E-04s)\n", - "2023-08-25 15:26:02.900 - amici.de_export - DEBUG - Finished computing dJrzdz +++ (2.28E-02s)\n", - "2023-08-25 15:26:02.901 - amici.de_export - DEBUG - Finished writing dJrzdz.cpp ++ (3.12E-02s)\n", - "2023-08-25 15:26:02.928 - amici.de_export - DEBUG - Finished simplifying root ++++ (1.70E-04s)\n", - "2023-08-25 15:26:02.929 - amici.de_export - DEBUG - Finished computing root +++ (9.40E-03s)\n", - "2023-08-25 15:26:02.931 - amici.de_export - DEBUG - Finished writing root.cpp ++ (1.89E-02s)\n", - "2023-08-25 15:26:02.999 - amici.de_export - DEBUG - Finished simplifying w +++++ (3.15E-02s)\n", - "2023-08-25 15:26:03.000 - amici.de_export - DEBUG - Finished computing w ++++ (4.06E-02s)\n", - "2023-08-25 15:26:03.035 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (2.51E-02s)\n", - "2023-08-25 15:26:03.055 - amici.de_export - DEBUG - Finished simplifying dwdp ++++ (1.14E-02s)\n", - "2023-08-25 15:26:03.056 - amici.de_export - DEBUG - Finished computing dwdp +++ (1.05E-01s)\n", - "2023-08-25 15:26:03.078 - amici.de_export - DEBUG - Finished simplifying spl ++++ (1.36E-04s)\n", - "2023-08-25 15:26:03.079 - amici.de_export - DEBUG - Finished computing spl +++ (8.40E-03s)\n", - "2023-08-25 15:26:03.101 - amici.de_export - DEBUG - Finished simplifying sspl ++++ (1.03E-04s)\n", - "2023-08-25 15:26:03.103 - amici.de_export - DEBUG - Finished computing sspl +++ (1.16E-02s)\n", - "2023-08-25 15:26:03.110 - amici.de_export - DEBUG - Finished writing dwdp.cpp ++ (1.66E-01s)\n", - "2023-08-25 15:26:03.219 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (8.22E-02s)\n", - "2023-08-25 15:26:03.318 - amici.de_export - DEBUG - Finished simplifying dwdx ++++ (8.91E-02s)\n", - "2023-08-25 15:26:03.319 - amici.de_export - DEBUG - Finished computing dwdx +++ (1.91E-01s)\n", - "2023-08-25 15:26:03.359 - amici.de_export - DEBUG - Finished writing dwdx.cpp ++ (2.39E-01s)\n", - "2023-08-25 15:26:03.369 - amici.de_export - DEBUG - Finished writing create_splines.cpp ++ (3.98E-04s)\n", - "2023-08-25 15:26:03.404 - amici.de_export - DEBUG - Finished simplifying spline_values +++++ (1.11E-04s)\n", - "2023-08-25 15:26:03.405 - amici.de_export - DEBUG - Finished computing spline_values ++++ (9.25E-03s)\n", - "2023-08-25 15:26:03.417 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.12E-04s)\n", - "2023-08-25 15:26:03.427 - amici.de_export - DEBUG - Finished simplifying dspline_valuesdp ++++ (9.45E-05s)\n", - "2023-08-25 15:26:03.428 - amici.de_export - DEBUG - Finished computing dspline_valuesdp +++ (3.99E-02s)\n", - "2023-08-25 15:26:03.429 - amici.de_export - DEBUG - Finished writing dspline_valuesdp.cpp ++ (4.87E-02s)\n", - "2023-08-25 15:26:03.464 - amici.de_export - DEBUG - Finished simplifying spline_slopes +++++ (1.12E-04s)\n", - "2023-08-25 15:26:03.465 - amici.de_export - DEBUG - Finished computing spline_slopes ++++ (9.20E-03s)\n", - "2023-08-25 15:26:03.476 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (7.68E-05s)\n", - "2023-08-25 15:26:03.485 - amici.de_export - DEBUG - Finished simplifying dspline_slopesdp ++++ (9.28E-05s)\n", - "2023-08-25 15:26:03.486 - amici.de_export - DEBUG - Finished computing dspline_slopesdp +++ (3.93E-02s)\n", - "2023-08-25 15:26:03.487 - amici.de_export - DEBUG - Finished writing dspline_slopesdp.cpp ++ (4.71E-02s)\n", - "2023-08-25 15:26:03.523 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (7.05E-03s)\n", - "2023-08-25 15:26:03.538 - amici.de_export - DEBUG - Finished simplifying dwdw ++++ (3.78E-03s)\n", - "2023-08-25 15:26:03.539 - amici.de_export - DEBUG - Finished computing dwdw +++ (3.13E-02s)\n", - "2023-08-25 15:26:03.543 - amici.de_export - DEBUG - Finished writing dwdw.cpp ++ (4.30E-02s)\n", - "2023-08-25 15:26:03.588 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.64E-02s)\n", - "2023-08-25 15:26:03.599 - amici.de_export - DEBUG - Finished simplifying dxdotdw ++++ (4.12E-04s)\n", - "2023-08-25 15:26:03.600 - amici.de_export - DEBUG - Finished computing dxdotdw +++ (3.57E-02s)\n", - "2023-08-25 15:26:03.610 - amici.de_export - DEBUG - Finished writing dxdotdw.cpp ++ (5.61E-02s)\n", - "2023-08-25 15:26:03.642 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.69E-03s)\n", - "2023-08-25 15:26:03.656 - amici.de_export - DEBUG - Finished simplifying dxdotdx_explicit ++++ (1.36E-04s)\n", - "2023-08-25 15:26:03.657 - amici.de_export - DEBUG - Finished computing dxdotdx_explicit +++ (2.76E-02s)\n", - "2023-08-25 15:26:03.660 - amici.de_export - DEBUG - Finished writing dxdotdx_explicit.cpp ++ (3.91E-02s)\n", - "2023-08-25 15:26:03.691 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.63E-03s)\n", - "2023-08-25 15:26:03.702 - amici.de_export - DEBUG - Finished simplifying dxdotdp_explicit ++++ (2.59E-04s)\n", - "2023-08-25 15:26:03.703 - amici.de_export - DEBUG - Finished computing dxdotdp_explicit +++ (2.19E-02s)\n", - "2023-08-25 15:26:03.705 - amici.de_export - DEBUG - Finished writing dxdotdp_explicit.cpp ++ (3.14E-02s)\n", - "2023-08-25 15:26:03.747 - amici.de_export - DEBUG - Finished running smart_jacobian +++++ (2.99E-03s)\n", - "2023-08-25 15:26:03.834 - amici.de_export - DEBUG - Finished simplifying dydx +++++ (7.46E-02s)\n", - "2023-08-25 15:26:03.834 - amici.de_export - DEBUG - Finished computing dydx ++++ (9.83E-02s)\n", - "2023-08-25 15:26:03.853 - amici.de_export - DEBUG - Finished running smart_jacobian +++++ (2.80E-04s)\n", - "2023-08-25 15:26:03.865 - amici.de_export - DEBUG - Finished simplifying dydw +++++ (1.05E-04s)\n", - "2023-08-25 15:26:03.866 - amici.de_export - DEBUG - Finished computing dydw ++++ (2.15E-02s)\n", - "2023-08-25 15:26:03.951 - amici.de_export - DEBUG - Finished simplifying dydx ++++ (7.01E-02s)\n", - "2023-08-25 15:26:03.952 - amici.de_export - DEBUG - Finished computing dydx +++ (2.25E-01s)\n", - "2023-08-25 15:26:03.981 - amici.de_export - DEBUG - Finished writing dydx.cpp ++ (2.62E-01s)\n", - "2023-08-25 15:26:04.018 - amici.de_export - DEBUG - Finished running smart_jacobian +++++ (2.60E-04s)\n", - "2023-08-25 15:26:04.028 - amici.de_export - DEBUG - Finished simplifying dydp +++++ (9.56E-05s)\n", - "2023-08-25 15:26:04.028 - amici.de_export - DEBUG - Finished computing dydp ++++ (1.88E-02s)\n", - "2023-08-25 15:26:04.039 - amici.de_export - DEBUG - Finished simplifying dydp ++++ (9.50E-05s)\n", - "2023-08-25 15:26:04.040 - amici.de_export - DEBUG - Finished computing dydp +++ (4.04E-02s)\n", - "2023-08-25 15:26:04.042 - amici.de_export - DEBUG - Finished writing dydp.cpp ++ (5.07E-02s)\n", - "2023-08-25 15:26:04.061 - amici.de_export - DEBUG - Finished computing dzdx +++ (1.50E-04s)\n", - "2023-08-25 15:26:04.061 - amici.de_export - DEBUG - Finished writing dzdx.cpp ++ (8.54E-03s)\n", - "2023-08-25 15:26:04.079 - amici.de_export - DEBUG - Finished computing dzdp +++ (1.53E-04s)\n", - "2023-08-25 15:26:04.081 - amici.de_export - DEBUG - Finished writing dzdp.cpp ++ (9.44E-03s)\n", - "2023-08-25 15:26:04.098 - amici.de_export - DEBUG - Finished computing drzdx +++ (1.45E-04s)\n", - "2023-08-25 15:26:04.099 - amici.de_export - DEBUG - Finished writing drzdx.cpp ++ (9.13E-03s)\n", - "2023-08-25 15:26:04.117 - amici.de_export - DEBUG - Finished computing drzdp +++ (1.53E-04s)\n", - "2023-08-25 15:26:04.118 - amici.de_export - DEBUG - Finished writing drzdp.cpp ++ (8.88E-03s)\n", - "2023-08-25 15:26:04.144 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.40E-04s)\n", - "2023-08-25 15:26:04.154 - amici.de_export - DEBUG - Finished simplifying dsigmaydy ++++ (8.96E-05s)\n", - "2023-08-25 15:26:04.154 - amici.de_export - DEBUG - Finished computing dsigmaydy +++ (2.02E-02s)\n", - "2023-08-25 15:26:04.155 - amici.de_export - DEBUG - Finished writing dsigmaydy.cpp ++ (2.86E-02s)\n", - "2023-08-25 15:26:04.183 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.20E-03s)\n", - "2023-08-25 15:26:04.193 - amici.de_export - DEBUG - Finished simplifying dsigmaydp ++++ (1.58E-04s)\n", - "2023-08-25 15:26:04.194 - amici.de_export - DEBUG - Finished computing dsigmaydp +++ (1.88E-02s)\n", - "2023-08-25 15:26:04.197 - amici.de_export - DEBUG - Finished writing dsigmaydp.cpp ++ (2.99E-02s)\n", - "2023-08-25 15:26:04.208 - amici.de_export - DEBUG - Finished writing sigmay.cpp ++ (1.52E-03s)\n", - "2023-08-25 15:26:04.232 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (7.05E-05s)\n", - "2023-08-25 15:26:04.243 - amici.de_export - DEBUG - Finished simplifying dsigmazdp ++++ (1.14E-04s)\n", - "2023-08-25 15:26:04.244 - amici.de_export - DEBUG - Finished computing dsigmazdp +++ (1.93E-02s)\n", - "2023-08-25 15:26:04.244 - amici.de_export - DEBUG - Finished writing dsigmazdp.cpp ++ (2.79E-02s)\n", - "2023-08-25 15:26:04.256 - amici.de_export - DEBUG - Finished writing sigmaz.cpp ++ (5.24E-05s)\n", - "2023-08-25 15:26:04.272 - amici.de_export - DEBUG - Finished computing stau +++ (1.40E-04s)\n", - "2023-08-25 15:26:04.273 - amici.de_export - DEBUG - Finished writing stau.cpp ++ (8.13E-03s)\n", - "2023-08-25 15:26:04.292 - amici.de_export - DEBUG - Finished computing deltax +++ (1.35E-04s)\n", - "2023-08-25 15:26:04.293 - amici.de_export - DEBUG - Finished writing deltax.cpp ++ (8.38E-03s)\n", - "2023-08-25 15:26:04.313 - amici.de_export - DEBUG - Finished computing deltasx +++ (2.33E-04s)\n", - "2023-08-25 15:26:04.314 - amici.de_export - DEBUG - Finished writing deltasx.cpp ++ (1.03E-02s)\n", - "2023-08-25 15:26:04.332 - amici.de_export - DEBUG - Finished writing w.cpp ++ (9.05E-03s)\n", - "2023-08-25 15:26:04.343 - amici.de_export - DEBUG - Finished writing x0.cpp ++ (1.96E-03s)\n", - "2023-08-25 15:26:04.375 - amici.de_export - DEBUG - Finished simplifying x0_fixedParameters ++++ (2.13E-03s)\n", - "2023-08-25 15:26:04.376 - amici.de_export - DEBUG - Finished computing x0_fixedParameters +++ (1.20E-02s)\n", - "2023-08-25 15:26:04.380 - amici.de_export - DEBUG - Finished writing x0_fixedParameters.cpp ++ (2.49E-02s)\n", - "2023-08-25 15:26:04.409 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (2.16E-03s)\n", - "2023-08-25 15:26:04.420 - amici.de_export - DEBUG - Finished simplifying sx0 ++++ (9.81E-05s)\n", - "2023-08-25 15:26:04.421 - amici.de_export - DEBUG - Finished computing sx0 +++ (2.15E-02s)\n", - "2023-08-25 15:26:04.422 - amici.de_export - DEBUG - Finished writing sx0.cpp ++ (3.13E-02s)\n", - "2023-08-25 15:26:04.450 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.50E-04s)\n", - "2023-08-25 15:26:04.460 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.11E-04s)\n", - "2023-08-25 15:26:04.471 - amici.de_export - DEBUG - Finished simplifying sx0_fixedParameters ++++ (1.77E-04s)\n", - "2023-08-25 15:26:04.472 - amici.de_export - DEBUG - Finished computing sx0_fixedParameters +++ (2.92E-02s)\n", - "2023-08-25 15:26:04.475 - amici.de_export - DEBUG - Finished writing sx0_fixedParameters.cpp ++ (3.96E-02s)\n", - "2023-08-25 15:26:04.499 - amici.de_export - DEBUG - Finished writing xdot.cpp ++ (1.48E-02s)\n", - "2023-08-25 15:26:04.513 - amici.de_export - DEBUG - Finished writing y.cpp ++ (4.81E-03s)\n", - "2023-08-25 15:26:04.537 - amici.de_export - DEBUG - Finished simplifying x_rdata ++++ (1.97E-04s)\n", - "2023-08-25 15:26:04.538 - amici.de_export - DEBUG - Finished computing x_rdata +++ (8.23E-03s)\n", - "2023-08-25 15:26:04.540 - amici.de_export - DEBUG - Finished writing x_rdata.cpp ++ (1.77E-02s)\n", - "2023-08-25 15:26:04.566 - amici.de_export - DEBUG - Finished simplifying total_cl ++++ (1.07E-04s)\n", - "2023-08-25 15:26:04.566 - amici.de_export - DEBUG - Finished computing total_cl +++ (8.30E-03s)\n", - "2023-08-25 15:26:04.567 - amici.de_export - DEBUG - Finished writing total_cl.cpp ++ (1.67E-02s)\n", - "2023-08-25 15:26:04.592 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (8.56E-05s)\n", - "2023-08-25 15:26:04.601 - amici.de_export - DEBUG - Finished simplifying dtotal_cldp ++++ (9.78E-05s)\n", - "2023-08-25 15:26:04.602 - amici.de_export - DEBUG - Finished computing dtotal_cldp +++ (1.75E-02s)\n", - "2023-08-25 15:26:04.603 - amici.de_export - DEBUG - Finished writing dtotal_cldp.cpp ++ (2.57E-02s)\n", - "2023-08-25 15:26:04.630 - amici.de_export - DEBUG - Finished simplifying dtotal_cldx_rdata ++++ (1.07E-04s)\n", - "2023-08-25 15:26:04.631 - amici.de_export - DEBUG - Finished computing dtotal_cldx_rdata +++ (8.89E-03s)\n", - "2023-08-25 15:26:04.632 - amici.de_export - DEBUG - Finished writing dtotal_cldx_rdata.cpp ++ (1.72E-02s)\n", - "2023-08-25 15:26:04.662 - amici.de_export - DEBUG - Finished simplifying x_solver ++++ (1.60E-04s)\n", - "2023-08-25 15:26:04.663 - amici.de_export - DEBUG - Finished computing x_solver +++ (9.63E-03s)\n", - "2023-08-25 15:26:04.666 - amici.de_export - DEBUG - Finished writing x_solver.cpp ++ (2.06E-02s)\n", - "2023-08-25 15:26:04.692 - amici.de_export - DEBUG - Finished simplifying dx_rdatadx_solver ++++ (6.79E-04s)\n", - "2023-08-25 15:26:04.693 - amici.de_export - DEBUG - Finished computing dx_rdatadx_solver +++ (9.45E-03s)\n", - "2023-08-25 15:26:04.694 - amici.de_export - DEBUG - Finished writing dx_rdatadx_solver.cpp ++ (1.80E-02s)\n", - "2023-08-25 15:26:04.722 - amici.de_export - DEBUG - Finished simplifying dx_rdatadp ++++ (8.74E-04s)\n", - "2023-08-25 15:26:04.723 - amici.de_export - DEBUG - Finished computing dx_rdatadp +++ (1.08E-02s)\n", - "2023-08-25 15:26:04.725 - amici.de_export - DEBUG - Finished writing dx_rdatadp.cpp ++ (2.03E-02s)\n", - "2023-08-25 15:26:04.749 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (6.97E-05s)\n", - "2023-08-25 15:26:04.758 - amici.de_export - DEBUG - Finished simplifying dx_rdatadtcl ++++ (8.82E-05s)\n", - "2023-08-25 15:26:04.759 - amici.de_export - DEBUG - Finished computing dx_rdatadtcl +++ (1.66E-02s)\n", - "2023-08-25 15:26:04.760 - amici.de_export - DEBUG - Finished writing dx_rdatadtcl.cpp ++ (2.47E-02s)\n", - "2023-08-25 15:26:04.770 - amici.de_export - DEBUG - Finished writing z.cpp ++ (4.95E-05s)\n", - "2023-08-25 15:26:04.779 - amici.de_export - DEBUG - Finished writing rz.cpp ++ (5.57E-05s)\n", - "2023-08-25 15:26:04.812 - amici.de_export - DEBUG - Finished generating cpp code + (2.65E+00s)\n", - "2023-08-25 15:26:59.498 - amici.de_export - DEBUG - Finished compiling cpp code + (5.47E+01s)\n", - "2023-08-25 15:26:59.945 - amici.petab_import - INFO - Finished Importing PEtab model (5.85E+01s)\n", - "2023-08-25 15:26:59.954 - amici.petab_import - INFO - Successfully loaded model FullModel from pyPESTO/doc/example/amici_models/FullModel.\n" - ] - } - ], + "outputs": [], "source": [ "%%capture\n", "# PEtab problem loading\n", @@ -365,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-07-13T09:24:50.218430Z", @@ -375,36 +154,7 @@ "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PEtab benchmark parameters\n", - "{'edatas': [::value_type' at 0x12c917d80\n", - " condition 'model1_data1' starting at t=0.0 with custom parameter scales, constants, parameters\n", - " 16x3 time-resolved datapoints\n", - " (48/48 measurements & 0/48 sigmas set)\n", - " 10x0 event-resolved datapoints\n", - " (0/0 measurements & 0/0 sigmas set)\n", - ">],\n", - " 'llh': -138.22199656856435,\n", - " 'rdatas': [::pointer' at 0x12c917c60> >)>],\n", - " 'sllh': None}\n", - "Individualized parameters\n", - "{'edatas': [::value_type' at 0x12c7dadf0\n", - " condition 'model1_data1' starting at t=0.0 with custom parameter scales, constants, parameters\n", - " 16x3 time-resolved datapoints\n", - " (48/48 measurements & 0/48 sigmas set)\n", - " 10x0 event-resolved datapoints\n", - " (0/0 measurements & 0/0 sigmas set)\n", - ">],\n", - " 'llh': -185.54291970899519,\n", - " 'rdatas': [::pointer' at 0x12c917060> >)>],\n", - " 'sllh': None}\n" - ] - } - ], + "outputs": [], "source": [ "# Simulation with PEtab nominal parameter values\n", "print(\"PEtab benchmark parameters\")\n", @@ -435,21 +185,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "185.54291970899519\n" - ] - } - ], + "outputs": [], "source": [ "class Objective:\n", " \"\"\"\n", @@ -518,27 +260,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "data": { - "text/plain": [ - "(185.5429188951038,\n", - " array([ 4.96886729e+02, 1.50715517e-01, 4.44258325e+01, 7.14778242e+02,\n", - " -5.19647592e-05, -1.66953531e+02, -1.50846999e+02, -6.86643591e+01,\n", - " -1.59022641e+01]))" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "petab_yaml = f\"./{model_name}/{model_name}.yaml\"\n", "\n", @@ -573,320 +301,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[ message: STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT\n", - " success: False\n", - " status: 1\n", - " fun: 483.97739572289964\n", - " x: [-3.352e+00 -2.216e+00 -1.774e+00 -1.776e+00 6.234e-01\n", - " 3.661e+00 1.261e+00 6.150e-01 2.480e-01]\n", - " nit: 18\n", - " jac: [ 2.192e+02 3.002e+02 7.278e+02 -2.483e+02 3.634e+02\n", - " -3.849e+01 -3.175e+01 -1.073e+03 -4.504e+02]\n", - " nfev: 520\n", - " njev: 52\n", - " hess_inv: <9x9 LbfgsInvHessProduct with dtype=float64>,\n", - " message: STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT\n", - " success: False\n", - " status: 1\n", - " fun: 242.1633515170673\n", - " x: [-1.690e+00 1.599e+00 4.108e+00 -2.908e+00 4.344e+00\n", - " 2.980e+00 2.152e+00 1.482e+00 1.401e+00]\n", - " nit: 19\n", - " jac: [-1.918e+01 -8.125e+00 8.223e+00 -3.257e+00 -1.422e+01\n", - " -1.719e+01 3.437e+01 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message: STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT\n", - " success: False\n", - " status: 1\n", - " fun: 249.5523160565121\n", - " x: [-2.706e+00 -3.267e+00 1.257e+00 2.064e+00 1.969e-01\n", - " 1.412e+00 1.874e+00 1.758e+00 1.297e+00]\n", - " nit: 15\n", - " jac: [-2.522e-01 -1.616e-01 6.545e-01 1.178e+00 4.326e-01\n", - " -2.745e-01 2.402e-01 -5.134e-02 4.700e-01]\n", - " nfev: 530\n", - " njev: 53\n", - " hess_inv: <9x9 LbfgsInvHessProduct with dtype=float64>,\n", - " message: STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT\n", - " success: False\n", - " status: 1\n", - " fun: 249.74599444226348\n", - " x: [-4.686e+00 2.111e+00 2.352e+00 3.564e+00 3.211e+00\n", - " 3.715e-01 1.873e+00 1.759e+00 1.299e+00]\n", - " nit: 36\n", - " jac: [ 5.684e-06 5.684e-06 -5.684e-06 5.684e-06 5.684e-06\n", - " -8.527e-06 -9.237e-04 2.177e-03 -2.240e-03]\n", - " nfev: 520\n", - " njev: 52\n", - " hess_inv: <9x9 LbfgsInvHessProduct with dtype=float64>,\n", - " message: STOP: TOTAL NO. 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-1.86005028, 4.82608847,\n", - " 2.24886943, -1.09100022, -1.53563431, 0.22579574]),\n", - " 'x0': array([ 2.20258998, 4.3092804 , -2.2015135 , -1.86005028, 4.82608847,\n", - " 2.24886943, -1.09100022, -1.53563431, 0.22579574])},\n", - " {'exitflag': 1,\n", - " 'fun': 1146659.4928225973,\n", - " 'message': 'Finished Successfully.',\n", - " 'x': array([-0.53682615, -4.81637178, 4.95364701, -4.57752447, -0.60577667,\n", - " -2.88604054, 0.85270085, 0.07054205, -1.27549967]),\n", - " 'x0': array([-0.53682615, -4.81637178, 4.95364701, -4.57752447, -0.60577667,\n", - " -2.88604054, 0.85270085, 0.07054205, -1.27549967])},\n", - " {'exitflag': 1,\n", - " 'fun': 1236788.617249787,\n", - " 'message': 'Finished Successfully.',\n", - " 'x': array([ 2.69036786, -1.85327759, 2.85858288, 4.70288006, 4.02501682,\n", - " -4.23172439, -0.72188414, 3.55067703, 4.87046828]),\n", - " 'x0': array([ 2.69036786, -1.85327759, 2.85858288, 4.70288006, 4.02501682,\n", - " -4.23172439, -0.72188414, 3.55067703, 4.87046828])},\n", - " {'exitflag': 1,\n", - " 'fun': 441.8147467190984,\n", - " 'message': 'Finished Successfully.',\n", - " 'x': array([-1.88273814, -0.60977486, 2.87775688, 2.25140401, -2.65489216,\n", - " 1.01047951, 3.69127283, 4.44893301, 2.65440911]),\n", - " 'x0': array([-1.88273814, -0.60977486, 2.87775688, 2.25140401, -2.65489216,\n", - " 1.01047951, 3.69127283, 4.44893301, 2.65440911])},\n", - " {'exitflag': 1,\n", - " 'fun': 4453.420140298416,\n", - " 'message': 'Finished Successfully.',\n", - " 'x': array([0.66867122, 3.97870907, 2.13991535, 0.2612146 , 4.9597103 ,\n", - " 3.23568699, 4.22366861, 0.40266923, 3.15201217]),\n", - " 'x0': array([0.66867122, 3.97870907, 2.13991535, 0.2612146 , 4.9597103 ,\n", - " 3.23568699, 4.22366861, 0.40266923, 3.15201217])},\n", - " {'exitflag': 1,\n", - " 'fun': 324.16551565091083,\n", - " 'message': 'Finished Successfully.',\n", - " 'x': array([ 4.03440768, 0.83760837, 3.55179682, 1.57898613, -4.87401594,\n", - " 4.33049155, 3.79628273, 1.90990052, 1.02667127]),\n", - " 'x0': array([ 4.03440768, 0.83760837, 3.55179682, 1.57898613, -4.87401594,\n", - " 4.33049155, 3.79628273, 1.90990052, 1.02667127])}]\n" - ] - } - ], + "outputs": [], "source": [ "results = []\n", "for x0 in x_guesses:\n", @@ -1265,83 +447,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine will use up to 8 processes (= CPU count).\n", - " 0%| | 0/25 [00:00\n", - "* message: ABNORMAL_TERMINATION_IN_LNSRCH \n", - "* number of evaluations: 141\n", - "* time taken to optimize: 6.583s\n", - "* startpoint: [-4.32816355 -4.74870318 -1.74009129 -1.26420992 3.53977881 0.54755365\n", - " 2.64804722 1.62058431 1.57747828]\n", - "* endpoint: [-1.56907929 -5. -2.2098163 -1.78589671 3.55917603 4.19771074\n", - " 0.58569077 0.81885971 0.49858833]\n", - "* final objective value: 138.2224842858494\n", - "* final gradient value: [-0.00783036 0.05534759 0.00129469 -0.00675505 -0.00121895 0.00394696\n", - " -0.00021472 0.00294705 0.00089969]\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "results_pypesto = optimize.minimize(\n", " problem=problem,\n", @@ -1362,30 +470,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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cLJKSkhrcl9NNBrukc8pNN92k/nF///33btO2b9+uTquebvvoo48KQCQmJtZY34QJE9RlqqebN8att94qAOHl5VXrh2Fjg12XXXaZenNw9OjReud97rnn1HWvXbu2xvTqF9arrrrKo/1pzsCKzWYT8fHxAhDjxo2rdZ6WCnYNGTKkxk2GS2VlpVuKfkpKSo15Grphveqqq9TpH3zwQZ1tSktLE97e3gIQvr6+td5AyGCXJEmSJDVd9YdDT39OLBGwfv36Rn15WJtTGewyGAy1BtiEEOLo0aNupTkuuuiiOrf5yiuvqPN98cUXHu1XbZYvX66u58svv6x1nsbc3y1YsECd991336133r1796qBqsmTJ7tNM5vNahc2g8Eg9u3bV+d6Hn/88VMS7DrZe1BPeHIPXv38URRFJCcnN2lbJyopKRE+Pj4CEHfccUet81QP2oSHh4uioqJa51u3bp3be1BXKRsh3IO6q1evrjH9gQceUAO5DQV2Zs+era6rtuBaS5y7QrgfNx8fnwaDk0ajUQDi8ssvb7CdrZEsUC+dU+rryuj6v9FoZPDgwerrriL1e/fuJScnx22Z1atXA87Cgq75Gmvo0KGAM613+/btTVqHS2FhIb/99hsAN9xwA1FRUfXOf/PNN6u///HHH/XO+8ADD5xU25pCq9UyaNAgAP7++2+EEKe9DXV59NFH6yz4ajKZuO+++9T/11dEvjZms5nff/8dgJCQEO64444653UVjgVnscyG3kdJkiRJkk4/b29v9XdX96XW5LLLLiM2NrbWaVFRUW6lNu6///461zN8+HD19927dze5Pa77Y6BGt86m+PLLLwHw9/dn8uTJ9c7bqVMndUT2E++r1q5dS25uLuA8Zh07dqxzPY8++igaTfM/bp/Ke1CXxt6Djxgxgl69ejVpWyfy8/OjR48egGfv/S233OLWVbK6AQMGuHXBbOq5K4Tg66+/BmDcuHENdk+dOHEiOp2zYtTJ3ps317l7IldZmvq4Prf279+P1Wr1tMmthqzZJZ1TPAl2DRgwwK3u0YgRI1AUBSEEq1atYsKECYAz+JWdnQ3UrNdV3caNG/nqq6/YsGEDhw4dorS0tM4Pi6NHj9KvX78m79/atWtxOByA8yLVUD/36u3Ys2dPnfNptVqGDBnS5HbVxeFw8NNPP7FgwQKSkpLIzMyktLRU3YfqSktLKSkpqfNidrpV73/f0HRXfQ5Pbdu2TR0NZvTo0RgMhnrnv+CCC5g9ezbgPN9c56gkSZIkSc3L0+DBifUwu3XrRtu2bcnMzGT27NkIIZg8eTIDBw48JQGRxnIFNurSpk0bUlNTAdSH6brmc3GNcF6b3NxcvvjiC/744w92795NYWEhFRUVtc579OjRetvmiTVr1gAQGRnJ4sWLG5xfq9UCzpGsKysr8fLyAtzv6caNG1fvOiIjI+natWuNkSxPVnPcgzb3PfiIESM8bL3zS9358+fz888/s23bNnJycigrK6s1oObJe1/fuavT6QgJCSE7OxsfH596axTXd+7u2rVLHeXSz8/Po1pivr6+FBUV1fuM5YnmOndP5Ml7dv755zN//nz27NnDeeedx+OPP855551X5zpbGxnsks4pCQkJREVFkZGRwc6dO8nPzyckJAT4J9g1atQot2WCg4Pp2rUru3btYuXKlWogofowutWDaC4Wi4U777xTjcZ7oqGhmhtSfZjYWbNmMWvWLI+Xre+GJCQkxC0A2ByOHj3KlVdeyZYtWzxeprUEu4KDg+sMbrokJCSov2dmZjZq/VlZWervnTp1anD+6vNUX1aSJEmSpOZ15ZVXNmk5rVbLRx99xDXXXIPFYuGzzz7js88+IzAwkCFDhjB8+HDGjx9/Ul96ngzX/XBdXAWrG5q3+nxVVVW1zvPdd99x9913U1xc7FHbTvb+uKysjPz8fMD5ZfVVV13VqOULCwvVh/vq93TV7/XqkpCQ0KzBrua4Bz0V9+AN9SZx2bFjB9dccw379+/3eLsN8fTcDQ4OrjMjrvp8UPPcrf6MtWDBAhYsWNBgu1zqe8ZqSHOeuyfy5D17/fXX+euvv8jMzGT16tWsXr0ao9FI//79GTZsGGPHjmXs2LGtZhCxE8lgl3TOGTVqFN988w1CCFavXs1VV13lNqpibd0RR44cya5du9yywar/Xluw6/7771cDXUajkYsvvpgBAwYQFRWFj4+PGnVfvnw57733HgB2u/2k9s3Tm4baWCyWOqc1d/TearUyfvx4NUU4NDSUyy+/nO7du9OmTRtMJpP6LWf1kYdO9vg0l+pdEeri4+Oj/l5WVtao9ZeWlta6nrr4+vrWuqwkSZIkSa3HpZdeyt9//83UqVP57bffsFqtFBUVsWjRIhYtWsQzzzxD9+7d+d///seFF154WtvWmOyyk8lEW716NTfeeKOaQdS3b1/OO+88OnToQEBAgFvAwfVg35L3x+B+j1z9nq6x94PN4WTvQU/VPbgnzwoFBQWcd955ajfQmJgYLr30Ujp37kxYWBgmk0kNRrlGK6wt0+xEnp6PJ3PenqpnrFO53Ya27cl7FhcXR1JSEi+99BJfffUVRUVFmM1m1q5dy9q1a3njjTcIDw/nv//9Lw8++GC9wcSWIINd0jnHFewCZ8DqqquuUgNXOp3OrUaAy8iRI5k1a5YaFAsLC1OXqa1eV1pamtqtLDo6mlWrVtG+ffta25ORkdFs+1Y96PHZZ59x++23N9u6m9O8efPUi+z555/PwoUL67wZcPWPb03qSrOvrry8XP29+vviCT8/v1rXU5fqNzLVl5UkSZIkqXXp1asXCxcupLS0lLVr17Ju3TpWr17NunXrsFqt7Ny5k4svvpgvv/ySm266qaWb2+ymTp2qBjA+/vjjOmsQeXL/46nq92EjR46sUcqkqetq7P1gczjZe9CWvAefOXOmGuiaNGkSn376qVrX6kSvvPJKs277ZFU/js8//zwvvPDCad/uyZ67TRUeHs57773H9OnT2bx5M+vWreOvv/5i+fLllJSUkJuby8MPP8yuXbv4+OOPT3v76tPyHcQl6TSrrW6X69++ffvWGpioHsxavXo1Bw4cUNOCa6vXtXz5crXf+X/+8586A13g7E/dXKqnozZHfYNTZenSpervb7/9dr3fejXn8WkuBQUFDaYkHzhwQP29oeKPJ4qMjFR/9yTNu/o8jd2WJEmSJEmnn5+fHxdeeCEvvvgiK1euJCsri0ceeQRwFsN+9NFHW01Ge3OxWCxq/aH+/fvXW2y7Oe//AgIC1Pv7k70/rn6fVf1ery6ezNMYJ3sP2pL34K5t63Q6ZsyYUWeg61Rs+2S11DNWc567J0uv1zNkyBAee+wxFi5cyLFjx/jss8/UbMxPPvmEHTt2tGgbTySDXdI5p1OnTmowYfv27RQWFqrBrrpGVGzbti0dOnQAnLW6GqrXVX3URtdydVmyZEm906un3DY0EsrIkSPV9NHWPCqfp8cnNzeX5OTk09Cixlu+fHm9011p3+Ac9KAxevXqpV44Vq5c2eDoJ9Xf69qKxrrOodY0mqUkSZIkSf8ICQlh+vTp9O/fH3DeA534hVdj7glbo/z8fGw2G3Dy98fQ+HtkgEOHDp1UAKr6fVZD94JZWVknXZy8NidzD9qS9+CubYeEhBAYGFjnfElJSWp5mdaiT58++Pv7A7Bs2TKPulfWpyXO3eZmMBi4/fbbeeCBB9TX1q5d24ItqkkGu6RzkqsIvcPh4JNPPlFHVTyxOH11rhErVq1a1WC9rur96Q8ePFjnOn/++We2b99eb1urZ5o1lAodHh6u1nj466+/Wm3Ay9PjM23atFY7zO3bb79d5zSz2cwHH3yg/r+xxSSNRiOXXHIJAHl5ecydO7fOeY8cOcK8efMA57kyfvz4GvO4zqHmTqWXJEmSJKl5xcXFqb+7AkMujbknbI08vf8rLS2t9z7LpTHHY9KkServzz//fIPrrsuwYcMIDw8H4Jdffql3P2bMmHFKsvNO5h60Je/BXdvOzc2tt8bsiy++2KzbbQ5arVbtVpyens6nn356UutriXP3VKnvM6ulyWCXdE6qHqB66623AGeEffjw4XUu44qq79y5Ux32tbZ6XeD+Lcqbb75Za7rxxo0b+de//tVgW4ODg9XRT5KTkxuM/r/88svqiBjXX399g0PUpqen8/jjj6t96E+H6sfnueeeq/XbkY8//ph33333tLWpsdauXct///vfGu+H1WrlzjvvVIfnvvTSS0lMTGz0+p944gn1W5/HHnus1m9KCgsLufbaa9WL5D333FPrSDnx8fGA8xvVw4cPN7otkiRJkiSdnCVLlvDOO+/UW3D6wIED/Pnnn4DzYfjEzBvX9Rxg69atp6ahp1BAQAAdO3YEYPPmzSxcuLDGPGVlZVx33XUcOXKkwfU15nhce+216v3nvHnzePjhh+st3l1ZWcncuXP59ttv3V7X6/U8+OCDgLNb5oQJEygoKKix/E8//cT06dMb3IemOJl70Ja8B3dtWwjBs88+W2O6EILnnnuOn376qdm33Rz++9//qhlpDz74IF988UW98+fm5vLSSy/VmtjQEuduY2VlZfH444+r51NtKioq+Pzzz9X/9+rV66S22dxkgXrpnFQ92OUK8vTs2bPelFpXUEsIQV5enrpMbcP/DhkyhH79+rFlyxbS0tLo3Lkz99xzD4mJiVRWVrJ8+XK+++47AG666aYGC0COHTuWhQsXcvDgQSZOnMjVV1/t1tZRo0apI2r07duXWbNmMXnyZAoLC7nooosYNmwYF110EfHx8ej1egoKCkhJSeGvv/5i8+bNADz88MP1tqE53X777bz66quUl5ezcOFC+vbtyy233EJ0dDQ5OTn8+OOPrFq1ioiICHr06KHe+LUWbdu2JSoqimnTprFy5UpuuOEG2rRpQ3p6Op9//jm7du0CICgoiPfff79J2xg8eDBPPfUU06ZNo7S0lFGjRnHDDTcwduxYvLy82LlzJ59++qmaEt6zZ886vwkbN24cv/zyCwBXX3019957L5GRkWowrUePHh4PGS1JkiRJ57LGPIhfcskl6heQWVlZPPzwwzz55JOMGTOGQYMG0b59e7y9vcnLy2PTpk3Mnz9f/QLr4YcfrjFa2rhx49Tfn3zySY4dO0ZiYqJa+ygqKooePXqc5B6eWg888IAaLLr22mu56aabGD58OH5+fuzcuZO5c+eSmZnJrbfe2mAwYdy4cWpQ5o477uCRRx4hNjZWHfE8ISGBhIQEwPml9oIFCxgyZAgZGRm88847zJ8/n+uuu45evXoREBBAWVkZhw8fZvPmzSxbtozy8nJeeumlGtt94okn+OGHH0hOTmbr1q107dqVyZMn061bN8rLy1m8eDELFiwgMDCQXr16uZU/OVknew/akvfg9913H5999hl2u513332X5ORkrr76aiIiIjhy5AjffPMNSUlJdO3aFS8vL7Zs2dJs224O0dHRfPvtt1x++eWYzWYmTZrE9OnTufzyy+nYsSNeXl4UFxezb98+NmzYwNq1a7Hb7YwZM6bGulrq3G0Ms9nMW2+9xVtvvcWAAQMYMWIEXbp0ITAwkOLiYvbu3cs333yjDrY2YsSIehNHWoSQpHNURESEANSfBx98sMFloqKi3JZ56KGH6px3//79IiYmxm3+6j8mk0nMnj1bzJkzR31tzpw5ta4rKSlJeHl51bmu1NTUGsv88ssvok2bNnUuU/0nJCREHDt2rMY6YmNjBSBiY2MbPDaNXeann34SJpOpzjZFRUWJv//+W0yaNKne/VyxYoU6fcqUKbVua9SoUeo8J6P6vqWlpYmuXbvW2f7w8HCxadOmOtfV0H65PPfcc0Kr1db7/o0aNUrk5eXVuY7S0lLRqVOnOpev67yTJEmSJEl4dC9V209hYaG6jrlz53q0jKIo4qGHHhJ2u73Wttxwww11Ljtp0iS3eRu6//HkHtTTdbmkpqbW2R4hhHA4HOKmm26q9xhcccUVoqKiwu0+pzY2m00MHz68zvXUdl+YmZkpxo0b59F7odVqxSeffFLrtnNyckSfPn3qXDYwMFD8+eefHt/vNaQ570Gb6x68MeePywcffCA0Gk2d2+7SpYvYv39/g+fblClT1OkrVqzw+NjVx5NnCiGEWL9+vWjfvr1H55Cvr6/Yvn17jXW01LnbmOOWlpbm8WfdmDFj6n0WaSmyG6N0zjqxPlddxemrc9XtcqmtXpdLQkICSUlJPP3003Tp0gWTyYSvry+JiYn8+9//ZsuWLR51YwTo3bs3W7Zs4c477yQxMdGtv31dLrvsMlJTU/nwww+5/PLLiYmJwcvLC4PBQFhYGEOGDOGBBx7g119/JTMzk9DQUI/a0lyuuOIKtm7dym233UZMTAx6vZ6QkBD69evHiy++yLZt2xpd2P10io2N5e+//+a1116jb9++BAYG4uXlRZcuXXj66afZs2ePWmT2ZLz44ots376dBx54gK5du+Ln54fRaCQ6OpprrrmGBQsWsHLlSkJCQupch6+vLxs2bOCZZ56hb9++BAQEuBXGlCRJkiTp1Lr11lvZuHEjr7zyCpdddhkJCQn4+Pig1WoJCAigd+/e6v3hjBkz6rxOf/nll8yaNYvRo0cTGhpa74h2rZGiKHz11Vd88803jBkzhsDAQAwGA9HR0Vx66aV89913/PTTTzWy2mqj1Wr5888/ee211xgyZAhBQUFqZkxdIiMjWbp0KStXruTuu++mW7duBAYGotVq8ff3p2vXrkycOJFZs2Zx5MgR7rzzzlrXEx4ezsaNG3nvvfcYPHgw/v7+eHt7k5iYyCOPPEJycjLnnXdek45RQ072HrQl78Hvvfde1q5dy3XXXUdERAR6vZ7w8HCGDh3K9OnT2bx5s5rR1FoNHjyYvXv38tVXXzFhwgTi4+Px9fVFp9MRHBysjjT63XffkZ2dXWu2ZUueu56KjY3lwIEDvP/++9x444306NGDgIAAtFot3t7edOzYkRtuuIFff/2V5cuX1/ss0lIUIc7AoTwkSZJaQFxcHOnp6cTGxpKWltbSzZEkSZIkSZLOAfIeVJIaT361L0mSJEmSJEmSJEmSJJ01ZLBLkiRJkiRJkiRJkiRJOmvIYJckSZIkSZIkSZIkSZJ01pDBLkmSJEmSJEmSJEmSJOmsIYNdkiRJkiRJkiRJkiRJ0llDjsYoSZIkSZIkSZIkSZIknTV0Ld2Ac43D4SAzMxM/Pz8URWnp5kiSJEmnmRCC0tJS2rZti0YjE6wlSZIaQ95LS5Iknds8vZeWwa7TLDMzk5iYmJZuhiRJktTCjhw5QnR0dEs3Q5Ik6Ywi76UlSZIkaPheWga7TjM/Pz/A+cb4+/u3cGskSZKk062kpISYmBj1eiBJkiR5Tt5LS5Iknds8vZeWwa7TzJVu7e/vLy/QkiRJ5zDZ/UaSJKnx5L20JEmSBA3fS8tiIZIkSZIkSZIkSZIkSdJZQwa7JEmSJEmSJEmSJEmSpLOGDHZJkiRJkiRJkiRJkiRJZw0Z7JIkSZIkSZIkSZIkSZLOGjLYJUmSJEmSJEmSJEmSJJ01ZLBLkiRJkiRJkiRJkiRJOmvIYJckSZIkSZIkSZIkSZJ01pDBLkmSJEmSJEmSJEmSJOmsIYNdkiRJkiRJkiRJkiRJ0llDBrskSZIkSZIkSZIkSZKks4YMdkmSJEmSJHkoLS0NRVFQFIW0tLSWbk6rNHfuXBRFIS4urkW2P3XqVBRFYfTo0S2yfUmSJEmSWp4MdkmSJEmSVC9X8MDTH0k6FZKTk5k6dSozZsxo6aacNqNHj671b0yj0eDv70/v3r15/PHHSU9PPyXbX7lypcd/9ytXrnRb9tdff+Xxxx9nzJgxdOjQAX9/fwwGA23btuWiiy5izpw52Gy2U9JuSZIkSdK1dAMkSZIkSTpztGnTpqWbIJ2jkpOTeeGFF4iNjeXhhx+uc77Q0FASExNp167d6WvcKabX6wkODlb/b7VaKSgoYNu2bWzbto0PPviABQsWcNFFF52yNgQFBWEwGOqcfuK0p59+ml27dqn/9/PzQ6vVkpWVRVZWFosXL2bmzJn8/vvv8nNFkiRJanYy2CVJkiRJkseys7NbugmSVK9///vf/Pvf/27pZjSroUOH1sicqqqq4ueff+b+++8nPz+fm2++mcOHD+Pj43NK2vDjjz82qmvotddey0MPPcSwYcOIj4/Hy8sLgMzMTD799FNeeOEFtm7dyqRJk1i8ePEpabMkSZJ07pLBLkmSJElqgBCC/Px8zGYzRqORkJAQ2V1PkqQWZTKZmDhxIlarlVtuuYWCggJWr159SrO7GmPq1Km1vt62bVuef/55qqqqmDZtGkuWLOHo0aNER0ef3gaeQkIIKisrsdls6HQ6vLy81GtGfdNOZr3nOrvdztblO1kyZxnlRcX0KtlIeHAllvh2dHpsCqu3bSM1t5jIQD8mjBtHUEAQiqJgFw72lmRSaCknyOBDon9btIqz0o883q2DEHawbAbHMdCEgaE/iqJt6WadlLNxn1ojWbNLkiRJkuqRlZXFsmXL2LBhA0lJSWzYsIFly5aRlZXV0k07I8TFxaEoCnPnzqW0tJSnn36axMREvLy8CA0N5corr2Tjxo31rsNut/PZZ58xduxYQkNDMRqNREVFcd1119XIdmkOGRkZ3H333cTExGA0GomOjub222/nwIEDHi1vsVj44IMPGDNmDKGhoRgMBiIiIrjiiitYtGhRvcuWl5czZcoUunTpgpeXF+Hh4Vx88cUsW7YMcD+e1Z1YOP/gwYPcddddxMfHYzQa3YrFFxYWMnv2bCZMmECPHj0IDg7GZDIRGxvLjTfeyIYNGxrcxw0bNnDllVcSGhqKl5cXiYmJPPPMM5SVldW7XEVFBfPmzePWW2+ld+/ehIWFYTQaadu2LVdeeWWdx0dRFG6//XYA0tPTa9SLqh5Y8aRAfVJSErfeeiuxsbGYTCaCgoIYOnQoM2bMwGw217rMiYX3t2zZwoQJE4iMjMRoNNK+fXseffRRCgsL6z0Gza13797q77Ud/+Z4v0+FwYMHq79nZGSc9u1nZWUxderUZv8sLy0t5cCBA+zdu5d9+/axd+9eDhw4QGlpab3TTma957o1P27kyqBJ/Hf8y3Sc9xEvL3qR69cuYeyvq7nw3a+Ii0uky/tzKSrU8OOGHM5/+nOe/Wgu647s4t6/P+WxLV8wdfv3PLblC+79+1P+zjsgj3crIaqWII6NQRTegih+1PnvsTGIqiUt3bQmOxv3yVOn6nO3Ls0a7CopKSE3Nxe73d6cq5UkSZKkFpGVlcWWLVuoqqpye72qqootW7bIgFcjFBYWMmDAAF577TXS0tIwGAzk5+fz888/M3ToUD777LNalysuLua8887jjjvuYMWKFRQVFeHt7U1WVhY//PADY8aM4Yknnmi2dm7dupUePXrw8ccfc/ToUbRaLcXFxcydO5d+/frx999/17t8eno6ffv25f7772flypUUFBTg7e1NTk4Ov/zyCxdffDH33ntvrcvm5uYycOBAXnzxRVJSUrDb7VitVhYtWsT555/Phx9+6NE+rFu3jt69e/PJJ5+Qm5uLXq93m/7OO+9w55138v3337Nnzx719cOHDzNv3jyGDh3Ku+++W+f6P/vsM4YNG8bPP/9Mfn4+RqORtLQ0Xn31VQYMGFBvsGf+/PnceOONfPnll2zfvh2r1YpOpyMrK4uff/6Ziy++mMcff7zGcm3atMHf3x8AjUZDmzZt3H58fX09OjYAb7/9Nv369ePLL7/k8OHDmEwmysvLWb9+PY888ggDBw5s8G/7m2++YciQIXz//fdq5kdqaipvv/02I0aMaDDo15y2bdum/p6YmFhj+sm+36fKmjVrAGcgs3379qd9+1lZWbzwwgvN+jleWlpKamoqxcXF6PV6fH190ev1FBcXs2/fPvbt21frtNTU1HqDKPWtt6Flz3ZrftzIi9e+SVWZmTvFdiawr8YDriIcDFzyf9y8bAGD4wIJ8dWzaFsRH3y3mqKSYoxaPYEGH4xaPalluby3/Vc27tkmj3cLE1VLEEUPguOE8gmOHETRg2dkcOhs3KfGOBWfu/VpcrArLS2Njz76iBtvvJH4+Hj1W7HIyEgMBgPBwcEMGzaM//73vyxZskQGwCRJkqQzihDCrbhybXbt2oUQ4jS16Mz2wgsvkJuby/z58ykvL6e4uJjdu3czatQoHA4Hd999N1u3bq2x3B133MHKlSsxGAy8++67lJSUUFhYSGZmJv/6178AePPNNz0OBNWntLSUq666isLCQtq1a8cff/xBeXk5paWlrFu3jpiYGO6+++46ly8vL+fCCy9k165djB49mpUrV1JZWUlRURFFRUVMnz4dX19fPvzwQ955550ay0+aNIndu3fj5eXF7NmzKS0tpbCwkMOHDzNhwgQeeughjh071uB+3H333XTr1o1NmzZRXl5OWVkZf/zxhzq9bdu2TJkyhc2bN1NRUUFBQQGVlZUcOnSIhx56CIBHH32UpKSkGuveunUrd999Nw6Hg9GjR7Nnzx6KioooKytj3rx5ZGdn8+KLL9bZtqCgIB5//HH++usvysrKKCoqory8nMzMTF544QX0ej1vvfUWv/zyi9ty2dnZ6jGLiYkhOzvb7ae2AFlt/u///o9HH30UIQRXXHEFhw4dUtv/xRdf4Ofnx/bt27n22mvrvHc9duwY//rXv5g0aRKHDx+mqKiI0tJSZs6ciV6vZ9euXbzxxhsetedkmM1mfvjhBx555BEALrjgAnr27FljvpN5v5tbWVkZO3fu5Mknn+Stt94C4JZbbiEsLOyUb/tUE0KQnZ2NxWJRgyOKoqDX6/Hx8VEzu06c5uvri8ViITs7u9bryYnr1el0Hi97trPb7XzwkPOLEp1wcC37ADixo6Hr//2W/I6fIugS7osC7DlYRVd7MAZFhwYFo1ZPkN6HaIsPB4sy8fH1kce7hQhhR5S8AtR2nJ2viZJXnd0BzxBn4z61do2q2eVwOPjpp5/46KOPWLZsGUKIOv/Qi4qKWL9+PRs2bOD1118nPDycf/3rX0yePNktlV6SJEk695xMrYKmLtvY5fLz82tkdJ2oqqqK/Px8QkNDPWr72SAiIqLe6RMnTqw1kFNcXMzSpUsZN26c+lqXLl1YtGgRvXr1Yv/+/Tz33HP89ttv6vSNGzeyYMECAN577z3uuusut3bMnj2b4uJiFixYwHPPPcdtt92GyWRq8r7NmjWLw4cPYzAYWLx4MV26dFGnDRkyhKVLl9K1a9c6l58+fTopKSmMGjWKP/74wy2jKiAggEceeYS4uDiuvvpqXn75Ze6//350Ouet2F9//aUW6f7444+5+eab1WVjYmL45ptvOO+881ixYkWD+xESEsLSpUvdsp06deqk/l79OLooikJ8fDwzZszAZrPx/vvv8/777/Ppp5+6zffss89is9no1KkTv//+u1p0XK/Xc/311xMUFMSFF15YZ9uuuOIKrrjiihqvR0ZG8vzzz+Pt7c0TTzzBu+++y+WXX97gvjbWk08+CcCIESNYsGABWq3zM8BgMHDLLbcQGBjI5Zdfzrp161i4cCHXXnttjXVUVFQwadIkPvnkE/U1b29v7r//fg4dOsT06dOZN29evUG/xlq3bp3b355rNEZwnh9PPvlknTWyTub9ru7qq6+uczTGl156icmTJ9c6bcOGDQwZMqTG61qtlkmTJjFz5sw6twnOoF71rqUlJSX1zl8f10iQgBpcrx5kj4yMJDIysknrrqyspKysDJPJhKIoVFVVYTab0Wq1CCGw2+0oioLNZsNqtWK1WjEYDBgMBkwmE2VlZVRWVuLt7V3vektLSxFC4OPjg1arrXfZs93ONSnkZTj/Di7jAPXdCSg4M7x6LF+C+byLCfDSUVRpI+eAnapOVVh1An+DN75CTzje5NrLybeU4WXV4HA45PE+3Syba2Y/uRHgyHLOZxx02pp1Us7GffLAqfzcbYjHmV0///wz3bp147rrruPPP//E4XDQvn17brzxRv73v/8xZ84cfv75Z/744w++/fZbPvjgA5544glGjBihpu+/9tprdOrUifvvv9+jbyYlSZKks8/J1Cpo6rKeLudwOCgvLyc3N5ejR496tD911fc5W+Xk5NT7U1xcXOtyw4YNcwt0uXh5eandEBcvXuy2/HfffQdAdHQ0d955Z63rfemllwDIy8vjzz//PKl9+/bbbwG47rrr3AJdLhEREdxzzz11Lj979mzAmSVzYtdBlyuvvBJ/f3/y8vLYsmWL+vr3338POGty3XTTTTWW02g0PPvssx7tx7///e9Gdes70SWXXAI4A3DVFRUVsWSJ82/miSeeUANd1Y0fP77WwEZjt71+/fpm7xWwfft2tRvfs88+qwa6qrvssssYOHAgAPPmzatzXXW9F65A3oEDB6ioqDjZJqusVqvb35kr0AXOQHJBQUGdf3sNqev9PlFhYWGdf/fl5eV1LmcwGNTuptWDZXfffTdTpkyp9Tyqbtq0aQQEBKg/MTExjdg7dx999BH9+vWjX79+anBu8uTJ6msfffRRk9dts9mw2+1qANtms2Gz2TCbzVRUVGCz2bBYLBQWFlJSUkJFRQUlJSUUFBRQWlpKRUUFZWVlNRIJqq/XarVisViwWq3qdK1Wi8PhwGazNbntZ6r8rH+6TLel7nOwusCcbHQaBYPO+RhcWSYIsRupsloos1ahR4MeLRbsVFqcgVar1aq+L+fy8T6tHB7GCjydr4UIey6iagmOktcQJZ5dw1v7PjXWqfzcbYhHmV2jR49mzZo1CCHo1asXN998MzfeeKPHETiHw8GyZcv46quv+Omnn5g1axZff/01X375JZdddtlJ7YAkSZJ05lBrFZyYwn28VgGB76KYxjfrsg0tV2V6gxLzYMrKyqioqFBvaC0Wi0f7ZDQaPZrvbNHUrhtjx45tcJrD4WDr1q2MGTMGgM2bNwMwZswYNJrav5/r0qULUVFRZGRksHnz5ibfV1gsFnbs2OFRW6dNm1bj9YyMDNLT0wFn18vaAikurnpO6enpDBrk/PbW9S3nyJEj6xzta9iwYeh0ugYfsoYNG1bvdIBDhw7xwQcfsGLFCg4ePEhpaSkOh8NtnhMDvlu3blXnaegYrV+/vs7pOTk5fPDBB/zxxx9qDaMTA1sVFRUUFhY2a9ak63zS6XSMGjWqzvnOP/98/v77b3X+EwUHB5OQkFDrtLZt26q/FxYWNlvmx6hRo2oMxlBSUsKmTZuYOnUqn376KUuWLGHZsmV07NixxvJNeb9PtGLFinqL/telb9++ZGc7sxkcDgeHDh1ixowZzJo1iy+++IKvv/663iy+p59+mkcffVT9f0lJSZMDXnfffTeXX345lZWVDB8+HICZM2eqAdqTyS7Q6XRotVpsNht6vR4vLy8MBoMa8KqeKazT6bDb7WpdPofDgRCCQ4cOkZWVhZ+fnxrcq75eVwDVZDKpnzF2ux2NRqMG2c4lIZFBIBxMZB8XkurRMkVWE9p9x/D98wBmfxMiMJqOShDhdm8qq2wYtRoUAV7o0Fqd1zuDwaAe33P5eJ8uwlGAMK/0bGZN6+kCLYQNbHvBkoSwJoE1CeyefXHqphXtU3M4lZ+7DfHor3T16tWMHz+eqVOnqjdljaHRaDj//PM5//zzqaio4L333uOtt94iKSnpjAl2bd26la+++oply5aRmpqK0WikS5cuPProo1x55ZUt3TxJkqRWr6FaBQIFR/HLmG1D4MTuhcKOsfxlFESNWhz1LtvQcgKU8tfIyf8AjneA0Ol0+Pj4EBkZSXFxcb2ZWyaTiZCQEA/2XoqKivJoWm5ubo3f61sWnJlfGRkZbss2VkFBgRpEqm970dHRtb6emZmp/p6Xl+fRNqtn/rgy3qsHS05kNBoJDQ1VAwd1CQ8Pr3f6woULueGGG9zObX9/f7WblCv75MRsnerHtynHCJwZWxdffDFFRUXqa76+vnh7e6MoCna7XT1+5eXlzRrscrXfNaJnXVztr+t88vPzq3PZ6g/A1bNvTgV/f3/GjRvHwIED6dGjB+np6dx9990sX77cbb6mvt+ngkajISEhgZkzZ9K+fXsee+wxbrrpJvbt21fnA4/RaGy2LxVc3WWq72vv3r3p27fvSa/by8sLX19fiouL1QCVVqtVuym6gl1BQUE4HA7sdrua/VVRUYFOp1Ozi8vLy8nOzkZRFEwmE2azmZKSEhRFQavVqtlwQgiqqqoICAhoMEPurGOx0P3nT/k/5ReMwvnZ7bq7qO3rAgEIRSFl+HkUpBdSvDMLnYDMtalkzdDiG+dP0NAwNMNCoY0f7ZUAHOVmLDod3t7eOBwOtXvqOXm8TwNhz0GUz4bK70BUNjC3ApoIMPQ/LW2rjXAUHQ9sJTsDW9btIE7M6NWArhPo+4C+F5S9CY58ar8Xbvl9OhVO5eduQzwKdq1fv75JQa7aeHt789RTT/Hvf/+btLS0Zlnn6fDGG2/w559/cvXVV3PfffdRWVnJvHnzuOqqq3juueeatS6DJEnSWamBWgUKAkXkkH3k/yi3dneb5qPfSXxQTqOXbXA5BQzafNoEHUHnPQwfHx+MRqOaWdO9e3e3rmYn6tatW51ZONK5pXpm0p49e+jcuXOT1tMc51N9WWX5+fncdtttmM1mxo4dy/PPP8/AgQPdHtyWLVvGeeedd9LtOJHNZuOGG26gqKiI3r178+qrrzJ8+HC34NHBgwfVrClZANozfn5+TJw4kTfeeIMVK1aQlZWlBo5a8v1uyH333cd///tfdXCD6tlbZyJFUYiIiHCrsaXVarHb7VRVVeHv768Gp0wmE3q9Xp0WGhpKVFQUWq1WLWRfXl6OzWZTR/usrKxECIFeryc/P19dt9FoxN/fH5vNphZTP6uVlMADDyDmzUNrtaLFGTbICowg0yuYflm7EbgHvFyfJNsuvBRtXAiFe/IwBXlTVVgBAoTZTuneQkr3FsKcfaSYtIS3D6HjkFg6D++ATqdTsyFNJhN+fn5uXValkyNsRxHln0Ll98DxLwl03cEwBCpcdQSrXw+c767i/1+P672edBuFA+yH/snasmx1/v9Eih/oe6MY+qgBLkXzT1kBofE53ttAoaX36Vzg0V9ocwW6qvPx8aFbt27Nvt5T5YEHHmDu3LluhW8feOABhg8fzrRp03j44YcJDg5uwRZKkiS1ch7WIDAZy7Bp3L/FN+nLmrSsp8uFh2pQvGpmaEVGRtKvXz927drl1gXFZDLRrVu3U5p6fbbJyMjwaFr1rKTw8HD27t3bYPcq1/SGMprqExwcrD48etrW6qoXD09PT290sCssLIy9e/e6ZYidyGw2e5w1Vpfff/+dkpISgoKC+PXXX2vtZldX5lj145uRkUH79u1rna+uY7R+/XrS09PRarX83//9X63ZYQ1lrZ0MV/vz8vIwm811Zgs1x/l0usXGxqq/p6WlqZ9NJ/N+n2omk4ng4GCysrI4cOBAi7QBGh50ozH8/PyIj48nOzubsrIyzGYzGo2GgIAAdTt1TXMFfYOCggBnl8/KykoKCwvJzc3FarVis9nU7C+NRqN2ryssLKSoqEjNJDMajeq/RqOx3gD4GSMzE3HXXbBoEYrD4QwVKAoVffvy+vjR7J5XSHFaCXdi41r2uRWrF4qGTRdczDdjriYlvRhd90jGXdsNH28vklfup2hzJsUH8rAWO6/z9io7Wbtzydqdy+rZm9AZdbRJCKXryAR6j+tGoX8hxcXFGAwGvL298fLywtvbWwa/GknYDiHKP4bKX4Dj3fP1/VB87wPDcBRFQRh6OnsFVP+yVBPhDArVUfaiWdrmKHNmalmTEZatYE0GUcvgGNp4MPRF0fcGfV/QdUBR6i6LrpjGQ+C7LbJPrUlzfu7WR/5Feqi2+hdarZarr76aDRs2sG/fPgYPHtwCLZMkSTpDeFiDoG1ULxRjottrwlyEKKxjgXqW9XS5+toWGRlJREQE+fn56gNySEjI2f/teTOrbxRB1zSNRkOfPn3U1/v378+aNWtYsWIFDoej1rpdKSkpanBlwIABTW6fwWCgZ8+eJCUlsWLFCv71r3/VOt+JXcRc4uLi1Nphv/76K+PHN+6GtW/fvvz111+sWrWqznnWrl170kWRjxw5AkBiYmKd9aSWLl1aZxs1GufIZCtWrKgz2FXXMXJtOywsrM5ukHVtG1Df/6ZmfPXv7+waYrPZWLVqFRdccEG9bTiZ8+l0qx4Q9vHxUX8/mff7VCstLVW779bXNfRUa+6HLj8/P3x9fdWMLJ1Oh5eXl3rNqG9adRqNRs02rqysxGQy4e/vj9lspqysTK25ZrfbKS0tRavVotfr1Syw6nQ6XY0AmMFgqLMWYquyZ48zyLV2Lcrxv32h0VA2ejTHXp/GvXmLKaQKhoYRvlNh/9/teTJvCB3+SqatowxxSVd6fTCdPdu2EZhbzFXt23BBv4GYKyuw2+306hjP0v57KSyuxG6xk7MjC8eeXMoO5WMpcXb9tZltZOzKJmNXNn/O+gudUUfbxDb0GNOZrsM7EtOlLRqtRga/PCSsKYjyWVC1GDW7yTAcxfdeFIP7565iGg/G85o8grdH7REC7EfA6sraSnLW3sK9riGKF+h7gr4Pir4PGHqhaBqf7HI69qm1k8GuM4TrG9iwsLOrkJwkSVKzM/QHTQTCkV1rPY16axUcXxZHDo2qc9DU5U6cS1GatXbQueivv/5i5cqVNQpcV1VV8dZbbwHOkfwCAwPVaddffz1vv/02GRkZfPrpp9x111011vv8888DzjpMJ9sVa+LEiSQlJfH999/z7LPPkpjoHnTNzc3lww8/rHP5yZMnM3XqVGbPns0dd9zhFrg7UUFBgVtG+LXXXsu7775LWloa33zzDTfeeKPb/EIIXn311Sbu2T8CAgIA2Ldvn9qdqrrk5GS++eabWpcNDAzkggsuYPHixbz55pvcdNNNNZZfunQp69atq3fbrhH82rRp4zb96NGjvPvuu3W23d/fH8Ct3ldj9OzZk65du7J7925efvllxo0bVyPj5ffff2fjxo0A3HDDDU3azulWVVXFjz/+CDgDKdXP25N5v0+GK5BTn//9739q8LYphe9bM0VR6gwu1jetNnl5eTgcDnx9fYmKilJr27lqe5WXl2O32xFCqKMEumqGueZ11QarXjNHURT0en2NAJjBYGgdX+asXYu47z7Yvl29ZxA6HSWXXUbV66+jbRvOzX/9DxsOQBDnXcq3tyWhPPQGimkcX7+ygPef+5aI3WFc0iaW2y77Z1AJIQRZWVmUlpYSYjRy71Xns3jDDpL3pdFpaBxh4ztRUmljxfZszIfyic6vovJQHmUFzuNnM9s4vD2Dw9sz+O2dZWj1WqIS29BtdCKdh3QgrkcMWr1WDX65AmDnevBLWLY5g1zmal+IGMc5g1z6nnUupyhaMDZfTzMhzGDd6QxuWY4XknfUkjWtiQJDH2fWlqEv6BJRlNpHWm6s5t4nqXZnQDj/HxUVFSxatIiXX36Zq6++mtjYWBRFQVEUpk6d6tE6SktLmTp1Kj169MDX15eAgAAGDBjAW2+95fHIWy4ZGRnMmTOHQYMG0aFDhybskSRJ0rlDUbTg94yzPkaNuFP9tQoURYvi/4zbvJ4s29TlpOYXEBDANddcww8//KA+4KakpHDJJZeQkpKCVqutUf9y4MCBXHPNNYCzdMDMmTPVou7Z2dlMnjyZ77//HoCXXnqpxoN8Y917771ER0djNpu58MILWbZsmZpFtHHjRs4777waI9hV99hjj9GjRw+qqqoYM2YMM2fOJD8/X51eVFTEokWLuPXWWxkxYoTbsiNGjOD8888HnEGzuXPnqgXFjx49yk033cSaNWtOenS/Cy64AI1GQ0FBATfddJOaFWexWJg/fz4XXHBBvVk2L730ElqtVn3v9u7dCziDG/Pnz2fChAluAcvqhg8fjo+PD0IIJkyYwL59+wBnZsqSJUsYPXp0vQ/Z3bs76/GVlJQwf/78puw+r7/+OgBr1qzh2muvJTXVOYKb1Wrl66+/VgNcQ4cOPSMGINq9ezfXX3+9+j7cf//9bt0zT/b9birXCIs//vijW6F/h8PB9u3bueuuu3jppZcAZ++JCy+8sNnbUB+tVsvFF1/MxRdf3Kq7+FVVVVFS4uw6FRYWpv59aLVa/P39iYyMpEOHDkRHRxMUFKTWAlMUBYfDodaY8vf3Jzg4mKCgILy9vdFqtQghsFgslJWVkZ+fT2ZmJmlpaezfv5+0tDSysrIoKCigrKwMq9V6+mro/fwzokMHxPDhKMcDXQ6TicI77iA/PR2f+fPRR0Vw2Zo3sAlnoCvBVMS3PTJRQhaimMYBcM0jlxLSNojstGP8+sESt0246quZTCYcDgelJcVcP34Yt10+hmBfE8IhsNod9O4cQqcR8RwbGUfmDX0YMes27pp5O/0v7IVPwD+fxXarncM7M1k0cwVv3/Qpj/Z9gdeufZ8f/vd/bFy8hbRDaRw8eJC0tDRycnIoLS096SzdM4UQAmH5G0fBbYiC644HuhQwXYwS8guaoFn1BrqapQ32HETVYhwlr+LIn4DI6YsouAFR+gaY/zwe6NKDvjd4344S+C5K2Go04SvQBE5H8bkVRd+92QJd56oW+dwVjZSSkiImT54sevfuLXr06CFuvPFGsWLFigaXi4iIEFqttrGbc7NixQrB8cE0TvyZMmVKg8unpaWJuLg4dRlvb29hNBrV//fp00cUFBR41Jby8nIxYMAAYTKZxLZt2zzeh+LiYgGI4uJij5eRJEk6WxQVFYnUlI9E1dHBwp7V8Z+fnJHCUbm4weUdlYuFPWdEo5dt6nKnwpl4HZgyZYp6rWzTpk2DP2vXrlWXjY2NFYCYPn26SExMFIAwGo0iICBAXaeiKOLjjz+uddtFRUVi1KhR6rw6nU4EBQUJRVHU1x5//PFm29dNmzaJwMBAt3sFX19fAQg/Pz/x3XffqdNSU1NrLJ+RkSEGDx7stm+BgYHC39/f7b4lISGhxrJZWVmic+fO6jx6vV5ti0ajER9//LFo166dAMS8efPclk1NTa23XdU99dRTbm0JCAgQer1eACI+Pl58/fXX6rTafPTRR27HPyAgQL2f6ty5s5g+fboARGxsbI1lZ82a5bZtX19fYTKZBCBCQ0PFL7/8Uu9+jBs3Tp3u5+cnYmNjRWxsrHj77bfVeVzn66hRo2pt//Tp093aHxgYKAwGg/r/Hj16iIyMjBrLzZkzp879cmnM++AJ17mv1+tr/J15e3u7HcuJEycKi8VSYx0n835Xv/f25H7fxXWsXD8+Pj4iNDTU7TgDYuzYsSI/P79Rx+RM/AxtCofDIY4cOSJSUlJqPR/rWqayslIcO3ZMpKWliZSUFLef1NRUcezYMVFRUSEsFosoKysTBQUFIisrS6Snp4t9+/bVWMb1s3//fpGeni6ys7NFQUGBKC8vF1artfl2+OOPhSMiQhz/PkwIELaAAJH35JOiIC9P2Gw2IYQQx8qLxKBF/xUDFv1HDFj0H3HrysnCXvSccDiqaqxy0exl4jzlWnFV8CRRUlBaY7rFYhEHDhwQKSkp4ujRoyIvL0/s3r1HfPHzn+LGKR+Jh9+cKx6bMVc88f5ccde02eLCJ2aKIQ+8I6b/sEoUlJSLzEPZ4udZi8UT500Vl/reJM5Trq315wLddWJSl/vFtMlviwUf/yySNyer70d2drYoKSlp3mPZCjgcDuGoWiXseROr3Xt1Efai/wiH9dAp3K5FOCw7hKPsc2EvfFjYc0a73/up94BDhL3gPuEo+1Q4zJtrPX+k1svT60Cjcil//fVXJkyYgMViUaP7u3bt4ttvv+XGG2/kww8/dKsTcCLRDN8IBAUF0bdvX/XnkUce8aiwps1m47LLLlOLdn7xxRfqN7Tff/89kydPJikpiZtvvpnffvut3nVZLBauvvpqtm7dyvz58+nZ89RGoyVJks4GQghycnKoMg/GFHAJbYKONrpWQVPrHMj6CM0nJ6fu0S1dasuUDgoK4u+//2batGksWLCAI0eOEBwczLBhw3j66acZMmRIresKCAhg2bJlfP7553z55Zds27aNsrIyIiIiGDp0KP/+97+btQtU//792b59Oy+++CKLFi3i2LFjhIWFcc011/Dss8822A2lbdu2/PXXX3z//ffMmzePzZs3k5eXh0ajIS4ujh49ejBu3DgmTJhQY9mIiAg2bdrEG2+8wfz580lLS0On03HxxRfz5JNPMmrUKJ544gmAOrOnPPHaa6/RrVs3Zs6cyY4dO7BarSQkJHDVVVfx5JNPkpSUVO/yd911Fz169GDatGmsXbuWiooKYmNjueaaa3j66adZsGBBncvec889tGvXjv/9739s3rwZm81GVFQUF198Mf/5z38azLL/4YcfePHFF/ntt984fPgw6enpQOO6Nj7yyCOMGjWKt99+m1WrVpGTk4OXlxd9+/Zl4sSJ3HvvvXUWr28pVqu1xt+eyWQiPj6eQYMGMWnSpDqzo072/W6KSy65hE8++YSVK1eSnJxMTk4OhYWFeHl50b59ewYMGMD111/PxRdf3OzbPlu4uihqNBqPy6UoioLJZMJkMhEaGorVaqWsrIyysjIqKysxm82YzWby8/PR6XT4+vri4+NDQEAAGo0GIQRWqxWLxYLZbHb71263U1lZ2WA9MNe/HtUDczjg1VcRb76JUlysjk9nCw+n6PHHMdx1F8H+/mpG25GKPK5bPR3H8bIEvXzy+bj/xSheV9a6+vMnjWLB2/9H2q4jfDttIZPfuMVtul6vp23bthw9epSSkhLy8vIwmUxcPmoAvbp34+Uv/8BhrSQq0ERQiJbw4EAKiiys2LyTH//azoRRvbn1llFcfs94hBCk7z7K5iXJrP3lb/b+fRBrlXNkQYddkJGSQ0ZKDss+XYuiUYhICKPT4PZ0GtSehH5x+AR4YzQa1XpfZ2q3RyEcYF6KKPsQbDuPv2oA72tRfCajaGuv19jk7TkKwZKMsG49XmtrB4jKE+bSgC7xeJfEvs4MLm1M6+iuK51SivAwApWZmUnXrl0pKSnBy8uLMWPGYDKZWLt2LTk5OSiKQvfu3fnzzz9rHb0mMjKS3Nxct6G5G8tut9dIeYuLiyM9PZ0pU6bU25Vx9uzZ3HnnnQCsW7euxk31vHnz1PoYS5cuZdy4cbWux2q1ct111/HLL78wZ84cJk2a1Kh9KCkpISAggOLiYrX2hCRJ0rmguLiY9PR0NBoNnTt3PiNv4prDuXYdcF2n58yZw2233dbSzTmj7d+/n06dOgFw+PBhYmJiWrhFknT6nQufoUII0tLSsFgsBAcHN0ttYFedr7KyMsrLy926ZLuK4buCXyc+b4njXR6rB8DMZjNWq7XO7en1erdaYK5/FUUBiwWeeALx8ccox0c6FoAlLo7iKVPwvvZafHx83IIRqSU5XL/uHcTxQNdAvxLeG/gQir5Tvfu98fetPHvpNPRGPXNS3qFNbM1jWVJSwoEDB6iqqiI4OJjExEQURaHSYmXmT3/x/apttA0wEtvGRHxMIJWFdrIKyjhSVElxlWDimD7cOK4vfl7/BMntdjsHtqayZel2Nvy6iX1bUrFb63gOVqBNfCgdB8XTcUA8HQe2xz/E94wKfglhg6pFiPIPwbbf+aLiBV7Xo/j8C0Xbpv4VeLQNB9gOgnXrP4Xk7ak1Z1T8Qd8bxdAX9H1A3wNF43vS25daD0+vAx7/xcycOZOSkhJiY2NZsWIFcXFxgDNj6q233mLKlCns3LmTMWPGsHz58hpFR5vDyfTt/PzzzwEYM2ZMrd8eX3/99TzzzDOkpqbyxRdf1Brsstvt3Hjjjfz888/MmjWr0YEuSZKkc5UrqwuchcRb8w2bJLVW06ZNA6Br164y0CVJJ6G8vFz9cj43N7feniktoaioCIvFglardRvI4mS46nz5+/vjcDiorKxUs75sNhulpaWUlpaiKApeXl74+vri6+ur1gBzBa6qczgcNTLAzGYzNpsNq9WqZpapbSgtJeLll/H57TcUm03N5DJ37UrJ66/jN24c4V5eNdq+v/AQN2/8RB1mZliAjemDXvcogDHwoj70HtON5BW7mPv8tzz1+QM15vHy8lIz0ex2O1VVVXh5eeFl0PPEhDGM6tmBp+f+xtq9RRzMqmRE7zZc1KETWw9kcKyonKUbt7NgdTI3jOvHxFG98TYZ0Gq1JA5IIHFAAjc+fTUWs5U9G/aRvHwnG37bzMHkdITj+B4JyDmUR86hPP6atwmA0NhgEvrH0nFgexIHtSewTYBb8MtVe62lCWGByp8R5R+B/bDzRcUXvG911rpqwmiF6rodZWDdVq2QfDKI0pozatuDoe/xERL7gLY9inJGlSY/J7TE567HTxt//PEHiqLw9ttvq4EucKauPvXUUwwbNoxrrrmGlJQUxowZw4oVK05JwKspKioqWLt2LQAXXXRRrfMoisKFF17IrFmz+OOPP2pMdzgcTJo0iR9++IG3336be+6555S2WZIk6WxSUlJCVVVVo7pjSNK5JiUlhTfffJNbb72VPn36qIXDU1JSeP3115k7dy4A//nPf1qwlZJ0dnANdtHa2O12dWCL0NDQUxLQcGVy+fj4EB4eTlVVlZr1ZTabqaiooKKigtzcXEwmk5r1ZTQa3bKtNBqNMyh0QoDKbrer2V8WiwVrWhpBTz6J95o1KMc7FQlFobhPH4488wy6Ll3w9vbGUVxMVVWVmgmm1WrZmfcXd275XQ10jQnx47X+T3vcBU1RFO58/Rb+PfA/LPtqDdc8fCkJfeLd5snPz1cDXlqtlszMTNq1a4de7yxIPrBzO36e8i8e/vxHtm7LZv6qw2yPKeb+8f0pKi1jy76jFJRW8se6ZH5Y6Qx6XTuyFybDP4/aBqOeXqO60WtUNya9MJHKskp2rEkhafkO/l60lcO7M9zalJdeQF56ARsWOLsZB0cF0qF/LAn940gc3IGQKOeABK6sr9Md/BKiCiq+R5R/Co4s54tKIIrP7eB9M4qmcQNfCCHAfsSZtWVJAutWsO2jxkjaihfoe4G+D4qhjzODSxPYLPsknXqn+3PX42DXgQMHUBSlzmDR8OHDWbVqFWPHjmXv3r2MHj2aFStWEBER0WyNbao9e/aoqbqukXxq45qWnZ1dY0jwJ554gq+//pohQ4YQGhrKV1995bbs0KFDad++/SlovSRJ0pmtelZXWFhYq/gmUpJao6qqKmbPns3s2bMBZ70yq9XqdnP44IMPcsstt9S1CkmSznAFBQXY7XaMRiMBAQGnfHuuTC4vLy9CQ0OxWCxq4KuyspKqqiqqqqrIz89Hr9ergS9vb+86A05ardaZfZSairjrLli3Th0PWWg0FI8eTe6LL6KLjMRosyGEULdT3RHbb/z3yDH1/xeERvNCn3saXWspsX8HxtwwjBXz1vLJf77i9SXPqdPMZrM64mX79u3Jy8vDbDaTmZlJTEyMmvHl62Xk03tu4KWl/8cvv+1nz5FiHvxsOfdc1J+bLhjI7kMZbNl/lOLyKpas28qC1UlcP7Y/Vw3rjkFf85Hby9eLgRf1YeBFfbj7f7dSkl/KtpW7SFq+g81/bCProHuNvoKMIgoyitj08zYAAsL9aN+vHQkD4uk0sD0R7cNOS/BLOMqg8ltE+WfHRzEENOEoPv9ydlnUeDZisBBVYN0JliRnvS1rEjgKas6ojXYGtvR9wNAXdJ1QFNk7QPKMx2dKRUUFAQEB9Rbs7Ny5MytXrmTMmDHs3buXsWPHtooMr8zMTPX3qKi6i+JVn5aZmekW7NqyZQsA69evZ/369TWWnTNnTq3BLte3Gi6uD1NJkqRzRfHxb2q1Wi2hoaEt3RzpLLdu3TquvvrqRi0zdOhQfvzxx1PUIs916NCBN998k6VLl7J371611mlMTAxDhgzhrrvuqrOmqNR6NeWLX08GX5LOPhaLhcLCQsCZ1dUSBbQNBgMGg4GgoCDsdrta46u8vByr1UpRURFFRUVqQKvWOl9r1iDuuw927lSDXA69nrIrr8T6v/8RGB1N4PH5XUXxq2eCWcwlHKyYyX+P/JMdNNo3llv8xnDgwAH0er2a/eWqBWYwGOotin/7yzew5ocNbP1zO5v/2Eb/C3oBkJeXhxACPz8/fH19MRgMHD58mKqqKrKzs4mMjHR7H54ddwn+4YtZ8FsKZYcFH/y2ieXb03n+xtH07NiO5L1pbNmfQVmlmcV/bWbh6iQmjOnHZUO7oa8n+OQf4seIawYz4prBznZl5JO0fCfJy3ey5c9t5GcWus1fnFtK0qJdJC3aBYBvsDfxfds5M78GdaBtpzZu9b5ONvglHMVQ8SWi/HMQxc4XNVEovneB19UoSv2Degh7drXuiFvBugc4seabHvTdq2Vt9UHR1qwFLkme8jjYFRwcrEa66wt4derUiRUrVjBmzBi3Lo0tqbT0n7693t51R5urT6u+DMDKlSubtO1p06bxwgsvNGlZSZKkM50QgtzcXODUdceQWre0tLTTuj2LxeLRiJHVFRTU8m1yC/Dz8+Oxxx7jsccea+mmSM2oseejdO5yBV5cXQxbmlarJSAggICAABwOBxUVFWrw68Q6X97e3gQsX47P88+jpKf/E+Ty8qLk1lvh5ZfxDw6uEZBSFEUNVvn5+SFsh1l+6EWePhLrmoPLQ3txW8QwZ5fI47XArFYr5eXlbuuprSi+q+ZYZHwbrrj/QhbM+I1PnvqSPuO6Y7FYKCsrQ1EUQkJCAGewzzVCY2lpKUajUZ3m2s6/e4zHqrOxfOsBjm2ClCO53PbWAu67fCjXDu9Pn87t2bT7IEkHMqiosrBozSZ+WpPEhDH9uWhQF7QejFQZGhXC+beM4vxbRiGEIPNgNknLdpK8Ygdbl+2gNL/Mbf6yggp2LE1hx9IUALz8TcT3iSGhfxwdB8QT2z0abx9vtwCYJ/dkwp6PqJgDFV+DOH68tfEovneD6TIURV9zGWEF2x73URJdXR2r04RVC2z1BX03FMXQYJskyVMeB7u6dOnC6tWr2bBhA6NGjap3XlfAa/To0ezdu5cxY8bUGKb2XPH000/z6KOPqv8vKSmRRWUlSTpnyKwu6XQbPXo0Hg40LUmnhTwfJU9UVlaqgaOwsLAWyeqqj0ajUYvWu7odugrce33+OaHvvYeu2hcHtoAAiu67D8OTTxIQEODR/oiqP1mcOp0p6b2Pv6Jwa9xI/t35QnUeVz2wE4vi2+12Z1aYxeKWtKDRaNTA10X3jWXRnOUc2pbO0q9W02VMBwD8/f3dkjm8vb0JDw8nJyeHvLw8jEYjvr7/FMPXKhoeSLyQMttP7GhzlIK/tRQftTPjxzWs3HaQFyaN57IxQxjYLY8NO/aRfDCDSrOV31Zv5Oc1W7lm9AAu6J+IRuN53bGohEiiEiK59O7zcTgcpO08QtKyHSSv2Enyyl1Ulbl3Aa0sqWL3qv3sXuUcGdHobSC2VzQJ/WPpNLA9cT1j8PX3rTP4JezZiPLZUPEdcHzdukQUn3vAdCGKUm1eR8Hx7ojHR0i07vhnmX+OGugSwdAHRX98lERtVKs7z6Wzi8fBLldNru+//77BYBc4A14rV65UA15CiBY7mV0FXqH+omjVp1Vf5mTUNnKJJEnSueDEERhlVpckSZIk1VQ9C7qhsjGtgaIoeBmNmF5/ndC330Y5XqZFAJawMI7efz+V11yDTqfDUFBAlcWCr68zsFLb86AQVkTpdH45/AevHh3o2gqTO4xlcsfz3OZV64FV65EjhMBms9UIgFksFhwOh1s9sPPvHMnPby3h06e/4pHv7sDL14uQkBAqKirUovgAgYGBmM1mioqKyMrKol27dm7vi1Gr54mul/OC/Xu0I48RdtiLjE02kg9mcv0rX/HI1SO4ZkRPrmwTxpBeefyVlMK2QxmYLTb+b+V6fv1rK1eP6c/Y3h0b/Yys0Who3zOW9j1jueaRS7Hb7OzdfFANfu38KwWbxea2jLnCwr71h9i3/hC/swK9UUe7HlF06B9Lx4HxdOgTh3+gHyZDGV78hJeYj1ZzPFlF3xPF5z4wjgEcYDuAULskJoE9rWYjlQAw9P4nsKXvgaJp+WxF6dyiCA+/btqwYQNDhw4lICCAtLQ0jwsm7tu3j7Fjx5KZmYmiKNjt9pNq8Ini4uJIT09nypQpTJ06tdZ5tmzZQv/+/QH4/fff6yyy/8EHH3D//fcDzlE5mmuo3+pKSkoICAiguLgYf3//Zl+/JElSa1FUVMThw4fRarV07txZBruOk9cBSZKkpmuOz9DKykr1eWDRokU1RhM83UpKSsjKykKj0RAfH49O14oLcFdVIR5/HGbPRjkeQBKANT6ekpdfxnT55Wqtr4qKCnWQMHAGqqoXuNdqtQh7DqLoYX7IzOfNjH5wvAPkAx3Hc0uHhhMs6lO9HpgrAFZaXMZz571BYVYx5987nPPvHOkWOKteD0yv11NQUIDFYsFgMNCuXbsa702hpYznt31PrrmYNo5gyjYZST7grBc9uEssU245nzZBftjtdrKP5bFqyy52HMrCYrVhtQs0BhPXjBnA8B7tmy0xxFxpZvf6fWrwK+XvAwhH/Y/8Wp2G6K4BdOitoWNfDQm9ITA0DK+AoXiZjHjrdqKxbwVrMoiymivQJThHRtT3BUMfZ1dHpeHumtK5ozk/dz29Dngc7AJ49dVXsVqtXHXVVfTs2dPjxhw8eJBXXnkFIQRz5szxeDlPeBLsqqiowM/PD4fDwRtvvMETTzxR63z33Xcfs2bNIiIigqysWvoVNwP5kCNJ0rlACMG+ffswm820adOmxQcqaU3kdUCSJKnpzrbPUIfDQVpaGlarldDQULf6UK1KYSGOe+9FWbAAxebMGhKAuXt3yt96C7/RozEY3OstVa/zVVZW5pb0oCgK3oZMvB3v8muJlfezu+MKdD3e+TImxA05Zbvy84e/M/O+OZj8jLy94QVMfka1HtiJHA6HOsCYt7c3ERERmEwmt3pg2ZVFPLf9O0qslXTxi6JDXjwf/LwOs9WOr5eRpyaO4eKBnVEUBYfDQXZuHkv/3sHutCysNjt2ITB6+XDlyH4M7hbf7L2hyksq2LF6jxr8OrQ9vcFlNFqFtp18aN/bQMe+Ojr1FQSHluNlKMDLWImXbwxaY6/j9bZ6o2hO/cihkuRySoJdrZEnwS6AkSNHsmbNGsaOHcuyZctqTBdCkJCQwKFDh7j11lv5/PPPT0l7z7YLtCRJUm1kVlfd5HVAkiSp6c62z9D8/Hzy8vLQ6/XExcXVO6Jgizh8GMfkyShLl6Icz9ISikLl4MFUvfMO/n36eJSJJoSgsrKS8vJySktLsZavR1g28lOZjh8rTep8/0m8jKviBp+y8jcOh4NDhw7x8uXvkJGSzTUPX8I9028DUOt+VR8Z0vV7SUkJQgiMRiM+Pj4oiqLWAzMYDOQ4yngrdTFmYWNgSAJXBQ3hhS/+ZGeac2TVsb0TeObGcQT5eavHIzM3jyXrt7E3PQub3YEQ4OPny2Uj+jKwS/wp2X+Awtxiti39P5L++I3kNWVkpjbcbVZRICIhkPZ9Y0gY0InOgzsSFhXa6IL3ktQcZLDrBLNnz+bOO+9EURTWr1/PoEGD3KbPnz+fiRMnArB06dJTNrT32XaBliRJOpHM6qqfvA5IkiQ13dn0GWqz2UhNTcXhcBAZGdm69mf7dhx33YWycaM6sqLQaik//3ys775LQIcOTQrMCUchjsInsFRs5f2jPZlf3FaddnfocIb7dcBgMODr64uPj0+ddb6aqqCggGPHjrF/Yyrv3DYbnV7LZynvEBlf+72KEAK73U5hYSGZmZnYbDa8vb3R6XRuXTQBDpqP8VneeuwIhvp34Ma2Q1i4YR+fL9uG3eEgyNeLZ286jzG9E9zWn5GTz+9rt7L/SI66zsAAfy4d0Zc+nWJpLg5bJlTOh8ofwZGtvp57VE/yWl+S//Ilea0f+dnuIywqilLrQBthscG079eOhAHxJA7qQNv4CDXwJYNf0ql01ga7CgsL3VJg+/bty5EjR3jiiSd48skn1ddNJpPbqBk2m42+ffuyY8cOoqKi+Pzzzxk3bhwOh4MFCxZw5513UlJSwkUXXcTvv/9+ytp/Nl2gJUmSalNYWMiRI0dkVlcd5HVAkiSp6ZrjM7S8vJy4uDgA0tLS8PFpmcLZ2dnZFBcX4+XlRUxMTOsYmW7lShz33YeyZ48a5HIYDJRdfTW8/TZ+bdo0uZ3Csg1R9BA4Mnn7SG++y/8n6PN8xyvobYyioqLCLbCi1WrVUSC9vb1PKvPNbreTmpqK3W4nIiKCaRPfY8sf2xh9/TCe+ebhBpd3BcoURaFt27YYDIYamWBbi9L4Kv9vBIKxfolcENCFtNwSPliSzJF8Z62r83rF89CVQwkO8MNoNKr7dDg7j19WbyEtI0c9BuEhgVw0rA89OjTu/BDCAtY9YE1CWLaCZSOIwpozaoJBPxDF0Bv0fRG6LmTszyNp2U6SV+wgecUuSgvca3RptAoOe80QQnBUAPF929GxfzyJgzsQ3bGtGrCUwS+pOT93T2mwSwjB6tWr+eOPP9i1axeZmZnqEK9+fn60bduWbt26ccEFFzBy5Mhm/eB2ZXI1ZNKkScydO9fttbS0NMaMGUNaWhrg7HftGqEDoE+fPixbtoygoKBma++J5EOOJElns+pZXREREYSHh7d0k1odeR2QJElquuYKdrm+FC8rK2uRYFdVVRWHDx9GCEG7du1avEg+8+fjeOIJlMOH/wlyeXtT+q9/oX3pJXwCApoe5BICKr5GlE4DrLx2dAQ/5f2TSTWj7ySGhicCzoCUq85XeXm5W5KDRqPB29tbzfpqbCH/Y8eOUVBQgNFoJDY2loPb0riv31MIIZi5cRqJAxLqXd41ynRxcTFarZZ27drVqFMmhGDJ0SRmH1qJEIKrQvswxDuOisoqftiwn1+3HEIICPY1cff5PegVF+5WFN9gMJBTVMb/rd3Gkaxc9b2ICg/mgsG96FZH0EvY852BLWsSWLaCdSdgrmUvFNB1Aq8JKKaxoGlb7/vqcDg4tC2dpGU7SFqxkx2rd1NV7r5erV6L3VpzEDr/MF9n5lf/OBIHdSCuWzsZ/DqHNefn7ikLdv3www889dRTasAIqJHWWP0PJi4ujjfeeINrrrmmMZup08kEuwBKS0t58803+fHHH0lNTUWj0dCpUyduuOEGHnjggRofWM1NPuRIknQ2k1ldDZPXAUmSpKY7G4JdQggyMjIoLy9XEwVahBCImTMRL76IJi9PfdkWFETZQw9hfPxxvE7y2AhHOaLkWaj6DYAXD1/G7wX/1Ij6aMBk+oTUXp/KVefLVeC+egF5RVHUnjy+vr4NPsNZrVbS0tJwOBxERUWp7/8bt83kzy9W0XNUV95cPrXBgJ7D4eDo0aNUVlaqIzTWdq+z4PBG5h9eD8CDiRcxODjBmfm1/wjTvltNZoEzUeS8nu24aURnTHr3wJ2iKBwrqWTFtkPkFRWjVRRQILZNKOcN6kZidCUae7Iza8uaBPbDtbRWA7i6WhrB+3oUn7tQtGH17mN9rBYrezcdVIvd71m/D6vF5jaP3qjDZrXXGAHSJ8iL+D7tSBgQR+LADnToHYevr68Mfp0jWn2w68UXX+SFF15Qg1t+fn507tyZqKgodcjWiooKMjIySElJUbO9FEVh6tSpPPfcc03eoTPd+++/z/vvv4/dbmffvn3yIUeSpLOOzOryjAx2SZIkNd3ZEOwqKysjIyMDRVGIi4s75V+212CzIV58Ed5+G6XM2UVNALbISMqeew6fO+5oljYJ2wFE4b/BfgjQ8czRm1iWVw44x138ZODd9Az2rCaVEAKLxaIGvlw9c1xcdb58fX0xmUw1glZ1dRnNPXyM2xIfwmq28vKv/2HQJf0abIvNZuPw4cNYrVZ8fHyIioqqsT0hBHMPrWJxVjJaNDzV7Qp6BTn3tdJs5d2f/uK7lckARIX48+S1w+gUGaR2iaxeD+xoXjYrtx+guEyLXqugKIL2YbmM6bqehIi9aDXHg03aBNAEgC0VRIHzNcUfvG9B8bkVRdP8vZeqKszsWpuiBr/2bzmE44Qgl9HbgNVsw2F3r3Fm8jMS3zuGDv1jSRyUQMe+8fgH+Mvg11mqVQe7VqxYoRZtHz9+PE8//TTDhw+vs9+0w+Fg7dq1TJs2jcWLF6MoCsuXL2fUqFFN2J2zh3zIkSTpbCWzujwjrwOSJElNd6YHu4QQpKWlYbFYCA4OJiys6Vk2jVZZiePhh1E+/xzF7OyKJgBL+/ZUvP46flde2eiugXURlb8gSp4DUQmaNjyRPoE1+VmAM9D15ZD76RQQ1eT1W61WtavjiXW+dDodPj4+ap0vm81GWlpanV1GP3nqK+b/72diu0bzUfKbaHUN379UVVVx5MgRHA4HQUFBtX7B5xAOZu5dwtq8vRg1ep7rcTUd/SLV6Rv3pPPCl3+SXViKosCt5/fn7ksGY1AO46jagsO8BcW+DY0jFUURpGSFMW/9WArK2mPSaZxBr3Abfdobad8mg2CvX9Aqzgw9oQSB9+1ofG5G0fjWaNupUlZUzvZVu9XgV9quI27TFY2CydeIpdJao+ujwVtPbI8oEgbE0XFgezoP6EhgcIAMfp0lWnWw6+qrr+ann37innvu4YMPPmhUY+677z4+/PBDrr76an744YdGLXu2kQ85kiSdjWRWl+fkdUCSJKnpzvRgV1FRETk5OWi1WuLj40/PA3x+Po577kFZuBDleA0sAZh79qTq3XfxGz682dohhBlR8ipUznO+YBjKQ/uHsLHQWYZGAb4b9jBxfs13n2C32ykvL6esrIyKiooadb5cmVJBQUHExMTUWL6sqJxbE/5NaUEZj35yDxfdMc6j7ZaWlpKZmQlAREQEAQEBNeaxOey8vvtnthcdxk9n4oWeE4jyDlanl5QX8tb83/j172MAtA8r4sWr/qRzZJ77irTtQN8Hu6YnSWlRfLfqGGXl5fgatGg0gs4ROQxP3Ep0SA5l9ksotlyAwIRer8doNKr1wFz/nq7BEAqyC0lesUsNfmWn5rpN1+g0ePt7YS43YzW7d4fUGbS06xFF+77tSBzcga5DEgkKCcTb2xuTySSDX2eYVh3satu2Lbm5uRw7dqzRBdwLCwsJDQ2lTZs26gfCuUo+5EiSdDYRQpCfn09BQQFFRUX4+PjIrK4GyOuAJElS053Jwa7qowG2adOGwMDAU7vB9HTsd9yBZvlylOOPfEJRqBw2DOv77+PXvftJjW54ImE74hxt0bYTUMDnPu7dZSKpyBno0qDw/chHiPEObbZt1miDEG4F7isqKigpKQEgMDAQPz8/tcB99a6aC97+Pz587HNC2gYxZ++7ePmYPNpeXl4e+fn5KIpCTExMrQMNVNktvLRjAQdKcwgx6nkxMZJgZRdYksC2B7Czam8cr/w6ioJyb7QaB3eOzuT28yPQm/qAvjeK9p9jJhyFiPIvSNq9mC/X9qesKo4Akw6tBjq1DaFn+yj8fb3qDGgpioLBYFCDX64AmF6vP+VBsKzUHJKX7yRp+Q6Sl++kMKfYbbrepMfb34uqsirMFRa3aVqdhqiukXToF0vHAfH0GN6FkDbBeHt74+Xl1aznstT8WnWwy2Qy4e3tTUFBQZMaFBwcTGVlJZWVlU1a/mwhH3IkSTpbZGVlsWvXLre6GQaDgR49ehAZGVnPkuc2eR2QJElquub4DK2srGTkyJEArF69+rSNhHjiaICnLLCQnIzjzjtRtmxRR/MTWi3lF16IeP99fNu1a/Zti6rliOInQZSAEogS+Cb/2rKTXSVHAWeg69eRTxDmHdis262Pw+EgLS2N4uJidDpdjTpkRqNRDXxpFC13dn2Y7LRj3PbS9dz0jGeDqwkhyMrKorS0FJ1OR7t27dDr9cenWcC6G6xJFFck8cJ+M5kWHW0N5Uxttwk/3fGC+5o2YOhLkaUX0xYaWJbszPLqGtuGFyeNp31kiHN99mOIijlQ8Q2ICudrmvZsypzEFysUKsrKCPHRo9Np6REXwYAu8YQGB6IoClarFbPZjMVicct8q06j0dQIgBmNRrRa7Sk5V4UQpO8+qga/tq3cRXlxhds8Xr5GvAO8qSitpLLEvU6bolFo2ymc9v1i6TSwPT1GdCE8KkwGv1qp5vzcbfZgV0xMDJmZmRw9erTRDzGZmZlER0cTHR3N4cO1jRRx7pAPOZIktTau7Cyz2YzRaCQkJKTBm5qsrCy2bNlS5/R+/frJgFcd5HVAkiSp6c7Uz1CLxaLWjao+GmBzEkuX4rj/fjT79qlBLofBQMXEiWimT8fLg+t7o7cpbIiyGVD+sfMFfS+UwHe4Zf0C9pU5a3RpUfht5H8I9vZr1m03xDUQgEajIS4uTn2trKyMysrKGnW+tv+Zwgf3zcXbz4u5+98jKLxmt8TaOBwOjhw5QlVlAUZdNtFhO9DYtoJ1J/BPdlKe1cSU9EEU2Pzp6OvNM50HYPIagKL9535JCMHiTXt57dvllFaaMei03H9ZD24YuAZN1feA+XiDO6P43gfG81EULUII1u1K47NF6zFXlBPuZ8Co09KjfST9O8fRNqKNmkljs9nUwJfrX4vF4lYUvzqtVusW/HL93txZ/Ha7nQNbU0k6Hvza9VcK5kr37C6/YB9n8Ku4gtKC8hrraNMhlA79YkkYEE/PkV2Iim+r1vySwa+zR7MHu26++WbmzZvHVVddxXfffefxyW2325k4cSILFy7kxhtv5Msvv/RsD85SZ+oFWpKks1Nt2Vkmk4lu3brVGawSQrBs2bIaIyFVZzKZGDdu3GmrCXEmkdcBSZKkpjtTP0MzMzMpLS2tc/S+kyG++Qbx1FMoR4+qQS67jw8Vd92F/sUXMZ2CwBoczzQqegSsfztf8L4Vxe9Jrv/rfVLLndlJOjQsGvdfAvTep6QNdbZNCNLT0zGbzbUOBFC9zld5eTkOhwOHw8H/JnzIkV2ZnHfbCO6dcRs+Pj61PvcKYQPbPrAmISxJWCt3cSSnHTa7AV+vHCKDklEUQAkEQ18UfR8w9OWIpS1Td/xCud1M76A4nuhyGTpNzfXnFpXx4he/sG5PDgB9YzN5/orlRId3QPG5F4yjaz2HhBCs3HaQ2Ys2YKsqJ9LfiJdBT68OkfTp2I424WH4+/vXOnqkK/ureiDMarVSV7hAr9fXCIAZDIZmCypZzFb2bNhH8vKdJK/YyZ4N+7Hb3LPSgiMD8fL3ory4gqLs4hrrCG0XRHtXt8cRXYhNjJHBr7NAswe7kpKSGDRoEHa7nR49evDEE08wfvx4QkNr73Odn5/P4sWLeeutt9i2bRs6nY6NGzfSu3fvJu3Q2eJMvUBLknT28SQ7KyIiArvd7vbNX35+PocOHWpw/YMHD67zGnEuk9cBSZKkpjsTP0MrKys5fPgwiqIQGxuL0Wg8+ZU6HDjeeQdefhlNtTIztuBgKh9/HNPjj6vd6U4FYfnbGehyHAPFB8X/FRSvi7lm9ZscqXC2R6doWDT29Ae6AIqLi8nOzvZoIACHw0FFRQXl5eVsWbqNt2/5BI1Ow7O/Pkib+DC8vLzw8QFfw0F0IhmsSWDdDsI9s6jSEsTRgvEITVtCQtoQ2qYPaGt2V91XksnLO3/E7LAxIqwz93W6AI3yT+BFWPcjyj9EVP7Gwq2JvL1kGJVWPV4GDY9eO4arh/doMFjqcAiWJu3j09834DBXEhVowtdkoE+HtvRIiCY8NISAgIAGAz4Oh0O9/6seCLNarbXOryiKWhS/eiCsOeqBVZZVsmNNCsnLd5C0fCcHk9NqBOLC40Lx9vOirKic/KMFnBjpCIz0p33fdmrwq333ONnt8QzU7MEugE8//ZR7770Xu92unqzh4eFERUXh7e38EKuoqCAjI4PcXOdIC0IItFotH374IXfcccfJ7NNZ4Uy8QEuSdPbxJDtLp9MRFRVV40aivLyc/Pz8BrfRp08foqKaPqz42UpeByRJkpquOT5DKyoq6Nq1KwC7d+9Wn2NOBSEEhw8fpqqqisDAQNq0aXNyK7TZsD/3HMp776EpdwZbBGBr25bKF17A5/bbT+kgMUI4oPxTRNl0wAG6jiiB76Ho2nPFyjfIqioCwKDR8ceYp/HWn556aNW5anVZrVbCwsIIDg5ueKHjhBD895JX2Lx4G33Hx3D76x3Bng0OZwDPqC/B15SLjykXo8GBYugN+j4ohr6g70VJqYPs7GwAIiMj6zxHtxak8ubuX7Hj4OK2fbg1fiTYdiHKZoH5z39mNI4io/I2Xvg2g637MwAY1i2O528+n7DAhjP27A4HizalMPv3DQirmZggE4E+Jvp0aEu3+EhCgoMJCgpq9Dljt9trBMDMZnOD9cBOzATT6XRNDoKV5JeybeUuko5nfh1JyXCbrtVpiOzQBi8/E6WF5eSmHcNhd7+n9Qv1pX3fGBKOB7869ekgg1+nSHN+7p6SYBfA+vXrefLJJ1m7dq1H8w8fPpzXX3+dIUOGNGYzZy35kCNJUmuQl5fHhg0bGpwvPDwck8nk9i1dVVUVKSkpDS4rM7tqJ68DkiRJTXemjcZYUlJCVlYWGo2G+Ph4dDpd01ZUUYH9wQfRfPklisVZx0gAlo4dMb/1Fr6XXHLKH86FoxhR/BSYlztfMF2B4v8CisabS1ZM45i5FACjRs/yMc+e0syy+hQWFpKbm4terycuLq7B4yIc5WDd4eySaN1K2vbd3DsuEodD4X8/HSGmqz9lVWFUWeMRmkhnjS1tBDpDOL6+ztEdvb291aCNayACjUZDTEwMJlPtIzuuzt3D+/uWgKjk+rAMrgj84/gUBYzjUXzvQdE7gwMOh+Cb5VuZ+fNaLDY7/t5Gnpo4lgsHJHoULLLa7fy2YQ+f/L4BbBbaBZsI9/emb0IUnWPbEBQYSFBQ0Em9Z0IItTfAiTXB6qsHVldR/MbKy8gnecUukpbvIGnZDo4dcf9i1uBlIDoxEpOPkZKCUrIO5GK3ugfnvAO9iO8dQ8KAOLoP70yXAZ3w9fOVwa9m0KpHYzxRamoqf/75Jzt37lT7oAP4+fnRtm1bunfvzvnnn098fHzT9uAsJR9yJElqDTIyMkhKSmpwvu7du9OuXTu3C7ys2XVy5HVAkiSp6c6kYFf1DKPQ0FBCQkIav5K8POyTJ6P59VeU41kzAjD36YNt5kx8hgw5LddaYd2JKHoQ7EcBA4r/c+A1AUVRGL/sFQqtziwzL62BpaOfabFAl91uJzU1FbvdTkREBAEB7kXmhRBgz1ADW1iSwZYCuAc93n4slsXzAuk2xJfpS29CMfbFIQLUAvcVFRVuARytVouPjw8+Pj54e3uTnZ1NeXk5er2edu3a1QhyCiHAso7f0r7gyyzntMkRKYyN6IviezeKLqHW/TuUlc9zcxez57CzF9V5fTvy9PVjCfLzLEvGYrXx07pdzF60EcVupV2wF1FBPvTrGEXH6DACAgIIDg6uMXLlyXDVAzsxE8xisdRZD0yn09UIgDWmHpgQgsyD2epIj8nLd1KcV+o2j0+gN+26RGH0NlCcX8LRlCysVTa3eUy+RmJ7RZPQP5buwzvTbUgX/AP8ZPCrCc6oYJfUOO+//z7vv/8+drudffv2yYccSZJalKeZXXVlZ8nRGJtOBrskSZKa7kwKduXn55OXl+dxhpGb1FTst9+OZvVqlOOPa0JRqBo5EseHH+Kd6FlGz8kSQkDld4iSlwAraKOd3Rb13QA4b+lLlNgqAfDVGVky6r8tFugC5/1Nfn4+RqOR2NhYZ5utu8C6FWFJAmsyOHJrLqiJBENvFH1fMPQhLyec2xMfw1xpYeqPTzDsyoFus7vqfLkK3Nts/wRJFEXBZDJRVlaGoih4e3sTExODRqNxHk/zckT5LGfdL+Cb3M78WtgdjSaYR7peycCQ2gNdLla7nTmLN/Hp7xuxORwE+3nz3E3nMapXB4+PU5XFxg9rtjN3ySawW2kXbCI+zJ9+HaPp0DYEPz8/goKC8PI6dd1QHQ5HnUXxa1NbPTBX18iGa5g5SNt5RA1+bV+1m4rSSrd5giICie0WjcFLT9GxYtJ3HsVc7j4apN6kI7ZnFAn94+g2vDM9hnUlKCQQk8kkg18NkMGuc4B8yJEkqTVojuyspozkKMnrgCRJ0sk4U4JdNpuN1NRUHA5HvbWbTiQ2bcJx991okpLUkRWFVkvlZZehzJyJ12mshSkcFYiS56HqF+cLxnEoAa+haAKwWq2MX/UqZTYzAP46L5ae99xpa1ttbDYbhw7uRNgyiQxJwVe/Eaw7gRODJzrQd3XW2tL3AUMfZ9fEE8x5dh7fvPoj0Z0i+WTHdHT62rugCiGoqqpSs74sx7uZ2u12iouL0Wq1BAYGEBO2H4PtIxT73uNLmsB7Inj/i49Td7IiZxd6Rct/u19F14DoBvd3z+Ecnp+7hINZzu56lw3pyuPXjcbPy/MBECrNVr5bmcznf25G2G20CzKR2DaQfh2jiY8Ixtvbm+DgYLdumqda9Xpg1bPB6qoHpihKjQCY0Wistx6Y3WZn7+aDavBr19q9WM3u50lE+3Diukaj99JTkFNE6rbDVBS7B8h0Bi3R3SLp0M+Z+dVrZHdCwoNl8KsWMth1DpAPOZIktRbNkZ0lhCA/Px+z2YzRaCQkJER2XWyAvA5IkiQ13ZkS7MrJyaGoqAiTyUS7du0avDaKxYtxPPAAmgMH1CCXw2ik6qab0L39NobTfL0QtoPObou2/YAWxe8x8L4DRVGwWq2ct/IVKu3OoE6Q3ocl4545re0DEMIGtn1g2YqwJpGTW0xxqRdehiKiQzeiHnJNsFtgC30PFKX2OlrVlZdUMCnh3xTnlfLgB5O57J4LPGqXxWJRA18lJUWUFKUh7Jl4GzLw987E16sEn6DheAfdhEbnzJ63CwfT9/wfmwsO4aM1MqXHtcT6hjW4LbPVxqxf1/Hl0i0IARFBfky59QIGdW7nUVtdyirNfLM8ia+WbsHhsNMuyET36GAGJMbQLtyZuRQcHIyvr2+L3efZbLYatcDMZrNH9cCq1wWrrR6YpcrCrnV71eDX3k0Hcdjd1xvbLZq47jHoTDoKsgo5sCWV0nz3ETk1Wg1RXSLo0C+WrkM70WdMD8LbhsngFzLYdU6QDzmSJLUmWVlZbNu2zS39XmZnnVryOiBJktR0Z0Kwy2w2k56ejhCCdu3a1dsVzP755yhPP42SlaUGuey+vlTdey/Gl19G14y1kzwlKn9DlDwLohw0YSiBM1AMAwCwWq2MXfEyZoczCybU4MfvY58+Pe1yFIE1+Xh3xCRnN0BRAYDF6kP6sWEIoRATkYeXb6fjIyX2BW3Dwca6/DRzEe8/+BmB4QF8vv89vP0869YnhBkqF2Atnkt2fgg5Rd2x2f3w9QvB4N0dFJNa58vX1xcfHx+sws6ru34ipSSDQL03L/aaSBtTQMMbA5IOZDDl8yUczSsGYOLo3jx45XC8jI3rUlpSXsWXS7fwzYokHHY7MUEm+sSFMigxhqjQAAwGA0FBQfj7+7eK4M2J9cCq/9tQPbATA2HV96e8pIIdq/eQvHwHSct3cmh7uts6NBqFjv07EN8jBo1BQ15GAfv+PkhRdonbfIpGISIhjIT+cWrwKzI24pwMfp3xwa7IyEiOHTvm9tAkuZMPOZIktSYOh4MdO3ZgNpuJiIjA29tbZmedYvI6IEmS1HTN8RlaUVHBgAHO4M2mTZvw9vasuLcnhBBkZGRQXl6uDtxVg8OB/c03UV57DU1hofqyLSQE83/+g+mRR5o0Gt3JEsKCKH0NKr5yvmAYjBLwForWmWFUYa3kghXTsDicz3oRpkB+Gf3kKWqLA+yHwJKEsCaBZavz/ydSfEHfh6yiYZRVtcPHP57oaM9rVzXEarFyZ/dHyTyQzS3PX8etUyfU325HBVR+iyj/TK0NJpQQjlXdR1FFL2x2DQEBATW65Wk0GmfRcy8909P+4EhlPm1MAbzYcyKBBs/Oz4oqC+8s/IvvV28DICYskBdvG0+v9rWcgw0oLK1g7h+b+X7VNux2O1GBJgYltGFQ52gig/3R6XQEBQUREBDQIudqQ4QQtXaFrKseGKDWAzsxEKYoCkXHitm2YhdJxzO/Mg9kuy9r0NF1aCLte7VDo9OQc/gYKRsPkHe4oMZ2wtuH0qFfO7oM6UifsT1plxB9TgS/mvNzt8WCXbm5uXX2p5XkQ44kSa1LRUUFBw4cQKfT0bVr15ZuzjlBXgckSZKarrV/hpaVlZGRkYGiKMTFxbmPamezYXv6aTQffICmwpmRJABbdDSWV1/F66abWuyBV9gzEEUPqUXT8bkHxfdBFMVZp6rEWsFFy6dhFc7nvGivYH4c9Xjzbd9R5ty2NRlh2eosJC9Kas6ojXfW2NL3dmZt6RKoqjJz+PBhFEUhNjYWo9HzmlWeWP3Del6aMB2Tj5G5+94jJDKolvaXQsVXiPI5IIqcL2oiUHwmg/d1gJGjR49SUVGhjtDo6u5YXl6u1vkCKLFX8VH+WooclcT7hjO153X46Bvudumyfnc6L3z5B7lFZWgUhUkX9OfuSwZjqKPmWH2OFZcxZ/EmFvy1A4fdTmSAiRGdIxncOYbwQN/j9cgCCQwMrDHiZGvkcDhq7QpZV7KOoig1MsAMBgOFWcUkr9hJ8oqdJC3bQX5modtyXr4meozoQoe+cWh0ChkHs0lZv5/sg8dqbCMkOpD2/WLpMqQjfcf2JL5r7DkR/DoZMtjVSrX2C7QkSeeWgoICjh49iq+vL+3bt2/p5pwT5HVAkiSp6VrzZ6gQgvT0dMxmM8HBwYSFHa+5VFaG7YEH0H79NcrxzBIBWBMTsc2Ygdf48S2aUS3MqxFFjzuDNEoASsAbKKYx6vRiawUXLX8Vm3DWMIrzCWX+iEebvj0hwH4ErK6srSSw7QVOrL1kAn3P48GtPs7REjXBNdblCiIFBAQQERHR5HbV194Hhz5Dysb9XHLX+Tz84V3/THMUIMo/d2bDiVLni9p2KD53gdeVKMo/wU673U56ejpWqxVvb2+io6NRFEXNQnLV+aqqqiLPWsasY2sod5hJ8GrDIx3GE+TvrJ3lyblSWlHFG/NX8tvGPQAkRIXy0qTxJMaEN+kYZBeUMnvxRn5euwu7w0EbfwNju0UzpEsMof4+aDQa/P39CQoKcg/wniFsNlutXSHrimtoNBq30SDz0gvYvXYf21ftJnnFLkoLytzm9w/xo9eYbnTqF4+iU0jfc5Q96/eTkZLFidGYgAg/2vdpR5ehHekzpgcJPdvj7e0tg1/VyGBXK9WaL9CSJJ17MjMzycvLIzQ0tPauFlKzk9cBSZKkpmvNn6FFRUXk5OSg1WqJj49Hc+wY9smT0f7+O8rxItoCsPTvj33WLLz69WvZIJewI8reg/JZzpbpuqMEvoui+2ckwIKKUi5Z/Rp2nI+MHX0j+Hr4g43cjtk5KqI16Z96W468mjNq2lYLbPUBXWcUpf6aU65MOo1GQ1xcHHp942pUeWrnX3t4ZOTzaLQaPt7+Fu066Z1dFSu/BXF8hD5dAorPvWC6SM2IO5HZ7MxCczgcBAYGEh4eXuMcsNlslJWVsSfvMO8cWYrZYaWbV1tuCu6PQW9Q63x5EgBZnnyAV75eSmFZJTqthrsuGcxtFwxAp21a4OTosSI+WbSR3zbswSEEoT56xveKZWiXaIL9nCM2+vn5ERwc3OwZdqebEKLOovj11QPT6XTkHMgjZd0Bdq/dx661e6kqN7vNFxYdQu+x3UkcmABawaHt6exau5cjuzJw2N3X7RviQ/u+7Ugc1IE+Y3vQuV9HfHx8zunglwx2tVKt+QItSdK559ChQ5SVlREdHU1wcHDDC0gnTV4HJEmSmq611uyy2+2kpqZit9uJKC3F+5FH0K1di3L8UUtoNJhHj0b55BOMrSCTWtjzEcWPgmW98wXvG1H8/uuWiXSsoojLVv8Px/FAV1f/KOYOvd+Ddee4B7asu4ATayXpQd/VOUqioS/oe6NoG5eVVWcm3Sky9eo3WPvTJoZc7MfUTzei7pOuG4rvvWA8D0VpOABRVlZGZmYmQgjCw8MJCqrZLdJle0Ea03b9hMVmY6BPLFf491CDYxqNBm9vb7XAfV3dCAtKKnhl3jJWJB8AoHtcBC9MGk98RNPv+9JzCvn4tw0s3pyCEBDkrePSvu0Z1iWaAB9nl0tfX1+Cg4PrHaDhTOTKxDsxAGa1WmsNgtmtdo7uyebA32mkrD/A/s2p2Czu3SajO0XSe0x3ugzpiKJVSNl8gN1r95K2/Qg2i3tsxcvfRHyfGBIHJ9B7dDe6D+mKr2/rD37Jml3nAPmQI0lSa7J7925sNhsJCQnNWqBXqpu8DkiSJDVdax2N8dixY5StXEnElCmY9uxRR1YUOh1VV1yBbtYs9Kc4GOMpYdnirM/lyAXFC8X/ZRSvy9zmOVKRx3Wr31YDXb0DY/l48N011yWszi6Ilq0Ia7KzS6Ijo+ZGNSHVAlt9QN8NRfG8DlVtSkpKyMrKUjPpTmWhdGFLJT3pPe4eehiHXeHNhQfoMTzRGeQyjGx0hl5BQQHHjh1DURSioqLqPQc35O1nRspvCAGXRfThgoAulJWVuRVbVxQFk8mEr68vvr6+NboSCiH4/e8UXv9uBWWVZox6LQ9cOZzrR/dBo2l6duHBzDw+/L/1LEtyBtL8TTquHJjA8C5R+Hk5M7u8vLwIDg7Gx8fnrB4AyeFw1NoV8sSi+JZKCwe3Hmb/xkPs25hK+s6jCId7SKZD7zh6j+lOt2GdQeNg14a97FyTwqGkw1ir3Ndn9DYQ1zuaToM60Ht0N3qN6IF/gF+rO9ZnxGiMq1evrnPaVVddRVFREStXrqwztW/kyJGN2dxZRz7kSJLUWlitVvbscdZy6N69e6v/RuhsIa8DkiRJTdcag13mhQsRDz+M8fBhNcjlMBoxT5qEYcYMtK0ks0UIARWfIUrfBOyg7YAS9B6KLsFtvrTSXCaunYHraW5gcAdmDrzDuQ5HIViSEdbjReSt2//pxqfSgK4T6PuiGPo4g1vamGZ9+HY4HKSlpWG1WgkLCztl2enCmoIo/xCqFgGCd/8TxW9fhNJ5YCTvrJvR5HsnIQTZ2dmUlJSg1Wpp165dvbWu/szazqcHlwNwe/vRjI/shdlspry8XK3zVZ3RaFS7O1av85VTWMoLX/7Jhj3pAPTvFM3UWy+gbUhAk/bDJeVILh/+up7VO5wjZvoZdVwzpBPDO0fhY9KrbQoODsbPr/UFYk4lu91ea1dIV4JQRUklBzalsW/jIfauP0jWgVy35TVaDYkDOtBnXA+6D++MUBxsX7OHXX+lsH9zGuYTukjqjTra9YwicVAHeo7sSp/RPQkKCWzxY35GBLs0Gk29B0oIUed0RVHqHOngXCEfciRJai1KS0tJTU3FaDSSmJjY0s05Z8jrgCRJUtO1pmCXbfZslGefRZOdrQa57H5+WB56COOUKWha0eh0wlGKKP4PmP90vmC6FMX/JRSN+77vL87i5vXvqYGu4SExvNUt+J9C8vbUmitX/JzdENWsrZ4oGt9Tuj+FhYXk5uai0+mc9dGa+Qs7YdnmDHKZl/3zonEshaW3cFu3WVSVm3lu/qOMvHZIk7fx/+ydZ3gc5dm2z5ntfSWtrOYqufeCcS/YppgONiX0lhcCSYAQkjeBQEjIF96EEJoJEEpI6L0FY3DFxr33Jlf1ur3PPN+PldaSbdmyLBdgzuPwYXt2yjNFM5prr/u6VVVl//79RKNRjEYjnTt3PqI77YN9y3h33xIk4Ge9pjIm+8DvbolEIt3ZMRwONzOe6PX6dKmj1ZrK1fpg4Xqe+OAbovEkNrOR+6ZP4JLR/Y5bENmwu5x/fLYkLabZTHquHtOHMX3ysDR0gzQYDGRkZOByuX6wX7Q25oEdzgnmrfSxffkuti/dxbalu6gtad7p0WA20HtEdwaf1Z/BZ/UHSbBm3gY2LNrCjuW7Cfuai886g45OffPoOaKQgeP7MmzyYLI6ZJ508es7IXZNnDixxQOzePFiksnkEd1b8+bNO5bNfe/QXnI0NDROF6qrqykvL8flctGlS5dTPZwfDNpzQON0YuLEiSxYsICHH36Y3//+96d6ON95tON54jnlYpeqkvjzn9H99a/IPl96cjwri/gDD2C7555T7qA4GJHYjPD+HJR9gAHJ+QBYfnTIODfUbuO2Fa81CF2CSa4K/l+3bw9doa4bGIc2BMkPBV1hq7Kq2otm+Wi5ubhcx+dKaoqIr0AEn4N4435LYD4PyXYHkqEPAP955D3+/ci75HfP5aWNT2Awtj0UP5lMsm/fPhKJBDabjYKCghavHyEEr+yax1fl69FJMv/b9xIGZhz6+5uiKGnHVzgcbhYxJMty2vFVH07wyOuzWVtcBsDY/t343XVTyHYdv1C5ekcJ//hsCat2lABgNem5dnw/xvbOw9gQjq/X63G73bjd7hNagvpdQghBIpFoJoCV7ixn06JtbFtazLaluwjUNO/0aLab6D2yOwMm9GHghL7IOpn132xm48KtbF9+6PyyTiK/dy49zyxkwNjenDFlCDkdD22U0N58J8SuI6FldrXMjBkzmDFjBoqisH37du0lR0ND45Szf/9+6uvrycnJIScn51QP5weDJnZpnE5o4kz7ciqP5/z585k/fz5du3blpptuOqnbPpmcKrFLxGIkf/UrdP/8J3Ik5ZwQQLxjR8p/+UtMV15JXl5em8ZzIhHh9xH+R4AYyAVIGU8hGQamnD/KfkisRsTXsLpmM3fuOFDOeK57L490XQmSBQwDU3lbhiFgHIwktxyofjKoqamhtrYWo9FI165dj/slXQgB8YWI4D8gsaphqg7MFyPZ/wdJX9Rs/kgwwo09fkZ9pY+7nrqFS3829bi2H41G2b9/P6qqHjVoXxUqz2z7ksU12zHJBh4aMI3ujpaD/YUQhMPhtOvr4Jwvo8nMrHV7eeWr1SQUFafVxG+unsy5w4/f8S+EYPm2/fzjs8Ws31UOgM1s4PqJAxjbKw+dlJIhZFnG7XaTkZHRYsj+D52meWC7Nuxl3byNbFq0nW3Lion4m5ewOrJs9BxRSN8xPek/rjcAmxdvZ8uSHexYvpv6cl+z+SUJcrt3oMeZ3eg/tjdnnjuU/C557S5+aWLXDwDtJUdDQ+N0YceOHUQiEbp06dKu34pqHBntOaBxOqGJXe3LqTyev//973nkkUeYMGEC8+fPP6nbPpmcbLFL+Hwk77wT/XvvITUIBQJI9O1L6C9/oap7d2RZplu3bqfVi7oQEYT/DxD5IDXBOB6s1yEldzTkba0BtQ6ApV4P9+yZkF72Ek+S3/YZmHJt6XshSafPfiWTSXbv3o2qquTn5+NwONq8LiFUiM1GBJ+H5MaGqQawTEey3Yak79Tisp+/8DVP/eRFXB4Hr+14Bpvr+HLfGsP2gaO61RJqkv/b/CkbvPtw6M08MvBKCqxHzywTQhCLxQgGgwSDQWKxA1lP+2sDPP/VBnZVegE4e2gPfvOjybjtx583J4Tg2017eO7TxWzdn8qjspuN3Dh5IGN75YGa0g4kScLpdJKZmXnE/DKNA8TjCbau2M7q2etZP38z25YVEz8owD4z303PkYX0GllIr5HdEapg27Jiti0rpnjlXmr21R2y3uyuWfQY3pX+Y3sz/JyhdOl5/Jl7p0LsOn3uXBoaGhoaJw0hRDrM1Gw+vm5IGhoaGhoa3yUkSUqX77f0AqeWlqL8+MfoZ83CoKpAg8g1YgS8+CL6/v2p37MHEgkyMzNPL6EruQdR/xNQigEJ5ByIL4b4NzR3ORiYFziD3+wpSE+5qvMo7ut7EacrtbW1qKqKxWJJvzgfK0IkITozlcmV3JGaKFnAchWS7RYkXctOqUam3jqJD5/8nP3bynjnL59wy5+uadNYGnE6ncTjcWpra6msrMRoNGJpobGBQdZzX+8L+cPG99kVrOL/bfqIPwy8kizTkYW/xo6NZrMZj8dDPB5Plzt2liQeuXIkHy/fyUfLi/l69Q5Wbt/Pr6+cwNln9DkuoUOSJMb278aYfl2Zv66Yf3y+hJ2lNcz470pen2/m5imDGNMzj2Qijs/nw+/3Y7fbyczM1H5HPQpGo4GBY/oxcEw/eBjisQRblqbErzVzN7J9xU7qyrws/XA1Sz9cDUBuUXYqv2tiHy6/fyqxaJzty3dRvHIPxav2UbWrhuo9tVTvqWXxe6t4kTfILHDRfXg3+o3pxfCzh1DYr+sxl5625r7b3pw+d2UNDQ0NjZNGPB5PNxTRvj3T0NDQ0PghYdHp2H7nnYjiYqRnn0XcfTeSyQSAsnkz6m23oV+6FENDAYyQZeJTpiC/+CLJ7GySySSB8nLi8ThGo5GMjPYr6xNCgfhKUKtBzgbjGUjS4V8qG+cVSjkQBjUOsZmpLolpWUuAWgGAT/Hw6L7RlMVt5Fs9jM0eyp+L/5te3w1dx/HT3sdXknciEEIQiUSIRCLU1NSg0+nweDyHfWE+0vETIg6RTxChF0FJBagj2VOON9tNSHLrOzrq9Dpue+w6Hr7sL7z/xGcUDe6KklTJysug/7jebcqgysrKIh6PEwgEKCsro1OnTiSTSZLJJHq9HovFkt5ni97I//a9lN9veI+ySD3/b+NHPDRgOqWROurjITKMNno589EdIUvNaDSmr9/GnK+b3S6GFeUy48u1lNYF+d9XZjFr+WZ+cv5wOmRlYLZYWL+7ghpfCI/LxpDuBegaQuYbz9PhxgspgeOswd2ZMLCIr1dv54X/LmVPRR1PfrKM1+wWbjlnKGN65hKLRggEAvj9fqqDMYJJGU+Gi6E9OrZ6Wz9UjCYDgyb0Y9CEftz8x1TJ7cZFW1kzZwNr5m2keM0eKoqrqSiu5ps3lyFJUjq8fsg5A5j+vxcSi8TYtqyYHct3s3vtfip2VFNX6mN56VqWf7yWV3kHm9tCdlcPPc8o5LxbJ9H3jF5Hveb1egMvPf4vakrrWPzFcsZfPAbjceTdtQatjPEko5WvaGhonA74fD727t2LxWKhR48ep3o4Pyi054DG6cTRyu5ee+01brvtNpLJJL/97W/505/+dFzb27NnD08++SRff/01e/fuRVVVOnfuzLnnnst9991H586dm83/k5/8hOeffx6Xy8XatWvp2rXrIev8xz/+wZ133olOp2Pu3LnpRkl79uyhW7duAOzevZtEIsGf/vQnZs+eTXV1NTk5OZx//vk89NBD5OfnH9d+NdL0eD788MO89NJLvPTSS2zZsgUhBP379+euu+7iuuuuO+J6Nm7cyNNPP828efMoLS1FlmUKCwu56KKLuPfee/F4POl5m+5nS7z66quH5Hgd67kA+Ne//sXNN99Mly5d2LNnD/PmzeOpp55i2bJlVFVVcf311/Ovf/0rPX9xcTGPP/44s2fPpqSkBIPBQPfu3bn00ku555572nQPPN57aOyeezA+8wwVqsoLwO1AriwTv/xy5C1b0G/alO6sKPR64tOmof/HPwjr9VRUVBAMBkkmk4TDYYxGI926dSM39+hOoNYgorMQ/j+lxSkA5Fwk5wNI5nMPzKfWIYKvQuR1EKGWV6jrAabhSIah3LR6F1sCVS3O+uOiSfy4x5T22I12JRAIpI97KBRCURTsdjs9e/Y8pISxpeOH41dIaj0i9BKoqVJBJDeS7aaU0CW37VkshODWfveyf2tps+mejlnc+eTNjLt8xDGvU1VV9u3bRyAQIBqNotPpUFUVnU6H3W4nNze32X5XR/08tP4dSsP1xJQEkiShCBW9JNPJ5uG2okmc6el+hC0efgz1Pj//+GwJHy3ZigA8DjMT+3Vi7sb91AUPZER1cNu5/8qJnNk9N32eFEVpcbxNUVSVmSu28uLnSympSeVIeVw2bj1nGE6jYPH6HYSjcQD80STBpMytF45hRI+8Y96WRgp/bYB1CzazZs4G1s7beMi1qzPoKBrShZ4jCul+Zle6DuxILBJn+4piti/fzab526nb7z1kvSabkV6jiug3phfDpgyi34jezdyuHzz/KW889CE11TWUsIuOFOLJ9nDtHy5n2h0XH/t+aJldpyfaS46GhsbpQGVlJZWVlWRmZtKxY8dTPZwfFNpzQON04khi12OPPcZvfvMbZFnm6aef5q677jqubb3xxhvceuut6ZwYk8mELMtEGsK+HQ4H77//Puecc056mUgkwvDhw9m0aROjRo3im2++afYL9MaNGxk+fDjRaJSHHnqIRx55JP1ZUxHo7bff5sc//jGBQAC73Y6iKOntZmZm8vXXXzN06NDj2j84cDwffPBBNmzYwCeffIJer8dqteL3+9PzHTzWpvzlL3/hN7/5DWpD6ZzVak23qIfU79v//e9/GTJkCJBqNjJ8+PC0GGAwGMjMbO5Qeeqpp7jqqqvS/2/LuYDmYte9997LvffeixACl8tFOBzmmmuuSYtd7777LjfccEN6Gw6HIx2wDNCpUydmzZpFnz59jukYH889NHbPPRifegqANcAwYBUwpOHzRpFLNZuJ33YbpscfRzKZCAQC7N69m3g8jtlsJhaLEYlEUFUVt9tNt27djvslW0RnpTomcvCrmZSaZrkaRCyVtaXsOfLK9EMh4wVkXSr36cZvZ7AlUNri7B2MDj6f9JvjGf4JoelxNxgMBINBhBAYjUbMZnOz497y8TsIORvJdgtYrkaSjy9na+GHy/jD9McP/aDhQnrovV+2SfCqr69n06ZNJBIJrFYrLpeLZDJJNBpNC6xNr7fPS1bx500fowgVs85ItslBQiiEkjHMOgO/6z/tmAWvRlZt38/Dr82irC5wxPnuPLsfgzunyg71ev0Rx3swCUXh8yWb+efMZVQ02Y5ZL9Mpw0y+y4Tc4NqSJThrQBc6uO1t2pZGc2rK6lg7dyNr5m5gzZwNVO+vbfa5yWqk98ge9Bndg6SS5P3HPj/qjxiA2WGicEhn+ozuQTwW57O/zwbAL+pZzhzOZDJOKeWIveO5649Z8Grtc6Bde8ReeeWV3HDDDe25Sg0NDQ2NE4CW16WhodESQgjuvvtufvOb32AymXjnnXeOW+j6+uuvueGGG1AUhV/96lfs3r2bSCRCKBRi69atXHHFFQQCAa644gr27duXXs5isfD2229jsVhYsmQJDz/8cPqzSCTC1VdfTTQaZcyYMTz00EMtbv/222+nW7duLFu2jEAgQCgUYtasWXTu3Jm6ujouu+wyAoEjv8wdCzNmzGD+/Pn861//wu/34/P52L9/PxddlMpCevTRR9mxY8chy7388sv8+te/xmq18qc//Yny8nJCoRDhcJiVK1cyadIkysvLufjiiwkGU+3kO3XqREVFBb/85S8BGD16NBUVFc3+NBW62noumlJZWcl9993HjTfeyL59+/B6vUQiEX73u98BsHr1aq677jpisRhjxoxh/fr1+P1+wuEwn376KXl5eenj0bgfJxoRi2F85hkAokCjty5KSptokJSIPvggUjCI+ZlnkEwmhBBUVFQQj8ex2+3IskwsFkOWZTIyMojH41RUVHA8/gEhlJQj6bBvkQ3TIm9D9KOjC10AajmSnMqz8iXCRxS6AKriAXyJ8DGN+URz8HGPx+NIkoTFYknnWzUe9yMfv0ZkcPwOKXsuku3W4xa6FEXhuXtebWHwqb/+ce+rx2wCEUJQU1ODwWDAaDSmhXmDwZA+Dk2vN0WofFq6CpNOj06SSYgk3kQYk2wgw2gjqiR4uXguilDbtJ/DenbirQeuw3KUcrM3F20nFosTj8dRFKXF8R4Og07HZWMH8PHvb+L+Kyamha1oUmVHdZjFu73sqYuQVAUd3Waq6+rT25Ek6Zi2pdEcT34mU64bz/2v3MUbe/7Bv7Y/zT3P/w8TrxqNO9tJLBxn3dxNvP3ox7z/5yMLXSabkcIzOmEw64kGYmz+ZgcfPPZFWuhShMImlgOgcuB6fOPhj4jHE4dd5/HSrmLXU089xauvtvBDr6GhoaFx2qCJXRoaGocjHo9z9dVX8/TTT+Nyufjyyy+ZPn36ca1TVVXuuusuVFVlxowZ/N///R9du3ZFkiQkSaJXr168++67XHzxxfj9fp544olmy/fv3z897bHHHmPevHkA3HvvvWzatAm3282bb755xLwQvV7P119/zZlnngmksmPOOeccvvzyS4xGI/v27eP5558/rv1sSn19PR999BE33nhjOmS6Y8eOvPfee+Tn56OqKu+++26zZQKBQFqwev/99/ntb3+bLo/T6XQMGzaMWbNmMWzYMEpKSnjppZeOeVzHey4aiUajXHLJJbz66qt06tQpPcaioiIAHnjgARKJBN27d+err75iwIABAMiyzEUXXcR///tf9Ho9xcXFRz3usVgMv9/f7E9bSDz1FBWqyhpgNbClYXrj/1cDFYDscCA1uZYikQjBYBCz2YwkSWn3G6Q6iiUSCWpqaqioqCAcDretwiW+snnpXUuYLwb7fUefTy1PrRP4w/r3WzWE6xc9w2ZvSavmPRk0Pe6qqpJo6ITZ1MUTDAZT56NVx09F0vdEkkztMr6NC7dSU1Lb8gwCqvfXsnHh1mNab+N+22y2tEMpEolQX19PNBrFZDId2G9gm7+M/aEabHozJl1KkAorMaJqShy06U3sC9WwzV/Wth0FtpVUEzmKGOGNJNhR6ScajeLz+dJClNlsbjbeI2E06OnR0YN6kFiVUAS7ayNsqgiiCPBHEgTCsfQXAcAxb0vjUCRJoqB7Hhf8z9k88Na9vFP+T15c9zg/eeIm+ozqedTlY6E4Nz9yDS/v/Du/fv8uzr97Evl9coiJCH5RT4B6QqS+VApQj1+k/tRUVfPNp9+ekH1qV7FLQ0NDQ+P0R1GUdBmJJnZpaGg04vf7Oe+883j33XfJy8vjm2++YeLEice93m+++YYdO3bg8Xi47bbbWpyvsTpg1qxZh3x2xx13cPnll6OqKtdddx0vvvgiL7zwAgD//Oc/D5svdfDyHTp0OGR6nz590mLe22+/3ep9OhpjxozhrLPOOmS6yWTi3HNT2Uvr169v9tkHH3yA1+tlyJAh6XkORq/X86Mf/Qg4/HE6Gu1xLhr5zW8OX/bm9XrTy91///1YrdZD5hkyZAiXX345AG+99dYRx/znP/8Zl8uV/tMorh0roriYF0iVLo5tMv2nDdOGAS80zNeUZDKJoijp8lmj0ZgWVoUQqKpKNBqlurqa/fv3s3PnToqLiykpKaGqqgqfz0ckEjmyCKZWt2ofJNNEJF3B0Wdsss6ySH2rZq+I+bhp6XOc+eVvGf/Vw/xo0VP8ZdMnrKrddUoiapoed1mW0810FEUhHA4TCAQIBALU1tYSj7Xu+LX2OLeG2vLWHdfWztdI0/02mUxYrVYkSUJVVcLhMH6/n2AwiM/nI6kkWVW3i0AySm0sQExNCVIGSY9BSl2vBllPUqjUx4+Q7XYUKo5SwtiIP5IqtTYYDMgNQfKNmWPJZLJV61i1o2XBVS9LxJMq++oiJETK/dUogrZlWxpHRpZlbG4b8VjiyMJuE8LeKLl5OXTu0gmn2UmgOkQJu1jOHFYyPz3fNtaynDksZw4l7KKmtO6E7IPWjVFDQ0PjB0aj0KXX60+rVukaGhqnjvLyciZMmMDatWvp2bMns2bNOmwYfFv49tvUN7Y+n++IQfCNmVR79+497OcvvfQSK1euZN++fdx+++0A3Hbbba1ynk2aNOmIn7355pusX7+eRCKBwXD83aFGjGg5p6fxGNTVNf/lvvE4bdmy5YiB542uhZaO05For3NhsVhazDhbvXp1uoxoypSWA8/PPvts3n333aMe99/85jf84he/SP/f7/e3SfCSioq4HbgYiHBA8HoWGNXw7zxALSlJdyuG1LNSp9ORTCbTpWVNhZfGEHG7PVU2mEgk0h30QqHmAoPBYMBkMmEymTAajZhMJgwGA5Kc3bqdaO18TebNt2RQHKxs/XJAVE1QHKykOFjJ+/uXIQFWvYl8cwZ9XR2ZmNOXkVk92tRxsLUcfNwdDgeqqhKPp0rlotEoQgh8Ph/hgBWTMgq7uQK7pRKj/vAlmSL8NhgGIOm7HPf4svJa14GztfM1cvB+WywWzGZzOu8uGo2SVBRW79vGju3fsCtZh1XVEySORW/Erjdjkg3p3LCEmkQvyWQYj71sUwjBgvW7eObjRa2a3+Oy43a7m10XiqIgy/IRf99MKirz1u7k9Tmr2bC7vMX54kmVpCow6GSsVgugNBO2WrMtjaMTqA+y8P2lzHlzIesXbD6mZRd/vJxXH3iTij0HhOWOFJJNPipKWvDqxWBcZAFgwoynoPWdUI+FdrkSVFVl1apV7N27l3A4rOV2aWhoaJzGaCWMGhoaB/Piiy8CqfvC7Nmz2+yeORxlZanymUQiQWXl0V+6WypBycjIYMaMGencq8LCQp5qCBs/GgUFLbthGj9LJpPU1dWRk5PTqnUeiSMFJDe+iDV1JMCB4xSNRtP36SPRWL5zLLTXucjKyko7Nw6mqupAx78jHffG5ihHO+6N4tDxYrj7bnJ/8xtyVZWmR24wMJRUFI0E8MUXJDp2JP7aa1gnT8ZisWC32/H5fOj1+rQIBinng6qqZGVl0a1bt7QDJxaLpcWJxr+TySSJRIJEItEspyyVOZSNMTYBo64Ekz6A0RDEoIsgSY3lXFKqq6DxjIYN54JaSYsBOrInPe9DA6dz9pxHj3p8njvjJuaUb2ZRzTZqYn6UJusWQCgZY0ewgh3BCj4pXYmEhFVnpIPZRW9nPhNz+jImuydGnfGo22oNhzvusixjNpvT14PZbMblchGJ6IlG84nGndT4e2IyBHBYKrCbKzEamgiOieWImqkI61VItruQdJ4Wtn50+o/rjadjFjWltYc/DRJkd8yi/7jex73fkiRhMplIyoLqcC2lCS81iQhGdBRIDrIkMz4RJS5DvRwn2XDdCCEIJWMU2jvQy3lsHWe3l1Tz+HsLWLl9PwCyJB1SXtiUDJuJwYV56HQH7gtCCKLRKC6XK13O3ZRgJMbHizfx1tw1lNelypP1Ohm9TiYaP9Sd5YskqQnG6eqxkZvpxOf1IoQgmUyi0+mOuC2NIxOPxln6+SrmvrmQ5V+sIdHk+A+c0Jezrh7D6398P+VUPEJu14L3lgBgtBjoObqQnqO7Muu5hYRqwyjiwDoduNMB9Y5sO+MvHnNC9uu4xa5nnnmGRx99lJqamvS0pmJXfX0948aNI5lMsmDBgnb5BUJDQ0NDo+1oYpeGhsbBXHjhhSxcuBCfz8fNN9/Mp59+etjys7bQWAI1YsQIli5delzr+uc//5n+d2lpKTt37mTgwIHHtc7ThcbjdNVVV7VrSeXhtnG85+JEOnpOFJLJROxnP8P41FOHvKs1/l9xu5G9XgxlZejPPpvYiBGI994jNze3WYaUTqdLu7pMJhO5ublpEUyWZSwWyyEv3I0RAgcLYYqiEI8niCuXIUIz0/PLkoLREMSoD2IyhDBl3ospqaYEEOcDDZ0HJQ775imioOwGfXdcBit9HAVHDKnv4yjgDE9PzvD05NekRIodgQrmVGxgWc1OdgUriKrNxQeBIKTE2B2qYneoipnla5GQsOgMeExOejnzGJfdmwm5/bC0QQCTJOmIx91sNtO1a1ccDgfJZJJg3QQC1f8mHMsklnAQSzio8ffAZAhit1Rgz7oSk7QYYgsg/AYi8hHCeguS7ZZ0mP+xoNPpuPPJm/nDFY8f/jQI+Mnfbz7mn5XD7bdXibDbX4k/HCQqJdlq8mGxWpnq6UtfYw67akpZULGZZEwlO2EmpFOokkKUqUHMegO3Fk1CJ7UuvajWH+K5zxbz8bcbEQKMeh2XjexFtsPIszPXHjrehl2/dmxPwuHQUX8+AMrr/Lw9by0fLdpAMJpykbrtFq4YP5ArJwxibXEZv3rx88bD2IwtlUHOHVpEOBRKi8uNLr/DbUujZRRFYf2Czcx9YyELP1xGyHfga4BuAzoz+drxnPWjMXTolBKFXdmuw3cfbcBoMTDknP70GltI56H5mCxGbDYb7kw3r9z7TovLXfvIZRiP0gChrRyX2HXXXXfx/PPPI4TA6XSm28E2JSMjg6FDh/LGG2/w3nvv8dOf/vS4BqyhoaGhcXxoYpeGhsbBDBs2jIcffphzzjmHOXPmcMEFF/D5559jsx1fxzIgXZLXlrK7pjz77LN8+umn6HQ6evXqxebNm7n66qtZuXLlUYW50tJSevXq1eJnkHJcZWaemFKK1tBex+lUb6NpNlpJSUk6tP5gSkpS2Twn87ibnnySGMDTT0PDO0sugCwT/9nPMD35JMqbbyLdcQdyIIB52TJE166EbryRgv/7P+q8XoLBYLobo8vlIjc394hOvkZ0Oh1Wq7XZtdroSkmJX9nEghIx/xfE4wqq0BGNu4jGXWAYhqR0g+pd6HQ6jMY+GKUnMSZexaTbi9EQRCcnQM4BDKCWIOpugMz/IOmLeG3MXdz47YzDCl59HAW8NqZ5t1VJkujpzKOnM4+f9DwHIQQ7AxXMr9zEyrpd7PRVEFZjqIhmYoRAEFbi7AvXsC9cw9cVG5A2vIdJNuAxOejuyGVMdi8m5/THbjy6+8bhcNCtWzcqKiqOeNz1ej3uDufhckok6/+PYEQQjOQQjnmIJXOJx6+grroIk+ksbKYbsEsvYFSXI4WeRUTeBNtdYL0KSTo2UW7c5SN46L1f8tw9rx6SaZSR62bURcOOaX1N97tT186sKN7E/sp9RBIxFEngk+OYs5z8pOtoBmd0RW4QsDrm5mN025m7ew2JaByjCp2x09eUzaiCvgy0d2xWmns4Yokkb81dw8tfLifUIECN7dORK0d2J9uVumatJiOvzt1Itf+AKNIhw84vr5jImd1zj3qeNu6p4PXZq5izZgeKmrpyuuZmct3koZx/Zh/MxpQ0MXlID/7yPxfy13fnU+UNtrit2tradJfO7OzsVv8s/pARQlC8bg9z31jIvLe/bZaVld0pi0k/Gsvka8fRbcCBUt9kIsmauRtZMXMNVqeFsL+549dkNXL2zeOZeMtIYokYiqJgNpvJysoiLy+PAQMGIMkSb/zhQ2jwRxkx48i2c+0jlzHtjotP2P62Wez68ssv+cc//oHD4eDf//43l1xyCXl5ec2sy41cc801vP7668yePVsTuzQ0NDROIY2WctDELg0NjeacccYZzJkzhylTpjB//nymTp3KF198kc4iaitjxqTKEyoqKli5ciVnnHHGMa9jw4YN3H///QA89NBD3HjjjQwePJgtW7Zw7733psPqW2LevHkt5nY1dnccOHBgu+R1tZUxY8bw2muvsWrVKsrLy8nLyzum5RvLCg/+4vngbcDxnYujMXTo0HR535w5c1oUu2bPTrWjHzRo0Ek97qYnnyTx0EN4OncGRSHzgQfg/vvTpXG6a66Bq69Gue8+5GeeQVIU7K++iuXdd5Efe4ychgoWvV6PxWI5LhdJqoTRgMFgSAnLmVMR4hxEbAWJeA2x4FJioVXEVT1x4wQSiQSKohCJRIhQCPwBomUgwuj1NozWIkzGJIbYHzFJGzHW3IzO8xqSvhuvjbkLXyLMH9a/T1mknnxLBg8NnI7LcHQHpyRJ9HDm0cOZx48BVajsClYxv3Izq+qK2RWswh8PIxrEr+YCWCoDrCRSR0mkjvlVm/l/mz7CKBvINNkotOcwMqsH5+UPwnWYXCmHw4HdbicSiZBMJo943CXzuehzp+COr8StVqMID8F4L0KhMKFQqMFZ14E6fodRLsYmvY/DtA6j8kek8GtgvxfMU5Fa6YKClOA1+pIz2LhwK7Xl9dhcVv5607PUV3j56rUFnH/b5FavC8AbDzO7Yj1fla/HFw9j0+uxGgwM8XTj0s5D6GI/NLdNp9Mxpkt/Rnbuy+bafdR66zHGBJl6K5IqsXfvXkwmEy6XC4fD0SzTSgjBnDU7eOrDhZTWpkoJi3LdXD++N73yM5EkCYfDgcvlomfPnlxx9ijW7CylxhfC47IxpHsBuoZ7z+HOkyoEc9fu5PXZq1hbfKAj5Jm9OnHdlGGM7tsVWT70XE4e0oOJg4qOuC232015eTkWi4Xu3btrjq4jULm3mrlvLmLOG9+wd/OBJgB2t43x00cy6dpxDBjXJ/0ciccSrP56PQs/XMqST1YQqD9QDuzMctB7RHe69O1I4Rmd6Tggl2gsSjgSxmg0pkVOp9OZPidnXTmWohGd+Wby5wgB//v8zzn3yiknzNHVSJvFrueffx5JkvjDH/7AJZdccsR5R41KxT5u2LChrZv7zjNjxgxmzJhxSrqZaGhoaDTSGJoLmtiloaFxKEOGDGHu3LlMmTKFhQsXct555zFz5szj+rb8rLPOonv37uzcuZN7772XOXPmpAO+D0ddXV0zp08kEuHqq68mGo0yduxYHnjgAXQ6HS+++CJXXnklL774Iueccw7Tpk1rcZ3PP/88d999Nx5P84yebdu28f777wOp8sFTyRVXXMGvfvUrvF4vv/jFL3jzzTdbfHlTVRW/34/b7U5PczqdQKobYksc77loDW63m3PPPZeZM2fy17/+leuuu+4Q5926dev44IMPANLdJU8m9sxMqpvkZh2CLKP7+9/hd79DmT4ded48dKEQ7p/9jMTf/kbwtdewjRlzQl6uJUmHZB6JyQxG+xjs1ZNAFCO5z0cYJ5NIJJqVQsZiXVOh+EAyHCEcBsTdiMjHSKIGfdVTmDNuxmQpwGg08v/6X4XRaDyuscuSTHdHLt0dudzGpLT4tbBqCytriykOVhFKRkk0lD4eTgCLqQnKI17KI16+rd7GE1s/xyDryTDa6GrN5kxPEVPzhuCxpF6YW1tWLUk6MKUaROgBtxXc7gwURSEUChEIBAiFQsTVIuLcT71vMwZ1AXZzMfbIQ5gsLyM7f4lkGt3q46HT6Rg0sV/6/9f8dhr/+MW/eP0P7zHlunEYzUd3jO0OVjGzbA3fVm8nKVLvi5kmO+fmDWJSbn+chqM74XSSzABPV/B0RQhBOBzG5/Olhb6qqiqqq6ux2Wy4XC721Qb52/vfsGZnyvGXYTPxo7G9GdM7H5PRiNvtxul0NhPHdJLEGT0Pn+nY9DyFo3Hemb+WN+euoaTGB6TyuKYO7821k4fSs+PRmy3oZPmI23K73dTV1SGEOKpz7YeIvzbAgveWMPfNhWxctDU93WAyMPLCoUy+djzDpw7BaEoJTrFIjJWz1rHwg6Us+WxlMwdXRo6LMZeeybjpoxgwrjf+gJ+6ujoSiQT+gB9ZlnE6nbjdbjp06HDIFxjhcBiny0lpeelxf4F2LLRZ7Fq2bBkAt9xyy1HndblcOJ1OKioq2rq57zx33XUXd911F36/H5fLdaqHo6Gh8QOl0dVlMplaDBfW0ND4YTNo0CDmzp3L5MmT+fbbbzn33HP58ssv02LKsaLX63n++ec577zzWLRoEePHj+dPf/oT48ePT/9CvGvXLr766itefvllLrnkEh588MH08vfeey+bN2/G7XbzxhtvpDNwrrjiCm699VZefvllfvzjH3PmmWe2GKyfSCQ4++yzefHFFxk+fHjKzTBnDrfddhuxWIxOnTpxxx13tGn/2gu3282TTz7JTTfdxNtvv43P5+Phhx9m+PDhaafUtm3b+OKLL3jppZd44IEHuO6669LL9+/fH4BNmzaxePFiRo8+9GX9eM9Fa3n00UeZPXs2O3fu5Nxzz+W5555jwIABqKrKl19+yY9//GOSySRFRUXpzpqnJZmZ6ObOhRUrUK+4AnnvXox79pAxYQLhiROJ//vfuDt2PGEv2ZKcgbBeD6HnEcFnkLImHzawP5X7FW+SBWYjqptGMvg+iUQdieq3CVouAzn1DiJJUrqrZNPukAaDoU370lT8urnoLBShsidYxcLqbays3UlxoJJQMoaCiqI2/eI/ta3Gcsi4mqQy6qMy6mNZ3U6e3T4LvaTDbbTS2erhjKwipuYPJt967GWvOp0Op9OJ0+lsJnyFwwNJKL2oj62lLrgao86Hvf5P2J1dMGfehWzsd/SVH8SFd5zN+3//jOr9tXz+/Ndcfs8Fh51PESora4v5omwtW/0HSkx7OHKZmj+EEVnd0ctty8eTJAmbzYbNZkNRFAKBAD6fj2g0yr6KGt7+YBGLtpQhAKNe5qJhhVw0vIgstwu3243Vam3TtVBZH+Dt+Wv5cOEGApFU92+Xzcz0cQO5cuIgsl3tJ3Q0dhVPJpPEYjEtmJ6UYLX0s1XMeXMhK2auIZlI/bxJksSgs/ox+ZpxjJs2Apsr5aKMBCMs+HQlCz9YwrL/riYaiqXXlZWfwbjLRzJu+kj6jemFLMv4/X727d9HMpkkEokQj8exWq2YzWY6dOiAw+E45LppvDdJknTSz1Gbxa66urq0FbI1ND6kNTQ0NDROHVoJo4aGRmsYMGAA8+fPZ9KkSSxZsoSzzz6bWbNmNXMSHQuTJ0/mvffe44YbbmDZsmVMmTIFg8GQznyNxQ78gn3ppZem//3hhx+mSxT/+c9/0rlz52brffrpp/n222/ZunUr1157LfPmzTtsIPQLL7yQFsTsdjuqqqa7Gbrdbj788MM2i3ntyY033kgkEuHuu+9m5syZzJw5E5PJhN1ux+/3N+vgePALxcSJE+nVqxfbtm1jzJgxZGRkpPfp8ccfZ/r06UDbz8WxMHToUP7zn/9www03sGjRIgYOHIjT6SQej6efQ506deKzzz47qd/yt5nhw5H37EE8/zzcdx9SOIxt/nyshYV477gD3R//iMPlOjFOL9stiPB/ILkVYl+D+dxD5tHpdIcJxe9IItaBWNVPicfqiSmVxE13k0jamoXlBwKB9BKyLB8igJlMJnQ63THtm06SKXLkUuTI5abCCWnxa3H1dpbX7mRnsJJwMpZ2MKEqyMhIEkiSnMoyEykJLCEUqmMBqmMBVtXv5oWds9FLOpwGC52sWQzN7MbU/MF0tXc48qAOOl4HC1/BYCbB4ADi0RXUBTZQF1AxVP4du7MbzuwfYbZ1a/UxMJqNXP/QFTzx4+d5688fct6tk7A6DpybUDLG3IqNzCpfR3UsVTqoQ2akpwdTCwbTw3FsJcyt2V+3243JYuOlz7/lzQXriTWIIMO7ZXHJsC50yfWQk9OBjIyMNjWg2LKvktfnrObrldtJNrzzd+7g5tpJQ7lwZF8spvYvV5MkCbPZTDAYJBqN/mDFLkVRWDdvE7Pf+IZvP1xOOHDAkVU0uCuTrxnHWT8ag6cgC4CQP8zcNxfyzQdLWTFzDfHogedKh84exk0bybhpI+kzsgeynPp5DIVC1NTUpDvLRqPR9HPD4XCQk5PTzAHYlFAoVQJpsVhOenOTNotdTqeT+vp6EonEUevs6+rq8Pl85OcfW8tTDQ0NDY32RRO7NDQ0Wkvfvn3Tgtfy5cuZMmUKX3/9NRkZGW1a36WXXsrOnTt57rnnmDlzJjt27MDr9WKz2ejduzfDhw/nggsu4Pzzzwdg//793HbbbQDceuutabGmKVarlbfeeouRI0eycOFCHn30UR5++OFD5hsxYgQrV67kT3/6E3PmzKG6upqCggLOP/98HnroITp27NimfToR3HHHHZx33nnMmDGDr7/+mt27d+P1enE6nRQVFTFq1CguvvjiQzLI9Ho9c+bM4fe//z1z5syhtLSU+vp6AIIHlewd67loC1dddRXDhg3j8ccfZ/bs2ZSUlGAwGBg8eDCXXXYZ99xzzykTGCORCFOnTgVg5syZrX5Jlu64A267DXH77fCvfyElk2Q8+yzJ11+n6m9/w3H11e3WxTS9TdmNsN4IoecQwWfBdHarM6UMphz0+TOw1l0PyiLQ7YGM/6CIDod0hYzH4+nOdo2/KzSi0+maiV+N/27ti2tT8ev6wvEoQmVXsJLlNTtZVrOTncEKwskYCaGkPF4SGNChk2SMckpoi6tJomoCVQiSQqEuHqQuHmSddy+v7pqPXpJx6C0UWDMZlNGFc3IH0sd99J/rpsKXquYSChUS8J1JsH4+ifgO6usqqK97CoOlL46sKTic+ZjN5qMKX+fcOJF3/vIJpTvK+eipL7j2wWmUhuv4snwtCyq3EFNTAoNDb+bs3IFMyRtAlunEhKsnEgk+WbSOF79cSU1DaVr3XDc3T+pPz/xMFEVBCEFtbS319fXpMsejubtUVbBwwy5en7OaVTsO5ECd0bMj104eyrj+hYfN42pPTCZTWuz6ISGEYMfqXamg+XcWU1den/4sp0s2ZzUEzXftl3I7B+qDfPXafBZ+sJRVX60jET/QXTW/KCctcPU8o6jZOY9Go1RXVxMOhxFCpL8Msdls6PX6Ft1cTWkUu2RZZuLEicCx3XePB0kcKcXyCEyaNIkFCxawYMECxo4dC5AOqD84l2rGjBn87Gc/45JLLuGjjz46/lF/h2ksY/T5fKfFN4gaGho/LHbs2EEkEqFLly5aSfUpQnsOaGicHPbs2UO3bt0A2L17N127dj21A9JoF9rjHhoKhdKOsmAw2LauoyUliOnTYdkyJBpyqPr0oe6f/yTrjDMOKTU8HoTqRVRPAhFEcj+DdBh31xGXVyoQddeDshd0nZAyX0fSNXcPCSHSeWBNhbBEItFi0wODwZAWwJr+fawxCUlVYVewkpW1u1heu5MdgZT4FRcHXshlZEw6PTadCaveRExJ4E9ECCsxkuLw1UM6ScauN5NnyWCAuxNTcgYwOLNrqxxaqqoS9K0jUP0BoVAtqqoHyYBkGIbeMgyHMxOHw3FE4Wv+O9/ypx89idlhZvjMC9mklqc/62z1MDV/MGOye2HStb/rqTGza/nmXbwwcxXbG8SQLIeZ/znvDC4eMzB9jSaTyXSZY1NnZ6Nzx+l0Nsv2i8QSfL50M2/MXc2+Ki8Aelnm7DN6cu2kofTtktPu+9MSwWCQ0tJSTCbTD+IeX76rMh00v3/bgcB/R6adCVeMYvK14+g7OlVy6Kvx8+3HK1j4wRLWzNmIkjyg0XTqlc/46aMYO20ERYMO/ZlIJBLU1NTg96ech4qioKoqOp0OWZZxOBx06NChRTdXI6qqUlxcjKqqZGdnk5WVcpe1+b7bQGufA212dk2fPp358+fz+9//nq+++qrFm9q6det48MEHkSTplARQamhoaGik0DoxamhoaGhotBMdOyItXQpz5iCuuw6pogLzli3kjRtH4PzzqX/uObLy8tql0+QBd9cMRPCZY3J3AUi6XMj8D6LuWlD2I+pugMz/pKY3ztMkx6tpTI2qqs3ywBqFsEQikf7T6NxoXE9TEaxRADtSKL5e1tHTmU9PZz7XdBtLUlUoDlayum43y2p2pMQvJUZUSRBR4hAPoEPGpDNQYM2kizUbVagN5Y5+/IkISaGgCBVfIowvEWarv5T39i1FRsKmN5NrcdHX2ZEpuQM4I7PwEJeaLMs4M4bgzBiCEllEqOYlAsEgoWiEhH899dER1Nf3Qa83pjtGNu0SGVUSxMfaMfdwEt3hZ8VzS7Dd0ZVhmYVMzR9CP9eJyXpTFAWfz8eukgr+PW8Di7amBBGTQce1Zw3ilqmjsB4UmK/X68nIyMDtdhOLxfD5fAQCARKJBLW1tdTW1mKxWEhIBr5YuZMPv92IL5T6fdJhMTFt3ACumjiYnIwT40w7Eo2/z8bjcRRFOellcicDX42fBe8uYc4b37B5yfb0dKPZwKiLz2DSNeMYft5gDEYDdRX1/PeFr/nmg6WsX7AZVTkgBHcb0DmdwdWl7+GvP0VRqKuro76+vlnwf+PPdVM3V2sIh8Ooqpq+J5xs2uzsSiQSDBkyhC1btjBx4kTuvfdebrnlFmpra9m6dSt79uzhs88+4+WXXyYSiTBq1CgWLVr0g++SoH2jr6GhcaqIxWJs27YNWZbp16/fD/5+fKrQngMaGicHzdn1/eS0cXYdzGOPIR5+GCkeB0A1mai99174xS/IzMw87pdwofoa3F0BJPdTSOapx74OpQxRdx0oJaDrhpT5HyRd67OumtIYit+8M2Ssxc7zjWLawS6w1oTiJ9QkOwIVrK/fy7KanewIlBNW4sTVZEO0fcrFZZINOA1m+rk609WeTXXEx65QNSXhGrzxMEmhcLgXXxkJq95ItslFX1cBZ+X0Z7SnB3rdAV+IECpEv0AJPEk4HCEYySUY64UwjAV9EZASjVSTzIrQPmbXbiakxogvqSP46y3ozHqe3Phnehd2bdPxPhKNX2Z6vV5q6r18unwnn63aRTyZEjrOH96Luy8fT7a79fl4qqoSCoXw+Xxs2VvB56t2sXhbGYqaOoL5WU6umzyUi0f1O0Q8O9ns2rWLRCJBp06d2r2M+FQRDcdY/MkK5r65kJWz1qVdWbIsMXhSfyZfO54xl52JzWmluqSWRR8uY+EHS9m4aGszJ2aPod0Ye/lIxk0bQadeBS1uT1VVvF4vdXV16Z9hg8FAMplMr8/pdNKhQ4djupdVVVVRX1+P2+3Gbre32323tc+BNotdAHv37uW8885j27ZtLd6khBAMGDCAWbNmkZube9h5fkhoLzkaGhqnCp/Px969e7FarXTv3v1UD+cHi/Yc0NA4OWhi1/eT01bsAohG4frrER98gNTwipXo0IHKv/8d29SpuFyu4+qErAaehtCzoO+BlPXZMbm7GhFKKaL2OlBLQVfYIHhlt3lMB5NMJg9xgcVisRYblcmyfIgAdrRQ/LiaZGeggg31+1hWu4PtgQoiyThxNYF6kPhl15vo7SpgaEY38ixudvgrWF23m92haryJEAk1eVgBTELCojOQbXLSy5nP+A59GJ/TB5MMhN9GBGegqj7CsSyC8RHsiZ7NjoBCWbgeFUFSUpHMBobl9+DL6z9m65IdXPSTc/n5jNva6UiT7rLo9XqJRKN8u7WMt7/dRl0w5boaXJTPL6+YQN8ux/4OrqqCxZv38PrsVSzftj89vVd+BhcM7cawwhxMJiNOpxOXy9UuDsa2UlZWRiAQIDs7m8zMY+/UebqgJBVWz9nA3DcX8u1Hy4kED+SQ9RjajcnXjmfi1WPIysugYk8VCz9YxqIPlzZzewH0HtGDcZePYNy0keQVHrmkVAhBIBCgpqYm3QClMY8vEknlu+n1enJyctrUTGT37t3E43Hy8/ORZfm7JXZBypr2t7/9jVdeeYW9e/c2+6ygoIAf//jH3Hfffe33EPmOo73kaGhonCoqKyuprKwkMzPztApj/qGhPQc0vqtcfvnlLF68+JiW+fDDDxk9evQJGtGRaavY9V3bzx8ap7XY1ciOHYjLL4eNG9N5XpGhQ6l67jky+/Q5aqBzSwjVj6g+K+Xucv0dyXJBm4YnkvtTDi+1HPTdkTL+g6TLatO6WrU9IUgmk4eE4sdisRbzwI4lFD+mJNgRqGCjdx8raovZHignosSJKUlUUiJbo/hl0Rnp7cxnUEZX+rgKyDG7WVi1hWU1O9gVrKQuHiKuJloUwMw6A1lGBz0cHkbYS7Er85nny2F3xImdLFwil57GfHrZc8m1uJGQ2LVqH09c9yI6vY5XtjxJftHxGUCi0Sg+nw+/34+qqmwvq+ffCzZTXOkDUq6rey4fx+QhPY75OovGk/x32WbemLuGPRV1qWMnS0wZ2pNrJg2he64bv99PIBBo5uKzWq24XC7sdvtxCbptoba2lpqaGhwOx3euIZ4Qgu0ri5n9+jcseHcx9Q3nECC3WwcmXzOOSdeOo3PvAkp3lrPwg5SDa/vK4vR8kiTRd3RPxk8bxdjLz6RD59aJ1+FwmOrq6nTEiV6vx263EwqF0sJXW9xcjcTjcXbv3o0kSRQVFRGNRr97YldTysrKKCsrQ1EUcnNz6dKlS3ut+nuD9pKjoaFxqtizZw9+v5/8/Hw8Hs+pHs4PFu05oPFdZeLEiSxYsOCYlpk3b166+9J3hR/Kfn5X+U6IXY188AHixz9GauiKKWQZ35VX4nv0UTy5uW3argg+iwg+nRKpsj5DktpWHimS+1IZXmol6HsiZf4bST65rhghRLM8sGMNxW+aB9ZUYIkqCbb7y9nk28+q2l1sC5QRVRLE1ARKQ5i9XtJhkvWYdUa623MYlNmVPs4Cejnz0SExu3Ij31ZvozhQQU0sQKwFAQxAAnSSQp4hxDnu/VxZ0BuD5XaCYSvBYBBFUXj2tn+x9dudjLh0CD997hYcDsdRux02RVXVdIh8o+Om2h/hncXb+XZrKQBWk4Fbp57JNZOGYjIcWzR3rT/EuwvW8d436/EGU+u3m41cNnYAV581mLzM5j9rqqoSDAbx+XyEw+H0dJ1Oh91ux+VytaprZXsQCoUoKSnBaDSmv+A43SndWc7cNxYx582FlO440LjAmeVgwpWjmXLdOPqM7Mm+raUs+mAZ33ywhF3rDhiLZFliwPi+jJs2kjGXnYknv/U/u7FYjJqamnSXXp1Oh9vtJplM4vf7EUJgMBjo0KFDm9xcjdTX11NVVYXVaqVTp07tet89JWKXxtHRXnI0NDROFVu3biUej1NYWHhcDy+N40N7DmhoaGi0nfYSuzp0SGVVVVVVndgKFFWFBx5APP44UjLVXVC1Wql88EGU66/H4/EcU9MYoQYa3F1+JNcTSJYL2zw0kdzT4PCqAn2vBsEro83ray+OFIp/OBrDsw92gjXmgUWVONv85Wzy7md1/W62+8uJKnFiapKkSLmTGsUvk85AF1s2gzK60MdZQG9XAR6Tg4gS5729S/i8ZBWlkfoW8r9SUyTAIKlkGHR0tXdllKsXHXZK/N9FzyBJEr/95Kfk9chJC0N2ux2bzXZYYSgej+P1evH7/WknVSyhMHPdfj5auo14UkGS4NLR/fnJRaPxuI7tWt5ZWsMbc1fzxfKtJBpyofIynVwzaQiXjO6H3XL0rqKJRAK/34/P52t2joxGIy6XC6fTedSufceDoijs3LkTgO7du5+2IfX1VT4WvLOYOW8uZOuyHenpJouR0ZcOZ/I14xh69kBKtpXxzftLWfjBUvZuLknPJ+tkBk/qz/hpIxl96ZlkdDi2rurJZDLdYbExeN7lcmG1Wqmurk6fO5fLRXZ29nEfx5KSEkKhULq8tD3vu5rYdZqiveRoaGicChRFYdOmTQD07dv3hP7SoXFktOeAhoaGRtv5zt5DvV64+mrErFk0ShrxTp0offJJzKNG4fF4Wp17JIIzEMGnUplbnv+22d0FIJK7EHXXg1oN+j5Ima8hye42r+9E0jQUv6kT7Eih+IfLA0ugsi1QxmZfKWvqd7PDX05UTRBTEiSail86AyZJj8NgQSAIJCMYZQN6SaanI4/JuQNR1CTfVG9lm7+MqqiPsBJLh+Y3GQkSYH20HP3iIFnj87j0/y6ghy0XuyEldOp0Omw2Gw6HA4vFQjgcxuv1NndN6fUsK67hX3PWUutPTT+jZ0fumz6BXp1a32hACMGSLXt5ffZqlm454BYa0C2P66cMZeKg7uh1bciDE4JIJILP5yMYDKZz2iRJSpc52my2E1Lm2BhS37Fjx9MqPikSjLD4k5XMeeMbVn29Pt0dUZYlhp49kEnXjGP0JcMp21mRFriaOr30Bh1Dzx7IuGmjGH3xGTizjr3jpaIo1NfXU19fnz4nDoeDjIwM/H4/Xq8XSLkmc3Jy2uX4qapKcXExqqrStWtXTKaji6bHgiZ2naZ8Zx/QGhoa32nC4TA7d+5Er9fTt2/fUz2cHzTac0BDQ0Oj7Xzn76GrV8OVV0JxKnNHAKExY6h48kmcnTuTlZV1VEeFUIMN7i4fkutvSJaLjmtIIrmzQfCqBX0/pMx/IcnH5ho5VQghUBTlkK6Q8Xi8xVB8nU7XTABTdLA7VsPWYDnr6/eyI1BBRIkTTsaIqwoCgQTIDblfBdYMhmd1Tzu/OlkzkRuaBSiKwtLa7cwrm8sm304qYzrCqh4VCXlfHPtP9iEJCP69E/aeLvKwUSA78egd5JpddDS6Mas69Hp9ukTTbrezuzbMc58vZ+v+KgA6Zru49/LxTBxU1OpSwVgiycwVW3lj9mqKy2sBkCWJSUO6c+3koQwqbL+8K0VR0mWOjWWXjcfe4XDgcrkwmUztVuZ4OoXUJxNJVn29nrlvLmTxxyuIhmPpz3oNL2LyteOZcOUoKvfWsPD9JSz8YCkVe6rT8xhMBoafN5ixl49g1EVnYHe3TXwSQuDz+aitrSXZ4Cq1WCxkZ2ejqiqVlZVpN5fb7cbj8bSbK66xtNRgMNCtW7d2L2c9YWJXbW0tL7zwAitWrEBRFAYMGMBNN91Ejx49jrjcmWeeSW1tLcXFxUec7/vOd/4BraGh8Z2krq6OkpIS7HY7hYWFp3o4P2i054CGhoZG2/ne3ENffRV+/nNoyM0ROh11N99M/f33k+nx4Ha7j+iAEcF/IIJ/B103JM8Xx+XuAhCJHYj660GtA8MApIx/IcnH7iI5XRBCkEgkDskDi8fjLeaB6fV6vESZ79/BIt8OwkqMmJokIRQMkh4JSIiU+GWQdBh1BsyygQyjjf7uTvRxFtDHVUBXewd0kowQKkQ/Jel9knXBGHO8nVj0OzPRryIkh1oJPVrQMFhwYSJbWPBgxSx0GGU9Dp2ZDJ2Tsn1JFm8upy6UwGoy8uMLRnLVhEEYW5nLVR8I894363l3wTrqAilHmNVk4JLR/blm0hAKPCdW2IzH4/j9fvx+f7MyR5PJhMvlwuFwHHfFQV1dHdXV1acspF4IwZZlO5j7xkIWvLsYb7U//Vl+91wmXzOOiVePxl8T4Jv3l7Low2VUl9Sm5zFZjJx5wVDGXT6SERcMxeqwHNdYgsEgNTU1xONxIFVS6vF4sFgs1NTU4POlgvANBgO5ublYrdY2b+9wVFVVUV9fj8vlIjf3+BoyHI4TInYtW7aMCy+8kLq6umbT9Xo9v/rVr3jkkUdavCnn5eVRVVXVos30h8L35gGtoaHxnaKsrIyamho8Hs93rlPN9w3tOaChoaHRdtrjHhqNRpk2bRoAH3zwwTFlZrUryST8/OeIF15AanAhKU4n5X/8I/ELLyQrKwun03lYV0TK3TUJhBfJ9VckyyXHPRyR2JZyeAkvGAYjZbyCJH+/Mj4bQ/EPdoJtDpaxKLCTbdHK9Lw5Bidj7IX0sxVQSYhd8Rq2hyvZE6khrirE1QQxNZkWv0w6AybZgNNgoY+roEH86kih3Y0x+i4i+A8q9oa5dVwvkgmZm14/jx2FZraGK6hOBgmrcRQhcGLEk7ThqXZi8plJV0W6FQwdIb9DNhM7D2BgZtcj7uuu8tpUHteyLcQSqXfwnAw7PzprCJeN6Y/DenKveyEE4XAYn89HKBRqVuZos9nSZY5tcQGFw2H279+PwWA4qV/qlmwvY84bC5n75kLKig9cO+5sJxOvGsOEq0aTiCVY+MEyvv14OXXl9el5LHYzIy8axrjLR3LGeYOx2I7/fEQiEaqrq9NuOr1eT1ZWFi6Xi1AoRFVVVVpwzMjIwOPxnJCy0t27dxOPx8nPz8fhSInm7XnfbXexy+v10qdPHyorUyexb9++mM1mNm7cSDweR5IkJk+ezMcff3xYZVATu1JoLzkaGhqngl27dhEMBunYseMpt3f/0NGeAxoaGhpt5zvVjbG1VFTAFVcgFi1CIqVtxHr0oPSZZ9D16oXH4zmsCCCCzyOCTzS4u/6LJB1/HqdIbEHU3dggeA1Fynjpeyd4NRJVEiys2sLMsrWUhutSji8h6GcvYKyjiK66jHT5V1NCSpw98Vr2JOsojteyP1ZHQijE1SRxNYmKwCDpMen0mGQDNr2JXs58ejuy6CpWM/uBFSx4R6XbQAP3/msYButYnO5uuN1u9oRreWb2fJZ9W0EyCk6zng4FEjmFEmbTAVFCkVTqiRHSK5isZvq6OzEltz9D3F1ZtbOMN+asZtHG3en5+3bJ4frJw5g0tDuG0yDAXVGUdHfJaDSanq7X65uVOR7L+oqLixFCUFRUdEKzaesq6pn/dipofvvKA1VrZpuJMZeeyYQrRyPrZBZ/vJzFn6xo5vKyuayMuvgMxk0byRnnDMJoNrbLmOLxODU1NQQCAQBkWSYjI4OMjFTDiaqqKvz+1DiMRiM5OTnt7uZqOpbdu3cjSRJFRUXp0sjTuhvjn//8Zx544AE8Hg+ffvopI0eOBFJljQ8++CAvvPACkiQxduxYvvjii0MGr4ldKbSXHA0NjZONEIItW7aQTCbp3r37CXu4abQO7TmgoaGh0Xa+l2JXI998A9dcA6WlAAhJInD22ZQ/9hhWjyddhtRIc3fXX5Asl7bLMERiU4Pg5QfDsAbB6zQ5Ru1ATdTPrPL1zKnYQEhJ5SlZdEYmdujLefmDybW40/MenAd2uFD8oBJjd6yG4lgNu+K1lCd8JFBS7i+RRBUCvaTDKMkY0GGoU4nctAERE1z5bIIrLtqF03khK0qv5IkPN7CjtAaALh0y+MX08XTp6uDL8rVsqtlLJBzBGpcxiObCV52IUlWZpGa7jqTvgJg1sHcHfnreOIb17NTuuUntRSwWS5c5NhUXzWYzTqcTp9PZqiypRjdRQUFBu3cdDwcifPvRcua8uZA1s9ejqikJRdbJnHHuICZcMQqz3cKy/65iyScrCNSH0ss6Mu2MuWQ446aPYsjk/hiMrWtE0RqSySR1dXV4vd50h0Wn00lWVhYGg4FgMEhlZSXJZBJJktLZXCfCzdVIfX09VVVVWK1WOnXqlJ5+WotdY8aMYenSpbz00kvcfPPNh3z+zjvvcMsttxCNRhk9ejRffvllsx3QxK4U2kuOhobGySaRSLBlyxYA+vfvf0IfcBpHR3sOaGhoaLSd77XY1cgTT8ADD0CD40U1Gqn56U+p/5//weFw4PF4MBpTjhARfAER/BvouiB5ZraLuwtAJDYg6m4CEQDDcKSMfyLJ390vy4QQbA+U80XpGlbUFqOQKqHrYHIxNX8wE3P6YtW3zkl0tFD8gBJlV7SGndEqdkaqqVQCJIRCApUECioC8ys16N6rRSoyYv6rh+jmDGr2p7KzHFYjt18wiismDDqsC0sIQYmvioX7NrCvshJfZZx4nQRJCVUIaiMJahwB6grqUe0KEhIWnZFss5PejnzG5/RmQoc+GHXt4ypqL4QQhEIh/H4/wWAwna0mSRJ2ux2Xy4XVam1RtCsvL8fv9+PxeMjKyjru8SQTSVbOWsecN75hyacriUXi6c/6jOzB+OmjcHocrJ69niWfriTsPxDE7852MuayEYyfPpKBE/qib2W+WmtRVTXdYbFRX7HZbGRnZ2MymVAU5RA3V25ubjOx/ERRWlpKMBg8pFnAaS12ZWZm4vP5CAQCLboCFi5cyIUXXkgwGGT06NHMnDkzvUM/dLFrxowZzJgxA0VR2L59u/aSo6GhcdIIBALs3r0bk8lEr169TvVwfvBoYpeGhoZG2/lBiF2QErpuuQXefhsaXteSHg9lf/kL0TFjcLvdZGZmopNjDe6ueiTXY0iWy9ttCCK+DlF/M4ggGEciZbyAJJ34l+X2JKkqLKnZzhdla9gVrEpP7+/qxNT8wQzJ7IZOap8vAYUQxGKxtAgRDodRFAVfIkwJAUoJsDdZT1UySNwXI3njZqSwSmBKD+JF2SAJbEUBsvvW0ynTQ5+MM+jj6kwfVwHZpubZbXsr63lz7mo+W7IZvSzoYDfSNdtO5442FFccvxImrMaoFmGqpTA1RFCkA6/9EhJmnQGPyUEPRx5js3sxOW8AltNEAEsmk+kyx1jsQDdDg8GQdns1Cr6NNDqK7HY7BQUFbdquEIJNi7elgubfW4K/NpD+rGPPPMZNH0VmrotN325j6eeriIYOjC0zL4Nxl49g3PSR9B/bu906Gx48Pr/fT21tbTp7y2w2k52dndZoAoEAVVVVaTdXRkYGWVlZJ+XLblVVKS4uRlVVunbt2qwU9bQWu4xGIw6Hg9ra2iPOt2TJEqZOnUogEGD06NHMmjULq9X6gxe7GtFecjQ0NE421dXVlJeX43K56NKly6kezg8e7TmgoaGh0XZ+MGJXI7t3w7RpsGYNkMrzig4cSNnTTyMKCsjIyMBt+BAp/FfQdUbyfNlu7i4AEV/bIHiFwDgaKeN5JOkUBfofA754mK8r1vN1+Xq8iVQHQoOkY1yH3pyXP4QuNk+7bi8Wi+H1egkEAun3XVmW044kWZaJx+PE43HK/LW8uXQ9i59dimnpPhSXmfDPB2IZEEZxxEgKgUESmCSBSWfBqLOTbXbR11WAyWdn7coqlm/e36iB0qtjNtdNGcbZQ3sg1FQWViAQSG1PSbIrWMm+SC17EnXsTdZTLoIkJPWQfZAAk2wgy2SnyJ7LaE8PzskbhN146gTORgGx0XTTVEuwWCy4XC7sdjs6nY5IJMK+ffvQ6/UUFhYeU9nm3i0lzH1jIXPfWkTF7gOiaEaOi7GXjSCrIJOdq3ex4su1zRxe2Z2yGD9tJGOnjaTvqJ4nTFBqDPevrq5Oi38GgwGPx4PD4UCSJJLJJFVVVencLpPJRE5OzklxczUSCoUoKSnBYDDQrVu3ZufgtBa7PB5POkDuaCrlkiVLOO+88wgGg4wZM4aZM2fSvXt3TexCe8nR0NA4+ezfv5/6+npycnLIyck51cP5waM9BzQ0NDTazg9O7Grk88/hppugwXggJAnfZZdR+Yc/oDfpyNT9L07zFmTX/0OyTmvXTYv4KkT9rSDCYByHlPEcktT68PCTyZ5gFV+UreXb6m0kReq9M9No4+zcgUzOHYDL2H6lmKqqEgwG8Xq96e53kDKJuN3uw2ZNLd68hyfeX8Cu8jqIK3jeWYMIx5n86NnI52axPVRBdbyWhBohLgQJJBKKBGUWEsUWFO+B9eV1sXDO6CIuHDyAro4OzRxqjV0nA4EAwWCwmTsqKVR2J2pZGy5hfbSMyrifqJJAcKgsIAFGWU+G0U6hvQMjPN05N3cQmWZHux3H1qKqKqFQCJ/PRzgcTpc5NoqKDoeDsrIyhBAUFhZiMBw5G6umrI55b33L3DcXsnPNgTB/i93MiAuGkt3Jw74tpaz+eh2J+IEssbzCHMZNG8m4aSPoNbz7Cc9Ci0aj1NTUEAqlcsB0Oh2ZmZm43W5kWUYIkXZzKYqCJElkZmaSmZl50qNLqqqqqK+vx+VykZub2+yz01rsaszsWrx4MSNGjDjq/IsXL2bq1KlpwWvTpk14vV5N7NJecjQ0NE4yO3bsIBKJ0KVLF1wu16kezg8e7TmgoaGh0XZ+sGIXgKrCH/4A/+//QUMJk2o2U/W//4tvWi8M6iw87gD2jm8iy+1bjibiKxD1t4GIgGkCknsGknR6lLwpQmVV7S5mlq1ls78kPb27PYfzC4YyIqs7ern9Ssri8Tg+nw+fz5d+tz1artTuijr+/sE36Q6JbpuZOy4ajbRqHy/+8t9kd8ziX9ufxmg2UhGqZ13tDlaVfsPitX4qdzhRIg3j1wl0nWKYi+KYnRJGSYdB0mPVm+huy6G3M59+7o70dBdgNprS44jFYgSDQQKBQDPhS5IkrFYrJquZtZESvqnZxjZ/GdVRP2El3qIAZpD1uA1Wuto6cEZWEVPzB5HTJNj/RJNMJvH7/fh8PuLxA06rQCCATqeja9eu6U6ETQn5Qiz8cDlz31zI2rkb04KZTq9j8KT+5HTJpmJPFevnbyKZOKBbdOyZx/jpoxg3bSRFg7uelLD/RCKR7rDYGD7vdrvJyspKi6jJZJLKykqCwSCQcnPl5uZiNp8a92Vjk4D8/HwcjuaC6Gktdt1///088cQT3H///Tz22GOtGkRTwavxBGlil/aSo6GhcfIQQrBxY+ph3qtXr2Nq46xxYtCeAxoaGhptR7uHAsEg/OhHKbdXA4mCfEr+PIlYXzdW1ySyC85r9/IlEVuGqP8xEAXTWUjuZ06p4BVKxphXuYlZZWupiqWCuHXIjPT04Lz8wfR05rXbtoQQBINBfD5f2mEDqVIyl8uFy+VCrz+0fNQbjPDif5fy3jfrUFSBXpa5+qzB3DZ1BE6bmXg0zk09f051SS0/eeImLr/nAvZXe3lr7ho+WbKJSCwlajqsUbr3qUTtksCvcxHHRlwVxNUkCaGgR8Yg6RrELx1GWU8XUxY9bDn0cRbQy12A3WxNh5c3Or6iDU0Q4IDwZbfbsdvtSJLE4prtzK/czGZ/CZVRH+FkDPUwAhikykSdBitdbB6GZXbj3PzBdG7nctGDEUIQjUbx+/3NMr4sFgsejweXy4XJaGbVrHXMeXMhSz9bSTyaSC/fa3gR+UW51JTWsWnxNlTlQHln1/6dGHf5SMZNH0nXfievm6WiKOkOi6qaGk9jh8V0Y4oW3FxZWVmnrOtmPB5n9+7dSJJEUVHRCcksa6Tdxa65c+cyZcoUcnJy2LNnT6tfmBoFr0AgoIldaA9oDQ2Nk0s0GmX79u3Isky/fv1O27bTPyS054CGhoZG29HuoU3YuBGmT4dt24BUnld4eAGlf7kCkfc/OByuZp0b2wMRW4yovx2IgWkykvupky54lYXr+bJ8LfMrNxNTU8KFXW9mSu4AzskbSJap/UrsEolE2sWVTKZK2RpFIbfbjc1mO+zvVglF4f0F63nhv0vwh1NOqgkDC7nn8vF0yWnuOPripTn8/X+ex5Zpp+vvLmLhtr3pPK7uBR6unzyUc4a4MMRmIMIfUZUwsSnsYXN8JJsjBdTFYsTVBDElSUxNkFAVdMgYJRmDrMeAjE7W0dHgppspix7WHHo68nBZ7M2yxA52fFksFhwOB3a7PS3kKYrCmvo9zK7cwEbvfioiXkJKDEUcmgEGoJdknAYrHa2ZDM7ownn5Q+juyD3svMeLqqqUl5c3lDJC9Y46Vny+jjVfbiTsO1Bmmt89l0698vFW+9m+opimckjR4K4NJYoj6dy7bSH3xzN+n89HbW1tWjOxWq1kZ2c3c2olEgmqqqrSbi6z2Uxubu4p/0Lb6/VSWVmJ1WqlU6dOJ3Rb7S52qarK7bffTjKZ5M4772T48OGtHszSpUv57W9/ixCCefPmtXq57yPaA1pDQ+Nk4vV62bdvH1arle7du5/q4WigPQc0NDQ0jgftHnoY3ngD7rwT/Cl3k9BJ1F17ATX/+xcknQ6Xy0VWVtZhnUdtQcS+bRC84mA6B8n9dyTpyPlIx71NIdjg3ccXZWtYU78nPb2TNYup+YMZm90bk659xtAYBu71egmFQmkxRK/X43K5DtsJsOmyizbu5u8ffMOeynogJVjdN30CI3p3PmT+pKLy1YqtPH3RX1Fqg4SHdyIytBNj+nXluslDObN352ZimkhsRwSfgNjc1P+xUK67ga2J8Wz217DJtx9vPExCJIkpKQEsoSaRkTA0uL4MyMiSTJ7BRTdTVsMfDw69GVVVSSaTKIqCXq9Hp9Olha/GXKzDXUfr6/fxdfk61nv3URapJ5iMtiiA6SQZh95MviWDARldOC9vEP3c7SOObFu1g09e/JKVn6+jvtyXnm7PtJFblE00EKNka3mzZXoNL2LctFGMmzaC/KITI8QdiUaXVk1NTbrDoslkwuPxNBNTGzsxVldXp91cWVlZZGZmnhZfZpeWlhIMBsnOziYzM/OEbqvdxS6N9kF7QGtoaJxMKioqqKqqIjMzk44dO57q4WigPQc0NDQ0jof2uIdGo1Guv/56AP7zn/+csnybdkVV4d57ETOeRWooxVIdDioefpjAhRciyzIZGRntFlotYt8g6n8CJMA8Fcn1t3btAtlITEnwTdUWvixbS0mkDkhlRg3J6MbU/CEMcLdfeVljDpTX602LDpBy17hcrnTXu5bYWVrDEx8sYOmWfQBk2C3cefFoLh3TH91BxzwQifHxtxt5a94aKuoCGHfW4JizHZ3FwF9XPMaAvocKY00R8RWIwOOQSHXpRMpAst+JsFxNeTTEZl8Jm3wlbPLtxx+PkBQKcTUlgMUbxK/GkkcdMhKQrXfQzZRFoclDF30GZkUmkUigqio6nQ69Xo9er8dms6WPx5FC4Lf7y/mybC3r6vdSEqklkIiQPIIAZtOZyLO46e/uzJTcAQzJ6Nqqa7W6pJZ5by1izpsL2bVub3q6yWakc6+OhANhSndUNFumcEhnzrxoCBOvGEO3Pl1OepB7I40dFhvLSfV6PR6PB6fT2exaSyQSVFZWpktoTxc3VyOqqlJcXIyqqnTp0uWw99T2vO9qYtdpivaSo6GhcTLZs2cPfr+f/Px8PJ4Tm5ug0Tq054CGhoZG2/lBB9S3AlFdgri0H9JiPxKp0sZEURGlTz1FvHt39Ho9WVlZuFyu4xaJRGw+ov4uUoLXBUiuv7ab4FUTCzCrbB1zKzcSTKaEALNsYGJOP87LH0Se5dDw8bYghCASieDz+dJB4JDqeOd0OlOZT0cRFOoDYZ7/fAkfLNyAKgQGvY5rJg3hlvPOxGFpvmxZrY+35q3l4283EoqmgtUz7BauGDeQFQ+8z75N+7nqV5dw22PXtWrsxL5GBP4GSkM3QV1HJPs9YL4QSUp16iuN1LHJmxK/tvhK8CcjKKqaKn1UkyRUJd110SDp0JMSfjJkK12NmXQzZNJJcmJRdM1KOXU6HRaLBafTmS7pbHSCtcSeYDWzytayqm43+8M1+BMREuLwMUcyEla9iRyzi36ujpyV05+RWd3R6XQEvSEWfrCUOW8sZP2CzU2C5mWyu3iIR+PUlXrT65Ikif7jenPmBUPoM6E7ZtcBZ55Op8PhcKTP9clwScViMWpqatKliLIsk5mZSUZGRjPhTQiBz+ejpqYGRVGQZZmsrCwyMjJOCzdXI6FQiJKSEvR6PYWFhYcd22kdUK/RPmgvORoaGieTrVu3Eo/HKSwsTD9gNE4t2nNAQ0NDo+1oYtfREaHXEPMeRLqjGqk0lcMkgPDkyZT85S9gs2E0GvF4POkg8jZvKzoX4f0ZKcHrYiTX/yFJbQumFkKwI1DOf0vXsKK2GIWUC6iDycV5+YOYmNMPm759nCyKoqS7+TXNqjKbzbjdbhwOx1HdPomkwtvz1/LPL5YRjKTWMXlId+6+bBwds93N5l2/q5w35qxizpqdqA2v34V5mVw3eRhTz+yNyaBn6eer+N3Fj2GyGHlt57Nk5bVO0BMiCZEPEMFnQK1KTdT3QXLcD8Yxzc6vKlRKwnUNzq/9bPaVEkxGUYVKTEkSV5MkhQIiJX4Zdfq0+OWUzRQZsuiKixxhwyL0NL1y9Ho9ZrMZp9OJxWLBZDJhMpkwGo1HDCsvj3iZVbaG5bXF7AvVUB8PkxTJQ2PwEyrG5WFM80PIKwIQPzBHh84elIRCbXl9epqskxl8Vj/GTRvFmEuHk5Fz4JzE43H8fj9+v7+Zi89kMqVda+1V9tuUZDJJbW0tPp8v3cCvpTLjeDxOZWUl4XAYAIvFQm5ubrtm8LUXVVVV1NfX43K5yM09fCnoaS12LVy4kAEDBuB2u9s8KA3tJUdDQ+PkoSgKmzZtAqBv374n5KGtcexozwENDQ2NtqOJXUdHiCiiegqoVUhvjkF68B2IpAK6hcFA/U9+QvWddwKku9ZZrda2by/6NcJ7N5AE86VIrj8fk+CVVBWW1Ozgi7LV7ApWpaf3c3Vkav4QhmZ2Qye1T5lZNBrF6/USCATSne5kWU67uFpTWiWEYP66Yp78cCH7q70A9O7UgfumT2BYzwOREYqqMm/tTl6fs5r1uw7kRI3s05nrpgxjVJ8uzfO4hOCesQ+yecl2Lr7zXH727G3HtG9CRCD0L0TonyBSjiGMo5Ac9yMZ+h92GVWo7A/Xscm7n02+/WzxlRJSYgghiKlJ4koiXXpokHWYdAYM6EACp2Sij74DnYQDtzBhw4DUIH/p9XqMRmNa6DIYDBiNxrT41fh3S4JibTTArPK1LKnawa4lxYS/qkK30I8UOlAGqTpTy8r+A9MkvUSX0V0ZefZARl04jL6D+hzlmKXy2Rq7bDZeE5Ikpcs1W2pCcCyoqkpdXR319fXpbdjtdrKzsw8Rr4QQeL1eampqUFUVWZbxeDy43e7Tys3VlN27dxOPx8nPz8fhOHyDiNNa7JJlGbPZzOWXX86tt97KWWed1ebB/ZDRXnI0NDROFuFwmJ07d6LX6+nbt++pHo5GA9pzQENDQ6PtaGJX6xChfyMCj4Kch+T6L9LtP4V//5vGNn9qVhblf/4zwfHjgdSLt8fjaXMGkIjOQnjvARSwTENy/gnpKAKVLx5mdsUGvi5fT30ilUVkkHSM7dCbqflD6GJrn/gFVVUJBAJ4vd50NhIccPE4nc4jOo+asr2kmsffW8DK7fsB8Dit3HXJWC4c2SedyxWKxvlk8UbemruG0tpU0wCDXsfU4b25dvJQehS0vF/r5m/il5N+j96g45WtT5HXLeeY91eodYjg8xB+A2hwLZnPR7Lfi6TvcsRlFaGyL1STdn5t9ZWlxa+4miSmJlGFihCgl+WU+NUQep8trHTXZ5Er2XHLFhySCdEg1jQVvhqRJKmZCNYogBmNRnZv2MecNxYy761FVJfUppcx2AwokkANJg/sr0EiOcxKYoydxJk2bHYzZ6i5IEvstATp7sxldHZPzskdiM3QspipKAqBQACfz9fsOtHr9c3KHI/pXDSUIdbW1qZLQC0WC9nZ2VgslkPmP9jNZbVaycnJOS3dXI0kEgl27dqFJEkUFRW1+LN02otdQFpNLCws5NZbb+Wmm25q0aqmcSjaS46GhsbJoq6ujpKSEux2O4WFhad6OBoNaM8BDQ0NjbajiV2tQ4gYonpyyt3l/D2S9RrYtw+mTYOVK1PzAMkBA9j/1FMk8vORJAmn04nH42mTG1xEvkD47iMleF2B5PzjYQWvvcFqvihbw7fV29J5TRkGG2fnDWRK7gBcxra7zJoSi8Xw+Xz4/X4UJbUdSZJwOBy43W7MZnOrnTK1/hDPfbqYjxdvRAgw6nVcP2UYN507HJs5JUSU1/l5e95aPlq0gWBDHpfbZuaKCYO4YvwgPK7WXWe/PvePrP56PWffOIFfvfrTNux5CpEsQQSfguinpM62HqxXIdl+iqTLatU6FKGyJ1jNZl8Jm30lbPGXElHiIARxVSGmJlCFQAJkScak02NGj0s1kaWa6WRwk210kKG34tRZMOkN6HS6wzq66sq8rPzvOlZ+tp6yHZXp6XqjDr1BTzR0oNzUZDEyfOoQRlw6jOBQIytie9gZqKAmFiCaTDBWFCALieVyORGpIWcMMMoGMo02Cu05jPR059z8wbiNh56XWCyWLnNsFKmAdJnm0QRSIQShUIjq6mri8dS1cKTS4e+im6sRr9dLZWUlVquVTp1a7qp52otdJpMJu91ObW1KXW0Mpjv//PO59dZbueCCC05ZJ4PvCtpLjoaGxsmirKyMmpoaPB4P+fn5p3o4Gg1ozwENDQ2NtqOJXa1HhP6DCPwR5Fyk7NlIUoM7ZNYsuOEGqEqVDApJInzppZQ88gg0lJZlZGSQkZHRasdTepuRzxG+XwIqWK5Gcj6CJEkoQmVV3S5mlq5ls78kPX+RPYfz84cw0tMDvdy2rK+mqKpKMBjE5/Ol3TGQEhoaXVzHIuTFEknenLuaV75ckQ6UP2dYT35+2Tjys1LX3+a9Fbw+ezVfr96OoqZerbvmZHDN5KFcMKIPFmPLHQsPx7YVO/npiN8gyxIvrv8bXfq2LCC0BpHYkurcGF+YmiDZkGy3gPVmJPnY8lxT4lcVGxvEr62+UqJqAgQkhEJMSSAa0rYkJGyygUxhwa2acKoGMgw2ss1OPCYHOfYMpCisnbWJRR+sYNvSnentSLKETq8jGT8gNJmsRgZO6svIi4cy/LwhODMcmEymQ0LxY0qcuZtWsLe+ktVqGduTtUSUeHpcTZEAg6wnw2Clq70Dw7OKOC9vMB0srtSxaxCt/H4/wWAwHYQvSRJ2ux2n03lImWMkEqGmpiZ9/el0OrKysloUruLxOBUVFUQaSo2/C26uppSWlhIMBvF4PGRltSyinvZiV25uLnv37uXDDz/klVdeYc6cOaiqmj5pubm53HTTTdxyyy0UFRW1efDfZ7SXHA0NjZNFcXExoVCIjh07kpmZeaqHo9GA9hzQ0NDQaDua2NV6Uu6uKaBWIjkeQrId1OHv0Ufhj3+EBueJMJup//Wvqf7Rj4Cjv6S3uN3IJwjfrwBByHQN34Qu5svydVTFfKn1InOmpzvn5w+mhyOvXZwr8Xg87eJq2jHQbrfjcrmwWq3Htg9CMGfNDp78cCFlDaWI/brkcN8VExhcVICiqnyzfhevz1nNmp2l6eWG9+rEdZOHMqZfN2S57fv1+2l/5duPljNu2ggeeu+XbV5PU0RsCSLwV0huTE2Qs5DsPwXLlUjSsQlyjSRVhV3BqnTZ4zZ/GTE1CQKSIuX8QqTOhaxCB8mGO2ogvqSOqjml1K6oRiQPZG7pDTqSiQPdGS0OM4Mm92Xg2X3oPbo7RvOh49TpdM2ywEwmEz6fD5/PR0ZGBh06dCCpJPm2ZjvzKzezxV9KVcRLWImjtiCA6SUdLqOVLlYPw7IKmZo3hByTk0AggN/vP6TM0el0YrVa0109gaOKxkII6uvrqa2tRVVVdDodHo+nXTqlnixUVaW4uBhVVenSpcsRM+++E2JXWVlZetq+fft4+eWXee2119i3b19qhQ0nZsKECdx2221MmzatzbXf30e0lxwNDY2TgRCCzZs3oygK3bt3P67gWY32RXsOaGhoaLSd9riHNoZSA8csgnzXEOE3EP5HQM5pcHcd9F4WDsN118FHH6UnqQUFVDz5JIEBAwAwGAx4PB4cDkerj1VZ/VvM3PsW3/jyiZIFcjZ2g5nJuf05J28QHtPhQ6yPad8aXDder5dwOJx23RgMhnTgvMFw7CLO5r2V/O39BWkRq4Pbzs8uHcvU4b2JJZJ8smQTb85dTUl1SrzT62TOO6MX104eSq9OHY57vwD2bNrP/wy8DyEEzy5/jF5ntI+RRAgBsS8RgSdA2ZuaqOuCZL8XzOcdNWftaKTEr0o2+UrY5Cthe4P4JRRBfI2X6FeVKAu9EDogaCEDB/QujE4jvab0YspV45h04WjMFlMqMyweJx6PE4vF0n8nEgkOJ2fEYjHC4TAWi4X8/PxmeWCNlWiKorCyfhdzKjay2bef8oiPUDJ6WAEMUgKY02ChkzWLYY4ujLYVYk3KJBIJIpEI0Wg03ZHS4/GQnZ3d4vUXi8WoqKhIi2Y2m42cnJw2Xa+nklAoRElJCXq9nsLCwiPeH9rzvntSxK5GhBB89dVXvPzyy3z66afE4/H04N1uN9deey233norgwYNauPufH/QXnI0NDROBolEgi1btgDQv39/rcT8NEJ7DmhoaGi0He0eemwIEW9wd1UgOX6HZLv+8DNu2QLTp8PmzanlAGXUKPb//e/E3W4glVeUnZ3d4hdoQgg2ePcxs2wta+p3I1Q/qJUUGENMzS1kXOf/xaw//tKsZDKZdu4kEon0dJvNhtvtbnP3vGpvkGc//ZbPl25GCDAb9NxwzhncePYZBCIx3pm/lg8WrscfTmVHOa0mpo0byFUTB9PBfWzlgK3h/258htn/+YYzzh3En2c+2K7rFiIBkfcQwWdBrUlN1PdPdW40jWqnbQi2rS7mo9e+Yul7KwhXBg98KEFTTUl2G3CNzqZgbEcyB3mQ9SknlN6gJ9Plpqsnnz6ZnSiwZjY7t6qqNhPBGoWwSCSCz+dDkiQyMjLSyzSG4h/sBDMYDEiShBCC9d49fFW+gQ3e/ZRH6ggmYyiiiRrXOGYh0RkHA+hAB9lOps5KltVFjisTo9GYdhVaLJb09oUQ1NXVUVtbixACnU5HdnY2TqfzOym6V1VVUV9fj8vlOqk57idV7GpKTU0N//73v3nllVfY3HCzbDxxw4YNY/ny5a3dh+8l2gNaQ0PjZBAIBNi9ezcmk4levXqd6uFoNEF7DmhoaGi0He0eeuyI8JsI/+9B7oCUPedQd1dT3n0Xbr8dvN7UsrJM9KabKPnVr9LmG5vNRnZ2drp6J6YkWFi9lS/L1rI/fKBz3tCMbpyXWUt/HkOSANttSPb72/RS3+gK8fl8zbKTdDodLpcLl8vV5oyjaDzJ67NX8epXK4jEUuLZ+Wf25meXjqU+GOH12av4auV2kmrqCHTKdnPt5KFcNLIvFtOJc+KU76rk5t53oyQVHp/3ewZN6Nfu2xBqCMKvIkIvg0h1xMQ4DsnxSyRDnzats2JPFXPfXMScN75h35bSFuczeMzox2ein5CJfoAToQNFSZIhLGSpJiyKDj0ysiQBEnFZIWGUyHF76JGZT19XRzrZPOgO40ZLJpPs2LGDeDxOVlYWQghisVi6UcHBSJLUTABr/Fuv1yNJElt8pXxVvo519XspCdVhTUp0Uh2YRSr/LSjF2Y8fk6QnX9hxyWYcegsdzC6K3Ln0yemKxWKhtrY27eay2+106NDhO+fmasqePXuIxWLk5+fjcBy/W7O1nDKxqylLly7lpZde4t133yUYDKbCCVu4wH4oaA9oDQ2Nk0F1dTXl5eW4XC66dDlym2mNk4v2HNDQ0NBoO+1xD43FYtx+++0AvPDCC9/7yJWUu+tsUMuRHA8i2W448gKqCr/+NTz5JDTkXwmHA9+f/kTVuecihECSJCSLgRXxEuZUbyKYTL3Am2QDZ+X047z8QeRZMlLLht9C+B9Ordv2P0j2+1oteCmKknZxNXa1g1QZlMvlwm63t9m9LoRg1sptPP3RIirqUzlLAwvz+MX0CXgbRK6V2w+E6Q/tUcB1k4cxbkA3dCfJMf/0XS/x2T9m0Xd0L55c+McT5v4RSi0i9ByE3wYSgATmi5Ds9yDpOx51eX9tgAXvLmbOmwvZ9O22FufL7pjFuGkjGTdtBH1H9yIhFHYGKtJljzsC5WkXlaSCJSmTJcxkCBOKqiIhIUsycVnBJ8eJGyS6uHPo7S6gj7OAQnuHdKOD/fv3Ew6Hyc3NxeVyIYRAUZRmZZCN/1bVQ51bkBJTmwpgiqLg9/tJJBJ44yG2hytZlyhjU6wSfzKS6i4qwIGRPGEjGyt6IadzwFQdWM0WRnfuz8ROA9rU9fR0IZFIsGvXLiRJoqio6KjNLNrzvntaiF2NhEIh3n77bV555RW+/fbbY17++4T2kqOhoXEy2L9/P/X19eTk5JCTk3Oqh6PRBO05oKGhodF2tID6tiHCbyP8D4Gc3eDuajlIOk1dHVx1FcyenZ6kFhWx5am/sMptpDRcjyKp1OiiCJuBcwoGMzGnHzb9oS+xIvQ6IvCH1H9sdyLZ725RuBFCEI1G8Xq9BIPBtBCh0+lwOBy43e7jFig37C7nb+8vYP2ucgByMx385MLRROMJ3py7hr1V9altyhLnDOvFtZOH0LfLySvTaqS2vJ4bu/+UWCTOo5/9LyMuGHZCtyeS+xDBv0P0vw1TDGC9Fsl+B5LcvNlRNBxj6WcrmfPmQpZ/sQZVObxglNutA+MuH8G46aPoNbzoiOJkTEmwPVCeCrz3lrAzUIGCiizArhhwJPVkCDMGSSahpkw0SVng08Xx6eIoOokezjz6uAroojixxSSyMjKP+LuwEIJEInFIHlg8Hk87CJPJJOFwOF022+godLvdmM3mtBhWGfMxs2wtq+p2sSdUTSAWwSWM5AobbkxIInXNJyWVGiL49HGsZiu93QVMyOnLWE9PDLrvhtPL6/VSWVmJxWKhc+fOR53/OxdQr3HsaC85GhoaJ4MdO3YQiUTo0qULLpfrVA9Hownac0BDQ0Oj7WhiV9tIubvOBbUUyfFbJNtNrV945UrElVci7d6dWhewfkQfPvztTyhwd6K7M4eOdg+ezFTnxpbEDBF6DRH4EwCS/WdI9p81+1xRFAKBAF6vl1gslp5uNptxu904HI7jziCtqAvwzCeLmLl8KwAWk4ErJ6RypT/+diO+UEOJmcXE5WP7c/XEIeRmnrzyrMPxz1+/zrt//YTCQV34x6q/nJQcVpHYmOrcGF+SmiDZkWw/RjFdz7r5xcx5YyELP1hKNBQ77PIFPfIYN20k46ePpPuQbm12pEWVBNv9ZWzylbDZV0JxoBIhVByqAZdixJbUY5R06OWU+BVDwa9P4tPFMaoyXZMObBYrnoIc+jgL6OnMP6wYe9hj0FA6W1lZme7yqSgKRqMRs9l82PPQmAdmMBiIRCKEQiF8SpRlkT0UJ+qIRSJYEzIG9cCyYSlBpRSigjAJScWiM+IxOejlzGd8h95MyOmLWXf8WXftTWlpKcFgEI/HQ1ZW1lHn18Su7zEzZsxgxowZKIrC9u3btZccDQ2NE4YQgo0bNyKEoFevXt/78ozvGprYpaGhodF2NLGr7YjwOwj/747J3eWLh5ldsYGvy9cz+MPZ3PT0x5ijqXJCodcTv/tuyu+8k1iD4+VonRtF6BVE4DGAVImc/U6i0Sg+nw+/3592ccmyjMPhSAd8Hy+RWIJ/fbWC/3y9imgiiSTB+AFFGA065q8rJpFMuYQKspxcM2koF4/uh818eggM/toA1xfdRdgf4YG37mHiVWNO2rZF7FtU/1/ZuWY3cz7IYN5HmXhrDl+u1rVfJ8ZePoLx00fStX/nE1JyGVXibPOXs9G7n82+/ewJVGNVdbgVI07FCEJgkHXoJB1xNYFdMRDVKawx1SJkkJHoYsumjytV9tjLmY/LeGjDBUVRqK+vp76+Pn1NOhwOPB4Per3+EBdYLBYj2VDym0wmCQaD6fgmk8mE2+3GYrGkO0L642GWlW2lpK4KbyxERImjoFBHlAopTA1hGkxgSIBZZyTLaKe7M48xnl5Mye2PzdAKd+YJQlVViouLUVWVLl26YDYffSyntdj1yCOPYLfbue+++9o8KA3tJUdDQ+PEE41G2b59O7Is069fv+9kd5fvM9pzQENDQ6PtaGJX2xEijqg5D5QSJMdvkGw3tzjv3lANM8vWsKhqayqHCHAbrJyT3Y8L/9/LmF59LZXtBYjMTMLPPEPlyJHpMi+z2YzH4znssRWhl1B8jxOM5uJL3kpUnJn+zGQy4XK5cDqdR80Aag2qKvhixRae+WgR1b5UAHthXhZWk4GNeyrS8w0qzOO6ycOYOLjopOVxHQuv//F9Xnv4HQp65PHypr+j0x//sTka5bsqmfPGQr7693zKiysPO0/hoC6MnzaKsdNG0KXP0bO92ptwMsZWfxlbfCVs9O6nzu/FoRhwqUZkVcKjmDCgo94Yp1IXoUoKEZHVlILUQEdLZkr8cnWklyMPXUShtrY2LVZZrVY8Hs9RRddEIkFlZSV1dXUkk0mEEJjN5hZzuWRZRq/XpztKRuIx9kdrKYnUU5Xws1fxsl/4CRBvNl5I/dcoG8g02Smy5zAyqwfn5Q/CeRjh7kQQCoUoKSlBr9dTWFjYqneN01rs0mgftJccDQ2NE43X62Xfvn1YrVa6d+9+qoejcRDac0Dju8rEiRNZsGABDz/8ML///e9P9XC+82jHs21oYtfxIcLvIvwPguxpcHcdeIFXhMrqut18UbqGzf4DweyF9g6cnz+UUZ4e6fBvyspg+nRYsuTAugcMwPvKK9S63WmhwGaz4fF40s6PeDyO1+vFV/01SnQpALJ5NPaMCWn3S3t9Sbe2uJTH31vA5r0pocZtM2M06KnyBlPblSQmD+3BtZOGMrAwr122eaIIByLcUHQXvpoAv/jnHUy9dfIJ2Y632seCd5fw5Stz2Llmz2Hn6TEwxrgLaxl7gY+Cnn2QHL9CMg4/IeM5VkLJGNv8pWys38+u2nJs3iRuxUhAThCRFYQQyHqZuAFqpAgVajAlbgpwqQbyklYyZCvZJgfZdjc987vSOTP3qKWjkUiEioqKdBMFp9NJhw4dkGU5HYp/cB7YwaH4jfM1CsZ6vR5VgjLVz8ZEJetjZVTEfUSUBIJDJRwJMMh6Mow2utk6MDyriPPzh5Blbv8y3Orqaurq6nC5XOTmti7L7lSIXd/d+H8NDQ0NjcPS2NK4NZZiDQ0NDQ0NjR8Qlssg9DwoJRB+E2y3EkrGmF+5iS/L1lEV8wGgQ+ZMT3fOzx9MD0feoQJUfj4sXgzz58M110B5OdKGDWSceSau6dOpefxxvNEooVCIUCiEwWBAluUDWVz6MzBaFZz6N3Ba52FwRZGsLTvNjoWyWh9PfbSIr1dtB8Cgk9HrdHgb8rhsZiOXjenP1WcNJj/ru5FranVY+NFvLuf5+17jP394j8nXjsPYTmWWkVCUJZ+s4L//nM2Gb7ZwOC9MrzO7M/HK0Yy9fAQ5na2I0MsQfgUS6xB11yJMZ6W6bBp6tsuY2opNb2JoZiFDMwuhCPZXlrGzbC+VSpC98Tpi4QiSIjAokIcZj2RA0umwYwAVomocn4iwLV5HnW87+JeTYbDRx1VAb2cBfVwFdLRmIksp8UtVVWpqavB6vQgh0Ov15OTkpEUdSIlWer2+mbhzcCh+owDW6PRKJBJp4csjjEykE5NMXbBl2rDax9bZcgABAABJREFU7WxRq1jq28WOUAVVUT8RJY6KIK4mqYz6qIz6WFq7g2e3f4le1uM2WOls8zA8s5Cp+UPIs2Yc13EOhVIuSav15DjJ2oomdmloaGh8z9DELg0NDQ0NDY3DIUkGsN2J8P+Wsrr/8FV5J+ZX7SCqptwkNp2JybkDODd/EB5TKxwhEyemXF5/+Qs89BDEYsjvvUeHTz/F/bvfsXv69Ga5RxaLBY/HQ0ZGBjbb9YigF0LPIgJ/BnRIthvavG+haJxXZ63g9dmriDdkcMmSREJRSSgquZkOrjlrCJeM6Y/D8t3LM73oJ+fwwd8/p3p/LZ+/8DWX331Bm9elJBVWz17Pp/+Yxcov15JMKM1nkKDPiB5M+tE4xlx2JtkdmweQS467EdYfIYLPQeQdiM1DxOYjLJch2X+OpMtv89jaE7fVQb41k0JzPld36UIgHmFD1W721JRR561HiiqYEim3oiKphHQCr0XBYrLiEUZqY0HqEyEW12xncU1KPLXrzfR25lNkzsYTN5KNFVmScblcZGdnt6r8VpKkdH5XU2FMCNHM/RWJRPD7/YRCIZLJJIFAgEAggEeWudzYC1PmQCwWC3q9ns3xSpb4i9keqqAq5ieUjKEiSKhJqmN+qmN+VtXt4vmds9FLOlwGC52sWQzNLGRq/mC62LNbdUwbhThJkk57V2ybxC5FUairqyM7+9ADEovFmDlzJjt37sThcDBhwgR69+593APV0NDQ0GgdmtiloaGhoaHRMlarlaqqqvS/f0gIIdgQG8oXpeNYGzAj5G9BzqDAksnU/MGM79AHk85w7Cv+1a/g5z9H3HgjvPceUiyG8cEHKXz6aUoee4y6gQORZRmz2Zx+kbdarUj2nyFQIPQPROBRQEayXXdMm1ZUlc+WbmbGx4uoDUSafaYKQf+uuVw3ZSiTBvdArzv98rhai9Fs5LrfTefvt7/AW//vQ6beOgmLvfXh/UIIti7fwcfPzGTxJysO6aQoSdB7RE/Ovn48Yy47k8zcI7t/JF0HJNfvEbYbEYG/Q+xLiHyIiHyOsF6PZL8dSXa3ZVfbjcbfhWOxGKqq4jBaODO3J0WGLLwWL8FYhNqQD18yTFUyiC4p4wnLKBEVn07FLukxWEzYDea0aBRMRFlatZ3FyhYATLKBPu6ODLR2pU9QociRg0Fum6dIkiRMJtMhzaVUVSUQCFBfX4/P5yMejxOPx4k2uCdNJhNdjHa62YaALbUeg8HAzkQNSwK72BauoDLmJ6TEUIRKUijUxoPUxoOs9e7llV3z0EsyDoOFjpZMBmV05dy8gfRyFRwyxkZXl9lsPqZcvVNx3z2msxCJRLjvvvv497//TSQSSQfW/+53v0OSJJYtW8ZVV13F/v37my137bXX8tJLL2E0nh4dLTQ0NDS+ryiKks4L0MQuDQ0NDQ2NQ5Ek6bBf2n9XUYTKNn8Z9fEQGUYbvZz56KTmok5MSbCoeiszy9ayP1wLahegksGW3UztPISBDh2SLgKHySYSQhCJREgmk+j1+sPmaiWTSXyhEL4//xlx660U/PznmLZtw1BVRddbbqHryJGE//MfagwGotEo1dXVeKuqyPvoI0wlJdCpN1yzGRH4AwIdUekSEok4erEZnVzD+0sUSuqddMp2c8WEQRgbQr9XbS/hr+/NZ3tJdbPxSBKcNag7108ZxsDCw5Rhfkc556aJvPPXTyjbWcEfrnwCo9mAxW5myvUTGDKp/2HFh/3by/jgic9Z8N5igvWhZp9JskTvM7tz3s2TGH3pcNzZx17WKem7IWU8jYivQwT+ConlEH4ZEXkPbLeD7fpmnT+FUCC+EtRqkLPBeAaS1D6B+wdfq42CTOM1l0wmCYVC6VLN3Kxs+vfqg9FoJBwOU+mtZU9NGVVhL5URH4FEFDWh4tdFCOriJElil4xYZAtJScVPFEWGzaEyNofKQIBbmCi0dqCbqwO9sjrRy5WPWWds1c8ptPDzLKecYy6XCyEEoVAIv99PIBAgkUigKArRaBSdToder0eWZeLxOJ1x0tk+GBrMY5IksSday4ZwKdujVexK1BFUow0CmEp9PER9PMQG335e37MQnSRj15vJt2Qw0NWZszJ64wil3F3/n72zjq+rvv//88h1i0uTStpCoYK1xZ3izmAw2JDhMH7IgOGd4GNs8IUhQ4cMH+6lQIeVAoVSgUrSpk3S2HU7+vvj5p4mjTRJU4PzfDxum5zzsXOu5J7Xeb9f7+Li4l7Pe0+fERlD5aXIXFam2qmOFHHq6H3wSBtWHxqQ2HXsscfy3nvvWS+OeDzOn/70JxRF4dJLL+WYY45h9erulRqeeuopdF3nqaeeGppV29jY2Nj0SD6qK+8PYGNjY7OxePzxxznrrLPQNI1rrrmGm266ab3Gq6ur4x//+Afvvfcey5cvxzAMRowYwcEHH8zvf/97RowY0aX9+eefz/33308oFGLu3LmMGjWq25j33XcfF1xwAZIk8cEHH7D33ntbc9XU1ABQW1uLqqrcdNNNvP/++7S0tFBeXs5hhx3GDTfcwLBhQ5+eY5omDz30EA899BALF+Y8cyZOnMiFF17Ir3/dd5TLSy+9xCOPPMKXX35JOBymsLCQqVOncuaZZ3Lsscf22Of000/n8ccf57TTTuOxxx7jscce44EHHmDBggWIosjkyZO54YYbrPOjaRr33Xcfjz32GIsXL0YQBPbYYw9uvPFGdtppp17XFo1Gueuuu3j55ZdZsmQJqqpSXV3NtGnTuOKKKxg9evTgT9p60tjYyAMPPMC5555LZeXmbU7eF7Nbl/DQ0g+oT7aimQayIDLcV8JZY/Zn55KxtGXjvNv4He83zSOh5b4juEQH+1bswUGumxjmWAnm5xAjZ3ktVkDwWgT3wUDueq+pqYlEIoGu60iShN/vp6KiAr/fTzqdJhKJkEgkrGtEafRo4rNmIX38MY5zzkFob4fPP8c3bhzeM84gfvvt6FddRcHDDyN0Muk2rxVQzhzG8vM+JqFmMbRWfmxyM7vWx4LVSVoTdQD8/cVZHL37BFqiSf73fW2X8+FxOjhmjwmctN+ODC8t2NCnf6MjO2SGjxtGw5Im5rw919o+48lZePxurnjsd+x13C60N4V57o5Xmfmf/9HeGOkyhiAKjJsyhsPOmcaex+5CoNDPUCA4t4eiJ0D5GDN+B2g/YCb+Cql/g/9i8BwD2RmYsZvAWFMJc+3X3GDp6bXqcDhIJBKkUimam5uRJAmn00lRURHDhw/vEmHk8/kY7fNREihg5cqVRIkSz6aI6mniqESUNFE9TVTMEhHTpBwGoiRR4Q4SdHiRsyauhIZHFZFSCo1t9fxQt5RVzjS4ZZoyERJqBgRwCFKX92medb2fISdY+f1+/H4/uq4Ti8WIxWLWNQBgRVE6nU6r2mM+BdKlKOxkFDNFLMXpz6VRtoppPk/VsSizmkYlSkLLoJkGumkQVVM4FJOVYXirtoVKfIBAS+M3yMV+dioZQ2XKSTqd7vYZEQjkUqFvmvcSr636ikx7nJY3v6H0sB15dOmHHFk1mWsnHbdez3tf9Lsa40svvcTxxx+PIAicdNJJ7LrrrsyaNYsXXngBt9vNlVdeyZ///GcuuugiLr30Uqqqqli2bBk333wzTzzxBIIgMGvWLHbfffcNdjBbAnYVLhsbmw1JW1sbq1atwu/3b9KLCJvesf8O2Gyp9FU98NZbb+Xqq69GFEXuvvtuLrzwwvWa66mnnuLMM8+0zKxdLheiKJJO51KUAoEAL7zwAgcddJDVJ51OM3XqVObPn89uu+3Gxx9/3EX0//7775k6dSqZTIYbbriBP/3pT9a+zmLXM888w9lnn008HrcuJvLzFhUV8d577/Up8PSX/Pm87rrrmDdvHq+88gqyLOP1eonFYla7tdeaR1EUTj31VJ599lkgd3GT/2zJ+yP96le/4vHHH8fh6JqW1lnsgpxQmb8bH4/HgdxNk//+978ceOCBHHXUUbz77rs4nU4cDkcXc+KPP/6YyZMnd1vf/PnzOeSQQ1i5MlfVz+1243A4rPFdLhdPPfUUv/jFLwZ03obiMzSbzfKb3/yG559/ns8++4xdd911UONsama3LuEv379IRlfxyS4cooxqaCS1LLIgsn3hKFYkW9HJvR7KXEEOHrYD+5VPwKt9iBm5qIdRc9EYQsHdJNTdqa2tRVEU3G43siyjaVqX92Hn6A2Px0NBQQF+v39N9TrDgOuvz3l6aVpumyxjdvzcOfYjf1Ha9Ks9abtkF75aXsYPjZWE3BJJ1eDzugitCbXHc1Ea8nHy/jtx3J4TCXh/upHtD175BM/f8WqfbUKlQaItsS7bBFFgqx1rOPL8g9jr+N3wBTdsGplp6pB5FTN+FxgNuY1iRVeRa83qcv8W3D1owSsej3d7raZSKdrb21FV1RK+nE4nkiTh9XqpqamxxJjexpEkiWQySTQazYm5DomMwyBGlmYlziojRkRSEEwYp4TwmDI4JGRJRjDBVDUiepqPWUEracSO94ssSJiAV3LyhwlHs3/FxD7fz27JwfUTf9FFGFubTCZjCV/5aqiA5enV0tJCNpu1/h7kvcEEQaCgoKBbFt5qJcbnqTpWptqpzLiQDYGMqVJILnU2hUpKUJktNtEmpHELDookHyOdhWwnlbO9dzhbjx3L3XXv8cqqORiKxrI7XiM8axHb3nkq/m1zKZJHV00ZsODV378D/U5afvLJJxEEgYsuuoinnnqKiy66iOeee47TTz+dTCbDzTffzMknn8xdd93FqFGjcDgcjBs3jscff5xjjjkGgCeeeGJAB2FjY2NjMzBsvy4bG5uNiWmaXHzxxVx99dW4XC6effbZ9Ra63nvvPU499VR0XefKK6+ktraWdDpNMplk0aJFnHDCCcTjcU444QRWrFhh9fN4PDzzzDN4PB4+++wzpk+fbu1Lp9OcdNJJZDIZ9thjD2644YZe5z/33HOpqanhiy++IB6Pk0wmeeeddxgxYgTt7e0ce+yxlmAzFNx77718+OGHPPbYY8RiMaLRKPX19Rx55JEA3HjjjSxevLhbv2uuuYZnn30WQRC4/vrraWtro729ndbWVq655hoA/vOf/3D99df3Ovcrr7zCc889xwMPPGBdJC1atIjJkyejaRoXXXQRl19+OXPmzOG5554jkUgQj8eZM2cOY8aMIZVKcfHFF3cbNx6Pc+SRR7Jy5Uqqqqp44403rLSbuXPnsuuuu5LNZjnllFP49ttvh+hM9h9N03j++ecBulwUbknopsFDSz8go6sUOn24JAdix3bV0AgrST5t+QHN1BkfrOb32xzBP6aczhFVO+GV5Fx0TY/kJCcjejNNTY0oioLf78fhcKDrulUhLpFIEA6HrQvlkSNHMmLECILB4BqhC3JpkTfdBG1tcOihuW2ahkBXoYtOv1c8+wlmVmHeyjKymkFzQsXnkNi2zN+tz6jyQm4641Bev+lMTjtoyk9a6FIUlRfufG2d7fJClyAKjNlhFJc+eC6vRB7n3i9v45DfHrDBhS4AQZAQPMcilL6DELgKCPUidEH+NWfGbs6JZAPENE2ampq6vFY1TSMajVrvb8MwcDgcFBcXU1BQgKIoNDU1dak8ufY4ecGss4BWVljM1DET2bViGw6t3IFTKnblBP/2HGiMoszwkiYnbrUocZrVOK1mGtkQ2MYowiM6cYgygiCgY6CbOlE1xR+/e56Lv3yUG79/kbiaJih7cu9nQcAlOSh0+sjoKg8v/QDdNHo7DbjdbsrKyhg9ejTDhg3D7/cjCAKpVIrly5cTDocRRRGHw4HX66WgoICysjJLDKuoqKCoqMg6h+XOIEeFJvEr93bs6h7J7gVj2cVXQ5kcQJZkEqKGDyfjjEJME9KmyiotwqepWu6Pf84Fq5/nsE9u55VVc3Ln1zAIz1pk/ZzntVVfkdaVAT/v/aHfYtdXX30FwCWXXNJle/4Lja7rXH311T32veqqqzBNk88++2yQy7SxsbGx6Q95scvj6b9hqY2Njc1gUBSFk046ibvvvptQKMTbb7/N8ccfv15jGobBhRdeiGEY3Hvvvdx2222MGjUKQRAQBIFx48bx3HPPcdRRRxGLxbjzzju79J84caK17dZbb2XmzJkAXHrppcyfP5+CggKefvrpPk11ZVnmvffeY+eddwZyKSMHHXQQb7/9Nk6nkxUrVnD//fev13F2JhwO89///pfTTjvN+uyurq7m+eefZ9iwYRiGwXPPPdelz6pVq7jrrruA3PfsP//5zxQUFABQWFjITTfdxGWXXQbAnXfeSWNjY49zRyIR/vWvf3HOOedYc48bN86KFqurq+Oee+7hlVde4YQTTsDhcCAIApMnT+bBBx8E4JNPPrGit/L885//pLa2FofDwdtvv81hhx1mCSDbb7897777LqNGjSKbzXLttdf2eX6y2awlxOUfg6WxsZGvv/6auXPnWtvmzp3L119/zddff93redoc+SHWQH2yFZ/sQhAEVEOjMROhXUmgmjqiIGAC54w9gOnbHc/OJWPX+AMpc/oQHgBM0pkMiehy3G43hmEQjUaJRqNWtKXb7UYURSorKykvL1/3TbZgEN58Ey6/vM9mAiAYJuZjqwm41kQkRjMapQEnIU9Xi4bj9pzEoTtvg2MARtlbKq/98x1Mo19JWRx78WG8Fn+C+7/+K4edNW1ARvZDiSC4EHy/hYLb1tHSBKMx99ocIOl0mkQigdvttioWhsNhVFVFFEWcTmeXCERBEHC73SQSCStKsfM4LpeLTCZDJBKxXu8ej4fi4mJ0XScQCFBTU8OIESMYVlLOCF8JZZKfGn8ZU3wj2ME5jDGuYnyyCw2dsJmh2PTgMUV008AlOih0+Ag4PDhFGdXUWZxooiUbRzE0mrIRGtNhMppirdcnu1iRbOWHWMM6z4coigQCAaqqqhg9ejTBYNDy0lIUhVgsRjgcRtd161xkMhkcDgelpaVWv7Fjx1JWVoYoipYHl2wKFMlexvsqOTAwju3kCg6QRvNL50S2lUopEj04OvzXTCCLjtKeILmkidTSNXZXqWWrSS5pIrmkiUx7nH8v+2jAz3t/6LfY1dzcjNPp7OZ/sNVWWwEgSRLbbrttj30nT56MLMtd7r7Z2NjY9BfdNPiqbRnvNHzLV23L+ryrsTn03VRzzmlbyodti1iUXY3DZRcEsbGx2XDEYjEOOeQQnnvuOSorK/n444/Zd99913vcjz/+mMWLF1NSUsJZZ53Va7tTTz0VgHfeeafbvvPOO4/jjjsOwzD49a9/zYMPPsgDDzwAwL/+9a9uXl899S8rK+u2fdttt7XEvGeeeabfx7Qu9thjD/bbb79u210uFwcfnEvp+e6777rse/HFF9E0DbfbzVVXXdXjuNdddx0ulwtVVXnhhRd6bDNixAhOPvnkbtvHjBnD2LG5dJm99tqLPffcs1ubffbZx6oYtvb68mLZ8ccfz8SJE7v1DQQCXHnllQC89dZbRKPRHtcHcMstt1jGzKFQiOHDh/fadl088MADTJ48ucvx/O53v2Py5MlMnjzZep1sCYSVJJppWFXfFEPr9p1BNTQeWfohty94heeWf8bstiU0Z6KYevM6x9d0N7qeS3GKxWJommZViguFQhQUFCDLMv10xFlDJ1+hvnA3tBJ0rxG2FN1AFgWcctfL11Wtvb92fmo0LO3ujd0b2+y8FS6Pa90NNxKCmepfQ6Nl3W3WIl+VMJFIEIvFUBQF0zQxTRNBEKzXaOfXqiRJGIaB1pFOmxd082OkUikMw0CSJILBID6fD1mWrT6CIFhpuz6fz5rLIcoUOLyM8ZRx6LAd2KmoBkMwcQkybhyYmGQNlZiWxie7KfcU4JNd7FK8FQ5BwinKCAjopkFcW/NecYiyZSDfX0zTRFEUNE2z1med5k7Hvva5yPfLi4bpdBpVVdE0DUmSrEf+HDsEiZ0d1Zzr24Ub/AdwX+UJPDDiJM4o3IUCwU3Lm9+w4KJHWXT5k9b8K/75HgsuepQFFz1Ky5vfsDLVPuDnvT/02704b+a2NvkcyZKSkq4hq2v1zedU2tjY2AyEmU3f87eFr9OcXXMnt8wV5PfbHsF+Fd2/QG/qvpvLnP/+4qt+zWljY2MzUBobG9lnn32YO3cuW2+9Ne+8806PZvCD4ZNPPgFyxuZ9GcHnq84uX768x/0PPfQQc+bMYcWKFZx77rkAnHXWWf2KPNt///373Pf000/z3XffoapqNy+swbDLLrv0ui9/Dtrbu14IzJmTi36YOnVqr34lhYWFTJkyhU8++cRqvzZTpkzptUpdeXk5S5YsYerUqT3ulySJkpISVq1aRTgctrYrimKJX9OmTev12A488EAgd9H19ddf9yj4AVx99dVWlBrkhNbBCl7nnnsuRx11FOl02hK87rnnHnbbbTeALcqovtDpQxZEVEPDJTnwSW5kl4RiaCiGRlZXMTFI6Vm+aq/lq/Y1Ru5eUWWkNIVR7jij3DFGueIMcyWRhTVigCxlQHASiUQQBMG66M9f7+WjZgZcDGfMmH41y1SVIItrXptOSUQzTBStq6BXPYjqgVsqw8aU97ttcWXhBlzJwDHF4nU3glx1xn6SzWaJRCK0tbWhKAqSJFlRwEAXoSv/+s3v03UdURTRNI3Vq1cTj8fJZDIYhoEgCDidTlwuV5eosHwklGEYtLa2kkgkrLTe/FwOhwOXy2XdCChw+vAIuYjYArcfBAdt2TiaqdOSiRJ05CKhJhfX8Hnrj7ik3N+UlmwMrVNKp2poyIJIodPX+znuEKlSqZT1MAwDVVUtYUqWZRwOh+Vflj8ugFQqZRWbyGazVoV3RVEQRRFJkqw0yPx5zj9cLpflcZb/u7iXbwzNSozmw3akYNetMLKqJXiNuOBA/NtWA+Ao8lPtLer38z4Q+v3pVFpayooVKwb9hz2bzRIK/Xw+jGxsbNafmU3f84e5T3fb3pyN8Ye5T3PbDif3KuZsir5b2pw2NjY2gyGfvuZ2u3n//ffXK9JmbRoacikaqqr2WOF7bTqnoHSmsLCQe++91/K9Gj16tJX2ty6qqqrWuU/TNNrb2ykv7//FZ2+sbZDcmbyQoKpdTbmbm5u7rKc3qquru7QfzNwDXV97e7t18dTX+vJr62t9QJcLx/WlsrKSyspKy2AfYIcddhiSggMbm3HBYQz3lVCbaM5Fg3T4+7gkB6ZpElaSjPKVcsk2h1GfaqM20czyZAsrUm2kdJmFSikL00Xk/ZIcgsFwV4JRrjgj3XEqZT8p3YuuKHg8ni5Cl2maZDIZQqHQwG0TLrggl8rYi1dariKkQOyEqQhNIIkCumEScss0RLNE05rVVhQETthn+0GcvS2TIy84mPsve3yd7Uqqi5m41zYbYUX9wzTaIfHQOloJOQN755S+xzJNEokEkUiEVCoXLSYIArIsk81mrc8KSZKsyMN8FFY+1TZfwVCWZZqbmy0xKx+plc1muxRfME3T8gBzOBxd/jYJgkAoFLLeE2sXbSh2+imVfNQZERKCikOUKHOHaMvGyegKbdkENf4yDqrcnldXfkVtopmQI/ee0k3DEuuSWpbR/jLGBbveBNI0zRK2ksmkFZ2VR5ZlAoEApmmSTqetmyN5371sNksqlUIURStls3PffBqzpml4PB4cDocV2SWKovVZv7ZXX/58HF+6E28lF+Es8qNn1vhyeUeX4xtbAYCIwKmj9+nzeR8s/Ra7qqurWbFiBfX19d0qfP3nP//p84MuHxI4lF+GbGxsftropsHfFr7eZ5s7FrzGREfFGg+KTn3/uqDvSjVD3XdDz/nX+a+xrViKJIjW3RndNLh9ft/97lz0BnuXj+82p42Njc1gOeKII5g1axbRaJQzzjiDV199tUv59vUh/8V5l1124fPPP1+vsf71r39ZP69atYolS5aw3XbbrdeYNjabC5IgctaY/fnL9y8SVpI9Vm87e+wBTCgYzoSCNddgmqGzMtVGXfg9atufZXkmQF02QNqQWJYJsiwTxIxIqLoXk1kU4KEyE2S4UESVs5By0Y+smrhcLioqKnqNDOwVpxMuuwz++tduu6xqjCcdgOCSKAsmaEs6ccsiSUVnYXOCzkmTv562E86BRpZtoRiGwSW79+1vl+eCf5zRpy/hxsRUvsSMXAbGanLSg0bOma3zM9kRiRW8BkHoed2qqlq+cXlBJx9pZZomgUAAXddRVRWPx2M9wuEwpmlaPlzpdJpMJoMkSQQCAev/YDCIx+MhkUhQW1tLPB63ijJkMhkURbEq1oqiiNfrxe/34/f7kSSJgoICamtrLe8wSZKsvmMKhvG/xCrC6pr3aUD2oBgaJgZJLcvbDXM5c/R+3Dj/JaJqCkwwMUnpClldxS05OHPM/ggmJFNJkskkqVSqm0CV99fyer2WPqOqqnX+Vq1alXteOq4lDMOworVkWcblcuHz+fD5fLjdbpxOJ6qqsmLFChRFsapZ6rpOKpWyboYkk8lux+1yuRhRNYK9U9vwYfPCXl8jR1ZNxiNtGPuVfn867LTTTnz66ad8+umn3cSuE088sc++eWP6CRMmDGKJNjY2P0fmttd1ScvriRYlzsxl37CNq+ud9UXZ1bQqiY3ad0PP2arG+Xj5d93mbFP77rc6E2Vuex2Ti0f32c7Gxsamv0yePJnp06dz0EEHMWPGDA4//HBef/11fL7e0yv6S0VF7k5vb+mJ/eWee+7h1VdfRZIkxo0bx4IFCzjppJOYM2fOOoW5VatWMW7cuF73Qe6Od1HRhkm76A95T7G1jeHXJr+/Jw+yDUVRUZF1wdPX+jrv25jrW5v8a25LZOeSsVw/8Rc8tPQD6pOtJLUssiAy2l/GmWP2Z+eSsd36yKLEKH8Zo/ynsE9pCWbsJgy9iRbVQ102wJLUKBZGhrHKMIibUZIOiUVaKwtizeQECoECh5exciVbtcUYlS1jlK+UMncQsb831m6/HQDzzjsROkd4iaBccAKJ/3cTZuMPlAZaMcwUny9zsGB1ktZELoJQFAR+PW0nLjlu7/U8g1sGhmFw3o5XUDsv538tOyQ0Te+qFwGegJsrHv0dex3Xe2r0xsI0DUg+gJm4G9BBqkEouAv05blKoJ0LJIgVOaHLffBaY+SikfKpddbN3g6RSxAEK5rI4/FQVFRkiUD5NNuSkhKy2SyJRMJKXXS5XJSUlFBWVmaJVfn5RFEkGAyyevVqotFol8iwsrIySkpK8Pl83cTEvGl9U1OTFTEliiKhUIhxFeMIZiu6vU8nhKoZ7i1hXnQFT9X9j4Mrt+faCcfyyLIPWRBdiW4YpFWFCb5KflmxM5VpF0uWLOnmk+d0Oi2xShRFFEUhHA7T0NCApmmW2JXvl7+pJEkSfr+fsrIyqypjPhV0bSRJ6vHY8p+fve3z+LwYgFd0EmdNZJej0IeIwJFVk7l20nEDf4H1k36LXbvssguPPPIIX3/9Nb/+9a8HNMlTTz0FMCTGpTY2Nj8PWrP9K+uuuIRufiVKbN3Gq0Pdd2PMqblFCgtyHgyCIKBH2vrVr7/n0sbGxqa/TJkyhRkzZjBt2jQ+/PBDDj30UN588038fv96jbvHHnsAuS/Oc+bMYcqUvlNaemLevHlcccUVANxwww2cdtpp7LDDDixcuJBLL710nSbkM2fO7NW3K1/dcbvtthsSv67BMmXKFJ544gnmzJlDNBrt0SokEol08fbaWDidTrbbbju++eYbZsyYwZlnntlju/fffx/IRSJs7DRCj8dDbW3Ow2pdBQs2d3YuGcvk4tH8EGsgrCQpdPoYFxzWr4huwX0wuKYhKXOoMFooUIooaS5hZ2cUt/4E/uBH1OujqRMvZGk8RV2yhdXZGBnB4PvEKr5PrLLG8khORvpKGOUrY5S/lFG+UoZ7i5HFniN1zJsvwLx0GebtHyD8I5yL7Zn1Fq7dD2GsaeJ0OolGI2w1NsIx06Ls/ocIAEftOp5rTjngZxXRddbEy6hflDvXvgIv/15yD26/m1fueYvv/7cIj9/NtN/sw477T9wsIrpMvQ0zegUo/8ttcB+NEPwjgugDxzbgmtZREbQl59HlnNIloiufZti5+mdeoMn7TkHuu7Df7ycUCuH1eq0or3g8TiQSIZ1OW1FdXq/XErJKS0utdEfDMEgkEtYjP09BQQG6ruN2uwkEApaI3xeBQAC/3086nbaqH+arGO4cCPT6Pn1j1dc8Ufsx7zR+S3s2wV+3O5n75r9FY6SVqf5RTCmsQVAFkkrSEvryaYSCIKAoiuWxlRe28qb0ea8tt9ttRW05nU7cbjdut9ta37ro69iAXve9sOJz6lNtlHiCFDm8yP/+fwiCwNHb780l44/YYBFdefr9KXHyySf3WK1lXei6zjbbbMP06dM56qijBtzfxsbm50mJq3ePkM5sWzWaUcWjumxrazOg4YON2ndjzDluWA3Di9ekImztUaDvm/pA/8+ljY2NzUDYcccd+eCDD5g2bRqzZs3ikEMO4a233urT42ld7LfffowdO5YlS5Zw6aWXMmPGjB4LJOVpb2/vEmGVTqc56aSTyGQy7Lnnnlx77bVIksSDDz7IL3/5Sx588EEOOuggfvGLX/Q65v3338/FF19MSUlJl+0//PCDVdVwXVkNG5pf/OIX/P73vyeTyXDbbbdx8803d2tz8803k81mcTgcfR7vhuCkk07im2++4YUXXuCaa67pVpExkUhwe0d0z2GHHbbRfX1FURyyogqbA5IgMj5Uve6GPSAIErh2IZ1Os6plFYah4/GVM6zicsTo9xTq89lOuhWh+gkEqZKMrrIi2UJdsoW6RAu1yWbqk22kdYVFsQYWxRq6rKvaW5QTwHyljPKXMsJbjE95ETN+OwgZuKoGHkxBKovw+H9h90MsESOTySC7x+EOViBLd6HpBtuMKP/ZCF2apvHbbS+hsaMKY6DIzxNL78EXykXRnnDZUZxw2eZ1fW0qszvSFpsBN0JwOniO6yKo5F9za5M3nI/FYhiGYXll5aO48mKTw+GwqrPmhS/DMIjH41Ylxc7G9J3TFPMG8/F4nHg8TjKZxDDWFD2QZdlKT8wLaANBEIReo4d7e58eUrE9Qd3Bq8u+IN7Yyn9a3qXGEUAzkuhZlWQiiSiKVrph5yIR+UfeWD9fVNDj8eB0Oq3j8Hq9lrH8YOnr2HratyLZykv1swEY5S2lPt3GiFEjEQWRE2v22OBCFwxA7BoskiRx7bX9yy+2sbGxybND0SjKXME+UxnL3SF2KBq1WfTd0ua0sbGxGQq23357PvjgAw444AA++eQTDj74YN5+++1eKwSuC1mWuf/++znkkEP43//+x957781NN93E3nvvbUVSLVu2jHfffZeHH36Yo48+muuuu87qf+mll7JgwQIKCgp46qmnrIujE044gTPPPJOHH36Ys88+m5133rlXL1lVVTnwwAN58MEHmTp1KqZpMmPGDM466yyy2SzDhw/nvPPOG9TxDRVVVVVcfPHF/O1vf+PWW2/F4XBw6aWXUlBQQCQS4W9/+xt/7fBEuuyyyzZ6lcHzzz+f+++/n9raWg499FAefPBBDj74YERRZN68eZx33nnU1tbicrm48cYbN+rabLqTSqVYtWoVhmHg9XqpqqpCFEXMoicw238D+grM9l9D0RO4pWFsHcw98uimwapUO8uTLdQmWjr+byapZ1mebGV5spWPAEwNjNWUyW2Mco1jpL+UmuLfst3E83HMngMzZlhj5t/v+eIHTllC0w1WR34e0eqapnHa2N/RvCIXxV9QGuTxJffgDQywIMBGwjR1SN6Pmfg/wABpDELB3QiOrdbRLxeJFY1GLcN5XdctAUqWZUvs8nq9FBQU4PP5rCiudDpt+YPrnVJivV4vwWCQQCBgGannBa58lcI8DofDErj6G+m0PuTXnUgkLHHOp2kc6BrLV7HlrFYjrMi20WamSQomu/i2tkS6fBqnrutWlJvL5bJSOvN+XV6v19q+KdBNgwcXv49uGkwurGFhhwjuk92kdWUdvYeOn4csbmNjs8UhCSKXjjuMq797ptc2l21zeI9h+pIg8vttj+ixSuGG6rulzWljY2MzVEyaNIkPP/yQ/fffn88++4wDDzyQd955h4KCgkGNd8ABB/D8889z6qmn8sUXXzBt2jQcDgfBYNDyBMlzzDHHWD+/9NJLVoriv/71r27paXfffTeffPIJixYt4pRTTmHmzJk9pqU88MADliDm9/sxDMO6CCsoKOCll14atJg3lNx8883U19fz3HPP8ec//5kbb7yRUChENBq1LuR+9atf8Ze//GWjry0QCPDqq69yyCGHsHLlSg477DDL7DgWy92gcblcPPnkk2y//cavpqcoinUz/qabbuozevCnTmehy+fzMWzYMCtyRJCqoOjJDsGr3hK8BKlrlU1JEBnhK2GEr4S9yrYFchf0rdk4dR3C1/LobGpji2kzZZpVH836SGanQ9D6P47aqYJTZoNRW8t/ln7MqGA5w6QgJqYldrmcMqmsSnssyU8dRVE5dfSFtDWEASiqLOSxH+/G43Nv4pX1jKm3YkZ/D0rOpxvPcQiBGxDE3v0R1zacz0dxQS5KKC92yrJMKBQiGAxa71NVVYnFYsRiMRSlkw9UR8RXIBDA6XSiaRqxWIxEItEl2ivfNp+a53a7N6gopGmaJWzl16JpWhfBTZIkSlxBdisbxxORL2k2EsTUDNvoEi6Xq0taYv685L3EOhvSbypxa23ebpjL4kQTXsnJ/hUT+Spci9uQmHv/K2R0FeXejRNtbItdNjY2my0TxDLOL9yDZ2Pf0K6nrO3l7hCXbXM4+1VM7LXvfhUTuW2Hk/nbwte7RD5tyL5b2pw2NjY2Q8X48eMtwWv27NlMmzaN9957j8LCwkGNd8wxx7BkyRL++c9/8tZbb7F48WIikQg+n49tttmGqVOncvjhh3PYYYcBUF9fz1lnnQXAmWeeyfHHH99tTK/Xy3/+8x923XVXZs2axY033sj06dO7tdtll12YM2cON910EzNmzKClpYWqqioOO+wwbrjhBqqrB5cuNtQ4nU6effZZfvnLX/Lwww8zZ84cwuEwxcXFTJkyhbPPPptjjz12k61v4sSJzJ8/n3/84x+8/PLLLFmyhGw2y5gxYzjwwAO5/PLLGTNmzCZZm6qq3HHHHQD88Y9//NmKXclkkoaGhh6FrjyCNKyT4LUi93/hvxHkvt8HgiBQ6g5S4tSYLP4DXO9AGSSEHVkuX8bytJxLhUy2MPPI3Tn5/tcRDYPl/32KV3fdFtkU2C5bTNDpwanXInRctbbGU33Ou6WjZBROGXUBkeYoAKXDi3lk4V24va5NvLKeMbOfYUYvz/lvCR6E4HQET8+G4z0ZzhuGYYlceYN1yH1e54WrfFRTXuDqLFyJotglTTEvLK1evdry7MrjcrmsCK4NEfWkaZrln5WP3EqlUmQymS7CFuTeH/l0w3yqoSzLmKbJNWWVvLzsc5qVMEJaYFmkkWpvsWWun4/cyleG3NxYnYny7PJPAfh1zV6sSLYCMM5Xwb+fehcA7R/qRlmLYK5t52+zQYnFYtZdt83hrqCNzeaKoij88MMPmKZJ9Yjh1OphWrNxSlwBdiga1e9oJd00mNtet1H7bmlz2mxc7L8DNjabH3V1ddTU1ABQW1v7k/Jz+qkxFJ+hyWTSKqaQSCSGpJLolkZnocvv91NZWdnnhbOpN3UIXstBrEIoemKdgpeZmYEZuw6MNkBG8F8EvrMRhK7xFlldRS4uRYpGWXbYPjxy83msSLSydTqAYAosckVY/LaOEgZ3Mex+XEmHB1iHF5ivFL9j84x6GgiZVIZTRl5ArC2XqllRU8bD8/+O0735ibGmqWMm7oXkvYAJ8lYIBXchyN0rgOYN5yORCIqiWFFc+UqH+SguSZIIBoMUFBTgdDotcSwWixGPx7sIRnkxzO/3WwJXIpEgnU53mdvtduP3+61or/U/7lwaYV7UUhQFRVHIZDJkMhnLQ6tzSmX+2LxeLz6fj0AggM/nw+VyoSgKqVSqmyimGTrPLv+MiJlGcMocPnoqB4zYYbMoQNAXpmly0/z/Mi+ygvHBam6Y9Av+PO9FFsRWckrlrhw9djcA5jUtZWL54CvF9/fvwEaJ7Bo9OncggiCwdOnSjTGljY3NFk5jYyOmaeLz+SgMFVAkDC46QBJEJhcP7sN0sH23tDltbGxsbGxsfj4kEgkaGhowTbNfQheAIFVA0ROY7aeCXrcmpVHu7n1nGgnM+M2QzhV0QN4KIfRXBMf4Hsd2SQ6YOhXef5/Rcxdz4/YnoZsG835cSGsqSo1Xpdm3gJZwBl2B+lQb9ak2ZrUsssYocQUs4WuUv4waXynFrsBmk9a1LpKxFL+uOZ9EOBe5Vr1VJQ/O+xsO56ar+tobpt6ci+ZSPs9t8ByPELweQejqJ7a24bxhGFZaqizLlnDj8XisKC5RFFFVlba2tj7TFE3TJJFIUF9fTyaTsdoIgmBVUPT7/YOumpsXtToLWvmf835Znasf5o9LkqQuJvGBQKBLtci8uBUOh0mn091EMVmWrcgt0j4WhhvxSA4eXjWLsJjllyN226xf0x82L2BeZAVOUebcraaRNTR+jDcCMGGQRTTWh40idtXV1QFs1k+MjY3N5kMymSQazYVvDxs2zP7ssLGxsbGxsbEZAjoLXYFAgMrKyn5/z+oqeNV2ErzW+OOZ2S8wo1eBsQoQwHcmgv9iBGEdaXgnnQTvvw+NjaAoSE4nJb4QHmSmVlTwXXmCd1f+SFD08ofxB3Yywm+hORulNRunNRtnTvsya0i/7GaUr5SRvlJq/Ln/q7xFm13keyKS4JRRF5CK5aKSRk6o5v6v/4rs2Pwch8zsJx1pi20geBGCf0LwHL1m/1qG850N1SGXfp03Uw8Gg4RCIdxuN4ZhkEgkuhjVw5o0xbwQlkwmWbVqVRcRLG9en09RlAdQrTMfZba2oJUXtTqTF7fyflv56of51EK3243P57PEKlmWURSFdDpNU1MT6XTaStnMk4/46qliYoW/kMKkj60CFSxJrOal+tm0ZeOcM3Yasrj5RXhFlCRPLPsYgBNG7EqFp4Bvw8vRTJ0SV4Byd8FGX9NGeQftvffe9sWqjY1NvzBNk4aGXMWOoqIiPJ7Ns+qMjY2NjY2Njc2WRDwetyLnByp05RGk8g7B6zedBK8nQSrDjN8JqcdyDaVqhNBtCM6p/Rv4lFPgrLPANOE//4HTTutSkbE4mDM7zyoaOxWNZqeiNdHsSS3L8mQLdYmWDh+wZlYm20loGb6P1vN9tN5q6xAkhvuKqfGVMcqfiwQb4SvFLW2aCKpIa4zfjL6ATCJXeGPMDqO4d/atSPLmJWaYpoaZuAeS95FLWxyHUPAPBDnnu9eT4Xw+ZTEvCEHON6ugoMASr/JCUE9pisFgEFmWSSaTrF692oqegpzA5fP5LIFrXel9AxG1OvfJ/5+vENm5+qEkSZY5vM/nw+FwoOs6qVSK1tZWUqlUlzUDA6qYWOkpRBAEqrxFHFAxiYeWfMBHzQsJK0ku3eZwvPLm5eP2yNKZJPUso/1lHF61EwDzIisAmFQwYpPoQRtF7Prwww83xjQ2NjY/ASKRCOl0GlEUKS8v39TLsbGxsbEZBMcddxyffvrpgPq89NJL7L777htoRRuGn8tx2mz5xGIxmpqaME2TYDBIRUXFoC8+BamsU4TXMsy2X4LoBz13YYvnRITAHxBEf/8HdbuhogKamuCZZ7qJXaWhnK+aonYXJnyyi/GhasZ3SpNSDY2VqXbqEs3UJVupSzZTl2ghY6gsSzSzLNEMqzuOh5ywUOMrY6S/lJqOaLCQs/dqgkNBuDnCb2ouJJvORSmNmzqGuz69abPzZTL11ZiRy0D9MrfBcyJC8FrAZWVj5A3nNU1DVVXLgF0URURRxO/3U1BQgNvtRtM0IpEI0Wi0ixjkdDoJBAI4HA4ymQytra1dIqFEUbQELp/P1+N5yq+hp/TDtU3i8+TXmn+9dY7gyu8XRdFKkcyLW/mItLzpfiqV6lItON833ycf/dVfU/mKjkiohnSYC7Y+mEKnj78vepPvIiv447znuWr8MRS5BvAe24DMbl3CF21LkBA5b+yBVvRkZ7FrU7D5xUba2Nj8bNF1ncbGXF53WVnZoPPsbWxsbGw2Le3t7axevXpAfTqnpWxsRo0axWBqNm1px2nz86Sz0BUKhSgvL1/vKAtBKsMsfBTajgGzHfR2EIoQCm5FcO07uEH32ANefBFmzwaw0tE0TaOsMJD7We9ZsFgbhyhT4y+jxl9mbTNMg+ZMjNpEcy4FMtnC8kQLYTVJQzpMQzrMJ60/WO2LnD5G+TpM8P05L7AyV3BIIlRaG9o5bezvUDI5sWfiHtvwt4/+tNlV1zOzszAjl4MZBsGHEPwLhvNQopEYkUijFb2VF5Py0U6QE68KCgoIBnPnLB6PWxFPeSRJwufz4XQ6URSFSCTSJdIqvz8vcOXPT37OniK11iVqdX4IgoCqqqTTaVKpVLe+TqfTErc8Hg+CIJDJZEgmk7S0tJDJZLr97ciLWx6PB4/HM2jxstJTAEBTOgLAjkU1TJ90PLcteIXlyVau+/YZrp5wLMN9xYMaf6hIaBkeXvoBAEdXT2GkvxSAqJKiLtkCwITQcFA3fl1EW+zaSNx7773ce++9vYZJ2tjYQEtLC5qm4XQ6KSkp2dTLsbGxsbEZJD+XqP6fy3H+1PB4PHz//ffWzz9lotEoq1evHlKhC8DUlkLkipwIYiGCtB4RHKeemhO72tshEsHRkfqmqiqVRbmKa4ZpWlX8BoooiFR4CqjwFLBb6dbW9oiSsiK/6pI5L7DGdJh2JUm7UsvX4VqrrVdyMnItI/wqb9GAPJRWL2/mjHEXoyq5yKEd9p/I7e/dsFnZ/uTSFu+C5AO5DfI2KJ47icYCxGLLMAzDqkoIOVFIkiQEQegSxZXJZGhpaemSppiPdnI4HBiGQTKZJBaLWXNLkmSlJ3q9Xiv9MBKJDFjUcrlcXcStfJphKpUiFot1SzPsXDUx77uVyWRIpVJW9klvglj+MVSReRUdYldcy5BQM/gdbsYEyvnL9idy6/yXaUiHmf7dc1w+/sguUY0bmydrZxFRUwzzFHLsiJ2t7fM70odHeEsocHoxZINf/uePRNUU7o30uWuLXRuJCy+8kAsvvNAqk2ljY9MVRVFoacmp//2pCmRjY2NjY2NjMxhEUWTChAmbehkbnM5CV0FBAWVlZestqJimAaknMON3AFkQguC/HNJPgLY4l9pY9Ljl5TQgjjgCRBEMAx5+GMfFFwO5yK7q0tI1x5XIUBAYuovlAqeXHZyj2KFwlLUtrSnUp1qpTeQ8wOqSLaxItpHSFRbGVrEwtspqKwsSw71FHUb4ZYz0lTLSV9Kjp9KqpY2cOf5S9I50zJ0P3ZEbX7968xK69CbMyKWgfoVpCsTN3xJLnUC6TcU0w5bPlSiKuN1uBEGwKiWGQiEMw7CiCTuLSbIsW1kb2WyWdDrdZZ/b7e4SbdXW1kZjY2OvopYoit0itVwuVxeT93yaYSwWI5VKdancmB+js4eW0+lEVVVSqRTNzc3rrJiYN5XfELglJ0VOH+1KksZMmK0clQCUu0P8ebtfcvuCV/kx3shN3/+XC7c+iN1Lx22QdfTFvMgKZq6ejwCct9WBOEW5yz5Yk8IoiiJFo6tASWy067yNInYpisIzzzwDwKmnnroxprSxsdnCyBum+v1+gsHgpl6OjY2NjY2Njc0WSyQSsVJsCwsLKS0tXX+hS1+FGb0alM9zG5x7IYRuQpAqMD0HYbafBtoPHYLXvwcueIkijBgBdXXw0ktIl12GKIoYhkHAs0ZQWNkaHVKxqyc8spOtg8PYOjjM2qYZOqvS4Q4fsJwh/vJkC0k9S21HWuSHzQus9hXugi6VIJ0NGpfvdD26lhNP9jh2Z6a/cPnmJXRlP8SMXImqZoimJxLTz0QXxqLrGStl0eFwWMbqPp+PUCiEx+MhkUjQ2NjYJU0xb+QuCAKapqFpmlWdURAEZFlGkiR0XSeRSPS4prVFrXy0VmdRy1q/aZLNZkmlUiSTSTKZTDexrLOHlsfjsaK9wuEwqVSqx4qJawtiG+s5q3AX0q4kaUpH2CpQaW0PODxcN/E47vnxbWa3LeWuH96iPZvg8KqdNtraMrrKg4tnAHBg5XaM6/ReMU2T7yyxa/hGWU9PbBSxKx6Pc/rppyOKoi122djYdCNfahgYVGUgGxsbGxsbG5v+oigKN998MwDXXHMNTqdzE69oaAmHwzQ3NwNDI3SZpgnp/2LG/wJmEgQPQuAq8JxkjSuIRVD0eCfB6zcdgtfYgU12wAHw8MPw7bdWxFA2m+0QR3LFGhvaokysqRj08QwWWZQY6SthpK+EfTq2maZJSzbWpRJkbaKFdiVBUyZCUybCF22LUZcmiJ/5LXToLjVHbM1B9x9FYzpChSeEKGzajAbTVDHifycdfolIchRJZSKm82BU3YuqJhFFEZfLhSiKyLJMKBQiGAyiaRqxWKxLBJZhGIiiiK7raJqGYRiWyJV/TjunPeb7dRa1Oqcf9iRqdSYfiZVMJkmlUt0isRwOR5dILIBUKkUikaC5ubmbj+JAKiZuaCo9BSyIraQhHe62zyU5uGSbw3li2ce81TiXJ+pm0ZqN85vRe1sG8RuS55Z/RnM2SrHTz8kj9+yyb3UmSms2jiSIbNuRYqkoCl/+61XSuoJyxy82+PpgI6cxDsb408bG5qeNaZqWKX1RUdFP3jvDxsbGxsbGZtOiqip/+tOfALjiiit+UmJXZ6GrqKiIkpKS9RO69DbM2HWQzUVw4NgJIXQbgjyyW9s1gtfpoC3qELyeGJjgdfbZObErmYTly5FlmWw2i6ZpyJKIqhmsDscHfTxDjSAIlLlDlLlD7Fyy5jhjatqK/Pp69gL+d+YnltDlPKSU6JWl3PXDWwC4RAcjfSU5E3xfKaN8ZQz3FXdJCduQaMpKog23EI2mUbSpGNL2KOyIkRFwOMDny1XCzEdxOZ1O4vE4K1eutFIa89ULDcOwHnlkWcbpdOLxeJBl2RK11vbTWpeolUfXddLptCVurS1WdY7Eypva53232tvbh7Ri4oYm79u1Oh3tcb8kiJw2eh9K3EGeqM2JXm1KnN9tfQguacMV+loSb+Kthm8AOHvsAXjkrp+h+RTGcYFhuKU1VVW/eug1ALRbunqlbShszy4bG5tNSjgcJp1OI4oiFRUb/y6djY2NjY2Njc1Pgfb2dsv/tLi4mOLi4vUTujLvYkav7zChdyD4LwbfmQhC7wbcXQWvhZjtv4bCfyM4tu61Txd22QUcDlBVePBBHP/v/wG5C2WnLKNqCs3R5KCPaWMRdHjYrnAE8g9p/u+Xr1lC156/3YPd/7J/zgg/0cKKVCtZQ+XHeCM/xhut/hIiVd6iNZUgfaWM9Jfil91DtsZMJkOk5WNirW+j6yKaUYkq7A5CJW63G0mSkCSJYDCIz+cjlUrR2NhIMpnsInBBTjDqLBDlPbjyJvOdI7ZkWR7Q69I0TasCYt53q3MQTWexKl/ZMZ/K2NTU1GPFRJfL1SWVcahM5YeaSk8hAI2Z7pFdeQRB4IiqnShy+rj3x3eZ3baUG79/iSvHH0XAMfRBBJqhc//i9zAw2at0G3YsqunWJi92TdyEKYwwALHr448/HvQk+fQkGxsbm87ouk5TUxMA5eXlVolpGxsbGxsbGxub/tPW1kZraysAJSUlFBcXD3os04hhxm6EzMu5DfI4hNBfERzb9Ku/IBZ2ErwWYIZPhcLHERz9NNDeaitYsABefx3H738P5MQut1MmmVFoi23+YhfAtx/P54r9/kheZzn24sO44O9ndGmjmwaN6XCnNMgW6hLNxLUMK1KtrEi18nHLQqt9mSuYqwbpX1MRstjp77d4ZBgGiUSCcLiVdHQmhvIdWTWAbpbh8E7G4yrCMAwkScLlcmEYhuXDZRgGpmlaaYJ5P658aqPH4yEYDBIKhaxKhoMRW03TRFEUq2piTybx+QqIPp8Pt9uNpmmkUina2tr6rJiYj/jaUq45KjsiuxrTkXVWId29dBwFTh93LHiNH+ON3PDdc1w14RjK3UNbHO/llV9Sn2ojKHs4dfQ+3fbrpsH3kVwlxrw5/aai38/yvvvua/vo2NjYDCktLS1omobT6VyvL2U2NjY2NjY2Nj9HTNOkra2NtrY2YAiEruwnORN6owkQwXc2gv8iBGFgqZ6CWABFj2G2/xa079dUaeyPYHbYYTmxa9Eiq9Kdqqr43E7aYikiicw6Btj0fPX+d1x18F+gQ+g68Q9Hc9Ytv+7WThJEqr3FVHuL2ZPcuTFNk3YlQV2ihdpkM8s7zPCbszHr8WX7UmuMgOzuUgmyxl9Kpaewi2+TqqpEo1Gi0Siq0o6amEk6o2GY1UiOSnBsRVYR0I00siyjKArhcBhd1zEMwxKbHA4HPp8Ph8OBLMv4/X5CoRCBQGC9oqPyYlX+0bmKI+RSE/PilsfjwTRNUqkU0WiUpqamTVYxcUNT7g4hAGldIaqmKXB6+2w/PlTNn7f/JbfMf5mGdJjrvn2Gq8Yfw5hA+ZCsZ2WqjZfqZwNw+ph9CfYQOVaXyBVt8ErOIZt3sAxY0rR9t2xsbIYCRVGsUPthw4ZtNrnxNjY2NjY2NjZbAmsLXaWlpRQVFQ1yrDRm/A5IPZHbII1ACN2O4Nxp0OvLCV6PYrafsZbgtW3fHc87D+64AxQFx/z5UFiIqqr4PS4Aosn0oNe0Mfj8jTlcf+Rt1u+/ueF4Tv3jif3uLwgCxa4Axa4Ak4tHW9uTWpblyRZqE80d/7ewKtVOXMvwfbSe76P1VluXKDPcU8xoZzHlupegKuNBRk2vRs02AEEkUUd0jEIQS3A5cibsiqKQyWS6GMe7XC48Hg8OhwNJkvD5fPj9fnw+36AFLsMwSKfTlriVyXQVMNc2iRdF0Wrf2tq6WVVM3JA4RJlSV5DmbIzGdHidYhdAtbeYv2x3IrcueJnlyVb+NO8FLtnmMHbqId1wIOimwf2L30M3DSYX1bB7Sc+pyfkUxvGh6o1ilN8X/Ra7ioqKCIfDPPTQQxxwwAEDmqS9vZ3JkycPeHE2NjY/LfJfyrLZLJFIJFdKOhAgEAhs6qXZ2NjY2NjY2GwxmKZJa2sr7e3tAJSVlVFYWDi4sZRvMaNXgl6b2+A5GSFwJYK47gvrdSGIoVyEV/gMUOflqjUWPYbgGN97pzFjwOuFVArno4/CZZehaRoFvpxfVSKd7b3vJuZ/L3/Bn467w/r9zJtP5qSrjh2SsX2yi/GhasZ3VLcDUAyN+mQbtfHVLI01UZtoZlWyDW/GhHiCVWaa1aaI15SRMEBI4xRVArKDkGcMFYFheEUHiqJgGAaCICAIArIsWwbysix3EbgGc4PaNE3LRyufmrh2qmFnk3in02mZyjc2NvZaMTEvcLnd7p+EuNUTFZ4CmrMxmtJhtg1V9atPkcvPHyedwN8XvcF3kRXcseA1zhy7HwdUTBr0Ot5t/JbF8SY8kpMzx+zf6/nOi12bOoURBiB27bTTTsyYMYPGxkZGjuxefaMv/H7/gBdmY2Pz06KxsZH58+d3uXMjSRLjx4//yf5xsrGxsbGxsbEZakzTpKWlhXA4Z1o9WKHLNBXMxD8heT9ggFiGELoFwbXXkK5XEINQ+Chm+LegftdJ8JrQe6eJE2H2bIQZMxAvvxzDMCgO5MSuVGbjVHIbKDOf+R83n3yX9ft5d57GLy45Ykjn0HUdRVGsRzabRVAUhqsuKvUqpgjFZB1ZEkKWpJYhqymkTIV2s404KlkhS5us0SbKGMnv8McXMkwIMgw/hbKXIk+QUn+QAk8Av99vGcwPRuBSVZVUKmUZy6+dauhwOLpUQFQUhXQ6TWtra7dIr825YuKGpsJTwHeRFTRmIgPq55Vd/GH80Ty45H0+al7Ig0tm0JZNcMKIXQd87dWcifKfuk8AOGXUnhS7eg5UyOoqP8QagC1M7Jo8eTLvv/8+X3311YZcj42NzU+QxsbGHj87dF1n3rx5OJ1OKisrN8HKbGxsbGxsbH5uuN1uZs+ebf28JWGaJs3NzUQiESBX4KegoGDg46iLMaNXgLYgt8F9JELwhlwk1gagq+D1bc68vuhRBMfEnjscc0xO7KqrwyFJZA2D0kAu0iyjaj332YS88/hM7jjjn9bv/+/eszjy/IMHPV5nUSubzVo/r+1llTdzz2QyVmQWgF92UewL4nE2IWkf4XetwHQlWJQ5gbpoGYlsCkVXyBgagiAQFTK0ammiqUZiGQXTITEyVcqo1Boj/CpPIbLYe9qiruuk02lL3OopGquzj5amaaTTaSKRyBZdMXFDU+nuqMiY7r0iY2/IosT5Wx1EsSvAS/WzebH+C1qzcc4Ze0Cfz2VnTNPkX0tmkDU0tg1WcUBFL+9Z4IdYI6qpU+T0M8zTVYB3u90c9+g1xNQ0ro30uTugyC6Ar7/+eoMtxsbG5qeHaZrMnz+/zzbz58+noqLCjvCysbGxsbGx2eBIksTUqVM39TIGTGehSxAEysvLCYUGJk6Zpg6pxzDjfwcUEAoQQn9CcB+6YRbdCUEMdAheZ4L6TSfBq4fUqrPPhmuuAV3H9/nnZCdPpjiY8+xSNjOx6/UH3+Wu8/5l/X75I+dz8On796tvXtTqLGhls9lunlSdkWUZSZJQVZVsNosoirhcLhRFQRAE/H4/kiTgMD4i5HgZn6uZWHYHVoXPx6t4GA84/BW4XC4cHhdph8FqkixX2skkW0inMuiGxoLYShbEVq6ZV5AY7i2mxl+aqwjpK6Vc9KNnVct3q7NgtXY0liAIlhjW2traLY2xc6TXllQxcUNT0VGRsSkdHVR/QRA4ceTulLgCPLxkJh81LyCsJLhsmyPwyOsuPPFx80K+i6zAKcqcM3YaYh8+XGtSGId3u66TJImy8TXISmKjCZf9fgXtvffeTJ8+HUEQ1ln2cm2Kioqora0d1AJtbGy2bNra2rqFIq9NJpOhra2NkpKSjbQqGxsbGxsbG5stB9M0Wb16NdFoFEEQqKioIBgMDmwMrR4z+gdQ5+Q2uPZFCN6IIJVtgBX3jCD6ofBhzPBZoH6dM68vegTBsV3XhiUlEApBNIr/+edpnzyZog7PLm0tkWRT8tLdb3LfJY9av1/95P9j/5O7p4Hqut5N0FIUpU9Ry+Fw4HQ6cTqdOWHK4UDTNOLxOMlkEk3TUFUVVVVxOp0dIpeEy5HAZ/wfglFPMlvGyvDRpLTtkCQHXq8Lv99PQUEBfr8fl8vV7bpeM3RWpdqpTbZ0McRPaQoN8Vbi0SjLjOX4DQeSKeCX3RQ4vRQ4fRR5gwwrKKE0WIQsy5ZPVyQS6bFiosfjsSosOp0Dq/j5cyEfIbU6E8EwjT7Fpr44oGIShU4f/1j0Jt9FVvDHec9z1YSjKXT2bjkVUVI8vuwjAI4fsSvDvH2nS29Ofl0wALGrvLyc6dOnD2oSQRAG7PNlY2Pz0yCb7Z+JaH/b2djY2NjY2NisD4qicNddOW+liy++eLO/yDZNk6amJmKx2KCELtM0If08ZvwWMJMg+BACV4PnhE0SVZ8TvB7qKngVPoLg3L5rwylTYMYMXJ9/DkChPyd2mWaumt+m9mx69vaXeeiqp6zfb3jh9+x21BQrha+zuNUfUStvCJ9/5KNfdF0nFouxevVqstksqqpaFRPzqX6SJOUqKGa+wMjOoFXxY5rbk9R3R3TUUFoYoLCwkEAggMvl6vO4ZFFipL+Ukf5SNE2zfLeaY+20pWJE1CQRJUVESRE3MtSbURaqbaQ1DXdSwr/aQangpUT2U+D0WUJYwOnB5/X95CombmhK3UEkRLKGRruSpKQXv6z+sFPRaKZPOoFbF7xMXbKF6759lmsmHEuVt+cqro8unUlSz1LjK+OIqr4rs8bUNHXJZgAmFgzvtl9RFOY+8Q4pPYsy/heDPoaBYMcG2tjYbFDW9Qd1oO1sbGxsbGxsbNYHVVW58sorAbjgggs2a7FrbaGrsrJyQFWsTb0ZM3YdZD/MbXBMQQjdhiB3vxjdmKwRvM4BdU6uWmPho10FrxNPhBkzEFavBkUh5F5THbIlmqS8cNNU8zZNk3//6Tme/PML1raLHz2Tyu1KWLp0aa/9HA5HN0Grs6i1NplMhkgkQjweR9M0MpkMqqpa1RHz/XLG7nGczMEtLUbHi04ZzsCh1JTUEAgE+v0aNwyDdDptVU3snJ3hRma4v4StPcPxer24XC6imST1kdWsjrUTTSUIq0kSegYNnUY9whK1lURaJSGqGJLISH8po/yljMrm/h/uLcYh2pJEX0iCSLknREM6TFM6sl5iF8CYQDk3bn8St3z/XxozEa7/9lmuGH9Ut0qPs9uW8HnbYiREzttqGtI6IsrmR+oxgeHe4h6jxVRV5fN7cu8Zbfo/1usY+ov9yrKxsdmgFBcX43Q6u5lUdsbtdlNcXLwRV2VjY2NjY2Njs3ljmiaNjY3E4/HBCV2ZtzCj08GMAA6EwGXgPR1B2DyMvnOC1786BK8vMcOnd0R47Zhr8JvfwDnnIJgmgbfeIv2LNdEgK1ujG1zsMk2zx/TD5299lbfv/6jjIOB3D53OVrvWoOs6giAgy7IlanUWt/oTiWYYBolEgkgkQiqVskQuwzBwuVwEg0F0XUdVVRKJBJqmIQpx/I5PcIjteJzt+Ap2I1B2Nk7nuk3ATdO0Ug1TqRTpdLqbl1bedytfzCGTyRCPx2ltbcU0TQpwUuCtQPDlIs0cbhftZFilR1iebKUu2UIk1YZiqPwQb+CHeIM1toRIlbfI8gGr8Zcx0leKT7Zvgnemwl1AQzpMYzrcY9TUQCl3h/jz9ify1wWv8mO8kRu/f4nfbX0wu5VuDUBSy/LIkpkAHFk9mVH+dac6b24pjLABxK4777yTRCLBDTfcMNRD29jYbIHEYjGCwSCtra29tpkwYYIdxmxjY2NjY2Nj08HaQtewYcPw+3v31unS14hixv4MmddyG+TxCKG/Iji22oArHhyC6MsJXpFzQfkiV62x8GEE507gdkN5OaxeTfD114kffTSyKKAZJo1tMRiiwzFNE03TLEGrcwri2j5TL972FjMf+yS3dgH+8NxFbL/P+C6RWoNJr1RVlUgkQjQatYzns9kssizjdrsxTRNVVYlGo2iaZn1vdssrKPG9ScBdj8+j4ii+GcG19zrnyqcmplKpbseYN4r3eDyIokg2myWdThMOh/tdMXEY0Llmn24aNKTC1CWbqUu2UJfIeYEl9SwrUq2sSLUCC632Za4Qo/wljPKVWUJYkdP/s71eqPAUQBga05EhGzPo8HDdxOO458e3md22lH/88CbtSoLDq3biqdpZhNUkwzyF/GLELv0ab16kHsiZ028uDLnY9de//pXm5mZb7LKxsSGdTlNfX4/X66WmpobGxsau4dBuNxMmTKCysnITrtLGxsbGxsbGZvPBMAwaGxtJJBKIokhlZWX/ha7sLMzo1WA0AxL4zkPwn48gbL6pmoLohYIHMCPngfJ5rlpj4UMIzsmw++7w3//imTcPAL9LJpJWaY4kBjzP2qJW54ittQUfa22CYKUfPnn9i2uELlHg77P+woTdxg36uE3TtMzbE4mEJXJpmobT6cTj8aAoCvF4HMMwLKFHFEX8fjeVwdcIOZ5BEjVwTEUo+BuCVNFtHl3XrSqIeT+xzoiiaIlVsiyjqirpdJrm5uYhq5goCSLDfcUM9xWzF9tax9+Wja9lhN9KSzZGczZKczbK7LY1aaFB2ZNLgfSVMspfxihfKRWegnWm1/0UqMxXZMyEh3Rcl+Tgkm0O5/FlH/FO47f8u/Zjvo/U83V7LQhwzthpOPuRZro6k3u+JES2DVYP6RrXBzuN0cbGZoOgqip1dXUYhoHf76empobx48fT1tZGNpvF5XJRXFz8s71DY2NjY2NjY2OzNoZh0NDQQDKZRBRFhg0bhs/nW2c/00hhxm+H9NO5DVINQuj27qbvmymC6IXCBzDD53YSvP6FcOqp8N//IkYiiIkEAY+TSFqlJZLsday8qNVT9cO1xRtr/k6i1toVEEVR5G9n3cd7j34MgCgK3DP7VrbaafSgjlXXdaLRKNFolEwmg6IoZDIZRFFEkiREUSSVSmGaJoIgIAiCJUgVFxdTVpRCTv0etB8AAXwXIPh/hyDI1vFnMhlL3MpkMl0isgRBwO12WxUQ8z5d7e3tvVZMzItbDodjyL67C4JAiTtIiTvI1OIx1vaEmsmJX8kW6hK5SLCGVJiYlua7yAq+60iXA3CJMiO8JTkRrEMAG+4txiU5hmSNmwsV7lwVxKGM7MojCSJnjN6XUleQf9d+zJsN3+AQJU4YsWs3H6/e+L7jOdkqWIFH3nyEdVvssrGxGXIMw2D58uWoqorL5WLkyJHWH8aSkpJNvDobGxsbGxsbm82PtYWuqqoqvF7vOvuZyteY0StB7xABvL9BCFyOIHg28IqHFkHwdAhe54HyGWb4bDjkAQRRRDAMgi+8QMi/NfXt0BZPWql9PaUf9iVqdU457Cxu9Sbi3Prru5jx9P8AECWR+765ndETRw74+DobzucFLlVVEUURQRDIZrOWwCWKIqIo4na7KSoqorS0FI/HA5nXMGM3gJkCsQgh9Ddw7o6qqiSTYct3a23Ryul0WqbyeZ+ueDyOqqpd2uVFtU1ZMdHvcDOhYDgTOqXDZXWVlan2XBpkooW6jmiwrKGxONHE4kTTmmNAYJin0BK/8tFgAceW9X7ozLCOyK7VmSi6aQx5NJsgCBxZPZm54TreafwW0zBZnmglrqb7dd7WpDBuPn5dYItdNjY2Q4xpmtTX15NKpZAkiVGjRvVaZcbGxsbGxsbGxiYndK1atYpUKtVvocs0FczE3ZB8CDBArEAI3Yrg2n3jLHoDsEbwOh+UTzCj50B1GcKKJvzvvIP/V+MBWN0aZsmSJf0StdaugDgQ8ebPJ/yNWS9+DoAkSzw472+MGNe/aBdYYzgfDodJp9Nks1kymYxlZq9pmpWiKEkSsizjdDopKCigpKQEv9+PKIqYZhozdi2kO6rZSbuTdvyRdNhNKlXbTbSSJMkylRcEAUVRrJTJtc9T58itfPvNDZfkYEygnDGBcmubbho0pSOWB1jeDyymplmZbmdlup3/tSyy2hc7/ZYBfl4AK3UFN8vjXZsilx+nKKMYGq3ZOOXu0JDPsTTexILoSkpcAVyizNLkam747jmumnBMn/MZpmFFdtlil42NzU+a5uZmotEogiAwcuRIXC67moqNjY2NjY3N5oPb7WbmzJnWz5saXddpaGiwbhRWVVXlonj6wFQXYUav6EhlA9zHIASvQxCDG2HFG4Y1kVoaWfNmsvF7yabrKdrhS0IrmnAvWoTPmYtoiaWylki0tqCVTz9cXxHj+qNu5fPXvwJAdkg8NP/vVI3tn8+soihWqmLe4D2dTiMIAqZpWiKdKIrWmv1+P8XFxYRCoS5eWKa2BL39MtKpFlLZcaQ4DoXdgGzHY41olTeV1zSNdDpNIpHolsK4tqn8YAz1NwckIVfJscpbxB6lOe800zQJK8kuEWB1yRZWZ6K0KQna2hPMaV9mjeGTXIxaqxJklacQWdy8btSLgki5O0R9qo3GdHjIxS7N0Ll/8fsYmOxfMZFjq3fmlvn/pSEd5vpvn+UP44/uIjR2ZnmylbiWwS06GOPvuQ3kPmuP/OflxLU0ro30uTvkYtfaFRpsbGx+PkQiEVavXg1AVVVVv81UbWxsbGxsbGw2FpIkse+++27qZQA5oWvVqlWk0+l+CV2mqUPyYczEXYAKQiFC6C8I7oM23qLXk7yotbanlqqqa0VqnYxpvEH46AShVxciptNUK3EAFANqamqG1EOqM384+C98/d53ADhcMo8suouKkWXrPK5kMkk0GiWRSFim8KqqWmvMpym6XC5cLhdut5vCwkIKCgpwuVxd2mWzWZLht0m2/5dMthqDbRHcB4KUS+/Li1aSJGEYBplMhvb29h4rJno8Hnw+X5eKiT9FBEGgyOWnyOVnp6I1nmpJLctyywg/9399qo2knmV+dCXzoyuttg5BYrivmBpfWYcIVsoIXwluadN6UVV6CqhPtdGUjkDh0I79yso5rEi1EpQ9nFazDyGnl79sfyK3zn+FFalW/jTvBS7d5jB2LKrp1ndeR1TX+FB1nyKhJElUTR5Hu5LYaK/BIRe75syZ02s1Cxsbm58uyWSS+vpcvnZpaSlFRUWbeEU2NjY2NjY2Npsvawtd1dXVfUaamdpyzOgfQP06t8F1AELwLwjS5umHappmj35aiqL0GiCRj3RaE6F1Jo7KZZiygKCZ7PHpuzxesRcZJVexcEPw+31v4LuPFwLgdDt4bPH/UVpV3Gv7vOF8JBIhmUySTCZJp9PW8UDuXDidTtxuN263m2AwSCgUwufzWW1UVSWVSnUYy0fRUjNBXQgEQarG4T8Ur68Uh8NhiWHRaHTIKib+lPHJLsaHqhkfWlMpUDW0Dh+wli4+YGldYVmimWWJZqutAFR6CrtUghzlKyXkXLen3lCRN6lvSA9tRcZVqXZeqp8NwGmj97GOqdgV4E/bncCdi95gXmQFf13wGmeN3Z/9KyZ26T9vM01hhA0gdlVXbz6lJm1sbDYOiqKwfPlyTNMkGAxSUdG97LGNjY2NjY2NzeaAqqo8+OCDAJxzzjk4HBu/cpuu66xcuZJMJrNOocs0TUg/gxm/Fcw0CD6EwPXgOXaz8BvqLGp1FrT6I2qt7afVU6SW6b8Lc/QzCD9GGD33c6RD9yKjahvkWC7a7RoWfbEYAJfXxeOL76a4sucbuHnD+fb2dkvgyhvO5+nsneX1egkGgwSDQWRZRtf1TuJWCkVRcp2MdszM24g043ZFcAUPQHAfgaYZJJPJboEl+Tk2RMXEnzIOUabGX0aNvww6su8M06AlE+tSCbIu0UJYTdKQDtOQDvNp64/WGIUOH6P8pbkoMH8pNb5SSt1BxCE2kAeo8ORSF5uGsCKjbhrcv/g9NFNnp8IaKx00j1d2cdX4o3lg8ft83LKQB5a8T1s2zvEjds15wRkai2INwLrFLlVV+f75mST1LOqE44fsGPrClnltbGzWC13XqaurQ9M03G43w4cPt//A2tjY2NjY2Gy2KIrC7373OwBOP/30jS52DUjo0pswo9eCMiu3wbkrQugWBKn/JulDhWEYvaYfDkTUcrlcyLLc7++LguCEw0+DH+8itKoZt0Miqw2t2GUYBhdOvYol39QC4PG7eWLZvYRKgt3axWIxWltbiUajlkglCIL1yItPHo8Hl8tlCVxOp5NsNkskEiGVSpHJZLqdN7e0AJf5NIIng04JafEc0ulhkI5abURR7JKW2Dn90Wb9EAWRck8B5Z4Cdi3ZytoeUVIdKZDNLO/wAWtMhwmrScLhJN+E66y2HsmZM8HvMMKv8ZVS7S1ebx+wSk8usqtxCMWu9xq/48d4Ix7JyZlj9+/xdSSLEhdsfRAlrgAvrZzNC/Vf0JqNc/bYA/gx1ohiaBQ6fFR7+87qURSF/93xNADqH24fsmPoC1vssrGxGRCmadLW1kY2m8XlchGPx8lkMsiybFdetLGxsbGxsbHpA03TWLlyJdlsFlmWqa6u7rGYj2makHkDM/ZHMGOACyHwe/CeirABokY6YxhGl+is/ohakiT1WP1wIKJWXwjnXQB/vwtJ1dk23MBXvjLM7CwE117rPbZhGJy7/eXUzc/ZcXhDXp5Yeg/BooDVJpvN0tLSQmtrK8lkEkVRLIP8vMDldrvx+Xw4nU58Ph+BQACn00k6naatrY10Ot0tKiuf2igICkbyaTLJH4moFSANz/mwmZ4tpmLiT5kCp5cC50i2LxxpbcvoCiuSrZYHWG2ymfpkG2ldYVFsFYtiq6y2kiAy3FtseYDlxTCv3P9CXpWeAgBaszE0Q19v8awlE+Ppuk8AOHnUHpS4Ar22FQSBE0ftTpHLzyNLZ/Jh8wLCStISuCYVjNgsX5NDInaJokhlZSWrVq15QisrK2lpaUEbYtXdxsZm09HY2Mj8+fPJZDLWNkmSKCoqYtKkSRvMO8HGxsbGxsbGZkun30KXEc6JXJm3chvkiQgFtyPIY4d0PWuLWvmIrf6IWmunHw6VqNUrW2+N4XYjZjIc/f2nzNnlGJTWC3CW3IPg2mfQwxqGwZnjL2Hlj40ABAp9PLHsXnwhH4Zh0NbWRktLC7FYjGw2i67rGIaBKIrWucgLUG63G7/fjyRJZLNZWltbUVW1y3ySJFkVEAVBIJvNEovUYmbeBiMMBBBcu+AO7IHX69viKyb+lHFLTrYODmPr4DBrm2borEqHrSiwug4hLKlnrcqQH62xAqPMFaLGvyYKbJSvjEKnr8f3UoHDh1t0kDFUVmeiVK0jkqovTNPkX0tmkDVUtglWMa1iUr/6HVi5HcUuP/9Y9CbfRpbzacsPuGUnEwuGD3otG5Ihi+zq6QPRrsxoY/PTobGxka+++qrbdl3XaWlpIRqN4vVuPJNGGxsbGxsbG5stBU3TqK+vR1EUHA4H1dXVPd4kNDMzMWPXgdECSAj+C8B3HoIw+FTLzqJWXtDKZrNomjYgUcvlciFJ0iaL4NC22Qbn3LnsWPcD7CLQFHUyXLoACu9FcO078PE0jTO2uZimZTn1IVQa5PHF/4eOxpIlS2hra0NRFHRdR9M0K4IrbwDvdru7nBdVVWlra+syhyAIuN1uSwxUVZVkMrnmvKsLMLMf4XTE8Pp0vCXn4QvtbGdKbKHIosRIXwkjfSXsXbYtkNNEWrIxy/8rL4S1KQmas1Gas1G+aFtijRF0eDoM8Ms6BLBSKjwFSIJIpaeA2mQLTenIeolds1oW8W1kOQ5B4pyxBwzIY2ynotHcMOl4bp7/X1am2pC1TJ9RYZsSO43RxsZmnZimyfz58/tsM3/+fCoqKjbLEFYbGxsbGxsbm02FqqqsXLmyT6HLNBI5A/r0c7kN0phcNJejfxEXkBO11jaIz6cf9oYkSd0ELafTuUlFrd7QDjsM59y5lEdawTBYldif4UWvYIYvhIJ7ENz79X8sTePU0b+jZWVOnCooD3HTB1fy49IfSKfTaJqGpmkYhtFF+MsLV7IsI0kShmFYlRfz5NtC7qZwNpvt1kaWDDzCy3gd7+ApbMPh2Rmh4O8Iol3N/KeGIAiUuUOUuUPsXLwmOjOmpnP+Xx2VIOsSzTSkw8TUNN9FVvBdR5VDAJfoYKSvhNZsnKSW4dvwciYVjsApDlzOiSgpHl/2EQDHj9h1UKLZ2EAFJ47YjZvnvwzA3xa+zpXjj2Kb0Mb3EuwLW+yysbFZJ21tbV1SF3sik8nQ1tZGScnmWf7axsbGxsbGxmZjo6oq9fX1qKqKw+Fg+PDh3QzxTeVLzOgfQF8JCOA9DSFwGYLQs2m9ruu9ph/2hizLvXpqbSkYZ56JefPNSKbBzg2LacqeB64sZN/GjPwOCv4Pwb3/OsdRFJXf1FxAe2MEgFB5gEuePYPmtmY0TUPX9S4+XA6HA0mSkCQJURStlMK8/5Ysy5ZJvGmaZLNZ4vF4lzk7V0z0OFYiJ3+PYNSSi967FHxnbXAvNpvNi6DDw6SCEV2qGGZ1lfpU2xoj/EQLK1KtZA2VH+ONRJUUcS3NMys+5f2meQzzFnakQJZ1RIOV4nf0/LmR57FlH5LQMozylXJE1U6DXv/KVDtl7iAOUSapZ7nx+5f43bhDuhj7b2q2nE83GxubTUY2mx3SdjY2NjY2m559992Xjz76iOnTp/PHP/5xg81z+umn8/jjj3Paaafx2GOPbbB5Nhfy0TAzZ85k33333bSLsdmkKIrCypUrUVUVp9NJdXV1F6HLNLOY8X9A6hHABLEKIXQrgmsXoKuo1Tlia6CiVj7NbkvHUVWF4fcjJRIcvWg2K6KnIRT8DTMqQOYtzMhFUHAXgntaj/11Xae9tZ1zJ15JvC0BQEFFkPMfPwXVUDG0nBfX2uJWPvotLwyKoojL5UIURUzTtFITO5OvmJgXuCxvtvSzmLEbAQXEilw0l3PyBjtnNlsWLsnB2EAFYwMV1jbdNGhKR6hNNPNB0/d81LwAAdAxqE+1UZ9qY1bLIqt9qSvISF8JNf6ynCG+r5RiVwBBEPiybSmftf6IhMi5W01bL5P7eZEViILIRVsfwv9aFjGnfRn/WPQGv6nZm8PXQ0QbSmyxy8bGZp309wtSTyarNjY2NjY2NhuHxx57jLq6Ovbdd19baOsDl8vF66+/bv28IVin0KUuwIxeAdpidMOBIp2A6jgXJSqhKCstT63eyEcT9ZR++FNFlmUyEybg++ILdmpcxlfRZM7LLPQ3TATIvIkZ+X8dgteBQC5dMZFIkEgkiLRGuHa/20hFc9kKRdUhzn7oJAQ5F5GVj9oSRdGqkuh0OhEEocu51TStW1pivmJiXuDyeDxd0kBNI5HzYsu8mdvg2hchdBuCWLgRzpzNlowkiFR5i6jyFlHmDjE/tpIip58btz+xw/+rhbpkM3WJVpqzUVqyMVqyMea0L7PG8Mtuqj1FfBdZgWbqHFK5AyN9pYNeU0smRmMmgoTIdoUj2blkLI8t+5B3G7/j37Uf05qN85uavbp4gblcLg7920XEtTTOjXTNaItdNjY2fZJIJGhra0OSpG7lkjvjdrspLi7eiCuzsbGxsdkSqKysZNy4cVRWVm7qpfzkeeyxx/joo5wXiy129Y4syxx++OEbbHxFUaivr0fTNJxOJ8OHD0eWZXRdJ5NJoUSfJRt/A0UNoeiHocuHgFwDxLqN5XA4ekw//CmLWr0hSRLJww7D98UXlCajRMO58yUIMoTu6BC83kBp/QNJWSehjCeTyWAYBpHWCDcedg/ZRC4LoWRkIWfcfzyClBO68imLa5vOC4JgGfx3NvMXBAGXy7UmNbGPiommugAzcjHoywEZIfB78J5hpy3aDJhKTwEA7UoCv+xmp6LR7FQ02tqf1LKWAX5eCFuVaiehZfis9UeSehZZkPiweQGfty1muLe4SyXIEb4S3NK6i2F83+EnNiZQjk/OCVe/Hb0fJa4gT9f9jzcbvqE9m+DCcQdbvmKyLDNyz+1oVxIbLX3aFrtsbGx6xDRNWlpaaGpqAqC8vJyGhoZe20+YMGGzMzK1sbGxsdn03HLLLdxyyy2behk2NhuFbDbLihUryGazVvpbY2MjiqKgKS2Y2fdBXw1UgzwmV0VQ8NiiVj9Jn3AC5vTpiMDYz2bC744DQFUN4vrVxCNVZJLLgbfQZJ2sVkG8PcFfj30AJZVL/ywdXcTp9/4Ch9OBw+HoFo2VT01cO13U6XRa4pbX613nc2OaJqSfxozdDKggDutIW9xxA5wZm58DAYcHn+QiqWdpykQZ6evqleyTXYwPVTM+VG1tUwyNj1bP564f3kYQBLYJVBFRk2QMlaWJ1SxNrIbVubYiAhWeAmp8ZYz051IgR/pKCTm9XeaZF6kHYGLBcGubIAgcXT2FYqeffy5+l8/bFhP5PskV2x61Th+xDYUtdtnY2HRD13VWrlxJNBoFoLCwkKqqKiorK5k/f34Xs3q3282ECRPsO/Y2NjY2NjY2WwSKovDoo49iGAYnn3wywWBwwDfsTNNE0zRisRjpdBrDMFBVldbWVnQ1jCRpBAMe4vGOcdV5mNlPcchxnB4NV/BYnP59fhbphwPFNE2rIqIsy11EKN3lIuUP4EvEOe6Np/nqbB3x7NORPTnBMJ3cn5mPv0jd3CYcnlcZu+sEXrn9U/RsLjuhcusizvjnifj8foLBIG537iJcVdVu3rOyLHcRt9YuLLBmvTooc8BoAbEUnFPATGFGr4Xs27lGrv1zfmxiwQY7bzY/DyrdBcyPrWRG43fsWro144LDkHqIEtRNgx9iDTSnozxT9xleycmB1VM4e+wBGKbB6ky0SyXI2mQLUTVFQzpMQzrMJ60/WGMVOf2M8pUywleCgMBHzQvQDYMJnUS1PHuWbUOh08cdC19jUayB6797lqsnHIMbmc9fepe0rvCMv5pLJx2FR3J26z+UCGbneMxBIooiFRUVXaI+KisraW5u7jPt6edILBYjFAoRjUYJBoObejk2Nt3IZrPU1dWRzWYRBIFhw4ZRVFRkfQk0TZO2tjay2Swul4vi4mI7osvGZgAM5d+BvLfIokWLGDt27Lo72Nh0Yl0G9Y8//jhnnXUWmqZxzTXXcNNNNw1qnr4M6juv4brrruP//u//+Pe//83ixYvxer3svvvu/OlPf2L77bcHIJVKceedd/LMM89QW1uL2+1m2rRp3HzzzYwZM6bb3I899hhnnHEGI0eOpK6ujvfee48777yTOXPmkEgkGD16NL/61a+4/PLLrYve9aWzQf3kyZO57bbbeOGFF1i+fDler5fddtuN66+/nl122aXPcd544w0eeeQRPv/8c1pbW/F6vUyaNIlf/epXnHnmmTiday4S8sfZF7W1tYwaNarLtk8++YT77ruPWbNmsXr1alwuF+PGjeMXv/gFF154IX6/v9s4nZ/PRx99lIcffpjHHnuMhQsX0t7ezqOPPsrpp59utf/www+59957+fTTT2ltbSUQCLD99tvz61//mlNPPXXAIs/6fobG43GWLVvGDjvsAMArr7zCtttuS0VFBYFAoFt70zTRdb2LQbyiKMRiMWKxGIqiYBgGAIaexCE24HaEKfQvxe2M4XRKuJwyTmE+TjmJ6N4FIXQLgmTfJOyJeDxOU1MTiUQCXdeRJAm/308gEKChoQFp+p/Z7vVX6PzN0xBEFh55FE+5duLLF7+DXq5ua7ZzcsUjDkQ5iOE4BEPs+pnRuWJiXtxa13dcM/MOZuwmMJrWbBQ7bD2MNnJpi1eA93T7+7LNejO7dQl/nvcCbUoCpyjjlZwM95Vw1pj92blkbJd2Dy39gPpkK0kti2rqeCQnf5x0AnuXb9vr+BEl2ZEC2WqlQjZmIgCkNYWwkkQ1NIyON1mhw8deZduyZ9k21PhKqfIWWab3y5Ot3Dr/ZdqVBLFsitZ4mDnH3gHApIfPxTusmCOrJnPtpOMGfB76+3fAjuyysdmC0Q2Db5asojWapCTkY8exVUi9+AX0p28iHqe+vh7DMHA4HIwYMQKfz9elnyAIlJSU9DKqjY3NxsTTcSfbFrpshppbb72Vq6++GlEUueeee7jwwgs36HyqqnLIIYcwY8YMnE4nDoeDlpYWXnnlFWbMmMHMmTOpqanhwAMP5JtvvsHtdiMIAu3t7Tz33HN8+OGHfPnll4wYMaLXOf75z3/yu9/9DtM0KSgoQNM0FixYwPXXX89LL73EjBkzKCwcOrPoxsZGdtppJ5YsWYLb7UYURdrb23njjTd47733eO211zjooIO69Uun05x66qm88MIL1rZgMEg0GmXWrFnMmjWLf//737z55pvWej0eD+Xl5bS3t6OqKj6fr5tQ1VlUMgyDSy+9lLvvvtva5vf7SSaTfPnll3z55Zc8+uijvPPOO4wcObLH4zNNkxNOOIEXX3wRURQJhULdPIsuu+wy/v73vwO57w+hUIhIJMIHH3zABx98wJNPPsnLL7/co8i0IYjH49TW1hKLrfHGikQiRKNR0uk0w4cPx+l0dquAuPbNe0VRiEQi6LqO0+lEFEUyqVYwWhHELGMq36bQv2qt2R0IgWvBe7Lt1dQL+edHURTcbjcejwdN02hubmbZsmVU//N+xr/+Srd+gmkw/tWX2Z4FfCls1+v4+5/iQTc96KoJ6luI3kPxBrbrUjFxIIKUmXknZ4i/trpmtHUsrAih8AEE5/b9HtPGpjdmty7hL9+/SExNIwBOQcIlOahNNPOX71/k+om/YOeSsVa7jK7iEmU0U0cg95n914Wv4pYcXYSxzhQ4fexYVMOORTXWtrSm8Maqr3lgyfuAiSiIGB1jRtUUbzR8zey2JXhkJ7IgMdxbxCh/GaN8pZxesw83zn+JNjVhCWQAajiJMayIV1bNARiU4NUf7E/ajcS9997L+PHjmTp16qZeis1PhBnfLObwax/mnL+/wDWPvMU5f3+Bw699mBnfLB5U30OvfpAXZ36JYRj4fD7Gjh3bTeiysbHZvKiuru6zBL2NzUAxTZOLL76Yq6++GpfLxbPPPrvBhS7ICVFz587l+eefJ5FIEI/HmT17NqNHjyaRSHDxxRdz9tlnEw6Heeedd0gmkyQSCd5//31KS0tpbm7mmmuu6XX8lpYWLrnkEo4//nhWrFhBOBwmFotx33334XK5+OabbzjzzDOH9JguvPBCnE4nH3zwgbXe2bNnM27cOBRF4ZxzzrEigjpzzjnn8MILLzB69GieeuopotEo0WiUVCrFK6+8wujRo/n888/57W9/a/U58cQTaWpqYvfddwfg8ssvp6mpqctj+PA13irTp0/n7rvvpqysjHvvvZe2tjbi8TjpdJqZM2ey44478sMPP3Dcccf1uEaAl156iVdeeYU77riDcDhMe3s70WiUgw8+GIB77rnHErrOOeccGhoaCIfDRKNR/v73vyPLMh988AFnn332kJ3zvjBNk6amJhRF6fL9RlVVdF0nHA6zaNEi6uvraW5uJhKJkE6n0XXdqsbn9/utaHeXy0V5eTmhUAjTNHCIK/C7m3A547THx2Gaa4kmYgF4f2ULXb3Q+fnx+/0IgkAqlSIej5NMJlGTSbZ9/VUA1paj8r8fz4/IZs+vV4AX/xbHJbdTHFjK8JIvGV10O1VVlRQVFVkCev/Xq+ciunoLIwMQHOCY2O8xbWx6QzcNHlr6ARldJejIpfXqmLgkB4VOHxld5eGlH6AYmtUu5PCQ0HKZOj7ZTak7aLXT+3ifrI1Tknm36Tsgl0Lplhw4BIlCp58SVwBZkFAMDa/oRDN1apMtzFw9n0eXfchfF7zK6ky0z/FfW/UVaV1Zr/PTG/an7UbiwgsvZMGCBXz55Zebeik2PwFmfLOYKx98neZIosv2lkiCKx98vU/Bq7e+rbE097wzjx9bMowePbpXXwIbG5vNh8MPP5xMJmNVX7OxWR8UReGkk07i7rvvJhQK8fbbb3P88cdvlLkjkQgvv/wyxx9/vJU6NHXqVP71r38B8Omnn/L222/z3nvvcdBBB1kpvAcccAC33norkBNfehN/U6kUu+++O88884wl+ng8Hs477zzuvfdeAP773/8O6fc0WZaZOXMm++23H6IoWsf0/PPPA7B8+XI+++yzLn1mzZrFk08+SVlZGR9++KHlJwU5j8yjjjqKjz76CJ/Px8svv8zcuXMHvK66ujpuueUWPB4P7777LhdccAFFRUVArvJfPrW0urqar7/+mldffbXHcRKJBHfeeSe///3vrTX6/X4qKytJp9NMnz4dgF/96lc88MADVFRUAODz+bjkkku48847AXj22Wf56quvel1vNpu1Ugbzj8GQTqepra1l2bJlLFy40Nq+YMECFixYwOLFi2lsbETTNCRJIhQKUVlZyahRoxg7diw1NTVUVVXh8/lQVRWv14soiiSTSUw9BigIAgjoRJLVpLIFXRdgtGAqswe19p8D6XSaRCKB2+3GNE2i0SiZTAZN0zAMg+FvvIFoGN2ErjwCIAFH0vt34EizQfuPCygOLsHjakc0G3JeW4NBmdM1dbEnjNWDH9/GphNftC5mSbwJzdSJqCkAS7DKiVkulidaeHzpR/wYayRrqDRmIqimhohIgdNrtVuRbOWHWO9FxzqjGhrvNMxlcawRxdBoyETIGioI4JVd+Bxuil1+BODy8Udy95Qz+P02R3Dc8J0Z6yunORtDaU+QXNJEaulqa9zUstUklzSRXNJEpj3Ov5dtmO+xtthlY7OFoRsGf33uwx7vI+W33fH8h+gdd2JN07Qemq732jfPwzO+xVh/Kz8bG5uNwNVXX01paSnnn38+jY2Nm3o5NlswsViMQw45hOeee47Kyko+/vhj9t133402/5577smee+7Zbfs+++QMvAGOP/74HlN285FE6XSaxYt7v9C97rrruqXZAZxxxhlUV+dMdp955plBrb8nzjnnHMrKyrptnzRpEjU1uRSR7777rsu+hx9+GIBTTjmlSyRWZ6qrq9lvv/0AeOeddwa8rsceewxd1znkkEMsL7S1CQQCHHPMMX3OUVhYyLnnntvjvvfee4/29naAHv3gAC644AKruM3TTz/d63pvueUWQqGQ9ejtvKwLTdN44YUXOPHEE/n1r39tbb/zzjv57W9/y9lnn80rr7yCYRjouk40GqW5uZmmpiaam5tpb28nkUhY0V6ynHODcblciIIGgKq7yaoBEukKljfvTWP79kST1aiaJzdZ+HyMyMWYqRcw9XUIJT8zNE2zzqsoitb7Pv8d1tfUv/N1DvO4xfyY3cxVPUZ5XXXiGI7eaiK/P3Y0M14sINtyLWbiXkx1PgOysjZahradjU0ndNNgYXQVT9f9jyu+fpJb5r9MSsuS1VVMTCRE/LIbzdBJqBniaoaolub5FZ+R0RU0I5d67RAkil1+xI6IUocoo5kGYSXZ47yqobEouooXV3zBX+a9yJmf3899i98jrSuohoaJidgxt0OQuoz5TXstD/z4HrcteIV7fnib91fPI2OotLz5DQsuepRFlz9pzbPin++x4KJHWXDRo7S8+Q0rU+0b5Dzanl02NlsY3yxZ1S0qqzMmsDqc4IV3ZrFtVVfvkYWrwn32paPvN0tWMWXrwX2ZtLGx2XgsXLiQm266iUsvvZTx48fzm9/8hj322IOysrI+TZ/33nvvjbhKm82dxsZG9tlnH+bOncvWW2/NO++8083IfEOz884797hdkiRKSkpYtWpVr1YQ5eXl1s/hcLjHNrIss9dee/W4TxRF9t13X5588knmzBm6KIy+DOiHDRtGbW2tJQjl+eSTT4Cc6NWXAJSvlrx8+fIBrys/x7vvvmtFW/VEIpHoc46pU6d2McnvTP48Dh8+nK233rrHNpIksf/++/PUU0/1ed6vvvpqLrvsMuv3WCw2KMFLlmWOP/54pk2bhqZpnHLKKUBOBN1uu+1QFIVQKGT5h+XTG3Vd71KFWlXVXFqdqlpVFL1eFyiNGIaDjBpENzRME+LpCuLp3Dl2yCm8rja8rq/xOt9HklRMeStw7ong2gucUxEE14CP66eCLMtIkoSmaTgcDvx+Pz6fj1QqRTqdJtXHa7XLOMAUmplCMxoCi80C3qKG/1FNXHACApmkxPdfBPj+iwC3X2Ti9s5g7MTXOfBXGvv+cjKewgPAuTuC2Iedh1javwPrbzubnz0RJcW34Tq+CdfxXXg5SX1NdVBZkJBFCY/owCHJaIZBQssQUXOiVV6o9Ts8ZAwVr+TCL7u7eTmrhoYsiBQ6fdbvS+KrWRBdyYLoSn6M5yK4OhOUPUTFFB7Jic+RE7lM0ySpZYhrGbK6ioHJ3T++3e2YBKD0sB0p2HUrjKxqCV4jLjgQ/7a5m0yOIj/V3qKhOYlrMSRi16mnnkpBQUGXbb/85S8HHWZsY2PTO63RnpX4tYmmsv3atj5z2NjYbFr23XffLh4j9957r5WS1RuCIKBpWp9tbH5ePPjgg0AuTe79998fdOTM+tCXQXk+gqa3Nvn9QK9pjCUlJVakSE9UVVUB0NzcvM619pf+HNPa681XNu9vul4qlRrwuvJzJJNJksl1/73vbY6eotby5M9j/rz2Rj6irq/z7nK5+nzu+ovH46GmpoZoNNolwm/bbbdl0qRJJBIJQqEQo0aNQhAETNPsUn0xb1YP4HQ6yWQyGIbR8RnsxVRGgplFM9z4XM0U+OowcKBpHlTdi6qXETV+TTRRj6AvxyUtwetqxet6BbfzcUTBgencOSd8ufYEaczPqnqfx+PB7/cTjUaRZRlBEBAEAYfDgSRJrDj8cCY++hiC2XMqowkYwMdUMYVmAqjImGxLmG0Jcylfs0oIsGBMDa+2DOOHmL8jJUIgk5L4fraf72fD3y9dgtv7I2Mm/p39TyjkgFP2x1tyIIK8VvEL5xQQK/pIZRRy+51ThvI02fyE0E2DZYnVfNNexzfhWpYlun4OBmQ3kwpGUuUtJKOrPFH7MVElhaSLXYzrnKKMYZqM9JXw4C7n8P/mPEZtohlxrc8P0zRJqBkqPQXMj9Tz4ooveha3HB7GB6sZH6pmQkE1Fe4Czv3iXyyON5DqqOxo9JIn5BQkSt1BJhaM4IiqyUwqGM7+7/8ZZ5EfPbPGl8s7uhzf2JyALSJw6uh91udU9sqQiF1rl5EGuOuuu4ZiaBsbm07ouo6o90+w2n781ozfqrrLtrQjCO/PX2ffkpBtTG9js6UwoLSLQbS3+elzxBFHMGvWLKLRKGeccQavvvoqXq93Uy/rZ0m+6t99993Heeedt0Hn+MMf/mD5nQ2GvqJHN0cEQaCioiIXJZRKcckll/CPf/wDQRBIJBK4XC4qKiosgSlvQr+20GaaJuFwmNraWrLZLLIs54QxaQRapglJUnDKKZLZNZFIJoBzTxAq0IVSDHEHNEEjozTQnlmBqNfhdqzE66rH6/o/XI6bEaRKTFc+6ms3BDG0Ec/Wxqfz85NIJJAkyRIaBUFA8niYd/gRbPf6q5h0NanP/1V7ga15qKMa4ygzwrEsYQ8aCKEgANVGnOrF33EQ36EVe2icujtvRUbwzo8asXA6vxIyKYn5s33Mn63wf1e8hcvzBmMmmex97CimnXo0wYo9EAQHpvdkSNzZ09Hk/g1egyBsWe8Tmw1LQs3wbWQ537TX8m14OTEt3WX/aH8ZWwUqcYkOmrNR5kWW82nrDwC4xJyfpYmJR3TilV0ICKR1Bbfs4HdbH4JbcnLWmP35y/cvElaSeCUnJpDWFVJaFhOTqJrmhfovrDmDDg8TQsPZNljFhIJqqjxFNKTb+W/9lzxR+zGrUu1doszWRhZE9izdhgu2PohR/u43QY6smswrq+YgOmSqf7svKx/5EEGWuuz3SD1HCa8vdhqjjc0WQP6LVVNTE2VuKPS5CCd7/tARgLJCP1PGjegWujpl3AjKCvy0RBI96vH5vjuO7ftOrI2NzeZBb1XSbGwGwuTJk5k+fToHHXQQM2bM4PDDD+f111//SVXkbW1tRVGUXtPuVq1aBfQdrbQxqKioYPny5YNKTxzIHD/88MMGnSN/HleuXNlnu/z+jXXeA4EANTU1NDU1ceihh5JMJikuLiYUClFRUdFnNF4eQRAoKirC4XDQ1NREIpHAMAyczio8hQIFzudwiI0oqg9F85PVKzEd+4I8BsilzRqGgapCRq3EMMqBqSSMFFJ2NRKrcApL8blX43V9htf1Og45i+nYHsG1Jzj3Asekn6SI4vf7KS8vZ8WKFcRiMQzDQBRFiouLKSsro+lP0/lKg53eeR2hkx+XKYgsOPIovnPtBC9+BybUCQX8nSn83TQZ54ly+dQfGf59M1J7LiVVbksz/O0ZnAOc5fWSPmw/Fo3ajpcXGcz7uoFkZI34lU1LLJgNC2av5P6r78Hl+T9GT3Cy28FhDjxBpqjMAXQSLcSKnNDlPnijnTubzRPTNKlLtvBNey3fhOtYEm/qEhXlk1yMD1VT4gqQNVQWx5t4p/HbLmN4JScTC4azXcFIDNPkxfovqE+2ktFVZEFktL+MM8fsz84lY1EMDb/DzT5l43mv6TuaszFM08xFSQoSBU4f5Z4QE0LDGR+qYkJoOBWuEPOiK3ht1Vc8uPh9mrNRFEPrdq0okIvCQhCQBRGP5KSm09y9ce2k44Bc1cXiAyZhZDWcpUFEBI6smmzt3xAMWuxasWIFkiStMzw5T0NDA5qmMWLEiHU3trH5GWGaJm1tbWSzWVwuF8XFxV3C1pPJJA0NDaTTuT+ibreLS47dg+lPfoBA14LH+V6Xn7BvN6ELQBJFrvjlvlz54OsD7mtjY2Nj89NlypQpzJgxg2nTpvHhhx9y6KGH8uabb+L3+zf10oYETdOYNWsWBxxwQLd9pmlaFU2nTNm0KUd77LEHy5cv5/XXX+eWW24ZcP98el5fEZx77LEHH330Ee+//z6ZTAa32z3o9fZG/jyuXLmSH3/8sUffLl3XmTlzJkCvfmwbgkAggN/vp6qqil133RVZlvF4PANOGcyPk06n0TStY5wdgEM6KvW1gFiK6ZiMYWClQXZOi9R1HdM00XUdTXOiqj5S6nBMcyrtmTiS0I5EEy65Ab+7iaD3Jfye+5EkL6ZrdwTnnuDaC0Hqn5/V5oppmqRSKdra2kin0wQCAXw+Hz6fj8LCQgKBAIIgUFlZSeu/H+Kqx15j388/wL+6Cfe22zDs2supKS1lusPB6r+u5rX73mXxl7V4/G52PnoHxu06GtHTQswTw700jPmf2Tifew65JWceL6ZS+N54g8m8wU6ShLrLLkT23oev/SN598NVLJldSzqe920TyKYFFs7RWDgnwCM3jcfphlHj3exyyDAOOm0a5WMP+EmKkTb9I6ll+T6ygm/CdcxtryOsdk0XH+4pZpS/FIco0ZyJMTdch2rq1n4BGOuvYFLhCHYoHMUYfzmyuOb1NK1yEj/EGggrSfyyG1EQWBRdxVsNc1kcb7TGKnD48EhO3JKDrQOV7FG6DRMLhlPiCvDR6oW82zCXuxa9RVhJoJvdExMlQaTA4WPrYCUHVU5iWvkkZEm25i50+hgXHIYkrPu68dpJx3HZ+CP497KPWDlhD6q9RZw6ep8NFtGVZ9Bi16hRo6isrLTuhK2LPfbYg/r6etsnxMamE42NjcyfP7+L8anb7WbChAkUFxfT1NREJBIBcl9gy8vLKS4uZhtRxOv18tfnPuxiOF9W6OfyE/blgB236nXOA3bcitvPOWJQfW1sbGxsfrrsuOOOfPDBB0ybNo1Zs2ZxyCGH8NZbb/Ur2mVL4KabbmK//fbrVpHx8ccfp76+HoATTzxxUyzN4pxzzuHpp5/m+++/57777uP888/vtW0ymcThcHSJVgsGgwDWd4ee+O1vf8vtt99Oa2sr06dP57bbbuu1bV6YGajoeeCBB1JcXExbWxt//OMfezTbf+CBByz/sF/96lcDGn990XWdN954A4Bjjz120N5YgiD0kPIrgWtNcQIBEMWcT9va0ZKapnURwBRFIZPJkMlkUFUfqlpMRqsho2SJZmKsDEeRaMflaMfnasHv/g9+7524PaVI7l0R3Xmj+6EXMDcEa4tckPu+W1BQQGFhYRc/PlgTVdemwow9DsTj9XLjeSd0aTN85HAuuPVMVFUlkUgQj8dJp9OklQrSSgVCqYD3qv3x/fnPuH/8EePpp3E9+yxyh2+coOs4P/2Usk8/5RDgwK23JvWbA2na4QDmxDx8/uq3LJ1TSzaVv6YVUDLw49dZfvy6lidu/hcO10OM3LaYqYfsykG/PYjqsZUb+lTabEJM02RVup2v22v5pr2OH2IN6KyJPHSJDsYFKgk5vWR0lSXxJma1LOoyRpHTzw6FI9muYCSTCkbgd/T8HlYMjSXxJuZH6lkQXdVF3MpT4PDm0hJDVYwPVeOTnLzZMJeX6r/gjoWvEVfT3Ty3BMAhSpS6gkwqGMHhVTuxc/EYq4pjZ8aHqrtt6w8OU6RoboQiRI49dj9kacMnGa7XDLZPiI3N4GlsbOSrr77qtj2TyfDVV19RWlqKx5MrVV1UVER5eTkOh8Nqd8COW7Hv9mP4ZskqWqNJSkL/n73zDI+jPNv2OW27dtW7LffeC2BjcMemmA6hg5OQRviAhJA38CaUQMJLCIEkEJJAKIHQIXQbd2yMDbbl3m1JVrX69joz34+11pIl2bIsWy5zHsce0u6U55mZ3Xlmrrnv67Yzul9eh6KyjmVZAwODkxNN01i7di0lJSUEAgFuueWW7u6SwSnIyJEjWbx4MdOnT+err75i1qxZzJs3LyGinKrYbDZWrFjBDTfcwJNPPkl+fj6hUIhXX32Vu+66C4DLLrus3aqQJ4rJkyczd+5cXnrpJe644w527tzJnXfeSZ8+fYB4dFBhYSHvv/8+//rXv1i/fn3C5B1g2LBhfPjhh3z22Wfcd999bWZg9O3bl1//+tc8+OCDPPHEE1RWVnLfffcxbNgwIC7AbN68mY8++ogXXniB//znP0yaNOmotsNqtfLQQw9x55138sYbb+B0Onn44YfJysoiEAjwwgsvcO+99wJxgXHs2LGd3WWdIhwOc+211wLxqpOHiionClmWkWW5lWCmqmpC/GrysGqqSqiqMYKqj4DfQ43XjaB7kaUgZsWL3fIKNvOfMdsKMNuGY3Kcg2zu30rg7W7aE7lcLhepqamHPR6SJOGPqCRbJGym9udTFIWUlBRSUlKIRqN4vV68Xi+hUChRnEFITcV23304Hn4YZcsW9LfewvzeeyiVlQfb27mTpJ07SQL6paVxxYwZeB6DolwnWzansG6Bzp7CMmLhg+JGNKyze30tu9d/whuPf4JiEskfmMWYmWOZddtUeg8zMp1OdUJqlK3u0gPm8sXUhFsWFMm2JJNnS0VCpDrsZqN7X4vpZlFmsCufkckFjEwpINea0qboHj4gjm05UC1xl7eK2BHErUgsyicVhbyydxllgXqCagS9DXHLIpnItaYwLrUPl/c4iz6OzONaFKM7zrsn7MweCoW6bSAxMDjZ0HWdLVsObxRfX19P//79ycvLS4hehyKJIuMGdK5q1rEsa2BgcHLxl7/8hUcffZTa2trEZ83FroaGBs477zxisRjLli0jKyurO7ppcIowfPhwli5dyrRp0/j666+ZOXMm8+fPb1V5+1QiIyODX/ziF9x555289dZbpKSk4PP5EtUQR44cyYsvvtjNvYzz/PPPI0kSL7zwAk8//TRPP/00DocDRVFwu90tvPoOvTG59dZb+eMf/8ju3bvp2bMnGRkZiTTFFStWJISxX//618RiMR599FH+/e9/8+9//xur1YrNZqOxsTFhYt9WGx3lpz/9KXv37uVPf/oTf//73/nHP/5BcnIyXq83kekxdepU/vnPf3Zq/aczkiRhtVqxWq24XAeN6TVNIxAI4PF48Hq9+P1+IpEAsVgj0YgbX8iNKARRpCCyvANFWo+iyJgseZhtAzDZhmG2pGIymVAU5YRXezwWkas5gUgMMGEzdSxVUFEUUlNTSU1NJRKJ4PV68fl8LYWvzEzs//M/2B95BGnDBnj7bSz//W8L4Uuoq8Py1ltY3oIMRWDMeedy1SUX0vD4ZRR7YNvKnayfv57iTftpXuAuGtEo2lRJ0aZPeO+pT5AVkdx+OYyeNpwLbptC/zF9zqjKm6cqVcFGChvi0Vtb3WUtIqoUQaKXIxOnbMGvhiny1VAVamyxfIE9nZHJvRiR0pNBzlwUsfX3PazGfbu2HkbcSlHsDHHlMyQ5n0FJuVQGG/isopC/7VpAdchNtA2/LREBu2yhwJ7O+ZmDuChvLJmWU/shVkc4IepTRUUFNTU13W76aWBwslBXV9cidbEtVFUlOTm5XaHLwMDAAOCOO+7g+eefR9d1nE4nPp+vVSR1SkoKY8aM4fXXX+edd97hpz/9aTf11uBUYciQIQnB65tvvmHGjBksWLCAlJSU7u5ap7njjjsYMGAATz31FN9++y2iKDJo0CBuuOEG7r333pNmvDWZTPzzn//ku9/9Lv/4xz9Yvnw5FRUVhMNhMjMzGTRoEOeffz5XX311q8it/v37s2TJEn7/+9+zevVq6urqEsJScysRQRB45JFHuPbaa/nb3/7GkiVLKC0txe12k5KSwoABAzj33HO54oormDBhQqe35amnnmLOnDk8++yzfPXVV9TV1ZGUlMSoUaO4+eabueWWW065qo7diSiKOByORFqprutEIpFEup7X6yUWqUeN1RCKNhII+RHFCIrfg9L4JbL8BaKcAVJPRKUAkyUfs9mKyWTCZDJhNpuPiwim6zrBYJC6ujoCgUBiW45W5GoiGIkLvlbl6L87JpOJtLQ00tLSEsKX1+slHA7j8/nw+XyIeXnYHniA2KOPIq5bh/Duu1g/+qil8BXVURavwLV4BS6g5/DhnHXBBVzx4AU09OlL8eYKdqzcxKYl2yjd1kgzP31iUY1928rZt62cD5+dhySLZPfOYuSUocy8ZTJDJw40xK+TgIgWY7u7nMKGYgrri6g8RLxKMznIsrpAF6gOudnlrWwx3alYGZlcwIiUAkYk9yTZ1LroS5O4tcUdT0vc3Z64lZzPEFc+fe2ZbHOXs3D/ZuZXbjiC35aN/kk5zMwZzvTs4dhkM2cagt7B3MIvv/ySpUuXJt4/9NBDOByORAhyW+i6TmNjI5999hm7d+/m0ksv5YMPPjjmTp/KeDweXC4Xbrf7lE8JMOg85eXlFBYWHnG+0aNHd7gIhIGBwalBV44D8+bN46KLLiIpKYlXX32Vyy67jJycHKqrq1tEZjSf99JLL+W///3vMbVrYHCq8PLLLzN37lwKCgooLi7u7u4YdAFdcQ71+/0Jwcjn8502lUd1XU9EK8XTHr3EIuWokQrUaC2qGkQQNCQhhiSGkWUdQc5HkHqC3BMEB4IgJMSvQ0WwzqRDNkVydYXI1cS5/+8ZzumVzIiCDG658sIuEYbC4XBC+IpEIonPRVHEbrdjtVjgywcRP1iMbX4pyn5vYh6dg4WeAPTsbKIXXIB/2jQaxo7FH9PZs3Y3O1ZuZNOyPezf26xyYxuIkkhmz3SGnz+EGTeex6hpw066VNTTlZoDhvGFDUVsbiwl3CxET0Qgz5aKXbbgj4aoCNajNpOZZEFikDOXESkFjEzuSU97eivPq7AaZae3skXklqq3rKydarIz2JXPUFc+WeZkChuKWF69nSL/frzREPohSYnCgbYzLE6GuXpwcd5oxqX1bTNyrDvpyvNuR8eBDu+BJUuW8PDDD7eqEvfwww8fcVld17FYLPzqV7/qaHMGBqc1ZnPHlPWOzmdgYHBm8vzzzyciNC677LLDztsUnbFp06YT0TUDAwMDgxOMIAiJ9EeIZwkEgwUEAgECgQDhUB3E9qHH9qHGKlFVFUnfg6huR4ipaHommtSbcKwn4XAuzW8VBUFIFEQwm80txLC2hJhDRS5BEBIiV3MP2s4QjmnoOphkgWg02qJIQ2cxm82JqujNI74S/zfuROipYv/lEKK/fx5xvRfxvfewf/opyv79ifXogFBVhenVVzG9+irJFgva1Kn0mTGDs350Eb5fuvA1+Ni5qpAdX29j21fl1JW3LOCmqRpVRdVUFVWz4JWlCKJARn4aQycOZNoNkxh/4WgjIrKLiGkqO72VFNYXUdhQTGmgrsX0JNlChtmJhs7+YGOr6bnWlET01hBXPhap5Xc7rEbZ4alkm+dw4pYjnpboysMqKnxbt5dvanfz/r7VBNVoG35bAhZRjvttpfXlktzR9Hd1rCrimUaHxa5evXoxefLkxPtly5ahKMphQ5tFUcTpdDJs2DBuvfVW+vXrd2y9NTA4DdB1HU3TkCSpVeRFcywWC2lpaSewZwYGBqcaq1evBuLV1Y6Ey+XC6XRSVVV1vLtlYGBgYHASIElSi7THaDSPYLD/AZ8qH2qkAl3dh6buA3U/kliDWdyLLASRAE0aQkQfSkTrg6anJEzzfb6D1bwFQUCW5YQIpmkaPp+PSCSCKIpdKnLBgetoHSKqhkWRCIVCXSJ2NSEIQgvhKx7x1YCn6iWiuoQ3Ng2fNxOxfzb23/6W2O9/D6tWIb//PvbPP0c5UNURQBcEhFAI6fPPsX/+OXZAHzuWyKxZFEyZwlmXTCEai9FQUcGOlWvZsWoP21bW4ms4ZJs1nep9tVTvq2XJm1/Fq1LmJDP4nAFM+c5EJl42HsV07Pv2TKE+7GNDQ9xYflPjPgLqwUg+QRdItyRhk0x4YkEawn68sYPWM3bJzLDknoxM6cmI5AIyDvG9CqlRdnoq2eYuY4u7jN2+tsWtoa58+idlE9NUvq3fw5KqzbxVspKoprYSt0QEbLKZAlsGkzIHcmHOKHJsbRvaG7Skw2LXrbfeyq233pp4L4oiqampLFmy5Lh0zMDgdCQcDlNaWkogECAlJaWFmfShDB061DiJGRgYHJb6+npcLhdJSUkdml8UxRbm1gYGBgYGZw6KoqAoCk6n84DfV08CgRHxyC9/PVp0HwF1H0T2ge7DJFdhM28m2VSHyWxHlSYSYRwRfTDRmEI4HEZVVaLRKMFgkEAgkCj6AGC320lOTkYQBPx+f0IQO5aopGgs/qA4HNMwKzLBYPC4WcMIgoDFYsEc+5DU9PcJx3riM92Dz0+iwiOANGQItnHjiDz+OOIB4csxbx5yTU1iXbokgaoirF2Lee1azL/7Hal5eWgXXURgxgzyv3M5E67R0LQoVbu2sGPlJnauKmXHt37CgZb90nWduooGVry/mhXvrwYBUjJdDDqrH+ddPYHzr5mA2dJ1AuCpjqpr7PFWHaicWESRv9lx0XXMkkKayUFM16gLe1tUVpQEkb5JWYxMKWBEcgF9k7JaRFDFxa0Kth4Qt/b49rcrbvWyZ9IY8bG2fi+fVRTSGPG367flUqz0d+QyPWcYk7MGk2JyHJd9c7rT6UTOl1566aQx8jQwONnRdZ26ujoqKyvRdR1RFBkwYAA9e/Zk69atLczqLRYLQ4cOJScnpxt7bGBgcCrgdDppaGggGo0e8Yl5fX09breb3NzcE9Q7g9OJK6+8kpUrVx7VMu+//z4TJ048Tj06vmRnZx/1MkbU5KmDyWTipZdeSvx/JtI8giklJQVdzyUY7EsgEMDv9xMOVhKNldAY2kejvxxBiGFW9mAzf4PD3IDFno+YfC5BdTz76zMIhWJIktQi3VEURUKhUKuiTE2RYM09wUwmU2L5w1Hr9gMQUXVsJjlR1fF4ocf2ofueRRDAmn4nNmsfMg54ozVVdWwhfA0bhu2sswg//jjCypWY/vtfHPPnIzd7wK0rCug6Qnk50j//SdI//0mSzYY+YwbhWbNIO/98CoaNZ8ptEdRILaWbNrD96x3s+qaa3YVR1Ngh+0iHhv1uvv54LV9/vJYnbv0rrgwn/cf0ZtKVZzP1+knYHGfWfbs7EmBjYwmF9cVsaCzB1yw6S9M1UhQ7JknBHQ0QUWMtzOczzc4DvlsFDE3ugb2ZsXtIjbDFU3lQ3PLuR6W1uDXM1YNMs4vqcAOF9SX8t/RbfLG2/bYUQSLNnMTw5B7Mzh3FmNQ+Ldo8XeiO826HDeoNugbDoP70pUnQCofDidBnQRCIRCKUlZUlQr4dDgf5+fmJH3l7yxkYGJyedOU4MG3aNJYtW8ayZcuYNGkSQLsG9c8++yx33nknl1122RlfLMbg6JkyZQrLli07qmWWLFnClClTjk+HjjOdGYeNS+oTg3EtfWKI+30FD4hfbiLBYvTYPlD3gVaPKKhYzQ3YzHWYTUFU+oLpXGzJF6BYClBVNZH2GA6HE/83j/w6FEmSWglgJpMJWZYTv8nC3WV874/vMDDTzk8vGEpaWhqDBg06LvtA13X0hu9BZAWYJiCkvNzq3NBUFMDj8eDz+VpUPZUkCZvNBqqK+NVXmD/6iKQvvkCuO+j7pJvNIMsIfv/BlQoCnHUWsYsuwj99Ot6ePQmGQmhqkIh/L3vWbGLn6mJ2feuleItGS3v8tklKddB3VC/OvWw8M26ejCP59CjK0ISmaxT5aihsKGJ9fTG7fVUJUUnXdUQEkk12oloMTyzYwjjeLCoMS85nRHIBI1MKyLYkJ45zSI2w01PJlgOG8m2JW2kmB0Oc+dgVMxWBBra4yygP1hM+4LfVUtwSMIsyOdZkxqb15cKckQx05WE6yczkT3Y6Og50idi1ceNG5s+fT0lJCcFgkBdffDExLRqNUlNTgyAIRqQKxgB9ulJZWcmWLVtaRWgVFBQQDofRNA1RFMnOzjbELAODM5yuHAeee+45fvrTnzJt2jS++OILRFFsU+zasGEDU6ZMwePx8MYbb3Dttdce62YYGBgYdAvGtXT3EI1GCQQCNDY2Ul9XghYpQRErUaT9CESQpQhWUx02Sx02qxPFfjaCeRKYxiMIB6OKNE1rVwRr77ZUFMWEALZyezmPvvUlvdOs/PyiEdjtdkaOHHlctlkPfoLu/hlgQkj/BEHudfj5dZ1gMJiI+GoufMmyjMVigVgMYflybJ98gmPBAuT6+oPL22xgtyM0S38EoKAA/ZJLCF9wAZ7RowmoKuFwCNT9+Bt2smvVdnZ9u5+d34aoLOrYPYbdZaPPiALOvmQss26bQnKGq6O75aTBHwuzsaGEwoa4ubwnGo/y03WdmK6RJFtQRAlPNIiA0EIT7OPITIhbA5JykMV4am1IjbDDU8kWdylb3WXs9Va3ErfSzUkMTMpFFARK/XXs8FZQE/IQ07VWflsSAhbZRIEtnXMzBjItaxi9kjINM/lj5ISIXW63m+9+97uJEua6riMIQosL7EAgQEFBAQ0NDWzYsIGhQ4d2trnTAmOAPv2orKxk7dq17U5PT08nPT2dHj16GNUVDQwMunQciEajjB49mm3btjFlyhTuuecevvvd71JXV8f27dspLi7m448/5sUXXyQYDDJhwgRWrFhhCO4GBganLF1xDo3FYsyfPx+AWbNmIctGVMWRCAaD1NXV4ff7E8WWTCYTiiwRCZWiRUvQDxjdg45J9mMz12Eze7Am9UG2TgTTeSD3b3MM0jSNaDTaQgBrejW/XZ2/voiXl24jP9nMXTMGoigKffv2xWKxJAQxRVGOeZzTNTd67WzQ6hAcdyM4fnJ0y+s6gUAAn8+H1+ttcX8sSRIWiwU9GkVcvhz7p5/i+OIL5MbGg8snJSGkpkJlJUQOGqiTlASzZhG78EL8kyfjs1gIBAJoqgdiJdRX7GDXqiJ2r/WwfXWUhuqOiSrWJAu9hvbk7ItGc8FtU8jITz+q7T0R6LrOvkBdonLiLk9lQojS9HiFTodiJqzGiGoqUrMqoSmKnREpPRmZ0othrh64TDag4+JWb3smUU2lNFBDka+GxmgATW8ZtyUAoiCSJFsZkJTNtOxhnJM+gBxrsnHdRdeed4+72BWNRpk8eTKrV6/GZrMxdepUFi5cmDAqbM59993Hk08+yYMPPsiDDz7YmeZOGwyx6/RC13UWLVrUyougOSaTiRkzZrRZltnAwODMo6vHgZKSEmbPns2OHTvavZjSdZ3hw4czf/78TnkRGRgYGJwsdMU51O/3JyoU+nw+7PbTK6WrKwmFQtTV1SXsOARBwOl0kpqa2sKS42DKYx1h/x60WAnE4kb3gqBjVjzYzLXYrAKWpBFI5klgnoggphy2/biR/kHh61/z1/DG8q1kO838/IKBCIJAZmZmC99KQRBaeII1F8E6ej2uuf8Xgm+D1Bch/UMEofMeQ03CV1PEV/N7ZVmWURQFPRJBXrEC+2efkfTFF0hu98Hlk5MRcnOhqgqaRYIhijBxIvollxCaORNffj7+QIBwOABqGVq0mP17drH72xp2r/Gx/Vsdv6dj22+2mSkYnMe42aOYNXcquX2659ohGIuwyb2P9fXx6on1kfj3UNd1IloMq2RCEkT8sVA8FfDAdZAiSAx25TEyuYDhKQX0tMUza4KxCDu8FWxpLGObu4y9vtbiVqbZSbbFRUCNUBaopyxQj78dvy1ZkOIeXcn5zMwewajU3qSaDTP5tujK8+5xF7uef/55fvKTn9C3b1+WLVtGbm5uuz4hq1evZsKECUydOpVFixZ1prnTBkPsOr2ora1l1apVR5zvnHPOIT395HtCYmBgcOI5HuNAIBDgj3/8I//6178oKSlpMS0vL4/bb7+dn//858YNnYGBwSmPIXadGDoicrVHk9+X3+8n4NtHJFiEHisBtRxQm/l91WOzZ2Kyj0O0TAJlJIJw+GiPx15fwHsrNpPltPDwVWOJRqMJL9wmQay9qsPNzfObe4I1mek3oUfWoNffEF8m9XUE0/ij2HOHR9d1/H4/Xq8Xv9/fSviSZTkufH35JY7PPydp4cKWwldaGkKfPtDQALt3t1x5375w6aXxqK/Ro/FHIvj9frRYLaglqOEiSreUsGedh53fhti1TiAS7pj4ZbIo5A/MY+zMEcyeO5Weg/O7ZH8ciq7rVAQbKGwoprC+iG2e8kR1w5imouoaVslEWIsiIraI3uphS0ukJg5y5mKWFIKxCNs95Wx1l7PVXUqRr6Z15JbJQYopCX8sRFmwjv1BNxEt1krcEhFQRJlsi4sxqX2Ynj2slYG9QfucUmLX5MmTWbFiBR9++CGXXHIJ0L4pbiQSwWazkZWVRXl5eWeaO20wxK7Ti/LycgoLC4843+jRo8nLyzsBPTIwMDjZOd7jQEVFBRUVFaiqSnZ2NgUFBV3ehoGBgUF3YYhdx5e2RK6kpCTS0tI6XUGtye/L73cT8O4iFi4BtQS0eJSSLEWwmuuwmYPYkvpjsp8D5vMQpNbXzr/8x8csKNxNptPC7687h2AwSJ8+fRJRy7quE4vF2vQFO/QetTlNIpjJJKIE/weTsA1T0gUoKY91aps7gqZpLSK+mot0kiQhSRJaKIR5xQqSPv8cx8KFSAeqPgLomZkIQ4ZAMAiFhS3THZOTYfbseNTX1Kn4TSb8fj+hoBvUfeixYqKBIoo3NrJ3XTzlce9mGU07JEJcANpQCxSzTG7fbEZPH86s26bSb3TvTu+HsBpli7vsgLl8CdXhuLin6TphNYosSohAVNcwiweLFSTJFkYkFzA8pScjknuSZk4iEAuzw1NxWHErzeTAJpnxxIJUBhqoj/hQD/htNd9UCRGzpNDTnsbE9AGcnzWEvo4szNLhq18btM0pJXalpqbi8XgIBAKJE197YhdAWlpa/Ad2mHSvMwFD7Dq9MCK7DAwMjhZjHDAwMDDoPIbYdXw4HiJXWzSlJQYCAfzeCgK+nWjRfaCWgh4GOOj3ZbNidQ5Htk4C01kIgpWfPPMuq7aXkp1s4/9umIjX6yUnJ4fevQ8vtui6jqqqbXqCNTeSJ7IGPbIKBCuC7UYUU1KbKZGSJHXZPoG48NU84utQ4UsURbRQCMvy5XHha/HilsJXTg7C2LGgafDtt9Dc5F6S4LzzYM4cYhdeSCAvD7/fj9/vQ41UoKvFECsm4K6mZIObXWv8bFutU7a7tagjCEKbhQQkRSKndxYjpw5l1q1TGHR2295sTewPuRPeW1vdZfFIKl0nqqlEtRhmSSGsxTCLcsI8XkJkgDOHEclx761ejgzCapTtngq2ucvY4i6jyFeNdog655JtmEQZd9TP/pAbbyzUpt+WJEjYZRP9HDlMyR7CuNS+9HJkGGbyXcQpJXZZLBasVisNDQ2Jzw4ndjmdTjRNS5xAz1SMm5zTC6/Xy4oVKw77pMhisTB9+nTDmNDAwADo2nFg2rRppKWl8c4773Ro/uuvv57q6uoz3lLAwMDg1MUQu7qWUChEfX093gPCyfESudqjye/L7/cR8O4iFNiLHisFtQrQEQQdi+LGavZgc+Tx45cHsaVUpSDDyeM3TqKuro6UlBQGDRrU6WvtpkiwcLCEcO3/EomaicrXERMGtruMLMvtimDHes3fnvCl63pi/XoohHXFCpLmzYtHfPn9ieX1/HyESZPAZIK1a2HLlpYNDBoEc+bEo75GjyZwIN0xGKiFWHFc/FJL8dQEKC5sYOe3IbaskqitbJliKojx7dS1NsQvWSSrIIPh5w1h+k3nMXTyILZ7KuPRWw3FVATjGoKqaYS0KCLxADIRAbN0sMBAtiU5biyfXMAQVz46sONAWuIWdynFvpoW4pau6dgVMxIijVE/dWEfQTWCDoeIWwKKKOJS7AxL7sGUrCGMSC4g15pi3LMdJ04psSs/P5/Kykrcbnei0+2JXUVFRfTt25d+/fqxc+fOzjR32mCIXacPDQ0NlJWV4ff7qa2tbXe+sWPHkpOTcwJ7ZmBgcDLTleOAKIpkZ2dTUVHRofl79+7Nvn37DivQGxgYGJzMGGJX1xAOh6mrq+s2kas9En5fvloC3q2Eg/sOGN3H+3nfu/mUNZoYkBXlkaus1Lp7k+TKYeDA4cfUb13X0Ru+C5GvwDQRIeUlNE1rEQHWFBUWjUbbXY8kSS3Er6b/ZVnulIiiqmoL4avp1l3XdURRjAtfwSC2r77C+fnnOBYtQgwEDq6goABmzgSnEzZsgGXLoHkkW1oaXHQRzJmDOmMGfkk6UEXSjRopQY8Vg1qCrrpprPCxZ20jO76JsXm1BV9jy+2RZBFdB01twzNNBCHDjDw8CWFGCrHRNkyKgqprWCQTiiCBAFbJxDBXD0amFDAipQCHbDmiuGWW4sb0jRE/jRE/UU1tx29LItPiYkxqb87PGMyQ5HzSzElHfUwMOkd3iF2drvd49tln89///pdPP/2U73znO4ed9y9/+QsA5513XmebMzA4adB1nZqaGqqqqoC4yNuzZ0+2bt3aIk3XYrEwdOhQQ+gyMDA4adA0zXhiaWBgYHAG05bI5XA4SEtLw2zufqNtSZJwOBzxm+LsXkSj0QNG90UEvDsJxeIPd0ySSGOjl3CoCkHdQU3x66Rn9MJknwjKqCMa3bci9Elc6MKE4HwYQRCQJAmr1YrVam0xa3MRrHlaZDQaTYh1wWCwxTKiKLYpgimKcthxWZIknE4nTqezTeFL13V0kwn/tGn4p02DYBDHV1/FUx2XLEEsKYEXXoivrFcvuOMOyMqCrVvh00+hrg7+/W/497+RFAXn5Mk4L70U/ZJLCPc5H79/bNyKKFBBau8iUnoWM+7SStCi1BQ1sGuNj22rRbZ+ayYSOkTkMgmgATEdNND3h4nuD8PCWhAgnG5CGZpE1sX9OO+ysYzJ6EOONYU9viq2uMtYvH1zK3FL1TRkQURDxx0J4I0FEymJTXMJgCiImEWFPFsqE9P7c076AAY6c3EolqP7Xhic0nQ6suvTTz9lzpw59OvXj6VLl7ZbjfHvf/87P/nJTwBYuXIlZ599dtf0/BTFiOw6tdF1nfLycuoPlP3NyMggOzs7kb9eV1dHOBzGbDaTlpZm3FQaGBi0orsiu1RVxeFwYDKZcDer7GRgYGBwKtEV59BoNMo//vEPAH7wgx+gKKe/4fTJLnJ1BF3XmXHf8zT4Qkzo5+K7k0x4vSqCECTJWonNXI9J8WMz+7E5CrA5xyBZJyHIh68cqGuN6LUXglaH4LgHwfHjTvVP0zSi0WibvmDt3XI3F8EOfR3uPkJVVXw+H16vl0AgkFh/i4dawSBJTamOS5YgNhfgeveGq6+GAQNg+3b45BPYsaNlI8OGwZw5cOmlqGPHEgiF4lFf3lpi4SL0WDHhaClVEZHSgEThxgD713nxFQZRd4Th0AAviwiqDtE29oUAcqoZYZAdZWoapilpiCaJqBZDQEDVNTzRQDwlUaeVuCULEhbJRN+kLM7PGMTo1N70S8o2zORPIrryvHvc0xgBrrnmGt577z2ys7O54YYbeOGFF/B6vfztb3+jpKSETz75hM2bN6PrOrfffjt///vfO9vUaYMhdp26qKrKvn37EhcJubm5hum8gYHBUXMs44DH46GxsTHxvlevXmRkZLBmzZp2L6R1XaexsZGXXnqJP//5z4wfP57Vq1cfyyYYGBgYdBvGtfTR0SRy+Xy+xDjRlK54qohczTnvnr/iD0WZM74fd1xyNqWlpUTCXmyWRixyCXqsDPR4pkWT35fNqmBzDsTqPAvBdDaCaGuxTs39AATfAakvQvqHCELXpnHqut6uCNbciL45giC06QmmKAqi2NIwXVXVREXH5sKXqqoJ4UsIBklavjwufC1diti8aFzfvnDttXD22bBrV1z4WrECmlseZGbCxRejXnIxe88ZQWGwmt21ZXh9XmyagK5FiWkxmmoa+oNe6rfU4FnvIVwYJlYcoQUiiHYZLaJBuO19oLskYv3NRM91EJuchG4RDywqIIsSSbKVoa48JmUOYXhyPr0cmYaZ/BnCCRG7QqEQt99+O6+//nqbynPTqr/73e/y/PPPI8udzpo8bTAG6FOTaDRKcXExwWAQQRAoKCgwjp+BgUGnOJZx4OGHH+aRRx5JvNd1/agjSJ955hl++tOfHtUyBgYGBicLxrV0xzjdRK4mzrnzz0RiKjdNHsbcWeMoLy8nFAqRmppKQUHBAaP7bQS8O4mEyhJG9wCiGMNq8mBzZGJ3DsdknxD3Amu4CQAh9XUE0/gTti1NIlhbvmCHE8EURWkzJVIURWKxWCLiKxgMJo59LBZLZKJIoRDOA1Ud7cuWIYbDBxvo3z8ufM2aBSUl8Mkn6J9/juDxJGaJmGQ2je7HqnMG8uU5/ahPSyZdsJIh2EjRFUyahkuOkCXXkyb70aUIRTUhCr+Jsm+NSqQwjLb/EN8zBTSbiBDWIaRz6JWNDghJEq4haYy/dAxX3Dab/tm5RhbNGcoJEbuaWLFiBS+88AIrV66koqICVVXJzs7m3HPP5Qc/+AHnn3/+sTZx2mAM0Cc3baUihsNhioqKiEajyLJMr169sNlsR16ZgYGBQRscq9j18MMPJ963VwK8LfLy8vjRj37EAw88cFRtGhgYGJxMdMW1tKqqLF++HIh7CkuS1JVd7FYikUgiXfF0ErmaGH/H06iazk8vHsc1k0dRXl6e+E7069evxbGMRqP4fTUEPBsI+IqIhcsSRvcAshTGZm7AZq7Bau+NKeOPCGJqd2xWC3RdT1SIPNQX7HAFZppEsCYBTBRFIk2VFg+kMOq6nliHruvIzYWvL79sIXx5+xawZvpY5p/dB6vby+ivNjNu5VZyKxtatFvUP4/N548iMPsCks+ZhIJEZWMdle4aQgEvgh4FPYqma0QQ8epBqiq91BW6ia73IW0MIHpbinu6TUS0yUghnZiv7YIA9mQbfUYUMPHSccy4ZQrJ6ca99clMV553T6jYZdBxDLHr5KWyspItW7a0MJk3m824XC4sFgtms5nevXt3a4UaAwODU59jGQfcbncijVHXdfr06UNGRgbffPNNu8uIoojT6cTlch1Ltw0MDAxOCoxqjG3TnsiVmpqKxXL6mHKP/fGf0IFff2cSs84aSmlpKR6PB6fTSY8ePdp9IK3rOqFQiIBvDwHPZoL+fWjRcpobS5kUPzarA5tzADbnWUiW0QjCyeP51CRUHSqARSIRYs0rLB6CLMvIspxYNhaLJTKumpaLqDE8nhqUxQvJWrCYgm/Wo0QPrrO4Zzorpoxg9ZSRKLKJ8Su3MmnVTvI37ERoJifUpzlZd+5Q1k4cyrrRfXCLKnZdwa7KODUTVl2GZobzEUHFrQfwFNUhb3Vj3qxTt85HNNBS4ErOcmGxmfG7A/ga/G0+6LMlWek1rAdnXzyWWXOnkJbT/cKlwUG6oxqjIXadYAyx6+SksrKStWvXtjs9Ly+P4cOHG6m4BgYGx0xXjgNTpkwhPT2dd999t4t6Z2BgYHByY4hdLWlL5Goynj+dRC6Im6+Pu+MZAJ7+/gWcM7w/RUVF+Hw+HA4HmZmZpKSkdGhdaqSYYOVNBMJOAupEwuEYulqXmC4IOhZTCJs9B5tzGFbnRESlx3HZrq5AVdU2PcGi0dZRUU3zNkT81IY91Mb8NEYDKLqApkMMFdXvYfA3mxi+fC391m5GbiZ8NQ7ozZ7Z57Pk/MHsMEUZtWorY7/awohvtmMLHvTmCpllvhnVm+Xn9GPFWf2oS3Vg02UyBDtZgp1+io2epiDpciWpsgdF1LAobhShjtId6Wz9Nov1y1W2f1OOpraM/MrunYliVvDWe3HXetG11pKGxW6m5+B8xs8exYXfm0ZWQWYX7nGDo+W0ErtqampYsWIFoigyefJkkpOTj0czpxyG2HXyoes6ixYtahHRdSgWi4Xp06cbeeEGBgbHjDEOGBgYGHQeQ+yKcyaJXE00eANMvy9e8Oz1n13OwL4F7Nq1i0AggMViISUlhezs7COuR9d19Ia5EFkJpnMRUv6Fpmn4faUE3IUEfHuJhKoSRvdwwO/LImNL6oXdORqT/WxE6eT/3jRFc0UiEbyhAJsb9rHJW8pWfyUNMT+CBkmajFMzk4QJCREFCZsgYxJlnIoVWzhC3srV5C1aRo9vNyDHDqZS7u2VyeLzh7DgvEFUZDoZvWkf563eyaTVu8mp9rToS9nQvvhnz8R65dXknj0ZURAJhUL4fF78np2Eg3shVgxaLQCSGMFmqUOI6ezdXMDGr1NYv6yeok1lLdYrySI9BucjiSKNNW4a9rtbiWMAZquJ/AG5jJ01kgu/O438Abldv8MN2uWUErvWrFnDc889x9ChQ/n5z3/eYtqbb77J9773vYR4YLfbeeWVV7jiiis609RphXGTc/JRW1vLqlWrjjjfOeecY1RfNDAwOGaO5zhQU1NDSUkJgUDA8Ms0MDA4LTnTxa5IJEJ9fT0ej+eMEbma2L6vmht+/zoAnz14PdnZ2ezevZtgMIgkSdjtdgoKCo64Hj34Ebr7XsCMkP4pgtyz1TyRSIiAdzMB9yb8vjLUaA3N0+9kKYbNnozNMQBb8lkoliEn5UPxymADhfXFFDYUsaWxjKAWIaxGCalRIloMkyBjFmVMgoRdV+grppCh21A0kaAWIahGsCCDrhPWVWK+RgauXs/w5WsZULgNuZmotKt3XPhaPmU4kd69uKhWYtI3u+i5+GtMa9a17FiPHjBnTvw1dSqYzUSjUQKBAD5vGQHPdtRoCcRKgXhUmcXkwWZuJBrKZfvaAtavkClcvJf9JTUtVm2xm+k9PH5M6ysbqa2oR4229jpTzDJ5/XMYPX04s+dOpc+IXl267w1ackqJXT/72c945plnePLJJ7nnnnsSn1dUVNC/f/+ECV4TZrOZzZs307dv3840d9pgiF0nH+Xl5RQWFh5xvtGjR5OXl3cCemRgYHA6czzGgY8++oiHHnqIDRs2AHHj+ub+HQ0NDVx//fUAvPXWW4Z/l4GBwSnLmSp2tSdypaamYrVau7l3J4blm/Zy13MfAjD/kZvIyMigqKiIYDCIpmmYzWb69et3WNFJ1xrRa2eDVo/guAfB8eMjthv3+6on4P6WgHcHQX8lmupvMY/JBDZ7LnbnMKyuCchK9zwgj2gxtrnLKawvorChmPJgfULcCqlRREHAIilYJAWzqJBqcpBlcSGLEg0RP+WB+nglSFXFFpVwqSYsuoyu65gQsQkKAhBGA5+bYV9vYNhXaxlQuB2pmfCljRyJcO21CNdeC/36QVUVfPopfPQRLFgAzbUCuz1e/XHOHLj4YsjIQNd1gsEgfn8jfs8WQv4iUEtAi0eLSWIUm7kOq82Cp34gm1dlsX6Zh/VLtuCu9bbYJ8kZTvqO6oUa06gpq6O6pIZopLXHmaxI5PTNZtSUoVxw2xQGjj/8d8ng6OgOsavTBkRffvklAJdeemmLz//xj38QDAYZMWIE7733HhaLhZtuuokvv/ySP//5zzzzzDOdbdLA4LjQ0co0p0MFGwMDg9OPxx9/nAceeOCwVRlTUlKwWq189NFHvPvuu3zve987gT00MDAwMOgsbYlcdrudtLS0M0bkaqLOGwBAFISECCHLMpIkoWkamqYRjUYPW0xK9z4BWj3I/cDesbFQEASs1jSs1tmkZc9GVVVC/l0E3Ovw+/YSDtYRicSIRCpobKhAEOZjsdixOXphd43GkjQGUTx+Ba5qQh4KG4oorC9mc+M+PLFQQuCK6SomUcYqmXAqViySQpYlGbMk44uGqA55qA97CajxKK6wFiWmaQdj2AQdq6CQqVvJxI4KyIhkyA4y0jIwXTqUyiu+iycQJG35ctKXLMH17beIGzbAhg3wwANEhg8nevnlaFddhXLjjZhUFXHp0rjw9cknUFEB778ffwkCnHMOwqWXYpszB9uQIWRk5BKLxfD7/fg9ewh4txOLlOANmvAGdWA/QyftZvyMAFZHb/bvO4/1K2wULtnDpmVbaazxsHbBxsT+yu6dSb/RfYhFIlQWVVO5Zz+RUJRYVKV0ezml28v5+PkvkGSRrIJMRkwewoybz2fE+Sdn9J5B+3Q6sisvL4/9+/cTCoVamHaPHz+edevW8eGHH3LJJZcAsHHjRkaNGsXQoUPZtGlT1/T8FMWI7Dq5CIfD7Nu3j927dx+2lK/h2WVgYNBVdOU4sGrVKs4991xkWeaJJ57g5ptvZujQoVRXV7c6p7333ntcc801XHvttbz55pvH1K6BgYFBd3GmRHZFo1Hq6uoMkasZ/5r3DX/98CsUSeSzh28iLS2NiooKvF4vqqoiSRK5ubkkJSW1ubwe+Ra9/kYAhNT/IJjGdUm/YrEgAfca/N5NBL2lRCK+FtNFUcRqz8Ce1B+bczxmW+9juqeIaSo7PBUUNhSzrm4vxYFaQmo8PTGsxeLRW6IJi6SgiBJppiRssomgGqUx4kPVdQKxMAE1TERTUfWmiCw9IXIJgCiImESZJNnCsOSejEnpRX9rFlnYCPmDRKPRRJXLcDiMpmmIoojc2Ejy0qWkL1mCc80ahGbXI8Fhw/BdeCGhOXMQ+/TBpChYt23DsmAB0mefIRyabdO7dzzi69JL4bzzwGRKtOn3VeN3byIUKEKP7Ut4rIliDJupAZsjFbNlGHu29qZwmYf1izezbdUu1FjL66M+IwroP7YPoUCY8p2VlO6oIBwIt9rvoiSS0SONYZMGM+2GSYy7YCSiKHb6OJ5pnFJpjBaLBYfDQW1tbeKzYDCI0+lEURQaGxtbqOoWiwVFUfB6vW2t7ozBELtODnRdp6GhgYqKCjRNIxQKUV1d3e78Y8eOJScn5wT20MDA4HSlK8eB66+/nrfffptf//rXPPTQQwDk5OS0KXbV1taSmZlJ//792bFjxzG1a2BgYNBddMU5NBKJJLJN7rrrrsNGAp1ootEo9fX1uN1uQ+Q6hD++u4zXF63DapL5+MEbSU1Npbq6moaGhsQ8aWlpbXrs6noEvfZSUPeC9VpE16PHrZ+RUAUB92r83u0EfPtbjceyYsPmyMWeNBSrczwm85GtBerDPtY3xL231jeU0Bjxx6O3tCiarmMSZSySgkmUsUkmkhQrUU3FGwsS0zQCsRBBLUpUjRGP2xI4VNySBQmzJJOs2BmT1psRyQUMcubRx5GJLEot+qPrOuFwGK/Xi9frJRqNomlaogKkIAjIsozS2Ihz0SKSFyzA/s03CNrBVMfg8OF4Z8/GO3s2sQNWMZbaWpxffolt8WJMy5cjhJuJTk4nzJ4dF78uughSU4G4Cb/P5yXg2YLfu5NYuAS0g5U1TYofu0XF5hwAwhi2rkmhcPEOChdtYu/GkhbbJckSg87ux8Bx/Qh4gxRv2UfJ1jKC3taFzARRID0vlSHnDGTq9edyzpyxSJLUaj6DOF153j3uYpfD4UhUdmjiyy+/ZMqUKUyaNCmR5thEeno6fr+/lZfXmYYhdnU/sViM8vJy3G43EL+A6NGjB3V1dWzZsqVFVUaLxcLQoUMNocvAwKDL6MpxoGfPnpSXl7N///7ExX17YheQeNp9pj94MjAwOHU5Xa+lDZHryPzm5Xl8snobTquJD359AykpKdTX11NTU4MkSaiqisPhaNNjV/c9h+57GsQ0hPR5COKJ8a7UNJVwYCMB9zoC3j0EAx40vXlUl4jJkobdUYDNNRKrYxiyLKPqGru9VRTWF7Guvoid3sqEuBXRYoiIWA+IW5Ig4TJZ0XQdfyyUiNwKa1GimtpM0jposS8ioIgSJlEhy+LirNS+DEnJZ5Azj3xbKqLQ8Yilpkgrr9eLz+cjGo0mqkBGo1FEUcRsNmPxeHAuXIhr/nxMq1a1EL5CI0fiaRK+Dtx3CYEA9pUrsS9ZgmPpUuS6gwKWLkkwcSLCpZfGxa+BAxN9CYfD+L378Ls3EvQXo8fKaTK5FwUVq9mD3ZGD3TUKv380G1c0ULhoE+sWbaKqqGXwg8VuZvj5Qxg4vj8Bt4+da/dStGkffneg1X4QBEjJTmHQ2f2Ycu1EJl15NopJ6fB+NOg4x13sGjZsGNu2bWPVqlWMHz8egHvvvZc//elP3Hffffz+979PzKuqKhaLhdzcXEpKStpb5RnB6TpAHw9UVWXz8u3UVTaQlpPCsPMGdVgtb29Zn89HaWlp4olDVlYWGRkZiVBiXdepq6sjHA5jNptJS0szUhcNDAy6lK4cBywWCxaLhcbGxsRnhxO70tPT8Xq9hMOtw/MNDAwMTgVOt2tpQ+TqOPc89yHLNu0lPcnK2/dfR3JyMh6Ph8rKSmRZJhaLoSgKffr0abGcHitGr70EiCC4nkKwXtI9GwCoMQ8h7yr87o0E/KWEwyr6AfHLp8GumI3dup2tERGPJhDWNMJaFI149JZJVBABh2xBEARCsQgxtANpjDFiesuxv+lGX0JEkSQsokIvRwbjUvsyyJXHIGcuGWZnl93vNBe+vF4vsViMWCxGOBwmFoshSVJc+GpsxLVoEa4FC1BWrkRoJknExo8nOGcOvtmzCaalEY1GQdOwbNqEY/FiHEuWYN65s0W7sb59ic2ejX7JJShTpiCZzQiCgKqq+P0NBNwb8Hl3EguVgn7wgZ8iB7DbTNicg7C7JrC/Ip/1i7dRuGgj6xdvprHG06IdV3oSo6YNY/A5A/A2+NiyYgd7NhTjrW+ZutpEcqaLgeP6Mumqs5ly3blYrIYHdFdw3MWue+65h2eeeYZx48bxl7/8hcrKSm655Rb8fj/Lly9n4sSJiXnXr1/PmDFjmDBhAl999VVnmjttON0G6I7QGdFq+furee7ul6gtO6jgp+en8ZOn53LelWd3YtlUrn/wCnqNzwXiZvM9evTAZrMdw5YZGBgYHD1dOQ6kpaXh9XoTZdehfbHL5/ORnJxMRkYGlZWVx9SugYGBQXfRFedQVVVZt24dAGPGjOmW1KO2RC6bzUZaWppxfdoO3//j26zbXU5eqoPX7rsWl8uF3++nrKwMRVHiogjQr1+/xDHVdR294TaIfA2mSQgpL540D7JVTWV3QyHfVK1kTX0Vu4IqYV0goguoxFMLTQhIoogiKZglK+EDHlvhAxFeqq7RtDXNUxIlQcIkStgkM4OcuYxM7cVgZx4Dnbm4TCfm+9VUUbF5xFdz4UtRFEwmE5aGBlKWLME5bx7yqlUthC/OPRft6qsJX3IJkYyMRGaZumcP5gULcCxejO3bbxEOHHsA1eUiMHky4QsuQJ05EyU9HZPJhMlkQlVVAt6dBDxxsVGPVSX2nCBoWM0h7Ek9sLtGI9smsm9b4EDU10Y2LttKyN/yYWFWQQajpw9n6LkD8dZ5WbdoM7vW7mlVEbIJZ5qD/mP6MPHys5hx03nYks6c33pXnnePu9hVXl7O8OHDE6lgEP9CT5s2jYULF7aY97HHHuM3v/kN99xzD08++WRnmjttONPErs6IVsvfX80j1zwJh34zD5zJf/POvZ1e9tYnr2bKNeeSk5Nj5FQbGBh0C105DkycOJHVq1ezZs0aRo8eDbQvdr322mvccsstzJo1i88///yY2jUwMDDoLk51g3pD5Oo81z/2GjvKauiX7eKFe67G6XQSDocpLi5GkiREUSQajbZ4oK0HP0R3/wIwI6R/iiD37NZt8MVCbGwoobC+iFV1u6gN+wirceFK13VkASQ0QEXWdWK6gAbEEIgBTdJW3HGLA/8LKKKISVRwKlaGuwoYlpLPYGce/ZNysMrd70nXXPhqiviKRqOEw2FUVT0ofNXXk9pM+EogCDBpElx7LVx1FeTkJDzCIrW18MUXyJ9/jnnRIqRmHm66LBMYNw7ftGn4p05FLShICF+KoqDGvESDWwgFdhMLlydM7gEUKYTN7sTuHIQt+Vx0oT87vt1D4cJNFC7exLZVO4lFW15r9RrWgzHTRzBs0iA89T5WfbyWHWt207C/sfX9KeBIttN3ZC8mXDaOmbdOwZni6Opdf9JwShnUA2zYsIGf//znfP311yQnJ3PJJZfwxBNP4HIdzIHWdZ3+/fuzd+9ePv74Yy6++OLONndacCaJXZ0RrVRV5abed7QQxw5dNiM/jX/vfbaVWNWRZdNyU3m9+DlD6DIwMOg2unIcePzxx7n//vu5+uqrefvtt4G2xa6ysjLOOeccKisr+fvf/873v//9Y2rXwMDAoLs4VcWuWCyWELm0A15Fhsh1dFz2m5corWlkREE6f7njcpKSklBVld27dwPx9E+/309mZiYpKSnoWgN6zWzQGxAcP0Nw/OiE91nXdfb5aylsKGZV7U62ussJqhFCahQNDUEHSZQQBQERAQ0dTddRNZWorrbhtgUiIAMmQcAlyQxzpDMsbTgjMvrTz5mNIsonfDuPBl3XCQQCLSK+mgtfJpMJs9mMta6O1CVLcHz+OfLq1QdXIAhw/vlwzTVx4Ss7++A0VUX76iu0jz5C+PhjpEPSHcP9+uGbOhXf1KmERo6EA/eEgiAgiqBHK4lFi4iFKxGpQRKjB6brWMwa9qRe2F1jMSdNIBwU2bx8G+sOiF971he3aEuURAad3Z/R04Yx/PwhNFa7WfH+arat3kl9RSNtyTA2p5Xew3pyziVjuWDuVFKzkrtkn58MnHJiV0dQVZWysjIA8vLykOWT+8d3vDlTxK6OCk/PbXgcURTQNA1N09i8fDuPXfXMEdd/35s/YdCEfi0+2/71bp647rkjLvvk4ocYOWVoh7bDwMDAoKvpynHA5/MxePBgKioquPHGG7nvvvuYOXMm1dXVhEIhiouL+fjjj/m///s/ampqGDhwIBs3bkRRDMNUAwODU5NTTew6nMhltVpPmpS6U4FZ//MPatx+zhmQwxO3X4LD4UDXdXbt2oWu64nvhcvlIjs7G819PwTfBbk/QtoHCMKJiXAKxiJscu9jTe0evq7dRXXYTViNxsUrXUcSRERBTIgdOjqqrqHqWlvBP0iCGK+2KApkKTqDTQF6CF7yBYVMUUcUAEQEORurLR+bcwi2pEFYrbaT/vul6zp+vx+fz5cQvsLhMJFIBE3TMJvNmEwmbHV1pC1Zgv2zz5C++ebgCgQBJk+OR3xdeSVkZbVsYPdu+Phj+Phj9C+/RGj2IFBNTSU4dSreKVPwTpiA7jgYVaXrOtFII7FwJWqsBl2tQxJDSGIEWYpgkkPYHak4nIOxJU9CNvfEXeth/ZItFC7cSOHiTVTs2d+iK2arieHnD2b0tOGMmDKE+spGlr75FVtW7qCmrA5da330LQ4LBUPyOevC0cz+7jQye7SuNHqqcNKKXSkpKeTk5LB169bEZ6+++ipWq5Vrrrmm0508EzlTxK4NS7dw77SHjjjfj/95M/3G90q8X/f5Zl7/1QdHXO7G31/BmAuHtfiso8v+6vW7mHb9pCPOZ2BgYHA86OpxYP369cyaNYuampp2L2p1XSc3N5dFixYx8EDFIgMDA4NTkVNF7GpL5LJarYlIrpNdhDgZmfLz5/AEwswY0ZMHb5mdOG579+4lGo2SlpZGXV0dFouFnjn70etvAkBIfQPBNPa49UvXdSqCDayrL2JFzXa2uMsIxMJEtBiaHj/2kiA289cSUNHQ2rgNFwBZkDBJMlbJRF9HJsOSezLImccgVx6ZB8zkdc2HGvwav3stAd8uAkGIxppFCApWRCUfm6MXNucI7I4cTCbTSf290zStVcRXOBxOFNVpEr7stbVx4evTTxHXrDm4AlGEKVMOCl8ZGS0baGiAefPi4tfnn0Oz4j66yYR6/vmEZ87EP20awQP+YE2/XVUNEw2VEwnXEo14QI8iitGE+GW1QJIrh+TUkVid4xBFhf0lNQm/r8JFm2msdrfojjMtiZFThzJ62nBGTh1KTWkdi177kk0rtlFdUoumahyK2Waix8A8xs0ayezvTiOvX06X7PsTQXeIXR0Ks3K73a2qgdx2223k5OQYYpdBm9RVNhx5JiDsjeBwOBBFEUEQyOvdsR9srwE9yW4esgr0GuBpZ+6WpOWkdGg+AwMDg1OBUaNGsWHDBh544AHeeOMNQqFQi+kmk4kbbriB3/3ud63OmwYGBgYGXYshch0/IrF4VE6SRWmxH2VZJhqNIooiAOFwEK3xN3Fhyfqd4yJ0hdUoW9xlrKrdydc1u6gKNRLWosS0A6mHiaituIAV0VX0NuK24n5bEmZRwS6bGZSUy9CUHgxy5jLQmUdyO2byguhAts/EZZ+JU9dBLSHqW4Hfu5GAr5xAKAk1sgtf/S589QtAzEA252NP6o/NOQS73XXSZVyJoojD4cDhcKBpGn6/PyF8NRnTe71efBYLDZddhvnaa3HU1pK2eDHWTz5BXLsWFi+Ov+64A6ZOjQtfV1wB6emQkgLXXx9/RaPw1Vfw0Ufw8ccIu3cjL1yIvHAhdoARI9DnzEG96CLCw4cTicWIRLIS4ls4WEUkVEk00kAgHCIQ1qlzh2DfahRpJVablSRnPuMvHcHk6yegKAql2ysSfl8bl23FU+dl+burWP5u3Jsss2c6o6cN57aHr2PUtKFU7qlmwb+XsWHZFqqKqlGjKuFAhN2FRewuLOLNx/+LYlHI75/DmBkjmPXdqfQe2r2edCcbHYrsMpvN2O126uvrE5+Jokh2djYVFRXHtYOnG0ZkV0sOTSlMpD+W17Vp4tchz65OLGtgYGBwojie40A4HGbt2rVUVFSgqirZ2dmMHz/e8IMxMDA4bThZI7tisRgNDQ00NjYaItdx4qyfPkNM1bhtyhBuv/T8RDBGeXk5Pp+PzMxMamtr0UKrKUj+IyazFSH9cwTRdYQ1d4z9ITdr6vawrHorWxvL8KthwmoUHQ5EcMVt44UD3lttISJgkmQsooJTtjIspSeDD0Rt9U/Kxiabj7mfuh5BD68l5FtFwLONQMBHKJKMph+4/xEUkPIxW3tgSxqCPakAq9V60t4fHSp8NaU5RiIRRFFMeHy56upIWbwY60cfIRyo+gfEfbmmT48LX5dfDmlpLRvQddixIx7x9dFHsHIlaM2iqrKz4eKL4dJLYcYMdKsVVVWJRCKEw2H8/lr87h34fVWEQgF0vXlEloAsmzGZUrA68rA5emI2m5FEiX2bK9j85XY2LN3C1pU7WpvdD+3BqGnDGD19OCMnD6F8dxVfvLKUwkXxFMlYJNZqX8kmmdy+WYyaOowLbpvKwHF9j/0AdBEnbRpjQUEBZWVlbN++nf79+wOG2NVZzhSx61iEp4SxPbRc9miqMXZiWQMDA4MTwZkyDhgYGBgcD042scsQuU4cY3/yJ3Qd/t9Fo7hu5gQsFgsA+/fvp7GxkbS0NPzeEoL1/yYndS3O7F8iWC/pdHtRLcbWxnKW12xjVe0uKkONhGKRAyby7clZLZEP+G1ZJRNp5iSGJfdgkDOPwa48+jgyT4iZvK7WoIW+IuhdQ8C7h0BIIRx1ousHvpuiC0HugdXeC1vSUOyONCwWy0n53dU0DZ/Ph9frxe/3EwqFCIfDRKNRJEnCbDZjNptJrqsjZeFCzB99hFBYeHAFsgwzZsTN7S+/HFJTWzdSWxtPc/z443jao9d7cJrFEhfO5syBSy6BvLwWi0ajYTz16/A0bMHnrSUU1oipJjQ9fpxFQUYxJ6GYsjBZ8xBFU/xeWIWiwlK2f72bzct3ULRhXwsDe1ESGTi+L6OnDWfMjBEMnjCAit2VzH9pKWsXbKB8VyWRULTVpkiyRHbvDEacP4QLbp3C0HMHddtxPWnFrrlz5/LKK6+QmprKtGnTcDgcvPzyy1itVr7zne90uFOCIPDiiy92eP7TkTPpJmfJ21/xu+ufjr/phGj13N0vtTC4z+iRxo//NPeIYtWxLGtgYGBwvDmTxgGD04MpU6awbNkyHnzwQR566KHj1s5tt93GK6+8wq233srLL7983No5WWi64ViyZAlTpkzp3s6cQnTFOTQSifC73/0OgPvvvx+T6eiNy1VVpb6+voXIZbFYSEtLw263n5RCwanOmB//CYAHrz6b2ZPGYjbHo6Dq6uqora3F5XKh+/6E2x0gNcVORu/fH/VxqA17WV27iyX7t7DVXYYvGiKqq6h6a/+kQ2ny2zJLClZJIc+SypADKYmDnXnk29OQBPGot7sr0XUNYttQAyvwezYQ8O8nEE5u5vclgpSDpPTAmtQPm6M/DocDRVFOuu+0qqqJiC+v15tIMYzFYsiyjMlkwmKxkFJbS/KCBZg+/BBhw4aDK5BlmDkzHvF12WXxNMdDiURg2bKEyT3FxS2njx0bF74uvRRGjYob5jcjGi7F37gSr3s7Po+HSMyMqpkOvMwIkhOTKQ3FkousJCf2sa8hwJ41xexaXcT2r3dTtbemxXrNVhNDJw06IH4Np++oXlQVVTP/pSWsmb+BfdvLCAcirTZHlEQye6YzfNJgpt04iTEzRiTSf483XXHebaJLxa7S0lImTpxIeXl5pzojCAK6riMIQotS6GciZ9JNTlVVFQvfWMZHf1hAQ9VBQ76OCk+qqrJ5+XbqKhtIy0lh2HmDOhxeeyzLGhgYGBxPjuc4EAwGaWxsJBpt/XSvOT17Gp4OBh3HELuOD4bY1Tm6+1raELm6h0AowqR7ngXg6blTOGfU0MTNstvtpqqqCrtpGzb1r1S7h+HImEt+z5FHXG9MU9nuKWdh5Wa+rd9NeaCekBptNw2xOQICiiBhkRVskpkCWzpDkvMZ5MpjsDOPLIvrpP8+6JoPIquI+L4m4NtKIBAjEE5D1Q5UbRasIPVEsfTAljQYuyMbm8120vl9qaqaiPhqLnypqpoQvqxWK2m1tTi/+ALlgw8QNm06uAJFgQsuOCh8udpIfdV12Lz5oPC1enXCmw2A/Px4tNecOTBtWjwKrBmaFiTk/Rq/uxC/p5hwREfTRFRNQdVM6EIKkpKFpPRAkLMQm0X91Vc0smPVHnau2suOVXvx1HhbrNuRYmfUlKGMmTGC0dOHk9c/h9qyOua/vITVnxVSsqWUoK+lpyuAIApk5KcyeMJApl0/ibMvHnNK3DN3qUF9jx492LJlC2+88QabNm0iEAgkIruuvfbaLuu0wemDpmnU19czYvpgLrr1Ako3Vhy18CRJUgs/r6PhWJY1MDAwOJXw+Xw88cQTvPnmm+zZs+eI8wuCQCzW2ufBwKC7ycnJYeDAgeTknDrVpQzOHFRVpaGhgYaGBkPk6gb2Nxy8uU+zt0yxkyQJ9CAx3zzMTi+C6SwisaR219UY8bO8ejtLqjazxV2KJxpE7YC4JQoCJiGekmhXzPRPyolXSXTmMtiVR7Kp66t6Hm8E0QGWGZgtMzCnQ3KsBD20nJDvWwK+YgIhO6GIj6hvB27fQtxiOsg9MVt7YXcOxGZzYrPZTlh0UHtIkoTL5cLlciWEL4/Hg9frJRQKEQqFCAQCuBUF85VXYrvpJtKqq3HOn4/8/vuwZQt8+mn8ZTLBrFlx4evSS6FJTBEEGD48/rr/fti/Pz7/xx/DF19AWRk8/3z8ZbPFxbM5c+J+X1lZiKIVm2saNtc00nWdWGgHAfdK/J7tBAINxFQf6KUQWYMesSCY8jBZ+iBbBuIYkE9O7ywmXjUOTdOo2l3NjlV72bFqD7u+KcLX4GfFB9+w4oNvAEjNTWHklCGMmTGcB9+7l/TcVBqrG5n30lJWf7qWvRtLCHiC6JpO9b46qvetZNlbKxEEgdScZAafPYDJ35nIuVechaKcXMLm0dChyK62MDy7Okd3P406UTQ0NFBaWoqiKAwa1H25wQYGBgYnG105DlRXV3P++eeza9cujmY417Qjp2MYGDRxoiK7zjSMyK7O0RXnUE3T2LZtGwCDBw8+7I16k8jV2NiYyFAxRK4Tz+rt+/jxM+8B8MbdF9G3b99EdFEoFKJ457+QtUIKcqvZ2/A4INKvXz8kSULVNbY1ljO/aj2ranZRHqwn1oG0REkQMYsyVtmMU7YxyHVQ3BrgzMXeBWbyJzO6HoFIIWpoOUHvBgL+BoKRNMLRpLjf1wGje1HqgcXRD3tST2w220nl9xWLxRLCl8fjSZjb67qOLMuYzWaSkpJIraoi6fPPkd57Dw6cGwAwm2H27LjwNWcOJLUjooZCsGRJorojzTPiBAHOOisunM2ZA8OGtUp31NQGwt6v8LvX4feWEYqYDnqqISDKmdgcPbA5R2Cyxj3Um7YlGAiye10R21buZueqPexdt6+V2X1Ov0yGThrIyKlDGTl5KCmZyYT9ERa9tpyvP1rD7vVxwawVAqRkuhg4vh+TrjqHqd+ZiMnSufTDoznvHokuTWP0eDyJUqBNTJ06lbS0NN59991Od/JUwufz8eSTT7JmzRrWrFnD/v37OxVmf6aIXbt37yYQCJCVlUVWVlZ3d8fAwMDgpKErx4Hbb7+dF198EZvNxs9//nNmzZpFVlbWEdMLCgoKjqldgzMLQ+w6PhhiV+foaoP6iooKXC4XVqu1xQ26IXKdXLy1vJD/+89SAM4Z4eLnV84kKGymMVJLkh5AqNgNSPQZeAVVtWl4/F5KJC/L3LtY59tHSD98ej/EzeQtogmbYiZVsTPYFa+SOMiZR9+kLEwnwEz+ZEZXayHyFbHAVwS82wiEJALhdKKxeFVMRCdIBUhKT2xJ/bHZU7Hb7Yf1+9J1FSJrQKtBF9IJqkNR1bgQdehv8liJxWKJNEe3250QiwAURcFkMuFyOkmtrMTx+eeI77wTr9LYhNmMftFFhC+9FO/kyeh2O3a7vXUhCl2HtWvh2Wfhyy9h796WHSkoOOjzNXlyPJKsxT6JoYbW43evJODZid8bxLSmHqk2gJpuIzBmACZ7T+zOAdhcY7DbUxAEgWg0SiQSwdvoY/NX29m0bCtbV+xk35aKFg9EBVGgx5BcBk3sy7DzBjHonP44nHbUsMrK/65l9cfr2F1YhKe2ZapkE9YkCz0G5jFr7hRm3jIFq93S5ny6rhMMBhMeaqFQhLS0uC/aa4+/wzX3XIbJpHTw6LWkS8UuURTJyclp4dn18MMPk5SUxM9+9rNOdfBUo7i4mN69e5OTk8OYMWP49NNPDbGrHQKBALt370YQBAYPHnzS5XQbGBgYdCddOQ7k5ORQXV3NW2+9xdVXX91FPTQwaMmRxK5XXnmF73//+8RiMe6//34ee+yxTrVzOM+u5n343//9X/7yl7/w6quvsmvXLmw2GxMnTuThhx9m5Mi4R08gEOCpp57izTffpKioCIvFwowZM/jd735H376tS7G//PLLzJ07l4KCAoqLi1mwYAFPPfUUa9aswefz0adPH66//nruvffeRAW4Y6W52DV27Fj+7//+j3fffZeSkhJsNhsTJkzg17/+NWef3b7HaSgU4vnnn+ftt99m27ZtBINBsrKymDx5Mj/72c8YNWpUm8v16tWLkpISXnrpJa699lqeeOIJ3nrrLUpKSkhOTmb69Ok88sgj9O7dG4Da2lr+8Ic/8MEHH1BaWorL5eKSSy7hscceO+xDzT179vDkk0+ycOFCysrKUBSFfv36cfnll3P33Xd36hzYFefQqqqqRLrs+++/T69evXA4HGRnx/2IDJHr5OKCvzxD7VaVRJWrA/ToI5A53E+mbmeInoFDFPFIFmLhGKDjJYKXCLVCkB1iPbVCMLGsACiijFlUcMhmMsxOhiT3YLArl0HOPHra07vdTP5kJm50vx09tJyIfzUBfxnBsKuZ35cIUjaC1BPZ0gubozd2u6OF35cemo/ueQy0KnzBTKoahuMLF6BJw5GU3MRvMqm9aKpjIBqN4vP5cLvduN1uIpFIwu9UURTMZjMpycmkVlRg++QThLffhl27EsurJhN1Z51F7bRp6BdfTM/Bgw/28/334a674mmNTSQnQ9++8XTJUDPvrKSkeMrknDlw0UWQnt6yo++/j37XTxHKKg/2PdPO/nvPxzetLyAhKnlY7QXYXaOwJxVgMpkS5yktOI/6oj/y9SIra5ZlsvPbCLWlwRZNKGaZPqN7MnBiXwae05ceQ3IxW8ygwbrPNrPmsw1sW72LgLvlck04Uuz0G9WbiZeNZ+Yt5+NIduD1eqmqqsLn86GqKvP+upQl/17JYu0DACYyG4fs5Kp7LuEHT9x8FEcuTpeLXYemLLYlgJ3OhMNhamtrycvLIxQKYbVaDbGrHUpLS2loaCA5OdkwQTYwMDA4hK4cB2w2G7qu4/P5TglDUYNTk8OJXY8//ji/+tWvEEWRP//5z9xxxx2dbqcjYtf999/P6tWrWbRoESaTCUVR8PvjqRcOh4MlS5bQu3dvZs6cSWFhYSKdJhiMX6RnZmby7bfftro+aS523Xffffz0pz9F13WSk5Px+XwJn7vRo0ezaNEiUtqq2nWUNN2M/Oc//+E3v/kNu3fvxmKxIIoigUAAAJPJxMcff8wFF1zQavny8nJmz57N5s2bgfgNms1mw+2OFwUSRZGnn36aO++8s9WyTWLX008/zYsvvsimTZta7aucnBxWrlyJpmnMmDGDoqIibDYbsVgsEQ3Rv39/1qxZ0+a57O233+aWW24hHA4DkJSURCQSSbzv0aMH8+fPZ/DgwUe13471HOr1etmyZQsTJkwA4LXXXmPkyJEEg0F0Xcdutyduxs1mM2lpaTgcDkPk6ibaE7oA0h0KFwxPRU6NISOiIKEgYkNBR6cKP7WEcAkmAsRYL1UTMOkkyWbybGkMcuYx2JnLIFce2ZZk4xgfA3Gj+9Vo4eWEfYUEAiEC4TSCkRR0XTxgdN8DQe6J2doXm7kUa/RRrKZ6AuE0iqvPIxJ1YDE1IIkRVGUWoWgWJpOJ3r17HxfBq4km4auhoSGR6tgkdCuKgtVqxWqxEFi1Csfnn5O1bBm2ZhqIajbjmTQJyy23YJVluOmmlsb1cDBt8fXXwW6Ppzp+8glUVR2cRxRh4sS48DVnDmzdCtdc02pdTdmNjc9Mpf68PkRVa7N1pKFY8rE7B2Eze7BG7iUYTm2xfz01QbZ/o7Lt22T2rNmPt9bXYv1Wp4X+43szcEJfBk7oS+Xu/bx495t0wNYOAJvTSlbfdAae24eJV41j2b9XsehfX6HqMZbwXwDGMYVkIS7sXXPvpUcteHWpQb3JZEpcSDSnk3ZfpyRms5m8vLzu7sZJTywWo7GxEYD0Q5VpAwMDA4MupUePHlRUVBhCl8EJR9d17r77bv785z9jNpt57bXXTkh04XPPPYckSbzzzjtcdtllyLLMmjVruO6669i7dy933XUXWVlZNDQ0MH/+fGbMmAHEo6euv/56qquruf/++3nttdfaXH9NTQ133303V199NX/84x/p0aMHwWCQV155hbvvvpvCwkK+973v8f7773fZNt1xxx3k5OSwePFiJk+ejCAIrFmzhptvvpkdO3bwgx/8gL1797bwN1FVlauuuorNmzfjcrl49tlnueaaazCZTIn98Mknn3DXXXfRr18/Lrzwwjbbfuihh0hOTuaLL75g+vTp6LrO4sWLufHGG6msrOSXv/wlxcXFuFwuvv76a8455xyi0SgffPABc+fOZdeuXTzxxBM8+uijLda7bt06brrpJqLRKOeeey5/+9vfGD58OJqm8emnn/LDH/6Q0tJS5syZw/r161tYpRxPdF2nqqqqVcXaWCxGNBpN3ORmZ2eTnp5uiFzdTE3Q067QJQCDsxyoIQFFF4gIGi4s6Oi4CePCTApWGqQoUVGgQEplUFIPevbuxSBXHqnmE/OdO1OIG91PR7JMx+YCa2wfaZHlqMGvCPq2EAjZCEb2EwrtJBRaSAgRGANoeIM5RGMOkqxlyFJcDJfVFTgct+Dz+amqqjquv0VFUUhJSSElJYVIJNJK+Gqq9hlNS0O4+WaKv/c9kouLyViyhLTFi7FWVJCyaBEsWoQuCAhtaSS6Hhe8fvlLKCqKpzBqGqxZc7C644YNsGJF/PXLX4IktRbNAEEHBIHkP+zG9f2/Egl+hd+zkYC/jkBYIxqoozGwgUYApuENZhON2XHaypClCKnZIhMvFZlwqYBP+zHBuij1uz2sX7yJ9Uu24HcH2LhoGxsXbTtwbIXDCl2KRcaRbKdxvwdd1wl4ghQVllJUWMq8vy474v5/70+fcNuj13U6pfFwdCg2Mz8/H5/Px9dff93lHTA4vaivr0fX9bgCbrUeeQEDAwMDg05z+eWXEwgE+Pbbb7u7KwZnEJFIhOuuu44///nPuFwu5s2bd8LSaBsbG/nvf//L1VdfnfCBGT9+PP/85z8BWLlyJfPmzWPBggVccMEFiKKIKIpMnz6dxx9/HIinrR0qdjQRCASYOHEib775Jj169ADAarXyox/9iGeffRaADz74oEt/c7Iss2TJEqZOnYooiolteueddwAoKSlpdQ3+7rvvsnr1aiAeQXXjjTdiOuD70qdPHz744APOPvtsdF3nvvvua7ftcDjMggULmDlzJqIoIkkSM2fOTOyrt99+m5KSEhYuXMg555wDxG8Mr732Wn7+858D8Oabb7Za7wMPPEA0GqVfv3588cUXDB8+HIhHm82ZM4dPP/0UWZbZs2cPzz///GH3TzgcTphLN706y969e/n2228pKipKfLZu3ToKCwvZsWMHjY2NCIJAZmYmSUlJhtDVzfzkrTdoS+gCcFllMhwm3KEY4UYR04GoLh0wCxKKIJMqWTnP1Y+Lc0czPnsAA02ZjHL0MISuE4Ag90Sw3Yic9hyOnvPI7P0bevYaQZ/8KnJSN+Cy70ORg0RjNvyhDFRNwhPMp97Xm0A4FXQfglaJxWLB5/MlIk6PNyaTidTUVPr27cvQoUPp168fKSkp6LqOrutomkYoHKY6L4+dc+ey9s03KfznPym57joiqaltC11N6DqUlsLy5fH3ohg3rv/tb2H9eigpift8zZoFsgyqeth1CaWliKursaTeTlqvv5A/+Dn6DZxNbq6LZPt+THKAaMx6YP/KuAM9aPAVEFPjqfgCPqxmD0lZNi747mQeev8+3qv5F39Z9Tu++9gNjJo2DEmR0LXDBzhFQzFu/r+reGbzw/zw+ZsYPnMgyTnOuEgGhPUgHr0B7wH5DcBLIx69AY/eQDDm5+Pn5nfk8Bw1HYrsuuiii/jrX//K1KlTGTFiROLpS319PdOmTetwY4IgsGjRos71lPgFyLJly1i7di3r1q1j7dq17Nu3D6DDpqler5c//vGPvPfeexQVFSFJEgMGDOC6667jzjvvTFwoGBw9uq5TV1cHxKO6jAsEAwMDg+PLfffdxzvvvMOPfvQjFi1aRHJycnd3yeA0x+PxcPnll7NkyRJycnKYN28eI0aMOGHtT5o0iUmTJrX6fPLkyZjNZsLhMFdffTX9+vVrNc+sWbMACAaD7Nq1iyFDhrTZxv/+7/+2WSVq7ty5PPTQQ5SVlfHmm28yfvz4Y9yaOD/4wQ/IzMxs9fnw4cPp3bs3RUVFbNy4kXPPPTcx7a233gJgwoQJbaY4yrLMgw8+yEUXXcTmzZvZtGlTQnBqzlVXXXXYfdXUv7S0tDbn+e1vf8uePXvw+/3Y7XYgLkjOnx+/cfnFL36BzWZrtezo0aO58sorefvtt3njjTe49957W83TxO9//3sefvjhdqcfDS+88EJCyGviqaeeSvw/d+5cbr75Zvbu3YvT6cRkMmE2mxN/ZVk2rm9PIHUNgXanmWQRWRSIxDR8XnC6JEQRJASsgowZCVEXkMMaPs0Tr8qoqrjdbsxmsxERfQIRBBOYzkIwnYXiUJE9j5AUfANdhwZfb7yhbBQxREyzoukiwUgKUdVGkhJAUiTC4XAilfxEYjKZSE9Px2Qy0djYSCAQSAheuq4f7NOgQTT27Utk0CD6d6SIS2Vl25/37Ak/+Un89a9/wfe+d1TrEsRkJPscnPY5OJyvg/dh6r298QZzkKUwMdWCpstEYnZkKe4bJokhwlEtsS2SLDHorP4MOqs/1//qCr54ZQl/mPvcEbth0i3k5eVhn20ne0gasiyjqipvPvAxny77iCK2tZh/B+sT//dmMBV79h95WztBh8Suhx9+mOXLl7NhwwbWrFmT+DwSibB06dION3asg8M333zDRRdd1OnlS0pKmDJlCsXFxUDc6yQcDicqLL7++utd5sNwJuL1eolGo0iShMvl6u7uGBgYGJz2pKWlsXDhQm644QaGDBnCD3/4Q8aNG3dEb4vzzz//BPXQ4HSisrKSyZMns379egYMGMD8+fPp1avXCe3DWWed1ebnkiSRnp5OeXl5uyJUcyP1hoaGNueRZZnzzjuvzWmiKDJlyhRee+21FtfDx8rhDOhzc3MpKiqivr6+xedN7TelabbF1KlTEzf4a9asaVPsam9/Nt9XHdmfjY2NCbFr3bp1CauTw/Vv5syZvP3222zcuJFoNIqitJ3C8qtf/apFQSyPx5OIujtavv/97zN8+HBisRi33norAPfffz/Dhw9HVVWcTmciui4UChFqbiRN/HvWJHw1vUwmkyGcHCfSUmw00rbgFYlpxDQdkyziD2ns2q2RZNFJtsskOyBmDiMjEQjFMEdkFFFCFiWqqqrweDxYLBbsB6rpNfnVGRw/9Ng+9OB7EPwAtLhPlSCAxdSIRfFgkv3IUhWRmB1/KJOYasbtlTFbg4ii2K0Fz2RZRlEUJElCURQ0TSMSiRCLxVBVNeFhqB6mWEcLDhTHOCx9+hz1unRdh+ha9MAbEPoMOLB/TW5Msp9ILIlgxEXznERVsxx2/2YVtH4Q0xbZPTNxOp3IskxdXR2yLOPz+SgYmUf+sj5kkIuGyhqWAjCQUbiIP0QxYyG3bwf33VHSoW9NSkoKa9euZcGCBWzatIlAIMBDDz2Ew+FIhDCfKFJSUhgzZkzidc8991DV3NitHWKxGHPmzKG4uJicnBxeffVVZsyYgaZpvPPOO9x+++0UFhZy00038emnn56ALTn9qK2tBSA1NbXNJ6IGBgYGBl2PLMv06tWLb775hkceeeSI8wuC0C1PSA1Off7xj38A8cp0Cxcu7LTgcCwcTshtulhvb57mF/PtpTGmp6djNpvbbaPJv7W6uvqIfe0oHdmmQ/vb1P7h/GQtFgvp6ens37+/3f52ZF8d7f5s3tbh+pefnw/Er9Hr6+vbrerYJCp1BX369EHTNGpra7n00kv56KOPGDFiBKNGjUoU+3C5XPTs2TPh4RUOh4lEIkQiEVRVJRgMtkqpUhSlhQhmMpkwmUzG9fAx8tx3rmfW6hdoK5XRHYxR44uQ6zRT7YuLDd5QDG8oRmkd5LjMBGMqQWLkZZhIMZlQRaipD5LkNuO02HFZHYljZ7PZEuKXUUm+a9C1AITnoQfeh+g3zaY4QYiBHsBqasRhqcYTyMduqYqLXvZSfKGeRHHgdrtxuVzdmn1ltVpxuVz4/X6i0SgmkwlZltF1HVVVicVicdF7yhT0/HyE8vI2vbYQBMjPh3YeqLTgvPPi83ZgXbrmg9CHcZErtrPZTEqL/QvxtEjhgNil4yAUceFyOdq1Hxp23iDS89OoLa9r27dLgIz8NIadNyixrxwOB1VVVYiiyLnXjuOLZ5dj1qxoukY2PaliH05ScArxACNREpnzk1ltrPzY6fAvWRRFZs2alQhrbhK7HnzwwePSsbY477zzWj3Z+p//+Z8OLfvKK6+wadMmAN57771EBRZRFPnOd76DpmnccMMNfPbZZyxatIjp06d3bedPc0KhED5fvJJDW6HuBgYGBgZdT3FxMZMmTaLyQBh7RwrHnEnFZQy6lksuuYTly5fjdruZO3cuH330UZspagYGJyuCIJCdnU0wGOSaa67ho48+QpZlotEooVAIs9lMdnZ2QrRqbpzfFM3RVFGySQSLRqOJV/OCXoIgJESv5iJYk9ecwZHJsDpJHyK1aVKvA9v2++iZaaJfnkaFP0q0QUGJKjhMEp5gjFUljdT6okiiQLpDIS9DISNTwp4qYA0qODxmUiQbdsVCitVBksWeqL5nt9ux2WxYrVbjeB0F8eiiQvTguxD6HPSm34QApkkI1qvAMh3CS9Eb/x+CANkpmwhFk/GHsjGbGpHFMNak/sT8YiKqat++feTk5HSLJ7QgCOTk5OD1emlsbCQcDqMoCrFYDE3TgHjUZ2pGBsIzz8DVV8fFqEOvt3Qdnn46bjx/JCQJ2lvXge+j/uS96L5HIPTRwf0sWMFyCYLtBlDLoPH/kZ2ymVA0GU8gF10X0BGIqhbCTMJstZCdnd3ud1ySJH7y9FweuebJNnZM/M+P/zQ3Ed0qCAJJSUmJQiCpqalMvvkclr7yNaIg0lPvTxX7EJpZx191zyXHxZwejkLsOpRbbrnlhHuDHEuI8CuvvALEQ7qbhK7mXHfddTzwwAMUFRXx6quvGmLXUdLk1dXkb2BgYGBgcPz5zW9+Q0VFBRkZGTz++OPMmjWLrKwsI6XG4LgwduxYHnzwQS644AIWLVrExRdfzCeffJJIXzsdqK2tJRKJtHstU36g3HxbHlsnkszMTEpLSykrK2t3nlAolLg+O5H9bd5WWVkZffv2bXO+pr7LskxqauoJ6RvEI9V69+6N1+vlBz/4AQ6Hg2g0isvlIjs7u91INlEUsVgsWCyWFp83pTE1CWBNIpiqqon3Xq+3xXqa+4A1/W9EE7XNF3fexQV/eaZNwcuaGWN7ZhGZup18l5W+uckkm/IIhiMs3l6GLumIIqiazn5PhP2eCOwBu0kiO1khJzNMSoYPs0PA6ldwSVackgWH2UaqPQmH2ZaI+mqK/Gov3fZMR1erIfjfeKqierAABFLPuMBlvQJByj74uWUWJP8Z3fMYDmsVvTKXU9UwHF+4gDDDkbRcMjIcpKSk4PF4iEajlJaWkp6eTkpKygkXIJOSkhgwYAAlJSXU19cTDserRjalN5rNZtxuN8mXXYb07rtw111w6PlZFOFoIqKvvBLaWJfusKD/ZQyc/1doCjKV+sQFLuvlCKIz/pkyFJL/jEN8jF6Zy9lVMRNvMJtQNAPB1BuXa8Bhz3lNnHfl2fzmnXt57LqnUGNa4vOM/DR+/Ke5nHflwVT8WCyGz+cjOTkZTdNQVRVfw8GHAGYs9GYwZiyIkshV91zCD564ueP75Cjp9Fn15Zdf7sJuHF8CgQBfffUVQLullwVBYPbs2fztb3/jiy++OJHdO+VRVTXhfWFEdRkYGBicOBYtWoQgCPznP/8xHtIYnBDGjRvHokWLmDFjBkuXLuXCCy/ks88+axEBcyoTi8VYvnx5m78nXddZtixeRn3cuHEnumstGDduHKWlpSxatIjf/va3bc6zdOnSRMpyV5npd4QxY8YgiiKaprFo0aJ2xa6FCxcCMHLkyBMuINjtdnJzc7n77rvJycnBZDJ1OoJHkqRWVcibzKubhK/mfzVNazMVUpblFiJY018jFRLmf9dBdfUf+OnCi3F7sshISebRSy/Gr22kMVJLsimdnrZzQYvvR4vFwvixIWKxGLIsU1Tt4f8+/YIde+uIBnT8EZU91Sp7qkPIokCqXSEnTSErI4It1Y0SFrF7TbgkCw7FgtNiJ83uwmayJLy+mqK+zuTjo+sRCC+JC1zh5TSlycWjiy6Mi1zKuHZ/V4JlFphnQGQNSa4aHDnpBNWhqKqOLMuJ32RKSgr79+/H6/VSU1NDIBAgOzv7hAvESUlJDB06lEAgkIjiVBSF/fv343a7aWxsxGQykXfFFQiXXRavulhZCdnZ8Ne/wvvvw3XXQWEhOJ0da/TKK+Gyy9CXvov+7l8Qn/8KrGGYvh9QwDITwXpDvABAG/u5aR8nudaQpddg9lpIThtIamraUZ3zBp/THzWmIQhwzz9+RG7fbIadN6jVw9Xq6uqE/2GPHj2oqahh3afx7LoxM4fT6+w89pfUMnTkYC776YXHLaKriS77hni9XtatW5fI08/MzGTMmDFHVApPBNu2bUuEGA4bNqzd+ZqmVVVVUV9f3+op01//+lcaGxsTFw4bN27k0UcfBeJmv2eq4W9DQwOaprUK9zYwMDAwOL40NjZitVqPqjKygcGxMnr0aBYvXsyMGTNYvnw5s2fP5vPPPz8prvm6gscee4ypU6e2uol95ZVXKC0tBeA73/lOd3QtwXXXXccHH3zA119/zRdffNGqImMsFkt4+A0bNuyw179dTXJyMrNmzeLzzz/nD3/4AzfddFOrdNcNGzbw3nvvAXD99defsL41EQwG6XPAANrn83V5Oq4gCCiK0krE03W9VSpkOBwmGo0Si8WIxWKtUiEVRWkVCXYmpULqgTfQvf9HhhXevr4fguPHzaa27wnX/JgO7WXj1TtuASASi/H56q38c9nXVFUGiMU0qr0Rqr0RKAaHWSIjSSEnQyU1PYzJ2YgpKONoUEiSLdjNVpJtDjLsyVgUc4uorzMlu0WP7jhgNv8h6M2KfShjEKxXg2U2gtixe0JBkMAcjwwSgLbihCVJIicnB7vdTnV1NX6/n+LiYrKzs0/4vacgCAlvtyaaKg96PB7q6+uxWq3xAJApUw4uOGoUrFkDe/fCj34Er7+eSEdsD11XIbwMPfAfGLoc+mvo70gI1SqsmAbX/BZBOnLUbmIfm8pQLH6SkpxHfc7bsGQLAP3G9OHC77X9cNXn8+H1ehEEgaysLERR5M8/fBFN1RElkbv++QPyCnIS8x5voQu6QOzatGkTDzzwAJ9//nlCUGpCFEUuvvhifvvb37ZZAeZEUVFRkfj/cEaZzadVVFS0EruefPJJSkpKEu8LCwspLCwE4MEHH2xT7GoaxJrweDxHvwEnMbquJ0Lk09LSzpiB18DAwOBkoKCggJKSEuPca3DCGTlyJIsXL2b69Ol89dVXzJo1i3nz5uHs6NPqkxSbzcaKFSu44YYbePLJJ8nPzycUCvHqq69y1113AXDZZZe1W8XwRHHVVVdx9tlns3r1aq699lqee+45rrnmGhRFoaioiLvvvpuvv/4agCeeeOKE9+/RRx9l4cKF7N69m1mzZvHcc88xfPhwNE1j3rx53H777cRiMfr27csPf/jDE96/7kIQhIRg1Vwc1jStRfRX0/+xWCwhjjVHFMU2/cBkWT6txgM9+F90z0PxN/YfgP1Hx7xOkyxz2bkjuOzcEQDUNPp4ZfG3fLp2C+6GKL6wii+sUlQbj/pKsSlkJytkZ6jYUoMo1gYsfoWkWjNWxYzDYiXF5iTD6sRqOej1ZbPZTquoL11zQ+gT9MB7ENt8cIKYGU+ds16JIHewgmAnEAQBl8uF1WqlsrKSUChEeXk5KSkppKend+u+TkpKIjs7O5HCV1VVlYgATJCSAm+8AeefH/87cybMndvm+nS1FoLvoAfeAu2gjoHjfLh5NDz9DsJr5XDd0aWnN3m2duYcsX5J/JiPntr2gxNVVRNBTykpKVgsFsp2V/LNvLhWcsGtU3ClnfgHYsckdr3//vvcdNNNhMPhNg1vVVXl448/5osvvuD111/niiuuOJbmOk3zPPnDqZjNpzVfponi4uKjbvv3v/89Dz/88FEvd6rg9/sJh8OIokhKSkp3d8fAwMDgjOLaa6/lt7/9LYsXLzaiuwxOOMOHD2fp0qVMmzaNr7/+mpkzZzJ//vwT7unalWRkZPCLX/yCO++8k7feeouUlBR8Pl+i2uDIkSN58cUXu7mX8UiH9957j1mzZrFlyxZuvPFG5s6di81mo7GxEYgLIn/605/atfA4nowZM4Z///vf3HLLLaxYsYIRI0bgdDqJRCKEQiEAevTowccff2xkBRA/VoemQgKtUiGb/tc0jVAolNiXTTR5Bx0qgp2KPo56aD66+38AHWw3ITh+flyEvIxkB/deOZV7r5wKwJodpbyxdB0rdxQTDmrU+CLU+CJsKotHfaU7FHJSdVLTVEzJPuSQgN1Tg000YVHMOK020u0u0qwu7M0qPJ6K0Xi6rkJk1QGz+QVAk+iqgHkagu2quOm8cOLSCU0mEz169KC2tpaGhgYaGhoIBoOJdOTuIjU1lXA4TCwWw+v1Ul5eTu/evVtGd06cCI88Ag88AD/9KUyYAIPiVQzjxv7fxqO4QguAAxVuhWSwXY1gvQ5B7gk/3glPvwOffw6lpUflAdak13RGGGwSu0a2I3bV1tYmKlU22Rr9/oanQQfFrHDns98jGmu7CvLxpNPfzKKiIm688UbC4TC9evXivvvuY+bMmYkywmVlZSxYsIA//OEPiXm3bNlC7969u6zzpwK/+tWv+NnPfpZ47/F4uqVU9/GiKaorJSXllBxIDQwMDE5lfvnLX/L+++9z++23s3DhwjNujDXofoYMGZIQvL755htmzJjBggULTukHYHfccQcDBgzgqaee4ttvv0UURQYNGsQNN9zAvffe2y3VwNoiLy+PNWvW8Le//Y23336bbdu2EQgE6NGjB1OmTOFnP/sZo0aN6rb+fec732Hs2LE8+eSTLFy4kLKyMhRFYdSoUVxxxRXcfffdp3wk4PFGlmVkWW4RIaLrOtFotM1USFVVCQQCBAKBFutpKxXSZDKdtOKLHl6G3vgzQAPrVQhJ/3vC+jpuYA/GDYzfq4UiMT5etYUPV21iR2lNIuqruC6Eskcgxa6Q4VTISQNbcgQlyYsSFElqrEGWZKwmC8k2B5m2ZNLtroTwZbVaT+r7Jj22Dz34AQTfB63y4AR5YDxN0ToHQTxxRSUORRRFMjMzsdlsVFVVEQqFKCkpITMzE6fT2S3f66ZKr5FIhOrqahobG6moqKBnz54t+/PLX8KiRbB4MVx3HfrKBaDPQw++AbHdB+dTRiPYro97nwnmg58PGACTJ8OyZfCvf8GDD3a4j52N7KoqrqaqqBpJlhg2aVCr6YFAIPGQpSl9cfNX29m5Zi8A1/3yckxmU7eIXYLeyRrkP/nJT3j++eeZMGEC8+fPb/epjN/v54ILLmDVqlX8+Mc/5q9//esxdfhQevXqRUlJCQ8++CAPPfRQm/N8/PHHXHrppUDcI2DEiBFtzvfhhx9y+eWXA/H0zOPhb+DxeHC5XLjd7lN+gI9EImzfvh2AAQMGtKpOY2BgYGDQmq4cB1599VUaGxt56KGHiEQiXHXVVZx11llH9E665ZZbjqldA4PTjZdffpm5c+dSUFDQqUh+gxNHV5xD/X5/4t7F5/OdFhVFNU1r5QcWiUQSUYmHIghCm6mQ3R2BpIdXozd8HwiD5SIE1x/jnkMnAaU1jby9bAOL1u+gqt4Pze6ik8wSaXYTWSkKaSky5hQN2QRmJKyiCVGWSDLHUx6z7SmkJyUnxK+TQXjU9SCE5se9uCKrD04QnHFxy3o1yEO6vZ+HEovFqKqqSnjdJSUldWtV6mg0SlFREXV1dSiKQo8ePcjKymo5U0UF+shhCLUN6N9NQ3/sgHAo2MByKYLtegRlcPuNvP463HRTPKqrqAg6uK3FxcWEw2F69OhxVJ5d815awh+/9xxDJgzgma8eazFN0zRKSkqIRCKJirYAN/e9g6qiauwuG+/XvYQoil163u3oONDpyK6FCxciCALPP//8YcOP7XY7zz//PCNHjuy2Koe5ubmJ/8vLy9sVu5rKSR+6jEHb1NfXA+BwOAyhy8DAwKAbuO222xIXnrqu89prr/Haa68ddhlBEAyxy8DAwOA0QxRFLBZLq2tyVVXbTIVs+jwcDrewbxFFsYX41fT/iRAP9Mh69MYfAuF4mpzrDyeN0AXQIyOZn189mZ9fPRlN0/hqawn/XbGJb3ftwxuI4g0HKa4PokgCqTaFdIeJ7FQBqyuE2aXTGPFR62ukSChHMsnxKo9WJ7lJaaQ7UxJ+XydKqImnzm04kKb4KehNxREEMJ0br6ZomdEysugkQ5Zl8vLyaGhooLa2Fq/XSygUIicnp1uicBVFIT8/n0gkgsfjobKyEqvVitPpRNfDEPoM3fwGPG1FuKkB4V916FN6IVx5d9z7rCPG/lddBXfeGU9j/OIL6GCqepO/+tEKlhuWHkhhnDK01bT6+noikQiyLJORkQHAkjdXUFUU9+/64R9u7lY/tU6LXWVlZSQlJXXIeH748OE4nU7Kyso629wxMXjw4EQJ5M2bN7frXbB5c/xAZmdntzKnN2iJpmkJsaspL9fAwMDA4MTSKjzewMDAwMCgGZIkJQzTm9B1/bB+YMFgkGAw2GI9siy3mQrZVTeyenRrPKJLD4BpAkLyMwjC8a/W1llEUeS8Yb05b1jcPsAXCPPBV5v4Yu0OdpTXst8bYb83wtZKSLLIpB1IeUxLVjG7Yij2EJ5QgFp3AzuqSzCZzPGUR2sy+ckZpB6I/DKbzV0+zutqDYQ+RA+8C+regxOkHnGBy3o5gnTqBH4IgkBqamrCvD4ajVJaWkpaWhqpqakn/DrJZrORn59PUVERgUCAfSVb6Je7AlPsHdAb4zNNT0a/Ixvh2XUIdxchTJ0C9g76F1oscMst8Mwz8I9/dFjs6kwao67rrF8c10hGTWup+4RCoYQekJmZiSRJaJrGX+6M+1qm5aYyu53KjSeKTotdiqK0GxZ7KE1ldg8tv3uisNlsnHvuuSxfvpx58+bxi1/8otU8uq4zf/58gFblmw1a43a7icViKIpyyqdjGhgYGJyqGOlWBgYGBkePLMv85Cc/Sfx/piEIAoqitLo3a7pnO1QEi0ajxGIxYrFYIl2s+XoOFcGONhVSj+1Gb/gu6B5QxiAk/+2kjiZqC4fNzM0zx3HzzHEA7Kmo462lhazcUkxlvRdPKEZRXRCTFK/wmOYwkeVSsDljWJI1FDVKIBigSqhlQ+UerBYLadYksu2p9EzNJjnJic1m6/T3VdejEF4aT1MMLwPU+ATBCuZZCLarQRmHIJy6FSStVisFBQVUV1fj8Xiora0lEAiQnZ19wnUIl8tBdnoVpftK8dT72BfbQ58sL6Kci2C7DqxXIzzlhFUTEdauhRtuiPt4dfT43n57XOz6+GOorIScnCMu0hmxq3x3FbXl9SgmmaETB7RY1/79+9F1naSkpIR9xtt/+AhvnQ+An7/woxZtdcd5t9Ot9OvXj/Xr1zN//nxmzZp12Hnnz59PKBRi8ODD5J4eZ2699VaWL1/OkiVLWL16NWeffXaL6e+88w5798aVbSO948g0GdOnpaUZUQUGBgYGBgYGAFx55ZWsXLnyqJZ5//33mThx4nHq0fGlyZ/kaKiqqjoOPTE4GsxmM88++2x3d+OkQxCEhGDVHFVVW/mBhcPhFp83RxTFhPh1aCrkofcNemwfev1toNWDPBQh5Z8IYsf9hE5W+uamcf8NM4B4Rsyiwt18/PUWNuytSER9bWse9ZWkkOIUMDtjWJwQ9ccIBQKU1O9nVfk2XFYH6TYXuUlp9ErLwelIwmKxHPE+TI/ujAtcoQ/j+7gJZfSBNMWLOpY6d4ogSRLZ2dnY7Xb2799PIBCgpKSE7OzsE1L5VVerIfgOeuAtMuX9hJLOoaphOPX+8djCV5KbPQ1BPCDBSMCbb8Lo0bB8OTz6KLTjQd6KoUPj1R1XroSXXoL77z9y3zohdjVFdQ2eMACz9eB5oaGhgVAohCRJZGZmAhAJRfj3I+8AUDA0n/GzR7dYV3ecdzstdl122WUUFhZy++FZzdgAAQAASURBVO23M3/+/HaFrK1bt/KDH/wAQRAS5u/HQkNDA6qqJt435Z4GAgFqa2sTn1sslhZf6FtvvZVnnnmGTZs2cdVVV/HKK68wffp0NE3jvffe4/bbbwfgwgsvZPr07g23O9lpqvLSFDJqYGBgYGBgYABx/479+/cf1TKH3ih3B7fddhu33XbbUS93tNtqYHAqIkkSVqu1hQeSruvt+oFpmkYoFCIUCrVaTwsBTGnE5J+LoFWD3B8h9UUE8fAFVk5FRFFk5tgBzBwbj4xp8AV4d9lGFhXuoqiqnqK6YCLqK9VuIs0WT3m0OTQsyTomK3i9Xrw+L7tqS1lRuplks4MMu4seKVn0Ss0hyeFIRMvomgdCn8a9uKKbmnUkI56iaL0SQe7bHbvihCAIAk6nE4vFQmVlJaFQiPLycpKTk8nIyOhyDyld1yGyGj3wHwgvBGLxfogp5PccTkQ5n7qGGJW1CvZkb8tqxf36wd/+BjffDL/9LUydGq+22BFuvz0udr3wAvzP/8BhtkvX9YTYdTTb3+TXNWrKwcJ9kUgkEfiSkZGR+N79/d5XiQTj4/n//Pv/dbiN40mnqzF6PB6GDh1KeXk5JpOJa665hunTp5OXlwfEPb0WLVrEu+++SyQSIT8/ny1bthyxQtSRaKq+eCRuvfVWXn755RafFRcXM3Xq1ETah81mS5yMAUaPHs2iRYuOa7ns06EaY2lpKQ0NDaSkpNCjR4/u7o6BgYHBKcXpMA4YGBgYdBddcQ7VdT3xkDw9Pd3IUuhCdF0nGo22mQrZ/LZTEhrIt/8Kk1ROVMulXv8zijm3RUTYmXJcNhdX8e6XG1i1rYTqxniaqAA4rTJpB1IeU5IkTHawpYAsgSSIRFFRxbi9ULrFSaY9Si/7VnqY5mMzVyMIOiCDeWq8mqL5PAThzErbbfqtN3lLmc1mcnJyWkUvdmrdmgeCH6AH3mjpfaaMRbDdAJZZCIKJcDjMjh078Pl8WK1WBg0a1No8/7bb4JVXIC8PNmyAjnhiBwKQmwtuNyxYADNmtN9XXWfnzp1APEOvI0UQdF3n2pzbaax289SyRxh+3mB0XaesrIxAIIDdbicvLw9BEHDXebk2+/toqsbw8wfz1NJH2lxfV513j3s1RqfTybx585gzZw7FxcX85z//4T//+U+r+XRdp3fv3nz00UfHLHQdK7169WLjxo08+eSTvP/++xQVFaEoCkOHDuX666/nzjvvxGQydWsfT3ZisRiNjY2AYUxvYGBgcDKxcuVKVqxYQVlZ2f9n777jo6rSBo7/zp0+k2TSG4RepCkCNhRBwAqrKBbslbWX9VV3Xcvqrq676667q6LuKmtvIPYuHUGRXqR3SO/J9HLP+8dNJoyEFhKSwPl+PtFk7p1zz2TCnbnPPM9z8Hq97O2zLCEEkydPPsyzUxRFaTt8Pl+s9Mbj8eByuVp5RkcOIQRWq3WPaypd12PBr3CwjMToY1hEPmE9g12ePxGRNvCU7zHOL0shzWbzERcE698lm/5djJLoUCTCN4vW8/mPa1mzvYgt5X62lPuxmTVSnRbSXBbSEiw4nDr2JIEjEbRohPJgBflVIZZpSdjNF5Bhj5LtzqBn1lA6JnRvtd7ZrU0IQUZGBk6nk6KiIoLBIDt27CAjIwO3292kvyUZXm1kcfk/B+qyF4UL7BcgnJcjLL3j9rfZbHTp0oUNGzbg9/vZunUrvXv3jg84Pf88/PADbNgAN9wAH38M+5ub0wlXXgkvvGA0qt9PsKvegT7m7Wt2UVVSjc1hpfeJPQAjyOTz+dA0jczMzNhY/7jxBfSojhDwuzfubHS81jjvNjmzq57H42HSpElMmTKFlStXxkoMTSYTxx57LBMmTODWW289LDWybdmkSZOYNGkS0WiUDRs2tLtP9KWUlJeXU1paSm2tkX7Zs2fP1p6WoihKu9PcmV0bN27kiiuuYOnSpXG3Syn37I1Sd9vu7QAURVHak+Y4h3q93ti1iQp2HV5S9yArr4PwStAykO43CEZz4jLBQqHQXl+nTCbTXvuBHYmKK2uZOncFs5dvYntJFVFdIgC3w0yay0qqy4LbacLmAocbbHaBAELohEQUadJw25yku9x0TMnkmIzOZCalNHspX3sQiUQoKiqKLbKQmJhIVlbWAWY5+cH/JdL/TnxpqLl3XRbXr/bb+6y0tJQtW7YQjUbJzs6ma9eu8e/Tli2Dk0+GUAieew7uuGP/D2r5cqPnl8UC+fmQkdHobtFolE2bNgHQq1evAwp4ffz8V0y6638MOvNY/vrNI0QiEbZt20Y0GiUjIyPWzih/UyHX9b4LJJx+8ck8MuX/Gh2vOc+7B/o6cMjBrt2Fw+FYimBqaupRG0Hel/ZYvlJYWMjPP/8cV3dvtVoZMGAAOQew8oOiKIrSoDlfB8rLyxk4cCD5+flkZWUxfPhwpkyZgsPhYPz48RQVFbFw4UJqa2tJT09nzJgxALz66qvN8VAURVEOOxXsar+k9CMrboLwIhDJiNS3EJZejewniUQicX3A6v+/t0tXs9kcC3ztHgw7UoI6UkoIr2TR6o/5cEE5S7ZlU+61AyIu6yvVZcFuF9gTwekGs0mgC0lARggKHYvJTLIjgYzEZDqn5tA3swvJzqMnKUVKSWVlJWVlZUgpsVgsZGdn43Q2viiCjGw1yhT9H4GsrrvVAvZzEc7LjdVDDzBTSkrJjh07KCgoQAhB165dycrKit/p3/+Ge+4BqxUWLoSBA/c/8IknwqJF8PTTcN99je4SDofZsmULQgh69drz31xjHhv/NPM/+okbnryCyx+8kIKCAmpra7Hb7XTq1Cn2uO885fesW7gRk1ljStErJKU2Xs3XGsGuZi3atVgsez5hSrtWWFjIkiVL9rg9FAqxZMkSBg8erAJeiqIoreRf//oX+fn5nHTSScyYMQOn08mUKVNwu9288cYbgPHm4o9//CNPP/00DoeDF154oZVnrSiKohxtpAwhK++oC3QlIFL/12igC4wyK4vFgsViiasOklLGBb527wcWiUSIRCKxrJ3dx/llFpjFYmk3pZAyWgaBT4wVFSObGJILQy4GTB0JmMbx+Yrj+GZJIWt3llJYE0QT4LabjUb3LguJDhNWu8DpNpPsAiEFvloP62qrWFW0lZnrF+N2JpCVnEb3tI70zeiEw3Lo/azaqvoF1pxOJ4WFhYRCIXbt2kVqaippaWkIIZAyDMGZRqli6IeGO5s6IhwTwHkxQjv4RdqEEOTl5eH3+6moqGD79u04nc74Vk933QXTp8Pnn8OECbBkCewvKDRxohHsevll+L//a7T88WBXYtR1nZWzfwZg4Mj+xgIJtbUIIcjKyoqN8/P8daxbuBGA8287Z6+BrtZydHWoUw6KlJKff/55n/v8/PPPZGdnt5sXDEVRlCPJF198gRCCP//5z3v9VNLlcvHXv/6VUCjEs88+yxlnnMEll1xymGeqKIqiHK2kDCOr7oHQPBAORMrLCEv//d7vl4QQsYDV7qLRaKNBsPrbf7niq6ZpccGv+u9NJlObuKYxgi1zjABXcA71q/uBHexnGc3mrSfiFBqXngGXnmFs3Vlaxfuzl/P9qi1sLathc5kPu1kj1WVkfaU4LdisYHNquFKsWK2CqC6pqq6mqLqc5Ts38qXZSnJiEh2SM+mdkUevlA5YTEdeyKA+O6m0tJTq6mrKy8vxeYrIcs/CEn4f9JK6PTWwjTCyuKzDEOLQMgU1TaN79+4EAgF8Ph8bN26kf//+DT3uhIBXX4XjjoP16+HOO+F//9v3oBMmwG9+Y/T7mju30dUcDzbYtWXFdmorvTgTHXQf2Jmdu3YCRvWe3W6PjfnXa58HwOa0cuNTVxzQ2IfTAf3lLlq0iBNOOKFZD+z3+9m2bRt9+vRp1nGV5lNeXr7HksG/FAgEKC8vJz09/TDNSlEURam3efNmhBAMGzYs7vZfvrEH+N3vfsezzz7Lf//7XxXsUhRFUQ4LKaPI6t9BcDpgRSS/iLAObtZjmEwmHA5H3Ap3Ukqi0WijpZC6rhMIBPa4zjGZTI2WQh6ufmAysgnp+wACn4De0Kwfy0CE4yKwj0Foe8+cyctI5r5LRnDfJSPQdZ35a7bz0bxVLN+cT0GBx8j6ctSVO5ZaSLCZsNjAkWghNcli9PoKRSktL2dXeTFLt63FbDaTnpxKl5Rs+mR2pmtiJtohBnzaCpPJRFZWJg7zeooLl+DzbGNHZYjMZEGiKw0clyCclyFMHZr1uBaLhR49erBmzRoCgQCbNm3imGOOaSi5TU+Ht9+GkSONwNfo0XDFPgJJiYnG9pdfNr72Eew60LLe5bNWAzDg9D5UVFYQiUSwWq2xPl0Ac6YsoHBLMQBXP3oJNkfbywg8oGDXSSedxNixY3nssccYNGjQIR3Q7/czadIk/v73v3Pbbbfx6KOPHtJ4SssJBoPNup+iKIrSvMLhMCkpKZjNDS/nTqeT2traPfbNysrC7XazcuXKwzlFRVEU5SglpUTW/AECnwFmRPKzCNvQw3JsIQRmsxmz2RzXG0hKSTgc3iMIFg6HiUaj+Hw+fD5f3Fj1pZC/DII1RxaY1Gsh8IWRxRVe0bBBS69b3W88wtzjoMfVNI1h/bsyrH9XADy+INPmr2L6kvVsKihnU6mR9ZXmspDmspLsNGMxC2wOEy63iWSnjWhUEohGKCguYmdJIT9uWolmMZOdkkH39Fz6pXWmgzO1TWTDHSypV4H/Q6TvPRKj27ClOCmqPJZAtA9FvrH4HceT6cpBtFDPt4SEBLp168bGjRupqqpi586ddO7cuWGHESPg4YfhT3+CW26Bk06C7t33PuDEiUag64MP4NlnITW+zPJgM7vqg119T+1FdbXRqywrKysWLItGojx3h7Gyd2JqAhff+6sDGvdwO6Bg16mnnsrnn3/OF198wQknnMBVV13FZZddRsZeuv3/kpSSmTNn8vbbb/Phhx9SW1uLy+Vi4IE0XFNazS9ThA91P0VRFKV55ebmUlhYGHdbVlYW27ZtY8uWLXTr1i12ezgcpqamJi4wpiiKcjQym81ce+21se+V5ielRNb+GfxTAA3hfhphH9na00IIgdVqxWq1xvVK0nU9rgSy/vtIJEI4HCYcDuPxePYY55dBMLPZvN+AgpQ6hBYaAa7AN0B94oDZKJlzjAfb6QjRfIu9JThtXHvmEK49cwgAm/JLmTJnBQt+3saqwloEkFyf9VVpwWk1YbaAw2UhNdmCZoFgJIo/EmGbfyfbi3Yx27QYm9NBrjuDnhkdGZDamXRb2+rZtLv6Jv/S/y74vyD2excurEkX0ilzAuXVKVRWVlJd7SEQ2EFOTk6LXeump6fj9XrJz8+noKAAl8sVXy316KMwaxZ8/71Rqjh/vtG4vjFDhhjN7JcvhzffhLvvjtus67rxUA8g2BWNRFk1dy0Auf0yAUhOTo5rlzHtX19QU258sHrbv67DZN5/9mNrnHcPeDXGadOm8dvf/jbWxb++k/+JJ57IcccdF1t+0mazUVlZSUVFBVu2bOGnn35i8eLFeL1epJSYTCZuvPFG/vjHP5KZmdnSj6/NaU+rMUopmTFjxj5LGe12O6NGjWqXEX1FUZTW0JyvA6NGjWL27Nls376djh07AjBhwgSmTp3KH/7wh7js6Zdffpmbb76Zrl27snnz5kM6rqIoSmtpT++lj2Z67b/AayyIIpKeQjjHt+6EmmhvpZDRaLTR/U0mU1z2V30wzGQyIaP5RjaR/yOI7mq4k7mHEeCyX4AwHf7WMLquM2PZJj5dsJqVWwup9YdwWLS61R2tpDjMmEwCqw2cSQJXIkQ0nQBRfESIoKNpgqjZyFjKS8mmT1oe/ZLzcFsb7yd6OEndB4HPjVUVI7v1ozb3MXpx2X+F0Bqy/7xeL0VFRUQiETRNIz09neTk5Ba53pVSsnbtWqqqqjCZTAwYMCC+B+vOnUb/rspKY6XFp5/e+2AvvAC33w59+8Lq1XGN6j0eD/n5+djt9vgMskasXbiRu075Pa5kJ0/N/x02m43OnTvHynn9Hj+XZN9E0Bciq3M6b2554bDHApp9Ncbx48dzwQUX8MEHH/DSSy8xd+5c1q1bx/r163nzzTf3er/6WFpGRgbXX3997I220vYJIejXr1+jqzHW69evnwp0KYqitJJhw4Yxe/ZsZs+ezVVXXQXA1VdfzZQpU3jiiScoLi5m4MCBrFixgpdffhkhBOPGjWvdSSuKoihHNOn5T0OgK/HRdhvoAiN45XQ64wIQUkoikcheg2B+vx+/3w+AIEiC5QfcthnYtRUIYVwbSxIQ9jEI58VgObZVr6c0TePMwb04c7CxOmalx8cHc1YyY9lG1hZVENV1UpwWUp0W0mqsOCwaJrPA7rCQkmzB7ICgHsUfiuCrqGVdZS1rtm7gPYsgNclN17RcBqR24pikDjjNh68iyOiB9h74PwJZ397BCvbzEM4rwHJco793l8tF586dKSoqwuv1UlJSgs/nIzs7u9n7t9UnEK1cuZJAIMD69esZMGBAQ+ZTXp7RoP7CC+Hvf4dRo+Cccxof7MorjYDYmjXwww8wtKFk+GDKGFfUlTD2OKELmqaRmZkZ97gnP/QuQZ/RG/beV25t07GAA87s+qWNGzfyzTffMHfuXBYuXEhhYSGRSCS2PSkpib59+3L66aczYsQIRo0ahcXSfKmY7VV7/DSqsLCQVatWxTU8ttvt9OvXj5ycnFacmaIoSvvTnK8DP//8M2PHjmXEiBG8+uqrsduvuOIK3nvvvbg3IFJK+vTpw4IFC3C73Yd0XEVRlNbSHOdQKWWsL5PT6WzTF2vtjfS+iaz9EwAi4X5EwsRWntHhI6U0Al+BAJHgCiyRT3GIGWjCG9vHFzmOmtBoPOGTQTga7QdmsVja1N/k6m1FTJ2znIXrdlBS5cVp0UhzWUl1WUh2WNA0sFgFDhckJINulviJ4CdCROggJH6ho1s0slPS6JXagf4pnemZmI3N1LzxAWMly+lI3zsQWtiwwdTJyOJyXITQUg5wLElVVRWlpaVIKbFYLGRnZ+919etD4fP5WLVqFdFolNTUVHr37h3/N3D77UbmVkYGrFgBe7sGv/56eO01uO46o7l9nZqaGgoLC3G5XLFKgL357Vl/Yun0lVz80Bh+detZ5ObmxrZVFFVyeadb0CM63Y/vwktL9pFp9gvNed490NeBJge7GlNVVUUgECAtLU0FtvaiPQa7AAoKCti1axdOp5Ps7GzS0tLa1ElYURSlvTgcrwNSSl555RXef/99du7cidvt5pxzzuH//u//VKBLUZR2rTnOoV6vl4SEBMAo79m9gbnSdNI3DVnzoPGD63a0xLv3fYcjjIyWQ+AToxdXZGPD7VouUcsF+OXZBMJpsUywvZVCapq2135grS0UifDNovV8/uMa1mwvJhAKk+w0mtynuSzYzRqaBla7wJUksCfqBIWOjwhBIkgBEaHj0SKYLGby0rPpk9KR/sl5dE/MxtTElR5ltBDpex/8U0EvrbtVA9sZRhaX9VREE8cOBAIUFhYSCoUQQpCamtoi18JlZWVs2LABgLy8PPLy8nafhNGkfuVKY3XGb76BxprnL1gAp54KDgcUFEByMgDV1dUUFRWRkJBAhw57X10yHAozLuVaQv4wD39+N6eedXLc391j4//O/I+MIOJLy56m+3FdDvjxNed5t9nLGA9Ect0vU9nTpEmTmDRp0l5Pam2druvY7XYyMjLiG+cpiqIobY4QgokTJzJx4tHzibqiKIrSeqT/C2TNQ8YPzusQCXe17oQOEykjEJyL9H8AwdlAfaWTDexnIxwXIawnYxIaVsAdu5+M6wdWXwYZCoXQdZ1AILBH32Sz2dxoEExroRUDG2M1m/nVKf341Sn9ACiurGXKnBXMWbGJn7ZXYatf4dFpwe21oBWB2WLC7jSRkWRHOHR8MoJdDxOJSqp2lTA9P59PTPOx2Wx0T+9I/5Q8+rvzyHOloe0jQGU0+p9vZHEFZwFGE3a0DHBcinBeijAdehVSfZ+rkpISqqurKS8vx+fzkZOT06wJPunp6Xg8nliSSUJCAikpKfWTgPfeMxrRT58Of/sb/O53ew5yyilGz641a+Cdd+C224ADL2Nc9f1aQv4wCakuBp46IC7QtX3tLuZ/bAS6Bp117EEFulpLs2Z2KfvXXjO7tm/fTnV1Nbm5uSrYpSiKcgia83Xgj3/8IwDXX399/CeAiqIoRyiV2dX2yMBMZNUdQAQclyGS/njEV4DIyCak/0Pwfwx6WcMGy7EIx8VGXyjt4P8+pZSEw+E9+oGFw2H2dtneWCmk1Wptlefgp/U7+GjeKhZv2EmVN0CKwxzL+rKZNYQwSh6dCQJHsk7EpOMlTIAo1GV91RDEb9Zx2Z30zehM/+Q8+ifnkW03msRLvdJo9O97F6I7Gg5uPdkoVbSNbtaVLHdXU1NDSUkJ0WgUk8lEZmZms17T67rO2rVrqa6uxmw2M2DAABwOR8MOkyfDTTeByWSs0njyyXsO8u9/wz33GI3tly0DIaisrKSkpISkpKS9tiGSUvLC/ZP5+JlvOGHsQJ785Pdxf0N3Df09a3/ciBDw+qbnyemadVCPrd1ndilHrvp+bG0hfVZRFEUxPP7445hMJh588MHWnoqiKIpyFJLB+ciqu4AI2M9HJD12xAa6pO6BwBdGmWJ4ecMGLRXs4xCO8QhLz0M6hhACq9WK1WolMTExdruu67HA1+5BsEgkQjgcJhwO4/F44sb5ZfCrvhSyJZ+fE3t34sTenQDwh0J8tmANXy9ax9JdpZiQdSs8WkgOWKgqB81kwm43kZoE5gQdv4hiw0Q0LCEMGz2bWSzWEDJJUhyC49xl9LMtoZ+zmDRLEEQiOC5EOCcgzD1a7HHVS0pKwuFwUFhYiN/vp7CwEJ/PR2ZmZrNk2GmaFmtYHwwGYw3rYw3ib7gBvvsO3n8fLr/cCGb9srru6qvht781enstXgwnnICuG1lv+3ruq6ur+fl7o4zypHMGx+27fNZq1v5olOaOuur0gw50tRYVuVAOSH35ZXOvQKEoiqI0XXp6OtFoVPXJVBRFUQ47GVqCrLoNCIHtTIT7LwhxZF0rGKVyi4wyxcA3QH1poQlsIxCO8WAb3mKZRPU0TcNut2O32+Nuj0QijQbB9lYKaTKZGi2FbIlrPIfVyqUjBnLpiIEA7Cyt4v3Zy5m3agtrCqtwO8zGCo8uCz6vBggsFgsOlxW7W4Itil2acGEBXRKNSJbUZjFTDCdq8tAlOYX+KcfT39SNfo4OJO5zNs3HYrGQl5dHeXk5FRUVVFdX4/f7ycnJ2eP5aer4vXv35ueff8bn87Fp0yZ69eplBJ+EgP/8B376CbZuhV//2gh87R7ESk2Fiy+Gt9+Gl1+GE07YbxljOBymYFchW5cZmXKDRx8b26brOs/8+iXAKEm9+e/XHPJjPFxUsEs5ICrYpSiK0vYcd9xxzJw5k/LyctLS0lp7OoqiKMpRQoZXIysngvSDdRgi+Z8IceRcWspoAfg/MrK4orsaNpi6I5wXG1lspozWm2Ads9mM2WyOWyGwvhTyl0GwUChENBrF7/fj9/vjxrFYLHsEwaxWa7P2A8vLSOa+S0Zw3yUj0HWd71dv5eP5P7N8cz6RSJg0p4VUlxV3yExNFQhNw2a1k5QA9mSdgBbBikaacICejr8szKyyFXyiLUZYTfRJyaOf2yh57JPUAYfZ2mxz/yUhBOnp6Tidzljz+h07dpCRkUFycvIhZ88lJCTQrVs3Nm7cSHl5Ofn5+Q2rKLrd8O67cNppMHUqnHkm/LJH68SJRrDr3XfhH//YZ7BLSklJSQmblmwlEo6S3iGVDj0bSh1nvvs9hZuLAbjw7jEkZ7SfhY6OnDOS0qJUGaOiKErbc/PNNzN9+nSeeeYZnnzyydaejqIoinIUkOENyIobQHrAcgIi5XmEaLnAwuEiZRAC3xkBrtACoK5HlnCBfazRi8tybJsv09y9FLK+RxIYGTq79wOrD4LVl0GGw2G8Xm+j4+weBLNYLIf8O9A0jdOP7c7px3YHwOMLMu37ZXy3eCkLd1STYLUZje5dFgIBDcoEZrMVu92GNVHHlBDFI0w4sJArQQ9IygtL+LhoO2+IMAlWBwPSjH5fA5I70SMxG6vW/NexTqeTLl26UFRUhMfjoaSkBJ/PR1ZW1iFfN2dkZFBbW0tRURE7d+7E5XI1NKw/6SR48kmjXPHuu40VGPv2bbjz6adDr16wYQO89x7yggsAGg1e1tbW4vF42LhwKwDHndEv9vyGgmFevOc1AOwuG1c/evEhPabDTUUulP3SdT0WDVaZXYqiKG3H+PHjuffee/nLX/5COBzmgQceUIuIKIqi7IfJZOLiiy+Ofa8cOBnZhqy8HmSVEfhJ+Q9COPZ7v7ZKSgmRn40yRf/nIGsaNlpPNsoU7We168dYT9O0WNBqd9FotNFSyN1Xi6ytrY0bp7FSyKYGd2Q0H2f0Pa4e+AFXH1sOwKbidKYsPYsfN2ZQ64/WlTtaSYqYER4QwoTVYsHlBLtbJ2KLYkEjRRhlhIFAhK35O1hasAGvFiXN7ub4tM70r8v86pqQiWkfKz0eDJPJRG5uLlVVVZSVleHxeAgEAmRnZx/y4hddunTB5/NRU1PDpk2b6N+/f0PD+vvugxkz4Ntv4bLLjNLG+m1CGNld998PL7+MPP/8upvjg5TRaJTS0lIAtiwxShgHnjEgtv3Df35OTbnx3F/z2KU4Epr+76A1zrtqNcbDrD2uxhgKhVi3bh1CCPr379/mP81QFEVpy5rzdWDkyJEALF68GK/Xi6Zp9OjRg8zMzL2+kRBCMGPGjEM6rqIoSmtpj++ljxQymo8svwL0QjD3RqS+idCSW3taTSL1CvB/amRxRdY3bNByjYbnjosQ5qN3lWMpZaP9wEKhUKzZ+S+ZzeZGm+I3lk0kZRRC84wVFYOziWXRaZnguBThvBRhygYgEtGZtWITnyxYzdrtRVhEQ6N7q8kY26QJ7HaByaljc0t8pjB+IiBACkk1QSpEgEoCRDTIc6RyXHoX+rs70S85j07OtGa5xg0GgxQWFhIMBhFCkJKSQnp6+iGNHQqFWLVqFcFgEJfLRf/+/Rve4xUVGasulpTArbfCCy803LG0FDp0gHCYsu++ozwvj4yMDFJTU2O7FBYWUlNTg4zAPcf/gWgkyptbJpHdJZPaSg8TOt5MyB/CnZ7Eu7tewmJtGz1i1WqMSrPZvV+XCnQpiqK0HbNnz477ORqNsn79etavX9/4Hdj3SjyKoiiK0hgZLUFWXGcEukxdESmvtrtAl5QRCM4zAlzBWUC4bovVyN5yjAfrKYhmyvhpz4QwmsVbLJa47KT6fmCNlUJGIhEikcgepZBx/cCsPuzyc0yhD+J7oVmHIpyXg23kHs3+zWaNMwf34szBvQCo9PiYOmcFM5dtorSyGrfdTKrLQpJuRviAMoHVYsdpB0tiFC1Bx4xGcl3WV0hGqfAEmONdxacsxmI209mRxvHp3eifnEd/dx6ZdneT3i/ZbDY6depEaWkpVVVVVFRU4PP5yMnJwWptWqmv1WqlZ8+erF27Fq/Xy+bNm+nZs6cxv+xsePNNOPtsePFFGDUKxo837piRAePGwdSpON56Cx58MO4xeb1eampqEEJQuqmSaCRKdtdMsrtkAvD6o+8T8ocAuOWf17aZQNfBUMEuZb9Uc3pFUZS26Q9/+ENrT0FRFEU5wkm9wihdjG4HU0dE6usIU/spmZeRLUaAy/8J6CUNG8z965rNj0Fo7afpdmvavY9XYmLD+oe6rseVQNb/38gOC6JFl+GKfoUjPB9NGL2gozKRAOcQtV6C2doDm8mG+QDCEykJTn495hR+PeYUAFZtKeSDeStYsn4n0UiIVJeFNKeVUFhArUAIMw6bDatNx5ESJWANY8VEtnAhhaQmHKIy4uHT2p94l/m4LDa6OzNjwa9+7jxSbQn7mVUDTdPIysrC6XRSXFxMIBBgx44dZGZmNjkbNSkpiU6dOrFt2zbKy8txOp0NDevPOgseeAD+9je46SYYMgQ6dza2/frXRrDrww8R99wTC3bpuk5xsdF0Pjk5mTnzFwIwcEQ/AIq3l/LZS98CkNMti5GXn9akebe2Zgl26brOkiVL2L59Oz6fj2uuaT/LUSr7p4JdiqIobZMKdimKohw8r9cba9zt8XgOua/OkUzqtciKGyGyEbRMRMprsfKytkzqHgh8ZQS5wksbNogUcFyAcIxHWHq33gSPMJqm4XA4GvpJYTwHUe/H4H8XTd8Yuz0Q7UVV8Fw84WFI6vuH5QPG9ebuJZD13+/rOnRAtxwGdDNWDwxFInz103q+XLiGjUVlOC2CVKfF6M0WAG+1wGyy47RpaM4o1uQwZk3DLWx0wU1YRKkIBSgIl/Fz9S50IXGZ7RyTkMPxGd0Y4O5EX3dHEiz2/f5OEhMTsdvtFBYW4vf7KSwsxOfzkZGR0aTr6uzsbLxeLyUlJeTn5+N0OhtKEp94AmbPNvp2XXml8b3ZDCNHQteuaFu3kvjVV4jbbgOgrKyMcDiMxWIhPT2d5bN/BmDgSKNf1wv3vIoeNcpV73j+xmZZlbM1zruHHOx67rnneOKJJygrK4vdtnuwq7KykmHDhhGJRJgzZw5ZWVmHekjlMFMrMSqKoiiKoijK0UXqPmTlRIj8DFqqkdFl7tTa09orKSWEFyF90yD4NUh/3RYT2E43yhRtI46IlSPbMhleb/TiCnyCJutLGu3gGItwXoHD3A9zOExSI/3AotEoPp8Pn88XN2ZcKWTdl8Vi2SMIYzWbuWBoPy4YWpehVFnLlDkr+H7lJrw+H8kOC6lOC5GoDj6gzIzDasdmBas7SsQVwoKJLGEEYmpFiIqQn3WVu/ipcgtmTSPJ4mBgSlej5DE5j2OScrGbGv+bslgs5OXlUV5eTkVFBdXV1fj9fnJycrDb9x8w250Qgq5du+L3+6mtrWXr1q3YbDYjaGSxwLvvwvHHw/z58Pjj8Kc/gaYZjep//3uSp04lfPvt+P1+qqqqAMjKysJX42fT0i2AsRLjxqVbWPDJIgB6DenGCWcPPKh5tiWHFL24/fbbeemll5BSkpSUhMfj4Zf97lNSUhg0aBBvv/02U6dO5Y477jikCbdXkyZNYtKkSbEsqfZEZXYpiqIoiqIoytFDyiCy6lYjK0okIVL+hzB3b+1pNUpGC8H/EdL/IUR3NGwwdUU4LjYyuUyZrTfBo4CUIQh8jfS9E59JZ+qKcF5hNP3XGkr46ksh6zN9wKgWqw96/bIfWP3XL/uB1Y+zexaYxWKJletlpSRy57jTuHOcUYb30/odfDhvJeu3F6LJKGmuuj5UIcADmmbFYXGiOSX2lAgWs0aisNJZuokInYpogEo9wPdFa5lZvBqz0Ei1JjA4rRv96lZ67JmYjUUzx80zPT0dp9NJUVERoVCIHTt2kJ6eTkpKykH1BjOZTPTo0YM1a9YQDAbZsmULxxxzDBaLBbp1g//+FyZMgCefhDPOMDK7rrsO+cgjOJYtQ1+3juLMzFj8xuVyseDTRei6JK93Lmk5KTx+0dOx4935/E3tutdrk1dj/PrrrznvvPNITEzkjTfe4IILLiAnJ4eSkpI9Ajr1+55//vl8/PHHzTHvdqs9riBTWFhIaWkp6enp5ObmtvZ0FEVR2rX2+DqgKIrSVjTHOVSVMe6blGFk1R1GE3fhMprRWwe29rTiSBmEwHQjwBX6nthqfsIF9vOMIJdlYLu+UG8PZGQn0v8e+D4AWVl3qxnsZyIcl4P1pEN+DqLRaFwArD4ItrckEk3TGi2F/GWVkj8U4rMFa/h28TrKKqtJtGqkuiyYtYb5WkwaNpsZW6KOdAeoJoSs2+wRISoJUCECVBMEAWZhItuexKC07vRP7kR/d0e6JGRiqlv0IBqNUlxcTG1tLQAul4vs7OyDrqCqqKhg06ZNRCIRMjIy6N69e0OW2003weTJkJMDK1ZARga+s8/G+e231N54IwX334/ZbKZLly6YTCZeuOdVPnr2S8befCYn/2oID499CoATzj2eP3/x+4Oa174053m3xVdjfOmllxBC8Mc//pELLrhgn/ueckpd87hVq5p6OKUVqcwuRVGUtm3FihVMmjSJ77//nl27dsV98vlLQohYebqiKIqi7E7KKLL6vrrVCm2I5JfaTKBLSgmRNUj/B+D/HGR1w0bLiQjneLCdjdCcrTfJo4CUUQjOMbK4QvOIBRq1bITzMnBc0qyZdCaTac9+YFISiUQabYqv6zp+vx+/3x83jtls3iMIdvHpx3LpiIEAbC+uZMqc5SzfsJ1QMEiK00KiDcK+EB4fUGLGarLhdFpwpEZxWb0kYCVPJBEVOpXSCHyV+Gr4xL+YT3ctxqqZ6WBPZXB6NwYkd6KfO48O2dk4nU5KS0vxer1s376d7Ozsgwr+pKSkkJOTQ35+PuXl5djtdjp27GgEFv/9b6OUcd06uP56+OwzaiZMwPnttzg++ABx111k5OTEru1X1PXrOvb0vjx/52TjAAJu++d1TX7O2oomB7sWLjQ69t9www373dftdpOUlERRUVFTD6e0ovpgl+rZpSiK0vY8//zz3HvvvUSj0T1aCSiKoijKgZJSR9Y8BIGvAAsi5XmE7aTWnhZSrwD/Z0az+ci6hg1ajlEe57ioTfcSO1LIaBn4pyJ9U0DPb9hgPc0oVbSNQIjDc70ohMBisRjle7vPUco9SiGDwSDhcJhIJEIkEtmjFNJisRi9r6xWbjl3CLZxp2IymZj/8zY+XbCanUWlWDVJqtMCMkqoJkpVDWjChtnsJMVtxZQWwi69pAsj0OoTYSoIUCH9bPWWsMVXwgc7fsSmWejszOSE9G70dmWTHrCQEIFdu3aRmppKenr6AWXCCSHo0KEDfr+fsrIyioqKcDgcpKeng8sF778PJ54IX3wB//43vjFjCGZnYysqIn3uXBIHGI3oq0qr2bJyOwDlRZUUbTVWKz3r6uF07NX+K7qa/NdYUVGB2+2OW3J0XzRNQ9f1ph5OaUX1GQAqs0tRFKVtWbhwIXfffTcAt912G2PGjOG8884jNTWVKVOmUFRUxPTp03nnnXdISkri2WefJScnp5VnrSiKorQ1Ukpk7RPg/xAwIZKfQdiGt+J8IhD6vq7Z/EwgXLfFCvbRRpmi9RSEUNcnLclo+r8Y6XsbAt8Rex5EMjjGI5wTEObOrTnFOEKIWNbW7nEKXdfjsr/qv49EIrHg2O40TaOz28r/XXAiNpuNUFTy1aKN/LhmCzUeL0k2Ewk2E6FwhOKyCJSBEHYcdiuZ6Tb8CTUk6FY6ykSiQqdKBqkUASqiAdZ78lnvyUcgcJisdLOk0sOeQXdfBn1rOtA7rxtW6/4XUdA0jS5duhAMBqmtrWXnzp3Y7XajVPDYY+GZZ+D22+GBB9BzcigeM4ZOkyeTPHUqoq6P+so5awDo1LcD7zwxDQCTxcT1f76iuZ6SVtXkYFdSUhKVlZWxJSv3pX7lAdXvqX1SZYyKoiht07PPPouUknvuuYdnnnkmdrvVamXkyJEAXHHFFdx1112cffbZPPLIIyxdunRvwymKohwVTCYT5513Xuz7o52UEun5O/jeAgTC/ReE/ezWmUtkq5HB5f8Y9JKGDeb+CMdFxop+WnKrzO1oIvVa8H9s9OOKbGzYYBmIcF4O9nMR4uBWE2xNmqbtUQoJ7FEKWf+9rusEAgECgUBs36HdUxjWywh+7arw8u2yTWzJL4VohBSnBZMGPn+QbTuDAEjhJD3JSXaOlUKtgupIACkk/vqsLxGgKhJkdbSQ1YFCBOAottB9Wxon5PRiUGYP+iXn4TLb9vq4rFYrXbp0YePGjfj9frZv306PHj2w2Wxw660wfTp89BEd7ruPtX/5C3mvvoo2dy5s2AC9erF8llHC6ExwsKPSyNa76K7zSM9NbeZnoHXOu00Odg0YMIA5c+awcOFCTjvttH3u++677yKlZMiQIU09nNKK6jO7VBmjoihK2zJ//nyEELHsrnq/LGccOHAgzz33HBMmTODpp5/mj3/84+GcpqIoSptit9v54osvWnsabYf3JfC+DIBIehzh2Hc/5uYmdY+xkp//g/iV/EQyOMYZZYqWYw7rnI5WMrwG6XsXAp+B9Bk3CgfYf4VwXoGw9G3dCTYzs9mM2WyO65clpSQcDjdaChmNRvH5fKTaBRNO6Qn0JKLrLNlcwuJNBVTWenFZNFxWE0JKyqu9lFd7ieoCszmBLplukrIkW8NleKNBokJSLYNUiAAVBPARZlW4iFU7inhtxzwSzXaOc3fi+PTuDEjOo1dSLnZTfKJRYmIiHTp0YMeOHdTU1LBjxw66deuGyWRCvvwy0YULcezaRae330aefTbiq6/g5Zfh6adZPms1ABuXbQXA7rJxxUPjW+R33Rrn3SZHLy6++GJmz57NY489xrffftvQ/f8XVqxYwcMPP4wQgssvv7zJE1Vah5RSZXYpiqK0UcXFxdhsNjp3bigh0DQt7pPIehdeeCEWi4UPP/xQBbsUpZmMGDGCOXPm8Ic//IHHHnustaezV126dGH79u28+uqrXHfdda09HaUNkd7XkJ5/AiASf4dwTjg8x60vj/NPg8DXDYEVNLANM8oUbWcgxP7LuZRDY6xs+ZXRcD68vGGDuYexoqJjHEI7sNZFRwIhBFarFavVGls9EIxSyF/2AwuFQhAOc1LPbE7qmQ1AtS/AzFU72VRQRjQSwe0wY9IEUo+ytagCWQhhXSPRlcLATmkEE3xs8ZcS0MMERCTW6L6SANURP3PL1zO3fD1moeG2ODkpvSf9k/Po786jR2I2Zs1EZmYmfr+foqIiKioqsNls5OXlUWuxUPnXv9Lp2mtJ/+oronfeaTyY11+n/I772LnOyOaKho3r/asfvYSE5CNnddomB7smTpzICy+8wKxZszjzzDP5zW9+EwuKbNy4kW3btvHZZ58xefJk/H4/p5xyCpdcckmzTVw5PKSUsQwBFexSFEVpW5xO5x6NTBMTE6mpqSEYDBpp7HUsFgtOp5Pt27cf7mkqiqIobZD0vY+s/TMAIuEuhGv/C48d8jGjReD/COn/EKK7vR6ZutaVKY5DmLJafB4KyMgOI4vLPw1kVd2tFrCfiXBeCZYhB9Qs/WihaRp2ux27Pb58MxqNxpVCOhxBLh7qisVGNuRXMH/dTkorPVhM4LKasJoEwUCQJRsKCEUlEgs5qamc0DOLLXoRO/0VBGWYahmiUvgpJ4BXhikPefiyYBlfFizDLDTSbYmcnN6L/sl5HOPOxR1IpqqyktLSUiwWC16vl8iQIRTdfDM5L76INnkyZGZCSQkFz/wn7nG4M5IYd+e5h+33eTg0OdhlsVj44osvOOecc5g1axazZ8+ObTvmmIY0UyklAwYMYNq0aeofSztUX8IohNhr9p6iKIrSOjp06MC6deuIRCKxUvPu3buzbNkyFi1aFNdmoKCggOrqapxOtSS7oihHN6/XS2ZmJgAlJSVxJUxHC+n/DFnzqPGD6yZw3d5yx5IhCM5A+j6A0HygbtEy4TJ6PznGg2WQulY8DKSMQHC2kcUV+r5hg5aLcF4GjksQpvTWm2A7ZDKZcDqdce+vpJSxfmAZGRkM6WM0kvf5Any/biert5XgCwRIsBmBL4CK6lq+XlSLNxzFZrHTKzeH7rkO1vgLKdO9+AhTsVvWVwSdokA1H+9axCe7FmEWJnraMhipdSU1aKNqs4e0BDdut5uaO+7AvWQJzp9+gqQkAJI+eBfoH5vzTU9didXecpmUrXHePaQmTJ07d2bJkiX84x//4H//+98enxZ36NCBiRMn8n//939H5YvIkWD3Ekb1AqQoitK29OnTh59//plVq1Zx/PHHA0ZZ1dKlS/njH//Ip59+it1uJxQKcddddwFGz01FUZSjnc/n2/9ORygZ+A5Z/QAgwXkFIuH+FnmfL8Nr6prNf7Zb5hBgOcEIcNnPRmjqGvFwkNES8E9F+t4HvajuVgHWYQjnFWAbrla2bEZCCCwWyx4L+Ukp6d69aywTbGdxOd8u3sD2kgrQdRwWjQSrCdDZWljO2p06wYiO2+5kUKdk0lKtrI2UUR71UKn7Y72+agkRJsqaQBEe6WOgnkmKtFFTs5VEPZ2eJBN+6gn6XXo5osh4/jsXrCObLhSJBHK6ZXLmNS2/+urhPu8ecsdxp9PJI488wiOPPEJBQQEFBQVEo1Gys7Pjeogo7ZPq16UoitJ2nXXWWUydOpXPPvssFuy6/fbbmTRpEjNmzKBjx4707t2bDRs2UFFRgRCCO+qWm1YURVGOfFJGIbQY9FLQMpAyAFX3AFGwX4hIfPSgAl2/HA/rkLggidQrwf+ZEeSKrG24o5YFjouMZvNmdY3YXPb1fEgpIbQQ6X8XAt8BRsUOIgWcFyMclyHMnVpv8kchIQQ2my3WZiI9PZ3j+/UmGo0SCoX4YfVmZi/bQHlVDVYT2M0adrMG6CzfXkHteuM5zE1J4sKevYg4oqz2FVAa8VAqfVSKAIXSQ5K00p90krw2ilaU4rNXsiFRY8VdV3HlH/4dm889LGGnTKLv0HPxVlVidjoJmSV/WjWNAn8lufZkLu9yCkEpSbG66J2Ui0m0n2qvZl1eLzc3l9zc3OYcUmll9cEutRKjoihK2zN+/Hh27doV99rbtWtX3nnnHa6//noqKir44YcfAKPXxP3338+VV17ZWtNVlKPO66+/zk033UQkEuH3v/89Tz75ZJPG2b0R/u9//3ueeeYZ3nnnHTZv3ozVamXIkCHce++9nHvu/vuthEIh/v3vf/PWW2+xadMmzGYzgwcP5oEHHuCcc87Z6/2i0Sivv/46b731FitXrqS2tpb09HSGDh3K7bffzogRI/Z63/fff5/XXnuNpUuXUlFRgcvlIiMjgz59+nD22Wdz44037tEH53ApKiqie/furXLsliYD3yBrntwtk2c39nMR7icRB3Hh2uh4WjYy8UGE5qprNj8dCNdttIB9tJHFZT1VZQ41s70+Hwn3ImS10Y8ruqVhm2WQkcVlP0c1/m9jTCYTDoeDkSf0Z+QJRmmhx+/n8+9XsnT9NgIBPxZN4HYY1+TeQIjPlm7DE4pgN5vokZnGiMw8SoWfYrz4SwUui5MUp5ncVBcmAduXFPL2X4tx0oML2QTAYEoZTCm8+Tzy7Rf4dPwwnrxhaGxemz3FzCtbj4Ygxeoiz5XOTd1HcmJ6j0N6vIfrvKsiGMo+1ffsUpldiqIobYuu6xQUFHD22WeTmpoat+3CCy9k+PDhfPnll+zcuRO3281ZZ51Fjx6H9uZEUZQD95e//IUHH3wQTdN4/vnnuf32Q++JFAqFGD16NPPmzcNsNpOQkEBVVRXTp09n+vTp+10V0uPxcPrpp7Nw4UIsFgs2m42amppY/91XXnmFG27Ys0l5dXU148aNi/XoNZlMJCYmUlhYyAcffMAHH3zAfffdx9NPP73HfW+44QZeffXV2M8JCQmEw2E2bdrEpk2b+OyzzxgzZgxdunQ51F9PkxypwS4Z+AZZdRcgG9/BdhZCHPil4F7H04ug+u74W819jQCXYyxCSznImSsHYp/PR80DDbcKF9jPRzgvR1iOQWk/EhwOJpx5EhPOPAkpJVvyS/nyh5VszS8CPYrdomG3GEHLgio/awprcVhMdE2145Lg1SNkJlgxaQJPuZfakloS0l1Edhp/NXvkc+o650+dQ7UM8PyNI+M3IakO+YjIEv60ehqP9B9/SAGvw3XebXIO2o4dO5r0pbQvqoxRURSlbQmHw/z2t78lNTWVAQMGcOqpp9KnTx8yMjJ48sknYyvopqamctVVV/Hggw9y2223qUCXohwmUkruvvtuHnzwQWw2G++//36zBLoAXnjhBX766SdeeuklamtrqaysZMeOHVx88cUAPP7443z66ad7vf+jjz7Krl27+Pjjj/F6vdTW1rJu3TpOPvnk2Lyrq6v3uN+NN97I7NmzsVqtPPvss9TU1FBZWUlBQUEsOPb3v/+dl156Ke5+33//Pa+++iqapvHXv/6V8vJyamtr8Xq9lJWV8c0333Dttdditaosk+YkZdTI+NlboAsBtX8zSuCaZby6MR1XIdI+Rkv/GOG6WgW6WsiBPR9mSHwUkTEPzf24CnS1c0IIunfM5M5LRvPMPVfx5K2X0r9LJiaTRlRKNCFIdVronekkzWXFatbIcdsRAnRdsun77dgT7fQ8IYeL6rK69jhG3f+v/PAnzKHIHtsj6CQIK4FomMmbZxKVegs+4ubR5Myurl27HvR9hBCxTKGjzaRJk5g0aVIseNReqGCXoihK2zJu3Di+/vrrWFCrXnl5OY8++igbN27ktddea53JKcpRLhQKcfXVVzNlyhTcbjcff/zxPsv7DlZ1dTWTJ0+Oy77Ky8vj/fff54wzzmDu3Ln8/ve/5/zzz2/0/j6fjwULFsStnN67d28+/fRTOnXqhMfj4fPPP48rd164cCHTpk0D4LnnnuPXv/51bFt2djaTJ0+murqaadOm8cgjj3DdddfFShIXLFgAwOjRo3nggQfi5pKWlsZZZ53FWWedtc/HHAwGCQaDsZ9ramr2uf++FBYWUlhYiN/vj922fPlyHA4HADk5OeTk5DR5/DYjtLjx0sUYCXqhsZ/tpGYYzxhT2M9GWPoezEyVpjig5yOCMPdEaAmHZUrK4SWlZGDXbI7JSaampgZ/OMKmEg92i4lAVCfBZsJh0RACaku8hPxhPOVeRpWtZl9X9QIw6ZKLv1jKexeeuMf28oiXVFsCO7xlrK8poK+7437n2prn3SZndkkpD/pL19t+9K+l3H777axZs4ZFixa19lQOiurZpSiK0nZMnTqVr776CiklPXr04MEHH2TSpEncf//95ObmIqXkzTffZM6cOa09VUU56tTU1HDOOecwZcoUcnJymDt3brMGusAIbF1//fV73K5pGg8//DBAbIXWxlx88cVxga56GRkZnHLKKQCsXLkybtv7778PQMeOHbnpppsaHfdPf/oTAGVlZXz33Xex25OTkwEoLS1t8ge+Tz31FG63O/aVl5fXpHEA/vOf/zB48GBOO+202G133HEHgwcPZvDgwfznP/9p8thtil56QLvJmkeQ3snIyLbmGS/wCVKvOqB9lUNwgM/HAe+ntDvhcBi/308oFELXdWwmjd5ZiZhNgqgusZk1NCHQhMBbaayAaKuo4PgV8w5o/I6FlY3eHpFRLJqZiNSpDHkPaKzWPO82OYKxdevWfW6vrq5m4cKF/POf/6S0tJQ333yTPn36NPVwSitRPbsURVHajrfeegswVmH85JNPYqv5ADz00EOMHDmSZcuW8fbbbzN8eMsvIa0oiqGwsJDhw4ezfPlyevXqxTfffNMiPahGjBix15Xzhg0bhtlsJhKJsHjxYgYMGLDHPiedtPcsnvqFLioqKuJuX7x4MQBnnHEGmtb45+R9+vShQ4cO5Ofns3jxYn71q18BMGrUKOx2O8uWLWPYsGHceOONjBw58qAqRB588EHuvffe2M81NTVNDnjdfPPNsay3pUuXMnHiRF5++WUGDRoEcGRkdYGxKt+BiG5D1v4Vav+KNPcA22iEbRRYBsQ3rj/Q8fwfIP2fIG2nIexjwTYSobkOfv7Kvh3o83Gg+yntSjAYpKSkhFAoRDgcxhOIsKaollA4So9MF2lOC1azRtAbZNP87SRv3MDNi7+gY0X+nn269mJXTuMlyGZhIqxHMAuNFOuB/dtuzfNuk4NdnTvvf8nYY489lquvvppRo0Zx4403smzZsqYeTmklqoxRURSl7Vi6dClCCP75z3/GBboAkpKS+Otf/8qZZ56pXm8V5TD773//C4Ddbmf69OmHlH20Lx06dNjrNrvdTlpaGsXFxZSUlDS6T2Ji4l7vX5/FHw6H426vH2tfxwYj8ys/Pz/u2N27d+eVV17hlltu4YcffoitDpuRkcEZZ5zBFVdcwfnnn7/XAB6AzWbb43zXVI2VywwaNCh20XXEsA4BLRv0Yhrv6ySMQIjrZgjOhNBCiGyCyCak9yXQMpG20Qj7aLCeeADjASIRtFyIrofgLGRwFggH0jayLvA1TK0A2FwO6PlINvZTjhhSSiorKykrK8Pr9VJa7WX1rio8wQg2s0Z2khWHRUOLRNkwcys9l8znlhUzSfZVxYJcEQSmur+Zxs66EtA1wQdjGj8nppldeCJBuiVk0jspt9F9fqk1z7tNLmM8UHa7nWeffZbCwsImL7estB5VxqgoitJ2lJWVYbfb95opPWTIkNh+iqIcPmPHjsXtdhMIBLj++uvx+XytPaU248orr2T79u289NJLXHbZZeTl5VFaWsqUKVMYN24cw4cPP6Q+XMqehDAhkh6q/+mXW43/Jj2C5roaLfVVROaPCPczYD/XWL1PLwH/O8jKG5AlJyOr7zO27XU8gXD/GS3jM0T6l+C6HUydQPoh8AWy6lZkyVD06oeQwR8OuDG+0rh9P791ZBX43j5cU1JaWCgUYufOnRQUFLBq43Y+WrCWHzeXEYxE6ZBsp0e6E1ckyvxnv6fD317g3pcf4pIfPiSlLtAVdDiZN3g0V3W4jin0AvYMk9b//PZFJxKx7nntb0bDI0PYTRZu7D4Sk2jxUNIhOywzHDx4MC6Xi88+++xwHE5pRqqMUVEUpe0IBoO43e69bq/ftnszZ0VRWt7gwYOZPn06KSkpzJgxgzFjxuD1Hlg/k4ORn5+/123BYJDy8nIAMjMzm+2Y9WPt2rVrn/vVb2/s2Kmpqdx8882899577Nixg02bNvG73/0OIQTz5s3jsccea7b5Hgiv18uZZ56Jw+HY5zm1PRP2sxHJz4KWFb9By0YkP4uwn92wr5aEcIxFS/43InMhIuUVcEwwsr+kBwJfgO9VjEtHyz7HE+YeaIl3I9K/Q6R9AM7rQMsEWQP+qcjKa5Glw9FrnkSGVuyx2IpyYPb1/GI9HQBZ+wR67TPqd9yOSSmpqqpi8+bNzF+xjndnLWfB+gIC4QiZiVa6pztI02DZk1/SbeJveWbuM5y6dCb2YAAJVKdlsOCue/jinXdZ9quLqanw84o4lin0Yo9u6prGp5cM5/kbR+4xDw2B2+qkW0Imj/Qfz4npB7/Cd2ucdw9Luo6u60SjUQoLCw/H4ZRmIqVUZYyKoiiKoigHYMiQIcyYMYPRo0cze/Zszj33XL788ksSEppvNbQ5c+YgpWy07G/evHmxDynrszybw5AhQ5g3bx6zZs1C1/VG+3atW7cuFog74YQT9jtm9+7deeqpp9i5cydvv/12XFP7w6W+N1l2dvZhP/bhIuxng2103ep9pUbwyjoEIfb+vl4IK9hOR9hOR8rHILwKGfwOAtMhugXYLSvL1A3s54O5+x5/l0IIsByLsByLTPwthBYhA59D4Bsjc8z3OtL3Opg6Ie1jEI5fIcwHfwF9NNvb8wsaeF9Eev4F3peQejkkPY4QqlKnPQmHw+zatYt5yzewblcpwXAUISDZaSEzwUKiZmLjE1MZv/Y7rqY8lsUkgapj+vLX289khaUD0quRWrqVzMEmct49gao1Vcz8sRdLQxqXmHYyMM+J3rULgRtu4Eynk+FmyZ9WTaPAX0muPZnLu5xCUEpSrC56J+UeUkbX4T7vHpa/+FmzZhEIBMjKytr/zkqboet67JMAFexSFEVRFEXZt+OPP56ZM2cyevRo5s2bxznnnMNXX321z15ZB2PHjh28/vrrXHfddXG367rOn//8ZwD69u3baHP6ppowYQL//Oc/yc/P55VXXuHXv/71Hvs8+uijAKSnpzN69OjY7cFgcJ/9tuqXnt9b43vl0AlhAtveFybY9301sB6HsB4HifchI1sgMAMZnA7h5Ubwy/svpPdfYOqCtI9G2EaD5bi4gJoxh5MRtpORSY9C8Hsj8BWcAdEdRmDG+yLS3Nvo72UfgzB3bJ5fwBFur89vwm2gpSJrHjMy6vRKSP4nQjRP/zul5UgpKS4u5tO5S9lSVE44YuRgOa0mcpJspNnMVD30CiM2z+FyvHH9uIqGDePuO05kZ1J9BmZl7L8WexZX9RjF6WP7YjNZ9jhufUc9J/CPwde05EM8bFo02BUOh/noo4+49957EUIwcuSeKXFK21Wf1SWEUG9CFEVR2oji4uJ9fgAhhNjnPkKIWPaHoijN77jjjmPmzJmMGjWK+fPnc/bZZ/P111+TlJR0yGO73W5uvfVWwuEwV199NXa7nZ07d3Lfffcxa9YsAJ544olDPs7uTjzxRMaPH8+0adO48847CYVC3HDDDTidToqKinjkkUeYOnUqAH/605+w2+2x+95xxx1UV1dz2WWXMWzYsFiJo8fj4a233uKNN94AYMyYMc06Z6VlCHM3SOiGSJiIjJZCcKYR+AougOg28L6C9L4CWprRmN42GmxD4wIsQljBPhJhH4nUfcYYgc8hOA8i65Ge9eD5B9IyCGEfA/ZzEab01nvQ7ZhwTgAtBVl1LwSnIytuhJQXEVrzBN+V5mU0oK/mvW/ns62onKgukYBZE+Sl2MmxadgeeIbhOxeTRMNCIj5hYcO4sfzmhmPw/iK6Y0JwqrMbF3U4gZO69DvqEliaHOzq1q3bPrcHAgFKSkqQUiKlxO1284c//KGph1Nawe4ljPtaJUdRFEU5fFTvDUVp+wYMGMDs2bMZOXIkP/zwA2eeeSbffPMNycnJhzTubbfdxrx58/j1r3/N7bffTkJCApWVlbHtDz/8MBdeeOEhzn5PkydPpqysjDlz5nDnnXfym9/8hsTERKqqqmLnpPvuu49bbrkl7n7hcJipU6fGgmEJCQmYzWaqqqpi+5x22mk89NBDKO2LMGWA8zKE8zKk7oHQ98jAdAjOAr3cyCbyTwXhRFqHGSs72kYgtIZePUJzgmMswjEWqVdB4Fsj8BVaCOGlyPBSqH0SaR1al/F1pgrUHCRhPxtSJiOrboXwT8iKKyHlFYSp+fr6KYdGSklpeQXvffsDO4sr0KVEl6BLna5pTrqbImTc/ziDStZhreu0JYFyzcUPv57Any/IJvqLDlw2zFzkPp4RyT3onNWBlJSUo/J6vsnBrm3bth3wvqeddhrPPfccvXr1aurhlFag+nUpiqK0LepDI0VpP/r27RsLeP3000+MHj2a7777jpSUlCaPabVamTFjBv/4xz9455132LJlC263myFDhnDvvfdy3nnnNeMjaOB2u5kxYwavv/46b775JitWrMDj8ZCdnc3QoUO54447GDFixB73e+SRRxg8eDCzZs1i7dq1FBUV4fF4yMzM5LjjjuPyyy/nmmuuUe812zmhJYD9HIT9HKQMQ+gnZHCG0edLL4LgN8jgN4AJaT3ByPiyj0aYcncbIxmclyKclyKjxRD4ygh8hVcagbTQ91DzKNI2AuEYawTOhH2vc1IaCNvJkPo2svImiKxDVlwOKa8izJ1ae2pHNV3X2VVYwgczf6KgzPjgIKpL/OEox2QlMKC2lM6/eZTu1bvi+nFtMafxyf9dxfsjnHUrKDYEulLNLiamnEJfVw4Ws4WcnBxcLtfhf3BthJBN/Ij49ddf3+d2s9lMSkoKxx13HB06dGjS5I5ENTU1uN1uqqurmyWdvSVVVVWxY8cOXC4X3bt3b+3pKIqiHBHa0+uAoihtw4gRI5gzZw5/+MMfDvvKhW1Nc5xDvV5vbOEAj8dzVF8MtiQpJUR+RgZmQHA6RNbH72DuW5fxNRrMvRvNPJGR7RD4Aun/DKKbGzYIF9hGIxy/AuspCLFnDyIlnozsQFZeD9GdoKUhUiYjLH1be1pHnWg0yobtu/h0zlJKKmsACEclNYEw/XOTGLZlFV3+9SKZvopYP64ogiXWPF57+DIWn7BnvlKPhGwe6f4rTLVhpJTY7XZyc3OxWNrOv4vmPO8e6OtAkzO7rr322qbeVWknVGaXoiiKoiiKcqTRNC22YqXqS9tyjBUZ+yMs/SHxbmRkh9E7KjADwksgsgbpWQOeZ8HUEWkbbQS/LINiKwcKc2ej2brrViMrKfA5+L8EPR8CnyADn4BIQdrPNTK+LIOMxvrKHoS5E6S+h6y8sS7D60pIftHI/FJaXCQSYfn6LXzzw0oqarwABCI6Fd4Qgzsmcd73s+n0pykkhP2x+/gx8a29N5MfHkvR4D2vyYdn9OHxAZdQVVZBbU0tEiMTNzMzs82d21rjvKvWH1X2SgW7FEVRFEVRlCONw+Fg0aJFrT2No44wdwLzDQjXDUi9AgKzkcHvIPg9RHeB7zWk7zUQyUjbGXVZX6chhKMucNYHYemDTPg/CC9HBj6DwFegV4D/HaT/HdBykI4xRo8vc5+jsk/RvghTRl1JY10Pr8obIfkZo7eX0iJCoRALVqxnzpI11PgCAHhDUco8QYZnO7jgu2nkzJyJRTeuvSVQhp2pzuOY9ruh1J4QX65rEhrXdh3GzT3PIhQKUZBfQCgUQghBZmbmIfeGbCmtcd5VwS5lr+pX6zKb1Z+JoiiKoiiKoijNQ2ip4LwI4bzIWJUxNL+uz9dMkFUQ+AgZ+AiwI22n1vX5OgOhpRqZW9ZBCOsgZOJDEPrRKHMMfgd6YcOqkKZu4BgL9rEIc5dWfsRth9ASIXVy3SqN3yGr7oKkx43VG5VmEwgEmP7TKhau3oQvEAKgOhCh3BNmnDvEeZ//j7QVK9HqOm9JYDNuXnYN4Yf7BxA4yRE3nl0z82i/ixnd4VgAamtrKSoqQtd1LBajP5fDEX+fo90BRTF27NjRbAfs1Ek1wmsvVGaXoiiKoihK87joootYsGDBQd3nww8/ZOjQoS00I0VpG4TmNFZatJ+JTIoYKzEGpht9vqK7IDjDCITVaEjLYIR9lNGvy9zJKHe0nYawnYaUf4TgbKT/CwjOhOgWpOdZ8DyLNPc3yhzt5yFM2a39kFudEDZIfhZZ8wfwT0HWPGqsoum6TWXDHQIpJT6fj8+/X8aKjdsJhozkkXJfmEpfhInhfIZPeYOEHTt268cFi8lmUsIJbLqnM5FT41ccdVscTBp8I72Sc2PHKCsro6KiAgCn00lOTo5KUGnEAf1Gunbt2iwHE0LEsoWUtk8FuxRFURRFUZpHRUUFxcXFB3WfUMjIBpg9e3YLzOjo5fP56NvXaMy9Zs0anE5nK89IqSeEGawnIqwnIuWDRlP7+j5fkZ8hvAgZXgS1f0Gae4NtlFHuaO5nBHDsZyPsZyN1j5G15P8cQgsgshpZuxpq/4q0noiwjzH21Zq+Omp7J4QJkv6E1NLB+wLS82/QyyDxEdX37CBJKamuruWj2YtYt72QSDSKBEpqQ3iDEX63awkDP5uGraoqdp8AJr6mM5Odx1N+dzaR0+MbrefaU/jfibeQ6mwIfkUiEQoLC/H5fACkpqaSnp7eLgKUrXHePaDVGJuzgZiu6/vf6Qg0adIkJk2aZKy+sGFDu1iFa/PmzXi9Xjp37ozb7W7t6SiKohwR1GqMiqIoTadWYzx6yWgBBKYbWV6hnzByYupoOWAfaZQ7Wk+MW53R6A/2lRH4Ci/ZbUQz2IYZ/b1sIxHa0ft3IL1vIGufMH6wn4dw/w0hrK07qXZASklpRQXTZvzEloJSdF2iSyisCWIOhfjDsq/oNms6proPLiRQiY13OYaP7T3x3plFZGT8eax/Uh7Pn3AdTkt8SaLf76ewsJBwOIymaWRnZ5OYGJ8F1pa1xmqMBxTs2r59e5Mn8kudO3dutrHao/Z0kbN+/XqCwSDdunWL/WEqiqIoh6Y9vQ4oiqK0NSrYpQBIvQqCc4xyx9A8kL6GjSIJbCOMckfrMITWcB0jowXg/8JY1TGydrf7OMB2BsL+KyMAdhQGeqT/c2T1b4EwWE9FJD8X97tTGui6zq6iEj6cuYhdpZVIKYnokoLqILneGn4/dyqZy5Yg6hJ9JLCNJF7iWJY6cvHdnE747IZkEgEMz+zLkwMuw2KxxB3LyBqrpqSkBCklVquV3NxcbDbbYXzEh641gl0HVMZ4tAeojlaqjFFRFEVRFEVRlLZGaMnguADhuAApgxBcgAxON3p16eUQ+BQZ+BSwIG1DEbZRRsmjKRcSJiISJiIjm4z+XoHPIbodAl8iA1+CSELazzICX9YTjXK/o4BwjAUtGVl1h7FgQMU1kPqKsZiAAhjXx5u25fPJvCUUV9QAEIrq7KoMcErpdp6Y+QEJWzbH+nHpwFIyeZbjKbQn4bspg/CYhiCXCY1LOp3MvX3HNno8XdcpKSmhuroagMTERLKystT1+QFSXcyURkkpVbBLURRFURRFUZQ2zejVdQbCfgZSRiG8wgh8BaZDdJuRARacA/wBaRlYt7LjKIS5ByLxbmTCXRBZZZQ5Br4EvQT8HyD9H4CWibSfa5Q6Wo5tF72RDoWwnQYpryMrJxp9zsovh5TJCHPH1p5aq4pEIqxcv4Wvf1xJebUXgEBYZ2eljys2LeHCOZ9hLSuLBbmCaEynE//hWPw2G/7rUwld0BA0tGpmbu0xmiu7nb7XY4bDYQoKCggEAgghSE9PJyUl5Yj/G2xOKtilNErXdeorXFWwS1EURVEURVGUtk4IE1gHIayDkAn3Q3RzXZ+v6RBeCeFlyPAy8DyNNHUD+2gj+GU5Fi3pWGTibyG0GBn4DALfGIEv3+tI3+tgykPaxyLsYxGWnq39UFuMsB4Hae8iK26E6FZkxQRI+R/C0qu1p3bYhUIhfli5ntlL1lDjDQDgCUYpKa/l/5Z+xyk/zsTk98f2r8LKFHoxlV5gNRG4Jo3g+IZFEFwmG48OuJgzsvvt87her5fCwkKi0Sgmk4mcnBxVbt0EzRbsKikpYdeuXXi9XvbVBuz00/cevVTajvqsLiFEsy5QoCiKoiiKoiiK0tKEEGDuAQk9EAm3IKPFEJxZ1+frR4huAe9/kd7/gpaBtI2q6/N1CprtJGTSoxCcbwS+gjMguhO8LyK9LyLNvY1sL/uYIzLrSZi7Q9p7yMobIbIRWXEFpPwHYR3c2lM7LAKBADMXrebHVRvxBozm8lX+CNH8In7/wyf0XBnfj2snifyXASwUuWARBK9II3BZQ5ArxeLi6eOv4tjUfbeHklJSWVlJWVkZUkrsdju5ubl79PFSDswhB7uef/55nn32WTZv3rzffYUQRCKRQz2kchjsXsKoUiUVRVEURVGUI4UQgr59+8a+V44OwpQFzssRzsuRei0E59b1+ZoDein430P63wPhQtpOR9jOBNvpaPYzkLqvLlD2BQTnQmQ90rMePP9AWo6vC3ydizClt/bDbDbClA2pbyMrbzYy4iqug+RnEfYzWntqLUJKidfr5av5y1m2cTvBkBG3KPOGyVy/jqcWfELG1k1x/bhWksFzDGSHcINZELksFe+VDeWKHRwpvHDCTeQ4U/Y84C9Eo1GKi4upra0FwO12k5mZecQknrTGefeAVmPcmwkTJjB16tR9ZnL9kl4XAT1atZdVuGpra9m6dSt2u51evY6+lFVFUZSW0l5eBxRFUdoidQ5VmpuUIQj9ZGR8BacbpYsxZrCehLCPrmtwn22sBBn41ljRMbQQI7cHQAPrKUZje/uZCC3x8D+YFiClH1l1lxEUxIRwP4lwXNTa02o2xmqHtXwydzFrtxUQjkSREoo9QU76aT43/PQVrrLSWJArjMYs8pjEcfiEFUyC6PhUPNemQF0Q55jEXJ478QbcFucBzSEYDFJQUEAoFEIIQWZmJsnJyS3zgI8AB/o60ORg13vvvccVV1yB2+1m8uTJnHvuubhcLrKzs9m1axdFRUV89913PPnkk1RVVfH+++9zxhlHZhT4YLSXF+iqqip27NiBy+Wie/furT0dRVGUI0Z7eR1QFEVpi9Q5VGlJUuoQXoUMzjACX5FN8TuY+yPsZ4JtFJh7GoGxwFdG4Cu8crcdrWAbbqxwaDsDIeyH9XE0NynDyOqHIPAxACLxAYTrptad1CGSUlJaXs6HsxazOb8UXdfRpaS4wsulsz5jzPJ5WPy+2P41WJlGT96jN7rQwARckEb1jUaQSwAnp/Xi7wOvPKiyw9raWoqKitB1HYvFQk5ODg6Ho/kf8BGkxYNd55xzDt999x3//ve/ueOOOwDQNI3s7GwKCgpi+3k8HoYPH86mTZtYsmQJPXr0aMrhjhjt5QW6vLyc/Px8kpKS6NKlS2tPR1EU5YjRXl4HFEVR2iJ1DlUOJxnZBsEZyMB3EF5GQxYXYOoEttFG1pfleIjugsAXRuBr9yCZcBn7OcaCdShCtM/+S1LqyNqnwTfZuMF5IyLxfoRoX2V2uq6zq7CYj+YsYWdxBVJKIrrEu6uIid99wAnrl6PVtfSRQIFI4BXZj+9FnjGABtrYVConpoJJYEIwpsNgHh5wcNluUkrKysqoqKgAwOl0kpOTg9ms1hDcnxYPdmVlZVFWVkZ5eXksxU7TNDIzMykqKorb94cffuDUU0/lxhtv5OWXX27K4Y4Y7eUFuri4mOLiYlJTU+nY8chruqgoitJa2svrgKIoSlvUHOdQn8/HCSecAMCiRYtwOg+s1Eg5usloGQRn1fX5mg+EGjZqqWAbibCNRlpPQUS3G43t/V+Cnt+wn0gxens5xoBlcLsLFAFI7yvI2r8ZP9jHGWWN7SCAF41G2bR9F5/OW0ZRRTVICEV1bD+v49YZH9Bt15ZYqaIE1pjSeTZ6HFtEXb8tDUxnp1FxawqYBRZh4sbuZ3BDj5EHPZdIJEJhYSE+n5E5lpqaSnp6+hHdQ7A5z7sH+jrQ5LBhVVUViYmJcbWkFosFr9e7x76nnHIKTqeT6dOnN/VwymG2e4N6RVEURVEURTlSSClZs2ZN7HtFORDClA7OSxDOS5C6F0Lf1/X5mg16Bfg/QPo/AOFAWk9D2Ecj06YhotuMbK/Al3X7vYP0vwNaDtIxxmhub+7TbgIdwnUTiFRkjVHWKGUVJP8bIdpm6V04HGblhq188+NKyquNWIUvHKX79/O4bv7npFWWxYJcUZOJ77WOPBseQI1uBwEIMI9OpfLONHQz2DULD/a9gHM7DmrSfPx+P4WFhYTD4VhlXGLikdHfbV9a47zb5GBXWloafr8/7rbk5GTKysqoqqpqtKHaLzO+lLZLBbsURVEURVEURVH2JDQX2M9G2M9GyjCEFht9vgLTQS+A4HfI4HeACWkdYmR8pbyP0HfWBb6+Bb0QvK8gva+AqSs4fgX2sQhzl9Z+ePslnBeBloysuhuCs5EV10PKSwgtubWnFhMKhVi4aj2zFq+l2mvELbzeICO+/IgLVszFsVs/roDdwceyO68Ge6HrWizIZRmeStU96USskkTNxlPHTuDE7N5Nmo/RCL+akpISpJRYrVZyc3Ox2WzN8XCVRjQ52NWhQweWLl2Kx+MhISEBgD59+jBv3jxmzZrFhRdeGNt36dKl+Hw+UlL2v+Sm0jaoYJeiKIqiKIqiKMq+CWEB2ykI2ynIxIcgsrZhZcfIOggtRIYWAk8izceAbTSk/A+ixRD4AoKzILoV6XkWPM8izf2NMkf7GIQpu7Uf3l4J+0hIfQ1ZeTOElyIrroSUya0+Z7/fz+zFP/PD6o14/UapaaisgvEfvc2wjcsx7daPqzoljcnh3nztzTXuXBfksp2aQtW9GVTbJWkmJ08dczHHdezV5Ow7XdcpKSmhuroagMTERLKystS1dgtrcrBr0KBBLF26lEWLFsVWWRwzZgxz587lvvvuo2PHjgwcOJAVK1Zw/fXXI4Tg1FNPbbaJKy0rEokAqAZ5iqIoiqIoiqIoB0AIAZa+CEtfSLwLGdkJwZlGn6/QIiP4FVkH3udB6wD2UZD8rFHeGPgSQgsgshpZuxpq/4a0nGA0trefjdDaXuKIsA6G1HeQlTdAZCOyfAKkTkaYux/WeUgp8Xq9fL1gOcs27CAQCgNg37iJKz5/h76/6MdV0KkLz1Qfw8qqhLhx7CenUHt/JtUOnQ7mJJ7s/Cu65XXDbm/6aprhcJiCggICgQBCCNLT00lJSWk3ZavtWZMb1H/66aeMGzeOW265hRdeeAEwVl7s06cP+fn5cU+elBKLxcLcuXM56aSTmmfm7VR7aUy8fv16gsEg3bp1i2XuKYqiKIeuvbwOKIqitEXNcQ71er2x97cejweXy9WcU1SURkm9wij5C8yA4Dwg0LBRJINtOFhPAr0Wgt9CeMlu9zaD7TSjv5dtlFFG2YbIyC5k5Y0Q3QoiGZHyMsJ6XMsfV0qqq2v4dN4S1mwtIByJIiV0+nE+l07/mJyq0liQSzeZWNd3AH8p6EFhRfw4jiEp+B7IoiYhSi9zOr/NOpOcnBxSU1PRtKYvIuD1eiksLCQajWIymcjJyTlqzzfNed5t8Qb15513HrNmzYrrop+QkMDMmTO57rrr+OGHH2K3d+rUiUmTJh31ga72RJUxKoqiKIqiKIqiNA+hpYLjIoTjIqT0Q3CBkfEVmAmyEgKfGF/YwHYqJNwP0gfBmRBZawTKgrMBO9I+si7wdTpCWFv5kYEwd4S0d5EVNxmZaZXXQvLzCNtpLXI8Xdcpq6jko9mL2byrhKiuo0cinPzlJ5y3cCaJgYZF84JOB0tOHsozq7KpXh2MG8cxMJng73IoToww2JLFrVnDSUhIIDs7G4ej6Q33pZRUVFRQXl6OlBK73U5ubi4WS9tftfJI0uRgl9lsZvjw4Xvc3rNnT+bPn8+uXbvYuXMnbrebPn3az+oSivGPUwW7FEVRFEVRlCOREILOnTvHvleUw00IB9hHIeyjkElRo+dVfZ+vqFH6SHAmRpf0QeC8CWQAQvMguh0CXyIDX4JIQtrPMgJf1pMQovWu3YSWCqlvIKvugNACo5eX+69GGWYziUajFBSV8vGcxWwvrkBKianWy9hpb3DamiVYokYrHgnUZqQyc9S5vD7bjGeWF2gIdDn7u4k+2IHS5AijbF0ZlzSAhIQEUlNTSU9PP6Rsrmg0SlFRER6PBwC3201mZuYhjXkkaI3zbpPLGJWmaQ/lK9FolJ9//hmA/v37H/X/MBVFUZpTe3gdUBRFaavUOVQ5kkkpIbIRgtON4FdkdfwOph5gORYIQ2gh6CUN27QMsJ9nBL4sx7ZaIFfKELL6AaMHGQKR+BDCdc0hjRmJRNi8M5/P5i6jsKIaJCTn72Tc1Nfpt3MzGkZIQwJFPbrw1ZgL+OyDajwFnrhxnH2S4MFOVKRHuNh1LCeaO+JyuWLZXLtXrTVFMBikoKCAUCiEEIKsrCzcbvchjansqcXLGN9++20uuuiiQ0rvU9qm+qwuIYQKdCmKoiiKoiiKohwGRoP7XmDphUi4DRkthMAMZHCGEdyKbjK+AEQmWEcBEQgvB70UfK8jfa+DKQ9pH4uwj0VYeh7mx2AF9zNILRV8byFrn0DqFYiEuw86ABcOh1m9cRtf/7iSsiojcNVtxRIu+Px9OlSUNPTj0jQ2nXAcn425kAUvb6Xm2fy4cZy9kjA9kEdNB8GNqafQTXdjMplwuVykpqaSkZFxyNe9tbW1FBUVoes6FouFnJwcFStpZU3O7NI0jcTERMaPH8/VV18dW5FR2bf28GmU3+9n48aNWCwW+vTp09rTURRFOaK0h9cBRVGUtkqdQ5WjldRrIDjH6PMVnAvSu9tWF1j6AlGjv5f0N2wy9zayvexjjN5ah2u+UoL3BaTn38YNjssQSY8dUKllMBhk0eqNzFqyhiqPH3SdU6Z/wZlzv8btb3jcIbuN5ecM5+vh57Dyn6up3lkTN46zWyK2BzoT6Gbj7swRpEZtBINBnE4nLpeL7OzsQ24YL6WkrKyMigqj6339uGZzk/OKlP040NeBJge7XC4Xfr8/Fp3t2LEjV111FVdffTXHHHNM02Z9FGgPL9C1tbVs3boVu91Or169Wns6iqIoR5T28DqgKIrSVjXHOdTv93P66acDMHfuXJV9obQ7UoYg9ENdn68ZoJftttVslDsKILIJiDRsshyPsI8xyh1N6Ydnrr73kDWPATrYzkQkP4MQtkb39fv9zF2yhgWrNuLxB7EGfJzz4TucvPxHbLv14/KmpTDvil/xQ58hrH16BVXbquPGcXZOwHlfV0S/JH7f6VyEP4LH40HTtLhsrkPtTx2JRCgsLMTn8wHEen6pXoB7as7zbosHu7xeL9OmTeONN95g9uzZ6Loee1IHDRrEtddey+WXX05aWlrTHsERZtKkSUyaNIloNMqGDRva9EVOVVUVO3bswOVy0b1799aejqIoyhFFBbsURVGarjnOoV6vl4SEBAA8Hs8hZ3YoSmuSUofwSmTwOwhMh+jW+B1MHQHNaHxP/aW/BtZT6jK+zkJoiS07x8A3yKp7gTBYTkSkvBg7ppQSn8/H1z8sZ+n67QSCYVJLizh/ymv027oeTTb04yrt2olvb7mcLald+PkvS6nYUhl3HEdHF4n/142UIdk81PsiPJWVeDweAoFAXDZX/b//Q+H3+yksLCQcDqNpGtnZ2SQmtuzvsT1rzvNuiwe7dldQUMBbb73FW2+9xerVRhM9IQRms5lzzz2Xq666ivPPPx+rtfWXRW1t7eEip7y8nPz8fNxud2zFBEVRFKV5tIfXAUVRlLZKBbsUZd9kZHNdn6/pRi+v3YlUEOb4xvZYwTbcWDXRdgZC2FtmXsEfkVW3GuWX5j6Q/DK1XhufzlvKz1vyCUei9Fy3ijEfv0vH0oKGflxCsH3wAL684xp8MpWlf/6Rsk0VcWPbc5yk3dudTsN68OCACykvL6empia2ImJCQgLJyclkZWUdcjaXlJLq6mpKSkqQUmK1WsnNzcVmazxbTTG022DX7lasWMEbb7zBu+++S1FRkXEQIUhOTubSSy/lxRdfbM7DtTvt4SKnuLiY4uJiUlNT6djx8NV1K4qiHA3aw+uAoihKW6WCXYpy4GS0BIIzjXLH0A9AuGGjcAJmkLv1uRIusI02Al/WoQhhad75hNcQrbiJsmonny49h01FmUQjUYbO+45RMz7H7a2NBbnCVis/nzWM6TdNwOVzMf+P31O8viRuPHumg4zf9GTQ2cdz27Hn4fF4KCkpwePx4Pf7cTgcuFwusrKymuU9l67rlJSUUF1tlE0mJiY2SwDtaHBEBLvq6brO9OnTefPNN/n444/xer0IIWIr/R2t2sNFTkFBAWVlZWRkZJCTk9Pa01EURTmitIfXAUVRlLZKBbsUpWmk7oHQvLo+X7NB1u621Vz3FWi4SaSA/VyEYwxYBiPEoa1WGI1GKSgu5ePZP7C9uBpTMMjYT97nxMXfY4s0BOG8yUn8dNmvWHnxGFKr7Ux/dAYFa4vjxrKl2ci95xjOuGAoE/qdQTQapaSkhKqqqlg2l8vlimVzNUez+HA4TEFBAYFAACEE6enppKSkqP5cB6g1gl0ttkSApmkMHDiQ1atXs3TpUtauXdtSh1KaWX1AUkWoFUVRFEVRFEVR2j+hJRjBK/u5dQ3uFxmljoHpoBfT0MheYGR8VYL/HaT/HdBykPbzEI5fgbnPQQV4IpEIW3bk8/n3y8kvryKlrJTrpr1Bnw2rMe3Wj6uiUwdmT7yc6uHDSK60sPQ3X1Lwc1HcWNZkG53uOoaLrjibUT0Go2kaHo+HoqIivF4vPp8Pu91OQkICmZmZJCUlNUswyuv1UlhYSDQaxWQykZOTo4Lk7UCzB7sCgQAfffQRb775JtOnTycajVKfPDZw4MDmPpzSAuqDXWq5VEVRFEVRFEVRlCOLEFawnYqwnYpMfBQiq5GBGRCcDpENxJU7ooFeCL7JSN9kMHUFx1iwj0WYu+71GKFQiDWbtvP1jysprfLQbdNa7vnobToW7Yrrx7XzuN7Mvv1iXH37kVSTzcd3TKNo1S+CXElWutzeh+tuHMegzn0wmUxEo1EKCwuprKzE6/Wi6zpJSUm43W6ysrKwWA69BFNKSUVFBeXl5Ugpsdvt5ObmNsvYSstrtmjGrFmzePPNN5k2bRoejycW4MrNzeWKK67gmmuuoX///s11OKUFRSJGVF9ldimKoiiKoihHovT09NaegqK0CUIIsAxAWAZA4j3IyA4ITjfKHcNLAT3+DtGtSM9z4HkOae5vlDnaxyBM2QAEg0EW/7yRWUvWUFnt5ZQfZ3Pzt5+SXFsVC3JFLBbWjRzKottvJCe9gq4la/nvLZ9RvDoUdyhLgoUet/bj5psvoVde11iQyev1UlRUhMfjiWVzuVwuMjMzcbvdzZLNFY1GY8cASE5OJiMjA007tHLOo9nhPu8eUrBr7dq1vPnmm7z99tvs2rULMKKfTqeTCy+8kGuuuYbRo0erOtZ2RpUxKoqiKIqiKEcql8tFaWlpa09DUdokYe4E5hsQrhuQegUEZhnljsHvgWD8zpHVyNrVUPs3fPpwvt8wgh/WCvzVHs778gNOWjgHe6jhPr6kRJZdPIbt11xB15QcjimP8uxtr1O2ojxuWLPTzDETB/Dr2y+mW17n2EqHuq5TWlpKRUUFHo8HXddJTEyMZXNZrdZm+R0Eg0EKCgoIhUJomhYLoilN1xrn3SYHu4YMGcKyZcsAI8ClaRojRozgmmuuYfz48aqGtR1TwS5FURRFURRFUZSjm9BSwTke4RyP1H0Q+r6u3HEWyCqkBG8wg+mrh7J0W28sJcWM//BN+q5ZgUkaGWESqOyYw083XUXo7LPp4s6mR6mfp6+bTNXysrjjme1m+t/QiZtuyqFjz4lxMQWfzxfLtPJ6vdhsNtxuNxkZGSQnJzdbgk1tbS1FRUXouo7FYiE3Nxe73d4sYyuHV5ODXUuXLgWgb9++XH311Vx55ZV07Nix2SamtA4pperZpSiKoihHkMcee4zHH3+c4cOHM3v27NaejqIA0KVLF7Zv386rr77Kdddd19rTURRlP4TmBPtZCPtZ6HqY6rIf+WLBWn7eYSF34yZ+/dET5OVvj5UqSgG7+vdk2V1XknDssXRO6kVliYfHrnyW2uUVcWObbCYGXj+Q62/uRE7SC7jsJYjwdqT8K1KaKS8vp7y8HI/HQyQSITExkcTERHJycpotm0tKSWlpKZWVlYCRiZSTk6MSQNqxJkcz7rrrLq6++moGDx7cnPNRWpmu67F+a+oftqIoiqIoLWHNmjW89NJLzJkzh23btuH3+0lPTycrK4u+ffsybNgwRo4cSa9evQAYMWIEc+bMadKxrr32Wl577bW421588UVuu+02AIYOHcr8+fPjts+ePZszzjijSccD2Lp1K126dDngcV5++WVuuumm/e63du1a+vbtC4DD4aCoqGify64rjfP7/Zx77rkAfPXVVzgcjlaekaK0D7quU1Zewadzl7JhVzHH//AT93/zESlVFQ39uMwmNg0fzPo7fkVOdg29LGWUlnzJb2/+CP8Kf9x4mlVjyLXHc8Xt59OxcwfjfBZIRFb/FgJf4A8EKfbdjccbwuPxYLVaY72zUlNTmy2bKxKJUFhYiM/nAyA1NZX09HTVjqkZtcZ5t8nBrn/961/NOA2lrajP6hJCqOZ7iqIoiqI0u6effprf//73sQVxwGj8W1VVRWFhIcuXL+edd96Jy0RLTU0lKytrj7FCoVDsU/iUlJRGP+FvrM/K5MmTY98vWLCA9evX07t379htVqu10eMBVFRUEA6HsVgspKamNrpPYx8Y7m08AKfTuddte5u33+/n3Xff5eabbz6g+yoNdF2PBU91Xd/P3oqiRKNR8otK+GzeUrbvLOacrz7gygWzcAQDsX38iS7WXDSWkuuvooPbR2+2kF9QxeOPBAkt88WNp5k1hlzenUvvOIOO3QaRkpLScO3p+BVSuCnf+Qxl1Wa8oW+JiIEkJCSTlJREdnZ2rIdXc/D7/RQWFhIOh9E0jezsbBITE5ttfMXQGuddVaemxKl/46lKGBVFURRFaW4ffvghDzzwAACnn346Dz/8MMOGDYv1Q8nPz2fevHlMmzaN6urquPs1ZvfMqQ8//JARI0bsdw4rVqxgyZIlpKSkcN555/H2228zefJk/va3v8X2GTp0KEVFRY3evz7LbOjQoQdVFrq38Q5UOBzmzTffBODOO+/kueeeY/LkySrYpShKi4lEImzZmc/n3y+nest2Lpr2Jrf8vBST3tCPqyonkzW/vp7oeeeTnpBMN5OJjYX5vHD/N4SX1MSNJ0yCEy7J5qKbdPI6riAl4WNMUTd4xiEdFyIsvQgGgxQVd6bGdzce70ospmrcju9Iz7qC9IxOzZZtJaWkurqakpISpJRYrVY6dOjQbGWRSutTEQ0ljmpOryiKoihKS/nHP/4BQP/+/ZkxY8YeH6516NCBCRMmMGHCBPx+f2NDHLL67KjLLruMCRMm8Pbbb/PGG2/w5z//uU1/2PfZZ59RUlJCnz59eOqpp3j11VdZtGgRq1evpn///q09PUVR2hEpJTW1tXy5cA2FJeUMnP05vaI1mHr2IOGWuwnpks07C5m5dB22lasY/9FbdNqxpaEfF7AhIZtp/c4h59JR9D+tN1ablRU7t/L6wx8S/WWQSxMMGX8s427rR17HjaTaPyIUlpTXdgcJLvuHOKyvUuUfSJnvHLzB7oTCdlzJg0nQPiDb/T12ZkHkf2DpiR71Q+3fILoNTF0g8QE0U+NlcVJK/H4/kUgEs9mMw+FASklJSUnsQ5XExESysrLUNfARpu2+oiutQgW7FEVRFKV1vf/++7z22mssXbqUiooKXC4XGRkZ9OnTh7PPPpsbb7xxj5WhvvrqK5555hkWLVpEJBKhe/fuXHnllfzmN79p1rntnkklpWTx4sX85S9/Yf78+VRWVtKxY0cuvPBCHnroIZKTk/e4//LlywE477zz9htYaol+HsFgkLfffhswenmddNJJdOnShW3btvHFF19wwQUXNPsxm0t9kO6aa67B5XIxfvx4Xn/9dSZPnsw///nPZjvOL5/jZcuW8fe//505c+ZQXFzMqaeeGpfRFgqFeOWVV5g6dSqrVq2ipqaG1NRUTjrpJG655ZZYj5a2qqioiO7du7f2NBTlsKmtrWXajB/ZVVTK6A/f5bLpX6DV9WwG0B/+E8vPOJftWR2Z+PWHpFaUNfTjEhpzZS4vMJBqrx1+8sBPn2C2m0nqkEDF5qq4YwkBgy8YwNg7RtP1mC6kp6cTDAbZuP1cyst2EQ75QPoxa15sFg9WSy2hSBlmUwHJDi+pyZ1Iy56IVrsBIpuQFVcgzT0gvKThIOH5EHgb3ToKLfXFPR5r/QqO0WgUk8mEw+GIZYcJIUhPTyclJUX15zqMDtd5VzVlUuKoMkZFURRFaT033HADEyZM4Ouvv6akpAS73U44HGbTpk189tln3HHHHXuUwz322GOcd955TJ8+nerqaiwWC2vWrOG3v/0to0ePJhQKtchcP/nkE0499VSmTZuGz+dDSsnmzZv5+9//zsCBA9m2bdte77tr164WmdP+fPTRR1RUVNCrVy9OPvlkhBBcc801QHw/rLYmPz+fb775Bk3TuOqqqwAjWAfw1ltvtdhzPG3aNE466STeeecdamtr93h/uH37dgYNGsTtt9/O7NmzqaiowOl0UlxczKeffsp5553Hrbfe2iJzay6HWl6qKO1JbW0tn8xcQHFJGadPfYuTvvscsVugC0DoktNmfMlV7/yXtLpAV9Dp5JtepzJGjuMpcTLVIv4Dl0ggEh/oEjDwvD489OVd3Pqv6xh0yvHk5uYSDAbZsGEDxcUlRKJWrPYMTNYOBKN5VPp6UFbbB4vJT2rCZjqlf0eG7R9olReC7gUtC2R1fKBrd6EZ6BUN55va2lq2bt0ae11MSEhACEFxcTElJSVEIhE6duzYrI3ulQNzuM67KtilxFGZXYqiKIrSOr7//nteffVVNE3jr3/9K+Xl5dTW1uL1eikrK+Obb77h2muvjesn8umnn/L4448DcMkll7Bjxw4qKyupqalh0qRJ/Pjjj7z44ot7O+Qhufbaaxk6dChr1qyhuroar9fL+++/T0pKCtu3b+fSSy+Nva+od+KJJwIwZcoU3nnnncPeHLw+oHX11VfHbqsPdn311Vct+gZ86NChuN1u7HY7eXl5jB8/nk8++SS2Cva+vPbaa0SjUc444ww6duwIGL3DOnfuTFlZGZ988kmLzPm6667jzDPPZO3atVRXV+P3+3n55ZcB8Hq9nHPOOfz888+MGDGC2bNn4/f7qaqqoqqqimeeeYaEhAReeukl/v3vf7fI/BRFOXBSSvILCsgvqaCsysdps74G4JdhHlH3JQFvejpL7/0NU9/8H3/flIMu9h8+6D28KxP/N4FLHh3L4FOPJy8vL1Y6WFRURG1tLUIIbDYb0WiUSCSCLgVCWEEkozlG0qnriTicqQ2z0wtBL97/gwzNQI/6Y8cKhUIkJCRgsVgIBAL4fD7MZjNSylhJo3LkUsEuJY4KdimKoihK61iwYAEAo0eP5oEHHohb6S8tLY2zzjqL1157jdzc3NjtDz74IADDhw/nvffeIy8vDzBKAG+77TaeffZZqqqqWmS+WVlZfPnll/Tp0wcwssIvvfRSpkyZAsCiRYv2aCz/2GOPYTabiUQiXHnllXTo0IHLLruMp59+mlmzZuH1eltkrgDbtm1jxowZCCHigl3du3fn1FNPJRKJ8Prrr7fY8X/44Qc0TUMIwa5du/jwww8ZN24cY8aMwePx7PV+Ukr+97//AQ3ZXGCU39RnedVvb259+/bl008/5Zhjjond1rNnTwCeeeYZ1q1bx/Dhw/n2228ZPnx4bIU0t9vNb37zG9544w0AnnjiibjVNw9GMBikpqYm7qupCgsLWbp0KcuXL48FjZcvX87SpUtZunQphYWFTR5bUdo6v9/Pms07qfZHOG3BDDQp9wh07U4A2y+6kK1njGDdV5uN6NcBOO68PnTpk4fFYomd8+qPX1VVhZQSk8lEMBgkHA7HygvNZjNms5mobiVsuRot42tE5mJIfAjMvQ78gdb+Db/fj8fjwW63I4TA4/Hg8xkrQjocDlJSUvD5fC3WG1Jp0JrnXRXsUuLUB7tUGaOiKIqiHF71Pa5KS0v3yIhqzMqVK1mzZg0ADz/8cMOy7buZOHEiHTp0aNZ51rv//vsb/VR89OjRDB06FID33nsvbtvw4cP5+uuv6d27N2CUMkyZMoUHHniAkSNHkpKSwpgxY5g7d26zz/fVV19FSsnw4cPp3Llz3Lb6IFJzB42Sk5O57777WLhwIV6vl8rKSnw+H8uXL+eyyy4DjIyyyy+/fK9jzJ49my1btpCQkMBFF10Ut60+K+3bb79l586dzTp3MJ7jvX0AWp8ld++992KxWBrdZ9y4cSQlJVFWVsaSJXspPdqPp556CrfbHfuqD+g2xX/+8x8GDx7MaaedFiv9vOOOOxg8eDCDBw/mP//5T5PHVpS2LhKJ4A+GCEV0MspLDug+jsJCwkSpKqw94OMEqoJYrVZ0XY8LckciEaLRaCybtf7/JpMJi8WCw+FA07RYtheA0BLRXNeipX8OllMObALRbbFjmc1mwuEwwWAQgISEBFwuF2azeY/5KS2jNc+7KtilxKn/B68yuxRFURTl8Bo1ahR2u51ly5YxbNgwJk+ezNatW/e6/+LFiwHjA6phw4Y1uo+maYwYMaIlpsvIkSP3u61+jrsbNWoUa9asYfbs2Tz44IOMHDkylsUWDof58ssvGT58OI8++mizzVXXdV577TWgIUC0u0svvRS73c6GDRuYN29esx134MCBPP3005x44ok4nU7AyMg67rjjeO+997jzzjsB+Pzzz5k5c2ajY9QH4C666CJcLlfctl69enHKKafEPb7mdOqppzZ6e35+Ptu3bwfgxhtvJDs7u9GvnJycWNZa/f4H68EHH6S6ujr2dShBvZtvvpklS5awZMmSWDnmyy+/HLvt5ptvbvLYitLWmc1mHDYrVrNGSWrmAd3H3yEXs9Bw5rj2v3OdxPQEpJRomhaXQGE2mzGZTHv0xxJCoGlaXPCr0cQLU7cDm4CpS+xYkUgklr1lt9tj2afRaHSP+SktozXPu80W7JJSUlZWxo4dO5prSKUVqDJGRVEURWkd3bt355VXXiEhIYEffviBm266iW7dupGZmclll122R3+nkhLjk/n09PTYG/jG1Pd4am77yhir31Y/x1/SNI3hw4fz5z//mRkzZlBeXs7atWt59NFHYwGdP/3pT3z++efNMtfp06ezY8cOnE4nF1988R7b3W4348aNA1quJLAxf/7zn2Mra3788cd7bK+urmbatGlA40E6aMhKe+211w6o/9fByMxs/IK4oKAg9n1ZWRnFxcV7/arvy1ZfQnSwbDYbSUlJcV9NlZOTw6BBg2JfQNzPOTk5TR5bUdo6h8NB3+55uB1m5p86Cl2IvVYmSkDXNArPH0eC2Ubnc7vs2dyrEQnpTnoM6UIkEiEhISEu+9fhcJCcnIwQIi57WUoZy7ISQuB2uxvvpZX4wIE90MQHcDgcJCQk4PF4CIVCCCFiY0opCQQCe8xPaRmted495GDX0qVLueiii3C73WRlZdGtW3zEtbKykptvvplbbrlF1cS2AyrYpSiKoiit58orr2T79u289NJLXHbZZeTl5VFaWsqUKVMYN24cw4cPP6SeRW3ZMcccw+OPP86nn34a++T/lVdeaZax60vufD4fSUlJCCH2+KovuZw6dSq1tQdesnMoEhIS6N+/PwBbtmzZY/s777wTe/88evToRud9yy23xO4/a9asZp3f3t4P7n6hunbtWqSU+/267rrrmnVuhyIQCHDXXXcBxMqbFOVIJ4SgQ24uHTJTSU92svisMcCerbjqf946/iJ0sxlNaFgcVjpf2HW/xzjr9mGYLWbsdjvZ2dlxWVxCCLKzs0lMTERKSTQaRdd1dF0nHA6j6zpJSUnk5OQ0ujqiZnKAddS+J2AdhWZyIIQgKysLXdcJhUKxDK5wOIzH48Fms+0xP6VltcZ595CCXW+++SannHIKH3/8MR6PJ/ZitruUlBQ2b97Myy+/3GIrxSjNR/XsUhRFUZTWlZqays0338x7773Hjh072LRpE7/73e8QQjBv3jwee+wxoCHrpqysLNYHozH5+fktMs99jVu/bW+ZQfsycuRIevToAcD69eubNrndlJeXN5o1tTder3ePXmOtpT5I11L7N1V2dnbs+6aWJ7amaDTK/Pnzgab9jSpKe5WYmMgFI4eSlZnO/EuvYtXY85G/CPhITWPLJRez87bbMZlMmEwm+rs70u2WAXBeaqPjWhwWLvrD2Qw+91jS0tLo0qULiYmJjR6/V69eZGVlxVZFjEajseBUz549G71fPS31xb0HvKyjjO11TCYTiYmJOBwOTCYTPp+PcDiM2+3e6/yUltMa590mB7vWrFnDxIkTCYfD3HXXXSxevJj09PRG97322muRUvLVV181eaJKy5NSqp5diqIoitLGdO/enaeeeoorrrgCgO+++w6AIUOGAEa/zb31mdJ1ndmzZ7fIvPaVRVS/rX6OByshIQFgn+WZB+qtt94iFAqRmZlJdXU1tbW1e/26++67gcNXyujxeFi9ejUAXbvGZ02sWLEi1tR90aJF+5z3Bx98AMCHH37YYqtv7q5Lly6xUtXPPvusxY/XknYP3CnK0SAxMZFrLxjN5WPPYN3Nd/LAX/6L32QsMpE/oBcbli/D+/Aj9O/fn/79+5ORkUFmZiYPjrqKaP/4sr+uJ+Rxw3OX8rcff88V91xC//796dGjxz4DSYmJifTr149+/fqRnp5OYmIiHTp0oF+/fgcUgNo9oIVIB/uVkLEi7vb6FktWq5Vu3bpxzDHH0LNnT3r37r3f+Skt73Cdd5sc7HrmmWcIhULcfvvt/Otf/2LQoEF7DZCMGmVEX5u6CotyeNT3VAAV7FIURVGUw21/af31vUXqV1089thj6dOnDwBPPvlk3Ot4vf/973/s2rWrmWdq+Pvf/04gENjj9lmzZsU+va1fcbDet99+u9++UitWrGDFihUAsf4eh6I+2+miiy4iKSmJhISEvX5NmDABgB9//DG20uWh2N9jffjhh2O/wwsuuKDReffp04chQ4bsc95jx47F7XYTCAR45513DnneB2LixImxeS5btmyf+1ZUVByOKSmKcoD+n737DpOiyh4+/q3OkyMMOYoSBFQEFWTJBiRLEkHQVRHFV38G1DWAgjnvLqKiCCpKZo2IioyigBIVBCRIZgjD5ND5vn80XUwzgQk90wNzPs/TDz1Vt+qe6m6qq07foGkatRISaN+iIQaLlZRoX4stj1EjMi4eq9WK2WwmOjpa/9FB0zTUX4Fj793xxigu692OOnXrEBMTQ3h4eKm6BmqaRlxcHJGRkfpg8uUSNhRD7GRfF8cCcnNzsdvtGAwG4uPjCQ8PJzo6utTxifNDuZNdK1euRNM0HnnkkbOWrVevHmFhYZUyJbIIHn8XRoPBUOT05UIIIYSoPBMnTmT48OEsXrw4YGD3nJwc3n77bT788EMAbrjhBn3ds88+C/iuy0aNGqUntux2O2+//TYTJ04kNja2UuJNSUnhhhtu0Lsaut1uFi1apA8Af9lllzFkyJCAbUaNGkXLli2ZOnUq69atC+h+efToUV5//XV69+6N1+vFZDLpLa3Ka926dWzZsgXwzbh4NldccQWNGjUCgtMlsHXr1rz++uts375dT0YqpdiyZQujR4/mzTffBOD666+nd+/e+nYOh4O5c+cCMGzYsLPWY7VaGTBgQNDiLo0HH3yQtm3bYrfb6dGjB//97385efKkvj4jI4Nly5Zxyy23FDtbqBAidPyzHkbYLKTE+npoRR45HjC+lcViAXz3iR6PB/ad/lEmvkEsYVG+CTbK0wrXYrFgNBr13kX+HkZlYmpcaJFSSj8XxcbGyvA8NVi5MxpHjhwhIiKi1DP8hIeHywD11Zx0YRRCCCFCx+VysXDhQoYOHUpSUhJRUVHExcURFRXFhAkTcDqdXH311Tz++OP6NoMHD9b/nj9/Pg0bNiQ+Pl7fplOnTkyYMKFS4p0zZw6rVq2iZcuWxMbGEhkZybBhw0hLS6NRo0YsWrSo0E2G2Wxm586dPPXUU3Tq1ImwsDDi4+Ox2WzUrVuXBx54gNTUVKKiovj0009p3759hWL0J36SkpL4xz/+cdbymqbpybqPP/4Yl8tVofp37NjBAw88QOvWrbHZbCQmJhIREUG7du30ZFbfvn2ZP39+wHZLly7VW0OVJtlVsNzGjRv1lnGVKTIykm+++YYrr7ySzMxM7r33XmrVqkVcXBwxMTHExcXRt29fPvrooxLHlBNChI7VaiU+OpyDSb57+ojMHOz47gndbjcGg0E/jzudTgzHTv9frt8ySX/un1W2LDRNw2azYTAY8Hg8pU52eT2Zp/8wXVBofU5OTkCrLlFzlTvZZbVacTqdpZri2OFwkJGRUWm/LJ4Lpk+fTuvWrenYsWOoQymWzMQohBBChM6TTz7Jv//9bwYPHkzLli0xmUzk5ORQu3Zt+vTpw6xZs0hOTiYiIiJgu2nTpvHll1/Ss2dPoqOjcTgctGrVihdeeIEVK1bov8wH28CBA1m9ejU33ngjNpsNpRRNmzblwQcfZPPmzYXGoALYuXMnCxcu5O677+bKK68kISGB7OxslFIkJSXRvXt3nn32WXbt2qUnncorPz+fTz/9FPB1YSzt9Y2/Bdjx48crPB7Vu+++y6233kq7du2Ij48nKysLTdNo3rw5N910E19//TVfffVVofFjCnZh9M/WeDbXXnstMTExAdtXtnr16vHzzz/z6aefMmDAAOrWrUteXh5Op5MmTZrQv39/3njjDX766acqiUcIUTZWq5V68dHsa+JLGlkcLk44fTP++pNP/u8Ql8uFlnF6JtZ6FyXprcPMZnO56vcnu9xud8AsryXy7D793NgiYFXBVl1xcXFyX1vDaao02aoiXHbZZfz+++9s27aNiy66CIC6dety/PjxQh/Uzz//XJ8uO9hTIp9rsrKyiImJITMzk+jo6FCHEyAjI4MDBw4QGRlJs2bNQh2OEEKcl6rz94AQZ5OcnEyPHj2As49HJURlCMY5NDc3V58EIScnp1ACWYiaIisri11/7+ODJT/yn6n/Dw1I/vIj6l7QEYvFQtOmTTl69CiZmZkkJCQwMmkC2qlT/z//O4JLe7UlMjJSn7CiPPXv3LkTr9fLhRdeWKrGMd7c+ZD9JACGOjsD1mVnZ3PkyBGMRiNNmzaVZFc1Eszzbmm/B8rdsqtv374opXjjjTdKLJedna1Pl+0fS0BUT9KNUQghhBBCnO8iIiJQSqGUkkSXqNGsViuRYVbMMdF4NF9qwLhmNeC7N1RK6S27Du06oie6ABq0qguUrwujX8GWXaXuNu7ZX+RiadVVvYXivFvuZNf9999PTEwM7777Lk8++WShaY7z8/NZsmQJnTp1YseOHdSpU4c777yzovGKSiTdGIUQQgghhBCiZrBYLBgMBqLCLORafEkr2x9/omkaXq8Xj8ejJ7u2rtqhbxdVJ5KoeF8rnYoku8xmsz4mWKnH9/aknHoSOKtidnY2DocDo9FYo4dPEqeVO9mVmJjIwoULsdlsPPfccyQlJZGamgr4+u/HxMQwbNgw/vrrLyIjI1m0aJH8clLNSbJLCCGEEEIIIWoGTdOwWCzERYWRFuUb8y/27wMYjL40gcvl0sfj2rNpn75dUotENM2XbKpIskvTNMLCwoAyJLu8J049OX3PKq26RFEqNA9n7969Wbt2Lffff3/AWFxHjx7Vn3fv3p3//Oc/tGnTpiJViSrg78Yo07MKIYQQ56/Vq1czZMiQMm3TuXNnlixZUkkRiWCT97hkdrudMWPGAPDRRx9V6GZdiHOd1WqlTlwUx+Jr0zQ1hfhj6RxSTmwYcbvdREREoGkaB3al6NvUuygJs9mMxWKpcGLJn+xyOByl28CbcerJ6UHxs7KycDqd0qqrGgvFebfCWY22bduyYsUK9u/fzy+//MKRI0fweDzUqVOHLl26cMEFhacDFdWTtOwSQgghzn9Op5Njx46VaZu0tDTA9yOmDExf/VXkPa4JPB4PixYtAmD27NmhDUaIELNarTSpE09KvYaw83eisnJJd+dRV4vC7XZjMBgwmUykH8nQt2ncqh4GgyEoCYvw8HCgDMkule37V/N1ryzYqis+Pl7uZaupUJx3g9aEp3HjxjRu3DhYuxMhIMkuIYQQ4vwnCavzn7zHQojSslqtxEeFs6HZhZAMZqebE85s6lqj9EHjLRYLuWl5+jYNW9cDKtaF0c8/1JHL5QroNlksdSoOzdciLDMzE5fLhclkklZdIkC5x+z65ZdfghmHqAYk2SWEEEIIIYQQNYfVagXgxMVtUPiGfXf/sRE4PcyN0WjE6/QCYIm2EBnvS1AFI9lVsCtkbm7u2TdQTt+/WhRer1dvlRofH4/BUO70hjgPlfvT0LVrV1q0aMEzzzzD33//HcyYRIjImF1CCCGEEEIIUXMYjUbMZjORcbG4Db6kU8S6DQB6y67je0/q5eMu9A0Ar2maniirCP8g+VDaQep9MWGIJSsrS2/VFRMTU+FYxPmlQqnPPXv28PTTT9OiRQuuvvpqZs6cSWZmZrBiE1VIKSUtu4QQQgghhBCihrFarSTFRZJn9XUNjN+xG5fXoye7/vp1j1424cJ4zGYzVqs1aC2p/IPU5+XlnaUkgO+eFUOitOoSJSr3J2L37t1MnjyZ5s2bo5Ri9erV3HXXXdSpU4fhw4fzxRdf6MkTUf15vV79uSS7hBBCCCGEEKJmsFqtNKodT3p0LAB19h8ly5WHx+PB6/Wy9/cDetnYC+IwmUxBnU3P30KsdC27fOMR2l3x+hhf0qpLFKXcya5mzZoxefJkdu7cqSe64uLicDgcLFq0iEGDBlGvXj3uv/9+1q9fH8yYRSXwd2E0GAySFRdCCCGEEEKIGsJqtVInLpLUxCQA4o5mkOn2JZ7cbjcHtx/Ry8Y1j0XTtKAmu/wtu1wul35fejbZeXGAtOoSxQvKp+LKK6/krbfeIiUlhaVLlzJ48GAsFgsnTpzgP//5D1dccQVt2rThxRdfDEZ1ohJIF0YhhBBCCFEThIeHk5OTQ05ODuHh4aEOR4iQs1qtaJpGapOmAETl5JHu8XUpdLlcpOw9BoDBbCAszhb0ZJd/kHqv14vdbi+2nNdzuuVXvru+tOo6h4TivBvUFKjZbGbgwIEsXryYlJQUZsyYQefOnVFKsX37dv71r38FszoRRP5klwxOL4QQQgghzmeaphEREUFERASapoU6HCFCzmw2YzAYSG19se9vj4cTdt9Y3G63m9wM3yyJkbUi8OLrDeQfVD4YTCYTJpPprMkuPLv0pw5PYxISEuT/8DkiFOfdSstsxMbG8s9//pNatWpht9vZsGFDZVUlgqDgtLJCCCGEEEIIIWoG/8yKrksvRQEaYP19M/Rpj91ux+P2je+c2CIeTpUNZsLCn+yy2+0lJ7vcvmSXUmCxJBAdHR20GMT5p1KSXWvWrOGjjz5iwYIFpKen68tr1apVGdWJIJBujEIIIYQQoiZwOByMHz8egHfeeUcfHFuImsxqtVK/fm3cBiNmr4fav28jr6eT1I179TINr6iP0RDcLoxQuGWXUqrIZJpy7cO/VFp1nVtCcd4NWrLr77//5uOPP+bjjz9mzx7f1KRKKaxWK/379+eWW27h+uuvD1Z1Isgk2SWEEEIIIWoCt9vNnDlzAJg+fboku4TAl+xqUCuGvLBwYnKzSfrzIJnOPHb8tEMvU7tlImbNWCnJLqPRiFIKt9uNy+Uqspukw74fX80aUVFRQY1BVK5QnHcrlOzKyMhgwYIFfPjhh6xZswbwJbgAunTpwi233MLw4cNl0LhzgIzZJYQQQgghhBA1k9VqxWgwkBUbT0xuNrUOHGOHK4/tq3f6CmgQkRCG0kxBT3YZDIZCrbvOTHZ5PB68rqO+DIZmkFZd4qzKndkYOnQoX331FU6nU09wNW/enDFjxjBmzBiaNm0atCBF5ZMxu4QQQgghhBCiZvInlzLr1afh4f0kpmeS6cojZfdxAEw2E14NrEaTfv8fTGcmu84cjys9PZ1wfIPma5iDXr84/5Q72bVkyRIA4uLiGD58OGPGjKFz585BC0xULenGKIQQQgghhBA1k9FoxGKxkNbiQli3mih7PpmuPHLSfDMxhiWG4cJDrMWK2+0Oeje0kmZk9Hg8ZGRkEBnmiwUteDNBivNXuZNdAwYMYMyYMfTv3z+o046K0JBujEIIIYQQQghRc1mtVjydOsEnszF5PaRlp+Fy+HoAxTaLxYmHaFs4TqeTiIiIoNZtNBoxmUw4nU4cDkfAIPXp6el4PB6MhlNJMC0sqHWL81O5Mxv/+9//ghiGCDVp2SWEEEIIIYQQNZfVaiWmW2cUoAFhP2/T19W9oi5OPMRaI3G5XEGv22QyYTAYAPB6vTgcDmw2G263m/T0dAAM2ql6tcig1y/OP4ZQByCqBxmzSwghhBBCCCFqLqvViiXMhtvoaxNT+6vN+rr6Hetix4PFYsHpdAa9bpPJhKZp+v2ovytjeno6Xq8Xm82Ghu+eFS026PWL84/0WRMopaRllxBCCCGEqBHCw8M5fvy4/lwI4eMfh8seEYk5K4PG+w4CtX3NvKwa2TgxGAyV1rIL0Ft32e123G43GRkZACQkJECO754VY0LQ6xeVKxTn3VIlu/wJkJYtW/Lnn38GLCsLTdP0FkSi+vAnukCSXUIIIYQQ4vymaRq1atUKdRhCVDsmkwmj0UheYiJRWRk0yPN1HzRZjHiBE/gGiHe5XAFjagWrbjid7HI4HKSlpeH1egkLCyMiIgKV4/UVNtQNWr2iaoTivFuqboxKKf1R1LKyPET14092GQwG/eQihBBCCCGEEKLm0DQNq9WKvUlTABK9eQCExYWhUJzQ7BgMBpRSQW/ddeZEaXl5eQGtugISa8aGQa1bnJ9K1bJr5cqVQGBzM/8yce6TmRiFEEIIIURN4XA4eOCBBwB47bXX9K5bQgh8Y3Jd2h5++I5IfAmtuMYxuPCSY3BhsViw2+04nU4sFkvQ6vXfi/rH7crNzcVisRAdHU14eDheT4FxwkzNglavqBqhOO+WKrvRrVu3Ui0T5yYZnF4IIYQQQtQUbrebt956C4CXXnpJkl1CFGCz2ci/ohMARhQW5SapTS08eMnT3JjNZux2e9Bbdmmahslkwu12o2kadruvFVliYiKapqE8e08XNl8U1LpF5QvFeVf6rAkZnF4IIYQQQgghBBaLBUfr1ih849JfTCpJF9ciHw+aQdNbc1XWjIzgawUEvvtTvXeZ+y+9nMEoY+6Jsyt3sqtnz54MGzas1OVvuukmevXqVd7qRCWSZJcQQgghhBBCCKvVimY24zb6Ek+tOUlcs1hyNScWgwmz2QxUTrLLaDTi8XjIz88HCOwm6dkf9PrE+a3cgzQlJydTp06dUpdfu3YtBw4cKG91ohJJsksIIYQQQgghhMFgwGw2k28Nx5yXRXMti30GA3m4sBrNegIq2N0YwdeyKz8/H03T9KSax+Px3ad6Dp8qFbwZIMX5rcq6MXq93qBOTSqCxz9mlwxQL4QQQgghhBA1m9lsJiMsGoD6Wg5eL+QrD+FGS0Cyy+v1Br1uh8OBUoqYmBgA7Ha7b4Xn+KkSMhKTKJ0q+aR4PB6OHz9OREREVVQnykhadgkhhBBCCCGEAMg4mk1qdCIACdhxOBQuPMSYwzEYDPp9Y7Bbd+Xk5AC+7otRUVFAgWSXSj9VShpoiNIp9SclKyuLjIyMgGUej4eDBw+ilCpyG6UUGRkZfPDBBzgcDtq1a1ehYEXlkGSXEEIIIYQQQgiAQ9uO4I6vx2V7NxOhnGTlubFGmIixROhdDD0eD06nM2iz6jmdTj2xFRYWhs1mIysrq0CyK8v3r2YpZg9CBCp1suv111/nmWeeCViWmppKkyZNSrW9pmmMGTOmTMGJquFPdkk3RiGEEEIIcb4LCwtj7969+nMhRKCdG/5Gq90EAINS5KRmY61lpG5YHOBreWW324Pasis1NRVN8832qGkaNpsN8LXsUkqBN9dXULMFrU5RdUJx3i1TdqNgCy5N04pt0XWm+vXrc9dddzFx4sSyRSeqhH/MLmnZJYQQQgghzncGg6HUP9gLURPtWLOL6AYJKHzDwcdt+ZPsVhfRODwBOD1LYrBmZHQ4HOTk5GAwGAgLC8Pj8ehJL7fbjdvtxqgcvsJaZFDqFFUrFOfdUie77r//fsaNGwf4kl7NmjWjVq1a/Pbbb8VuYzAYiI6O1geXE9WTdGMUQgghhBBCCOHxeNi35SAXRDfGbTRh9ripu3sXex0X0jw8CUCfKTFYLbtOnjyJUoro6Ghyc3NRSuH1erFardjtdux2OxGcqkuT3IIonVInu2JiYgKSVv/4xz9ITEykcePGlRLY+Wb69OlMnz5dTyxVF0op6cYohBBCCCFqDKfTyeOPPw7As88+q7dSEULA4V1HcTnc5Kbnk2cNJyYvi3opBzlx3Esdiy8fEMyWXXa7nezsbDRNIzExEYfDgcvlwu12Y7PZCiS7fL2RMCRUuE5R9UJx3i33bIzJycksWrQomLGc1+655x62bdvGunXrQh1KgILJN2nZJYQQQpx/pkyZgqZpdO/ePdShCKHr3r07mqYxZcqUKq/b5XLxyiuv8MorrwR9NjkhznU7ftsFQF5GPllRvjG66macIC3VS7o9Gzjdssvtdle4McfJkycBiIyMxGq16g0wPB5PwLhd4PVtYKhTofpEaITivFvuZBf4Zmj0Tw9akpycHLKysipSlagk/pOTwWBA07QQRyOEEEKImmDbtm38v//3/2jfvj0xMTFYLBbq1avHpZdeys0338zbb7/Nzp079fL+xEh5Hv5hOAqaMWOGvr5Lly6F1icnJ5e7Pk3T2LdvX5n289577xX5Oo0bN67YbSIjI2nTpg0TJkxg69atQXlfhBDir/W7AchLzye9Vl0AEnKzyM5UHMnxJaaMRqOelKpI4sJut5OTk6O36oLTvY38LbvAN6aXUqfuVU31y12fqFnK3W9tyZIlDBs2jBEjRvDJJ5+UWHb06NF88cUXLF26lAEDBpS3SlEJ/IPTSxdGIYQQQlSFl19+mX/961/6NQhAbGwsGRkZpKSksHnzZj755BO6detGcnIyAPHx8SQlJRXal9PpJD09HYC4uLgiu0UUNXbs+++/rz9fvXo1f/31FxdddJG+zGKxFFkfQFpaGi6XC7PZTHx8fJFlimotX9z+AMLDw4tdB74fJWvVqqX/7fF4OHnyJNu2bWPbtm3MnDmTd955h3/+858l7kcIIc5m18a/AcjPcpDWpRlsXUuE04E9T3EiL0MvZzabcbvdOJ1OPSlVVqmpqQBERUXp52//+dPtdmOxWDAYDL563OFYzblgal6BoxM1SbkzHAsXLgQo1ZfqHXfcweeff86CBQsk2VXNyOD0QgghhKgqS5YsYdKkSYBv/NcnnniCrl276jdKhw8fZtWqVSxevJjMzMyA7YqSnJxMjx499DKl6ar5+++/s2HDBuLi4ujbty9z587l/fff56WXXtLLdO7cmaNHjxa5fffu3fnxxx/p3LmznowrjeL2VxoNGzbUW4v5uVwuVqxYwT333MPff//N3XffTa9evWSWQSFEuXm9XvZtOQiA8irS27WHLz/BqLyE5eZxLOv0edlisZCfn1/ull35+fnk5uaiaRoJCafH4SrYskvTNGw2G7nZR3C4YnzJLvNFxe1SiADl7sa4adMmDAZDkU2/z9SzZ08MBgMbN24sb3WikkiySwghhBBV5dVXXwXg4osvZsWKFfTp0yegRUD9+vUZOXIkCxcu5LPPPquUGPytukaMGMEdd9wBwIcffhjQ0uxcYDabue6665gzZw7ga+W2bNmyEEclhDhXud0evpj5LfnZdn2Zp01zFKABL66ci23ax0ybt5TfU/djMBlxuVxkZmaSl5eHUuqsdSilyMvLIysriyNHjugzMBZslWs0+vabnZ1NXl4eFosFl+M4adlNyHPEoTLfwevJr4RXQJxvyp3sOnz4MLGxsaVqshgWFkZsbCyHDx8ub3WikkiySwghhKhe5s+fz/XXX09SUhJms5nY2FhatGjBgAEDmD59+qmBegMtW7aMPn36EBsbS2RkJO3bt+ell14K+iCwBcegAli/fj1Dhw6lbt262Gw2LrjgAh5++GEyMjKK3H7z5s0A9O3b96xDKISFhQUzdMA37svcuXMBGDt2LP/4xz9o0qQJx44d46uvvgp6fVXhkksu0Z+XZizds9m3b1/A2GN79uzhzjvvpGnTplit1kItx7xeL3PnzqVv374kJSVhsVioVasW11xzDZ9++mmpboBDqSIt7oQ4X3w3N5lBcbfw3wmnu3jHNYih/sv/0f++av92xi9fymOjbmRT71t5eM5SDh47wqFDh/jrr7/YvXs32dnZxdaRnZ3N7t27+euvv9i+fTuHDh0iPT09INGVnZ3N4cOHOXnyJCkpKWzdupWd25dzIvU4h1KvYOfh69iz7zg5B/rgTZtQOS+GqHRVdd4td7JL0zTy8vJKXT4/P7/CMzWI4PO/JzJmlxBCCBF6t912GyNHjuSbb77h+PHj2Gw2XC4Xu3fv5osvvmDixImFLhKnTJlC3759+f7778nMzMRsNrNt2zYeeeQRevfuHZSp4Yvy2Wef0aVLFxYvXqz/qr9nzx5eeeUVLrnkkkLd7go6dOhQpcR0NkuXLiUtLY0LL7yQK6+8Ek3TuOWWW4DAcbzOJb///rv+vOC4Y8GwevVqLrnkEmbOnMnx48f1Gdj80tLS6NGjB6NHj2bZsmUcP36c8PBwUlNT+e677xg1ahSDBg2qtM9gMEiyS9R0381N5qUx03Hknv5/Gtcghv9L2M2Vm38sVN6gFGN/X0mPj+axaMN+8pRvDMPMzEz27t1bZMIrOzubvXv36t9RmqZhNBrxeDwcOnSI7OxsvUxubi5Go9GXb8jehd1uxOUOA7yYTXlk5TVg3/Gu5GRukYTXOaraJ7saNmyI3W5ny5YtZy37+++/k5+fT/36MnNCdeNvsi8tu4QQQojQ+vnnn/nggw8wGAy8+OKLnDx5kuzsbHJzc0lNTWX58uWMHTs24Ffwzz//nKeffhqAYcOGceDAAdLT08nKymL69OmsXbuWGTNmVEq8Y8eOpXPnzmzbto3MzExyc3OZP38+cXFx7N+/n+HDhxf6obNTp04ALFiwgE8++QSv11spsRXHn9AaM2aMvsyf7Fq2bFmlXoB37tyZmJgYbDYbDRs25MYbb+Szzz4rd8snl8vFt99+q8822bp1a/r27RvEiGH8+PG0adOGdevWkZubS05ODt9++y3g+8F0yJAh/PTTT1xyySV88cUX5ObmkpGRQU5ODnPmzKF27dp8/vnnPPLII0GNq6LCwsJYsGABAFarNcTRCBE6breHdx/6KHChBi061eOK33/y/3nmagDG/JHM1l0ZHMpJw2A0EhkZidPp5OjRowHnNaUUR48exel0EhkZeapeNwaDgfj4eJxOJykpKXqZqKgoNE3D4bCjvPnYLCcADZcnEg1FhO0oTlckxzIuRjl+kC6N54hQnHfLnezq3r07SikmT5581rJTpkxB0zR9AFFRfUg3RiGEEKJ6WL16NQC9e/dm0qRJATP9JSQkcM011zB79mzq1aunL3/ssccA6NatG/PmzaNhw4aA76Ly7rvv5t///nexXQorKikpia+//ppWrVoBvlbiw4cP1y9m161bV2hg+SlTpmAymXC73dx8883Ur1+fESNG8PLLL7Ny5Upyc3MrJVbwdc9bsWIFmqYFJLuaN29Oly5dcLvd+vhXlWHNmjUYDAY0TePQoUMsWbKEQYMGccMNN5y1++HBgwepU6eO/qhVqxZWq5Vrr72WzMxMxo8fz6pVq4LeUj8hIYHvv/+eyy+/XF924YUXAvDJJ5/w448/0rJlS5KTk+nXr58+q2RERAS33HILX3/9NZqm8dZbb3H8+PFyxeBwOMjKygp4lFdKSgobN25k8+bN+gQImzdvZuPGjWzcuJGUlJRy71uIc9GG7zeTcSzw/1RUrQh6ndiCQalCiS4/DTAqxdA/f+H3PVmk5mfpg8nn5OSQn386AZWfn09OTg42mw1N0/R1NpsNo9GIzWYjMzOTjIwMbDYbBoMBr9eLx52D0eDAYACj0YnbY8bhikLTwGrJIDs/iXxnLGS/VHSQoloI5Xm33Mmue++9F4PBwGeffcbo0aM5duxYoTLHjh1j1KhRfPbZZxgMBv7f//t/FQpWBJ90YxRCCCGqh9jYWABOnDhRqqEf/vjjD7Zt2wbAE088gcFQ+LLujjvuqLSW9Q8//HCR42r17t2bzp07AzBv3ryAdd26deObb77Ru9sdPXqUBQsWMGnSJHr27ElcXBw33HADP/30U9Dj/eCDD1BK0a1bNxo3bhywbuzYsQDMmjUrqHXGxsby0EMP8euvv5Kbm0t6ejp5eXls3ryZESNGAL4WZTfddFOJ+/F6vRw7dkx/pKam6i0nsrOzyczM5MSJE0GNHWDixIl6S4wz+VvJTZgwgZiYmCLLdOjQgTZt2uB0Olm5cmW5Ynj++eeJiYnRH/6Ebnm88847dOjQgQ4dOuiTE9xxxx36snfeeafc+xbiXJR6JK3QMkuYmbick6XavmFWKpl5HhzKNz6k0WjE6/UGTPjhdrvxeDyYTCY8Ho8+lqT/+8PfndFfxul0njq/eTAYXHi9ZlAaoGEw+PZrMjjwKjNujw08+8r/AohKF8rzbrmTXS1btuTZZ59FKcWnn35K48aN6dKlC6NGjWLUqFF06dKFRo0aMX/+fACmTZtG69atgxa4CA7pxiiEEEJUD7169cJms7Fp0ya6du3K+++/z969e4stv379esD3g1XXrl2LLGMwGOjevXtlhEvPnj3Pus4fY0G9evVi27ZtJCcn89hjj9GzZ0+9FZvL5eLrr7+mW7duPPXUU0GL1ev1Mnv2bOB0t8WChg8fjs1mY+fOnaxatSpo9V5yySW8/PLLdOrUSW/1pGka7du3Z968edx7770AfPnll/zwww/F7qdx48YopQIeOTk5rF69mmuvvZZ58+Zx5ZVXsmbNmqDFDhQ767rH42Ht2rWAr7VewVZnZz7++usvAPbv31+uGB577DEyMzP1x8GDB8t3MPi6ZW7YsIG1a9fSv39/AGbMmMGGDRvYsGED48ePL/e+hTgXJdaLL7TMme8iPTKhVNsfjE4kKtxAmMnXLc3j8WAwGAIaUphMJoxGI263W59gxWKx6D/QeDwejEajXsbpdKJpGiYjKK8RlycMpQwYDU5MRt/2bq8Vg+by/W1sUpGXQFSyUJ53K9Sc55FHHiE6OppHH32U7Oxs1qxZo3/x+X9tio6O5qWXXuLOO++seLQi6KQboxBCCFE9NG/enPfee4+77rqLNWvW6ImLWrVq0aNHD0aNGsWAAQP0mRD93cISExNLHP+iQYMGlRJvSS3G/OuK67pmMBjo1q0b3bp105ft2LGDTz/9lFdffZXc3FymTp1Kp06d6NevX4Vj/f777zlw4ADh4eEMHTq00PqYmBgGDRrEvHnzmDVrVrHJw2B77rnnmDlzJna7nf/9738lJhDPFBERwVVXXcXixYu56qqrWLduHbfccgt//fVXka38yqN27dpFLk9LS8PhcACQnp5eqn2VZWKrgqxWa9DGd6lbty5169YlNzeXL774AoC2bdty2WWXBWX/QpxrOvS+hNik6ICujNkncllxdVu6a5+hFdOVUQFeTWNRmy7cekE0ibYolFLY7XZiYmICWv2GhYURGRlJRkaG3qrLZrP59lNgG03TyMzMxOl0YjAYsNqScORn4/Ua8SojUbYTmI12lAKHM5aYiIOEWTIgalIlvkKiokJ53q3wN+GECRPYv38/7777LuPGjePaa6/luuuu49Zbb2XmzJkcOHBAEl3VmHRjFEIIIaqPm2++mf379/P2228zYsQIGjZsyIkTJ1iwYAGDBg2iW7duFRqzqDpr2bIlTz/9NJ9//rme0HvvvfeCsm9/l7u8vDyio6PRNK3Qw9/lcuHChUXOJlYZIiMjufjiiwH4+++/y7UPo9HIrbfeCsDu3bv57bffghZfcT+GFuxmu2zZskKtzop6TJkyJWhxCSGCw2QycucrYwIXKtj12xF+bf+PIrfxDz3/UbvuXNwilmYxtXG73OTk5GC1WqlTp45+Dgdfa9Y6deoA6Elyo9GIy+XSt6lbt66+nb9MZFQUHhWJ2xOG0WgnwnYcl8dGrr0OFnMOSbFb0aw9MRgLd6cXAoKQ7ALfeAS33347s2bN4uuvv+arr77i/fff55///CfR0dHBqEJUAqWUtOwSQgghqpn4+HjGjx/PvHnzOHDgALt37+bRRx9F0zRWrVqlJw38rW5SU1NxOp3F7u/w4cOVEmdJ+/WvK65lUEl69uzJBRdcAKB3gauIkydP8r///a/U5XNzcwuNNVbdFRyDrKSur8GSkJCg/1Ba3u6J1YX/JlyImqrPzd2Z9NE9WGxmfVn6oUxeP3kBB+MKt+D1ahpz2vfgpzE3MbRDY6IMVlwuFzExMTRp0oSoqKhC20RGRhIZGakPSp+Xl1dom6ioKGJjY/Uy+fn5mMwJWC1ZRNmO4fJE4HKHExNxkCa1VxEZ0xZDfOXMNiwqV1Wdd6U5Tw1W8Fc5SXYJIYQQ1VPz5s15/vnnOXjwIHPnzuW7774D0GfIc7vdrFq1il69ehXa1uv1kpycXClxrVy5kmbNmhW7rmCMZeUfFD0Y3dc+/vhjnE4ntWvXZteuXSV28XviiSd48803mTVrlj6QbmXKyclh69atADRt2rTc+zl06JD+PCIiosJxnY3ZbKZTp06sXr2aL7744pwe60qSXUL4El7xteN49Npp+rK2vVsSt3kDpB8mJzqC5As7sMMcx1dXdCWqn5eJiW2oZ4ymVq1aREVFERYWFtCiq6Dc3FwMBgOJiYnUrVsXr9eLyWQK2Mbj8eD1eomLi6N27dqkpKQQGXaSuhHzsFmycBt6YzJkEBZeFy36dWnRdQ6rqvNuUFp2paamsnDhQl555RWeeeaZYOxSVIGCrbqKOzEJIYQQomr4u24Uxz8Gij9Z065dO1q1agXAs88+i9frLbTNrFmzAhIhwfTKK6/ogw0XtHLlSn755RcAfcZBv2+//VYf17U4v//+O7///jtAUMb08HdhHDJkCNHR0XoLg6IeI0eOBGDt2rX6TJcVcbZjfeKJJ/TXcODAgeWu49NPP9X/7tChQ7n2U1b+YUq+/vprvv766xLLpqUVnvFNCFG92PMCv4P63tODiAO+CSH+bt+SN68fzCftu2GNCCfcYuZobjpms5n4+HjCw8NLvJ/0j+0XGxtLZGQk0dHRhbbJycnB6/Vis9n0ZFi4cS1xkQcIt2YS2/BZIutPxxg3WRJdolQqlOxyu908+OCDNGzYkJEjR/LII4/w9NNPB5RJT08nLi4Om83Gvn37KlKdCDKZiVEIIYSoPiZOnMjw4cNZvHhxwMDuOTk5vP3223z44YcA3HDDDfq6Z599FvAlmEaNGqUntux2O2+//TYTJ04kNja2UuJNSUnhhhtu0Lsaut1uFi1apA8Af9lllzFkyJCAbUaNGkXLli2ZOnUq69atC+h+efToUV5//XV69+6t3+jcd999FYpx3bp1bNmyBfDNuHg2V1xxBY0aNQJOJ8kqonXr1rz++uts375dT0YqpdiyZQujR4/mzTffBOD666+nd+/eZd7/3r17ufPOO/XWeyNGjChx4oBgGj16NL1790YpxeDBg5k2bRpHjhzR1+fm5rJy5UruueeeYlsACiGqj9ys05NIaBqYDAa0U4nqAx3aYrD6kvcup8KqjBzLz0AzaGcd+9nhcJCXl4emaSV+H/nHSgwPDycjIwOAhIg1aJoCY8MKHJmoqSqU7Bo2bBhvvPEGTqeTNm3aFPlBj4uLY9SoUTidThYsWFCR6kSQyXhdQgghRPXhcrlYuHAhQ4cOJSkpiaioKOLi4oiKimLChAk4nU6uvvpqHn/8cX2bwYMH63/Pnz+fhg0bEh8fr2/TqVMnJkyYUCnxzpkzh1WrVtGyZUv91/phw4aRlpZGo0aNWLRoUaFrQ7PZzM6dO3nqqafo1KkTYWFhxMfHY7PZqFu3Lg888ACpqalERUXx6aef0r59+wrF6E9YJSUl8Y9/FD3YckGapunJuo8//lifOay8duzYwQMPPEDr1q2x2WwkJiYSERFBu3btmDt3LgB9+/Zl/vz5Je7n4MGD1KlTJ+ARHR1Ns2bN9EH8u3fvzsyZMysUb1kYjUYWL15Mv379cDqdPPnkk9SvX5+YmBj9c9uzZ0/eeustcnNzqywuIUT55BVIdhlMBsK3b0dTCgWc7NUTo82X7HI4vcRqNpxeDxle+1l7CPkTV5GRkZjN5iLLuN1ufcZWt9uNx+PBYvEQFeZrWUZY4Vl0hTibcie75s2bx2effUbt2rVZv349f/zxB/Hx8UWWHTZsGHB6/AZRPUiySwghhKg+nnzySf79738zePBgWrZsiclkIicnh9q1a9OnTx9mzZpFcnJyoTGZpk2bxpdffknPnj2Jjo7G4XDQqlUrXnjhBVasWIHFYqmUeAcOHMjq1au58cYbsdlsKKVo2rQpDz74IJs3by5yDKqdO3eycOFC7r77bq688koSEhLIzs5GKUVSUhLdu3fn2WefZdeuXXrSqbzy8/P17n1Dhgwp9fWOvwXY8ePH9WnSy+vdd9/l1ltvpV27dsTHx5OVlYWmaTRv3pybbrpJn9ipqAGdC/J6vRw7dizg4XQ6adCgAQMHDmT+/Pn88MMPZ91PsEVHR/PFF1/w9ddfM2LECBo1aqS34qhfvz7XXHMNzz//fFAmGggmm83Gb7/9xm+//YbNZgt1OEJUC3lZ+fpzo9lIzKkWo8pi4cJGrdDCTyW7XG7aRPhakO53nCxxnx6PR59BuKRWXTk5OSilMJvN5OTkAJBoW4yeRwu7vRxHJKqTUJx3NXW2wQSKce211/L9998zZ84cRo8eDUDdunU5fvx4wMDn4JvmOSoqigYNGpzzM7ZUVFZWFjExMWRmZoZ8psrU1FSOHDlCbGys3mRfCCFE5apO3wNClFVycjI9evQAzj4elRCVQc6hQlSOmY99zIIXPwMgIi6cuU3/JGLDBpyNG7N3+XIeWvMZh3/1tcx65b6rWXNwOyrKwhNXjix2n2lpaZw4cQKr1Urjxo2LbQV28OBB8vLyMBgMeL1ewsPDqWcejsZJ0CIwJG0K/gGLc1ZpvwfK3bJr0ybfB+7GG288a9nw8HBiYmICxp8QoSdjdgkhhBBCCCGEKNiN0WIzYd27FwDHqYlQjBGnG7REu8PRgH2Ok5x0ZBe5P6WU3oUxLi6u2ESXy+UiPz8ft9ut358mJib6El0A5o4VOSxRg5U72ZWZmUlMTIw+M9DZeL1emfGvmpFujEIIIYQQoqZxOp28/PLLvPzyywGTJAhRk+Vnn+7GaI2wYjw1g2J+584AmCNP3zOmpGYTb4nEbvCwOX1fkfvLzc3F5XJhNBpL7GLt78Lo8XgwGAxERUVh5cfTBSLvL/9BiWojFOfdcie74uLiyMzMLHLK6TOlpKSQlZVFUlJSeasTlcCf7DrbDBpCCCGEEEKcL1wuF5MmTWLSpEkVnoRAiPNFboFkVwtDhj44fc611wIQazvdyOVIWg51ImJx4S022eVv1RUTE4PBUHzaISsrC5fLhVIKTdNITEyEXP9kGwYMltYVOSxRTYTivFvuLMdll13G8uXLWblyJddff32JZWfNmgXAVVddVd7qRCWQboxCCCFEzbN69WqGDBlSpm06d+7MkiVLKikiEWwHDx6kY8eydf1p2LAh69atq6SIhBDVXX726UYsnfJPjbNtteKNjwePhxZR9dhkPIDXA8cy8uhwcTOwwx/pB3B7PZgMp+8pHQ4Hubm5aJpW4sD0TqeT/Px88vPziYyMJCYmBovFgtd9alILY4PKOFRRQ5Q72XXzzTfzzTff8OSTT9K1a1ciIyOLLPfNN98wdepUNE1j7Nix5Q5UBJ90YxRCCCFqHqfTybFjx8q0TVpaGgDdu3eXgenPAR6Pp8zvscxKKETNVrAbY4u8IwBo9evry/rWv4TFp5Jd+zMyqBcVT0x6OJmuPHZkHeHi2IZ6WX+rrsjISMxmc7F1Zmdn6618TCYTCQkJeD1pwKlubmFl+2FGiILKnewaNWoU7777LqtWreLKK6/krrvu0vtefvfdd+zbt0+fitjr9dK/f3+uPdUEUlQPkuwSQgghah5JWJ3/mjRpIu+xEKJM8nNOt+xKyj01OPwll+jLmkbXwWzWcDkVRzNzsFltXBLXhB+Pb2NT+l492eXxeMjKygIosVUX+Low5uXlERYWRlxcHCaTCW/G66cLhN0elGMTNVO5k12apvG///2PwYMH89NPP3Hffffp66677jr9uVKK3r17M3fu3IpFKoLO341RxuwSQgghhBBCiJqrYDfGcHuO70nv3voyg8GA2QLkQq7dhcVi4bL4pvx4fBsb0/Yypuk/AN9Edl6vF6vVWuJkdg6Hg6ysLDweDzabjbi4uFMrvj9VIgKD0RLMQxQ1TLkHqAffIPU//PADc+bMoWvXrlgsFpRSKKUwGo1cddVVzJ49m2+++abYbo4iNJRSeL1eQFp2CSGEEEIIIURNZs/1JbuaqAwMpwan58Yb9VaimqZhs/nSBw6HL5nVNrYRRgwcyU/nmD0TpZTehTEuLg5N04qtLzMzk/z8fCwWC7Vq1Tp9T6p83eYxX1YZhylqkAo36TEYDIwZM4YxY8bg9XpJS0vD4/GQkJAgLYaqMX8XRpBklxBCCCGEEELUVEopvWVXVw77FlosULs2ZGYCvvv+6HATx/Dg9vh6B5k1jYui67Et6xCb0/bRJbo5LpcLo9FIVFRUifUdP34cr9dLeHg4MTExAHjzvwNOdcGOur+yDlfUEEHNRhkMBt9UoaLaKzgTY0kZdyGEEEIIIc4nNpuNlStX6s+FqOlcDhcet6/XT1tSAfDUqVMoWVAnOoJdOPB60O8hL4lvwrasQ2xK30sbbwIAMTExGAzFdyLLzc0lOzsbTdOoV6/e6bK5b58qYcBgaRu04xOhF4rzboW6MYpzlwxOL4QQQgghaiKj0Uj37t3p3r27XAsLAeQVmImxMb7B5d2tWweU0TSNNrVrAaAUrD72FwCXxTUFYEv6AdKzs9A07awD06ekpKCUCmjV5avUt0+M9YveUJyzQnHeLVXLrgMHDgBgNpupW7duwLKyslqt0sWxGvAnu+R9EEIIIYQQQoiaKy/rdLIrBicArm7dOLP9TbOEOP35gj2/0jnpIhqEx5NojeJobjp/O07QsXYLzGZzsXU5nU7S0nzjctWtW1dvIeb1ZMCpugkbUuFjEqJUmY6mTX3Z2pYtW/Lnn38GLCtXpSYTHTp0YNKkSQwaNKjc+xHlJy27hBBCCCFETeRyuXj33XcBuPPOO0u8MReiJvC37GqkMjHiG5zeNXAggD5APUCM7XT6YNvBo4CvxVf7mMYcyTrJDvsx+sR2LLGulJQUPB4PFouFhISE0yuyXzv9POyOCh6RqG5Ccd4tVTdG/wyLBT/oBZeV9eFyuVi7di033ngjS5YsqbSDE8UrOGaXEEIIIYQQNYXT6WTixIlMnDgRp9MZ6nCECLkzB6dXZjPGevUCyvjv5f3DPWdlnv6/08KSCCh2OU+UOB6Tw+Hg5MmTANSuXTvwXtTx/akn4RiMloodkKh2QnHeLVXLrr179wIEZN/8y8rK7XZz+PBh3nzzTZYuXcorr7zCkCHSTLGqScsuIYQQQgghhBB5WXkAtDs1OL27Vq1CA8zrjSUMGm6Pwp3jW66Uoq47HJNmJFPZSbFnUD88vsh6UlNTcTgchVt1AShfEgzzZUE6KlHTlSrZ1bhx41ItK63mzZvToUMHYmJi+OOPP8q9H1F+MmaXEEIIIYQQQoi8Uy27/IPTOy66qFCyy+FwoGkaFpMBt8eDyvc1msjNzUXzKJrZEtnnyWBT2t4ik135+fmkpaWhlCIqKorw8HB9nTd/BXCqF1nU/1XCEYqaKGSzMUZERNC0aVOsVmuoQqjRpBujEEIIIYQQQgh/y65YHL6/r7qq0H2iy+VC0zQirad6e9kNrD7+FxkZGQBcFt8MNNiUvq/Q/pVSpKam4nQ6sVqtxMbG6gPTA5A749QTAwZL22AemqjBgprs8ng8nDhxghMnTugth0qya9cuvc+uqFrSjVEIIYQQQgghRG5WHg1VFsZTratyrrlGb9nlH7fbP85STLivsYpyGFh64Fdfyy5No3P9VgBszzpMntsRuP/cXHJzc3G5XISFhREdHR0YgHuH719j4DhhQlREhZNdubm5vPrqq3Ts2JHw8HDq1KlDnTp1CA8Pp2PHjrz66qvk5OQEI1YRRJLsEkIIIYQQQgiRm5XH1acGp3drBtx16hTbsisu4lTPLJfGtkzfNpGRkTSMqkW9sDg8ysvWjIP6dv5WXS6XC4vFgs1mCxjE3uvJAE4NWG4bXGnHKGqeCiW7Nm/ezMUXX8ykSZPYsGEDLpcrYMbFDRs2MGnSJNq2bcumTZuCFbMIAhmzSwghhBBCCCFEbmYe7TgBQE64r9WVwWDQW3WBr2WXpmkkRof5Frg1Mtynuj/GxgJwSVwTADaln57MLjs7G4fDobfqioqKCuzCmP366efh44N8ZKImK3emIyUlhd69e5OWlobFYmHo0KH07NmT+vXrA3D48GFWrlzJokWL2L9/P3369OGPP/6gXj1pmlgdyJhdQgghhBCiJrJarXz55Zf6cyFquvzsfJqcGpz+ZO0GGAwGNE3Tk10ejwdN0zAYDCTFRvg28mi4lQer1UpYmC8BdmlcE74+solNafv0RjCpqakopTAajRgMBqKiogIrd3536kk4BqOlKg5XhEAozrvlTnY988wzpKWl0bhxY5YtW0bLli0Llbntttt44oknuO666zhw4ABTp05lxowZRexNVCWlFF6vF5BklxBCCCGEqFlMJhM33HBDqMMQotrIy7YTd2pw+tRW7ah1xj2ix+PBZDJhNpupExvpW6h88ycWHGy+VUx9rAYz6a5c9ueeIMZlxuVy4fV6sVqtWCyWwokO76kxvM2XVuYhihALxXm33N0Yv/76azRNY+bMmUUmuvwuuugiZs6ciVKKr776qrzViSAqOHmAJLuEEEIIIYQQouaypRzUB6dP79VbH5zez3//aLFYaJBwKtmFhtcO2xzH9HJmg4m2sQ0BWH/yb30yOrPZjKZphbowevNXwKl6ifq/SjgyUZOVO9l17NgxwsLC6N2791nL9u7dm/DwcE6cOFHe6kQQFezCGNBfWgghhBBCiPOcy+Vi9uzZzJ49G5fLFepwhAi5i/b5xtd2oxHRrkWJya7Y8AJdDbNNfH54fUDZS+ObAvDbsZ14PB7MZrPeq6jQLIy5b596YsBgaRekoxHVUSjOu+VOdtWqVatMrYIMBgO1atUqb3UiiGQmRiGEEEIIUVM5nU5uvfVWbr31VpxOZ6jDESLkmqXvA+AkNqJrRen3iQXH7AJfsksphcHfXiLXyPaswwH7uiSuCSjF7pyj5Hqc+syLNpsNi+WMMbncO3z/GusG/ZhE9RKK8265k129evUiJyeHDRs2nLXs+vXrycnJoVevXuWtTgSRzMQohBBCCCGEEAKgvt3X3XAvMZhMpmJbdlmtVux2O2ajb73KMZLmzAkom2iNIskUjVKKAypD71V05sD0Xk8GnBonDNug4B6QEFQg2fXEE08QERHBHXfcoffFLUpaWhp33nkn0dHRPP744+WtTgSRzMQohBBCCCGEEAIgxpsPwO9abX3WRT+Px6PPpmg0GnE4HFjNp+4j7UacXnfAvpxOJ82N8QDs8aRht9uBwskust88/Tx8QpCPSIhSzsZ44MCBQsssFgvvvfce48ePp1WrVkyYMIEePXpQv359AA4fPszKlSt5++23cblczJw5s3CzRRES0o1RCCGEEEIIIQR79mA6NUj8WnMjuhkMAfeJ/ntHs9lMXl4eSikirGay8l0ou8E/vLwuNTWVi2xJ/JS7my1ZB+lruZCI8AjMZnNgQee3p56EYzBKnkAEX6mSXU2bNj1rmWnTpjFt2rRi148cORJN0/RWRSJ0JNklhBBCCCGEEMIzfwFGfIPTnwyLLbJlF/gau2RlZQEQHW4hJSMPHL5yv57YxRW1WmC328nOzqaJNZ4oazhZjjwOOjPomFSncMXeVN+/5ksq8/BEDVaqboxKqaA8/LMwiNDyJxxlzC4hhBBCCCGEqLm8P/wAQBo2rOG+1lcFB6j3J7u8Xi9utxtN00iICvdt7PKNVL/04G+Ar1UXQEx0DG1jGqG8XnY6jhcer8u+EvxtwqLuq7RjEzVbqbIde/furew4RBWSll1CCCGEEEIIIbRt2wDYRzS2SN/MiUW17LLb7Wiahs1mIyHKVw63L9m1I/sIubm55ObmomkaiYmJtMxL4me2scuVWriRRc6MU08MGCyXVt7BiRqtVMmuxo0bV3YcogpJsksIIYQQQtRUVquVBQsW6M+FqMmMJ44DsJla2KJ8/x/8yS5/yy6v14vT6cRoNGKz2agdfapll9eX7DrpyNZbdcXGxmI2m2lMNABHXBmkO3OIs0SertS9/VTldSv78EQ1EYrzbrlnY6yJPB4PL774IhdccAFWq5XmzZszbdq0c24cMkl2CSGEEEKImspkMjFs2DCGDRsmw3qImm3/frRT97I/04DwmDDg9H2iy+XSE14Gg4GIiAgMBgN1404luxQoBU6vG7vdjtFoJCEhAYfDgdmt0cASh8FgYHP6fr1KrycTcPj+sA2oskMVoRWK826Fk11er5e9e/eyfv161q9fz969e8/bsbnuvfdeHn30Ua6++mqmT59Ojx49ePLJJxk/fnyoQysTf7JLvtyFEEIIIYQQooZatAjwDU6fokUSeSqJ5W/Z5XQ69WSXpmnEx8cD0CDe30pLQ9k1fUbGuLg4jEYj2dnZALSLaQSaxqa0fafrzHnz9PPweyrryIQoXTfGoixbtozp06ezatUqcnJyAtZFRkbStWtX7rnnHq6//voKB1kdbNmyhbfffpu7776b6dOnA3D77bcTExPDa6+9xoQJE7j88stDHGXp+FuiScsuIYQQQghR07jdbpYuXQrA4MGD5QdgUXOdGpw+HV+3ssj4COD0faLT6dQTXVarlfDwcDRNIybCdnof2UYIc7PDcZT+cS1QSunJrk61WvB91g62ZOzH7fVgMhjBsfzUhmEYjJaqOU4RcqE475a5ZVdqairXXHMN/fr1Y9myZWRnZxeadTE7O5tly5bRr18/+vTpw/Hjxysj9io1b948lFLcf//9Acv9f8+bN6/qgyoHr9ert7yTZJcQQgghhKhpHA4Hw4cPZ/jw4TgcjlCHI0TobNkCwD5iAIip7Rtny9+yy+Fw4HK5MBgMxMbGommavs5o8I3XRY7vnvLn/H0YDAYcDgdOpxODwUCb2o2JNoeR53GyMzvFV97rG9sL8yVVcICiugjFebdMya60tDS6dOnCihUrUEoRGRnJjTfeyNSpU5kxYwYzZsxg6tSpDBkyhMjISJRS/PDDD1x99dWcPHmyso6hSqxfv56EhARatGgRsLxhw4bUq1eP9evXhyiysvF3YQRJdgkhhBA12ZQpU9A0je7du4c6FFEDzZ49G03TaNKkSahDEaLmOnoUgD9IBCCubgwGgwFN8yWycnJyUEphNBqJifElxPzrrOZTLXPyfPeUu/KPAeituiIiIjAZTVwa1wSATWl78dqTwd/pMer+SjwwIcqY7BozZgy7du3CbDbzzDPPcOTIERYuXMjjjz/O+PHjGT9+PI8//jiLFi3iyJEjPP3005jNZvbs2cOYMWMqHGxeXh7Lli1j2rRpDBkyhMaNG6NpGpqmMWXKlFLtIzs7mylTptC2bVsiIyOJiYmhY8eOvPrqqzidzmK3O3LkCPXr1y9yXf369Tl8+HB5DqnKFRyc3n+iEkIIIYSoSi6Xi1mzZtG3b1/q16+P1WolJiaGCy+8kB49evDYY4+xbNky7HY7AMnJyfo1X3ke+/btC6jf4/FQv359ff13331XKMbu3buXu75x48aVaT8NGjQo9Ws3YsQIfbvHH3+8XK+/EEJw4AC4XAD8iO8cFF8vVm8QoZQiKysLAJvNprfo8t9DhlnNvv3k+5JeJ505AV0Yo6KiALjEn+xK3wc5M05VrmGwXFpphyYElGHMruTkZJYtW4bZbOZ///sf1113XYnlIyIiePLJJ7n88ssZOHAgy5cvZ+XKlfTo0aPcwf7222/07du33Nvv37+f7t276xc84eHhOBwOfXD9uXPnsmLFCuLi4gptm5eXR0JCQpH7tdls5OfnlzuuqiSD0wshhBAilA4ePEjfvn3ZunWrvsxisWA0GtmzZw+7du0iOTmZF154gZUrV9K9e3csFgtJSUlF7i8tLQ2Xy4XZbNYHTz7Tma3Zly1bxpEjR/S/Z82aRZ8+fQLKxMfHF1mn0+kkPT0d8A3GbLEUHnPG3wKioIiICCIjIwstB6hdu3aRy8908uRJ/ve//+l/z5kzh2eeeUZa6wshym7JEgC8moEUfImpxAZxelIrNzcXl8uFpmlERETom/nXR1iMnARMDjNeTs/I6HK5MBqN+jbt4hpjxMDBvJOcyN9DLTNgqFtlhylqrlK37Pr0008BmDhx4lkTXQVdf/31TJw4EaWUvo+KiIuLo1evXjz88MN8+umn1KlTp1Tbud1u+vfvz759+6hbty7fffcdubm55OXlMW/ePKKioti0aROjR48ucnt/YqwodrudsLCwch9TVSrYsksIIYQQoip5PB4GDhzI1q1bCQ8PZ+rUqRw4cAC73U5aWhp5eXn89ttvTJkyhebNm+vbde7cmaNHjxb56Ny581nLNGzYMCCO999/H4B77rkHTdNYunQpaWlpAWWWLFlS5L6WnLpBLKnMm2++yZkeeuihYuPbuHFjqV6/jz/+GKfTSd++fWnevDmHDx9m+fLlZ99QCCHOtGIFANmW04ksW8TpFlz+YYhMJlPAvaOmaXg8HiIsvsYTZrevhZcCvSVYZGSkvp9Ik40W0XVBedicE32qooGVd1xCnFLqZNeqVavQNI3x48eXuZIJEybo+6iIrl27kpaWxvfff89LL73EyJEjsVqtpdp2zpw5bDk1AN/ixYvp3bs34MtMjxgxgnfeeQeAr7/+mhWn/uMXVK9evWK7Kh4+fLjYLo6VwuOB5GT49FPfvwXG4SrNdrHLlhGxbl3ptxNCCCGECIIffviBTZs2Ab6E0xNPPEHDhg1PjwFjtdKxY0cmT57Mrl27uOqqq4Iew7Fjx/jyyy8xGo3861//olu3bjgcDubOnRv0uoLNn6S75ZZb9CFC/MuEEKJUnE544w3ffSRwUIvWV33zzkoy0zNJS0vj2LFj+uD0DoeD3NxccnJyyMnJIT09nQjrqRkbcxWePWF4vTDu9/d49ch3eKyBaYZLYuqj3IdZkdGA1Vl12Oa8Bo/yVtURixqq1MmuI0eOYLVaufDCC8tcSYsWLbDZbKSkpJR524Iq0hppzpw5APTo0aPIC6eRI0fStGlTAD788MNC6zt06MDJkyfZtWtXwPKDBw9y5MgROnToUO7YymTJEmjSBHr0gFGjfP82aaI3Qz3bdtEDB9Lo0UepO2pU6bYTQgghRLUwf/58rr/+epKSkjCbzcTGxtKiRQsGDBjA9OnT9fGtClq2bBl9+vQhNjaWyMhI2rdvz0svvYTr1DgtwVJwTC2ATZs2cfPNN9OgQQPMZrM+CP7mzZv1bQYOLPmXff9U98H24Ycf4na76d27N/Xq1WPs2LGArytjdbZu3Tq2bNlCTEwMAwcO5JZbbkHTNL744gtOnDgR1Lr844xNmTIFl8vFq6++yuWXX67PxpZ86ibZb+vWrdx55520aNGC8PBwIiMjadeuHY8//jipqalBjS3Yjp4aoFuIGmHSJAgPh//7P8jJAaC1/TC3qz8A+Oa/P/LAZc/w0p3/4fjx4+Tk5JCdnc3hw4dZu3Yta9as4cCBA8xbvZP1e33/t11uL2prJN4vEjn+h2Kz4wiDf32DMav+DYA3bQLGnMmkOE2syqzHlP0deWjze0xY/Qi/pe4OzesgQqqqzrulTnY5nc4KXXBYrdYSB4CvTHl5efzyyy+Ar1tlUTRN07tnfvvtt4XW+wcDfeONNwKW+/8eOXJk8AIuzpIlMHQoHDoUuPzwYd/y4hJX5d1OCCGEENXCbbfdxsiRI/nmm284fvw4NpsNl8vF7t27+eKLL5g4cWKhi8cpU6bQt29fvv/+ezIzMzGbzWzbto1HHnmE3r17V9p12eLFi7niiiv45JNPyM7OLnac0ENnXpdUEX9S65ZbbgFg6NChREREsHnz5lJ3JwwFfwuu4cOHY7PZaNq0KV27dsXlcvHRRx9VSp12u53u3bvz0EMP8fvvvwfM0ub30ksv0b59e2bOnMnu3bvRNA2Xy8WWLVt47rnnaNeund6ar7qwWCxMnjwZ4JyfMV6IUps0CV5+uVDvHg0Yzk494YWCv387yJEdxwHwer243W7y8/NxuVzsS83lr+O5RVah9oTj2RoOwF+5Rxmd/Ai/ndzCzKOtcSsDGopwgxubwc3feV6m/jFTEl41RCjOu6VOdtWqVYusrCwyMzPLXElmZiaZmZkkJiaWedtg2L59O16vr5nkxRdfXGw5/7qjR48WGrehffv23Hnnnbz11luMGzeO999/n9tvv53XXnuNsWPH0rFjx8o7APCdlO67D5QqvM6/7P77C3dN9HhQ992HKut2QgghhKgWfv75Zz744AMMBgMvvvgiJ0+eJDs7m9zcXFJTU1m+fDljx44NGCj9888/5+mnnwZg2LBhHDhwgPT0dLKyspg+fTpr165lxowZxVVZIePGjaNPnz5s376dzMxM8vPzmTlzJgCdOnXSy911111VPpv1L7/8wo4dO4iKimLw4MGAb2yZIUOGAJXbJXDu3Lk0adIEq9VKbGwsl19+OY8//njAQPnFycvL08e+9SfpgEpvlTZ9+nT++OMPPvjgA7KyskhLS+PEiRO0a9cO8L1ejzzyCOHh4Tz77LOkpKToY+KuX7+enj17kpKSwoABA8g51YqkOjCbzQwYMACQSZtEDeF0wmuvFbnKn74eyk5MygsaNL+yETkn8/B6Tnc11DQNr9dLVr6LVrUj0YrZk9oTzqlbb3baDbxzsBX5XhORBieaBg5lxGrwEm+yk+9x8/7u76VLYw0QivNuqZNd/i+1pUuXlrkS/0Ce7du3L/O2wVDwIqKksbUKrivqwuO///0vzz33HKtWreLuu+9mxYoVPP300/oFXFEcDgdZWVkBj3JZtapwy6yClIKDB/n71NhkW7Zs4Y8//mDP7Nlohw4VcTIK3I4KjqcmhBBCiMqxevVqAHr37s2kSZMCZhxMSEjgmmuuYfbs2dSrV09f/thjjwHQrVs35s2bpw/QHhYWxt13382///1vMjIyKiXe1q1b8/nnn9OyZUt9WYsWLfR4/LMe/vDDDzRu3JjOnTvzf//3f3z88ceFhosINn8ya9iwYQGTC/kTSJ988kmR3UGDYffu3Rw5coSIiAiysrLYsGEDzz33HK1atTrr9fWiRYvIysqiefPmXH311fpy/3H8+eef/Prrr0GPOScnh08++YRx48bpr1dCQgLx8fFkZ2fz0EMP6fH961//0ieOMhqNdOjQgeXLl9OhQwcOHTrEe++9V64YgnYtDaSkpLBx40b9AQT8XdEhV4Sott56q8TGDRpgBPqzm6haEcQ3iCbnRC4n9pxuAKJpGocy7GTa3dSKshATVlTCQgM01N4w/e8dzjgijU7CjL767V4jKNA0iDQ62Z97gL+yzp70F+emUJ53S53s6tevH0opnnrqqUKtnkpy8uRJJk+ejKZp9OvXr1xBVlR2drb+PDw8vNhyBdcV3MbPZDLx2GOPsWfPHhwOB3v37uWpp57CbDYXu8/nn3+emJgY/XHmbEClVsoPgenECZRSeksuc2nHSZAvdyGEEKJaio2NBeDEiRP6rMol+eOPP9i2bRsATzzxhD4jVkF33HFHpU2u8/DDD5c4zurSpUu5++67MZvNeDwe1qxZwxtvvMGYMWO48MILadKkCU8//XSFkhpFycnJYcGCBUBg6yiAnj170qBBAzIyMgJmWwyG7t2788EHH3D48GEcDgdpaWmkp6fzwQcfULt2bbKyshgxYgRr164tdh/+JJ1/UHq/gi3UKqNVWps2bejfv3+R6xYvXkxGRgaXXnop1157bZFlTCYTN910E0C5Z40M2rU08M4779ChQwc6dOjAHXfcAfj+L/iX+SesEuK8s2dPqYrVIxdLmBmjxYTL4cae69DXKaXIc3pweryYDBoWUwmphNzT67xomDUvNoMHDXArA55TaQiz5sWjPKQ7i+4WKc59oTzvljrZNW7cOOrXr8/hw4fp1asXu3efvW/trl276NWrF4cOHaJevXqMGzeuIrGekx577DG9G2dmZiYHDx4s347q1i1dscsuo2XLlrRq1YpWrVpR//LLg7p/IYQQQlStXr16YbPZ2LRpE127duX9999n7969xZZfv3494Es0dO3atcgyBoNBHzQ+2Lp06VLi+oiICKZPn86hQ4d49913GTNmDK1atdITZPv372fKlClccskl7CnlDVppzJ8/n9zcXJo0acI//vGPgHUGg6HSZjecMmUK48aNo169evp4VzExMYwbN47Vq1cTGxuLy+Vi0qRJRW6/e/dufVb0M5NdcLor47x588jLywtq7CW9l/7xcLdv306dOnWKfTzzzDOA730tj6BdSwPjx49nw4YNbNiwQe+ZMXPmTH1ZeWadF+Kc0Lx5qYodIQJnvguP043ZasISfrp7vKZphFtMWIwG3F6F011C18OI0+sMKFzKgKYpEs351LPkYtR8613KgFEzEmeJKN9xiWovlOfdUie7rFYrs2bNwmg08scff9CuXTtuv/12vv76a1JSUnA6nTidTlJSUvjqq6+47bbbaN++PX/88Qcmk4n333+/UmbUKY2oqCj9eUkXAQXXFdymIqxWK9HR0QGPcunaFRo08LX3LIqmQcOGmHv2xGKxYDabMZvNGLt3L9V2FHMxLIQQQojQat68Oe+99x6RkZGsWbOG22+/nWbNmlG7dm1GjBjBZ599FjA25/HjvkGFExMTS7z2atCgQaXEW7t27VKXu+OOO/jwww/Ztm0bGRkZfPbZZ3o3vb179wZ1AiB/Emv06NGFBlmH00mjlStXlphMDKbmzZtzzz33AL6x2YoatHfWrFkopejSpQvNmjUrtL53797Ur1+f7OxsFi5cGNT4Snov/UN+2O12jh07VuzD30KvvIm4oF1LA3Xr1uWyyy7TH0DA33Xlx19xvrr7biihxa0CPMAXXED2iVzSDmURWSuCuhfV0s+XSikaxNmICTNxIttJZr67mD0ptKb5+t8tLenkeCwoBVaDB4OmTu0PcjwWGkc04qLoekXsS5wPQnneLXWyC6BPnz589NFHhIWFYbfb+eCDD+jfvz8NGjQgLCyMsLAwGjRowIABA5gzZw52u52wsDDmzJnDNddcU1nHcFYFx7AoaSDUgusKblMtGI3w5pu+52deoPn/fuONwiex8m4nhBBCiGrj5ptvZv/+/bz99tuMGDGChg0bcuLECRYsWMCgQYPo1q1b0Lv9lVdJXRhLEhkZyYABA/jxxx/p0aMH4Gultnnz5grHtH37dtasWQPAtGnT0DSt0MM/xphSig8++KDCdZbWVVddpdd7ZpLN4/EwZ84cwJcMKypuo9GoX8MGu1VaSe+lv0vtiBEj9CE0Snrs27cvqLEJIcrAYoEHHihylf+nkkVciFszgII9aw8QlRCOwWDQZ2FVSmHQNGLDLWw/nkPh6c98S7Tmefh7z19o8zK+4XbCDG7S3DYcXgNeBQ6vgTS3jTCjiX9e0BujVqa0hBClUuZP1YgRI1i/fj2DBw/WP/RFPTRNY/Dgwaxbt07vqx8qrVq10ser2Lp1a7Hl/Ovq1KkTMPhrtTFkCCxaBGeOsdGggW/5qZmEgradEEIIIaqN+Ph4xo8fz7x58zhw4AC7d+/m0UcfRdM0Vq1axZQpU4DTrXFSU1NxOp3F7q+qZ0IsLYPBwO23367//ddff1V4n2VNAs2ePVufyTuUli1bVqrZGv1WrVpV6YP8+/kHoy9v98RQq1u3LpMnT5bWXKLmeOklePjhQo0cvMACLuQ9zTchHRo0v6IRjdrWx2QyYTKZ9IYtZrOZprWiaFMvpsgJ0LTmeRgv9rXivCiiDh93f5FOCW15vNEGmtmysHtNZLht2L0mmocbeLLdHXRKvKByj1tUG1V93i3XnI8tW7Zk8eLFHD16lOTkZP7880+92XVCQgKtW7emR48e+pdgqIWHh9OlSxdWrVrFN998w8MPP1yojFJKHzgzlK3QzmrIEBg40Dd7YkqKb6ytrl3P3jKrvNsJIYQQolpq3rw5zz//PAcPHmTu3Ll89913AFx+arxOt9vNqlWr6NWrV6FtvV4vycnJVRlumURGRurPKzoMhsvl4qOPPgLgtdde0wfILUpmZiZNmjTh4MGDfPfdd8UOvB5M/oHpNU2jSZMmAev8SbrBgwfz4Ycflrifbt26sXHjRmbNmsXzzz9fKbEW1KVLF+bMmcOGDRtISUk555JGdevW1RPEQtQYL70E06b5ZmfcswdPkyZsvKgrOz76kUsycmnRqRn97+pDWEQYHo8Ho9GIx+PRk15KKfLy8rjkEnjKauWrdbs5nJpJQlwYW6K2k+KC+mENmHzJMGJPjcNliJ9Bp5h8OiS+yF9ZR8jw1iE2Ziyt4ppKi64apqrPu+VKdvnVqVMnqGMpVKaxY8eyatUqVq5cya+//soVV1wRsH7hwoX8/fffQOEZeqodoxHKM6hsebcTQgghRMg4HI4SEz5hYb4p3v2t2Nu1a0erVq3Yvn07zz77LD169Cg0I+OsWbM4dOhQ5QVdjK1btxIXF3fWmSALJnYuvfTSCtX5xRdfcPz4cQwGAyNHjgxIpJ0pMjKSXr16sXz5ct5///0KJ7v8vR2Ks3fvXqZPnw5A586dSUxM1NcdO3aML7/8EvD1rCgpboBhw4axceNG5syZw7Rp08rdnbS0hg0bxqRJk8jIyOCBBx7gk08+KfZYvV4vWVlZ+syiQogQsljg/vsBMAIdgY79OpV684LnotG9OxRYc1Wx2xiMYRjipnBxXNlCFaIizrlUanp6OqmpqfrD38Q8Ly8vYHlOTk7AdmPHjqVt27YopbjxxhtZsWIF4PvyXbhwof4r3/XXX1/kL6BCCCGEEKEwceJEhg8fzuLFi/XB5wFycnJ4++239cTQDTfcoK979tlnAd9g66NGjdITW3a7nbfffpuJEyeGJPGQnJxMs2bNGDFiBAsXLiQlJUVfZ7fb+fnnnxkwYACLFy8GYOjQoTRu3LhCdfpbR1199dWlan00fPhwAD777LMiB4wvixdeeIGxY8eybNkyMjIy9OVZWVl8+OGHdO7cmfT0dMxmMy+++GLAth9++CFut5uwsDD69etX6rhTUlL4+uuvKxR3acTGxvLGG28Avpkgb7jhBn799Vf92tzr9bJ9+3ZeffVV2rRpoyfuhBBCiKpQoZZdoXDppZcWOTbAyy+/zMsvv6z/PXbsWGbPnq3/bTKZ+Pzzz+nRowf79u2jd+/ehIeH4/V6sdvt+r7nzp1b6ccghBBCCFFaLpeLhQsX6jPtRUZGYjKZApInV199NY8//rj+9+DBg3n88cd59tlnmT9/PvPnzycuLo7s7Gzcbjddu3bl6quvrpLubgWZzWacTicLFixgwYIFANhsNsLCwkhPTw8oe8011zBr1qwK1Xf48GF9mIphw4aVaptBgwZx11134XQ6+fjjj7nvvvvKXb/D4eDDDz/UE5JRUVGYzWYyMjL0pFBMTAyzZs2iS5cuAdv6j71v375EREScta5mzZpx2WWX6V0Z+/fvX+64S2vs2LHk5+dz3333sWzZMpYtW4bVaiUyMpKsrCxcLpdetqQWbkIIIUSwnXMtuyqiSZMm/PHHHzz11FNcfPHFaJqG2WymQ4cOvPLKK6xdu5a4OGlbKYQQQojq48knn+Tf//43gwcPpmXLlphMJnJycqhduzZ9+vRh1qxZJCcnF0qITJs2jS+//JKePXsSHR2Nw+GgVatWvPDCC6xYsQKLxVLlxzJ+/Hh+//13XnzxRQYOHMgFF1yA0WgkMzOTqKgoWrduzS233MLXX3/N8uXLiYqKqlB9s2fPxuPxYDAYGDp0aKm2iY+P11v5V3R2w2HDhvHUU0/Rp08fmjZtiqZpZGVlERcXx9VXX80zzzzDX3/9xZAzJgv65Zdf2LFjB3C6xVZp+Mt++eWXHDt2rEKxl9Zdd93FX3/9xUMPPUT79u2xWq1kZGQQGRnJ5Zdfzr333st3330X8gmrhBBC1CyaUqrwrKEi6KZPn8706dPxeDzs3LmTzMxMoqOjQx2WEEKIKpaVlUVMTIx8DwghRDnIOVQIIWq20n4P1KiWXaF0zz33sG3bNtatWxfqUIQQQgghhBBCCCHOW5LsEkIIIYQQQgghhBDnDUl2CSGEEEIIIYQQQojzxjk3G6MQQgghhAi+1atXFxoo/Ww6d+7MkiVLKikiURFDhgxh9erVZdpmyZIldO7cuZIiEkIIIaqOJLuEEEIIIQROp7PMM/ilpaVVUjSiotLS0sr8fjqdzkqKRgghhKhakuwSQgghhBB0794dmaT7/JGcnBzqEIQQQoiQkTG7hBBCCCGEEEIIIcR5Q5JdQgghhBBCCCGEEOK8IckuIYQQQgghhBBCCHHekGSXEEIIIYQQQgghhDhvSLKrikyfPp3WrVvTsWPHUIcihBBCCCGEEEIIcd7SlEy7U6UyMzOJjY3l4MGDREdHhzocIYQQVSwrK4uGDRuSkZFBTExMqMMRQohzilxLCyFEzVbaa2lTFcYkgOzsbAAaNmwY4kiEEEKEUnZ2tiS7hBCijORaWgghBJz9WlpadlUxr9fLkSNHiIqKQtO0cu/Hn808V37VOpfiPZdiBYm3sp1L8Z5LsULNjVcpRXZ2NvXq1cNgkNEEhBCiLIJ1LV2dnGvfh1VFXpeiyetSNHldinY+vi6lvZaWll1VzGAw0KBBg6DtLzo6+pz60J5L8Z5LsYLEW9nOpXjPpVihZsYrLbqEEKJ8gn0tXZ2ca9+HVUVel6LJ61I0eV2Kdr69LqW5lpaflIUQQgghhBBCCCHEeUOSXUIIIYQQQgghhBDivCHJrnOU1Wpl8uTJWK3WUIdSKudSvOdSrCDxVrZzKd5zKVaQeIUQQgiQ75fiyOtSNHldiiavS9Fq8usiA9QLIYQQQgghhBBCiPOGtOwSQgghhBBCCCGEEOcNSXYJIYQQQgghhBBCiPOGJLuEEEIIIYQQQgghxHlDkl1CCCGEEEIIIYQQ4rwhya5zSHZ2NlOmTKFt27ZERkYSExNDx44defXVV3E6naEOT7dx40aefvppBgwYQMuWLUlISMBsNpOQkECXLl149tlnSUtLC3WYRcrKyuLFF1+kc+fO1KpVC6vVSoMGDejRowdTpkwhIyMj1CHqVq1axYgRI2jQoAFWq5XatWvTp08fPv300yqNIy8vj2XLljFt2jSGDBlC48aN0TQNTdOYMmVKidsePnyYt956i2HDhnHBBRcQFhZGWFgYTZs25aabbuKHH36oVvFOmTJFL1vSY/fu3SGP1W/RokX079+fevXqYbFYiIiI4KKLLuKOO+5g8+bNQYnT7+TJk3zwwQeMHj2a1q1bExERof8fGjRoEEuXLi1222Aca1XGW5wXXngh4LMghBBCBFNN/56pjO/uc8W5ci9YVWryZ6Gsaux5Q4lzwr59+1STJk0UoAAVHh6urFar/vell16q0tLSQh2mUkqpe+65R48LUDabTUVFRQUsS0xMVKtXrw51qAF++OEHlZSUpMdosVhUbGxsQNybNm0KdZhKKaUeeeSRgLhiY2OV2WzW/x48eLByuVxVEsvKlSsDYin4mDx5crHbHThwQGmaFlA+PDxchYWFBSy77bbblNvtDnm8Sik1efJkBSiz2aySkpKKfezduzfksdrtdtW/f/+AbSIjI5XFYtH/NhgM6rXXXgtKrEopZTKZCv3fj4iICFh2/fXXq9zc3KAeayjiLcqOHTuUzWYL2F4IIYQIFvmeCf5397niXLoXrCo19bNQVjX5vCEtu84Bbreb/v37s2/fPurWrct3331Hbm4ueXl5zJs3j6ioKDZt2sTo0aNDHSoAnTp14uWXX2bNmjWkp6eTn59PVlYW2dnZzJkzh1q1apGamsqgQYPIzMwMdbgA/PLLL9xwww0cO3aMIUOGsG7dOux2O+np6eTm5vLbb7/x+OOPExMTE+pQeeedd3jxxRcBGDlyJAcPHiQ9PZ3s7Gxmz55NREQES5cuZdKkSVUWU1xcHL169eLhhx/m008/pU6dOmfdxuPxoJSiV69ezJkzh8OHD5Obm0tOTg5//vknAwcOBGDWrFlBb9lTnngL6ty5M0ePHi320aRJk5DH+txzz/HFF18AcPfdd3Po0CGys7PJz89n/fr1XH311Xi9Xh588EE2bNgQlFjdbjedOnXirbfeYs+ePeTn55OTk8PevXv55z//CcCyZcsYP358UI81VPEW5PV6ue2227Db7Vx11VWVGrcQQoiaR75nfIL53X2uONfuBatKTfwslFWNP2+EOtsmzu69997Ts7BFtYb65JNP9PXff/99CCIsm+XLl+vxfvzxx6EOR+Xm5qpmzZopQN17772hDqdELpdLb3122WWXKY/HU6jMjBkzFKBMJpPas2dPpcdUVKurxo0bn7VFTkZGhtqwYUOx671er7ruuuv0Fkn5+fnBCLfc8Sp1umVXt27dghLL2VQkVv+vf8XFmpGRoSIjIxWgHn300SBE62sdWZLx48fr//cPHDgQsK4ix1peFYn3TG+88YYC1M0336x/TuQrVgghRLDI94xPML+7zxXn271gsNTEz0JZ1fTzhrTsOgfMmTMHgB49ehSZkR05ciRNmzYF4MMPP6zS2Mrjyiuv1J8fOnQohJH4fPTRR/z999/UqVOHl156KdThlGjDhg0cO3YMgAcffBCDofB/4TvuuIPY2Fjcbjcff/xxpcdkNBrLtV1MTAyXXXZZses1TeO2224DICcnh+3bt5ernjOVN95QqEisKSkpAFx++eVFro+JieHCCy8EfK9vMPTo0aPE9f5f2QDWr18fsC4U70tF4i1o7969PP744yQkJPD6668HLT4hhBAC5HumoGB9d59Lzrd7wWCpiZ+FspDzhgxQX+3l5eXxyy+/AHD99dcXWUbTNK677joAvv322yqLrbxWrVqlP2/evHkII/HxfykMGzYMm80W4mhKtn//fv1569atiyxjNBr1JMa58HkoScH3w+PxhDCSc0+zZs0Aiu2imJmZyc6dO4HiE2LBdq69n6WN94477iA3N5fXXnuNWrVqVUVoQgghahD5nim9c+1a42zOx3vBqnK+fRbKSs4bkuyq9rZv347X6wXg4osvLracf93Ro0er5UyHDoeDffv28d///pcxY8YAcMEFF9C/f/+Qx+XP9Hfo0IEDBw5w55130rBhQywWC0lJSfTv35+vvvoqpHEWpaSTtn/d1q1bqyqcSpGcnAyAxWLRE3jVwZ9//snFF19MeHg4kZGR+uyGmzZtCnVougkTJgC+1/Cee+7h8OHDACil2LhxI/369SMnJ4errrqqysZ48L+fAG3btq2SOiuiNPHOnDmTFStW0Lt3b2655ZYqikwIIURNId8zZXOuXWuczflyLxgK59tnoSzkvOEjya5q7siRI/rz+vXrF1uu4LqC24SazWZD0zRsNhtNmzbl3nvvJT09nS5durBixQqsVmtI49u3b58+Ve/ff//NxRdfzMyZMzl+/DgREREcP36cL7/8kn79+nHHHXeglAppvAUHPi8ukeV0Otm1axfga72Tm5tbFaEF3d69e3n77bcBGDFiBNHR0SGO6LTU1FS2b99OWFgYDoeDnTt38t5779GhQweeeOKJUIcHwD333MOkSZMwGAy89dZbNGjQgKioKGw2Gx06dGD37t08+uijrFixokq6EGZkZPD8888D0LVrVy666KJKr7MiShPv4cOHefjhhwkLC+Odd96p6hCFEEKc5+R7pmzOtWuN0jjX7wVD5Xz8LJSWnDdOk2RXNZedna0/Dw8PL7ZcwXUFtwm1OnXqkJSUREREhL6sR48evPHGGzRq1CiEkfmkp6frz6dNm4bZbGbhwoXk5OSQnp7O/v37GTZsGADvvfdeyPs7X3bZZSQlJQHw4osv4na7C5X5z3/+Q1ZWlv53wefnivz8fIYNG0ZeXh6JiYm88MILoQ4JgBYtWvDSSy/x119/YbfbOXnyJLm5uSxfvpwOHTqglOLZZ5/l1VdfDXWoGAwGnn/+eWbNmkVkZCTgG5vLn9y12+1Vlgz1er2MGTOGlJQUbDYb//3vfyu9zooobbzjx48nMzOTKVOm6N1GhRBCiGCR75nSO9euNUrrXL8XDIXz9bNQWnLeOE2SXaJS7du3j6NHj5KTk8OxY8d45ZVX2Lx5M506deKpp54KdXh6s2D/8/fff5+hQ4diNpsBaNSoEfPmzaN9+/YAPPfcc0UmmKqKyWTSX7ft27fTr18/Nm7ciNPp5OjRo7z88ss89thjevxAkYPYV2dut5tRo0axYcMGzGYzc+fOpV69eqEOC4Cbb76Zhx9+mAsvvFB/jS0WC9dccw0///wzHTt2BGDKlClkZmaGMlRSU1Pp1asX48aN46qrruLnn38mIyODlJQUlixZQq1atZgxYwZXXHGF3sWxstx33318+eWXAEyfPp127dpVan0VVZp4P/74Y7766isuueQSHnjggaoOUQghRDUze/ZsNE0r9+Obb74J2N/58j0T7NelOOfatYaoPDX5s3C+nDeC5dy6C66BoqKi9Od5eXnFliu4ruA21Unt2rV58MEH+eabb9A0jalTp+onolAp+Fq1aNGCQYMGFSpjMBh46KGHADh58mSxA35XlbvvvluPx9+iyGq1UrduXSZNmkSTJk2YNGmSXj4uLi5UoZaZx+Ph5ptv5n//+x8mk4lPPvmEa665JtRhlYrNZuO5554DfC2oVqxYEdJ4xo4dS3JyMt26dWP58uV06dKFmJgY6tSpw+DBg/n5559JTEzk77//5tFHH620OB566CH9F7XXX39dn2GzuipNvMeOHeP+++/HaDQyc+ZMTCZTVYcphBDiPCbfM2Vzrl1rlMX5dC9YFc7nz8LZyHmjMHkFqrmCLVoOHz5cbGa6YMuM6tIKpjidOnXi6quv5qeffuLdd9+lX79+IYulYP/2li1bFluu4MyH+/fv54orrqjUuM7m5ZdfZtCgQbz33nusW7eOrKws6taty4ABA7j//vt56aWXAGjcuDEWiyWksZaWx+Nh9OjRLFiwAKPRyMcff8zQoUNDHVaZFJwO+u+//w5ZHNu3b+frr78G4MEHH0TTtEJlateuzS233MJrr73GkiVL+PDDD4ssVxGTJk3Su3S+8sor3H///UHdf7CVNt5HH32UkydPMmHCBFq2bElOTk7Aen9XUUBfZ7FYzpn/i0IIIcrupptuqtA1bUxMjP78fPqeCebrUpRz7VqjrM7He8HKcr5/Fs7mfDpvBIsku6q5Vq1aYTAY8Hq9bN26tdgpZ/2DldepU4f4+PiqDLFc/Emm3bt3hzSO+Ph46tevf9ZuXAUHpg92QqC8unTpQpcuXYpc559hsnPnzlUZUrn5W3TNnz9fT3SNGDEi1GGds7Zt26Y/b968ebHlWrRoAfh+DTx+/Lg+HlwwPPzww7zyyisAvPTSSzz44INB23dlKEu8e/fuBWDGjBnMmDGjxP36f1297777eOONN4ITrBBCiGrHarUGbeKl8+l7Jpivy5nOtWuN8jhf7wWDrSZ8Fs7mfDpvBIt0Y6zmwsPD9YRGcX3WlVIsX74c4Jzp8uVv9VIdmtn6X7Pt27cXW6Zg8qBp06aVHlNFHDt2jO+//x7gnJhq1uPxMGrUqIBE18iRI0MdVrmsXbtWfx7Kz0nBcdr2799fbLljx47pz/2D2AfDQw89FHDB8fDDDwdt35XhXItXCCGEqOlqynf3+XovGEw15bMgykGJau+9995TgNI0Ta1du7bQ+vnz5ytAAer7778PQYSnud1u5fV6Syzz/fffK03TFKAmTZpURZEV76efftJfv6VLlxZa7/F4VLt27RSg6tevrzweT9UHWUput1sNGjRIAapTp05nfS8qS+PGjRWgJk+eXGI5t9utRowYoQBlMpnUvHnzqibAM5Qm3rO9lna7XV1xxRUKUBERESo9PT24QZ5Smlj37dunf6b79+9fZJmcnBzVrFkzBah27doFLb4HH3xQr/uVV16p0L5K+zmqiGDG6zd58mR9n0IIIUSw1fTvmcr47q7OzqV7wapW0z4LFVETzxs150jPYS6XS7Vt21ZPtvhPYh6PRy1YsEBFR0crQF1//fUhjlSpvXv3qvbt26u3335b7dmzJyBBcODAAfX888+riIgIBaj4+HiVkpISwmhPGzp0qAJUQkKCWrRokXK5XEoppfbv36+GDx+unxhmz54d4kiV2rNnj/rXv/6lNmzYoPLz85VSvs/Czz//rHr27KkAFRsbq7Zt21ZlMaWlpakTJ07oj4YNGypAPfzwwwHLs7Oz9W3cbrcaOXKknuhasGBBtY43OTlZ9erVS3344Yfq4MGD+nKn06m+//571bFjR/1z8uKLL4Y0VqWU6t+/vx7P6NGj1e7du5XX61VOp1P98ssv6vLLL9fXz5kzJyixPvzww/o+X3vttSo71lDFW5yaeDEhhBCi6tTk75nK+u6uzs6le8GqVBM/CxVRE88bNedIz3F79+5VTZo00T+g4eHhymaz6X9feumlKi0tLdRhqr179+oxAcpisajExEQ9weV/NG3aVG3cuDHU4epycnLUP/7xDz0+q9Wq4uLiAmKuzNYlZbFp06aAuOLi4pTZbNb/btSokdqwYUOVxuRvgXO2x9ixY/VtfvzxR3252WxWSUlJJT6C2eqrPPGuXLkyYF1YWJhKTEwMeO0NBoP617/+FbQ4yxurUkqdOHFCdejQIaBMeHi4MplMAcsefvjhoMS5f//+gNfhbO/nyy+/HLRjDVW8xamJFxNCCCGqTk39nqnM7+7q7ly5F6wqNfmzUF418bwhA9SfI5o0acIff/zBK6+8wpIlS9i7dy9ms5k2bdpw0003ce+991aLmRTq1avHwoULSU5O5tdff+XIkSOkpqZiNBpp1KgR7du3Z+DAgYwaNYqwsLBQh6uLiIhg5cqVzJo1i48++oitW7eSnZ1N/fr16dq1K/fee2+1Gey9SZMmPPXUUyQnJ7N7925SU1OJjo6mZcuWDBkyhLvuuovw8PBQh3lWXq9Xf+5yuQLGjypKfn5+ZYdUorZt2/LKK6+wZs0atmzZQmpqKhkZGYSHh9O6dWu6du3KnXfeSdu2bUMap19iYiJr165lzpw5LFy4kM2bN5OWlobJZKJRo0Z07tyZ8ePHc/XVVwelvoLvp9frPev7eeYMMVXtXItXCCGEqOlq8nf3uXIvWFVq8mdBlJ6mVIFp5oQQQgghhBBCCCGEOIfJbIxCCCGEEEIIIYQQ4rwhyS4hhBBCCCGEEEIIcd6QZJcQQgghhBBCCCGEOG9IsksIIYQQQgghhBBCnDck2SWEEEIIIYQQQgghzhuS7BJCCCGEEEIIIYQQ5w1JdgkhhBBCCCGEEEKI84Yku4QQQgghhBBCCCHEeUOSXUIIIYQQQgghhBDivCHJLiGEEEIIIYQQQghx3pBkl6gx9u3bh6ZpaJrGvn37Qh1OpRg3bhyapjFu3LhQh1IpmjRpgqZpzJ49u8rrnjJlCpqm0b179yqvWwghhBBCCFGYXKOL4kiyS1Qqu93OO++8Q79+/WjUqBFhYWHExMTQqlUr7rzzTlauXBnqEEU1MHv2bKZMmUJycnKoQxFCCCGEEAX4kwlnPmw2Gw0aNGDAgAEsWLAApVSoQz2vZGRkMGXKFKZMmUJGRkaowxHinGMKdQDi/PXdd99x2223cejQIX1ZdHQ0DoeDHTt2sGPHDmbOnMn111/PRx99REJCQgijFaE0e/ZsfvzxR4ASf5Vp3rw5NpuNmJiYKorstMTERC666CIaNWpU5XULIYQQQlQHSUlJ+vPMzEwOHz7M4cOH+eKLL5g9ezZLly7FarWGMMLzR0ZGBk8//TTg670RGxsb2oCEOMdIyy5RKebPn0/fvn05dOgQ9evX57333iMtLY3MzEzsdjvbt2/n/vvvx2QysWzZMq688kqOHz8e6rBFNbdixQp27NjB4MGDq7zuiRMnsmPHDj788MMqr1sIIYQQojo4evSo/sjNzWXr1q306dMHgGXLlvHEE0+EOEIhhPCRZJcIuu3bt3Pbbbfhdrtp27YtmzZt4p///CdxcXF6mZYtW/L666/z2WefYbFY2L17N6NGjQph1EIIIYQQQojSMhgMtGnThs8//5wLLrgAgHfeeQe32x3iyIQQQpJdohI88cQT5OXlYbVaWbhwIbVq1Sq2bN++ffVfgFasWMFXX30VsP7MQeX37NnDnXfeSdOmTbFarTRp0iSg/OHDhxk/fjwNGzbEarXSoEEDbr31Vnbv3l2q2J1OJ2+99RY9evQgMTERi8VCnTp1GDhwIMuWLSt2O3+MycnJHD9+nAceeIALL7yQ8PBwNE0rVP6rr77ixhtvpH79+litVuLi4vjHP/7BjBkzcDqdJcY4d+5cunTpQlRUFDExMVxxxRW8++67QRsnYcmSJfTr14+kpCQsFgtJSUn069ePpUuXFrtNwYHxlVK8/fbbdOrUiejoaKKjo7n66qv55JNPCm03e/ZsNE3TuzA+/fTThcaDKDiZQEkD1Bd8D06ePMkDDzxA8+bNCQsLo3HjxkycOJETJ07o5ffv38+ECRNo2rQpNpuNRo0a8eCDD5KdnV3kMRY3+KX/GErzKGpMsqr6zBXnzPfuvffe4+qrryYhISHgtfYf55n/5woqaRKIM7ffsGEDw4cPp27dulitVpo1a8YDDzxAenp6qWMXQgghROjZbDaGDRsGQHZ2Njt27AAgPT2d999/n+HDh9O2bVvi4+Ox2Ww0btyYUaNGsXbt2mL3eeZ11+LFi7nmmmuoXbs2BoOBKVOm6GW3bt3KlClT6Nmzp37tFx0dzaWXXsoTTzxBampqsfUUvLbMy8tjypQptGrVivDwcOrVq8eYMWPYu3evXj41NZVHHnmECy+8kLCwMOrUqcPtt9/OsWPHSnyNsrOzeeGFF7jqqquIj4/HarXSsGFDRo4cyZo1awqV7969O02bNtX/btq0acA1ZVHDfoTqmtLlcpGYmIimafz73/8useysWbPQNI3o6Gjy8vL05UePHuU///kPAwcOpFWrVsTExBAWFsYFF1zA7bffzp9//nnWOIrSvXt3NE0L+LycqTQD3O/bt4/777+fNm3aEBkZSXh4OC1btuS+++7jwIED5YpNVAElRBAdOXJEGQwGBahx48aVapvs7GwVFRWlAHX99dcHrNu7d68CFKDmzp2rIiMjFaDCw8NVRESEaty4sV52w4YNKi4uTi8fFhaml4+Ojlbz58/X1+3du7dQHPv27VNt2rTRy2iapmJiYvS/AXXXXXcVeQz+9TNnzlRJSUkKUDabTT8uv7y8PDV06NCAfUZHRytN0/S/r7zySpWWllaoDq/Xq2699daA+OLi4vTXe+TIkWrs2LEKUGPHji3Va1+Qw+FQI0aM0PdvMBgC9g+om266STmdzkLbFqzXvw//9gWP7dZbb1Ver1ffbt68eSopKUmZzWYFqIiICJWUlBTwOHDggF6+cePGClAffPBBse/BnDlzVIMGDfT9WSwWfV2rVq1Uenq6+u2331RCQoL++ptMJr1Mly5dlNvtLrT/yZMnK0B169YtYLn/GIp7xMbG6vteuXJlwLZV8Zk7G/97d8stt6gbb7yx0Hvvf60/+OADBQT8nztTwf+vZ/4fK7j93Llz9fc8JiYm4DPWpk0blZ2dXer4hRBCCFG5/NdAJV1fTJ8+XS/zyy+/FNrOaDSquLg4ZbVaA6573nzzzRLr7Natm3rggQcCrn2NRqOaPHmyXtZ/fei/FoqPjw+4/qxfv77asWNHkfX4t33jjTdU27Zt9X2EhYXp29etW1ft3btX7dmzRzVt2lS/Fyl4jdmiRQuVmZlZZB2bNm3Sr039r4X/es1/XM8991zANoMHD1aJiYl6mcTExIDry8GDBweUD/U15T333KMAdfnll5dYrnv37kXeJ/qvRwFlMplUfHx8wPW51WpVixYtKnKfxV2jK6VUt27dFBDweSnL9kop9fHHHwd8bq1Wa8DnIyoqSi1fvrzE4xahIckuEVSffPKJ/h//iy++KPV2/pvsyMhI5XK59OUFb54jIyPVFVdcodatW6ev/+uvv5RSSmVlZalGjRopQDVq1Eh9++23elJl9erVqk2bNgFJhzNvxHNyclTLli0VoLp3766Sk5OV3W5XSimVkZGhXnvtNT1x9sYbbxSKv2CMF110kVqxYoXyeDwBMSql1OjRoxWgmjVrpubOnat/Kebn56vPPvtMNWvWTAFq0KBBhep488039XomTpyoTpw4occ3ZcoUpWmafozlSXY9+OCD+pfjk08+qdLT05VSSqWlpal//etfet2PPPJIoW39X1AxMTFK0zQ1depU/diOHz+uJk6cqG9f1EVNab6IlCpdsis2NlZdcsklau3atUoppZxOp/r0009VeHi4/to1btxY9ezZU23dulUp5Xv9//Of/yij0ah/2Z/pbF+ERXE4HPqxNWrUSH/PlKq6z9zZ+N+7yMhIZTKZ1CuvvKK/d9nZ2erIkSNKqeAlu8LDw5XValW33367nsjMzc1V//3vf/UE2JNPPlnq+IUQQghRuUqT7Hr44Yf1Mtu3b1dKKfXOO++oyZMnq/Xr1yuHw6GU8v14+/fff6v77rtPaZqmjEaj2rhxY7F1+q+FHnnkEXX8+HGllFJ2u13t27dPL3vLLbeo2bNnq/379+vLHA6H+v7771WnTp0UoC677LIi4/ZfW8bGxqomTZqob7/9Vnk8HuV2u9W3336ratWqpQA1fPhw1alTJ3XJJZeoNWvWKKV815jz58/XrzEff/zxQvs/cuSIql27tgLUkCFD1Pr16/Ufjo8dO6aefPJJPamzdOnSgG1Luq4qqDpcU/7666+F3v8z7d+/X09C/vDDDwHrpk6dql5++WW1ZcsW/V7Q4/GorVu3qptvvlmB70fsw4cPF9pvZSa7vv32W2UwGJTJZFKTJk1Se/fuVV6vV3m9XrVjxw41bNgwBb4fzwt+/kT1IMkuEVSPP/64fqI7dOhQqbebOnWqvt3u3bv15QVP8o0bNy62xceLL76oAGWxWNS2bdsKrU9JSQlo9XXmF8Yzzzyjn+SKarmklFJLlizRf1kpmJBT6vSXRHR0tDp48GCR2//0008KULVr1w5orVTQwYMHVUREhALUpk2b9OX5+fkqPj5eAWrMmDFFbvvoo4/qcZQ12XXo0CH9i/axxx4rsoz/VzWz2awnQPwK/hpTXKLCn+iLj49X+fn5AeuCmexKSkpSqamphdY/+eSTepk2bdroFwEFjRkzRgGqV69ehdaVJ9nlf12ioqLUH3/8EbCuKj5zZYkRUP/+97+LLResZFdJn0//Z+yCCy4ox5EIIYQQojKcLdmVmZmp6tWrp1/n+RMlZ+NvDfTPf/6zxDofeOCBcseenZ2tt1ZatWpVofX+a8uwsDC1a9euQuvff//9Ul9jNm/evNC62267TQFq1KhRxcb42muvKUC1b98+YHlpk13V5ZryoosuKvFe4rnnntN/AC7Y06M0brjhBgWoqVOnFlpXWckuj8ejWrRooQD1zjvvFLv9gAEDFKDuu+++Uh6NqCoyZpcIqpMnT+rPExISSr1dYmJikfsoaOLEiURGRha5bt68eQAMGzaMVq1aFVpfp04d7rrrrmLrf//99wF44IFVeBDMAAAQy0lEQVQHMJvNRZYZNGgQ0dHRpKamsmHDhiLLjBkzhgYNGpRYx80330zDhg2LLNOgQQN69OgBwPLly/Xl3377LWlpaQA89dRTRW776KOPYrPZilx3NosXL8btdmOz2Xj00UeLLPPEE09gtVpxuVwsWrSoyDJhYWE89NBDRa7zx52WlsZ3331XrjhL44477ijys3fttdfqzx944IEip8X2l/njjz8qHMe0adOYM2cORqORefPm0bZt24D1VfGZK4u4uDjGjx9f4f2URnEzNQ0cOBCA3bt3B4zjIIQQQojqJyMjgxUrVtCzZ0+OHDkCwH333YfBULpbzBtuuAGAn3/+udgyBoOBRx55pNwxRkZG0q1bt7PWc+ONN+qD7BdU8PrxzjvvLPEac8+ePeTm5urL7Xa7PmZtScdwyy23APD777+fdeyvolSXa8oxY8YAvvGFVRFjCX/00UeA716oLOPLQuk+K8H2008/sWvXLhITE7n99tuLLed//wreu4nqwRTqAIQorS5duhS53Ol0smXLFgB69uxZ7PY9e/bk+eefL7T88OHD7N+/H4B//vOfGI3GYveRk5MD+AY3v+KKK0odI8Avv/wC+L6Qihqs3S8zM1Ovw2/9+vUANGzYsMgvYoCYmBg6dOig11MW/v137NiR6OjoIsvExcVx+eWX88svv+jlz3T55ZcXu32LFi1o0KABhw4dYv369fTv37/McZZGp06dilyelJSkP+/YsWOJZSo6SPq8efP05N7rr79O3759A9ZX1WeuLDp27IjFYgnKvkoSHx9f7Ge4Xr16+vP09HTCw8MrPR4hhBBClF5JSYrRo0fz+OOPByz7+++/eeutt1i5ciV79uwhOzsbr9cbUObQoUPF7vOCCy6gdu3aZ43ryy+/5KOPPmLdunUcO3asyB/NSqonGNeP4EsARkREAL7JeOx2OwDXXHPNWY8BfNd7Bfd3NtXpmnLMmDE8+eSTHDhwgB9//DFgwPcNGzawfft24HRy6Ey///4777zzDj///DP79u0jJyenUNKspPcw2Pz3VJmZmQHXqGfyTy5W8N5NVA+S7BJBVfDXjpMnT1K/fv1SbVdwlpTiWoQV90WXlpamT3FcUn3F/VLh/yXqzDhKUlyrk5K+jP31ZGVlkZWVVaY6jh8/DpR8fFD8MZ5NWffvL3+ms21fv359Dh06VOz2wRAVFVXkcpPJVOoyFZkye/Xq1frMhvfccw/33ntvoTJV9Zkri2Dt52yKe+0h8D1yuVxVEY4QQgghyqBgIsZqtZKYmMill17KzTffrPdO8Fu6dCk33XQTDodDXxYdHY3NZkPTNJxOJ+np6QGtoc50tusTr9fL6NGj+fTTT/VlJpOJuLg4/Ue8zMxM7HZ7ifUE4/oRAq9fCl7vlbbFVllbtlena8pGjRrRrVs3kpOT+eijjwKSXf5WXR07dqRly5aFtv3vf//LfffdpydCNU0jJiZG74mRn59PVlZWie9hsPlfW5fLVar3Lz8/v7JDEmUk3RhFULVu3Vp/vnHjxlJvt2nTJsDX1Lhx48ZFlinpl4qK8Hg8+vPt27ejfGPZlfgYN25cmWP01zNjxoxS1TF79uxgHqaoAn///TeDBg3C4XBw7bXX8uabbxZZrqo+c2VRWf+/hBBCCHH+OHr0qP7Yv38/GzZs4L333iuU6Dp58iTjxo3D4XDQs2dPkpOTycvLIzMzk2PHjnH06FEWLlx41vrOdn3y/vvv8+mnn2I0GnnqqafYtWsXDoeDtLQ0Pc6hQ4cCFNm1rjIVvN7Lz88v1fVewQRRWeuoDteU/lZbixYt0pM/brdbT0b6uzoWtH37du6//368Xi/Dhg3jt99+w263k56err+Hr732GlC176H/tb3iiitK9bpW9edLnJ0ku0RQ9ejRQ++nv3jx4lJtk5OTo4/h1LVr14BfR0ojPj5ePzkfPny42HLFratTp47+vDKbn/rrKU8d/l9aSjq+0qw/2/7P1jTYv764X35KG19VtSKqShkZGfTr148TJ07Qpk0bFixYUOxFQ1V95oLJ///S3xy/KP4uuEIIIYSo2b7++muysrKIi4vjiy++oFu3boSFhQWUOXr0aIXr8Y/be/vtt/P0009zwQUXFBozLBj1lEdVXO9Vt2vKoUOHEhYWRlZWFp999hngG3v4+PHjmM1mbrrppkLbLFq0CI/HQ6tWrZg3b16RQ2uU9z2syPVrRe7dRPUgyS4RVHXr1tUHmZ43bx5//fXXWbd5/fXXyc7OBuDuu+8uc50Wi4V27doBsHLlymLL/fDDD0Uub9Kkid797osvvihz/aXl7wf/5Zdflnnbyy+/HICDBw+yZ8+eIstkZWUVO+Bkafe/fv36Yk/4GRkZAWN7FWX9+vX6eABn2r17t54s89fn578oOVd/EXG5XNx4441s376d2rVr8+WXXxY7dhlU3WcumOLi4gBfF9aC3REK+vXXX6syJCGEEEJUUwcPHgTgoosuKnYMzu+//z5o9Vx66aVFrs/JyQnZ9UnBpE15rvcKJu2Ku0aubteUUVFRDBo0CDjdddH/7/XXXx8wKZmf/z1s3759sZMblPez4r9+9ddRlOI+H/57t6NHjxY7XrGo3iTZJYJu6tSphIWF4XA4GDZ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'message': 'Finished Successfully.',\n", - " 'exitflag': 1},\n", - " {'x': array([-2.59120601, 0.69656874, -3.88289712, -1.74846428, -1.58175173,\n", - " 3.39830011, 0.0917892 , -0.85030875, -3.77417568]),\n", - " 'x0': array([-2.59120601, 0.69656874, -3.88289712, -1.74846428, -1.58175173,\n", - " 3.39830011, 0.0917892 , -0.85030875, -3.77417568]),\n", - " 'fun': 36260050961.21613,\n", - " 'message': 'Finished Successfully.',\n", - " 'exitflag': 1},\n", - " {'x': array([ 2.10705352, -4.17386158, -4.95145244, -0.4940422 , 2.44773506,\n", - " -0.72754709, -3.38821849, 4.3015123 , -4.03270095]),\n", - " 'x0': array([ 2.10705352, -4.17386158, -4.95145244, -0.4940422 , 2.44773506,\n", - " -0.72754709, -3.38821849, 4.3015123 , -4.03270095]),\n", - " 'fun': 634294830118.3749,\n", - " 'message': 'Finished Successfully.',\n", - " 'exitflag': 1},\n", - " {'x': array([ 1.5071923 , 3.23408298, 4.40175342, -4.93504248, -3.68651524,\n", - " 4.89047865, -3.50203955, -3.98810331, -0.60343463]),\n", - 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" 'exitflag': 1},\n", - " {'x': array([ 1.11811741, -1.66877199, 4.78163474, 3.63123695, -4.84414353,\n", - " -4.69389636, -4.22521978, 1.05436896, 1.66464083]),\n", - " 'x0': array([ 1.11811741, -1.66877199, 4.78163474, 3.63123695, -4.84414353,\n", - " -4.69389636, -4.22521978, 1.05436896, 1.66464083]),\n", - " 'fun': 12556009991670.39,\n", - " 'message': 'Finished Successfully.',\n", - " 'exitflag': 1},\n", - " {'x': array([ 2.78765335, -3.97396464, 3.98103304, 4.06162031, -4.66533684,\n", - " -3.28137522, 3.15208208, -2.66502967, -4.85197795]),\n", - " 'x0': array([ 2.78765335, -3.97396464, 3.98103304, 4.06162031, -4.66533684,\n", - " -3.28137522, 3.15208208, -2.66502967, -4.85197795]),\n", - " 'fun': 16029712077044.059,\n", - " 'message': 'Finished Successfully.',\n", - " 'exitflag': 1},\n", - " {'x': array([ 2.62294851, -3.86768587, 4.24057642, 0.67648706, 4.94028248,\n", - " -4.53014752, -4.41436998, 0.48069498, 2.08662195]),\n", - " 'x0': array([ 2.62294851, -3.86768587, 4.24057642, 0.67648706, 4.94028248,\n", - " -4.53014752, -4.41436998, 0.48069498, 2.08662195]),\n", - " 'fun': 30002126964787.39,\n", - " 'message': 'Finished Successfully.',\n", - " 'exitflag': 1}]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# sort the results\n", "results_sorted = sorted(results, key=lambda a: a[\"fun\"])\n", @@ -1603,39 +505,18 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-5. , -2.2098163 , -1.78589671, 3.55917603, 4.19771074,\n", - " 0.58569077, 0.81885971, 0.49858833])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "results_pypesto.optimize_result[0][\"x\"][problem.x_free_indices][1:]" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "direction: -1\n", - "direction: 1\n" - ] - } - ], + "outputs": [], "source": [ "# we optimimize the first parameter\n", "# x_start = results_sorted[0][\"x\"][1:]\n", @@ -1698,20 +579,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(\n", " [x[0] for x in x_profile], np.exp(np.min(fval_profile) - fval_profile)\n", @@ -1739,28 +609,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine will use up to 8 processes (= CPU count).\n", - "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:13<00:00, 13.82s/it]\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "result_pypesto = profile.parameter_profile(\n", " problem=problem,\n", @@ -1788,182 +639,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "State([[-1.24027462 1.78932702 -3.93716867 -1.83911125 -1.99677746 4.14486841\n", - " 1.47913514 2.0021859 1.35932731]\n", - " [-1.47022978 1.7611698 4.98482712 0.85855303 -4.1989386 -4.3008923\n", - " 1.8615283 1.95714177 1.35676185]\n", - " [-0.41107448 -0.14952555 -1.77644606 4.16117419 -4.81759753 4.76263479\n", - " 1.60195281 1.74819962 1.4403907 ]\n", - " [-1.43602926 2.50778511 -2.30100058 -2.81499186 -4.86002175 3.36625301\n", - " 1.68878632 1.88056349 1.14940453]\n", - " [-1.22391434 1.81872122 -4.926788 -1.83840725 -1.59900807 4.96243857\n", - " 1.52924675 2.06366179 1.22987441]\n", - " [ 3.17131352 2.57330524 -1.1846534 -1.70751361 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3787178673, 3949825982, 2088303512,\n", - " 3672572846, 2002937565, 1152259001, 2024262702, 3512380730,\n", - " 1978640799, 689801872, 1484426853, 2228701662], dtype=uint32), 379, 0, 0.0))" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import emcee\n", "\n", @@ -2058,7 +736,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2068,20 +746,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(\n", " trace_neglogpost.reshape(\n", @@ -2104,18 +771,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:03<00:00, 311.04it/s]\n", - "Elapsed time: 3.9380469999998695\n" - ] - } - ], + "outputs": [], "source": [ "# Sampling\n", "sampler = sample.AdaptiveMetropolisSampler()\n", @@ -2129,20 +787,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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+/8rKSofb5SmOFJZtXQoDaD/vx5lFbPV6Pb4/VopydZNbe5mcNQmiNeOh03M1jQwi7GA8LN46Hw3w7Vm7RKR8ig/Q0tPT8eabb1q9/b59+zBy5EinHf/f//43SkpK5H+fOHECX3/9NW677Tb5tksuuQTh4eGoq6vDc88912aSQFVVFdavXy//u6mpyeLxNBoNNBqN/O+amhpnPA2ncSSnqnUpDMNjGP7beiao8bF6RIeirkGD7YfPdtjzZs6O3FLsLar0mV4m46HT7lGhDCIcZNybZpg1FWjHrF0iImdRfIDmabt370avXr1w4sQJvPTSS9i4cSPS09NRVVUlJ/vHxMQgIyMDy5cvx9KlS1FXV4d58+YhLi4Oe/bswf3332/ymO0Nha5ZswYrV6505VNyiCM5Va1LYQBot3q98bHmTeqPao2+Tc9bctcwqCCg6WDW5onSOp/qZTIeOh0YH4HuUcHoFzdM7h1kEGEbpU4yISL/pfgyGxqNRs7fskZ4eLjF3pSdO3di8uTJACyX2ejIqlWrsGLFCkRFRaGoqAjR0dEAWobQ7r33XmzYsKHNPr169cI777yDiRMnAmgZ7hw8eLDZxzfXg5aYmKiYMhtyXphRTpXdOWgCaNZbDqyMj1VWq4FeCLnn7flv8wAAT103DAA6zMH69vAfiutB84UcO/Iter0eWcdL0ajVt/vZJCLXU3yA5kzOCNDUajViYmKg1Wrx3XffYdKkSSb3//jjj3j77bdx4sQJREdHY+LEiUhPT0dlZSX69OkDoGXI0xDYdcTTddD0ej125JaaLAFkbwFPc6ytE/bt4T+gbmpu04N2/9SW2bQV6ia89dOfpU/WXj8cf0/tZVobTWHlElgjjVzFEGhVNehsSgUw1DgEOr7oISLX4hCnEb1ej1OnTuHMmTMYN26c2W0iIiIQGxuLs2fP4ty5c23uHz9+PMaPH9/m9m+//RYAkJiYaHVwpgQ7cktxzzs5LivgaW1Om2F90IZgFfQCcg22Pl0jUNNoflkd40kJCz88IN/3wo2jMLZvV6c9B3uxRhq5yo7cUruKSRtWCQHgUykBRN6IAZqRrKwsTJgwAZIk4ezZs+jWre2XmVqtRllZGQAgISEBAFBXV4d3330XJSUlyMjIMNszZ1ij85JLLnHhM3C+E6V1TlnSyRJrc9osrQ9q6CmIDgtqk8j94Z5T8qQE4+dwskzttPY7wlU10qxhvMyRlkNZPsfe5dj6xUXKPWiennjC9yj5OwZoRsaOHYvOnTujqqoKGzduxIoVK9pss379emi1WsTGxsq9bMHBwbj//vvR0NCA888/H5deeqnJPnl5eXj//fchSZLZlQaUrF9cZLuJ/I5ytE5Ye0VzjSclGD+HvgqZ4eiqGmnWMPQuAhzKchZ7hxVdwRBo2frZNawS0qjVe3z2Kt+j5O8YoBkJDQ3FokWLsGTJEjz55JPo1asXZs+eDUmSoNPpsGHDBixduhQA8PTTT8uzMYODg3HTTTdh06ZNmD9/Pr766iu5Ntq+fftwww03oKmpCbNmzcKoUaM89vzsMSUlDhtmpZrkoDmTq+qEAX+WTqhSa/D8DSNRUF6P5NgIXDo43qr9hRDYV1SJU5WNKFdrMCwhCqlJzrtyd+Vz74jxMke+MJRl3NvS4KRFz21l77CiK9i7HJuSVgnxtfeoP9HpdPj+WAma9S3/bvTQZ9LbMUBr5ZFHHsGxY8eQmZmJOXPmICMjA3369MHJkydRXl4OSZKwatUqzJ4922S/p556CllZWcjNzUVKSgrOO+88aLVaeWmov/71r3j99dc98ZQcolKpMHVwN6cOa7pL69IJhh/xH46VQCeA+iaB4krLXxo5hZXYkVviFVfu5lZpaBaWh4WMlzly51CWoZepWaeH3op2Wsu4t8VTAZK9w4rOpNfr8fOJUlQ26FBQVo/kuAjcNaEvAgIC3NYGZ/HUe9SYcaDBWa3W+/pwCaob/qy+oISLFm/EAK0VlUqFzZs346qrrsLGjRuRnZ2NAwcOID4+HjNnzsSCBQtwwQUXtNkvNjYWv/zyC5566il88sknOHbsGIKCgjBu3Dikp6djzpw5XvklaeALJSFar2TQ0ZdGfqnaa67cza3S0N6wkHFhVncOZRl6maxtp7WMe1s8FSDZO6zoTDtyS6Fuam4zKebyYT3c2g5n8NR71JhxoMFhVuvll6mh1enlf3vyosWb+VWANmnSJFhbVeTaa6/Ftddea9PjR0VF4cknn8STTz5pT/MULaewEne/vQd/H9Mbv5+tRlW9FpcMiveqIK31SgYdfWkkx0WgoFztFRX6za3S0F5w6anCrIZeJmvbaS3j3hZPBUj2Dis6g6EH9egftRDC9L3tzEkxrYeuANcNXymheLBxoOEtF2tKkBwbYdKD5smLFm/mVwEa2S+/VI2/j+mN139suYJ895cij15B2tOj13olg46+NFKTYqCSgJTuUahQazA0IcrlV+729lSaW6XB07PwzDH0Mjm7nca9Lc4OkKx9TTyZv2XoQe0aEezSSTGth64A3x6+Mg40nP15Mg52fS1H69LB8SaBvCcuWnyBXxWq9UaeLlRrsKegAtsOnsGbu02Lwc4Yk+ix9tha5LV1nla9VnlfivYWr7V1lQZz3DELsU0OmoV2AsBvpypRWquBXg9Aalkbs0kH1DY2o7K+CSMSO2OsG/KAvKGg8NbsYpypbsAne08h47LzoIeEwoo/J8U4K71i/XfHTYaugD9X9QCAxZen4K6J/ZxyLCVwZQ7aV7+dlYM/Xw5yyX7sQSOLjHsO+sdH4OL+sXj3lyKn98jY02tkT5FXJQyZdMTe4rXOeG7umIVobS/TnoIKnK5qkHvZACAsOABHz9W6PQ/IGwoKG3pQz1Y3Yt57+132Q9966AroePjKMHGhQavvcGKO0gQEBGDyINfk7xkPnzJHi8xhgEYW5RRWmvQcfHDnOJfU7Wp9HGt+dJ1V5LV1z5Onl4PyZPFaJcxCNMgvVaOktlHOUwOAQJXkkTwgT74m1jIM8T43YySKK+sxIN41Q0mth66AjoevDBMXrJmY40+Mg13maJE5DNDIotY9B8dL1ZgxJtHpP4j29FA4q8hr69mPnp6p5cnitUqYhWiQHBeBkCBVmx60+la5g+4Iljz5mljL0IM6srdrj2NPj1LLaiR6qybm+BPjYNffcrSMe1WNUxgMpXfwv2tiT6U1KAUDNLKodc9B/7gI7C2skAu3jugVBb0A8kvrOxyabK/oqz09FM4q8tp69qOnZ2p5snitJ2chtpaaFIPgALTkoP2vEy1QBYzo9b9yC+omDE/s7JZgyZOviS/oFxcp96ApIfhXClcOnyqdca+qgfHEIePbPJHWoBQM0Mii1j0HeiGwI7dU/rDMm9TP6g9Oe0VfPdlD0Xr2oxJnPrqLkqrIS5KE4Yn+8SXs66akxLX0lgSrFBH8AyxA62nGvaoGxqV3jG/z9EWzJzFAI4ta9xxszS42+bDY8sFpr+irJ3sojJeDigxWeXz9QSJfo1KpcNEAZQT+Bs4uQKuEpca8iXGvqoGlHjRPpDUoBQM0slrrwq0x4UFI7hqGf0wZAJ0QqK5vQvbJcrNXoEot+mpu9qNer8eO3FLsLapCdUMzpqTE8UuWyIc4uwCtEpYa8ybGvarGKQyRRv823OaJtAalYIBGVmtduLVP1zAkx0ValSPgiaKv9tqRW4p73smx+CVrfLWsbWd4xBeWx/JGravdM/nYOoYLkxOldegXF+nSCxNrP0Ou4uwCtEpYasybKLFXVYkYoJHVJEnC6KQuGJ3U8m9DcUxrrkBb76tkLfkRlr9kja+W2xsesaZ8iLt+FN1RhFYpWle7Z/KxdTq6MHEmaz9DrmI8g9IZaQ1KWGrMVxmC+XPVGtT5Wa4gAzSym+FLydcS6/vFRbb7JWt8tdxecGpN+RB3/Si6owitUrReqJnJx9bp6MLEmaz9DLmKs2dQunKpMX9nCOY9XQLJExigkUUdDdEZvpQ6hwXJV6CjE6Ohh8DW7GKvHdabkhKHDbNSTXq1jBlfLbcXnFpTPsRdP4pKKkLraq2r3TP52DodXZg4k7WfIU+wJ+HfG1Yp8VaGYN4fL6gYoJFFHQ3RmftS8oZ1CzuiUqkwdXA3i8GL8dVye8Mj1pQPcdePopKK0Lpa62r3TD62TkcXJs5k7WfIE5jwryy+OlJjDQZoZJE9Ff69Yd1CR1l7tWxN+RB3/SgqqQitM5nr5fXnAqCO6OjCxJmU3OPEhH9lMTdSo4RA3h0YoJFF9lT494Z1C53JkHzfqG3prrG16KW7fhTbK0LrzbNN7VnHlag1w9JDlQ06qCSge1QoACb8K4GSg3lXY4BGFtlT4d8b1i10JkPyvYEzE1kN5SIam11bhsCbgxx/6LFtzdMlKnyRYemhhR8eQERIIO6e0Bf94iKd1uPs6IUc+ScGaGSRLRX+W/fC/D21l1984RiS7w2cmchqKBfh6tlL3hzkuKvH1vAD26zTQy+A+iaB4krPlCvxdIkKX2RYekirE6iq1+Kp/xzDosvOw7zJ/dts2zrYarRiIoErL+TIdzFAI6ew1AtjGDqo1eh9siioIfneoHUia/+4CBwsrmhZ9Pt/hVINhVM7+pE3lIswF/SlJsXgQHElqtQaqLUCBWUtC9ZfOjgeAQEBNj0Hbx6WdlePreEH1jAj1JPJ454uUeGLDEsPGX8O+lr4HLQOtqx5L7jyQo58FwM0cgpLvTCGoQNvKQpqaz6WIfnecDXdekaaXgicLG80u+ZcR1/shnIR5mYvGXpRwoIDsPDDA/J9L9w4CpcPsy1B3puHpd21jqvhB9ZQU82TyeNKLlHhrQxLDz1/w0gUlNcjObblYsec1sGWNe+Fji7k+LqROQzQyGFCCHSJCDb7hWMYOvCWK0Zb87HaS74HWlZbKKltNCmUas2PvBACCZ1DEBxgvtL5h3tO4VxNIwJVksnjnCxT2/ycPblYvTHjoSOl5egYfmANwbUnk8eVXKLCW9my9FDrYMua90JHF3J83cgcBmjksJzCSmR8fABzxiejXtuMi/vHyV84hqEDbykK6ux8rOS4CIQEqcz2oLX3xW42UEztYvK4eiEQFhxg1bCMJxmvjdlevo7x0JHScnQMP7DNOj0ig1UeLVfi7lltxoGzNflWHTFMcjhV2YgiL1x2rHWwZc17oaMLOW/kzrVb/RUDNHJYfqkaZWotNuw6AQAY0jNa7u0wDB2EeklRUGfnY6UmxSA4AC05aP/rRDMUTm3vi72jQNHQi1Kl1lg1LONJxmtjtjesazx0pLQeV1/8gbWWceDsjNw7w/C8txaB9ef3gjF3rt3qrxigkcPaC2psGTpQAmfnY0mShOGJtgcWHQWK3lQbyHhtzPaGdY2HjnwhR8fQU1Sl1kAnIE8SAayb+acUxoGzM3LvDJMcWATWu7lz7VZjQgjsK2rpgS1XazAsIQqpSb4x6aw1vwrQ9Ho9JkyYgKysLGi1WgQGWv/0c3JyMG7cODQ3N0MIYXG7Q4cOYfXq1fjuu+9QVVWFHj164IorrsDSpUuRkJDgjKehON6cZN6aUvKxXHVOPVGU1nhtzPaGdY2HjnwhR8d4IkfrtUC9qffIOHB2Ru6dYXieRWC9mzvXbjWWU1iJHbklikqBcBVJtBdt+JiMjAw8/fTTAGBTgKbRaDB69GgcPnwYACwGaD/88AMuvfRSNDY2IjY2FklJSTh69Cjq6uoQExODHTt2YOTIkTa1uaamBtHR0aiurkZUVJRN+7qTt+eV+AtPrJVqbQ6ar9maXYwz1Q3ypBBjz3+bJ///4stTcNfEfu5untVszUEzfBecq9agzsxED+PviuLKegyI9473ghACv52qRHFlo0NlbXyFp3LQtmYX49CZarz1U6F829rrh2PGmESXH9vd/KIHTafTISMjA+vWrbNr/2XLlsnBmSUVFRW4+uqr0djYiEceeQSrV69GYGAgamtrcfvtt+Pjjz/G9OnTceTIEQQHB9vVDiVrnVeS3DUMKgiotf7zg+wqzgx+PVGU1hfXxrTmx8l4IkfrHjRv6j2yNefK8F1gaaKHYXh+ZG8XNtoFcgorcbqqweGyNr7CnWu3GkuOi0BBudrmFAjjFTgEvGM1B58P0PLy8jB37lzs2rXLrv2zsrLw7LPPIjw8HPX19Ra3e+GFF1BZWYlx48bhqaeekm/v1KkT3n33XQwaNAj5+fl46623MGfOHLvaomSt80runNgfJXVaq4dyvHk9SFdzZlK1MyZBGHpUqhp0fht8W5MgbTyRIzJYBeNONF9btN6Y4btAaRM9HJVfqkZJrWnunD1lbch+er0eTdqWgMrWSWfGK3AAypspbo5PB2jr16/HAw88AK1Wi8TERMyfPx+LFi2yen+1Wo309HSoVCqsWrUKCxcutLhtZmYmAOCOO+5oc19wcDBmz56NZcuW4b333vPJAK11Xomh0rm1SaTevB6kqzkzqdoZuW2GWX3elEflbNYkSHvTRA5nMnwX+MJED2OGkjlKL2vjyyx991hzMW+8AgegvJni5vh0gJadnQ0AmD9/PlavXo29e/fatP+iRYtw/PhxLFu2DKmpqRa3O3v2LAoLW8bDx48fb3abtLQ0AJAnKAQFBdnUFqUz9BY8N2MkiivrkdQlAjWNWquHcswNvRmWMyqr1aDBj3KXWnNmUrUzJkEYZvUpYRaep3pePZUgbStPrCFq+C7oHBbk9RM9jBlK5ii9rI0vc+S7x3gFDsA7Zor7dIA2ffp0rFixAn379rV53+3bt2PDhg0YPnw4li9fjqysLIvbHj9+HEDLj5+lY/Xp0wdAy4SDoqIi9Oun3KRge7TOKzH8MFg7lGNu6M24S9rXe2vaS6xuHfy2JFV7bljMMKtPCQGKp3pep6TEYcOsVJMcNCXyxBqivtpzaCiZM9wDueitA21vLdniKEe+e4xX4BDwjtUcfDpAu/LKK+3ar6amBrNnz0ZgYCAyMzM77O0qKSkBAERFRSEkJMTsNl27dpX/v6yszGKAptFooNH8uYxITU2Nrc1XBFsTi80NvRmWMwKcU39JyTpKrHZWUrVxoqy9vZKGchhKyKPyxKQHwHMJ0rZS0hqi1L72Zsu2DrSN+frFqzFHvntaXzgYzndIYACyCypR3aBVXICr+AAtPT0db775ptXb79u3z+ZSFq0tWLAARUVFWLFiBUaNGtXh9obJA6GhoRa3CQsLa7O9OWvWrMHKlSttaK1vMDf0ZtwlrYTeGleyJrHa1nIVxoVS1VqBgrJ69I1tGXoGTL/YX7slFZGhQVYNFSqpkrqzV37wNUpaQ5Ta196KDa0DbWP+FHQ787vHG3JpFR+gudu2bduQmZmJkSNHYunSpVbtY2sdnPZyZBYvXowHH3xQ/ndNTQ0SE32vvos1jLukldBb40rWJFZbu2SSgXGhVENpgNsuSkJMeEuZF+Mv9nJ1E+a+neP2oUJHpSbF4IM7x+FctQaNzTr8droagPDZyuK2UtIaotS+9lZsaB1oG7M16DY3XNoslF9ywtn5pkrKpbVE8QHaK6+8gpdeesnq7cPDw+0+Vnl5Oe68804EBQVZNbRpEBnZ8qFobGy0uE1DQ4NVbQwJCbE4TOpvfDWXxRxrEqutXTJJ3v5/vXKBKknePjwoEN2jWnp6jb/YK+u1ip/RZI4kSdAL4NCZasVPmXcFvV6P74+VolzdhEozP65K6u30Nx0V7G2tvRUbWgfajpRsMTdc+kdNIzqFqNCnSwQq1U349vA5AC3rBjfpgNrGZlTWN2FEYmeM9VDw5ux8UyXl0lqi+ADNnQHLvHnzcO7cOaxatQojRoywer/Y2FgAQG1trcUZmmVlZfL/x8dz5g+ZsiYYtXbJJHl7o0Kphu0/3FOEtdOHQS9Mv9ijw4K8dqgwv1TtkSnzHQVH7rAjtxR7iyr9MjhVCktrQ3aUV9qa8VJnrYMuZwba5oZL4yJDEBkaiDK11mTbsOAAHD1Xq4j3l7PzTZWUS2uJ4gM0d9q6dSsAYPny5Vi+fLnF7QxfwJs3b0Z6ejoGDRoEoOUL29IMzYKCAgAteWr+OmTZEU8tHeItLh0cL+egWfOlYlwo1bg0wMTz2i5PI4Tw2vVU7a0s7iglBEcnSuu8op6TL7O0NqStBXvd1dtpbri0pFaDyvqmNvltgSpJMe8vZ+ebekPvMgM0I4ZaZeZUV1fj0KFDJtt169by4sbExGDAgAHIy8vDTz/9ZDZA2717NwDgggsu8Nu12zpiTXV2f2brkkm2DBErZZF4e6QmxUAlASndo1Ch1mBoQpRbAswTpXUIlIB/Xj8coUEB0OgEfjlZgar6JrfNBusXF4nqBq3X9n4qka25TpZ6cNvLK/XkyinmhkujQoOg1evb5LeFBQegvtXkEk+9v5xRZNvbMEAz8uOPP1q8b+fOnZg8ebLF7WbMmIEnnngCr776Km6++WaT+5qamvDGG28AaJmVSuZZU52dqDVJkjA6qQtGJ7n3uP3iIqFuakZ9kw71Wr1HZoNNSYlDoAroFzdMHmb1hx8uV7I118lSD257eaWeXDnFXM+RYSH40lqNSX5boAp2LavkCt58EWkvBmhOct9992H9+vX44YcfcN9992HdunUIDg5GbW0tZs+ejfz8fCQnJ2PWrFmebqpieUt1dnIuw49DcWUjCsrqkRzXUqFd6T3NU1LisHFXPppsmLjhbCqVCpNSPHsRYyj/0tjsupmA5krGuOp9Ymuuk6Ue3PZ6sD1Vv88SQxFeUhYGaE4SHx+Pt99+G9OnT8eLL76Id955B8nJyTh69Chqa2vRuXNnfPHFFz63xJMzeUt1dnKunMJKnK5qkEuBBAVIeOHGUbh8mPXDuZ6gUqkwsFsnizPv/IWh/IsrF582VzLGlveJIYisa+q4fqCtuU6WenDbG8b0pvp9xsWtBYBmvfJLcvgKBmhONG3aNOzZswdPPPEEdu7cif379yMuLg7XXXcdli9fjuTkZE83UbGEENhbVIXGpmYMjA8HoMdXh855VY8K2Se/VI2SWtPF4E+WqT3cKuu0N/POXxjKv7gymdxcyRhb3ieGINKaYWhn5Tq1N4zpTflUxkvuAXBpIO5Knsz7s5dfBWiTJk2CEKLjDR3Yd9iwYXj//fftOoY/M3yZPX71UNQ16ey+UibvkxwXgZAglUmPQl8F9ygY84aZYK5mKP/SOhm+f1wE9hdV4FRlI4ocXCvSXMkYW94nhiDSmmFoZ+U6tTeM6cx8Klt6B+1hCI4NlDKr01aezPuzl18FaKRMQgj8droaWp2QvwjsvVL2NOP19Hx9KECv1+PnE6Vo0OpNFm8OVMFkQeeOlqVKTYpBcABMSoFcOpi1Ar2FofxL68Wn9ULg2B9qp0yesFQy5tLB8W2G4IC27zlDEOnOYWh3DWPa0jtoD+Ml9wC0u9qJkikt788aDNDIbs7qMs4pbJk9FBQgyVXu7b1S9jTj9fS8dSjAWjtyS6FuajY7Nd/4to5+OAwJysNZHtBunhy+sVT+ZWt2Mc7VNFrVa9WR9hLu9xRUmAzBAW3fc4Yg0p3D0O4axrSld9AexkvuCaBNIK7k4Vlj3pT3Z8AAjezmrC7j/FI13v21CHPGJ+NMZT1GJEZDJXlnj4rxenreOhRgrZayKHqzxS2Nb2PpFNdT4vCNoefF1b1WrYfgADPvucHdbKoh6AzuKgvh6t5BX1lyz5vy/gwYoJHdnNVlnBwXAbWmGRt2nVDMj4u9jNfT89ahAGsZ6oB11IPm6h9ob0z+dTYlDt8Yel6emzESxZX1GBDvml6r1kNwgH/NqPVE76A38sY6apKwN2ue3KKmpgbR0dGorq5GVFSUp5tjYk9BhVOu2uUfWKMrm45+YJVaO8tvc9BaFbfUC8i3dZSD5ihnvQ+9mT+fA2ty0LhkHHkjBmgKp+QAzVJg5Y41NfcUVHhl7Sxyvq3ZxVj08UH532uvH44ZY7w3oc0Q5Dfr9CaTLQxaBx+SJMmLdf9ZKNW3LgaI/BGHOMlulrqM3bGmpjfXzmrN8INc1aDjVb8dvDH5tz2GiSath4oNWifAdw4PatN7xuCM3MHQi17ZoFPUSIavYIBGTueONTW9uXZWa4YfZE+s5egLvDH5tz2GiSatJ1sYtP5sxYQHKyL/zHiosYFDjH7BMJObIxmuwQCNnM4da2r6Uu0sww8yZzvaxxuTf9tjmGhiqQet9Werc3iQInoQjSvO82LDPxhmcvvCSIYSMUAjp3PHmpq+VDvL8IPsTzPPyDLD8lHNOj0ig1Vo3YnWeraeJEmK6EE0LnfBiw3/YJjJ7QsjGUrESQIKp+RJAuQczEEjX2BcMJY9aP7BJAfNaCSDOWjOwQBN4RigEZE38IYcNF4MkTfhECc5lTtKbBCR8nhDxXlOyCFvwgCNnModJTaIiOzhjgk57KUjZ2GARk7ljhIbRN7OMBx4qrIRRfwRdxt3TMhhLx05i8MBWlVVFTp37uyEppAvcEeJDWfR6/X4/lgpytVNqPTR5ZichetdOpehJAV/xJ1PCCGvrFCu1mCY0coKhhmyrly30tm9dN6QNsLvUtdwOECbOnUqwsLC8Pnnn6NLF2XnH5DruaPEhrPsyC3F3qJKvPZDvl+uYWiLnMJKv13r0RUMJSl6RIdiwSUDoBMC+WV1yD4ZyB82B+UUVmJHbonZz7VKpcLFA00DJSEE9hRUIL9Ujf7xLQuv55fW230h4uxeOm9IG+F3qWs4HKAdPXoUcXFxDM4IAKBSqTB1cDevGNY8UVqHOk2zIqqwK11+qVoR58lXevKS41oCgXmT+uNEqZo/bE6UX6q26XNtfPExb1I/h18LZ/fSeUPaSEffpTqdDjtyS1Dd2IxKdRNGJHbGWF6IdMjhAC0oKAgRESxMR96nX1wkqhu0iqjCrnRKWe/SV3ryUpNiEKgCsk5U8CLByZLjIlBQrrb6/Wp88eGM18JcL50jvCFtpKPv0q8Pl+C309W8ELGRwwHaLbfcgpdeegkfffQRrr/+eme0icgtpqTEIVAF9IsbJudNePs6jq6ilPUuldKT5yhDSYqyOi3UmmaPBL+G2YaNWj0aFVq3zB6pSTFQSUBK9yhUqDUYmhDV7vvV+OIjMiRQERcixrwhbaSj79L8Mtt6NamFw4Vqf/vtN8ybNw+7d+/GxRdfjPHjx6NHjx4ICwuzuM/s2bMdOaRfYaFaoj/tKajwiR40A08mV397+A+U1WkA+Hflf3nYvEyN/v8bes4vq5cvRDgM57ivfjvLHjQ7OBygGZZ0EEJY/UbW6douAEzmMUAj+pPxjyl/QB3zyq4TaNC2fBd/sveUPFmhql6LUYmdOVmBnMY4B61K3YThzEGzisMBWp8+fWw+ySdPnnTkkH6FARoRuYJxDxoAFFXUs4eDSEEczkErKChwQjPcQ6/XY8KECcjKyoJWq0VgoPVPPycnB+PGjUNzczOsjWkdOR4RkSsZZhs2avU4VsIcISKl8auIYcmSJcjKyrJ5P41Gg1tvvRXNzc1uOR4RkasZzzaUpD88NlmBWOiVzHNqgFZUVIQvv/wSR48eRW1tLTp16oQBAwbgr3/9K/r37+/MQ9lEp9MhIyMD69ats2v/ZcuW4fDhw247HhGROylxRrOv1LyzhjMKvXINUN/jlABNp9Nh4cKFWL9+vTwBwHjSgCRJuOuuu/Dcc88hODjYGYe0Wl5eHubOnYtdu3bZtX9WVhaeffZZhIeHo76+3uXHIyJyN5VKhUkpypq56Ss176zhjKLZXAPU9zgltJ41axZeeOEFNDc3o2fPnpg2bRpmzZqFyy+/HN27d4der8fGjRtx6623OuNwVlu/fj2GDBmCXbt2ITExEWvXrrVpf7VajfT0dKhUKqxatcrlxyMiohbmat75qn5xkXINNgB2DTGfKK3DuZrGNqsOkPdyuAft008/xdatW9GpUye8+uqruOGGG0zuF0Lgvffew913340PP/wQN998M6ZNm+boYa2SnZ0NAJg/fz5Wr16NvXv32rT/okWLcPz4cSxbtgypqakuPx4RUXvaWwjc1yhl9Qp3cMYQs7PXACXPc7jMxhVXXIH//ve/2Lp1K6ZPn25xu48//hh///vfcfXVV+PTTz915JBW27ZtG4YOHYq+ffsCAHbu3InJkycDQIezKrdv346//OUvGDZsGPbs2YOsrCx5X0unzJHjWcIyG0Sm/Dmhek9BhcWFwH0Na97ZxlIOmiRJ+O1UJYorG1FQ1rII/KWD4+Uaps46DnPdnM/hHrQ9e/agZ8+e7QZnADB9+nT07NkTe/bscfSQVrvyyivt2q+mpgazZ89GYGAgMjMzERQU5NLjESmNkhO0nZFQ7a1sXQi8NcOPa7NOj3qtcMoPtqOEEDhQXIkqtQbqVm0a06eLX7yuzmBpDdA9BRU4XdWAhR8ekD8vL9w4CpcP62HXcZjr5j4Oh7zV1dVISEiwattevXqhtLTUpsdPT0+HJElW/+3fv9+OZ2FqwYIFKCoqwpIlSzBq1CiHH88WGo0GNTU1Jn9E7mZI0F708UHMfO1n5BRWerpJMksJ1f4gOS7CoVylHbmlOF2lQY1Gj/s/2I9nvj6K+97fh68Pl7iqyR3KKaxE7rk6VCuoTb4kv1SN4op6k8/LSQc+L8x1cx+He9C6du2K/Pz8DrcTQiA/Px9duij7amjbtm3IzMzEyJEjsXTpUrcff82aNVi5cqXbj0tkTMmLkveLi0R1g9Zv8pOM2boQeGsnSuvQoNUhUCU57QfbUfmlapyraVRUm3xJclwEQoJUJp+Xvg58Xrwh183QU9yo1QMAAlVQTI+xLRwO0C688EJ89tlneOWVV3DXXXdZ3G7jxo0oKyvDtddea9Pjv/LKK3jppZes3j48PNymxzdWXl6OO++8E0FBQTYNbTrT4sWL8eCDD8r/rqmpQWJiotvbQf5NyQnaSqzZ5S6SJGF0UheMTrJvf8OPa1hwgNN+sB2V/L8FypXUJl+SmhSD4ADg+RtGoqC8ZRH4SwfH2/14hhUonp0x0igHLc6JLXacYRjWICw4wGlDvO7kcIA2b948fPrpp7jvvvtQXV2NefPmITLyz2i6rq4O69evx6OPPgpJkjBv3jybHj8kJAQhISGONtMq8+bNw7lz57Bq1SqMGDHCLcdszZ3Pl8iS1KQYvDd3nEmCtk6nw/fHSlDXJDyaHKzEml3ewvDj2qzTO+0H21GpSTEIVAFVao1i2mSL1vmao3t3xt6iKsXkb0qShOGJXTDcSdf5lnLdlMTQU2zgrb2zDs/iBFrKSqxfvx6SJCEwMBADBgyQZx7m5eXJ61fOmzfPpt4wZ+toVqWtH6LNmzcjPT3d7uNZg7M4SSm++u0sqhu0TA4mMrKnoMKkoO6GWam4550cv5vAoiTfHv6DPWgGL774Ivr06YMnnngCVVVVbZZFiomJweLFi7Fw4UJnHM5l0tLSLN5XXV2NQ4cOmWzXrRt/mMh/5JepodXp2yQHTwU/B+S/WudrniitU2z+pr8w9BQb56B5Y++s09bifOihh3Dvvffihx9+QG5uLmpqatCpUyekpKRg/PjxCAsLc9ahXObHH3+0eJ9xb1h72xH5quTYiDbJ+UpLDiZyt9b5mv3iIhWbv+kvvGEY1hpOXSw9JCQEU6dOxdSpU535sESkAJcOjsf3x0oUnRxM5G6t8zVH9+7cJn+TyB5ODdCIyHcFBARg8iDl520Q2cueAs2SJLUpqMsCu+QMNgVovXv3hiRJ2Llzp7ycUe/evW06oCRJKCwstGkfIiLyfkpfS9RQoJkJ/qQENgVop06dgiRJ0Gq1JrfZwpMfxEmTJllcR9MV+zpyPCJP8ee1Lsm1cgorFb2WqJILNJP/sSlA27x5MwCgR48ebW4jIt/gz2tdkms5upaotQzre5bVaiAANDZbV7tPyQWayf/YFKDddtttbW678MILMWDAAF5dE/kIS2tdMkAjRyXHRaCgXO3yICinsBLHS/5cH9La2n3mCjQTeYrDkwSmT5+Ouro67NmzB127dnVGm4jIg/x5rUtyLUfXErWWYX1PA2tr95lL+CfyFIcDtPz8fPTu3ZvBGZGP8Oe1Lsm1HF1L1FqG9T0NWLuPvJHDSz11794dUVFROHbsmLPaREa41BMRkW3szUEjUhKHe9D+8Y9/YMWKFfjnP/+Jhx56yBltIiIispskSRjZm8OU5N0cDtDGjh2LtLQ0LFq0COvXr8dFF12EHj16tLu006pVqxw9LBG5CctuEBG5n8NDnCqVCpIkyfW+2vvSFkJAkiTodDpHDulXOMRJnvbt4T9YdoOIyM0c7kGbMGECr6SJfBjLbhARuZ/DAdrOnTud0Awi/2PPun+ewLIbRETux8XSiTzEW9b9Y9kNUhK9Xo+s46Wo0+ihbmpGFfMiyUc5NUCrrKzE9u3bkZubi6qqKqxbtw6NjY345ZdfMHHiRGceisjrecu6fyqVCpNSzBf2JHK3HbmlKKvToKiiXhF5kd7SE+4vhBDYV1SJU5WNKFdrMCwhCqlJ3hm8OyVAE0Lgsccewz//+U80NDTIt69btw4FBQWYMmUKLrzwQnz22WeIjY11xiGJvB7X/SOy3YnSOjRodYrJi/SWnnAlMNSnO1XZiCIX1aXLKazEjtwSRQTvjnJKgHbrrbfi3XffhRACsbGxaGxshFqtBgCUl5dDCIGffvoJEydORHZ2NsLDw51xWCKvxnX/iGzXLy4SZXUaNDXrFXGB4y094UpgWCPV2rVR7ZFfqlZM8O4oh8PWTz75BO+88w7i4uLw1VdfoaSkBCNGjJDvT0tLww8//IBu3bohNzcX//rXvxw9JJFPMKz7N2NMIvNnyC8IIbCnoAJbs4uxp6AC9lR5mpISh4TOIRiWEI011w3Dsr8NwrtzLvDYBY6hJxwAe8I7YFgjtfXaqM6UHBeByJBAn3hNHO5Be/XVVyFJEt577z1MnjzZ7DZpaWnYunUrJkyYgI8//hiLFy929LBERORlnDEcqFKpcPFA5eREsifceoY1Ul25NmpqUgxUEpDSPQoVag2GJkR57WvicIC2Z88e9O7d22JwZjB+/Hj06dOHa3YSEZnhjvwcT/PF4UBDT7gzn4fhvVCl1kCtFSgoq0dyXAQuHRyPgIAApx3H3VKTYhCoAp6bMRLFlfUYEB+JKSlxTj2GJEkYndQFo5Oc+rAe4XCAVldXhz59+li1bVxcHM6ePevoIYl8GpdW8i+GH+Oz1RpUN2hdmp/jaZwYYx1DrlZYcAAWfnhAPl8v3DgKlw/r4enm2c2wRurI3p5uiXdwOEDr1q0b8vLy5GWcLNFqtTh27Bi6dfOdLxvyXTqdDjtyS1Dd2IxKdRNGJHbGWDcFSTtyS7m0kh8x/BhXNWjRqNW1yc+ZCt/5zuxoONBQ46xRq0ezHn5b58yQqxWokkzeDyfL1B5uGbmTwwHapEmTsGXLFrz88sv4xz/+YXG7559/HtXV1bjyyisdPSSRy319uAS/na72SJDEpZV8n3HtLHVTM6obtOjZORR6fZBL83M8raPhQEONMwCKqXPmCYZcrbDgAJP3Q1/2OPoVhwO0hx56CO+++y4WLlwInU6HWbNmmdxfUlKCF198EWvWrIFKpcL8+fMdPSSRy+WXeW6qNpdW8n37ilpqNdVpmjG4RxTCggIQIEmoqG/EM9cPx6nKBiTHOT8/R+kMNc4A+PVFiiFXq0qtwfM3jERBeT2SY1ty0Mh/OBygDR8+HM8//zzuu+8+PPDAA3jggQfk++Lj41FeXg6g5Yrx8ccfx9ixYx09JJHLJcdGQK1p9kiQxKWVfN+pyka5dyg2IghPXTcMABAcGIHiinqkdO/kcxMErGGocQZAMXXOPMGQq0X+TRL2FKIx46uvvsKSJUtw4MCBNvf1798fjz/+OG644QZnHMqv1NTUIDo6GtXV1YiKivJ0c/yGcQ5alboJw92Yg0a+b3PWSazcdlj+92NXDkZ6Wl8PtkgZmING9CenBWgGhYWFOHToEKqrqxEREYHzzjsPKSkpzjyEX2GARuR79hSUY+ZrvxjlV12AMX26erpZRKQgDgdo33//PaKjo01WD7Dk66+/xvHjxzFv3jxHDmk3vV6PCRMmICsrC1qtFoGB1o/w5uTkYNy4cWhubm63+vXXX3+Nl19+Gb/88gvKy8sRGRmJESNG4Pbbb8ctt9xi8xWgvwRoXHCY/In8fjeazcj3OxEZczhAU6lUuPjii7Fr164Otz3//PORl5eHyspKRw5pt4yMDDz99NMAYFOAptFoMHr0aBw+3DIkYemUPfTQQ3j22WcBAJ06dUK/fv1w+vRplJaWAgCuuuoqfPTRRwgKCrK6zf4SoO0pqOCCwwqj0+nw/bES1DUJFPto4VQiIqWyaZJAbW2tHGwYa2hoQH5+vsX9hBAoLCxEbm4u9Hq97a10kE6nQ0ZGBtatW2fX/suWLZODM0veeecdPPvsswgICMAzzzyDBQsWyD9kH330EWbPno0vvvgCy5cvx5o1a+xqhy/zxQrj3u7rwyU+XziViEipbArQampqMGzYMDQ2Nsq3SZKEnJwcDBgwwKrHuPDCC21roYPy8vIwd+5cq3r4zMnKysKzzz6L8PBw1NfXW9zumWeeAQDMmzfPZCYrAFx//fWoqqrC3Llz8eKLL+Kxxx5DSEiIXe3xVawwrjz5ZWpodXqfLpxKRKRUNo1VJCQk4KGHHoIQQv4DYPLv9v569+6NF154wSVPxJz169djyJAh2LVrFxITE7F27Vqb9ler1UhPT4dKpcKqVassbldRUSHPXp05c6bZba655hr5MTvqjfNHhgrja68fjvfmjmNZCQVIjo1A96hQBAW05Eb5YuFUIlcTQmBvYQW+2H8Gm7NOYk9Bebt5zEQGNtdBe/TRR3HHHXcAaHnjJScnY+zYsdi6davFfVQqFSIjIxET494f3ezsbADA/PnzsXr1auzdu9em/RctWoTjx49j2bJlSE1NtbhdaGgotm3bhlOnTmHo0KFmtzH+QOp0Opva4Q9cseAwOebSwfH4/lgJnp0x0igHzb8KpxI5KqewpSixv66KQPazOUALCgpCUtKfy8RPmDABI0aMMLlNKaZPn44VK1agb1/b6wtt374dGzZswPDhw7F8+XJkZWVZ3DY8PBzTpk1r9/E+/PBDAC3nz9rhYCJPCggIwORB3rswM5ES5Jd6blUS8m4OrySwc+dOJzTDNexd97OmpgazZ89GYGAgMjMzbZp1ac7Zs2exYsUKAMDVV1+N6Ohoi9tqNBpoNBqTthARkXdKjotAQbmaObZkM4cDNIOmpiaUl5ejR48/r7g/++wzvP3229DpdPjb3/6GO+64w+Yp+unp6XjzzTet3n7fvn0YOXKkTcdobcGCBSgqKsKKFSswatQohx6ruroa06ZNQ1lZGSIjIzucwblmzRqsXLnSoWMSEZEypCbFQCUBKd2jUKHWYGhCFHNsySpOKWj0xhtvoHv37li+fLl82+uvv47p06fjs88+wxdffIG7774b119/vTMO51Lbtm1DZmYmRo4ciaVLlzr0WGVlZZg6dSr27t0LSZKwefNm9O/fv919Fi9ejOrqavmvuLjYoTYQEZHnSJKE0UldcNXInkhP64sxfbqyKDFZxeEetN27d2Pu3LkQQuDUqVMAWpLgly1bBgBITU3FuHHj8Pbbb+Pzzz/H+++/jxtvvNHqx3/llVfw0ksvWb19eHi4bU/ASHl5Oe68804EBQU5PLR5/Phx/O1vf8OxY8egUqnw6quvWhWghoSEsAQHERGRn3M4QFu/fj2EELj33nvlQrDff/89SkpKEBMTg507dyI8PBwzZszAhAkT8NZbb9kUoLkzYJk3bx7OnTuHVatWWbV0lSU//vgjrrnmGpSXlyM4OBhvv/02ZsyY4cSWEhERkS9zeIgzKysLMTExWLduHYKDgwEA//nPfwAAl19+udyjNX78eCQlJdlc6sKdDKVCli9fDkmSTP4mT54sb2e4LTMzs81jfPDBB5g6dSrKy8vRpUsXfPPNNwzOiIiIyCYO96D98ccfGDZsmBycAcC3337bJqgBgLi4OLmgqxKlpaVZvK+6uhqHDh0y2a5bN9OK6u+//z5mzZoFvV6Pvn374quvvsJ5553nugYTERG1QwiBnMJK5JeqkRwXgdSkGObAeQmHA7TAwECo1Wr532VlZdi/fz8AtAnQzpw5g8hI5VYi//HHHy3et3PnTvn5mNvul19+wa233gq9Xo9hw4bh66+/Rvfu3V3WViIioo7kFFZi5ms/s0iuF3J4iDMlJQV5eXk4ffo0gJaFwYUQ6N+/P5KTk+Xt/vvf/+LMmTMYPHiwo4dUHJ1Oh1tvvRVarRbx8fH4z3/+w+CMiIg8Lr9U3aZILnkHh3vQrr/+euTk5OAvf/kL/vrXv2LTpk2QJAk33XQTgJaZkW+++SZWrlwJSZJ8Mh/r008/xbFjxwC0rBTQ0XN88cUXHa6vRkRE1JHkuAi/KJIrhMCB4kqcq9agrqkZVfVajOgVjTF9unjtkK7DAdqCBQvw+eef4+eff8bRo0chhMCQIUOwcOFCAMCRI0fk/7/iiitwzz33OHpIxdm1a5f8/6dPn5Z7Ey2prq52dZOIiIiQmhSD9+aOQ36ZGsmxET5bJDensBLHS+pQVFHvM+ueSsJ4FW87NTU1YfPmzThw4AAGDBiAOXPmoFOnTgCA0tJSXHbZZbjllluwYMECm1cS8Hc1NTWIjo5GdXU1oqKiPN0cIiIixdmaXYwz1Q2oUDfhrZ8K5dvXXj8cM8YkerBl9nPKUk/BwcG46667zN4XFxen6NIaRERE5N2S4yKgFwJNzXqfGdJ1Sg+ascOHD+Po0aOora1Fp06dMGDAAAwdOtSZh/Ar7EEjMk+v1+P7Y6UoVzeh0gfyTYjIfsxBa8dnn32GRx55BMePH29zX2JiIp555hn8/e9/d9bhiMiPCSHw39//wG+nq30m34SI7CdJEkb29q3PvlMCtOeffx4PPfQQDJ1xUVFR6NSpE6qqqqBWq1FUVIQbb7wRhYWF8oQBIiJ75RRW4ugftajTNLcpIcAAjailhznreCmqGnQorqjHwG6RmJISzzxwL+LwK7Vv3z4sXLgQQgjcddddOHbsGKqqqlBcXIza2locPnwYc+bMgRACS5YsUfRKAkTkHfJL1egeFYrIkEAEBbQMX3h7vgmRM+3ILcXpKg0e3Lofa/97FPe8sxc7cks93SyygcMB2nPPPQe9Xo/ly5djw4YN6N+/v8n9KSkpePXVV/Hoo4+iubkZL7/8sqOHJCI/lxwXgVd3HUe/uAg8ce1QLLsiBe/OucBnSwgQ2epEaR3O1TSa9DCfKK3zcKvIFg5PEkhKSkJNTQ1KSkoQFBRkcbumpibEx8cjLi4OeXl5jhzSr3CSAFFb8vqCRrWdvDURmMgVvj38B8rqNHj080NyjuaGWamYOrhbxzuTIjgcoIWGhmLEiBH45ZdfOtz2ggsuwG+//Yb6+npHDulXGKAREZGt3J2DZphFWVarQUOzYN6bEzg8SSA8PBxlZWVWbVtaWoqwsDBHD0lERETtUKlUuHig+3rLDJX8AbDXzkkcDmtHjBiBgoIC/PDDD+1ut2vXLhQUFGD48OGOHpKIiIgUJL9UjXM1jcx7cyKHA7Sbb74ZQgjceOONFoc5f/75Z8ycOROSJOHmm2929JBERESkIMlxEegeFYruUaEmM6v7xUV6uGXey+EcNL1ej4svvhg//fQTJEnC+eefj9TUVDlvKicnB7/++iuEEEhLS8POnTsREBDgrPb7POagERGR0jEHzfmcstRTbW0tbrrpJvzf//1fy4MazaYyPPwVV1yBt99+GzExnAZvCwZoRERE/sepa3H+8ssv2LZtG3Jzc1FTU4NOnTohJSUFV155JcaNG+esw/gVBmhERET+x+mLpZNzMUAjIiLyP05bLN0gLy9PXu4pPj4egwcPRkJCgrMPQ0REROSznBagffDBB1i5ciWOHj3a5r4LL7wQTz75JCZMmOCswxEREfkkeaWMUjWS47hShr9yyhDn/fffjxdffFGeEBAdHY3IyEjU1NSgtra25UCShOeffx7z58939HB+hUOcRET+ZU9BBWa+9rNc7PW9ueMwpk8XTzeL3Mzhua+ff/45XnjhBQQEBGDx4sUoLCxEZWUliouLUV1djePHj+PBBx+EJEl48MEHkZ2d7Yx2ExER+aT8UrVJsdf8MrWHW0Se4HCA9sILL0CSJGzYsAFPPPEEEhMTTe5PTk7GunXr8Pzzz0On0+Gf//yno4ckIiLyWclxESbFXpNjIzzcIvIEh4c44+LiEBYWhqKiona3E0IgISEBKpUKp06dcuSQfoVDnERE/kXOQStTIzmWOWj+yuFJAhqNBn379u1wO0mSkJiYiEOHDjl6SCIiIp8lSRLG9OnCvDM/5/AQ57Bhw3Do0CGUl5e3u11DQwNyc3MxdOhQRw9JRERE5NMcDtCWLFmCxsZGzJo1C/X19Ra3u++++1BXV4eHHnrI0UPaTa/XY/z48ZAkCc3NzTbtm5OTg6CgoA67mT/66CNccskl6Ny5M8LCwjBw4EA8+OCDOHPmjCNNJyIiIj/icA7awYMHsWnTJrz44otISkrCnXfeifPPPx9dunSBWq3GoUOHsHnzZuzZswdDhw7FggULzD7O7NmzHWmGVTIyMvD0008DALRaLQIDrRvh1Wg0GD16NA4fPgzgz/VFW5szZw42bdoEAOjevTu6d++OvLw8qNVqdO7cGV9++SXS0tJsajNz0IiIiPyPwwGaSqWSe5WEEBZ7mNq7DwB0Op0jzWiXTqdDRkYG1q1bJ99mS4D28MMPm+xr7pRt2rQJc+bMQWBgIDIzMzFr1iwALQHWnXfeiQ8++ADx8fE4efIkwsPDrW47AzQiIiLzdDodvj9WgsZmQN3UjKp6LUb0isaYPl28fmKFwwFanz59nHISTp486fBjmJOXl4e5c+di165dJrdbG6BlZWVhwoQJCA0NlYdwzZ2ylJQUHD16FIsXL8aTTz5pcl9DQwMSEhJQWVmJLVu2yMGbNRigERERmffVb2dR3aBFUUU9Xvsh36eK+zo8i7OgoMAJzXCN9evX44EHHoBWq0ViYiLmz5+PRYsWWb2/Wq1Geno6VCoVVq1ahYULF5rdrqmpCVdddRUOHTpkNvgKCwvDgAED8Ouvv6KwsNDu50NERER/yi9TQ6vTo07T3Ka4r98HaEpmWLVg/vz5WL16Nfbu3WvT/osWLcLx48exbNkypKamWtwuODgYa9eutXh/bW2tvEbpgAEDbGoDERERmZccG4HqBi2amvUICpDkHjRfKO7rlLU4zbnvvvvw+++/Y/v27a54eKts27YNQ4cOleu07dy5E5MnTwbQ8RDn9u3b8Ze//AXDhg3Dnj17kJWVJe9ryyk7cOAA7r//fuzcuRNDhgzBvn37EBQUZPX+HOIkImpLCIEDxZUoq9WgoVmguKIeA7tFYkpKPFQqhwsUkJfw5Rw0l/Wg7du3D7t373bVw1vlyiuvtGu/mpoazJ49W074tyWgMrj99tuxY8cOeYWFK664Aq+//nqHj6XRaKDRaEzaQkREpnIKK3G8pA4A8Ojnh+Sekw2zUjF1cDcPt47cJSAgAJMH9fB0M1xC8ZcZ6enpkCTJ6r/9+/c7fMwFCxagqKgIS5YswahRo+x6jH//+98my1+dOHECX3/9dYf7rVmzBtHR0fJf67VNiYioZUHxczWNOFfTaJJ7dKK0zsMtI3IOxQdo7rZt2zZkZmZi5MiRWLp0qd2Ps3v3bjQ2NuL333/HPffcg2PHjiE9PR3/+te/2t1v8eLFqK6ulv+Ki4vtbgMRka9KjotA96hQdI8KNVlYvF9cpIdbRuQcLstBu/jii7F7926H65tpNBpotVqrtw8PD7eYf9BRDlp5eTmGDh2K8vJyZGdnY8SIEWb3teeUrVq1CitWrEBUVBSKiooQHR1t1X7MQSMiT9Dr9diRW4oTpXXoFxeJKSlxisrtYg4a+TrFz+IMCQlBSEiIW441b948nDt3DqtWrTIJzpzhoYcewurVq1FTU4N9+/Zh0qRJTn18IiJn2pFbinveyVFsbpckSRjZ27vLKBC1x2WXGUIIu3qaPGnr1q0AgOXLl7fJbTP0ngGQb8vMzATQcqVZVFSEn3/+2eJjR0REIDY2FgBw7tw51z0JIiInOFFax9wuIg9yWQ/ajz/+6KqHdpn21smsrq7GoUOHTLbr1q3latKw2oAkSTh79qx8uzG1Wo2ysjIAQEJCgrObTkTkVP3iIk3qSjG3i8i9FD/E6U7tBZXGOWittxs7diw6d+6MqqoqbNy4EStWrGiz//r166HVahEbG4tx48Y5t+FERE42JSUOG2almuSgEZH7OBygzZ492/qDBQYiPDwc3bt3x+jRo3HJJZcgICDA0SZ4XGhoKBYtWoQlS5bgySefRK9evTB79mxIkgSdTocNGzbIM0Kffvppu+qqERG5k0qlwtTB3TAVysk7I/InDgdomZmZJtV6zeWdtb7f8O9BgwZh69atGDx4sKPN8LhHHnkEx44dQ2ZmJubMmYOMjAz06dMHJ0+eRHl5OSRJwqpVq2wKaImIiMg/ORygbd68GZ9++im++OILhIWF4ZprrsGYMWMQFRWF2tpaHDhwAJ9++ilqamowYsQIXHDBBaioqMDOnTtx+PBhXHHFFdi3bx9iYmKc8Xw8RqVSYfPmzbjqqquwceNGZGdn48CBA4iPj8fMmTOxYMECXHDBBZ5uJhER+TC9Xo+fT5SiskGHgrJ6JMdF4NLB8T4xWuVvHK6DtmfPHlx00UVISUnBl19+id69e7fZpqSkBFdffTUOHjyIn3/+GcOGDUNDQwNuuukmfPHFF1i1apVDRWF9GeugERGRtb49/AfUTc1Y+OEBeYLHCzeOwuXDfHM5JF/mcJmNxx9/HHq9Hh999JHZ4AwA4uPj8cEHH6CpqQkrV64EAISFheGVV15BYGAgPvnkE0ebQURE5PdOlNahuKLepETKyTK1h1tF9nB4iPPHH3/EkCFDMHDgwHa36927N4YOHYrvvvtOvi0+Ph79+vVDQUGBo80gIiLye/3iIqFuajYpkdI3NsLTzSI7OBygabVa6PV6q7Ztbm5GY2OjyW0RERFoampytBlERER+b0pKHH4+UYrnbxiJgvJ6JMe25KCR93E4QOvXrx9+++03HDp0CEOHDrW43ZEjR3D48GEMGjRIvk2n0+HkyZPo1auXo80gIiLyeyqVChcNYGkUX+BwDtott9wCvV6P6667DocPHza7zbFjxzB9+nQAwN///nf59ueffx4VFRUYO3aso80gIiIi8hkOz+JsaGjApEmTkJ2dDZVKhfHjx2PEiBGIjIxETU0NDh48iN27d0On02Ho0KH45ZdfEBYWhhtvvBEffvghACA7OxujR492yhPyNZzFSURE5H8cDtAAoKKiAvfeey/ef//9lgc1U7j2mmuuwWuvvYauXbsCaBkaPX36NJ566incf//9jjbBZzFAIyIi8j9OCdAMcnNz8emnn+LQoUMoKytDREQEhg0bhmuvvRYjR4402fbbb7/FqFGj5ICNzGOARkRE5H+cGqCR8zFAIyIi8j8Oz+I0VlRUhC+//BJHjx5FbW0tOnXqhAEDBuCvf/0r+vfv78xDEREREfkspwRoOp0OCxcuxPr166HT6QCYLoouSRLuuusuPPfccwgODnbGIYmIiIh8llMCtFmzZuHDDz+EEAIJCQkYPXo0oqKiUFlZiX379uHs2bPYuHEjysvL5YkERERERGSewzlon376KaZPn45OnTrh1VdfxQ033GByvxAC7733Hu6++26o1Wp8/vnnmDZtmkON9ifMQSMiIvI/Dheqfe211yBJEt544402wRnQMrx50003YfPmzRBCYNOmTY4ekoiIiMinOdyDFh8fj5CQEBQXF3e4ba9evSBJklXbUgv2oBEREfkfh3vQqqurkZCQYNW2vXr1QmlpqaOHJCIiIvJpDgdoXbt2RX5+fofbCSGQn5+PLl26OHpIIiIiIp/mcIB24YUXory8HK+88kq7223cuBFlZWW48MILHT0kERER+SkhBPYUVGBrdjH2FFTAV+vtOxygzZs3D0II3HfffVi7di3q6upM7q+rq8PTTz+NBQsWQJIkzJs3z9FDEhERkZ/KKazEzNd+xqKPD2Lmaz8jp7DS001yCYcDtEsuuQT/+Mc/oNVqsXjxYnTt2hVDhw5FWloahg4diq5du2LJkiVobm7GPffcg0suucQZ7SYiIiI/lF+qhlbX0mum1Qnkl6k93CLXcEqh2hdffBF9+vTBE088gaqqKhw+fNjk/piYGCxevBgLFy50xuGIiIjITyXHRSAoQIJWJxAUICE5NsLTTXIJpy6WrtFo8MMPPyA3Nxc1NTXo1KkTUlJSMH78eISFhTnrMH6FZTaIiIj+JIRATmEl8svUSI6NQGpSjLy0pC9xaoBGzscAjYiIyP/YNMTZu3dvhw8oSRIKCwsdfhx76PV6TJgwAVlZWdBqtQgMtP7p5+TkYNy4cWhubrZpxoi9+xEREZH/silAO3XqlMMH9GQ35JIlS5CVlWXzfhqNBrfeeiuam5vdsh8RERH5N5sCtM2bN7uqHS6l0+mQkZGBdevW2bX/smXL2kx8cOV+RERE5N98PgctLy8Pc+fOxa5du0xut3aIMysrCxMmTEBoaCjq6+sBwKqhSnv3a405aERERP7H4TpoSrZ+/XoMGTIEu3btQmJiItauXWvT/mq1Gunp6VCpVFi1apXL9yMiIiICfDxAy87OBgDMnz8fhw4dwtixY23af9GiRTh+/DgyMjKQmprq8v2IiIiIAB8P0KZPn46jR4/ihRdesHl4cPv27diwYQOGDx+O5cuXu3w/IiIiIgOnrCSgVFdeeaVd+9XU1GD27NkIDAxEZmYmgoKCXLofERERkTHF96Clp6dDkiSr//bv3+/wMRcsWICioiIsWbIEo0aNcvl+xjQaDWpqakz+iIiIyL8oPkBzt23btiEzMxMjR47E0qVLXb5fa2vWrEF0dLT8l5iYaPdjERERkXdSfJkNjUYDrVZr9fbh4eFQqczHnTt37sTkyZMBmC+zUV5ejqFDh6K8vBzZ2dkYMWKE2X1bnzJ79zNHo9FAo9HI/66pqUFiYiLLbBAREfkRxeeghYSEICQkxC3HmjdvHs6dO4dVq1aZBFmu2s8cdz5fIiIiUibF96A5U0c9aLYuQ7V582Y5R86e/azBQrVERET+R/E9aO6UlpZm8b7q6mocOnTIZLtu3bo5tB8RERGROexBs2NfW06ZvfsZsAeNiIjI/3AWJxEREZHCMEAjIiIiUhgGaEREREQK41c5aN6IOWhERERt6fV67MgtxYnSOvSLi8SUlDiLdVC9EWdxEhER+TghBHIKK5FfqkZyXARSk2JsLhGlNDtyS3HPOznQ6gSCAiRsmJWKqYN9p0oCAzQiIiIfl1NYiZmv/SwHM+/NHYcxfbp4ulkOOVFaB62uZRBQqxM4UVqHqfCdAM13+gKJiIjIrPxStUkwk1+m9nCLHNcvLhJBAS29gEEBEvrFRXq4Rc7FHjQiIiIflxwXgaAASe5BS46N8HSTHDYlJQ4bZqWa5KD5Ek4SUDhOEiAiIkfJOWhlaiTH+kYOmq9jgKZwDNCIiIj8D3PQiIiIiBSGARoRERGRwjBAIyIiIlIYBmhERERECsMAjYiIiEhhGKARERERKQwDNCIiIiKF4UoCRERkli8usE2eIYTAvqJKnKpsRLlag2EJUUhN6sL3UzsYoBERkVm+uMA2eUZOYSV25JbgtR/y+X6yEgM0IiIyq7iiHnPGJ0Pd1IyI4EAUV9TzB5Xskl+qRp2muc2C7Xw/WcYAjYiIzOoUGoTXf/yzx2PDrFRPN4m8VHJcBArK1T63YLsrMUAjIiKzytUakx6PCnWTh1tE3io1KQYqCUjpHoUKtQZDE6KQmhTj6WYpGgM0IiIyq19cpGmPRxx7PMg+kiRhdFIXjE7ydEu8hySEEJ5uBFlWU1OD6OhoVFdXIyoqytPNISI/Is/iLFMjOZazOInciQGawjFAIyIi8j8sVEtERESkMAzQiIiIiBSGARoRERGRwvhVgKbX6zF+/HhIkoTm5mab9s3JyUFQUFC7CbI7d+6EJEnt/l1zzTUOPgsiIiLydX5VZmPJkiXIysqyeT+NRoNbb721w6DuwIEDAIAePXogOTnZ7DaDBw+2+fhERETkX/wiQNPpdMjIyMC6devs2n/ZsmU4fPhwh9sZArQFCxbgkUcesetYRERERD4foOXl5WHu3LnYtWuXXftnZWXh2WefRXh4OOrr69vd1hCgDRs2zK5jEREREQE+noO2fv16DBkyBLt27UJiYiLWrl1r0/5qtRrp6elQqVRYtWpVu9s2Nzfj999/BwAMHTrU7jYTERER+XSAlp2dDQCYP38+Dh06hLFjx9q0/6JFi3D8+HFkZGQgNbX9RYKPHj0KjUaD6Oho9O7d2+42ExEREfn0EOf06dOxYsUK9O3b1+Z9t2/fjg0bNmD48OFYvnx5h5MLDMObQ4YMwd69e7FlyxYcOnQIAQEBGDJkCG677TYOfRIRkUcIIXCguBKnKhtRVFGPgd0iMSUlHiqVT/fTeDWfDtCuvPJKu/arqanB7NmzERgYiMzMTAQFBXW4jyFAO3jwYJvetv/85z947rnnsHjxYqxevbrdx9FoNNBoNCZtISIickROYSWOl9Th0c8PQasTCAqQsGFWKqYO7ubpppEFig+d09PTO6wtZvy3f/9+h4+5YMECFBUVYcmSJRg1apRV+xgCtMbGRixduhT5+fnQaDTIy8vD/fffDyEEnnjiiQ7z4NasWYPo6Gj5LzEx0eHnQ0RE/i2/VI1zNY3Q6lqW39bqBE6U1nm4VdQexQdo7rZt2zZkZmZi5MiRWLp0qdX7TZs2Dbfffjvef/99rF69Gn379kVwcDD69++P5557Do8//jgAYOXKlSgrK7P4OIsXL0Z1dbX8V1xc7PBzIiIi/5YcF4HuUaEICmgpth4UIKFfXKSHW0XtkYQQwtONaI9Go4FWq7V6+/DwcItj6jt37sTkyZMBAFqtFoGBpiO85eXlGDp0KMrLy5GdnY0RI0aY3deeU6bRaBAbG4u6ujq88847uOmmm6zar6amBtHR0aiurkZUVJTNxyUiIjLOQSuurMeAeOagKZ3ic9BCQkIQEhLilmPNmzcP586dw6pVq0yCM2cICQnB4MGD8euvv+LkyZNOfWwiIqL2SJKEkb27YCSLDHgNhs5Gtm7dCgBYvnx5m9w2Q+8ZAPm2zMxMk/0bGxvbfXy9Xg8AVk06ICIiIv+l+B40d0pLS7N4X3V1NQ4dOmSyXbduLbNfDh48iIsvvhg1NTXIycnB6NGj2+zf2NgoLxfF9TiJiIioPQzQjPz4448W7zPOQWu93XnnnSeP42dmZpoN0F566SXU19cjNjYWl1xyiRNbTURERL6GQ5xOEBISggcffBBASyD2wgsvyMOZer0eL7/8MhYvXgwAeOqppxAWFuaxthIREZHyMUBzkiVLluDGG2+EEAILFixAXFwcxo4di27duuEf//gHdDodHnvsMdxxxx2ebioREREpHAM0JwkICMB7772HrVu34tJLLwXQUrw2KCgIM2bMwI8//ogVK1Z4uJVERETkDRRfB83fsQ4aERGR/2EPGhEREZHCMEAjIiIiUhgGaEREREQKwwCNiIiISGEYoBEREREpDAM0IiIiIoVhgEZERESkMAzQiIiIiBSGARoRERGRwjBAIyIiIlIYBmhERERECsMAjYiIiEhhGKARERERKQwDNCIiIiKFYYBGREREpDAM0IiIiIgUhgEaERERkcIwQCMiIiJSGAZoRERERArDAI2IiIhIYRigERERESkMAzQiIiIihWGARkRERKQwfhWg6fV6jB8/HpIkobm52aZ9c3JyEBQUBEmS2t1Oo9Hgn//8J84//3xER0cjLCwMQ4cOxerVq9HQ0OBI84mIiMhPSEII4elGuEtGRgaefvppAIBWq0VgYKBV+2k0GowePRqHDx8GAFg6ZX/88QcuvfRSHDx4EAEBAUhJSYFarUZBQQEAYPTo0dixYweio6OtbnNNTQ2io6NRXV2NqKgoq/cjIiLyVUII5BRWIr9UjeS4CKQmxXTYgeJt/KIHTafT4eGHH5aDM1stW7ZMDs4sEUJgxowZOHjwIAYPHozDhw/j0KFDOHnyJHbt2oX4+Hjs3bsXS5YssasNRERE1CKnsBIzX/sZiz4+iJmv/YycwkpPN8npfD5Ay8vLwyWXXIJ169bZtX9WVhaeffZZhIeHt7vdRx99hO+//x5RUVHYvn07Bg4cKN83YcIEPPXUUwCAN998E1qt1q62EBEREZBfqoZW1zKapdUJ5JepPdwi5/PpAG39+vUYMmQIdu3ahcTERKxdu9am/dVqNdLT06FSqbBq1ap2t83MzAQALFy4EN27d29z//Tp07Fy5UqsXbsWGo3GpnYQERHRn5LjIhAU0DKkGRQgITk2wsMtcj7rkrC8VHZ2NgBg/vz5WL16Nfbu3WvT/osWLcLx48exbNkypKamWtxOp9Nhx44dAIDrrrvO7DZRUVFYvny5TccnIiKitlKTYvDe3HHIL1MjObYlB83X+PQkgW3btmHo0KHo27cvAGDnzp2YPHkygI4nCWzfvh1/+ctfMGzYMOzZswdZWVnyvq1PWW5uLgYNGoSAgABoNBpUVFTgzTffRFZWFurq6jBgwADceuutGDdunM3PgZMEiIiI/I9P96BdeeWVdu1XU1OD2bNnIzAwEJmZmQgKCmp3+8LCQgBATEwMduzYgZkzZ6K8vFy+/9tvv8WGDRvwwAMP4J///KfPzTQhIiIi51J8Dlp6ejokSbL6b//+/Q4fc8GCBSgqKsKSJUswatSoDrevra0F0JKzdu211yIhIQHbt29HQ0MDzpw5g5UrV0KlUuG5557rMA9Oo9GgpqbG5I+IiIj8i+IDNHfbtm0bMjMzMXLkSCxdutSqfQwFaBsaGhAbG4tdu3ZhypQpCA0NRY8ePbB8+XIsW7YMAPD444+jstLydOA1a9YgOjpa/ktMTHT8SREREZFXUXyA9sorr6C2ttbqv+HDh9t9rPLyctx5550ICgqyamjTwLgEx/3334/OnTu32SYjIwMhISFQq9XYvn27xcdavHgxqqur5b/i4mKbnwcRERF5N8XnoIWEhCAkJMQtx5o3bx7OnTuHVatWYcSIEVbvZxyQjRw50uw2YWFh6NevHw4fPoz8/HyLj+XO50tERETKpPgeNHfaunUrAGD58uVtctsMMzgByLcZap8NGjRIvq+9GmeGWaMMwIiIiKg9iu9Bc6e0tDSL91VXV+PQoUMm23Xr1g0A0LNnTyQmJqK4uBi//PILLrvssjb763Q6HD9+HADQr18/ZzediIjIb/jDWpwM0Iz8+OOPFu8zrqFmbrtZs2bhqaeewquvvooHHngAnTp1Mrn/1VdfRX19PaKjozF16lTnNpyIiMiPGNbi1OoEggIkvDd3HMb06eLpZjkVhzid5OGHH0a3bt1w+vRpTJs2zSS5/+uvv8bixYsBtEwCCA0N9VQziYiIvJ4/rMXJHjQn6dKlC/7v//4Pl19+Ob7//nskJydj8ODBqK+vl4c2b7rpJjz88MMebikREZF3M6zFaehB41qc1K7U1FQcOXIE69atw2effYZjx44hPDwckyZNwp133omZM2d6uolERERej2txksdxLU4iIiL/wxw0IiIiIoVhgEZERESkMAzQiIiIiBSGARoRERGRwjBAIyIiIlIYBmhERERECsMAjYiIiEhhGKARERERKQwDNCIiIiKFYYBGREREpDAM0IiIiIgUhgEaERERkcIEeroB1D7DWvY1NTUebgkRERHZqlOnTpAkyeb9GKApXG1tLQAgMTHRwy0hIiIiW1VXVyMqKsrm/SRh6KIhRdLr9Thz5ozdEbglNTU1SExMRHFxsV1vHLIez7V78Dy7B8+ze/A8u4c7zjN70HyUSqVCr169XPb4UVFR/PC7Cc+1e/A8uwfPs3vwPLuHEs8zJwkQERERKQwDNCIiIiKFYYDmp0JCQrBixQqEhIR4uik+j+faPXie3YPn2T14nt1DyeeZkwSIiIiIFIY9aEREREQKwwCNiIiISGEYoBEREREpDAM0P1NfX4/HHnsMKSkpCAkJQWxsLC677DJ89dVXnm6aYp06dQoPPPAABg0ahPDwcISHh2PIkCF45JFHUFJSYnYfe89zeXk5HnjgASQnJyM4OBjdunXDddddh59//tkVT02xmpubcf7550OSJGRmZprdhufYPjt27MB1112HHj16IDg4GD179sTNN9+MI0eOWNyH59o25eXlWLRoEc477zyEhoYiKioKF110EV599VXo9fp297PnfBUVFWHOnDno1asXgoODkZCQgFtvvbXd19SbvfLKK5AkCa+//rrFbdz9nnXJayDIb9TV1YkLLrhAABBBQUFi1KhRonfv3gKAACAee+wxTzdRcb7//nvRuXNnAUAEBASI8847T/Tv318EBAQIAKJ79+7iwIEDJvvYe57PnTsn+vXrJwCI8PBwkZqaKrp16yYfe9OmTe54yoqwatUq+Xxt3ry5zf08x/Z55JFH5HPUo0cPMXz4cBESEiIAiNDQUPH111+32Yfn2jYFBQXy+QkMDBSDBw82OV/Tpk0TTU1Nbfaz93zl5uaKrl27CgAiOjpajBkzRnTp0kV+Tf/zn/+4+im71a+//io6deokAIjXXnvN7Dbufs+66jVggOZHbrvtNgFAjBw5UhQVFcm3v/XWWyIwMFAAEN98840HW6gslZWVIi4uTgAQf/3rX8WZM2fk+06cOCHS0tIEANG3b1/R0NAg32fveZ44caIAIP7yl7+IiooKIYQQOp1OPPXUU/IXzeHDh134jJVh3759IigoqN0AjefYdps2bZKf4+bNm4VerxdCCFFeXi7+9re/CQAiPj5e1NXVmezHc22bKVOmCABiyJAh4tixY/LtX3zxhQgNDRUAxOrVq9vsZ8/50mq1on///gKAuOWWW0R9fb0QQgiNRiPuvfdeAUB07txZlJWVufAZu893330nYmJi5O8GSwGaO9+zrnwNGKD5iePHj4uAgAChUqnE77//3ub+ZcuWCQAiLS3NA61Tpueee04AED179hQ1NTVt7i8pKZG/LN5++20hhP3n+bvvvhMARGRkpCgtLW2z38033ywAiFmzZjnp2SmTRqMRw4YNEyqVSu7ZaR2g8RzbrqGhQb6if/nll9vcX1VVJfdKvPvuu/LtPNe2KSoqkoOHrKysNvcvX75cvqgzZu/52rx5swAgevfuLTQajcl9er1eXHzxxQKAWLp0qROenec0NDSIFStWyCMX7QVo7n7PuvI1YIDmJ1asWNFuAHbq1Cn5TV9YWOjm1inTVVddJQCIO++80+I2hp6H++67Twhh/3k2XPFZ+sH68ccfBQAREREhX6H5ooyMDAFA3H///SIpKclsgMZzbLutW7cKAGLAgAGiubnZ7DaZmZni2WefFXv37pVv47m2ze7du+Xz0bonUgghtm3bJg99GrP3fBl6fCz9+G/ZskUAEMnJyQ48K8/Ky8sTiYmJ8jDj6tWr5e8GcwGau9+zrnwNOEnAT/z0008AgPHjx5u9PyEhAUlJSQCAXbt2ua1dSrZs2TK89dZbmD17tsVtxP/qPOt0OgD2n+eO9jv//PMRGBgItVqNPXv22PhMvMPPP/+MZ555BgMHDsSTTz5pcTueY9t98803AICrr74aAQEBZre57bbb8MADD2DUqFHybTzXtundu7f8//v27Wtz/8GDBwFAPmcG9pwvvV6PX3/9td390tLSAAD5+fkoLi625akoxqlTp1BcXIxx48bhl19+wdKlS9vd3p3vWVe/BgzQ/MTx48cBAP369bO4TZ8+fQAAx44dc0eTFG/s2LG45ZZbcMEFF5i9v6ysDDt37gQADBkyBIB951mv1yM/P7/d/YKCgpCQkGCyny9paGjAbbfdBiEENm/ejLCwMIvb8hzbzhAYDBkyBEIIfPLJJ0hPT8fUqVMxY8YMbNq0CVqtts1+PNe2SUhIwNVXXw0AuOeee3DixAn5vh07dmDNmjUAgAcffFC+3d7zdfr0aTQ0NLS7X2JiohyQe+t57tWrF/7v//4PP/30E1JTUzvc3p3vWVe/BgzQ/IShHERcXJzFbbp27QqgJfCgji1YsAD19fUIDw/H9OnTAdh3nisrK9Hc3Gzzfr4kIyMDx44dwwMPPICLLrqo3W15jm1XWFgIoOVHZuLEiZg+fTrefPNNbN++HR9++CHmzJmD1NTUNlf4PNe227JlC2bMmIHff/8dKSkpGDJkCJKTk3HJJZcgMDAQzz//PObNmydvb+/5Mi7xY2m/gIAAREdHm+znbfr3748rrrjC6u3d+Z519WvAAM1P1NfXAwBCQ0MtbmPotTBsS5atXr0a7777LgBg+fLliI+PB2DfeTY+3/74+uzcuRMvvvgiUlJSsHr16g635zm2XW1tLQDggQceQHZ2Np5//nmUlJRArVZj27Zt6NOnD3777TdMmzYNTU1N8n4817aTJAkjRoxAly5d0NzcjMOHD+PkyZMAgM6dOyM8PNxke3vPl7+fZ0vc+Z519WvAAM1PWMo7MUeSJBe2xPutXLkSjz76KICWnJ5FixbJ99lznm3Zx3g/X1BbW4vbb78dKpUKmZmZ7X7JGfAc284wDFNaWor33nsPCxYsQFxcHMLDwzFt2jT85z//QVBQEA4ePIg333xT3o/n2jY1NTWYMmUKli5diqSkJOzYsQMNDQ0oKyvDa6+9hqqqKtx5550mPWj2ni9/Ps/tced71tWvAQM0PxEZGQkAaGxstLiN4Uu89RUetWhubsZdd92Fxx57DABw2WWX4f333zf50Nlzng372LqfL3jwwQdRUFCAhx56yGKuX2s8x7YzPJ8RI0bgmmuuaXP/eeedh5kzZwIAPv/8c/l2nmvbPPPMM/j111/Rs2dPbN++HZMnT0ZoaCi6du2KOXPm4Ouvv0ZAQAA2bNgg56/ae778+Ty3x53vWVe/BgzQ/ERsbCyAlmUsLDGMjxuG6+hPNTU1uOKKK/Dqq68CAG644QZ88cUXbXp87DnPkZGRCAkJsXk/b/fVV1/h9ddfx6BBg7Bq1Sqr9+M5tl3nzp0BACNHjrS4zdChQwFATpYGeK5t9eGHHwJoyU81nHNjY8eOxbRp0wBATpGw93wZXpv29mtubkZ1dbXJfr7One9ZV78GDND8xKBBgwAABQUFFrcx3Ddw4EA3tMh7nDp1CmlpaXKpgocffhjvvfcegoOD22xrz3lWqVQ477zz2t1Pq9XizJkzJvt5uw8++AAAcOTIEYSGhkKSJJM/Q2L77bffDkmSMGnSJAA8x/YwnDONRmNxm8DAQACQf6iM9+O5to7hPZuSkmJxm8GDBwOAnJdm7/nq2bOnnHxuab/i4mK5BJAvnef2uPM96+rXgAGanzAMHxlqvbR26tQpFBUVAUCHs+j8yZkzZzBp0iQcOnRIHppYu3atxVwCe89zR/v9+uuvaG5uRmhoqEmdKm82cOBApKWlWfwzBAoDBgxAWloahg0bBoDn2B7jxo0DALlmkzm5ubkATMsF8FzbJioqCgBw9uxZi9sYZv4ZtgXsP1/nn39+u/vt3r0bQEvdtZ49e1r7NLyau9+zLn0NbC5tS17p2LFjciXm3NzcNvcvXbpUABATJ050f+MUSqPRiNTUVAFABAcHi08++aTDfew9z99884280G55eXmb/WbNmiUAiNtuu83ep+N1LK0kwHNsuyNHjsjV0829j//44w95qaf33ntPvp3n2jY33nijACDGjh1rdsWG8vJyeVHt9evXy7fbe75ee+01uUp962WGhBBi/PjxAoBYsWKFw89NSdpbScDd71lXvgYM0PzITTfdJACIwYMHi7y8PPn2t99+m4ulm7Fq1aoOF+U1x57zrNfr5cXXL774YnHu3DkhRMtCvU8//bTFhXp9maUATQieY3vMmTNH/gH64osv5NvPnj0rJk2aJACI4cOHt/mR4bm23sGDB0VwcLAAIGbOnGmypmN+fr4YN26cvBan8XJB9p6vhoYG0a9fPwFATJ8+XV4zWKPRiPnz58uvt68slm7QXoAmhHvfs658DRig+ZGysjIxbNgw+epi5MiR8hsdgHjiiSc83UTF0Gg08kLogYGBIi0trd0/43Nn73k+ceKE6NWrlwAgQkJCRGpqqujevbsAICRJElu2bHHX01eE9gI0nmPb1dfXy2vHAhCJiYli9OjRckCRlJQkjhw50mY/nmvbfPTRRyIsLEzueR8xYoQYOnSoUKlU8nk2F5jae75+/fVXER0dLYCWhb7HjBkjunTpIh9/x44drn7KbtdRgObu96yrXgMGaH6mrq5OrFixQgwaNEiEhISIyMhIMXHiRPHxxx97ummKsmfPHvnDbM1f625ve89zaWmpuP/++0VycrIIDg4WnTt3Fn/961998ku2I+0FaELwHNtDr9eLLVu2iMmTJ4vOnTuLsLAwkZKSIpYuXdruFT7PtW3y8vLE3XffLfr16ydCQkJERESEGDlypFi5cqWorKy0uJ+956ugoEDccccdolevXiIoKEjExsaK6dOni3379jn3iSlERwGaEO5/z7riNZCE+N9qz0RERESkCJzFSURERKQwDNCIiIiIFIYBGhEREZHCMEAjIiIiUhgGaEREREQKwwCNiIiISGEYoBEREREpDAM0IiIiIoVhgEZERESkMAzQiIiIiBSGARoROU1mZiYkSUKvXr3M3n/kyBF4y+pyp06dQk1Njclt6enpkCQJN998s4da1b6dO3dCkiRIkoTm5maXH++xxx6DJEkYP368y49F5G8YoBGRy9XU1ODee+/F8OHDodPpPN2cdjU1NWH58uUYOHAgSkpKPN0cIvJTgZ5uABH5jmuvvRbjxo1DUFCQye179+7F+vXrPdQq25w5cwaPP/642fvWrFmDjIwMREdHu7lV1jn//PNx5MgRAEBgIL/eibwZP8FE5DTR0dGKDV6coUePHujRo4enm2FReHg4UlJSPN0MInICDnESERERKQwDNCJyGnOTBPr06YPJkyfL/w4KCoIkSSgoKDDZ9/vvv8f111+PHj16IDg4GN26dcM111yDHTt2mD1Wnz59IEkSDh48iAULFiAmJgaRkZFITU1FRUUFAEAIgS+++ALXX389kpKSEBYWhrCwMPTp0we33HIL9uzZY/KYkyZNQt++feV/DxgwAJIkYefOnQDanySg0+mwadMmTJ48GTExMQgJCUHv3r1xyy23YO/evW22LygogCRJ6N69O4QQ2LRpEy644AJ06tQJnTp1woUXXojNmzfbNKnC0iQBQ7s3btyIgoICzJ49G7169UJISAh69eqFOXPmtHk9DPR6PTZv3oyLLroInTt3RufOnfG3v/0N2dnZHbbHltd09uzZkCQJ4eHhyMvLa3P/ypUrIUkSIiMjcezYMavPCZHXEkRETrJ582YBQCQkJMi3XX/99WLo0KECgAAg0tLSRFpamjh79qy8zSOPPCLfHxMTI1JTU0X37t3l2x555JE2x0pKSpIfD4AYPHiwSEpKEhdeeKEQQgi9Xi9mzpwpP0ZCQoIYM2aM6Nu3r1CpVAKACAwMFP/3f/8nP+a9994rxowZI++Tmpoq0tLSxN69e4UQQtx2220CgJg1a5ZJW6qrq8VFF10k79enTx8xZswYERUVJQAIlUolnn32WZN9Tp48KQCI+Ph4ccsttwgAonPnzmL06NGic+fO7T53S7777jt5P61WK99uaPfcuXNFVFSUUKlUYsCAAWLw4MHy9nFxcaKoqMjk8TQajbjmmmvkbfr16ydGjRolgoKCRFBQkHzu09LS2rTF1te0pqZGJCcnCwDi4osvFnq9Xr4vKytLBAQECAAiMzPT6vNB5M0YoBGR05gL0ISwHDgIIcTGjRvl4GTLli3y7Xq9Xrz//vsiIiJCABCvv/66yX6GAA2AeP/99+XbS0tLTdoSFhZmEoQJIcSRI0fEkCFDBAAxZswYk/sMgRMAkZeXZ3KfpQBt2rRpAoDo3r27+O677+TbGxsbxaOPPio/3scff2z2OAEBAeJf//qXaG5uFkII0dDQIG6++WY5iCwpKWl7ss3oKEADIMaNGyeOHj0q37d7927RqVMnAUAsWLDA5PFWr14tAIjo6GjxzTffyLefPn1aTJw40SToNmbva2ociL344otCiJbgt2/fvmbPO5EvY4BGRE5ja4Cm0WhEt27dBADxySefmH3Ml19+WX5M430NAdrFF19sdr+ZM2eKwMBA8dBDD5m9/6233hIAREhIiMnttgZoP/30k7x9VlaW2WPdddddAoDo37+/2ePMnz+/zT5lZWVCkiQBQHz55ZdmH7e1jgK04OBgk55Lg/nz57cJVpuamuQeQHO9VhUVFaJr165tAjRHXlMhhFi2bJkAICIjI0VRUZG49dZb5d67mpoaq84DkS9gDhoReczu3bvxxx9/oFOnTrj66qvNbjNr1iyoVCqcPn3abC6XpSKp7777LhobG/Hkk0+avT88PBwAoNFooNfr7XwGwLZt2wC0lLi46KKLzG7z0EMPAQCOHz+OQ4cOtbn/yiuvbHNb165dERcXBwCoqqqyu33GxowZg+7du7e5fdCgQW2O88MPP6CmpgahoaG48cYb2+wTExODG264oc3tjr6mK1aswNixY1FXV4fLLrsMb731FoKCgvD++++jU6dOtjxdIq/GMhtE5DGGYKWpqQkTJkywuF1AQAD0ej1yc3Nx/vnnm9zXXtmLgIAANDY24ocffkBubi7y8/ORl5eH/fv3o7i4WN5Or9dDpbLvejU3NxcAkJqaanGbAQMGICoqCjU1NTh69CiGDh1qcn9CQoLZ/cLCwgDAaasC2HKco0ePAgD69++PkJAQs/uNHDmyzW2OvqaBgYHYsmULRo0aJdd0e/LJJzFmzJh2nhmR72GARkQeU11dDaClFysrK6vD7c31JBmCi9a0Wi2WLl2Kl156CQ0NDfLtAQEBGDZsGM4//3x8/PHH9jXciGE5qI7qv3Xq1Ak1NTWora1tc19wcHC7+wonLY9ly3EqKysBAJGRkRa3j4mJaXObM17T/v37Y8iQIfJM0REjRnT4OES+hgEaEXlMREQEgJbep9YlLxx15513IjMzEwEBAbjzzjsxceJEDBkyBAMHDkRYWBi++eYbpwRohmE3Q2BiiSEQ8ZZhuq5duwJAm/VIjRkHvgbOeE3/+c9/Ijs7GyqVCnq9Hrfffjt+++03swEhka9iDhoRecx5550HADh27JjFYTwhBL777jvk5eWhqanJqsc9c+YM3nzzTQDAa6+9hldeeQU33XQTRowYIfe4nTp1ygnPAHLl/pycHIvbHDlyBGq1GkDLcKc3MLw2eXl5cttb+/333y3uZ+9revDgQSxbtgwA8Pbbb2PYsGE4ffo07r77brufC5E3YoBGRC5nnN9lPIw2YcIEREdHo7a2Fps3bza777vvvospU6YgJSXFJG+sPYWFhfJxzOWGGYqvGhgHEpbaaokhwf/XX3/F7t27zW7z3HPPAQB69eqFYcOGWfEMPO/iiy9GfHw8tFotXn/99Tb319fX4913321zuyOvaWNjI2bNmoWmpiZcf/31uOmmm/DGG28gICAAW7duxZYtW5z3BIkUjgEaEbmccR5TYWGh/P8RERFYvHgxAGDBggXYvHmzyYzKzz//XO45mTFjBvr162fV8fr374+AgAAAwFNPPYX6+nr5vqKiIvz973/HDz/8IN9mfL+ltlpy4YUX4oorrgAATJ8+XV51AGjJw1qxYgVee+01AMAzzzwDSZKseg6eFhAQIC8an5GRgffff1++r7y8HDNmzDAbMDvymi5evBiHDh1CbGwsXn75ZQAtM08ffvhhAMC9996LoqIiJz9TIoXyYIkPIvIxluqg1dbWysVJu3TpIsaMGSMOHDgghGgpXjp37ly5fldsbKwYO3as6Nmzp3zb+PHjhVqtNnlMQx201157zWxbFi1aJO8fFRUlUlNTxcCBA+XaYpMnTxaBgYECgNyW1o8dGRkpxowZI7766ishhOVCteXl5W1WEhg7dqxcRywgIEA89dRTJvu0V2+tdTs2b97c/on/n47qoFkq9Gp43ZKSktrcd++998qP2bt3bzFmzBgRGhoqJEmSVxloXajWntf0m2++kV8b48LDQrQU/B00aJAAICZOnCh0Op1V54PIm7EHjYhcLjIyEh999BFGjBgBtVqN/Px8nDx5EgAgSRJeffVV/Pe//8W1116LwMBA7Nu3D7W1tRg3bhxeeOEFbN++Xa5bZq2nn34aH374IcaPH4+QkBAcOHAAVVVVuOSSS7BlyxZs375drltmqGVm8NFHH+HCCy+ETqfDsWPHcPz48XaP1aVLF+zcuROvvvoqJkyYgKqqKhw8eBCxsbG44447kJ2djUceecSm9ivFiy++iE8//RRTpkxBXV0djhw5grFjx+K///0vpk+fbnYfW1/TyspKpKenQwiB6667rk19tZCQELzxxhtQqVTYtWsX1q1b5/LnTeRpkhBOmr9NRERERE7BHjQiIiIihWGARkRERKQwDNCIiIiIFIYBGhEREZHCMEAjIiIiUhgGaEREREQKwwCNiIiISGEYoBEREREpDAM0IiIiIoVhgEZERESkMAzQiIiIiBSGARoRERGRwjBAIyIiIlKY/wc0taq9YKgDOQAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot objective function trace\n", "visualize.sampling_fval_traces(result_pypesto);" @@ -2150,20 +797,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "visualize.sampling_1d_marginals(result_pypesto);" ] @@ -2181,7 +817,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2205,18 +841,9 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: You are loading a problem.\n", - "This problem is not to be used without a separately created objective.\n" - ] - } - ], + "outputs": [], "source": [ "# Read result\n", "result2 = store.read_result(fn, problem=True)" @@ -2224,20 +851,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# plot profiles\n", "visualize.sampling_1d_marginals(result2);" From 4847c3b4c7f87d653a5d9155cbc8d2d1556c88aa Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 10:39:49 +0100 Subject: [PATCH 02/31] rtd --- .readthedocs.yml | 4 +++- pypesto/visualize/waterfall.py | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index b75a9c82b..18a949b3e 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -17,11 +17,13 @@ python: path: . extra_requirements: - doc + - example build: - os: "ubuntu-20.04" + os: "ubuntu-22.04" apt_packages: - libatlas-base-dev - swig + - gfortran tools: python: "3.10" diff --git a/pypesto/visualize/waterfall.py b/pypesto/visualize/waterfall.py index 9132b32ff..8d09b1bc8 100644 --- a/pypesto/visualize/waterfall.py +++ b/pypesto/visualize/waterfall.py @@ -22,7 +22,7 @@ def waterfall( results: Union[Result, Sequence[Result]], ax: Optional[plt.Axes] = None, - size: Optional[Tuple[float]] = (18.5, 10.5), + size: Optional[tuple[float, float]] = (18.5, 10.5), y_limits: Optional[Tuple[float]] = None, scale_y: Optional[str] = 'log10', offset_y: Optional[float] = None, From f8f3858bfc63484d2ab80bdbe9809d47e5d0eccb Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 10:49:53 +0100 Subject: [PATCH 03/31] .. --- doc/conf.py | 1 - 1 file changed, 1 deletion(-) diff --git a/doc/conf.py b/doc/conf.py index 0cca2da1d..8182a3333 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -70,7 +70,6 @@ 'show-inheritance': True, 'autodoc_inherit_docstrings': True, } -autodoc_mock_imports = ["amici"] autodoc_class_signature = "separated" # napoleon options From 905281e7271cef0ae9b1714555c260e7370e9db3 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 11:02:27 +0100 Subject: [PATCH 04/31] .. --- .readthedocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 18a949b3e..2156a2efd 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -26,4 +26,4 @@ build: - swig - gfortran tools: - python: "3.10" + python: "3.11" From 787ed8de1b9b62061885513321ec4f00c41bb7e8 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 11:21:11 +0100 Subject: [PATCH 05/31] .. --- .readthedocs.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 2156a2efd..1b7a84f13 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -22,8 +22,9 @@ python: build: os: "ubuntu-22.04" apt_packages: + - gfortran - libatlas-base-dev + - python3-dev - swig - - gfortran tools: python: "3.11" From 151ad1a9be8a0e51308d28a25f06804dbb08fd86 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 11:33:09 +0100 Subject: [PATCH 06/31] .. --- .readthedocs.yml | 1 - doc/example/amici.ipynb | 2 -- 2 files changed, 3 deletions(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 1b7a84f13..eeb576ef0 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -24,7 +24,6 @@ build: apt_packages: - gfortran - libatlas-base-dev - - python3-dev - swig tools: python: "3.11" diff --git a/doc/example/amici.ipynb b/doc/example/amici.ipynb index 97257cc49..3d2a9bda2 100644 --- a/doc/example/amici.ipynb +++ b/doc/example/amici.ipynb @@ -933,8 +933,6 @@ }, "outputs": [], "source": [ - "%%time\n", - "\n", "result = optimize.minimize(\n", " problem=problem,\n", " optimizer=optimizer,\n", From ee68704284c81ab4c3b3fd59aeeae0b76c4558e1 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 11:41:52 +0100 Subject: [PATCH 07/31] .. --- .readthedocs.yml | 1 + doc/example/amici.ipynb | 2 ++ 2 files changed, 3 insertions(+) diff --git a/.readthedocs.yml b/.readthedocs.yml index eeb576ef0..bb2e572b8 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -24,6 +24,7 @@ build: apt_packages: - gfortran - libatlas-base-dev + - libhdf5-serial-dev - swig tools: python: "3.11" diff --git a/doc/example/amici.ipynb b/doc/example/amici.ipynb index 3d2a9bda2..97257cc49 100644 --- a/doc/example/amici.ipynb +++ b/doc/example/amici.ipynb @@ -933,6 +933,8 @@ }, "outputs": [], "source": [ + "%%time\n", + "\n", "result = optimize.minimize(\n", " problem=problem,\n", " optimizer=optimizer,\n", From 64f4216025df71215ebb495fb45ac9fc6a27efdc Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 12:15:21 +0100 Subject: [PATCH 08/31] .. --- .github/workflows/ci.yml | 2 +- doc/example/petab_import.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3a70f5765..4258a637d 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -388,7 +388,7 @@ jobs: run: pip install cffconvert && cffconvert --validate - name: Build doc - timeout-minutes: 10 + timeout-minutes: 30 run: tox -e doc notebooks1: diff --git a/doc/example/petab_import.ipynb b/doc/example/petab_import.ipynb index c2c4d2398..057e2ad11 100644 --- a/doc/example/petab_import.ipynb +++ b/doc/example/petab_import.ipynb @@ -29,7 +29,7 @@ "# install if not done yet\n", "# !apt install libatlas-base-dev swig\n", "!pip install pypesto[amici,petab] --quiet\n", - "!pip install git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python --quiet" + "!pip install \"git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python\" --quiet" ] }, { From 98568f05cbb37f95807d8eac5728438a297c38e8 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 12:40:07 +0100 Subject: [PATCH 09/31] .. --- setup.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.cfg b/setup.cfg index 64f714e15..7c300e9f1 100644 --- a/setup.cfg +++ b/setup.cfg @@ -150,6 +150,7 @@ doc = %(jax)s example = notebook >= 6.1.4 + benchmark_models_petab @ git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python select = # Remove when vis is moved to PEtab Select version networkx >= 2.5.1 From 7e9aa6cc0e5b0edf6dec9800a023cd0209ef6a82 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 13:04:09 +0100 Subject: [PATCH 10/31] .. --- .readthedocs.yml | 1 - setup.cfg | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index bb2e572b8..d82352514 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -17,7 +17,6 @@ python: path: . extra_requirements: - doc - - example build: os: "ubuntu-22.04" diff --git a/setup.cfg b/setup.cfg index 7c300e9f1..edcde6f4f 100644 --- a/setup.cfg +++ b/setup.cfg @@ -142,6 +142,7 @@ doc = ipython >= 8.4.0 myst-parser >= 0.18.0 ipykernel >= 6.15.1 + %(example)s %(select)s %(fides)s %(amici)s From 12b242b28785700c9f4a79d483f01d1c58af0398 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 14:23:06 +0100 Subject: [PATCH 11/31] jl --- setup.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.cfg b/setup.cfg index edcde6f4f..326a85cdd 100644 --- a/setup.cfg +++ b/setup.cfg @@ -150,6 +150,7 @@ doc = %(aesara)s %(jax)s example = + %(julia)s notebook >= 6.1.4 benchmark_models_petab @ git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python select = From 89e975f1f0e2de47e96b28625ba01f1133014c40 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 15 Dec 2023 14:50:29 +0100 Subject: [PATCH 12/31] exclude julia example for now --- doc/example/julia.ipynb | 361 +++++++++++++++++++++++++++++++++++++--- 1 file changed, 335 insertions(+), 26 deletions(-) diff --git a/doc/example/julia.ipynb b/doc/example/julia.ipynb index 48f8bcb6f..29ecc5afe 100644 --- a/doc/example/julia.ipynb +++ b/doc/example/julia.ipynb @@ -33,10 +33,157 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "ce619530-f9c0-4c7f-ba8f-e1c5a55fda4f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
# ODE model of SIR disease dynamics\n",
+       "\n",
+       "module SIR\n",
+       "\n",
+       "# Install dependencies\n",
+       "import Pkg\n",
+       "Pkg.add(["Catalyst", "OrdinaryDiffEq", "Zygote", "SciMLSensitivity"])\n",
+       "\n",
+       "# Define reaction network\n",
+       "using Catalyst: @reaction_network\n",
+       "sir_model = @reaction_network begin\n",
+       "    r1, S + I --> 2I\n",
+       "    r2, I --> R\n",
+       "end r1 r2\n",
+       "\n",
+       "# Ground truth parameter\n",
+       "p_true = [0.0001, 0.01]\n",
+       "# Initial state\n",
+       "u0 = [999; 1; 0]\n",
+       "# Time span\n",
+       "tspan = (0.0, 250.0)\n",
+       "\n",
+       "# Formulate as ODE problem\n",
+       "using OrdinaryDiffEq: ODEProblem, solve, Tsit5\n",
+       "prob = ODEProblem(sir_model, u0, tspan, p_true)\n",
+       "\n",
+       "# True trajectory\n",
+       "sol_true = solve(prob, Tsit5(), saveat=25)\n",
+       "\n",
+       "# Observed data\n",
+       "using Random: randn, MersenneTwister\n",
+       "sigma = 40.0\n",
+       "rng = MersenneTwister(1234)\n",
+       "data = sol_true + sigma * randn(rng, size(sol_true))\n",
+       "\n",
+       "using SciMLSensitivity: remake\n",
+       "\n",
+       "# Define log-likelihood\n",
+       "function fun(p)\n",
+       "    # untransform parameters\n",
+       "    p = 10.0 .^ p\n",
+       "    # simulate\n",
+       "    _prob = remake(prob, p=p)\n",
+       "    sol_sim = solve(_prob, Tsit5(), saveat=25)\n",
+       "    # calculate log-likelihood\n",
+       "    0.5 * (log(2 * pi * sigma^2) + sum((sol_sim .- data).^2) / sigma^2)\n",
+       "end\n",
+       "\n",
+       "# Define gradient and Hessian\n",
+       "using Zygote: gradient, hessian\n",
+       "\n",
+       "function grad(p)\n",
+       "    gradient(fun, p)[1]\n",
+       "end\n",
+       "\n",
+       "function hess(p)\n",
+       "    hessian(fun, p)[1]\n",
+       "end\n",
+       "\n",
+       "end  # module\n",
+       "
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from pypesto.objective.julia import display_source_ipython\n", "\n", @@ -53,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "96df9672-94ad-4671-9ff4-d6e426e321ce", "metadata": {}, "outputs": [], @@ -64,10 +211,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "724abb5f-3ce2-4ac5-89fe-2b48f89d8733", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 42s, sys: 3.78 s, total: 1min 46s\n", + "Wall time: 1min 48s\n" + ] + } + ], "source": [ "%%time\n", "\n", @@ -107,10 +263,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "8579d6f5-193e-444f-a8ff-44ae90293470", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "23.12228487889986\n", + "CPU times: user 1.36 s, sys: 12.5 ms, total: 1.38 s\n", + "Wall time: 1.37 s\n", + "23.12228487889986\n", + "CPU times: user 208 µs, sys: 11 µs, total: 219 µs\n", + "Wall time: 215 µs\n", + "[-38.6806348 19.9557434]\n", + "CPU times: user 1min 42s, sys: 2.21 s, total: 1min 44s\n", + "Wall time: 1min 43s\n", + "[-38.6806348 19.9557434]\n", + "CPU times: user 1.17 ms, sys: 0 ns, total: 1.17 ms\n", + "Wall time: 1.13 ms\n" + ] + } + ], "source": [ "import numpy as np\n", "\n", @@ -132,10 +307,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "38c8c4aa-3faf-4f96-bf9d-271fb8a8d8b9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(obj.get_grad(x))" ] @@ -150,10 +336,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "9a43d18f-ccf8-4c3f-a30a-2ad824e1e87c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([-38.75164238, 19.9661208 ])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from pypesto import FD\n", "\n", @@ -172,10 +369,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "3b826499-e4c7-443e-9a59-d8d90e10d3b4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sol_true = np.asarray(obj.get(\"sol_true\"))\n", "data = obj.get(\"data\")\n", @@ -200,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "d38e3540-9693-4b24-a734-cabdb5159d36", "metadata": {}, "outputs": [], @@ -226,10 +436,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c2267d70-85fc-46cd-bb81-5bf7742c8d84", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 77.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.38 s, sys: 88.2 ms, total: 1.47 s\n", + "Wall time: 1.47 s\n" + ] + } + ], "source": [ "%%time\n", "\n", @@ -251,10 +477,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "2b929224-6fa6-4f63-8892-37b2475b3738", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", + "2022-09-07 00:49:47,222 - pypesto.engine.multi_process - WARNING - Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", + "Performing parallel task execution on 8 processes.\n", + "2022-09-07 00:49:47,233 - pypesto.engine.multi_process - INFO - Performing parallel task execution on 8 processes.\n", + "100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 130.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 285 ms, sys: 177 ms, total: 462 ms\n", + "Wall time: 1.73 s\n" + ] + } + ], "source": [ "%%time\n", "\n", @@ -266,10 +512,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "05888c10-1027-4580-aa0a-1076410579c6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from pypesto import visualize\n", "\n", @@ -288,10 +547,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "b88b414b-598a-40f1-876e-ef1255a50c93", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████| 10000/10000 [00:08<00:00, 1234.51it/s]\n", + "Elapsed time: 8.135626907000017\n", + "2022-09-07 00:49:57,692 - pypesto.sample.sample - INFO - Elapsed time: 8.135626907000017\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 7.87 s, sys: 320 ms, total: 8.18 s\n", + "Wall time: 8.15 s\n" + ] + } + ], "source": [ "%%time\n", "\n", @@ -308,10 +585,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "1fa22993-8100-4ec4-9b75-64ea745e77f1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Geweke burn-in index: 0\n", + "2022-09-07 00:49:57,807 - pypesto.sample.diagnostics - INFO - Geweke burn-in index: 0\n" + ] + }, + { + "data": { + "image/png": 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KHHVNc0fLsx8IUuNle24pFr99CttzS0f4DAeXTuCIiAlhWLkGYBmAk1IfoChqMoD1AGYBSAPwCEVRSX2/uwfAEgBpNE1PAvCXIT/jIcAZYjcEAoFwp0LEJm4zmtV6LNlZwAoZHHkpc8g3XlWtOiSE+Dj9uIMtdB7CosIhp7ZNh9gg59/TOxnueFHrjVj89in2Pfh+07zb2lgEbIuY/AoYtWITFEUdB/A6TdNWiwdFUSsAPEjT9Lq+f/8zACNN0/9DUdQXAPbQNP2T3O8azWvUQMVuCAQC4Q6AiE3cKQy393prTjGeen9ocuMHm8d6uzYh3JpTjBW7Sb2Bo3DHy51S+8XlV2xE3c5cAzCPoqhAiqLGAHgIQHTf78b3/e4MRVEnKIqaKXYAiqI2UBR1nqKo8yqVaphO2zEMJjMxoggEAkEEYkjdRjR0WFSTtmSlDkv6WFUrPze+Ujn6cuNvN+ne2jb+Pa1RDe09HQ0pj85CeC23e+0XYeShKOqnvvom4f+WyPl7mqaLAWwDkAvgewCXAPT0/doNwDgAswH8AcAXFEVZeTVpmt5D03Q6TdPpwcHBTrgq5zKaalEJBAJhtEEMqduErTnFWLarP4oxWO+1SmOw+5mEEH5ufGLo6PNI1rY5Lsk7ksZFbBD/nsYFO3ZPHRFXuJM2QFLXcidEokYDo0G0YySgafo+mqYni/zvKweOsY+m6Rk0Tc8H0AmgrO9XDQAO0xbOAugFEOT8qxg6RmMtKoEw2iDvhTzk7DtvR4ghdRvQ0MGPYtS3Dy6KsS2nBA+/m49tOSV2P7slKxUH1mWMOvEEYGApco4YF0M1OW7JSsXn6x2/p46IK9xJG6ChvJbb+b44i9Ek2nE7QlFUSN//x8AiTnGg71dHAdzT97vxADwAtI3AKQ4YZwgDEQh3MneSw3IocWTfebtBDKnbgKhx/CjGYGS1VRoDDhU1oFVrxOGLDWiV4SEYrZEoR1PkHNmQD/XkOJBIlCONde+kDZCta2HSXQcCWQDvrIbNzoaiqKUURTUAyATwfxRF/dD38wiKor7jfPQQRVE3AHwD4BWaptV9P/8AQAJFUdcAfAZgDX0bqjvdrrWozmIwcwzhzuZOcljawmAyo1o18PdgIPvO2wliSN0mbMlKxZcbBx8ZCvZTYPn0KIT6eWLZtCjRnlO3A46myNW26WQbF6NxchyIuMKdtAESuxZhuqsjjMZnPBLciaIdzoKm6SM0TUfRNO1J03QoTdMP9P28iabphzifm0fT9ESaptNoms7j/NxE0/QzfamC02ma/nkkrsMZ3M6OmMEwmDlmOLDVT5Iw9NxJDkspcq414+28cqzcU4idx8oGlJ53p+w7pSDy579SWjWGIR/Mg5U3l3N85U2DXSNqa04xDhc1Ytn0SGzJSpV1XkJpdTl/09ChQ9Q4eZLmjnyWS6feOKo2uyMl497QocOyXYWs/PmXGzMcjtTezvL5zmYUjKtRK38+nJA1avTgjDlmKBGua4SRY6j3OiOFwWTG2ep2/OHLq3BzofBoWgSOXGzE8ulReCNrgsPHG4595xBC5M8JfIZ6MA912hRz/BMV7TY/J5YCKGfC40ZA5FyLI55LW5+15+0ZTUbUSMq4OyPd9U6K2A2W0TSuCITRgDNT6p1Ns1rPW9eaOklkaiS5E40owHJdt7p7sXR6JJ6aFY0jFxsHlZ53GxtRNiGGFMHpDHXalCPHH4xKnsLDTfS7hMaOI2Igtj57OxVjDreMuxhbslKxf5BCKHfqAkggEAaPs1Lqnc1w95Mk/HrJmhyO3y1KxpIp4Xd0et5gIIbUIPm1ygbbYqjzhh09vlAlzxHDjvtdrz+QgnfyKqyMHUc8l1KfHQ3FmI5IyQ9Wxt0ZbM0pxqq9Z0YkIuYM50CLWj9itVm/1powAsFRhjMS5ch+Yrj6SRIICg83xIb44o2sCfjm1TkDSutzBqN13SI1UoNge24pPj9Xj5Uzo0lTUBGGo0bK0eNnF9Rg588VeOVex+piDCYzmtVdeOr9/pz5b16dw/PK1LdrZS+6Yp/dllOCwxcbsGzawPKPB8NA8+1rVNoRMaKa1Xos2VnAPosjL2UOm1d2oGOIy/bcEig1RhwvVQ3qOAPBGec/BJAaKZAaqV8zZD9BIEgzCtYtUiPlbEazbPBIR8mYyMZQp005evzBpBzuyCvH7z4vYqMwYqFtRzyXYp8dKW/PYNL0nGFEDWS8jlRqizPSVpvVelyqv4njpaphVw0kaoUEwuhjNO8nhNypTVUJg2eo9p6jfd0ihtQAGaxs8FANuJFurjmSAgT2cDQlkOkfwtQ1NXQaMcbDBU/PjIb/mIEbibZS6EYi73gk0/QGM15HIrXFGWmr4f7emBo9FgtTgoddNvfXINdLINxu3C5tCG6nOl7C8DKUe8/Rvm6R1L5BMhDZ4KEK4av1Rix++xSb7vT9pnnDOiHXtumwYnd/6tvn6zNsbspVGgOCR8BwkJMSKEx125pTDJ3RjNzrykHd3+GUrHU09XGo0vSkzmOkx+tgcEbaanOnHv7eniOyKIxCuV6S2geS2vdrZxS0IZBEpTHg4XfzJVPbCb9ehmstH+F1i6T2DRUDiUQNVQh//9k6PJYW4bBXy1lhUkciGyPp2ZITiRIq623JSsVL8+IG5TUcTqW7gcjPD4URZes8RpsXlolAysEZk3l4gPeILQqjzIgiEAgY3W0I7vSmqoSBM1xr+Whdt0hEapAMxELenluKL87X48n0wUekmO9nPALmHhpZU0Lx+uIUULAMcFs4s4Bva04xyltuYv2CRMxOCJb83O3g2dqaU4wjFxuxdJp15GgwXkNbx3UWBpMZC948zt7f468vHLGoh5zz6NQbYe7pHXR0UqUxwN3Vxe6YF2M0NLdsVusR7v+rlTEmESk4b40aqWg/4c7nNm+q6jDkXZLPaI6oOgESkRoKBtp0dvPiFHy/aR7PiFJpDKhoke8RF34/4xFwd6MQ4a/AB/k1dvNVnVnAxzQIzCttx6bPLttsEHg7eLZs9Q8ZzEQhlGIfCkZLPrHc89hzslpWdNJWXeE7eWXYfbLaaszLiTI50gfMGYhdx9acYizZWcDWFo60YAzh9mUoov2jrbibMHKMxvV6qBiOzBmVxoBm9Z3RUPkONqJsQgypAaLWGx02QribI+6AO3KhDnvza7Bqn3yRBjEjiDHQVs+KkZU+6MwNt6MqaiPdj0AOQ9U/ZDjEHNZkxuH46wtHXN7a3nnI7Z9lq5BVpTFAqTHi68tNvDG/NacYy3bZf6cc6QM2WMSug3FCtGqNOFaqxF9+KBlRwRjC7YtKY0B+RSuWT4tEfmWrU/rRDdRhSBhdEGPYMYajt+OeExXYm1/Dc6IRbj+IITUAsgtq8NjOfDYnVI4RIrURVGkMiAvyxhGOR7y82b5HXMoICvD2dChf1ZkbbkdV1IbSs0UWjdGTT2zrPOREJ+3VFQb7KRDq58mrD9Qbux2KMtmKQDoLqevgOiFemJuAL843OK2GkrwHvy6C/RS4OykYhy424u7E4EHPsaNZdphEbeUzXMbwnSSNPtSZMyqNAQoPN97ez1YmD2H0QgwpB2EWlroOA76+3IjvN82za4TY2ggG+ylQ06bHUo5HPDlcnkfclhHERKeemx1j9zi2NrqOLpxD1c/HkQl6KBYNsmg7hiPCDfaik3IcA79dNB4b5sezKbMDiTINZSQKsH0djBNiRXqMbCdIVavte0wiCb8+VBoDz4EwWC/6aEkTFjLSbT5uJ4bLGL7dpdHF9hjctcnR+2ZvzxDsp4DBZObt/Qa7f7qTDNnbCSI24SD7z9Sipk0HXy93hPsrsGJGtKy/sycw0aox4KbeLNuIkiNysS2nBIeKGrB8ehRvkyq3eHIUdJIGIH0dYgyF0MJo7jiv1hsHJK4wlAyVcANTyOrINde3a4fcQHIUOQW59j5j7x6PFsERByBiE3DOGrUtpwSHLzZg2TT786VcRpNc/u3cNmGkyC6owXvHKvDyPUOzlqs0BqzedwapYWNRoryJj9dm3Fa1VPb2GI7uhRzZM7RqDDD39A7aiHJkn+QIo+ndH2GI2IQzMJjM+PxsLVwoF3xSWIeqVvlhWDGBCeaYgCXNTa4RJcfTLJXfK9drNFpSOhzNU2Y8qLGBCryRNWHQE8Bo7ji/+0QFPiqsxe4TFSN9KixCiXdnCjcEeHs67IkebUYUIK8g114kinuPK5XW93i0RhIIQ89Q1J+OpvEz2tom3A4Mdc3slQYN7pkQioKqdixMCYWvl/uQfM9QYG+P4eheyNE9Q4ifYsBGFHMuQ1XPZW+vOdojYMO1byWGlAMoPNyweXGKJWViAANWOOEPJPVG6qWuVvHTfMTye+W8bEw4WuHhhtcWj8eG+XH489LJI7aQ2spTllI5XJMZh9/cHY+t35UMOq3J39sTT82yLNorZzq2aA/kJZb7N2q9ER36bnxSWIcOffeoMPCyC2rwxyNXhky4YTQbtcNJQgg/bTExVPwePzkjCl++mDHigiOE4ed2igbIhTvfSzkmCdIM1RpuMJlxskLF1vocvdiIru6eIfmuocBeLZSjTqnhMvS5+0e59VyO7Em4e80frzehto2/3xrtqZzDmdpOUvsGwFu5pbjUoMbUKH+8NsCJfDCpN8Iwva00H2HPB1tpH8Jw9M5j5ejUd+Pry00jntYmvA5b1+zIvVVpDPDxchf9PeNtyS1uRc7VZmRNCcfqjFi759rYocNPpW0Op0U6kj7gaC8uOelwgwnhc+/53KQA/NeSKUOiTujMHmzA7ZW2IEzJrVRqJY2owxcbUNqixZER7o3lACS1D0PT6/BOYDT0eSNI89m5OlQodfjmSpPT5ubhxl5/LEfXiqHsqSS1x7F1DQMp1cguqMGP15swOTKA9/6N9l6gQ5TaTlL7nEmgrydKmrUI9O1/SWyFOMUK7weaeqPWG3lh+qLadl6aT1UrP81HOLil0j6E3v4mtR5N6i4rSemRQhiJOmxD5VDuvX0rtxRv5paJei3eO1aOvadr8NrBK9jxUzlOV7Tjnbxyux6drTnF2HLkusNpkY6mDziiKCQnHe6D09WD8t5w7/n9kyIkjajBpgI40xM9msQY7N0XMe+flBFlMJmh7zITNSjCHYG9+Z4wMIQRhsHw1MwYbF48/raOEtqL4ji6ER/KlFOpPY6taxjInmRNZhz++/G7rNL1Hdl/2OqRNVT9s4Y7tZ0YUg4iNiBthTht9bJxNG+ZuyFWeLihUqnD+ycreaovCSH2owBig14Yjo7w90aEvxceS4vAkrRQ7Hl2+qjJRU8K46c2MbVlXINVTv+ii/VqHC9VWU0uKo0B5l4aRy424nxNJxamBMt6IZnGrudrOpE1OdShl3ggL76cWgg56XAf5lfjbycqB10PZ++eOysVwBnjcLTUAAL278tA6gS9vdycqgZFIIwUUvP97c5Izjlbc4qxYrf8vpVyUHi4jZo9grMYTc42IcL1Vmw8cX/G7GMWjA+2e2zmuj87V4exCnfRdH2x/YfQIShsNC/3d85gOHtpEkPKQYQbXl1Xt+Qmh9lY2yq8dyQSJdwQJ4b6IDbIF3XtWuxcNXXQKQ9Cb/8r9yTjlXsSEe7vjfUfFY2qhnFbslLxyfP9fX/EDFape9us1iPYT4Fp0WNFjaRgPwVcXYCl0yLhp3BDVIBClsy9O4Bl0yMxI9YPfgp3JIf4oE3bJfuaBvLiM0axwWQWnUizC2t4/ZWEC51ab8SH+dWYmxyEUD9PbJyfKGtMSsmb20qhHOrmho4wGsQYqlp1su7LQPqZLJsWhd8tSnaorxvhzmWoPL/DhXC+v90ZyQ26UBCoRkUifGKMJmebFMy6JTaeuD9TeLghLdof40N9kBbtz1vvhNfFXLe5h0aFUofFb59CuJ+naJ9FYdkI1yHIbTQvzIqw9TtnMlzrOjGkBgB3w2trkyOnl43cl1OqgHFLVio235eK9Lggp1ybcKPdqe+2qxA2UnAjUXKV4rbmFOM/vrmOHXmlcHOlEBOgwO/vS+YZL9UqHX641gI/Lxe8OD8OCndXPPD2KZuL3tacYjy6qxDBPu74t0em4PNzDThd0Y4vzjc4pFw3kBd//5la/OvX160m0oYOHfYX1uNwUSPuSw3BUzMirP7W39sTS6ZG4kJtB16cn4i1c+Ptfp+tKKsUQ93ccCA4Yrg6axE1mMxoVOqwNacYT71fiC8u1Mu6LwNRYmtWd5FIFGHIPb/DxZ0UiRrJDXpsEH9fMhS1rHcCznK2cftJcSM2zlxThONJ7GerM2Kx59l0Xp23mAHGXPdDd4WxpR3vnaiCj5eH5DmIOQS5jeaFWRG2fucMnJm2KgciNuEkbBX5SfWykVP8xxQ4NnToEDXOZ0gLGKW+f0deOY5cbMTSaaO30HdrTrHdc2xW67FkZwG2LZuMn0pU+PGGUrIXSXZ+JcL9x0DX1Y238yowVuEBTZcJ32+abzWh5le04vefX2GP9dXGDPz9bMOw3DODyYwNH19AaYtWtLBSzn0BLIWx6lvdiA/2sfl9DR06LNtVyH7XlxszHFLms1fQOxjkCGrI7aHGxVn91PJuKHG2tgMLk4Ow6fPL7D38fF0GxijcnHZfmHf2NivOJ2ITcP4axcx5zFg78lImMa5HAUPd10kONSrtbW9EMfuioWQwgkSMgNe6uXFQ3zLjUFEDnpoZjUBfT6f16DSYzPjiQoPVeLI3xmwJMryVW4ryVi1iA71x5GKjLAERKSGzpk695Jxj63cDZQiFaYjYxFBjaxMkFYmy55XKLqjB+uyz2F9QhfoOPU6Wtsgyopyl7c94K6bHBODAutGdVrElK1U09MyF8YJ8caEOIX4eNlPemjUm/OnoddSrDVgyNRJKTRceS4u0mlArWnTYdayCV48SFugr63yAwXtOFB5umCqRogjIuy8AsPtkFVbusR1lMpjMsqKsthgqI0qOoMZAarQG4z0Weh/HKlxxpKgRfzvBHy9xob6y7ouc9zq7oAaX6jrtRpGFqV6OzhmjMc2FYM1Qe34J/Ui14xBjOOs3pBgKI2o4IwEDyYwAHJ+7dF3dDn2egVuOYTT3shGbSw1qp0UkmT2amwtlNZ7sjTGpiJtKY8Bn5+qRc02JY6VKfLkxQ5aAyG8XJYlmTdiac4YiEjUSaavEkHIC6gGo2dkLGzMbuHVzEqHuMuN4WQde//IattmZNJxV0M/dQP7zV9cQETC0qVjMZCLc4EnV4oghZ1O/JSsV//zwJGxalIJnZkWLqgxxX8YLtWpebZpw0ksK88HkqABcb+zEX1ZM4Rks9s7HGQW/zWo9vrrUhEqVDpMj/bB4gnUhqb3zqFbpbCo/AvwUALnG2XAhR1BDLPVA+N6KLWgDSe9QaQxW76HCww03DT1YOj0SJUo94sd5OeSckPNeM++s0FATqvsJU70cnTNGcwE2wZotWamkVm6I2ZpTjFX7HJvLHYly3A6Oi6EQsJDCkXR+Lo7OXYPZT3HLMTzdXNgU7qlR/k5JGeTu0d7JKxf9jL1jixlb3DT8e1PCZO2rmPuac13J/mwg++LBMlJpq8SQGgDMADGYzLI84VLY8hgwGzgvDwo9veA0AW6ULNQX2ywOdAIebH6wI9+bXVCD5/oWIe4Gb6AeJ3swXpC4EF/RCB/3ZUyLGmv3PmzJSsW/PjwF85JDZX2/wWR2muck3N8bD00JR0OnAckhvggfgIcnPpg/+QiVH8WiMs5stDtYmAVrblIA/vfJNNFnyl0cVmfE4OPCOt57a2uB5b6n9rzO23JK8Om5elEBiUUTQ/G7RcnYvzYDT2XES8qXC5Er1MG8syVKPWZIRJGFRb5VKp1DIiBqvXHUF2ATrJHy/CrV+hHZ8NyOVLWKv/tDLc9+OzguhjsScKK0lecssrUeMdF2rohCuVJrd+5yhkASI+C1cUESW+f62uIUp0QkHd2jqST2g2J/50hNrtj+YEdeGf7hs0vYkVcm/4KcxJasVHy+fngdvcSQcpDtuaV4bGc+3skrw5cXGux6wu1ha/CvyYzD1JhxrIKcvYJ07mZxZXo0cq4rZU3AUgupIy87N5LkyMTPvIQPTgrnTcQVyoF5nITHHijMy7h58QRZ98FWITQ3bYq5N1ebNU7znDjD47wlKxWfviA++YwGhTt7bF6cgsmRAfjd55eRnV8pmmLCLA7PzY7lvbetGoNd40Dh4WbX68wsvJ+drZd8XxUebkgKc+xZyxHqYN5hZqwunhQmaqgJU70Sgn0kjy28D9kFNXhsZz7rZR2tY4Egjx15pXj/dO2AHYGjiaE26BlxGLF3fyjl2bmb1CqlhnXkjDbjdzgjAdUqHW606HG9oROb70/GtcZOyb0XN6Kk8HDDa4vHY9n0SOReV+K945U2v+d4eZvkPN7cppOdxsh17AnXgsEid49mq2emFHLT8IX7A2N3D8Z5uWDTvYkY5+UybP1Hue/EcNf+jYjYBEVR/wlgCYBeAK0AfkPTdJPI57YBeLjvn/9J0/TnfT+/F8BfAHgAuABgHU3TdmfSwRbyqvVGLH77FJJDfJAa7oeT5a24Z0IojsosxhsMta1aKLzkFaS3agzw9XKXLCTkFmgyxZArZ1rOfyDF+Nzivk2Lku12lBYWiGYX1ODbyw2YERvIE0ZghBJWZURjzew4u0ICXMQEAuSIETibbTklOFTUgOXTo/DbRUlW90Z503DbFPwOpujWFgMZcwxcMZZluwoR6e+JWQlBOGKn2HR7bim+OF/Pvrf2CnOvN6jx+4OXkBo2FiXKm/jryhmiGyam4HZNZiyemOFcdUIpoQ7hOywHYZGv8NjC94dbmBwbqMDRl+c4W/SGiE1gaAWRuDSr9fjrsSqbgju3C84Sg5GiqlWHp97vF9g5sC5D1ElR3qwdEmXB7IIaVCk1UHi64+cSJe6dEIrDRY0Ove/DxXAJWOzIK4XW0INGtQ7r5yViemyg1WdUGgMefjeffW7fvDoHHq4uWPz2Kbtjnpnv3FworJwZhQ2ctiDZ+ZVo0pjsrjGjBZXGgNcOXpEUpHIWzFpsMJnxdl45jhQ1Yun0SPxuUfKQO9yk1sBmtR7h/k6rwxp1YhNv0jR9F03TUwF8C+BfhB+gKOphANMBTAWQAeB1iqL8KIpyAZAN4CmapicDqAWwZjhOmkkhyoj3x9eXm1Cm1ON4qRJHXsrE5sUpQ+olig2RV5AOWDwJUlEEbroct7bkRJlyQPnAwlQhtd4oGb1Q642i6XprMuPw0drZVpGVLVmpOLghA+YeOOQ1FQs1DyYFc6AIUwO0Xd14aWEi797cLkYUIM+D5ug74OiY43qe95ysZD1sjAjGSwuTcERGiomwZ5otz16zWo8jF+txz4RQFFS146Ep4Qj265eC5Z4TE/V6+Z5kpwtrSEWiBhIVF6Z6CSNRwveHO5+snZuAAG/P27430a+ZcH9vu4I7twPDISWeEMKPtkil5A6VPPuazDg8OzsBh4sakRo2ll1vxd73atXQiz3Y+o7hWs82LUrBulnRiA30xYufXBSNFIpF8qXayAhh5rsemoa/tye79tW26RDuP0bWGjNasPTM9BcVpJJKVXekNp2BOWZjRxd7f45ebERD+9D2jJRaA4ez7cOIGFI0TWs4//QGIBYWmwjgJE3TZpqm9QCuAHgQQCAAE03TTPLljwCWD+X5ctm8OAU0RbEL0OKJ4YgI8HZooy612XT2IrAmMw5HX85kN4fCAk19l6k/TWdh8oDygYWpQuEB3qKb0u25pTheppJM12NeQu4GL7ugBrtP1eCrS40I9fPC15cb2ZfE1r0SCzUPNgVzIIilWn6YX41/e2wiHpoUOurSMwaLnHeA+9xq2xyrz2HSInOvt2DvqUrsPVXN20BtyUpFWpj8FBPhIipmKGYX1GDjxxeQEj4WR4oaYe6hoTX0YPHbp7Ajr0w0jXU4e2TJ3RiIwR1/lcr+hVPKCcN9r++U3kS/ZjYtSsELc2JFBXduF4Yr7XhLVuqIKtcy6YMlypvs/CZ837fmFNtVXh0sw/Ed9mDWkC6KsiuSJFbrI3SiSSG2j4kN8kGz+hZfdXWQxuNg9n1y//a1xSl4ffF43vVszSnG9h9voKi2nffZwdamD2Wqqxhia+BwNfxlGLE+UhRF/TeA5wDcBHAPTdMqwe8XA/hXAPcDGAPgLICdAN4CUANgOU3T5ymK2gHgXpqmp0h8zwYAGwAgJiZmRm1t7aDOm+nLYe6hkTUlFP+wMBFeHm6ywsWAdAhyKNITmLSy/14yEfFBfkgK88HWnGLkXGvGs7Nj8cK8RABge1NJ9QGQg61+AExKZJCPO+aND8FRGX2NuKH1R9MicORiI5ZPt5yX3HvFTUUTpnINJ9xUSyZVQNvVg68vN43K9IyBwDxjJvXr4MZM3DL2IHSsFxo7upAU5sM+tz88mAJDVze0JjM0hh4cvdRod8xxU8s+fWEW3jh8Felx45Bf0YZ1c+OxYX4i7/PCFBOxfiP2epBwv3NuUgCmRo+D2tCN3OuWdKj544NQ0jy06RJycaS/nEpjwEeFdfj8XD22PDgeZa160b4bUqmcQ9SbiKT2YfhS+25nxMblUKUdjzSVSh0SQ/vnKCZ9UPi+V6t0WLmnP/3w0xcyrESDBstwfIc9hGu/3D6JQgab5t+k0sIExyNwwu8dzL5vMH9b0aLD9h9vICbIl5eiONg+kVyGKtVVCuE7MdCxYYPhT+2jKOoniqKuifxvCQDQNP1HmqajAewH8Krw72mazgXwHYBfAHwKoABAD22x/J4C8L8URZ0FoAXQI3UeNE3voWk6nabp9OBga2loR3EBsHR6JNzdKPh4uYOCPK9wQ4cOar0RF2vb8Y+Lx+NiXTsvuuLs9AQmrezhyaE4X3+TLZLfkpWKZzNisedkNetFZ87XEaUWIbY2Usz9add3I6Lv5bQ3sBlPY1KoD0exsEGWahgj7sDk6wLyvVDOQJj6wE21fGpWNFq1JrZj+FBEyJpUOrspHmq90akRUOYZxwYqsHFuPD4qrEVBhRJv55Vj1b5C7D1VyT63ZrUBYf4KZP9Sh28uN2HVrGisSo+0eXyu51lr7MFDU8JxobYDL8yzNqIA/gIn5mGT43Xbc6qKTYe4f1IEXn9gAjbfP55912fEBowaEQ65RtS2nBK8nVfBRmhjxo3hee4alVo2OiV1PaQ30Z2DlBLdaEVKyOhONKK25hTj6b38OYrZmArfd3vKq8DABSoYUQXud6zKiMY4bw87f+lcmH2SmwuFHnMPtuWUoLZNi/dWTXVoo+yMNP+IYF+HjSjh9w5m3zfYPWNSmA/Wz0/kpShWtWol+0QOZOwMpxEFWL8Tw9n2YcQiUuwJUFQMgO/66p1sfe4AgE9omv5O8PPFAF6gafpJe9/lLG/f7hPlULi7wdBtxsYFyQAsm3c3VxfRDQ0jxrDp3gTUq42i3t+BdDpXqvXo7qURNc4HLR060C4Ur7BuW04JHp0Sht9kn0er1ohFEwKx5YFJWP3BmRHxojviNWcQ69pt614xUThndw+Xi72u2gaTGe+fqoLGYMY3V5qcHiHbX1CFOrXRZiHsjrwyNHYacLxU5fR706oxoLhZg72na/D64mSs/6iIjVKtnhWLk+VK/OPi8bhUr0GjxigrOsmF63muVGrtSoiLedgoiuL97NDGDAT6Kqzq+Ra/fQraLjPS4/zxzlPTeGOXO5aF3vDBiGcMJUzxtbmHxrLpkfixuAX//NBEnK/rxJGLjXj9/iRUthlkd4V3cld6EpGCcyNS9sahvblqqLAXCZbCYDJj5e5fMCcxGPlVKny+4e470oACLJGop/f2z1H712ZYqX2KPd+qVq2oETUQQRpAfIxUK7U4fLnJ7vFqVTqMHePuVIGngxfqUaHUwdvTFZ8U1vGEJOSkVHMzJ4ZCYEVK3EDqewey72OQE3Gx9wykjlHfrmWNqIGOnTuQ0SU2QVFUMuefSwBYVZpTFOVKUVRg33/fBeAuALl9/w7p+39PAG8A+NtQnzOXjQuScV9qCGtEMQXze05WW32WW5fk7eUhWSMkR8aS63XYd6oCHxTUYcuhK9h9ohwfnqm3qll4I2sCgvw8sWx6JBZNCMSs+ECUtGqwJjNWthfdmVGLgU5Ywnsjda+44g5i3cOHuiZJTmNbhYcbfrtoPDbMj5dsBiyF2Plzn0+1Soeocd5WXibhMc7XdOJ4qWpICrRD/BS41d2LtOixuNHQyeaSZ00Ox4YFiZgcGYDXD13DOF9PrJsTZ9XvQWWjTkposMjpwyTmYcsr7u9B8mR6FPLK2q283EyEbfHEILzx4HirGTTA25O9b9xzclZDbC7Oej5MzZ67G4Vx3u5YkxmPfzp6DTNjx2H/2gxMjwnityBosV1ETSJRoxd747CqlT9XVSqHp2B+MPUXCg833J0UjEMXG3F3YvCoMqJszVtcbPWh477niaH8eUtoREk9X6lI1EBqhIW9oWpVWmQX1GDvLzV2j7cjrxQfn6lzWCTK3ly3MDkIRy422mwxYYvB1JTaw1bdqNT3DrSfVLNaj++uNiPY1xM515rR3Km3undynoFU2xNuJGok6svtIWesDCcjpdq3tS/N7wqAxQA2AQBFUekURe3t+4w7gFMURd0AsAfAMxyJ8z9QFFUMiwDFNzRN/zycJ28wmdlNhL2mbdyNXLuuSzRsymBrYeCmNDSr9Qj08cKRokY8PjUKYzzceZtnbmFdiJ8CW7JS8aesSQCAa00afH6+Hi/Mi7f78o5kI0C13sj7fuG9EbtXXHEHbvfw398/Hu8dr5Q1qQ/m5bSVXiE0gkL8FFaTuFRneKVaL5qOIHw+8cE+aOjQ8wphmXNgFNbU+m5MjvBjU9Y2zk8Y8PVKkTU5HK8sTMI9iSH4zfRofPJ8Bpt//XOJEvOSQ/Af3xTjo4JaXnrEO3lleDuvAu+INPEbzFjckpXKppOq9UbsPF6FU2Wt+ONDE/D0rGjJFInNi1OQEOKLoxdb7N57oH8uMPfQ0Bq7B9TAUYiz30Emfff5OfH47FwtHrsrHO26LkSOU9jdvAkZTQsZoR85jUTlKtE5E6HYkaO9AWsEjipnvF/OQK7zxFYfOrH3fEtWKvavtd7kOtoodqDGg7A3VMhYBXb+XIGcqy021R6rWnVo1TiWvr7/TC3+9evrduc6Zo3voWmMVbg5VIrArMFMmv/LC61TwuUiXM/liBtIlRcMxCEQ7u+Nh6aEQ6U14rG0COSWqHj3rlol/xnYqnNzpuEptV446uDef6YWf8+vxNnqduRcax7w+TiTEU/tG06ckTbBFPj91+OTkRTsg4QQH1kiDdxQKfe/5cAtemfS8Q6cqUGrrhvXGzsxLzkYHbfMNtOkmtV6/FSswrt9m0Z7ReJi3zlcHsDtuaW4VK9m+x6khHnjk7UZ0Bh6eIW3UnB74hhMZhi7e2SF850l+CFMr5ATGq9t02HF7v5Ujs/XZyAu2Bfbc0sQ6O2B945X8c6fAnjXxH0+ja1aGNE/QQrTM3bklaJTb8L4UF909wL7TlXhd/ePx7JpUQ5fa22bDrFB4s9ELC3k4Pk6vPlDmdWzUGkM2H2yGl9fbsJjaRHYMD+e9wydNRZrVTp8ebERX19uxEOTI/B/V5uwOiMGRnMvwv0VWDEjmv1sVasO+/KrrXrt2Lr37+SVQWMwOywkIpYS4ozrtlVUvedEBdpvdVulgVa0aO0aUUMgjjPqUvsoiloB4N8ApAKYRdO06OJBUdQmAOthuYb3aZp+u+/nU2HJlvACYAbwMk3TZ219p7NS++QKBw13Qbgwlchemh8zfnfklYICoDf2WonTDDRV0BmI9SoSi45UtOiwal///P7J8xnsfR/Iez4QYaiBpNYDfOEeJhXt9/ePxwMTQ62Ol11Qg8KKVkyIGAutoccqfd1gMkPX1c1LSTSYzNjw8QV0mbrx4KRwfH+jGR+tnc27B8JsBKmeelII1+DBzF9S6/kQiBvYpalTjwBvT9Hxw/TackYJwUDHDoPU/XY0bdBgMuOt3FK4uLo43KfKCSn3oyu173aFKfBLDfPGhbpO/ObvZ7E1pxj5Fa3Yumwyb0LjygkD4BlOjqqg7DpeadUDYN28JKydHYP/b+kUbFyQjN/MirJZWBfu741bpm5etELKiGrpi16MRCE9E0o+X9OJhSnBSAnzxvo58dibX2NVeCuFsHu4HK+KMwU/hJEoOaFxsc7wzWo9LtXfxIGzdXi0zwO4KiMGX19pxmM78/ul6wXPJzLElxeJYjxlOdea8cW5WoT6eOD+1FD4eblh36kqpMeNw9bvSvBhvnVqqi2kImiAZfyLpYmtSI/Bk+lRVs/C3dWF9Z59c6UJ7q79U5OzJI635hRjxZ5CuLkCBzdm9kePuszo6enBBIGRnhDiw+u1I+fePz0z2mEhEamUEDnXbSsVVCyK2R+ZNELh4SYayRYzorh9RYajd88o4RqAZQBOSn2AoqjJsBhRswCkAXiEoqikvl//D4B/7+uX+C99/x4W5AgHbc0pxuoPzgyrlDU3OmwvzY8Zv8dLlGjVmPBJYT0rTrNhfjwAS3PUG81aZOdXDvicqpQ62V5x4VgP9lPgHxcn48uNGfhjVgo8XMW3VLYkoQcyvw1EGGqgG2Fu1gCTivbUzBir4zHzQs51FUqab2L1zGheBObghXq8/VO5VfRO4eGGJ9KjMD0uELtPVWNGbCDvHgy2xYRwDW7VGAY8f0mt5waTGRdq2vHC3DhcqG1njznUc2NEgLfk+Nm0KAXPzIp2isiWI2NHeM1S68VA0gYVHm54dGo4r09Vs9p+ZHooUu65EEPKAZgB++ICS9PPsQpLzVNisDfGeXuwA0RMbUcKsRdNrTeyGx6VxoDPztXjm8vNGB/qg6xJoeznQgO8WaMsPNDXbs3CxgXJWDs7xqbBtT23BG/9VMFKdQ8kf3cwMEaPn8INkQEKfLI2A3FB/Lofe7UbYthT7XPWZl2ohudIaFyYrxzu742p0WMxNToApyta8e+PTcSGeQnY+XMF6joM+PpyI77fNM/m8+EqrD07OxZGM423fqrE6n3n8GZuGf7h3iScLm+DtsuMY6Wt7Lnby/kX5s8LmxLaShN7/YEJVs/C3n0aaC45A7d+7cCZeui7zHjl3iQ8MzsGgWNc0dUDrMu+YPXOblqUwm4I7N377IIarN53hk0vlZMKYS8lhHvdwrnCliErtkhxDTZ/b0+YzD2yHCvCDe9w9e4ZaWiaLqZp2l6BRyqAMzRN3+pLPT8Bi/EFWPoj+vX991gATUNzpuLY2mwONs1uMEQH+tr9fu743Xm8nHVo9NA0zL2WjZ2yQ4cmjQl/OnodTRoTlAO4hh15pThwTl4tj1Q6b0XbLfzT0Wu40aKzW4/CpDkLGcj8Npz96rhIve9ch+/sxBDEh/ryBHma1QaeAi+TkmgwmTEr1l/UqeMMp41wbQnxU8iev4QGtlrfLbpOKTzc8MjUKOw9XYNH0qKg8HAb1tIIqfETF+I7IIGvgSJ2zcx6sWhCIP62ehp7v7t7evFYWgRiAxVYOycO3T29sr7jrqhxdhUquTiaCjsQiCHlIGsy4zA1JgDLpkdC02XCHx8aj5CxY7D+oyLsOVWJihZxT7wYYoNu36lK7DpZxW54mJxgP4UbJkf48ybPgXSfDg3wltwwMREQRojgnbxyAJYXS24xrT3kHGfz4hR88+ocbFo0HsF+CtR18BvgMZtyR7pyq2WEpge7Wd+RV4Y/55RYPVMvdxcsmhACL3fbr1t2QQ2eev8M7283L56A3y9KwtGX5+LByRG8TezauQl2r2lrTjEu1LTjz0sn4YV5ifD2cuMJQKxIj8E/3JuER9PCUdOmx/EylSzvjVgETfi97VoD9jw7XXTj4OXuavUzOcYug61IjBjC+rX4EF+syYzDqlkxmBIVYNNQjw/1RVe3WfTeM2OQWfDLlHr8XNqCb16dI3odwrEpR0pcbFG2Z8gKNw9d3WYrg23dvES8OD/BpmNFasO7JjMO3746Z1idLKOUawDmURQVSFHUGAAPAWDyQ38H4E2KouoB/AXA/xM7AEVRGyiKOk9R1HmVSiX2EacjJXM8XNj7fu74nZ0QLNo8uKsXPM+0Qd4+jKWixVJHUtKstmpJIkRsQ78tpwS17RZHSGrYWHx3tdmqcbwQW2mUxm7JLi48WtR6nld/NGDL4QtY5rBwf4WVQAQzrxVUd4rOg85y2gjXFjlrvTCivzWnGE++b8loEFunhE4vZ0btpf6eu59yhkNrMMafrWtekxmH5NCx2PjJRdYhF+ynwDhvdyyeGIZ9p2uQc10p+xxr27TY/cw0WSmU3Np5R4RJHIHUSA0Ag8mMLYcu4Yn0aIxTeOD57AuspPDNW10I9FXYzZUVy4s2dvfgmyvNonVMwpzgoZKu3Z5bAqXGiBNlKvx2UTI83FxQodTxGuLaQ9hEkIGRJrd3HLF82k69Ee1aE2tESV2/2M8HK9+p1hvR3dNrM79WrTfiHz67xNZ1cZ+pnPosR/PkmZxxWzUwUnn5BpMZnXoju1gZTGY88PZJpMeNQ1zgGIdkZZtVWnSBQnxw//Pmfm9KmDf+unIGbwMx2PoauWNfrEmnmDxw3g0lztZ2SNYYCr+POa7w5/akbG2dty0pcamxIScnn5vbPtAcfrG/GwJJ3BGpkaIo6icAYSK/+iNN01/1feY4gNdt1EitA/AyAD2A6wCMNE3/jqKodwCcoGn6EEVRTwLYQNP0fbbOZ7gb8sqt15WSdWYYaP2Bve+3VZvR3KbD38/V4+jFRjw+LRJr0qMQ4WBvHzntIhi47/dDk0Lx8Lv5SIvyRUKIHwoqVbg7MRiHi+Svk1zkvk/M+nypvhOLJoThUFHDgN/BihYdksKcV1smp27LYDJD29WNED+F6LzGXZeEfzeckW+hXPn+tbN5a+mBdRl2BVoGI20uPI7Yeil3PyUHxuhhnsdL82OxfHqc3fEhfO+lrlmqBnwg+56B1g07WlcnAqmRcibG7h54urvj9YNXcaW+A8umR+Khu8Lw9eUmHCxqRrvWIBnCZxDztBi7zdB2mUTTbYSRKHtpGcJoTWOHTpZXhImAHH99IZZNi0SvuUc0HC/F9twS/Nu317E9lx/NkBteNZjMOHS+Dk+lR6GoL9e4UqlDgLcnLxLFvf7yZq3kfRmsfOfuExXYdaLKboQmu7CGp4bHPFO5qX0KDze8tDBR1PMmFnlTeLjZbSwolZev8HDjLVa7jlfi8akROF3e5rCsbPb5Bmz6rAjfXW20+t6UMG/ckxKK1R+c4TUh3HeqCqF+XvjgdJXDnjp7kRj2vCQ8a4wRxf3eRRND8btFyaIKWcLUO2WnHrqubtExaMvLae+dtZWWK+WVnRw1Fv/+6ERMjhor+bfc8catT3GE5zOieX83WiVxBwJN0/fRND1Z5H9fOXCMfTRNz6Bpej6ATgCM7OQaAIf7/vsgLHVUw4Lc9yo60NemJDdgW9YZkK4/kHMO9ow4WxH38CAfRPh54D8enYgIPw8rI0rO989NDsGRokZE+ntieoy/zfRA7vvNeLmvNGqRHDQGbz0xjX2/D19scErdjRBuxkhq2Fh2PR3IO2hLQXCgyKnbUni4sWuK2LwmNQ8Od/qwcN1m1rSsycHYtXqaLJXLwWa4ANJRHmemqzFr5eGLjXjl3iS8ND8WNOVqd3wI33u13ih5zWIZLJVKnc2Io1j2krG7Z8ARyqFMhSWG1ACJDFBgfKgPWm/1YEtWKp7PjGNfvDB/b1lKSE/OiOINulB/b3T30vByo7Ft2WTJDY+9tAjhJLk9twQ7fq6UHbIN7ytgVHi4oe2WSfbGukqpg1JjRGmLFkqNkdebRG54VeHhhgUpIWjRGuHv5Y4deeVW9WZcA2FVRjSC/Twk78tg5DvVeiOM5l67hmSzWo/9hfX4/FwDxni44NDGDN5EYi9lDbBMZh/mV+ONrBTe30oteHIX3+dnRNs06pmUjE8K65E1OQw9NI2kUB9ZhczVKh1+qVAhMzEY//Z1MbZxzvE3M6Lx15Uz2M0Fc44KDzcsnRYFpaYLj0+NcniBtJdSCFgMT1tpFVJ53GIiC9zUu+cyY/D3wno8/G4+atu1kkaqGD9cb+E5SBxNpRIuUAaTGf/x9Q28uP8i/vObG7I2btkFNVj+tzN25wDusbbmFGPHsQpoDP1pR/7elndpKHqx3I5w+hrGwFIfdaDvV00AFvT9970AyofjfLILavDA2ydx+GKD3c/a21AXVKhs1vBJbeiGsj6ElzI0JxFJIb5YM4cvZS3n+7MLavDHI1fwysJ4zEoIwp+OXse3N5RW6bfc7+O+34zhcN/EMCSF+Tq8wWOOK3eNYmpmF6YEo0R506FaTC5Sjkhn4Ohm1RnGhhjOEHkQrttbslIRG+iLFznpafYYrAEoZmhUKXU43JchMNh0NcZQi/T3RLCPB56cEYXl0+OseswJU0i57/3PpS34yw8lrFNX6pq3ZKWyfSO5OgJiY0DMOcM4jtW3TMNeu28PktrnIEwIfs3dsVg1y1q5plNvhJe7q+RgYkKhe09WYs+patH0JltpPkDfBt/YDRNNW23IxNK5/uO7G1YpZ3JecOZYbi4UVs6MwuN3RSDWRmGfnLCrvfCqSmPAawevQKkx4C8r7sIL2UWS3d0rlVocFemwLpYuMlD5zt0nKtCh77aS3BUyGOlTqftmSzIXsIzFL87Xi0qbKtt0+PBcvawUOCYlY2V6NF5amOjQ5P/d1Ub829fFvFTAD/JrcLioEatnR8PcA945DjQ0L0xh5Eryctl9ohx+Xh5oVBtw8EKDVYrBQL+/RqWFt6ebldTxTb3ZrtOkWqXDyj2FCPJxx/p5CUiP8ke0nQJZOTiSOsJct7bLjDnJgfjL8rtEU0K5aSSLUoLw3rEK+I7x5KU+bc8txQ/Xm7FxXiKWp0fbTC91gNEof74UwLsAggGoAVyiafoBiqIiAOylafqhvs+dAhAIoBvAazRN5/X9fC6AHQDcAHTBIn9+wdZ3DnaN4qbpni5vw0sLE/H8nHjRz9qbXwwmM57bV4jpcYE2W2sIU7qGsnWGMM1pR14Zztd0Ij0uAJsWjWfP2973cz+T/ZsZ+MOhawjycce88SG8sW4vDVmYkic3BU0qfV3OGtXcqYe/tycUHm4DXtdGQqp7uHB2a4ZqlQ7xwT7sPM6Mq09fyLArdCBkoFL93HRybZcZP95Qsvuy5zLjBuXM+vZqE3S3jIgN9EFtuw5PZcSz42NVhmUN/+F6MzbOT8LyGf0tUpj3/g+LU/A/P5TaLV9grqOpswtP7y2U3NeJtRTwcHWRVSIxxJDUPmfAjQJ8VFAr+pmvrzRLesK25ZTg959fxN5TFiNKymNuy4jKzq/CrpNVeHRXIfaf5XscDSazaDoX48VyxFu2LacE23+8gWXTI/uUkmiE+Nv2esgpDLXnOQn2U2Bq9FismhWDj3+pERWZYAj08RCNygiNKIPJbPOls+W92rggCS8uSLAbodmSlWqzaF+K2jbx8DaTvy4lmQtIR7q25hSjSm2w8ipJwXhWX1uc4vCG56EpkVg+vd8zpuvq4anjLUuL4J2jsbuHHYsLxgejS0aBtVgKo5gRVaPUoeOWGf/7UzlAAZ+uzbBaSAdSvLw9txRP7jmDw311glwvYFSgfU8gI3TRru9GqVJnZUQxKQyOCro44s1VeLhh033JWDY9EuPGeGDx26es5ihhGkmgjxdWZ8ShoEKFF+bEIil4DCpbtPj8XD3KlHrsOV2Jv/xgO730doam6SM0TUfRNO1J03QoTdMP9P28iTGi+v49j6bpiTRNpzFGVN/PT/el/KXRNJ1hz4hyBgoPN/zu/vE4Xd6GVq0RfztRKTm/2ZtfGCWyotp2vLXiLsm5TZjS5cg7xhVO4CImJiMcnyqNAY2dBpS2aNHYaWDnfznfz/2MtrsXy6ZHYsP8RJ7oTE2b1mZkWywrQG4kSuy4cjeGTMaII38jxJaCIPc8xXBknhqIINZgGKzIA6OWzLA1pxgr91giJ0LBIkeNKHty/7Zg9gSHixrZhsjubhT8FB6DNihmhvuhuqMLmz6/jOqOLrSotOz4WDM7Dj9cb8a85BBs+74Ef/mhf55n3vsn0qNlRVSZKHFNu95m43ex7CVnNgYeCkhEykFsRQFsecIYK/sfF4/HO8cqkB43DvkVbVg/Nx4vzBfvsC30bqn1RhwvU+HP35Ww33FoYwYCfRX44kIDzwsjbLTY2K6Ft5eHLM8x1yOQNTkYm+9LRX51u2wvD/e8xQpabXllatt0+IdPi/DApFBounrQoevCs5lxuCt6nOjn//JDCQ5eaJBsOGfPO2Xr9wMtoJbySgpFOOSKGDjSNJMp6kwN88aECH+bnuTBIIxCcCON9rydu49XwNjTA083V2xckGT1e+H3CD1RemO36PhpVuuxZGeBlVALA+NZBOQXL4t9f3dPL6s45YjnU0zoYltOCfIrWnF3Er9QvaVNBwMNnojHYGEaX4pFp5mxLhbl2n2iHB23zDgiiDL+4wMTsCOvDCG+XvD3dsOby9MGs8CNuojUSOAssYkP86vxtxOVsqKV9uYXW++KrWikvXeMEU44XqrivUO2RFm44/PJGVFsm46nZkVj/bwE9vv2n6lFztVmZE0Jx+qMWFnX1qjS4uPzDby5S/g+CK9pe24pKlpvYv28REyPDZR9/cxx/+2xSZgQ6uvU99wZOEPgYKgEsewxUJEH4flWKnW8yAkTgRKbx+3R0KHDsl39x/pyY8aAlDKZtfW5zBgo3N3xtxOVg468VbXq8NT70kIahy40YNv3JXajQbaio2J748YOg2g6PTOniGUvDbYx8CAhESlnwUhzi23abXnCGCv76OUGPDQlHNeb1Ph/D03AE5xQKRexHG9/b0/cvNUvRvFkehTyytqx4eMLVl4Y4cL4U1k7zwtty1PD9QjEBfohcpzCIS8Pc91i+ff2vDKxQT64OykYHxXWY4ynK/7p4UlWRhTjEduWU4LcGy3421NTsOSuSN5nGvrENWydN/f3hZWtqG/VsR40Jkf3rdxS0euVugdSufnC3mJioglcrxM3f12uEcXcv2XTI1Gi1CN+nBcOrHNcXMAeYhEi7oS3JSvV6nsZT9/OY+Vo01uaa+qM/dEoqQaGXE/UywsSsPtkleT4sSUlzvUsAvJz18U8YYzilKOeT+Hiy+SZz+lT+2rVGlGmVGN/QRU+PFfPO18pqlXyPb4KDzc8ODnMKjrNzUcXRrlUGgPGeLiznvoDZ+qxemYUvt80D8tnROHxqZFICPbGlXoNvr7SLPtcCEPL83PiZUcr7c0vUu+KPbEbW+8YVzjB3EOjsVMPlcZgV0yGOz4VHm74w4MpeDQtAp8U1uGdvAoAljlkx0/lOF3Rjnfyym2+m4zyKQBEBvsi2McdbzyQgmAfd6vvE5vbNy9OEa2b2XeqymaNFnPcy/Vqu++5MEoyUOxFkrhz8GAFDuyJ6wxlpGogdVdcUaFjpUr85YcS/CX3hmgEypYRJXVd3PrtVRnR8PXyEP2cPTl7JlK0dk4C/nai0iny6gkh/EibUEhj+YwoPJluvx7PloEjtjcWM6K4c4pY9tJoi0QxEEPKQbILavDIu/l2J0ixl/iNrAl460mL9v2zs2JxrUGD5z44g++v8TcgNvX45yTg6elR+HJjBl5emISdP1fgfE0nuzn6/f3jrfpRCA2GPSfFhSe438NN1xhIOpSYQSBHbXB7binKW27i3afSsGlRCttviDk3ZtOXc7UJh4oasDA5CN+XdvAMNsZYe+94hc3zZq4ra3Iw0mPH4ZML9Vi2qxDfXW3EoaIGaLvMaNF0Wd0rKWNJ6rlVKq17i0mJJthLt5EDU9T5VEa8LHUhZYcONUodu5GxhVyRC+73MqpfB8/XoUndha8uNfH+nimO355bKnpfmRTGRakhdsePWIoltxnvkYuNqGp1rLhaLIVS4eHGe04DqQFhHBb5VSr2WBsXJCFqHL8BtfB8mcVWaBzKYXVGLP79sUnsHCW2OWKuRa03IrjPaOSm2IYFeCPA2xMGkxkX69Vs3zln9EshOI+hVDpj5gFtlxmXG9QOq8YxwgkPTQnDM7Oj0dtL4eF383G6XGVXTIZ7XQuTg6zEgBxZr7gbt2qVDrtP1uC1g1ew51QN+94pPNwk53bh3FLbqsW+U1XYfbLK7jvRcrNLcl5SaQwwmMx2FRPlsi2n2KbyLHdNk7p/cgSjqlot64eUIBZzTQNNcZOLo2Of64R7YW4CvjjfgJzrKtS2a1k1V3uGqL3r2pKVisMbM2DuAc8BwYwPe44JhuRw3wH315Iai89nROPzdRlYNydO9Lpyb7Rg8/3j7crs27pH9gxcOXuL0dI3TQgxpBxArheamXjFYLzZxUodzla3ITMxGP/y1XXeBGfrJdmaU4wn957B/rMN7Of8FG5Ii/a3hEs7DVYvI7ez9IzYQOwVqc8SMw64E6XwJVDrjZKb7n2nKnClsZ23+UoO97WrNsi8SL9UqbHndA0+OF3N22CfKFWym74P8quwfHoklk6NYjedx0qVOFPZxlGKa8Ci8YFWLy/Xw7cmMw5/WDwRgT5e7HH+dqISy6dHYmZ8gNUm0dYYkHpuiaE+eGVhPPY8Ox0vL4hnPTFcFRsumxYl281ft4fYBkSMg+dqcKxchRMVSuzNr8GK3bYXOFu5ymKTHNfTt/d0FSLGeuKxtAj2773cXbHz5wqMVfTXu4m9WwHenrKbiAprDAPGuA8qt535fi4VLTp8d7UZwb6eyLnWLEv5SsyzvHF+PPY+NxNbslLxzatzMCs+CA0det67wz1fZrEtqm0fsHHIKHIC0psj7qK+YUES1s2Jw/61s3ljUirCRbjz8ff2xOqMaDyaFo6SZq1D0UjGa7958QQ8f3cc2vXd+PqyxbnyzrFKbJyfIDovihHsp+C928z4lROVEG7c/G3MEwoPN7y8IAHzxwfht4uS2XEurJsZ42Vpnj03OQihfp7YwEk3FCJVc/NWbinezC3DlxcabComSmEwmXlzjcUxKK08K7amSd0/WxLnW3OK8dT7hdh3qgJbc4pxraETf1kxBVuyUtGi1iO7oAY/XG+x6wwbKIONcjFOuBXpMewalxQyFklhvqwDd8+JCtF5XI6TGAA83N3YMXejqZN1av9wvdnhlhLqWyYkh/hAfctkdS5i2MqWWb+/CJ8VNVgZ24yjoEypx8ELdVbN6rlItUHg7gtsrQ/26qDkGpojAamRchA5+bdyaic+O1cHXw8X/Pu3/bmnwuanwhxrWw1WmRQFW8om1Sod1nx4lq3PemFuPNbPT3RYZWl/QTXq1F2iTQwbOnQ4X6vGn78rwcy4sVg3Jx6Rvp4I5Wx6bTVh3JFXhsZOA5pvdqG0RYtQPy8oNV1o1RpxV5Qv5iSG4LtrTciaHI5fKlT4nydScPRSB3KuNePZ2bE4XqLEpKgA2c1VGfadqkCrrpv3d60aAz7Ir7Gq97E3BsRy4+Xmix+8UC+7AfJAFYAY8itaMdbLBSpdN9xdXPDawavsGGAa5kkhzFW21VBSWDPFLBLM32cX1OCD01V4LC0SX5yvt5vbLjV+xO47c16v3ZeEWfGBVkaUVI1HXZsOwX5eNt+DrTnFqG3XitZHiH32cFEjVs6MwssLk9g+YFL3rFmlhYHmp5Fw3++sycGIDfSVrbzVotZj7BhPyevh5qOLzSO7T1ZJjl/G6B2kEUVqpDD8DXkZmIa7Us3Uhew8VgZfT3f89VilQ+p8YvPgzmPl6NR345srTZK1rlIYTGas3P0LMhODUFDVhs833O3QOBSreRargdmeWwKV1oSfS1pF13Xu32zLKcF315rw7Ow4vDAvwe45cP+WUa0tbdHCx9MV908Kc6jO9fDFBpS2aNm1+emZ0fiHT4uQmRjcpzwbiTdEjjPY5rHcOpu3V6axddwz48ZiRmwgrjdpUNqiZZURnV27KxxXg10bgf41jqkZZ56HVPNme7XB2QU12F9Yg0UTwnDzVhcSQnyx56TFqZ0S5o3FE8Ml6++FSO31pPYZzB7Px9MVz8+JxxMzLK1HmNqt5dMicagvsivci27NKcaF2nbMiA2UXAPElPZC/BQDatwuVgfFvd4V08Px4oLxiAiwvT4zf+cERVkGUiPlLLi9nyqV9pWFpCJTT82Mwb2pYVbeYCY0DlhvTGw1WAXsW/TxwT54aEo4LtR24NV7ErGirz6LG0n57aJk3t8IQ7WWdB8vvroRJ489apwP2nVdWDo9EudrbyK3WMUzoho6dDaLLDfMS8DxUhXO13Ti6VmR2DA/HqtnxyDUzxPLZ8TgjawJ+HR9Bg4XNeJKoxZrPryKTYuSsfeZmfiooBYhY8fgemMn3n0qzW5z1ebOfsWodfOS8MyMKF7jUYOphxd1YK7TnrdT+Ny4ef8Xatt5Xh2Dycw2xLR4Eg2yGiAPNj1CpTFg17EKUBTQqTehXW/kRUFsGVFqwURX26az6U0TptsFeHvy/n5NZhy+3zQfmxenyMptFxs/Yt42rsf5wLk6uLvx50Fu7wsu23NL8Nfj9vuu2esrwngumXGn7TJDqTFaeSArWm+iXqnj1TuFB/vyNnMqjYH3fieFjMWWrFR8+oJ9z/323BK89VOFzevhOnD8vT1584z6VrfN6Bc3wkW4/diaU4y3ckut6jjFYKIdPb3A3tPVsiIvDFJe+6Vp4XjlnkS7vfbEUHi4YXl6DI5easLyGTEOj0OxtF2hEdXQocOl+pv4uaRVcl3n/s0bWRNwcGOmLCNK+LfBfgpMi/bHwpRg6E09SAnzxVcvZeLF+faPZTCZ0WWyiMIE+bhjVuw4nC5vRWZSMAqqVPiXR1JFjShg8P2cuHU27bou9r9fWpiEw0WNbPlBu74bEyP8BtQYXAruuCpX3nRa6iCzRjFR++fnxONI3zx+o1mDZk6EMDu/0qq+jguzLyxT6lHTrsGjaZH4qKCWfX9WTI+W1W+SgVkLYgMVePWeJHi5u9qMiik83LDloQm4f1IY3v25AjvyLC3tmCwPbnq5MG1zS1Yq/r/H02xGR8UyGwbauF2sDiq7sAaPpUVgxfRwhPmPwe5T9tfn7bmleGxnPj7Mr5b1vYOBGFIOwGzWfqlol1x0FB5ueGFuvKxUF4WHGy9UzoTGbU0A9mRL7b2MW7JS8fFvZqJVa+JtIpmJ1NxLY8Gbx3HwQr1oqNbf2xMqTZfoplvZoUOlUodVGXF4flaU1WQpZ4JjjLoZsX4wmoH//LYYEX4eOLBuNjvJR/h7W6XQJYf7Yt28BFyo7cCyGdGYlRBsdWxuHvST6VHILVFhyc7T+OG6JS0lOsSXt0mPDbIYniqtEVmTw3nGhSMLNlMPxXjnVu07g+25JcgvU+LtvHK2vkvh4YZwf4XdRnty0wi4CBf+YD8FpkSNQ5vWjEAfL9BwQUXLTfzLIxNQrrwpeUxheH1rTjHWf3yec1/Fi1FtSfoD/fdT4eGGpg6dZC50fRv/d2q9UdJ5wSw2d0X54u7EYCzfVciO5YPn6/DF+QarSf5ibTsu1d/EpfpOZCYE4sCZGklniK3aK25tg5uLC5ZyUkXTonwR6uOOlTOjkTU5GHMTg/BJkbS4BPc9ZN7vx9Ms4ir20hS5Rf1y65jUeiN+LlFiVvw4HCtVwgNg3/fHp0XC29XmnxNGCXLqCRgj/+mMWKs6TuFxmPWvoLoTri5A1mSLU279vASsk2E0iKXmbs0pxuO7CrH7ZJXsQnJhatWazDgcfTlzwEaAve+NGudjs4UI931iHKEDbZAKAK8tTsHri8fj+OsLMS8xEPvPNchKaerUdcFg6sHG+XGYNz4EGmM3vr+hQkXLTWycn4DDRfV2hTcGw5asVOxfm4F185KwJSsVX27MwL0TwrBseiT8FG5s1HJJmuMNyW3BNDuPDVRg7dyEIUkdfCNrAh6YFIqVM6PYdNbcEhUAJpo7hldfJxRJ4Tqr181JwPnadmT1ObVfmBfP9mdyRExh8+IUrJuTgKuNN7HgzeO4UH9TNPW9ptWyZmbGB/Ac4IwxtCUrFTufnsaml4tlwSSF+UoKOXHvEffvnSVZ3tChw/5CS0/MZzLjZK1nar0RX11qxOz4QOw6XokPTg+tMUUMKZkwmzVtlxnjvN0kF52tOcX45EwtXpgXL3ti19zqQVUrf1Nmq+8Pk84HWEeMqlp1ogOW+zm/MeL9lwCwG9JmtbRCz+rMeLwoyGP/6mIdPj7fwHoKfihr502Wjmz+12TG4b8fvwuHixqRGuaNijaDldEq5kFjIhvzEoMAANV9AgpcmOjIywuTsL+wBvOSQ/DPR69LLlJSdUy2UHboePVjBpMZ5S03seXBCew9uNxwE/59jU65ghwrZkTjd/clsxOS2GZIbq0Qs+GQyo1+I2sCUiPGoqFDj3ZdF5LCxuLvv9Rg/bx4UJR1FFvoYapp68+fPlaqxMH14sWqUnCjrww78krxwS91ohuHHXmlyC60/G7nsXLWqNtzqkqypnDz4hTsXDWdve+HLzagQqnD+6eq8GhfrdaKGVFsCkeXyYzZCQG4JyUUBVXtuDcl1OEaB2Hks7enF4Fj3PHgxFBseSAZCSF+2PDJJbi5Apvvn2hTXEJMDGL3ySrJyAET3WRgivodqWPy9/bEA5PCcba6A4snhiMy2Acx/p7YtnQyYvz5abqE0YncegLGueROmfnGsgv/OB/mV7Nrw7acEqydk4C1mTE4uDETqzNiZJ8Xs8Fm0q8c3fSKiS9syynBkp0FkkIKzmDz4gn47T0JVmsOM7fmXm+R5QgVIrYJrFbpEOKnwDt5FXg7r0KWV39rTjHeOHIdu09WI6QvY2TPyUqkRY9FoK8C//FtCeanhA3aWLJlnG/PLe1zElrGXHSgZZ9yoaYdL8yNQ2FV26C+mwtTB1TRYhEIOVXWin/KmoBdxyp4Tl5nGmwhfgq8vDDJahMf7u+NZvUtLJ0eiSVpodjz7HSe05W5Z8yeJT0+CL2gEDjGDb9blAwPF2pARobBZMaPxUr2fP78f8XYtCgZhzdmsBHMHXml+OSsZc387mqLpDHE3KcQP4VoDZhab2Sf44XadslxKHQgRAYosGhCCCIDpB0L9hx7zH7H3Y1CWYtG1nrm7+2JFxck4HiZ5d7sPindT88ZEENKJkxDy0fTwnHgTK1oQzFGqa6uw4C9p6plFaAzEuFnq/lqRYmhvpJiDtkFNVi5+xdsFSjxSE3kwsiSlKeA6zUJ97et0BPg7clOFpfq2hEX5IPLNjwF3M3/k+lRCPK17bFjojgvLkhiN5k515p5RqvYC/ROXgU+OF2JncfK8NmFetHix4i+hoabF6fgm8tNdhcpucINar0R2fmV+OBMPU+0QeHhhvkTwrD1+xL2HqRFjYX6ltFKkIP5PJNfLLUZ4m5IxGA2HHtPVdpMNQ3xU2B1ZgIemRiKNdOj8OCkMBwr6xCNHPp7e/JkUOM4yoMPTQnHwYtNsotBxcZqQ4cOrRoTW3jOfSa1bfzfNd/s6k/bO1OHx+4Kt3g77wq3+q7ocT68sZwU6oN7J4TidEUrNt8/Hq8/YPGgBfspUN6qR1KwNye9spE3LoQbCbH0OqEMe2iANzYsSMJ9E0ORFh3IjueS5ps4U9VqU1yCmzKxJjMWSk0XfqlQYfm0SPxSqbJy4gjbDQCWTeDvFyU5lLojjGyvzkxAYog3VmfKS1cijBxyU2oYJ9OWrFSE+PogKUiBd59KQ1KQAuEhvrzj/P2Xary4IJG3eQkN8EbOdaXdFBshzKatWqWX3PSKqX8JHRRNfZLpciW5B0tUoK9VJIqZW309XWU7QhnEHFyMEiejHMs0X7Xl1WcMUiZ97vNzdWwD8ACFm6gBOBBsrUe2xtwjU6Ow93QNHkmL4vWXHCjczBam3KFd341rTRrcPzlCMrV/sDSr9ZKCUmvmJGLt7BiE+3tj/UdF7BwsvGfM5zctSsHDk8OwMCUEa+Yk8AQi5G74pcR+PrtgWYcv13fw1sw9p6qxcX6ClaotFymVSH9vT6THBWHv6RqkxwbJMvwMJjPeyi3DgbP1+N8fy0SvS8rJK4TZ76yYGYfNiydg4zz7Y/qhyeHsvVkwPhhdAjVrZ0IMKQdYNi0Sx0tVOFjUjHatAV+uy8BTM6LZzZWvB2RLVzd06HgS4f+bV4nV06NYCcqtOcWiCmrM5M3tPXP4YoOV3DgzkUstNFIpgIzXZMWMaDZU+9tFSahR6kQ9Fc1qPbw8XVDTpkOaDU9BdkENrjV0YufTaQjy9ZL98iQEeGFZX9g+a3I4Vu07I/l3zLU+clcUenpht9bogUnhskPPYvVwXLbnluJ4mQrh/mNE68fWZMbhoz7VsyMvZWLz4gmYMz4Uv5NQ6JOzGYoO9LWrlHfwQj02zBNPNVV26Nio0Pdl7cirauepF3K9xIxsrVAGlbmeNbPjZOdDS0VffTzdEeLnwW4cVszofyaxQT6834WP9bJ6du8dr8R//991XG3otPpOYdpBuJ8Hfr9oPAymbt7nnpuTgGkx40T7ZkhtJMTS67ZkpeLIRn6ELsRPgcTQfuNz/bxE7Pi5Ct9cacYDKeMkU3aZc18xIwpfXWxAZlIwDl1sxAOTwjB2TP/GRNhugIubq4tVc297CN8HKe/uaJWk/bUiJ6VG6Fzrpmm8mVuBJ/ecxV9+rEB9u5Z3nMfSIrF2Lr831UB6qTEYTGb0Arje0InN9yfjWmMnG4mVUv8S6xMnR5Jb6JCU6lnnKNxNtdbYY7MXjxCxe8dNFWaUY93dKPgp3Gym6zNOSiZ97v3nZll6+b1gSbOLCvSF+tbg3lF765HYmGM2yW4uFG/cSDl8APvKe9wo5rFSJapUWrbcYfPiCViTGYf3n5slmtrPxdFnzzUwpOrJ9N20VabS5+fqYe6hEeHnZmU8Rgb6sgIRy3YVYntuiWzDgoHbzuLJGVG85/TB6Sremsk8F6k0ezFHBfd+5ZW0YFb8OPxc2sLeP2WbTtIotifR7sj80azW89afxFBfWZkVkQEKjA/1QWSAYkh7UBHVPgdhFMb+vGQSzteroTX0oKRZjclRAThc1Ig/PjQeE8MCbBpRjLLKu09OwbGKDp7Sy7acEkyLGYs/Hb0uqaCWXVCDQxfqcHdiMI5cbMSyaVFsjZWYasy2nBIcvtjAfs4RCsqVuNKkQccts6RazbeX66HUGDE5zBe+Cg/Eh1h77ha8eRzmHhq/mROLTwrroO0yY2Z8AHY/M0NmXyotVu07Y1chaltOCUCb4aPwgMbQ06dUxL9uobqbrW7ZyjYdPjxXb6VWwz0GoygT5OOOlelRaNKYnKJKJKYoxVWhsaeUl3OtGVmTw/Hd1Wb87v5kLJsWzf7+szPVUOpM0HWZkR43Dv989Dp8PF2xOiPaSr0wu6AGAWPc8F/flsDcQ+Ohu8Lw0rw4RAo21mLnKzxn7vmJjdXs/CoE+XrClaLw4JQIq3tSp9LCd4wH+7yYZ6fWG/Hf/3cd43wVkuOUobZNhxW7CyXfLwbuuLCniCnEnvpiRYsWSWG+2J5bgssNN5EWNRabF9t+N1UaA87XduJfv74BNxcKj6ZF8I4vdk8NJjPeyavAoaIGPDUzGi8tTMR7xytFx81AFI4GosokAVHtg/PWqP2FNXjt/vF4cLL1OySlsCX1TtpSWT14oR6Grm5MiPDDrPgg0c8I51uVxoBgPwVyrjXjUr2aN9dInRuXpk49IgK8ecdt1Rjg6+VuVy01u6AGh87X4b6JYfiksM6msq5cmPPIvd4Cbw8X6E29WDwpjPcZsXdLTClP+Ay4appiMPcS4D+nbTklOFTUgBfmxqGjTyzGnmKsPaTmdy7MnCmlBCylPMxcu1DZVIytOcU4VqrEPSmh+O5qMx6eEoFDRQ2yn2V2QQ2+vdSA1+5PQWaSbYMLsGzil+wsYM/5yEuZksaI8PntPFaOUB93lLcZJFWOl+2y3I/544NQ0qx1SAWTe007f67Aa4vHo7HTwHtO1Uot/H3610zumLF3/sLv4I7X3GuNuFCvsbveiqnpSh1T6pwGMn6Z67S1v3MQyTWKGFID4OOCGkyK8MWhoib8eEOJf1w8Hv+TW8a+AF9uzJBceKpVOqzcY3lxYgMV+OL5mdB0W+qemEUk0t8TsxKCbG7GmcHJTLQVLTokhfmgUqkV9YbZm5AZuLKhBpMZ1a06XKhX490+z4FwImHOQ9mph87Ui0AfD3T39Fq9qJ+dq0OFUofcGy14fGokWjRdOF6qsjv5cRcguRKtzLVWKbXwUbjxrluOND0A1Cl1OHi5AfelhmD9R0XstR9Yl4HTVe28YxworEVVmx6nKlrxh/tTMSVsDAy98lMCpfjsXB2u1KtxV7Q/npoZw9u0rpsTZ3djX9+uw7qPziM9JhA+Xi74/f0pUHi4Ib+iFR4uFE5VtKO7h0Z5qw7jQ31xqKgBb2RNwJy4AJh6e9kc9wVvHsfshHFIDPGG1tCDry83SW6cHZFFlxqrA534rjZ0Yl32Bfae7F+bIdo9HbC9YDAS0MIFQM5GArC8E++fqsInhXU2N4SA/PH4Vm4pLtarkTUxBPXqLig8XEWPX96sZTcm2QU1uHnLxDouHk0Lh7urC368oeSNG1O3GZ+cbXDYIHLUuLQDMaQw+DXKVisLRuIckHauCY0mZkMu5gxoaNPhu+staL/VLbmZEo5vZhy/cHccosZ5IyLAC81qAy+quy2nBOdr2/DywiTcM4FvkEgd98P8auw6Xsl7j4QOk09fyMCL+y9g2fQofHC6hnUK/fGhVFmKg7bktG3dd1tzoNgmU0x+nYFZ51UaAz7IrxV9Nlxj9D+XTBJdu7nrqkpjwM1bPUgKsy8X7sjcLLVei829jEHBzFP29gZVKi1+99lFLEmLxO6+vpjC+y52bw0mM57bV4jpcYF2DQAGlcaAfSJtUBiqVTrEB/ffO+66ptYbUd2mw8ZPLooaj9z7YSl58HJYhl5s7HV190hGooVjRmjkM44Kqe9SeLihtk0HfZcZv8k+L3ldcrFlaDlixNq7TlvfIxMif+4sDCYzcm8okf1LNRs2PXq5QVbx/75TlTjTJzPJpKo9uvsMfqlpB9BfD9F004jIPk+51EvODAjGk8iEyqVSCuQYUUyIeeexsr7J1YibBiNumbp5uezMQN5/phYbPr6A/WdqERrgjZ9KlNh1ogoPv5uPj/KreCkVD04MxdeXm1DXYcCNFnkqYsJUKrkSrcy1JoT6WvXlkhNK3ppTjJI2LYqbtSiqaRdcu4I9xoEzNfgwvxpv55Xj5xIlFo4PxT8dvYqfytpExRocQZhfrNIYeKkVNGA3fSc60AeP3BWBMZ6uOHqpGe8dr4RKY8DlunZouszQdJnRrjfhSsNN3DR045tX52DZtCiEBnizY5gJz19uUGNCqK9o/RIXYYPez8/Vw8fTFSG+nlaflxqrA92MT4kKEK1dFOP5zBjR92trTjGezz6LrTnFVikWTDrs09MjbaagCNUXV2fEwMPVeqqVOx5VGgNaNJa+apcaNXh+ThyemhnNpjStmxOHnt5eAGAXMubYn52tx9Jp/YqB3JqLDfPisftkFd45ViWatmMv/cVZqkwE5yGVTiOsfZBqrCqsU5KqP9qaU4zDl5ug8HATVQIDrMd3i1qPFk0XMuP98EtNB57eW4gdeeVWRsMbWRMwIzYQbxy6Jpr+xT3uB6ercOhCA3Ydr0SkvyfC/TzZccvU2aaEeeP1xeOREOKLzYtTcOBMHR6YFIpl0yORe12J945X2rynjqjNLkkLxb41M9if20uJE9vYSRlRW3OK8dpBS/3Np+fqJZ8NN93RYDJbpUMy6+ruExU4U25pxC6VaifEkXdcbL1W642iysNMeiIzT9mbExOCfXF3UjCyC/vr1bnjXSpF7r3jFVg3L0FyzDKoNAao9UY2zXTcGHe2tkilMbDnxdS0ce+dmwvFpjv7e3uiruOWaC008/dM42ImNdHRejaFhxteWsivXxSuw8w1CceMWLo611ARpm0z9zc2yAcNar3kdTmCLePGBbAav/YQu05HUyYdhRhSDqK5ZcT9qaHwdHdHVasWj08Lw7tPz7Bb/K/WG+E/xgPV7V241tCJ/1k+hc1H5U4YzAL33JwEq4iGUq232sDZq42QQljvxOQeu7lQcKMofHGhHl9caMSVJg2ey4zHb6ZH8SY/g8mMy/VqlLZocaNJg++vNcNo7sWRi42I9PdEo8bIq/HibrqmRPrbzJ1l7pfYAjQYj4K9nF2g/37uOl6Bu5MCsetkLapaNfjrU2nY1NfRnjnGa/en4MN8Sy+Vu5OC2GuvU3eJ1rc5UksilNEP9lOw9+/lBQnQG7vtSt2r9UYoNUae8ePm6oKF48Px5YV6JAf7sIvWT8VK+HpZ978A+tUQH5gcYXfjzFxjrVKH7MIa/ObuGLZ3xe6TVTavmVtgPtC6G0aClxmn3N5M3MXv0Z0F+Ox8A+9vK5WWZz9W4cG+Ux+cruKdy+GiemSfq8eqvWex9xR/A8Zd9Lnqi909NG+xUuuNUCp1aOrsshqPYkX2Pl7u7HM6UaaCr5c7QvwU7FzRcasbK/ec4Z0PM057aBpJoT74yxN3scpHvgpXfL9pHh6aEobDRY2iBe1yFx5Hep8QhgdmM7YoxZJqJ1X7YM+5JlV/xPTF++xsPW4ZxZ1sgPV8O3aMJ46XqnDv+HArtVIutmo1hMfddN947Mgrw+qMSMxKCMKfjl5ne+QAlvng/tQwvPlDGbbnluKBSeFYMjUSrq6UXacQ4LjabEygDz4qqGPfncE4G7hrNLMuMbXRjINEqjaMmRs2LLBIkX/9UiaeyYjmratGcy/GKqyVYweKmNEjjOgz86DYpntLVir+vGSi3TUasKwVjLBXzrVmS52uSO3egTM17Jza0KHD5+ca8E5eOVbPjsafHkrG31ZPs9qcv5NXht0nq/FRYS27Id+XXwM3Vxe8lVuKD3+pxpcXGnC1oZM3NhpbtdiRV4qPz/BVZ5dMi8aL8xPY/RNzn5j36HRlJ14/eJWtp+ZGkIWI1SPtP1OLilYdkkN84ObCd+By77nwffZwdbFp5NtT/lw8ORJr0/n7Qu56O1iYdTpunBcOyOiXyCC8Tl8v9wHXcsqFGFIOsCOvFO+frsXe01WYEjEWW5dPxeTIcezkaEtq09/bE+O83XHkouXF+cdDV7FxfoLohCG2wO07VYEPCuqsPGNSTXptIabMwniEnpoVjSBfL2gMPfiksA4dejM0eiOyixqw+oMzvL85XqpCQpACj0wJw1s/loIGjaXTIvHSwiRRwQVm07Vp0Xie54U7OTD/PVTebuZ7xdTdgP772XTTCE8XYP28eFxp1OKGUt+v0NN3DGZRvlDbgUlhflg5M1ry2uXKETNszSnGl0X1eP3+8ewCwdy/Zo2RHQe27ou/t2dfoTh/kzwxyh+xQb74/EIdVmXEyJLFZn5nS850e24p/vv/rmNrTjFOVrVhf2E9fDzc7Xr/AGBbnwLlO3llDt8rBqVShxqlDt5elmlt94lynK5ow75TFcguqMFz+wqtFj/u+TBCEGMVLng2MxaxgQo8lhaJxW+fQnZBDWrbdAj08ULO1Wakx43DnpPV2HvSYryIGR4KDzfeYpV7oxlnypS43qDGB0X1eHpvIXRdJnz76hysyYyTLLK35QDo6e3FzyVKzEsOtpwPx5jiCsdobvU3lz5c1Ig2jalf+ppjXG1enCIrUsYYl8LmzITRwY68cnaOEBNpkItY5IqJ9PTQNMw0jXVz4iSVwJgx+OSMKHYc/1zWjNWzo/HM7Bisyoi2WrPknC9z3GXTorBuXgImRfiLzrtqvRFfnG9ApL8npkT6oVNvxLo5cdh0b7LV+iI2zuW2mgAsIjrcHjeME2YgzgbhGs2sS0zj1B6axliFm2TfH8Cyj2Acrx8W1uH9k1VouWlgr9vTzQU3DeLKsY5iz/EiRzzJYDIjKtBXMirD3SdwN8sPTY4QNeBTwrxxb0oYHn43H9tzS9ln2UPTCB7jDpW+Bxs/uYgdeaW8qA3jfBQaqxSAlptd6KEpvPtzBa7Ud/LGhomiJFVnA7w9kRzuy96n/WdqcbVZw977tXNi4enevx0X26MxmUcXKpXsvTCYzPj+Wgt+vKHE6Yp2vJNXzo5ji+HIv+fc99nWHkuu8mdosC+vxk2qF6JwLNiDcab4eLqiuqMLT79/RlbElIF7nQoPNyvFUWdDaqRk0qzW46/HqqzqCxzZRHx1uRHFTRqeQIStwj/ud5+p7sSfvyuRrMPi1kbYOxaTc/rbe+LwWFosLze6qV2LYqUO/+9Iv9jF/rWzeQWiB9ZlIDHUly0WzrnajIemhONYqRJbHkjFpEg/fGAjp5gLU0j4L4+k4FqTjldUKKwRYhhsrqu9Anllhw4aI81Lk7KVd83NG+/UG7H7ZBXv2h2pJTGYzGjs6MLaj84iPW4cTpe34YW58diwIBEAvzjVXj2ewWTGkp2nMSNmHLw9KGx+gF8LwIwZZuK1d09t1QEw1/jXp6fi1U8vwcfTFfdPCkNRbTtmxAbaHAvfXW3Ev31djFatEf8vazz2na51+B3LudqAug4DK4rywW+m4ZsrrThS1Ij18+ORe60Z0+MCUVTTjhlx4ufzlx9K0NNjBk254rurzXj13iT85Ycy3vUeOFODXlB4/2R/Xv4Pm+bxnq+wSJiprfrHByZgUqjlXXsu+zzMPTSWTY/E15eb8B9LJuFfvroONxcKm+5NxIy4ICSF8msWxIr23V1d8GVRA/acFK8TACwGZbCPB0pbb4nWXQpz4g0mM7640GCVq8/k0m/PLcUP15tx74TQvuLwQQtNAKRGCoBz6ngv1LThpf2XrOYIW7UPA6FepYWXp5vd9UtYz2Qwma0ET7jrYG2bDrFBPpLnazCZ0arpQmyQD+9nO/LKceRiI57LjMGTM6Kh6+pBmL8XDl+oR526C8dKlFZjlpm77dUq2hLc4LI9twRKjRFnqtvxWFokPj9X77CgRbNaj5V7zmCswgOaLhM+fSGDvQ/MnC2n5vmri3WIC/IB3UvjyMVGeHlanFqrZ0fjudlx7LzaqjHgpt7M2z/YqwnjYmtd4GKrxpS5///y2ERMCfdDbJAPb0ww+4SN8+PwwKQw9txs3QehcAmzllSptKjvuIU/fHmVNwczY+KdvDJoDGZ8c6UJa+fEYem0SIT4KXDwQj2Cvd3xj4euscf8+qVM6Lt7kRDiC6Vaj88uNEBr6ME3V5qwYkYU21pDeJ8YYYlIf0/8/t7x+KWmg937rMmMsaoLumWksWpfIX5/bxxq1PyaxP1nanG5Xo0TZSp2vt6WU4K8khbcOyG0r/7Kcm3clEMGqbo3uTXBAFBU244XOXVgn76QIZqi6og40dacYkyJ8MW/f1vCuxeOzmHbc0vx1aVGPH93PJ6fG+/Q3wogNVKDJdzfGzHjvFgPwrJpUZJeLMBaxtNgMuO/vy3G15easGpWNH67KAnbc0tZb4m9727XdYn23GBeDLmeJMbb99t74mDqdbHKjY4I9MWiieFYzrlOYdSLqW2ZEOqLI5zw+t+fnoHoAG+E+ClkNbJl0hUi/T2RFOxrlaL43rEKXG3UYNfxij4DQ2fX81Wr0ol6PJjnIealYf9WqbOEk3cV4tCl/rQve3nX3EkowNvT6to93V1lRdeY43d16/FsZiyuNaqRmRCIQxf7O9I74iFVeLhh9ew4/Fzaiugga7lQZsx8caGB9ZIZTGbRlALmeFKREca7dfC8pYeJ3tSDcWPcsGPlNFYiXUzivbZNh78dr8TSaZHImhyMLnOv3b4pQiqVOsQE+GBMX/RL22WGWm8xqHw8XeHj4Ybf3T8eR4oaca72Ji7UtuPAOv7YPHi+Dl+cb0CQj4JNGdl5rAIb5lvSK39//3i8d7wSv1S0ITPejycp7+/taTMdhfFIL58RhZu3jGjXW7zAD90Vxnov3ztejs33JWHj/Di03TLhu2tN2HmszOr+M7yTV4aDF+pxsKgBHxXU4v6JoQj188SLCxJ5n6tW6TDGwx1bvy9HQ4cOu1ZNxfOz+Q1UuQsTMwYDfTxwdMNsLEy2pIcxUcIjFy2iFKlhY9n31Z7cPWH4MJjMOFzUzGuuy/TsE25A7KW42JOizrmhxLFSFf7vcoPkZ8Sim8buHnYOPlGmZCOxH+VX8dp+CM9XrTfi26tNeDuvHOs/Po+D5+vY3yk83Czp9eszEDTGA9mFtdh10tIcflLkWBwpahQds8wabi8Ca8+IYtaczYsn4MV5Cfjq5TnsNTqaThTu742HpoRDqelC1uRw3n1g5myu8SDWb1LZocONFj3Wf1SEC3WdWJkew0bsDpypR5vGxH42xE9hVbfz+88v4eeSFlnnKydlHpBOA2buf6S/J6423MQ/fFrE65HJ7BOCfNzRquvmZeXYMia7TD2i625CsC+a1QarObiwSoWaNh1+u2g8NsyPx/eb5mHjgiSE+ClgMJnx5velyLnabNUjkDEYQv29ETTGHQuTA/Ffj03kGVHC+/TApDC8cm8Smm4a4e7ugsNFjTD30DCae0D1GXfciCyzB5sWHcSLvDYotWi5aUC7zogX5ydgTWYcWyNUptTjWKkSX27MsDSmP1aOvx6rtMr2YJrRy31eQqpadXj/ZKVkL0QGuVEuhi1ZqZgU5s+by8zdvZKfZ94xpqUL9zvrOgz428nKIVunSETKAQ5fbMDfjlcgJcwPte06LJ0WbaUUBEjLNXJVbJbcFe6w4lWNUgtXt/5JXWjdC9VXuCpNQhgZUm2XGRvmx+LRKVFWxflCb49QZe2zMzWo7jDg6MVGvPFAMspab0nKVEpFkbLzKxE3zhvm3l6crbuJ642deGlhEuYkhbDX98rCBDRrjLjepEFpi7Q86I68UlFVua05xTCbzXB3c0Nen1eS66VhPjMhzFcy6ifmdQPsR3G4sqQPTAy1esbMM+Me/64oX7y8IBGXGzQ4VCQuny3XQ2rr3nOvS9tlxvNzYtBDUwOSM1Xrjeju6YWmqweJIT6svLcUzLPddG8i6tVd+KVShT8/fhfW9kVqsqaEYv2ceMRJFF4Lv7ugSsVGpIqbNTAYu3HfxFA2QrVxfhxUum6ex5rxdla16vDi/vOYlxyCKw0dbATt9fuTMCMmCOEBXjB291i9r17urlYy/7bGw9acYvxcosT/PpGKII8x0AE4erkJX5yvx8sLEhDs5wlvdzf8UtXfEuGZmVGIFtRKqjQGvJ1XgQlhvnj/VBUeSwsDKBdcqlNjWoy/lYz67hPl6Lhl5kWjpBStFrx5nCfjf6SoEX94YDze7IvMPXRXGCL8FDhV0YqFKaE4KniPBgGJSME5Ean9Z2rRpr2FmHE+cHF1wZK0SKvP2IvA2JMcVmkMOHCujp1vn0yPsto4cr9LGN1kvN3//pglEtuqNWLPs9Ml235szy3FpXo1np0dg7/klmJecgi+EflelcaAilYd9uZX42qDBq1aIxZNCERy6FhWMls490udo1ykvOyOHFOY9SAnwgNIPyehzPjX6zLwQVEDjl5sxOPTIvG7vnpfIfkVrXj7xzKHVO3EroHZmNuLVgJAY4cOP5W2IdzPE386eh3Lp0XiUF8PSEaR9IP8Gpvrs/AcmPsijL5x6dQbcVNvwqFLTSisUmFGbKDNMZ9dUIOxXm7YX1iDxZPCkHujBW+tSLM6B3uRX7EMlx15pegy9aJdb8LxUhW2PDQBs+MCrNWRlVp8WNTArg8rpkfj6b39z5lRqhWqcqr1RryZWyaaUWVLmZN7zu26LqsoJaMiuTWnGLXtWqybm4D0OH4bBLXeCIOxG25urjbVD8XYX1AFnakHCnc3GLrN2LggWfRz2QU1KKppQ7i/t9UzdCSyZgcSkRosBpMZfz9dhblJwThb3YE1mQnYdbzSyuNkqziVm/vr7+3J8zqIvegqgUrSk3vPYP9Zi/ePa93n3mjGX37g15VIdahmaFbrsXJmFP5xcSK6zMBrBy/iu6uN7LUC1t6exFBf9pzUeiPe+qkCl+s6sG3pZNwVGSgpemErirQwJRShvl643qJDoILC5MgA7DpWgU8Ka9jrGzvGg9e5XczzVa3SieYnN3To0K41YFrcOBwqarTy0hhMZtbjtUfgVeFOkFxv0m8XJbNRHOaaxDw6XE/n//5YBi93V97vubVA3OMvnxGD2QlBOMRpuCz0pNgzorj55PZqn165Nwkz4wMQPnaMrHom4fF2n6jAVxcbsDe/Bk+/z3Sclz4/7th951glNs5PwDsrp2FylH9/zY6Xu5URpdYbYTCZUd+qQ2OHjh2nFkO0F4sm+OOJqWHYtnQyHpkahSCf/kLqPadq8PzsGHy2bjZ0Xb28WqSEEB/cOyEUpytasWJGDFucXdlmwFN9ymJi+eTC+2DrPjM532VKPdZ+fBU9Hi5IDPVlvX6LUkPwt+OVAGi2kfTRS43w9LQ+ZrCfAqF+ntB2mZA1JRzTogPwxbkGnK5oxxfnG9g5hxmTGxckY/WMKDYqKPU+MmNhw/xEXmPpvaer2AbFKaG+SAr1QXrsOKSE+RKhiVHI6oxYhPl5o67DAJPZ2oNrLwLDFMEH+bhjQpgvWkQEFsy9vbz59uAF6zmKQazmhRn3D04OZ2tdWtQGLJseiUUTAvHu01NZI4qZL87XdKK05SZemJeAbyS+N9hPgcbOW5ge48/Wt4wP9ceWrFTsXm0RhbLViF6OEcXNeLDlZRces1apE43yCd9HuREe5jkx83WtSss+S2EWSWioL+6OH4fdz0zD3fHjRI9Z26azpIXekyRrHRDCHPO9Y+XYe7pGtN5TyNacYizdVYg2bRfmJQfz6sC4QhpbslIxNWKszWwM5j5erG1n78uBM/W4ecsk+t0B3p6I61NyfPOJqTYFTgDL83xgUhhmxAVi7+kazIgNFF2HmR5ntu4TMzezddez43CtScPW123LKWGjpecqlaht1eFcpRKhob7YkpWKT9dZmr1zG7xvmBcHbw8KKo3BqrbR39sTEf5eVtkeUsqc3P3MV5cb8Tan5pL77JiMpi1ZqXj9/lQrIyo7vwr7z9Zi/7kGtGi68HOJEvemhKC3txc1Si3ve4R7qIYOHXb8XIVjJa2ICVDg8cnirRCY+ezJ9BjeM6xU8mvzh3Kdcn7V1R2KwsMNc5JCkFfSgtcXp2D5jCjoTGbW48S8EEzqFWN1C1805nO1bTr8XKLErPhxOFaqxFPpUTyVPq6X4Mn0KN4AeXJGFBJCfLFyZjS+OF+PDfOSsO37EnYyXz49kv18bbuW9Row1Lbp0Ko3IT3GH96ebjjy7Q1kJgZja04prjRocLioUdRTKfRcMN9/of4mFqaG8a5bKMPcqjWisLIV8xKDkBDSfy6xQT44UdwCVxdgYuQ47PipDIsnh2PPySrMTQ5CfkUbALDHlvLQxQf7sHL031xpYieKAG9PPHd3PP545Cp2PDEFAd6e6LxlRHSgL+uVfXP5FPb4IT7uknVHazLj8OSMKABgPYaHLtShRd0l6tFhFkThGAH4C3CLWo+KFh17/KbOLnbjznhSpKKVwh4WQH/kY/28BKxIt19btuSucDw5Iwo511uwdHokjvZFbdxFJLuFqPUW9af4IF/sPFHNjlHheOYidm3M9W3JSmXHN5cdeWUY4wa4uLig02BGq8bI6zWybEYML9d7TaZlMStv1bPRpQ8L6xDm68FbOJ6fE8su1CumR7ERV52J36X+iWlR2Lw4BWvniHs3Adt1BUxKLfN+cD2WXu6uMHWbcXdSMD4qrLG8v311lFKpK79dNB6tfTVSh4vq2efGzDnCdzUmxPp9fO9YBSsCwMDUsHxxrhavLIxHVIA3bhpMWDYjBitnRMHLwxXBfgosSYuAwsNtSBSQCIPj26tNKG/V4evLTXgsLcLqvZeal1QaA767rsSP15qwcX4cWnXd+PN3JaIe+nB/b9H5VgqxeYf5/BtZE9j3ELDMX68euMR+L3e+6IULHr0rAnXtt3DwQoPo9z6cFon/+r9inCpXIdjXE99da8KG+fHsfCR1nnLqbpno02v3JSEjIQjxwT4252nmmPtOVcDcS2OMhztumZpZzzr3fbxW34GK+CAkhfmwa4HUOan1RhwvVbLv/ZPpUThbpwbV24GUcD9MiQrAlqxULJ8aheRwX9SodJiVEGjzOmODfEBTrlDdNEjOVfZQaQww9/Y7g7hzrBCu0/mL8w14cobledeoLGvH2jlxvL+L6zMinpoZhbggX6vIDnMf/3qsnHf+XIPc39uTrcHjEhfkI+uamRRSsTWKwV60VywC5O/tifS4AISP9cKJMhV+uygZx8tUGB/shp/KOnCkqBFLp0dicnQgFB5uOFjUyB5jS1Yqnp4ehW9vNOPvhfWSTeBfuScZnXojXrknkR2njGgHE70K8VPwzu+3i5IsNXZ9afKR/go0t2uh76Z46yMzzrio9RYnuLmnF4UNnejQdeHhu8KhNfSgsKoNLi4u+LlEiU2LxuNao4bXNF7h4cbbSxfWdGJhqrghxcxnX/SVFTDPkJs9FeDtOaCG83IhqX0yUeuNeGxnPisA8Oo9SViRbtlUCzfIrx64gMemRuLry4149+kZrOV/y9TDe4G355bgcsNNpEWN5aXjiHV3lxJvYDaPwvAlE2qNDfQVDVfn3WjGv39bjDWzoxAW4I1//7oYoX5eUGq6rFIKatt0GOPhKtpxXlioKCZ6kV1Qg8LKVslzAQClSgtTL1DeqceuYxWYHheI768145nZsVg/zyK0IExnE3sxalq1GOvd38WbmTQLypQ4XtmBipab2LgwCVMi/bHgzeNwc6Hw1KxorJ+XgIZ2A+/cbb1475+sxN7T1fif5VPwhy+vWt0XLlIGzPbcUrSo9Qj0tdTl/GFxEirbLLKua+62pJ+5ubogwNtTdJMultZR0aLDy5+et0p9EZvc69t0+KKokZeWYjBZVBr/XlhvN9zPsPtEBbxcKTRpTTabSAuR29xRrTfiHz67hJcXJECpNeJQUaPNFE/u/blQ026Rj/fxxNrsC3htUZIlHfVSIx6fGom1mTEIFVk0vzxXi5td3Qj08UK7rgvr5iXZuQflfZukbsn0A8A67ePwxQbUd+jRqjFhYpg35o8PRnSgeDG5Wm+EodvMS9dlmo4G+bhj/bwEzIr2h4enm+i7ymAv5Uip1sPFxQUfFdZCqTGiWqXFwgkh0Bh6eIu03GbCMiGpfXBOQ96t35fgWEkrkoJ9kB4fgKdnxli9Z0KBmW05JbjWdJN9r/Y8Mw1/+uqGZBoV09j0sbQIKLVGvHKPdapYlVKHcT7udjcv3PlR2ESXm97HnS+a2nTooWjJyDzTAJ4x8pzhjWYEdRKCFEiLGcdLfRPOZY1tOri4UQj390ZFiw417Tqcq+tkN8S/mRWF8MD+xtnX6jvYdcBeOt3OY+WYEOqD/3fkOiL9PfHi/ESkRfghr7QVdTeNVil5jACGvSa3FS06fFhQjR9vKOHj6Yrf3B2HR9MiHFblfO9YOW4azPj+ejNeu288pkb7Wxku/ddShp5ewNUFeOWe8XaPnV1Qg28vNWBBSig+KqjlXY9wXqtRaXmpoTcaO5HcVycndY9r2nSIkzhXBnsNlm2lZYrt7bhzc6feyGauvJ1XjqVTwvGcoOmtv3f//J4S5o29z6WDAnD4YpOsJvBiMOuN2PkVVHegRX2LTZNn7p2tpvYM2flViAoYg4v1arRqjZgc7ou/Hq/C8mmRyCtVYl5yCMYq3HhN44XjVG4ZAzOPCEtQAMdELmxAUvsGi7+3J56fE4/T5W3QdplxrekmFrx5HN9ebYZKY2ALPv29PZGZFIwrjRpkJgYjwNsTH+VXYW9+Da+3kFKtxzhvT5Q0axHk68X7LrH+HVuyUvGpiJY+M8kJw5ebFiVj830TeV6Dqtb+FI1FE8Px8JRwfHSmAd5ublg+PRKmnv7mfU+mR7O5xit2F+Lry42iPUWEk6yY6MWTM6JsngtgkdGMDvXFvRPC8PzceORcbUakvwKNagMbbua+TFIS2XEhvuw5bc0pxrMfWPr9BPt5o0NrQHLYWLx64BLeO16BPzyYgkfTIvBJYR3eyavgnbstCe6tOcX4uLAWL8yLx4KUUJ4wh9jEZav4duP88ex9iQ/yYXt5aQw9ePjdfBwpahBtCFmt6vfmHStVoqLFcj+TwnywXpD60qoxYOfPFdB2mZFXrLRstnKK8bdT1VZpKQoPN7i4uuBQUQMSghSYnRDAa/YoxsYFSXhsWhTWzYmzKzDCpatbugEtAN47lR7rj2uNnWjXdSEteqxkiicD4+08V3sT+8/W42KtpbHyoUsN8FO4YtWsaPgpXK2MKLXeCJXGYmgxHnmVrtvmdbR06NBxy4x3f65Axy0zmm30mhGq45nNvdAaevDjDSWq27vg4eKCqlad1Tjad7ISu05WYePHF9gUXKBfirpd341SpQ6RfRsHsXeVwVYa085jZfjglzpcrFezUs4PTgpHTy94XuYmtX7Ie3MQHEfh4YaosQqsTI/CxAg/FFR24MP8Gt5nmBSoLy5Y0sSZ9J7KVh2bDtfQabCZRqXwcMMjU6Pw1+NVCPZTWL2DO/JKceBcnd02BsK0NmY8M9/LjWoz8/rOY2XIPluP5X87g/eOlYumVT81MwabF493akoPExljWlxou8y40axBdauWPTeDyYzdJ8rxaVEDfrrejJIGNZLCfKDwpNh0uaMXG0G79G+91mTG8daBIxcb2flciFpvRKe+G1ca1Vja16ajqOEmuikgKtCbl5JX1apFtapfkj3Ixx1jvaSjyElh/VkdelMPVDrTgFobvHxPMtbNjcNr943HjRataE9FBl1XLz4prIOuS1pEALDcV4PJjEPn67B4Ujg+Kqi1mnuE85owNfSJGdG8e8xI5DNszy3Fk7sLbY5XsVROrtiHvbRMqd5sDEzKeMvNLlS03ARNW8vTM8dICfPGPSmhWL6rEHnFrXB1gVVvMSnhKCHMeXxxwbo/2ZK0SDw8Jdwq3VOssbKQNXMSMD02AGM8XBA7ToExnm5YOTMa5+va2X2KsGm88LnKrQVn7jVjRDFptI6KXAwEYkg5wPNz4vHigkT2gbu5ULhlMFkZSV3dvci9rkRXdy9UGgPC/BXsINTcMmJ7bgn2n623qrHiLgjcHFcmJzuBk54jBjPpZRfUYMuhS+ilutlFaVVGNMZ5e/A+/0ZWKg5uzMTC1FC8kZWKD38zk+018/XlRnazbu6hMdbLDT5eLnh6ZjR8vGwPG24O+cEL9Xj7p3LsPlnGWyClwuIA8NCUSDwxIxITw8fiuyst+Liwjvd7OS9GbZsO33H6/XQZ9Vg5M4aXSjA90p+3OWzl1H+JHb9RpWPrqeo6DPjmciO25RQjv0KFf31kIitnLzwP4T3hws1lr27Tsb28mPMa5+MpWnMXH2z5O2YyXbXvDLsArEiPwZrMGDw/JxabFllUhzbdl4xH08JR0qxFSfNNyUasBpMZwX4K/O7eRKTFjMMfvryKDwQbMTECvD0R4qeQTOcTsjWnGD9ea0R5s5p3z7blFGPLocusAX/wXA225hTj/642Y5yPAqsy4vBkWiR+e0+CzZoGrrrhnMRAlLQa0KLW47eLktGpM2J5WgSWTeMX4TPG8+GLjVZ1ArYacdIuFLuxKm7WAC7yplWFh5ulELzP6FVpb+HDwjo89T5/4/HDtSZ4uLuioEKFzMRg/NvXxdjG+X24nwf+/dGJSIvyZftx+Xq52uwxIyYW0qzWswbTruMVrMH6/Y1mq0U6wt9bVh0HYXgxmMxwcQEoikKr1ojSFi0oqheVrf19Z4QGsGVjFol7U0OQc80y/2cX1mLj/ASbTeZt9fth6qfSonyxIDlQsm+Q0MEDwKbia22bDj29gNZgxL7npuFmlxkv77+AYxyFOUa1S+HhJhmJGwgGkxlPp0firih/rJwZxc6nJyvbAVhSKq83quHn5YGkQC80aLrxXPZ5bM0pxpykUCslNu68J6xpslVj+vXlJnxc0ICxXq54ZWEiQnw8ED3OB40dt3j9uRJCfBEf7IOp0WPx9KxIzBsfgj9/V8JrWCxk06IUPD0zetAGqK+XOxQerqK9vYB+h1VeSQsyEwLxc2mLpMOOMbYPX2zE5sUp+LiwFnOTg/rqghKs0laF7D9bh8fSIvBlUb2kkd6s1ouu+UJFRH9vT7Ze9Lf3JGL3ySorQ1HsvahR6lijRqw3W5VSxxub8cE+SA4biz/n1mCsJ/DhszPg59k/ft/ImoC9z6Wze4P3TlRh1axYrMmIYo9tr06ei0pjwPnqNmT/UodvLluUpZ+d1b8+CtWCGYegHLXoAG9PvHzPeKxIj8YT6TFYnhaJrMmR+OpiA56YEYUemsY4b3f85Ym77K4pQsNQal/FdT4PVU9SLsSQcpC1c+Ox+5kZeOXeJKybG4vwgP6i7GOlStS2aHkvpJurC1r6pDYXTQjE4klhuFR/E0cvNbGTweb7x+OdvAqr4swQP4VVZMSe/LfBZEZRTRvC/L3xzN4LiPb3xOcvZMDcA1HvIOOJULbqYDDSWDcvASqtEWvnJrCb9YfuCkNiiC+yf6nD23kV+KigTrIDOvd8LVLaBhwrVcJvjBdq27TYtWqqrIjFc7PjJDvP23sxGtt08HKj8FxmLE6Xt6FVa8TPFTdxrqad9e6sSI+C7xg3Ue+Q2PGz8yvx0fl6lLa0sRPKiwsScaioEVcatfjbyQp2E3vkQh3q+qTU139sWUhteWa3ZKVi/9oMrJgZhy1ZqXiUU4DdoTNKeoa3ZKVi51MzRGWozTSgN/bg7Z8qcPBCPe5NCWK9PUwOubARK3dsZSYFW3k3xRDzBtujWa1HhK8rtN3Alxeb8fC7+XjvWDlyrjbhUFEjz3PIROnKlHr8zw+laOwwICbUF1GB/XLudSqd6HlsyUrF4Y0ZWDo1ElOixiLYdwxeP3gV4f5j8Mn5Bt4iwzWeP8ivwcRw24XNXML9vXkbq9wSleR1C1mQEsqOtbVzE3hGc6NSi7oWHXpoQNtlwsaFiRzDvxGtGgOa1XrsPF6N//mhFB6urqxASXZBraxnoVTrsfNYOd7MLcPPHK9m000jQrw98MqCBHy0djZeuWc81mbG4NP1s9kNgCMF+oThwdjdg8/P18PHyw3HS1VIDfOG0QxWBEbMY761zxm0ICkIS6ZGQqU14rE0iwCS1LhnxGzENjtMZGPzfYlICPHDywcu4cDZWqg0BlSr+N57roOHiZABkHTIxAb5IDlkDHzHeOJYWTuKatoxPS4Qbxy6hs/OVGNrTrGVI4Ihu6AGS3aextlKJU8iWQyhKARTcP/4rkLsPlmFlxcm4XipCj6ernCBZf7wdXfBuZp2jFW4Ynwov0FwebOW1wZCrPn285kxdj38zNrk7kaBBoVH0yKwri/1fVVmPLrNNHKvK2Hu6f+bzYsn4NEpUbIdQ4mhvoPebB6+2IjSlpu8aAo3zW7x26fw3dVmLJoQioKqdtybEiqazcE1/N/JK8es+EA8PCUCF2o7sH5eAtbNS7B5Hmq9Edm/1OJwUSPCxo7BxvkJVkb69txSrPnwHC8bJ8DbkyfFz7A1pxi5N1qw+f7xmJscLBrhEqbyWyKU9Vi55wzbMJ17rdtzS/C3U5W8fR0jJHK+phM1HSY8//EFeCv4kd/ocT5W+5SIQF82EmVPPINhW04JXjt4BW//VIalfQ2LDd29iBC8+1JtTOQS4qfA1pxi/PO31/G3E5U4XdmJvJIWfPPqHFZm3taaIjQMpbKGhIJvje1arJsTN6SCE6RGagCo9UZ4urviw/xqeLu7oElrQkGlCmsz49Co6cJNgxlfXeLnZrdqDNDcMmPPqSqEjPVEa1/Tvt/em4z5yUGiebPCRq7fvDoHj3A+J1UbcrWhE+uyL/AkMVftO8OTvaTQ35Qt52oDLjVocaSoEStnRuHlhXxvQJ1Ki4sNatxo0dmsgRFrPPtTSSvoXhp/yS2DtsuMOcmB+PPjk+3m7jJS5rZy3Jm8dG4u/u4T5fDx6FfHa79lxv4zdXj9gRQ0qw3w8XBBTIA3rjRp8Nm5evzhwRQsSA5iz4fbBJA5frVKh/JWLVo6tWi91QtPVyA5xA/urq643KDGpQY1Vs2Kwb98dR0z48YiapwPZsT4409HryMzIRAFVe0OydwztGoM6OntRbi/t808YWGuMlNTVNqihZuLZbHNuWZpmnzkYqNoDrlYbveOvHIcK1Xihbl80QoAqFbq8EVRg+w6KiElDWrklrayOd2v3Z+Mn260IDMxGBWtN5EcOpYViahsM0jmYu88VmZVu8PAbfy5enYMNsxLQFd3D7q6zViys4Ctj1s53dIPREwmlbnvSrUeehPNE0rhYi833p6cNDPWmGf5+0WJqOnowrK0cBwvV8Fg7sUYN6DjVi8rRPFG1gQo1Xp8WFCHhMAxcHV1QZlSZ7f+i3tOPb29oGmKFSZ45Z5EmExmUK4uVu+ovWsYJKRGCs5Zoz7Mr8anZ2uRNTkUM+MC8doXV6xqnZjNnlAm+8C6DIzz8bA5R9kbB0xtaUWLFqv2nYGbC4VXFsSjzdCNTn03EoK98VympTHmV5cb8N+chpu2pL4ZmHN2c6Hwn49Pwv87bGmQ+ukLs7Dp88u8a2HSfJjm5Gtmx6BObV1HZOv6DCYzfrjewpPePrQxA2frLDVlzLr54KQg/FTSjrmJ49DQoccNpQHfX2vGs7Nj8cK8/obqnm7W9cafcqTk5dRxiK0HYuuvqdsMT3c3dm0MGOMJbw8XPJwWZfP4DGLCDPbgttRYmR6J5zLj2AwU7jnKbb7O1D79/v7xeHBiKPy9Pe02JOYKbNmSv+aeT0qYNz5cMxMRAd6itXoURfF+dmR9Bj4638Bbm4R1oxUtOhRUt+H9U1Vsff2G+fHseKhU6vBv314Xrfll1oIn0/v3ZEq1Hr2wOO+Y90yq1lhODRNTE8XUJ4X5uuGhSZGYEOUv51E7RKVSh6f3FrLfxW0gbIuKFh28vShek+LPN8zGk33PIjZQgYMbM0Gbe9ANS/SMufZVGdEw98AZ9VGAjTWK5GM4yI68MjR2GhAVoMAnhXVwc6Hw+0WJWDxhItxcKVxq1MDdlcJv701CZnwg+3chfgqE+AGhYz3R09uLqVFjce+EEDw4ORwAWOWUNZmx7NMSqpuF+CkkVeC4TIkKwLLpkcjpm8STwvoV/p7LjMUH+TXswFo6NRIxAT74168Z1b8GPDYlkheyjQn2RUywL+brjXhCoM6i6lMO8/f2FFVjWzEj2hKZumlAq9aE7p5ePPJuvs2i12a1HvsL69l+QqtnWk/6zETJ7R21c9VUjPFwR9Q4ywT7hy+vQttlRnqcP9Kj/RE/I5pNH/inr67D3EPjcr0aj0yxPAOhmg4zOY1xp9CsvoWMuBD89w+l7KQXG6jAb+6Oh8HYjWBvS3Rr8cRg/P2XaoRODMS7T05BhJ8nPh+nkFSYssUH+TVsL4w1s/n3itkINav1PPXH52bHICLAm1UAYsZpq9aInGvNvM7gXK8v46m+Vt+B39ydwKoTublQePOHMtR1GHg9t8Z4uIqq3zHYU8iZEOWPvPJWLJ0WiaOXLHVhc5JCkF/ZihfnJ+GhuyJ4yn9PTLPuc1at0lnV7jDnkV1Qg7IWLds348CZOjw3O7bv/ntizd0x0Bh68ElhHQymXryRNQHPzIqyUuWLDvTFZ2eqUd3RZXPzpfBww8b5Cdhzqsrq3RR6B5lnxIVJq9ySlYonpkXBxYXCWz8Vwt+LgrGXggt6MSkiEHcnh2DlzGjEBfng28v1aNaYUN5yExPDfNCs6YKfwhVr745FeIDCphHFnNN9E0LwU0krWrVGfHOlCa/ckyj6d3KugTA6eH5OPGga6DKb8eHpKiydHonjfQ4RZvPNjE8mpUxM6UoMJrWZq2YZOa7fUy4s6l42PRJjPFwRF+yNhooO3DL14K8/V8LY3YtnZsdi+w9lmJscBIPJhBcXyEsR5Z5zi/oW+9+17TrJa1F4uOH3942Hv8IdO36uYs+fq9QJWHuzV8+KQnSgLygXiqeMGRXoC1dXF2z9rn/dfGZmNPy93NBt6kVFWxfi/N2xamYM9pysRrCfF4qbNKyQEFcpradPSp6Zq744X29THVSq7093Ty+rorgmMwYfFdawa+Nr9yWhy9yLMqVFzbFEqbe7qRyo44SrChkf4stL4+fuETITxkGl7cY3V5rw6F0RuKkXr8liFAzfO25pJmtvQyw8b1tqq9zzWTyxv/kxVxGRG03jKfsF96kI9q1TYoqoSWE+yCttxrOZsXj/pEXV9v1T1VidEQuFhxu8AKRFj2XV+rgN1bnHBvr7AR7r64V5uKjR5r3YkpUqOk9z+4v6eLmza/DsOH+UqW7huezzks9cSjirqlUn6WRkYKTabakvC2Ge5SsL4/lKjEGWaNzXlxuxZnYsvrlsqWnmrtGrZ0XB18uDNZTtvVeDgUSkHIDr6ffxdMX9k8Jw9GIj1s+Lx9zEQDR23EJBjRpfX27Ci/PjcM+EMCtpagCoVWkRMta6QLdVY8CH+bU4VNSAZ2bH4LnZsdh/tg6NnQZEBijwyj39kqnChm5ifH+1CUE+Hrhp6MGiiaHo1BtBAVZeq8IqFS41aO0qrnGbyz44MRQfFdZCYzBbedHEPCQGkxkbPr7A3rvn58TjiRnWqocMtrwpzMv10Zp0fHyunl2AFk0IxMNTwmEwWfIa6tRGyWtiVJ2Yc39udoxNNZ39BVWYl+CNg5c7odQYcaJMhTeyJuDTwhq2eeGau2OwYkY0PsivQXyAO1xc3VDRZsCxEssm5smZ/KiOLZrVeizZWQBzX5dz7j3ec7ISe09V418emwil+hZadd2i18koAL2TV8FrzmcL5t5unB+HeyeEYdNnRfjXrBR4e3kiNECBdm036w1+NC0CRy81Wh2X2UwxUSBbk2WVUgsfhRt7r+15GoUwESnueXAby84bH8LKA3MjrVx1IqZhp9imoVmtR03bLUlPN8O2nBL839UmPJcZhxdE0k3seQezC2qw71QVlkyNxOfn6vFfj09GUV0njlxsxFvLpyDMfwwCfTyw+2Q1DhU1YNvyyQjy8cQrBy5i06JEBPl44a8/l2N1RgxKlbdwqKjB5iLb0qHDh2fqcdxGk1JHr2GQkIgUnJc1AVjm3B155aht0yIh2A8HL0iPCTG1VSm25hSjXHkTLy1MwvVmHeuBl2oyr1RqcbVNi+zCetYJ9dL8WLi5uUGpMcLY3S3aSNMe3HNWduqhM9GICPBCU6dB9P3Mr2jF6lnRqOrosrnWSY1zg8mMNq2BFwkS+ywTLapt02LFbksmyNsr09iI1tykAPzno1PgzZn3duSVIsDLDbFBPmhWG/BURhzvnJjNry3Vt53HymDuodGqNWLxxFDk3mjFuZp2ZMYHYX5KEH4uUYk2ZBWDicgkBCnw0sIkjB83BuF26l+FzjNbTdmZPcKOvFK0aowI8fPEpkX941KtN6JN281GlcSibWLnLoywfvJ8BjtG1HojdMZuK/VbpVqPW929iO+7PmbtZRQRp0aN5TmXuJkcQj7Mr8bfTlRaRVpqlFr8VNaK909Vs7/bmlOM76424x/uSUBCkDdudQPzxgeLHrdZrcdPxSq8+3PFgLJchI2KmfesUqlDUUMnNHoj5iQE8RQCmea+DFIqrVIGNze7h4utccFVJy5pULPn88zsGJQ232QbIe95diYC+iKTv/TVKEo1ayYNeUcZjNb/wpRgRI0bg4IKFR6fGoEfrjdDrTdC39OLry83IdLfE0pdN1buEc/Vjg32Fd1cUgAOFTXAzYWCzmjG/+aVI/uXWhw4W4+PCmrZ+hfmb23VSxlMZlxsuImXD1zC2doOGExmBHCiRty82pu3urFsWiD+/uwMyUWM8baYe2hUKHWsNLJYHZPYS63wcMN9E0LwxIwI3D8pDO+fqsKOvHKr8zeYzKht00nm43I9okV1KlZlKCXMGw9NicKM2EAcvdQEP4U7Hpk0TjLn/MGJobxzd3N1sammszozAW4ePti8eAI2zLUIHSybFoXX7k9hc88/KqiD5lYPDhc1Ii0qCPFBPjjSV9+z/ccySSUmMYxGGkv76tOY82xR67H3lMWIatUa4eFC4XhfncALc+NwobadV7DKKADJFQmpVPY34mzVdeOfj17Bvz6UgtzyDjyXfR67T1ax3uAemoafwlrQgKk10naZ0dhpsFnPBwAJob7svTaYzA4ZUYBFMndVehTvPBiP6IOTw3GsRIm5SUFo15t458JVPlo9Ow6HOSpczX355CqNAeH+3qht1/Fy/YWbNEb1rK7DgPdPVYkWTdvKL2ferbEKD7ZO65+/uoZNi5JxYF0G5qSE4ujlJnxUaHGyrJoZgdQAL9S265A1JRzenu6obdNhelwg6jpusZFCWwpFYeN8MG6MG1bNjEW4n4es/PHB5sgThp/DRY3/P3tvHh9Vfe//Pyf7JJnseyZ7Qgg7CRD2RRRFRRQVFVSKqLi13Kpt6b293W5vi7W2UouKAhYXVFBQtEZRZCdBIOxk3yfLZJ1kZjJZJpnfH5Nzcs7MmUlcem/v9+f78chDmUzOnPmcz+fzfn/e79f79cLHy9vO3ulmTox0iNLpTWJf0calWWREB7P5YIUsA+/r7cld0xPIjAngp9cPV/RfK9Tx95PV5CSGiGybyydpefe0jo8uNHL39CS3jK6uTHrPb56uY+tQn8nxyjbZ+4T1ebHeyI78Gh6YoWXXOte9SOvmJCv6DbWPlxOcTmlNCO85XtEufl+Vh4oV2fHMTQ9lQnwod207JSPx2bA4k4auPn7y3iWq2+X7h7Q3RMr6dte0BDR+3oA9AN15spZ3T+uI0vgyNsKfxDA/Fo2NJlzjw8uHy0VfmRSu5pEFw1pCSgQcSRGBbLgmVSQc2nlG5/QeqTn2q2w9UkZ+VRtbjygTWwifvWFxJuvmpMgOUR9f0PHS0UpR7BVc90U79sY6knYIc2TniUpeOlrpxH67/Vg5O/JrueuVU+Lrn1xqEhkRz+k6nSr00kOUVPh+Z341r52o4mdLM53QNsnRGh6clyb2/0hJq/52uJIgPz+XhyiDuZfYkAC6+/q5LTueYn2nUz+Xkgn3JsSJ+eUtop8L8FGxKa+Ie7YV0G7qodVsZfvJSpeEJ65EvCub5RXcSr1dFFqpB1DoEZZeV0oWsSmviB+/e54vi5uw9Fk5UNwg+t1IjQ8z0yLZdryamamR4neOClKj8lDRZuqR+WjpOv1ekPdf0DYsHiNm+l88XCGedCcnhfPq0VLunKZlsjaYX3xwRZxc7sRJpRYZpObu6QmEBXjz4uFKMqICxVL9sklxor4AjCys2dDRI6NbvTtbS6Dai8ggtazUXdNq4s9fVMiwwEr3KgSnZXoj+y804OWh4unr0tH4eYkZOQGepHRIFDL2f1wxkZ/uvcTtU+N566s62f0fLGnmkq5T1qvlaFJYR6NpgMcWptPUYcHP25NnPi1mwzVp5CSH87tPSoayhNGKMDMlGOLPlo7lzhzXjIJCiVy6EVxt7JRBAIT7u6BrxcPTy+F3o8v4Alxu6iDM34vUcH/ign2xDsI1mZE8tuucKFRs7rEyJSEYfVcv245XK8I9BQcrPN9bJsWSEK4Rs0XSrJFQeh8Xp+F3Hxdz+9R4Anx9ZU3Kt0/RyoQeDeZe2fgK43pBZ5BRmboTl4SRRQzdWVKURmwOF77PmlnJ6FpNWPoGudLYSXGj0elefrZ0LKtytTz57nlWTteKIr/HK9upbJbraOk7zKx0gAEJpiRqKPRRSr+zO5HH/7g5C28PFRXNZt48VSM+y7RoDeVNJt49XWeHWd42AWN/P68VNlJY3cZvl2eg9vSnRG9kTIQvpj6bbM65crIGc6+sP+GWKaPrmfgezvd/yxZmRnJB1yHCd0YDL3bcwx2hrfdMTxADMmmvg9rHi6eWZGKz2Xjm02IM3b3MSQkXA60Wcx9b7s7hN7eMR+3jJe7jlS1GGRRd2H9dZbQd76+8yU7xLVS7HPcb6fq8JjOG6HAN0S6u9e6pairb7SgCR0FzV6a0Jix9VjZ/USZCy5dkRbN8crzYN+YIHZTKWew7Vy8KvkohtYdK9Hh5qDhR3sx/3TKeRmMvC549PCxKPjSelv5BosM13DXNDmsS0APHy1rYcE0GcSFqXjpcgY+XB9ZBm8t9d3Z6FJtfKXC6J0dzZLm9fXI87d1Wth+v4bbseKr1RpKjNS7hX9JrGsy9JIT589uhvjmp2KsjRM8VxFHqnwA+u9JIsL8PWw5Xysbdy9OD8EA/th6tFsVma1qMbD1aRaCvJ09em8G8MREun7u0ErNhcYYYjz2TV8LS8bGK/k4JUrt0QqxTIqOqxURKZKATVFbfYWbNEFxv/fxUWUyodG8Pz0/hlSFI4ZbDZdw1XYu+q5e56ZGsf/MczcZeBmyIybc7s2NlVTwYhu1tXjmRYLUvnZZe8XukRg1/j5XTtByrbKOzu88J9i+0KUirVjtPVBLs70Nndx+Ls6I4O0Qcs/H9y9w1XUuExh/M3fz9vhyyEkIBFKF5yyfHY+mz0mm0cO/QehJMWN//DDif1L6vSH1Ns/RZxfreh+frRapwgOAAf74oaiLA28MlzeZI9ujCNHZ9VcuyyXHoDN0Eqb24NiuK9OhARXV6V1SRhbWt4gn9/lmJ7C6sl2UIhInlTrcD5PSfa2Yl8x83ZnHX9AQiNN7EhKjF/pyTFS1sO1qhWIGQZuw/utTE+nnJ9NtsMi0gAB8JjbS+q1fxWrp2k8hyF6HxY8GzhyltNbH7jH0j/+uhCtbPT2X3Q7lsWJyhyOwiZFMcMxV2xqdTilVEJatpNbH1aDWfX2ni8YVp3Dsk0LxxaRbTUqK5c3oy92VreeuB0WsrCd/x3TMNNHRYmJQYSFePvZenuLGLGyfGcramnQfnpXBbTgJPLRnLUwtS2bVupswRCpkeR9rShHANz+QVs27naTblFXH7y/lsP1Yp/t3GpVn09Q0OZeTMmHuddSzAng3ecqiMvx2qcBrfp5ZksvmuKaOmxxaSAoG+nvh4qMR7Hy0j4Ka8Ih57q5BNQ6yJO45V2OlPtxYQ6OfhllY1ISyQmWmR6Dst4sHP10MlOoJPLjfQYDATHRrgtn9ESmm7+WApv88rHrEaJ7VyvZHDJS3sOVsny2huyivisbfPiFXA6CA/ksLtlc56Qy8fnG9n5bZTaHy8mJwQiYeHJ4eG1uTl+g4qJSxpUvu2lLDCs/leP+pf19Q+XkxOCCFK40d6dKBTVrZC7zw3HFEO5U0mcb4JQajHENw4SO0l9joI8/Xg1UZ2n7GvHbWPJ5cbhplSF2VGkx4zjMYQdGhWz0q19zTkJvHK0Sr2nK1TzGgr3R/Yg9IpbrTlDOZeRcppx2u1dFlIDA/gULFdKPTZz0r502fyzwf7nJey+jlSZAtj//g16Xb/PS5GEkBrFNlABYZcR3mQ2JAA8fUH56ay+4y9svbmV7VOFYKNS7NkdPXCGhfopbesyiEnOZyXj9gTp59daXK6RlXzMA23q3sCZKyHUkrwldMS8HXQzFL7ePDcgWJ+9dEVNh+0s/m6oq0OCfClutWk6HMAMfkrxBRCsO6IAhD+xtJn5bkDJei7LE4Vi9iQALvY+twkrhsfwwtflvPBxQaRFbGqrZtbt+Qrsu069gq2d1h4cF6KW3/nuFe60mHalFfEXa8UsOdMrRMle3RogHhw33+xUdHHSO/tjYIaHpybQlK4mqmJ4TQZ7H5u21DvZFK4GpvNJkpbRGj8yYjViNUsgQXzwOV6jpS384OdZzhS3u70PXaty+Wxhels+bJc1IUS0D0Dg4NODIIGcy8NXb38/pNiGrp6CfD1lkmO7D6jY8nYSB6aP0Y8RAnPXzqeUvbQmHCNuJ6aDOYRGa6/S/u+R+pr2Duna6loNvHh+QZWz0wkxN9HpqQN8gyXOyytO9t+rJLdZ2v58bVjuGFCnNs+KKXfVTabuPvVAiZrNTw8L5Vwfz/ulrD2KSleK92rEvZVYPGpazfx8uEqAtVefHCungfnpYjNlIJz7e0fECsVz+QVs/ecjkcXpJES4S8jgvjtsvGkRGn4+FIDnaYeUiM1sp4UKYuNNAP02vEyrsuKpd5g4WxdlwwH60g2IGCJ919sVMzAnShv5sfvOrNbScdUKZv2z+obee5AMT39A0xJCOE3HxXJnl2PxYrNUyWyKTk+JyUVbwG3L2Dsb58azz8uN4pMQuvn26lkpf1F/33rBH61/wq/WpqJv68PYf5eRIcP482fPVA6Iube1dyt1psIUHuKGee953RUtpho7uqluLGT2el2atmRGAF17SZWvFTA7VPjef+cnbTiLysn8cN3Ljg9S3frqMPcy/6Ljbx4qJw31k7j/XONfHqlkRsm2IUI3VXKpKyR0j7K0TKR6dpN/HyfM3NTfXuPiPefpNXw3O1TyYjV8PGFOuoNPYyNCeIn712SzY1953S0d1vJr2hhdtrIY+iK8cmdCdXlGyfG2huBv0EVUcG+75Hiu+2REkxp3ivt7a6YJ4WKlGNfkaPP0LebeOdcvV1guqiJ1TMSaOu2MjNRQ0xQAFkJoS5JaKRMb/9+41heGArwpf5Kujc9PD+N68cPH1AOXK4nPsSf7t4+YkMDxD4Lx71QICly7Lk5/PRC9B09nKpuxcPTg2c/K1Xc1z68UM/Vhi72Fdaz8YYMSpu73fZ2udpzXLGwVjYbFas+DR1m4iTMoqtyleMPJXNc4wIL3o8WZ2AdtHG5rp1HFqTywYUm9ENVeemadrynTXlFfCmp2An/fmxhOgvHRNoPOKY+cb7clZPAS0crKKhs49YpcXT3DbpkJyxvMvHY22dYk6tlUkI4ET5exEbLP1sg7DD1DI6q93dTXhGHSvQ8eW0m42IDncZdiJcE5r5bJsWh8fPmowv13DA+lk+vNoo9OVLbcqgUbw8PFo3V8H5hO59cspNKPDTEyCe10SIuqlpM3PXK8L1clxUjElVJx2o0LLGOTL5Lnj8mqyJvum0i2hB/Vu+wM2veNV3Lw/PT2HywjL2F9bx6fzYPvV5oTyg+MYcfSPqnHKtWwj3tPqvjxUPlPH29nA3Z8X4Evy34tr+snEpalIZNeUUE+cLizFgSIwOd1o5QpT5SrOd8vYHmrj6ignxk0NBNeUVcaej6Wj54lPZ9j9S3NUuflZoWIx+ebxBZwG6ZFOvEeS99WN/kEPVMXjE786u5MyeRGybEideUYlKlpjQ5hHLrxXojB0taSY3RuO3/UbpXQcdAzCK0GGW6CglhgYQH+uDnBRtvyCQ2RC1m/X983Rj2nq2jocNAsc4ADGfsfzAnhe5+e8UjI9qfe2YkkDK0Sd88MY46Qy8/23tJzIT957JxNHf1OLEpdRkt2FSevHeukZ/uvYK3p0rMuAoVjrxLTfx0STr7Hp3Jf90yjp5+ZZxveZOJlw6VixmrB+Yk4eU5vDTcaZPEBfnym2XjiAuyb7KjVRIfyZ5aMpZ7pifyVWWbLJMWFaRmV6GO+3Z8xbZjFSJGOSLQmxlJYZQ76JgJ/RCC8xBgLicqW2Q6W68cqxSd/oZrM1iYGYm/nwez0yN5bPclPrzcJB6iwJ45jAvxcxL0dTSl+bnlUCn/uNLA26frePGQHUM/KyWUC3WdHC5pYU7asD6HkG10Na5Cxe1EZQtrZidy13QtF3QGRby0u400NMBX1LAYExtCkNqT9fNSxQyZdL5IbfPBEnadrmXJ88fYeqSc/oFBpiWFOGXH3VVutGHyjPq21RPZfHBYxDopXM3stEhW7zjFzvxqbp6cQEe3PdMqrBNhXa9fkME92VqevyvbaQxdfe+vY0ImOFjtwyeXGokO8mPH8crvK1P/wuY474VeSGEvFXo3lVAOO/OrqWkzc3d2jFNfkaPPMFvhrYK6ocN7PNuO19Dc2c2pWiMb3rvgVk9Pqim150yt07wW3vMfN2c5CcvWtJr4xYdF/OT9i3xR2i72wUghZ2qvQTblFfHA61+x+YsSnj1QKq633986kQ/P1dFotBDk70V3Tz8bb8hg36Mz+felmbJeItugjQGrlZ3rcogPC1DUEXI39oK5krIYCVYuoCg2LB4zah036Rpv6bIwPy2Cw08vZHVuEmtmJROuUdNs7Of80P7ruN85VqK+HKrYvZFfTbHOwN7CelqN/Vyu7+LZA6VsPVolojTWz08lJsSPwyUtBKt9aDP381VVK7dPjedIqV7Wr6drtx9yrx8fw4UGM+teP8sBSb+bNCZ5Pb+WVdPiXYqOCxXzmlYTn1xqxNvTk9/nFWEbdBZxFeIloeq3M7+Wfed0ZCeHs/VYFTlJ4Yr75MAgJIb50dfvK/Y7bTtW5dQLLW3D+PiCzkmjTPjuIK8CLsqM5unrxyr294yESHKsdoUE+IoV0skJIRx+eiGLsqLJiNXw+DXpDNhshAT4Ut/eI47xjuPDPVM6g1mx90wwofrj5aHi8NMLuTMnQTx1bMor4mx1G7+/bbwoJyD47UlaDbPTIrnn1VO8cqSctbMSsVhVbM+vcaomCVXq7ccqCdd42xM2V/UYLQNU6O1j3mgw02a0sOGaNG6ZHDsqRMx3Yd9XpL6GnSjTc6ikTexZenh+itOh5JvoLgjmipFnZ341hdWtjIkJYmAQPD3sTfYjWaXeSFigj5gBHC0jmrDRbD9RxQVdJ5O1wdyRrZVpKOx+KJeoYDXGnv4hFfNhFsHe/gHOVrfxVW2nS8poS5+Vvx4s431JxlyajUkKV/Pm/dN5q1Anssi1mPrFrMYdUxP45EqDSO3tWGnbmV9NSYOBIH9fTpa3MDs9kk8uOWspCbYpr4jL9R1sWJzGweL2IWpRLUvGRcs0uaSMbdLnNT05mJyk8O9cZ+dCXTv/+cFlZqVFkF/Zyp/vmMoDr58WK0m/XpZFY6fdaTSb+kVq1H3n6nlgTjIrpsaLVR/p3GzusqAC9l9oFCm7hfE4cKWJs7Ud7Bsa9yXjol06fiWCEamGh6NV6E0cKdWjN/aJz3Ld3GQGrQO8eUZnr0g1dTI7LZJ95+pZNyeZ9u7+Ece1rs2In7cXzx8s5/OreiICvXloXioLM6NGPCzo9SaMNjtEqE5vwuphd2jvnqqhsr1blomXZpjLm0y8ll/F51f1pEaoWTlNS52hh4EBG52WfsZEa1g9M9ltNlJ6PV2bkXCNWlaJujM7lofnZbB6x3BF+f1HZsqyec+vnCoGO9KM7cAgNBt7idL48qPFw/uFu+czktU0m3jnrI4vS5pYlDkyBe/XsO8rUvxzKlJK5q6Sbumz0tzVQ1SQn9ust7vrrpymJULjR3KYmp+8d2lUTGPSLPv05GD+fOcUEsLl81TIZBt7rExPCeX3y8ehDbdnssfGaGTMXXvX5/LO2QZ2n6lj5/3TuH/nGaYmBNPdP0hJk1FERPzh5gm8UVjHvsJ6nr5+DOdr2kmLCiQ80M8O/Zo33Kt7tlLPgZJ2tCFqDhc3MTs9QvF939TcVc2/rQn9X1KffEVnYO3OM2TFBDAxIZTmIUZaV1WuPadrsKlU/OmzUm6fGo/ax4a5X4W51zokBGyHfX50sYHHFqSyOCsKbVggO/Or2XG8kgdmJ9PQ5azj5Yg2cZx3pp5+IocEXfMctLmEcWvu6iEpItCpd0qYkw/MScLYM8juM8qaQtKqX5TGV1YVlcqGgN33P3+wnNXTE0DVz4cXW/ngXD23To1nTbaWOIl+WWd3L58VtfDxBZ1ijKBUHRaqgCPNB1e/d8Wk5+r9lj4rhm47qYXj3lChN4oxjxK7p1J17K8Hy3m/UMfra6aJzHtJ4WpWz0hi2/Eq0RdWNBu559VTTE8OJjVSw03jYmQSM9LnL8RaSeFqnl0xiSfeOa+4nwjf/T9uHMP0uDCsgM2TbxyXS+z7itR3YXXt3WjUnlybFUWQhLpUMCUl7JFMmsmVMon9ZEmmCGfY8mU59+YO6974eXqMyAC3+WAJXxQ3cbi0hZ0n7D0wo4lUhL6XW7acQIWK4kYjERo/WS/VymlaDpe3seDZw+Rd0Tv1bnVa+okL9ndSdpeaqaef94d+32rsprzJJMvGLJ0Qy6CnSsyOvHKsmvumaUUMeHpMIJ4eyLC40uexZlYyD8xJZ29hvVjhqG23iFpKjk5i49IsfnXTRLShgeJnXtB18vrJKpeMbVIGpccWpo9aSfzr2OSEMGanR/LB+QZmp0WSHqORVZJ+83ER12bFEB7oJzIEHirRs/vBXAzdVm564QSvn6h0mpuvnajhphdO0Gnpl2U1LX1WNL6e7BuqcmVEaujtd31/oQG+soBoU16RjG3J0cIDvUkM9xe1n/IrW/j4Qj3vnqvH39uDKdpg7stNZuPSLD56Yg7LJse6HFdpdTYhXENkkNpOnDA5jjZzP5Wt3S4PUbUtdpx93iUdOwrrOHC1jrfyK3mzsI67Xilg27EK9KZe8i41MiUhmJQIf7Yfq+SurSc5UqIH7AevqCAffjA7kftmJ5MSEUhzVx/vnNaxM7+Wv35ZTnOXRbEKCs49Gtpwe/+IlHkqXKMWs4ZCdk3a9zY7LVI8REkb0/dfaMBms3HzuCiWZEY5PZ/nDhR/7SrS1iNlHK1oRRviw2s/mC5+1u4zdd9XpP7FbKT+QqHPVCkxsflgGXduLeDFw+Wj7nMEe5Kk0WDmT3dOpLbNxNzUcGakhLNCwjS2eGw4f7tnisvqtdDPMTM1kttfLnDqb9CGBXLXdC3LJsdS3GjkYGmb+H2ma4NlWfP4cI1YwRmrDWFFdjxLxkWTGObPwsxIgtReLBgTSZ8K0VdtP17JD+Ym02zqtx/KTP1U64d9V7B/APsK63n7qxpmp0eI72sxudkkR2n/zJ4Oof9L6pPLm4z8/WTlEBOcGW2wL4/MszPSrsxxJp+xw7nLOFKi5+G5yaRHBxKs9mX5xDDum64Ve4tCA7z5dMM8Grt6xergmlnJfLphvj2RKbmH5i6LE9qkzWgR593T12fy14PlYr/cxqVZ3DvUSyeM08eXGnh+aM5+cqneqXdqbW4Cex7KJSJQLfZSK7FXSqt+N0+Klc0lR0KRvefqidL4UNth4qLORHqEmr/dPZn0CLV4iNp7TsfzB8u4ZUs+rcYe/rJyCnsL67lpQjS3TIxF32F2+u51bfa5lhqlGdV8UFqTjsQlUhbM+vYexetsPlgmMkM6VrOkMY8Su6djdcwe29mfQYtpuMf63pn2Q5TUF6ZFaXhiYSp3T0+k3dRPY5eFyQr9jkKstWZmAtvuzyYh1E+xx1eotk9PDmZg0Mbn5XreKqz72nH517XvD1KjNL3BjN7UR2//APMzIrgrO14GN3KEwimV+R1NaaH8bOlYrsuK4Y+flfDcgRJxkta0Wdh3boia2tzPqu2uSREq9CY8sNHWbeVQcRM5KSEum3elZjD30mDoYf+FBoLVPrxzuk426dfNSZY1FUp/Jy2XJ0UE0tDZ7bJhFIYPIXdmxxKuUYvB98alWbz9oH0ROxJhxEdqZJWRxxeN4a4crVN5XyiTCwHpicoW8To3TohzyTyWEauRNffmJIUQExJAk8HM1nunKgYdAmRx0dgYtxuvYHqDWbG0786EZk7h8x+cl8aDc4cbW5MiAmX0n4syo/H38xI3s6Rwfycoj/C7d8/UYewZDgLUPl4YewdYPz+Z1TMSOFHV7nauSU2Aebg6PAvEH/0Dg9w+dK+PLkgnLNCPtwrq2HashiuNXcxOtTeXRgWpZc9DGNeWLgs7T1RQ3mJk54kKYPiZ/2jxGB6en+KW7nTLoVLeOl1HW1cviaH2RvprxsSiDRsONN4oqCEiwIfVM4bEby828eF5HbPSIvnJe5f468FSDOZeNizO5IasWLYerqCqxSQerP5r+XgenmevWEvvX1q5dVxD0uDX0ZlJYTxCY7kj7bJ0rH68OIMFqSEcqhiirj9SJjYhuyNzcWW1zSbau6288GU5tYZeBnsHuT1byySthl8vG/dPh058b6M3V3u9tDkbUGQRlfqx3Wd0LEkP552HckfVA5cWHUhsSABP77lEbIidnEUQ9n5pVY5Im/74rvOKB/lNeUW8eaqGxxeli5A8JUjtYwvTnSBolj4r8UMCqVLCBRiulm9cmoWhu4/QAG/azb38YfkEFmZEy6BdizKj8ff2EkmPihq78JJM7fSYQJ68No0bJ8YRH6qWHQqEINjSZ6XKBcmL1KTrXWk/+K6SE+VNJiKD1NS2mWU+OT1Gw4SEMNqNFrbdl8PduSmkRmvYfVbH9c8f5cCVJlmMI5BXNBt7ae3u55OL9TR29fGDnZfYd7FJPLQ+vigDc2+/OI/yLjdS3WqUBcTS5Kc0MfTrZWPp6VeJ+93CjAjZwajBYObVY8PBuMHcK5JUNRt7eflIhehb1s9LYceJapa9VMCpmg62Hi1n2RAU/c4c9wQ7oQG+sj1WGt/YGU+refe0jpo2C4vGRDEzMZywAD/unJ4M2JN85h6rjDzB2g+/W56Fj48XP9h5htcKahXJoFzNB8c548pckYS4SnIKCTiBuVDfYR61ppxgUv8kfcaXGruYmRTKa/flMCYiUDExsyAziqoWMzdNiuZn+65gGxhg7cxE9kr2HUuflZ8tHUtiuD/vFTay/KUCvDxx8vMC8/CDc1NJiQgkNkSe0B9NXP5N7Hto3yhNbzCz7UQNH55vYO3sRDp7BhRLtCMRDwiwGlfNgq6E5+xQuHIyYwJdCo9JLb+8hXdO1xATEkCAj6dLCJyjbTlURoe5n8+LmrhxQhx7z+l4+vpMGg0WUXxXSrTx4+vGUN9hcSI3ANC3GOkfhF5UpEUPC+uFOMDApAJ6ggjcNyXtUCppC+Xo0UAbnztQQkFFC48tTGdmWoRLaItUOE5q+g4zgypE5XCpbT9WTrOpn7xLjdw/Sw5N+DomCPk5NrY2tRnpZxh/LxB8vHLvJD650iZCDx6YoeXvX9W7bdStaTVR3WbmJ+9dIj7ElycWZZAeHkCSC+Y6g7mXKzoDRyvbFcUuleZ1/8AgUUFqdhyrQG/q43CJXbhYiXJYgF08k1fMjePD+fhKqwg9bDEpQ/+kCu6CVbWY+OiiHRL6wGwtSRGBnNcZCfaFYLWvk4izFEr0hxUT+P0nxTKR5Gdum0Bpi1EkePjvmzNpNFnRhgSgM5hJjwrmB3//iiC1N8aefnbcP13M8AlN309fn0lls1mRytfRBJjghmszWCE5mElN32Hms+JmZiSEcv/OM6J4eE9vP2pfb642dol08ErEMEpW0WTiHsk6fffhmfxkz3myk8LtFMUj3Pco7F8O2qdSqZ4FlgF9QAWw1mazGRTedwOwGfAEttlstk1Dr6cA7wDhwFngPpvN1ufuM78ttE+AG3t5qLh7RgJ3T08gKkjNoSI9BdXtMkiVK+IHwY89tiCFxq6+EWG1jjTlrgQ3BZFXoeFdSmog9QMCBGj7iSqXEDNHwoTRNPILnx/o68l/3zKOIxXtDuKkwxCm5w4UKxIvAGw7VsErR6uID/FlRmqEbL/Iu9zI+TqDS0i7YI7ws9pWE4fKWrlc187aOamcrjV8IzkIxz3P0R82d1kwWqyyKoPU11r6rFz//FGWTogmSuOrCFusbjWxcmsBv1mWxa8kREiCdIoQ4wgwvKUTnElplHyxvs3Ia1/pRoyrhGcvzI2PLzVwSdcpew517SZUIEKgBeKGz4ua+I/rs9CGBziRR7mCPCuRN7kTeRX88+rcBNq7rbL7korMSkkblMhHHL+nK7p3VyYlCXEnVAx2tEF7t1UmPeMKAugqaea4nwjPuLHdhKeXp0wv0vEaWw6VEhXgQ8VQwUAaPwg+b+eaaZS0mEYV/zbojRTo2jH2WGno6nMrwP017Hto37c1X28vkWgi0M9HEW60cWkW7z4kz4ZJ4UfSjICrZkFHKlEhayLo3sxOC1fMYDjarPRIHphrb5Z3pKN0d5h4fFEGjy9KY8/6Wfxs6VgxKyQV35USbdwwLlqR3AAgOlLDm4U67tlWwOaDJYpU5I4Ceukx9pL28i3H+exKI+Dc1KxE2ws4lcmb2ozo2k3ihjHSIUpoTr7cYGLPuXoM3b2Kz2hTXpFM2M/SZ0XfbqK+1cRrBbViiVxqjQYz4YF+5F2yM+W9crSKbUcr3N6Pkgmla6XGVin9JwxXyyYnRhEX5DNEiuFDdLhGkQ4YhkX8kiIC0bWbeXxhCnMyIvnsqp47t51i7zmdS+IHs3UQX49Btt+Xg4/HoJj9aemyiNnMSVoNv7llPKEBvuLzeGBeGvfnaLkuK4ZnPytVbEYXKlHvF+rosAyIWabwQD9FaIRUxFJqKZHDkNDX8nUE+XizNlvLdVlaVs9KZXW2VqyIAoQH+rFyupY5GeE0GizcNlUukhwT5Mf24zUUNxjYeP1YotT+nK3r4gc7z3C2rosAbxU3ToylxdjL0gmxsgDGVdbVFTGEpc/K9mOVJIb5c0nXyYJnDys+j0EVbDtWRU+fHVaxdk4K+wrr2VlQh6W3n1/eOI7Hr0knKVzNfy4bx4uH5RT2wvqSUuBu3HdeLpMQEci6eWkiRNPdff8fts+BCTabbRJQCvzc8Q0qlcoT2AIsBcYB96hUqnFDv34G+IvNZksHOoB1/+wbjgxSs2Z2Issmx/FmQS2vnajB0mclxN9LXDOHSvT86bNiReKH5w6UUNbUyQt3T2bhUA+cu2yuUvUrPUYjq3wJJiAMpqeEOlWUpH5g6YRYHl6Q5kSkIK0KrJmVzMdPzGHF1HiX0Fmpbcor4rcfXWZFdjzhgd4E+fs6Vc+la1Op6gX2RMzr+TXMzYhAo/bhiq6Dp67L4HJ9BwZzL/7eHrIMuJK4sJS6u9XYzaa8Iu7YWoDGR0W4Rs1bBTWj+k5K3/FHb5/jZLmeRoPZiaK7rNFIVJDaScbBEZq/YfEYJsSHyOCNegmkOnnoOb5XWOcknSKNcTYuzWLn2mEI8OdXGkSqeCVfbOy3Q/mtAzZMvVaq9Ea+uNrI2eo2HpybwtmaNpq7LCx3IPm6eWIc/7Y4Q4y9njtQwrqdZ/jsSpMMpfH09WPZec80Tla3O5FHuarWOGpkCfGNK5FXAVZX225h11d1rMrW8uGjs3hkfiqACDF1ROooxXHSKs9IdO9KJiUJEdbX3PRQ/rxykuwQVdViIiLQd0TpmZ351Tz3WRFljQaxMiasSaXYLmqop235SwUy8Wmlg9jji8YwNyWcyEBvfnZ9JpGBdqFpaWXunTO1boV3pRYXrWFFThLXpkexKlvrFJd/1/b9QWqU1j8wKDKU2QYHRDath+aliDAuXbtJFvRL2d6UNjUl1p1NeUUcuNrEr5aNY9UMZ5xyVJBaEb6gZJMTwkT9mSC1p2LgrARB2HeugZtfOMHO/GqxHC/0nkgPeGofL7d6NFJ2qBZjn9OGZOmz0mQws2FxhojXt/RZeaugmoVjovjPD644OXpBjVsJaiYtkz88L5m/f6VzUjJ3Z299Vcu9MxNYPz+ZuCA1y7fk44WN3Q8PazRJD2t5lxt5/0wtu0/XsONUHV+Wtbrs5xE0K+6T9De9erzqa8M3gtSeLtXHlQ44UUFq8i41sOVwFY+8dY4Xj1SJm7BSj5/UmayelcqctCiRTS/Q15OSJqPiASUkwJeWrh56Bz1Y98ZZ+gY9SI7UyAKtp5ZkMictil9+eEUWeG3KK+LFE9Wi/owSfh2G4aA786vFMWgz9TglFqS9Qo7PYVNeEZd1Bm6aGMlHT8xhTmY0MdEa0bEkRmtkDkjt44WXhwe9/YO8drKGjy40kBAyjM9u6rLrkIyNC+Hfdl+ko3/4kPfBuXq6egadoJlSU/t4cbisdVSJDrWPF8unxKP28eTL4mYCfT3pMPVwpd7IiXK9+L7YkABunBjLfx8o477sMOamDidf1L7epMdoWDMrmR/MTiG/vE1cl00GM5vyith+vFycC1eH4Jqnazo5W9MmgxveMCF2RDbQ/8tms9kO2Gw2YYEWAEqqxTOAcpvNVjlUbXoHWK5SqVTANcB7Q+/bCdz6T77loQBHJTvgGnv6MXRbxQBE0CNyXGtC0HiwpI0fvnOBILW3W41BV8Gduz7JjUuz+K9l4xQTVGtzE9i1LpdH5qdS1WKSBVyOgdpzB0q46YUTvHKscsQ+LmE/mJ0WyZfFen4wO5m3TlW7hZ67SnSeLG9m6ZCW36LMSK6bEMdzn5dx3fg4QgJ86e4fZOV0LfPHRLBymrKArbCPTdJquGt6orhXJYYFcFnXQajG16UmlqMJgezhYj1nq9u4dlw0R8raWb4ln2J9hxinPHVdBoFqD9nfKNlzB0o4WtpEVKCPbB/rGxx0fo7LJ8qSx0rQ7uQIe3/n3PRQJsSH8sO3C/nkUr3iZ6fHBLJ6ZgIrsuM5cEVPoa6TS/UGspPDefVYFXMzInk9386Q+uJheRJS7eNFcqRGnMNZMcFsPVrNsdJmNt6Qyfr5qWw9UkZTX5/sHiv0RsXYTDB38Y2SGK6/J7KEQFK0hrdO62Rz15V+lJI5ikq72muVnqljPLBxaRYT4kP58bsXZWszJTKQ1qFeJiHJYR2w0d/fJxYCdO0m6lqNhAb68t65RlYPwf2XPH+M98/qFA+brvq/XJq3B1uPVvPknou8cqyahg4zah8vHl2YxuKx4dw5LZ5bJsdzT7Z2VPEvQHy0hqRoDSH+PiO+99vY99C+r2EvHirFz8uDZlM/l3Ud5KZF8kZ+Df9xcxZFDV2ykrRUmyA6yJf963LZUajjg3P1PH1dOlMTIpzKyMLfCFCckeABozVXUAslGNxrJ6p46bBdsG/x2HB+cdMEUiID2XKoFE9UxIb4sXxqgtO1Osy9qEBW2t1zupryVgsfnKtnVW4C1gE4cLWRRxdkMIhN1A2SwifeOlVDoI8H//1JiRO8sUJv4p5tzjBAqe08UYnGz5vEMDWP7RpmdXFVAhbMYO7loddPs2R8LNVt3SLz27wxUYoMQ/vO1fPQvBQiAn3x9/HkFx9cEZ+buzKyrtnIp8XNvHrcNWzFlWbVrvwqagw9tBst3DsrmckJYW6fJdghWT/eU8istEg+OF/Piqnx/EzhvtyV/gWYy8T4YF74shzrgI0/3DaWtMhQ2RyuazURqPams7uP5EiNEwvluw/PZKWE+fGjJ+bQZx1gxUsFMrYnJbiE1OraTSSEBcogn47QCCWYbaPBzPIt+S6ZmJRMqm8j6G8Iz03QZ5Fe90eLkukb9JB9tqvnKb2+VMfDnWac9F7mZUTQYOgWIRnCs9+UV0RNq5GkCA1fFuvZsHgMM5JD6R8YFL+vcC3puG+7P4d1O8/y8uqpPPLWOaYnB7NwTKS4hl3N6dGygY5g/3LQPqmpVKqPgHdtNtubDq/fAdxgs9keHPr3fUAu8GugYKgahUqlSgDybDbbBHef82191NYjZVgHbXRZBobW+zAMyNJnRddmISNWw58+K2HPWWdokhJkyR20WoAPC58zEoRIalKIj6BFtGZmIrWGXicI4i1bThCs9qGrp4+3H8rlVsk6/nTDPPy8PV2uGwG2VFjTRk5SOGeH/tvV3cs9M5KYOCT4qQQ5cmTp/Pm+86yZlUxogDcJQf4kRjszqwk+dCRYXnWribxL9Xh4eBAR6EdqmA/dAyp+/O7FYUbBW8ejdeG3BMjZz24YS1FDB7MzIqnv6JGxze1/dBaHS1upbO8WGV1dMW0K8OvfLMvivbN1pMcEy9a93mDG1GsTYfqO43Slro3PS9tlEHJBMqOm1cQP37b7ITsUWNkPSSHgmTEBrJmZzPMH7d/n3pmJHLjiXrdw88ESjJYBjpU3sygzeohBMoEVU+M5Xt7KmepWYkICnPazvEs6EkMDqe0wsXSiPV8ihawqaXE5Qi8FH7zhmlRmDZEAOc7dDx6bMyKLrCNUVmpKe60S9FApHpBqNynFRHXNRlSecL7BSJeplxpDD4XVbeQkh/PJpUZeWZXNgZJm3iyolbFwZsYEsGKKlnExGiIDfRmrDRHnxPMHy8T58G+LM0bN+rkqN4E1M5P58GIjBeXNTNCG0GUZ+EYw8j99VuKSqfFr2vfQvm9rW4+UYe4dxNRnV2nu7BnkaImeZ26dQGZ4gNPJW9rAetvUeKKjNeQkBPPWuhwqWi2KGTtfmz2bIUBxXDHAWfqs6A1mlxA3qe3Mr2bV9lNOZVolZheDuZfXTlSxZpaW99dPIyM6mLteKeCKzsDOk7Vs+qyU3+eVOJEIAOz6qpZnD5SyZUgXSNdu4tkD5VQ2d/G3uydz95Q4nlqSyXVZMRy40oi5xypWOqSNo5u/KCPIz0esfi2bFIex295WkBYdyB9uHUfeE3P4/fJxToeo8iYTWw5X8uSeizx/sHRUEEihImQw9bNyeiJHSvREBdk//8lrx8ifQ5uRli6LWBF8cF4aKg8VjYZu1sxO5O7pCcxICnUiARDsmbxiVrx6iq4eq0v9D1eaVS1dFuLD7I2TewobefiNQhE24qoCs+VQKZ9ebWBWWiT5lS386uZxis4LnCGW0uDnqSVjeXxBKnfkaFk3N4nn7phAoc4km8Ob8oq4fWsBW49WikGXNIv259sn4jGA2Ajs2GhsZ3vy4qMn5oibncHci8HcK4MJPXeghNtfKuC5AyWy4E7apLszv5qz1W38+c5JLskY3BGCSE3t48X6+aky/Q3huQkOUXrdvkEPWcZxU14RP/i7XfNLyV48XM7CzEhRx2MkR/PkkjHcnhPP9JQwJsUHo/HzdmDisq/r5HD7IeqmSbFc1HVy0wsneKOgTva9Hr8mHW8vFenRgXy6YR4TtaF2yM7ZOrFhV7qG781WKsiMDJn9VzaVSvWFSqW6rPCzXPKe/wCswFv/pHt4WKVSnVGpVGdaWlq+8XUaDWZOVxtoMfbhoRrk35dm8qPFw70tah8vMmI1PHeghANXG/npkkynwOKpJZmyNQju9RClEOGaVpPbfUQwYc+V9pvuLawnKyZYRvgi+KWQAF9umhiHvqvHThYUEuCySqA0JtuP1/D5lSaWTYpn7cxENt9lJw564poM8RDliiFNuh7TYwLJSQ7nt/8o5lh5O4nRzvp0lj6rmIgcCZaXHBGIv48X1kEbpXoT63ddorvHyorseILUXoyPC3Z5iJJCzs5Wt+Ll5cWH53R09/WLlbbHFqTQbbXRN2hj39D4Spk2Hav+QvXlvcI6MmKCuVLfwZ/utFed9pyuZkd+LfdsK2DrkTIn0qTe/gGq23uZGBfIq/dlMzEuUKY7mBQRyCMLpVDgekV4mrQCtGRcLKtmJvPIgjSig3yZEB/s9rlXtZhEHbNpSWGsytHy6YZ53D41nphgP7r7+kXyqJcdyKMu6Iys3XmGCzq7T3WErEo/S4kIQlp9+euhSoRilX3uxqLv6mHNzCTM7ihwFT5X+DzBlCpRjtUgV/GAK2ILg7kXvcHMF2Wt/Hj3BSy9VjHWuGF8rAhV7B/sFWHxAgun4MvHRAVyvMpObCTEBGofL8bFBbHxhkzGxQWNipBo49IsPnx0FtYB+OE759nyZTm3TI5nYJBRwcgd19uXRY1umRq/K3P7zVQqVTBwAxA/9FI98JlSw+3/y1beZMLfx5vtx2vw8lBxW3Y8V+o7uDNHS0FtB1d09hK6kIVOCNfw3IFiwv09efGeSXh42Id5yYQ4pzLyHVO1pMdoxKx/kK8HucnBNE/XUtRkZE5auCzg23tOx4DVSnmrxalS4pgZc1zwK3O04u8FZhfhngUIwmMLUqlqt9DWPSje51unqmXvdXSOBnOvnaDiqp5bJsfRYe4VF+2+c/UcLGm1b8Znatl9RseCjEgaO7uZnBBMbLAfR0pb+Pebsth+ooobJ8ZQ1WoUaeY1ak8SJY78dG0newuv2ql0x8eJrwsZTeEzpyaG89SSTFbP0Lo8RO3Mr6au1YiXlxfGHis1rUbWzUvlV/uvog1Vs3xSJLdlx/PBuXrun5XIG1/pZLpXAKYeK02dPah9vegw97Ht+GXFzIcUBvPumTrunZnotLE4zo07s7Uinj0ySE39EOb7gyFNjAAfD1q6LGIgLzyfuNAAqlpMDAzCmwW1YrVjWrI9aFAiYQD7Jnb7FK1i8JM09AwSw/yJDVKzr/CKeJ+3T9HK7vvuaVox+PrZ0rGsnZPEnrN1HK1op7bNyEurppKdHC773HtnaDlY2sbNL5zgySVjaDP1MiUuiKOVww3yj8xPlTmNB+YkO2UJv7iqFzUontxz0Un7ZuPSLO6fmTiqQxTYHds/LjXw0LxUVucmKb5HenCbk2GnGs+I1VDZbOJsdRs3TozllaNVDNrg4fnD5CC6dhPvntaJ2efFY8IVry98xlsF1SweG8PR0hbSIgNJigjkVFUbt02N54Pz9dw6JZ74MD87U2VFCw/NS6Wxs0d0QCcqmqlr1eLn40l33wBrZiXL9gRhfIQKdr3e6LSGleyfqX3zbUylUl1ns9k+d/cem8127QjX+AFwM7DYpgzfqAekJXrt0GttQIhKpfIaggcKryvdwyvAK2CvSLm7H3cWGxLAnPRwXjpcKWadl4yPBYafkTTw+uOBEq7Jihox0y6Y3mDG2GNzQlIIvRDSDLirfUTp+oKvOFSip749WNzjBL9k6bOKe+eXJU08MCeJp5Zkiuvf3T17eXhwW3Y8+RUthGt8UHl6EDckKyLsAQZzr0s/KTXHAFVpHxESFAJJgLt1Ud5kIsDHi86eAfIu28Wtf5dXzJ71s9z6LRg+cOw+U8ddMxJ56PVCmo29NHT28MfbJvPAzEReK6hlzWtfsTo3gduy4zlcMuwfXR1An1qSKVZfhEpko8FMSkQgzx4oJ9DXEy8PFTvya9k3VH0JVntzqdFE3qVGNixOJ9Tfm0kJctRKTauJzy43ip+/YkhcXsmkzxago7uXjKhA6ju6eWrJWKd9XzBpTKPx8yYxSiMjDfnN8vEszQrEy9tD9twc/e7tU7QyyOraOUnivbZ0WTBaBpyesTTekR5Sth2r4P0hKRG9qY8VLxU4VYkE0ipHqOzaOUnkXdG7JR0Reur3nNVJnqmvUzwg2MalWbK5tf1YBR2WfjyAd07rWD8vhVePVbJ6hn3OfHp1+JkV1plIClOTEhnAosxwZqZFsX5+KtaBQdqGtMGkY5gRq2H55Piv7R/8fLx493SdiLx4r7CO7KQw0c+5gpE77gPlTSY0vl7cMjlO1H41mPq+tgj9aMzlt1OpVPcDvwIOMOwAFgG/V6lUv7HZbK9/53fzL2rpMYEcLGnktux4yvWdBPl58tDcNEICvPnvT0ppNvaCChHGUKwzcL6uk56+flq77T0Tz9w2nlM1BtqMFtkkFxpzz9d1EuijIixAw4NvnOP27HjC/H146XAlHh4eIu2xucdKVox9UzP2WLna2EWl3sixyjaaOkzcnq0lwM+b2JAAXj1WyQ9mJ6Lx82HQAeMM9kV1X7aWHknFMjc1gj9/UcBXla2iQwvy93XrHAGx+f6jiw08vihNvP7aGVp6bCoqm028eqySZZPjOF7ezJKQGPSdPdw8KZbf3DKe+vYe/vvjIuZnRPDy0WriQ3x5ZH4aM1KHg0ulDU8IWIXXVSq9DE6i8VPGxta3m9jyZTnb19jhTBGB3tw0KZbaVjNLJ8bywbl6Cuu6iAr0ZuMNmYT4e/Gz96/INjmNnzebvyjj9qnx6Lv6+Pyq3mWQL1Rn9p7Tcde0BDR+3rL72ZRXROOQergwNxybglfNSqG5y8K9OVr2X2nkvcJ6Bmzg5aFyOiBISRU+OF9PT79NMeBxNHe0p7p2E8aeAeqHqO0/kBysV2THc7m+g0cXpjtlsLu6BxgYhMKaNrKTw3nkrXNOnx+uUYvBTHFTF/1WG4szIsXNuabNSJupTwweHAMBIWngCMFT2sBHe4iSOrZXj1Vyy+RYpw3c0mfl4/M6bhgfy/MHS8lOChMD1tSoQK6fYD9ENRt72X68ivtmJon3lF/RKj5vd9ln4bvNSg3nH5camJYcxkuHK7DZ4IG5KbxdUM2qGQnEhfqLdNNljfa1sfVwGbdNjSe/soXrx8fwZbGeBmOfW9iwUOmNj9aMuO5fOVIhE1j8F7PtgDMF5ChtiI3vp8ACm83W7eJtp4GMIYa+euBuYJXNZrOpVKpDwB3Y+6bWAB9+03sZra2dk8rgoI1XjlWJ898xwBhpDSkdKA5eaeByk5EOcz+pkQHcPytF/DtH6Y+7pynPF3fXF3zFO+cbCPf34KVVU8hOjgCGDye7TlVzTWYMN71wQkxWubsmQKCfN0khfqjSIvnN/iLunp5AuMZXZL60Dtrs+nBDY+Lu8KOUsFIyIUHR2z/g9lkJcYWvl4qlQ/pKK7JdHzAcTXrgEO4rJymclGiN7Jns+qqON9ZMZ82Qf1g/P3VE6m8YrkTGhgRwvKyF27Lj0Yao0fh5sfVoNRGB3nh7e5EQHsBv/2GH4v/xs1LeXJvrdM2kCDs1/smKFn55cxY3TYp3Kwwu3EOjwcy7p+17cFmziXumJ7jdv++flsDKHHtvWlWLPa4SEmu/3n/FKbHW0mURq6jSRLHgq6VB+3unayhr7WZfYT2rZybw6YZ5WAeG4yrHQ0pN6zAxyfyMCJFtbt+5en4wQ8vfHRgKpTHCiqlaNH7eIx7wn8kr5mBxE3++fSLRwfIDk6uEYUK4HXbfZRnAx8uTC3Wt1LSZuW1qLOEaX64ZG82u03X8bEkWdw2NpfRa1S1GxseFyZ7TZV2nU0wg2GgPUcKBS5okiA9V85tbxqP28aKuxci6ucmK60MgYpoQF0RisJ+YWH9gdpIsKZ/ignX425rLHimVSlUC5DpWn1QqVShwymazjfmn3NE/0b4t/rxKb2TApmLVdnsf04bFaTR19eLv4013Xz/rF2Sgazex/3wd3VYVE+KC+OWHV5mTFsrt0xJ48t2LNBt7uTM7lofnZTjRT6ZHa/j5+5eJDvIjxN/bSd1Z7ePF7tM1qLBR09Ej9hdtvHEsZY0GQgJ8xX6JX9+SxeHiVkIDfci71Chu1IIz1etNnKhro6Sl22UP0GMLUlgwJmpU1OOu6ECFwP3Ja9Oobu/hUImedXNSWTk9Ucx8PXegmHZTPxq1F4dK9FyTGc3eoayZErWoY++LKyyuEnYYYOeJClrM/TQPVQC9vLwIUnthHbDxZkEtgb6e/GB2MjdMiCEqSC1m5oR+gDWzkrgzR0tkkJqd+dW8f7aWxVnRGC0DI/b4NHdZnLJM0r6C5ZOjeWJhptsDzdmaNi7UGtAb+8SxcLXB1DQbUfvZxaMdMdKj6RES7JUj5VisA3h7qOgftDE2Sk1iiIashFA25RVhGaLWdqWmvuNEJRNig/nJ+5dcfv7O/Gp2HK9k5bQEIvy9SAz15/BQFSspXMPeIQd2/0zljKSUEtkVNfjXMYO5l61HqxRp4qta7ALS4IxHF+bdb5eP58NzdWTFhfDWqVoenpfKunl29qZqvYm3C+u4outg/cI0UoL80brZ5HfmV7PrVDUrcxLZerTSaV9wzPq1dFn45Iqetwqq+fkNWaREBrD/QgNjYzT84oMrw31rQ5TF38QEGmjHe/mG9o16pFQq1X4317vGZrONboIrX7sc8MVeXQJ7z9MjKpUqDjvN+Y1D77sReB47/fkOm83230Ovp2I/RIUB54B7bTabW2zJt/VRAJsPlnK6qp3pKWE8PC9VUcLBsecDoKrZxEtHKzhS2sKCMZE8Mi+V1Gh7RaKru58PLjTSZu7jcEkLD85LkVVXHXsbXFHpO1I6yz6/xcRdrxS4nJtCz6V1wMaNk2J46roxfHShkRK9EXOflRVT45g/Jlp8/1unarhS38n0pFD+8Kk90J8/JkKk/pf+f1K42ql/paXLQqCft9OcFqQY3Jkr3yM1gS66ssnI3dtPyZ5Rc1cPSRHKhwwle+dUFQlhAdS1m7k7137IVfKVNa2mUV1X6X36DjM9fYN8Uaqnw2JlUnwwfzpQwiPzU2js6kWFihB/b1blJites77FRD82kd1vJFp9sPudtu5+tz2aTe0mVB4qXsuvdbqmUJGS9rYCopyMlE5ckEkRTNqP1NJloabNzGO7zmPssXLvzAQ8VB4j9uwIFPA/uiadsmaz+DzuzU1w2a8k/Vx36+XTy4388sMrTE8ORhsW6DI55ohAEXqtDxXruSM7nq7eAQYHBxmw2fW41s1NYtkkZ71Nd71bYJe76bK6T8a6MqWqstIeJbUKvUnWr7f1SDnt5n5umRTL2iGK+cyYAF67J4dum7Ju3te0b9QjpQKUTlmD7i74/7KlRGvE7EWgnyeWvgE6hkQqO7rt2ExtWCBdveDvZSM9wi7G+YM5Kbx8qFzELodr1E6TbcVUrfierp4+Fo4JZ/XMRBlzz6a8Iv52qAIbKu7J0Yr9RZu/KGX5JC3+PsP9Ei8fqeDGidHsK6wnQSJK1tZl5khRE0fq2hWVzgFRFHfNnLRRB1lKdKDSrNhfDlbwg2laXrwnh5XT7Uni0ABf6ttN6Lt6+bxIj68XbL0nBz8fDxaPjcLLmRRHkfFG7eNFTmIof7lzEjmJoU4QFik21r6p+LP7tM7uhJvNrJ2m5YasGDxV9gqOuW+Axs5ecTOTwtQ+emIOxp4BO2PUkXKuz4rk3Ydns2Fx5ohCsIBTlsmR+jc2JMDtRmTps9LdYyUx3F+EbH1wXpkFCSApSkPU0IHvx++eZ83sRJ68LoP7Z7mGtwkUtYK1dFlQ+3iJormDgwNkRIWSlRAqMjNOjA9RZOgReg/mpgSTHOovroFbFQ46rcYe5qZF8O7pOhLCA/jVP4qYkaBhw8Ixw9nVU3V0mJ2leCx9VpEFc3VuktO1HVmNRhLM3HKojL8dquBEeTO/XjZO5ig35RXxu48v81VVqwzuI1AOv3u6johAb4J8PEiK0PDeWR0PzksRD1EAvr52p3W8ooOn91zCw2d4K1aijl4zK5kPHpvLunmpYs+ANHsu/b7P5BXz5J6LbPmynFK9mY37LhEd5IeXBzR2yIWyXa3vuhaTS5p74R6FbGt0kC/r5qT8b8H75gFbgecUfr6e8rWD2Wy2dJvNlmCz2aYM/Twy9HqDcIga+vcnNpttjM1mSxMOUUOvV9psthlD17lzpEPUd2EGcy//uNhAeIAvn1xqoKd/QJF9ThEaFRVIdJAvY6Lt/02NHq5IGLr7uNzQJfqc7Q6MoxuXZrHnoVysAyjSqgumxFQLQsA43Nd6/6xEAnzl88lO7Z7Eiux4vqpq4/1CHduOV+Lv7UG0xo+n91wSe0ssfVZOlLXQYe6hocsismJO0YaI47Fscpy4lh6YK6/S/PlACc8eKGXBs4f5+FKDbE+U7ptKPVCufI/UpCyEqTEa8Z7+c9k4Nh8s486to2ebrWk18ecvKli9/TR/OVgh0tQ7yrFsyisa1XVdvS86NICoUDWHipvxUql4/WSVnbziXD29/YNUt3Xz/BfliiLfm/KKuO2VAt45o3PLkie1li4L245X8/mVJh5fmMYDc5Kd3rP1SBmvnaqjWO/c8w32/t7181Jlc25nfjWvHqsU0QafXG6gweAsQuvjObwn7zmro6rFJDLbafy8R9Wzs3Z6Atvvm87tOYkytmUBCjhJq+FXy7JkME5pQtTVejGYe3nxkB1t8OCQzE2zsRcfj0HZeDrKgEh7rb08VFisg3gwyA1D5GbNxl52nKhx+h6uRL6ln7PslVO8f16n+HvhnpXMlfiwu0OUI3tzZbOJXusgtW1GvLz6RB93y+Q43iisZ9X2Uy7v/bswdwep/wYKVSrVSyqV6t+Hfl4GCod+9/9L25lfTVlTJ3+4dSL9g3bdg/gQX7ITQ0SthY1Ls1g2WUt6TCgbl2YRE+jLBG0oV+o72LFmIncosN5FBqmZlRaJp8rGr28aD9gp1zOiAmk19ogBW227hec+L8PDQ8Xj16STGRPAM7eNp6rDJGs0nZ0WycKxMayemcCcjDDuyLE3/d84Pp5ojR/bjlVS3mx0UjoXTIm2dSRznPiCbohw/ZhIjRNcLSzQT3TO75yux8tbxVsFdez6qo5dp+qciDZAOeOxZHwMU5PCWDI+xq6b4oK2NDYkgEaDPZgUGnqjIzWkRmvQhgcwIzmUl++d6jLDpALeL9QR6OtJW3c/t2zJZ/NBO8FGVJDa7eI3mHtd0uo6HhAdNx1pg3Zn3wD6Tous2dMdHEQ3BGOsN/TSZRngzYJaTD12SIJj0L4pr4i/fVnKJV2H+FrkUE/BbUOkEL1WlRhkCUrihbXt4iHtgTlJRGjUsg3y4V2XCA70IdjPk8cXphHs5ymjj61qsfcL7T3XwK1T4th+rJJFY6P59/3FnK1rV1RqF0zaKK4UzDtSJ2/KK+KuV1wHFJ9daaTB0MP+Cw1crDfy64+uisFQYU0bZU2dpMcE88Su8+y/0MCK7HiWT44mNiSAx94+w/r5ycwbE4Wpf1Cm+SXV4XFFfOGOOlr4bg/MTXFJViLAEc9Ud4gUyk9fn8lfD5bz6eUmEkIDWTcn2a2uxtYjZbx5ps6J5l56sEqPCeTGIRroB+el8PCCbyYu/R1YAdBts9mOOPwcBpSj+f+HLb+qnWvGRpNf2caizGhCA3wVgzFhfzGYe2WkAU8tGctvbx7P/TPl/YC56VFMSwpxS8sd5O8z4gECnKE+lj4r75+pJSxQTVlTJy+vnoypZ1AxcFs9I5H9FxrIignmkq6DRxekoVF7U9tm5JkVE6hu66J5KPGzIlvLA3NT2Xmylo8uNLBqRgK3T4kTx6O2rZu/n6zikflpsrFp6bJwrs6AbXCAHWumcEnXqXiwEPadt07VyA5U7iizhTF3HCfhnibGBrnU7pLu1ZY+q0g2dbhE7zI5khxp7zFzhF8qaYKB/VBW02rkhbsmMj0phHrJ+3Ttdkr6FdMS2fVVHccrOrhc38Gvb57IOQfiKOl4OBJbaTxsMvkYV4lDk2VATGzWG3owWeSHVl27SUwev3S4XDYGgo947kAJ92w7JdKlCz5J0NZMClezdEIst27Jlx0ABZ+x+WApjQYzfz9Zw18OVjAuOoBf3jiO2BD1iJIVW4+UseN0HauGqMJBrhe1cWkWC8dE8fZXOl4/USlbh1Jr7upxei0kwJcFmdHkV7YwaLUTlAiMsat3nGLzwRIOF+udSCcig9TUt9vjn4fnp/FWQR1//bKK//rHVZdETCNpWLmTG3EcT6UEi6uYyJVJZXVq2owioVNiiB+JERoeev0yU7Qatt2Xw9y08K+tv/VNzOVBymaz7QSmY8d99w79HAamAdX/lLv5FzdhER4saePnH1zC0mfl8YUpzEiN4BcfXOG1glrxvdIFEx0awMalWTyzbCIfXWx3GSg9ujCNksYuBj1shAf6sfu0juPlbew+o4OBQdlEjw0NYM2sZG6cGMcgKn61v5i6tm5unRLD20MB+eaDpWiD/fDy8MBms5ERFUidwYze2MOisdG8ll/DnJQw3l+Xy8oc+eFO2LRbRjHx3DETKYkUS03t48WGazOYPyaCHy3OcGKWkdI1K5n0wCH0AwhBtSvRvDVz0nhgZqLsvlq6LOjau8mvbGf9G+dcZlQFHPNIzIqOJt2YV+ZoFQPhYbpx+abjyCh188Q4VuQksnqalo+emMP9s5wP5oJtyiti1bavePLaNLbdN1XMonV29zgF7TWtJtqMFsI0atbtPCubow8vSGfdnGRFRsJH5qeSFhWIsWeA987q6Oi2DonF1ss2yJAAX8IC/SjRGwkL9JNtmKH+3iJblaenirljoinXd/LSqimsnpUqVkkdP9tVNkswx6Cl3EXmUnq95w6UEBbg7aSb1mgw8+rRCh5emC7L3t07TcsPF41lb2E9pXozMUF+is7dsfqzcWmWbDxHm6kF17hzYX4Gqb2ICfKTCf5erDfy8w8vATBoUwYVCMQ60rmt7zArChxnxQTylzsnERX4z9XocGc2m22pzWY7JBHBldov/8dv6H/RLH1WVLZBmahp/VBl2FGTaeUr+ew4Vs5LRytlWntbj5Tzzlmd4iFmw7WZ/HxIpP3G8dE4msHUz8ppI7PpNbabZH5F7ePFU0syh0hN2jhW3uEy+BEOKT6eNmJDAjha3kKonweJERp+tvcySeEaMai9dlwMXeY+UUvR0j9I0lCA3ds/wLun66htt/Dy0QrZoS8ySM2KKbGEadQ0dPbL1oJwAJH2ZF6oMzgx/kl9j+OeZCcIcB4ntY+XU/JR2DM25RXxwOtfcbhIz6eXGnj+YBn3bCugsKaNo6UtzE8J448rJnBvzjCzpsHcK/qOS41dbjXBBEuKCGRachhflrXz831XeOOMTvx8YZ6smKrl0YX2St514+NIj9EwNSHY6ZBt6bNi6bOKJBAig3GUZqifKInDxXrOVLXa58VQoqa62YTB3EtKdCBBak9WzUggSO0pJu8E04YFisnjqjYLiSG+Mh8h3fuvNnRQ0WwSg/YBm4306EDefihX3HMF/yH8nbHHynRtMHuH4HgDNhvF+m7SYzTcmZPAv12boajNCXZf6riPOsYILV0Wmrp6sQ0OUN/Vq6h56a6K+NSSTHauzSUnLZqNS7O4ZfKwJlmLsY8th8pk/kckV+npJysqgLFx/uJzyUkKd0rmCqiNkTSsXCUFpaK9IyVYXFXelExI3mbGBPDYwgz2FtZj6hkgMcKOsKptt/D8wXLU3l5MTgwX3/v0kjH/NIbZEXWkVCrVZeAN4I+AGrta+zSbzTbrO7kBleop4E9ApM1ma1X4/RrgF0P//N3QAQ+VSpUD/H3onj4BNrhgVRLtu8Cff3ihHtugDZWHiuWT4ynXm1gl0Tba81AuQf4+ihjx0WhsFNa08YdPirhhQgzWQZus/wrk+GyDuZcfvnMeHw+bk+aD8Dt9l4VHF6bzh6FGR0F/oc/UR9egPXiX6h9cmx7Fa2fqOFnewuz0SPYWuscAu2JMcsSvOpoUt6t0Den3dPUZjjh0R4z9aHs2nskrpqvHTksqEEa40qkQrLnLwo4T1U4YdCUTtDEEIgSpbpajOfYxffTEHG6WaDE5fid3WHNB3ygrJoD5YyLpsQ6K+jI77p8m4oilc/GSroN1O8+KrytpdUlt88FSJscHYbBY+f0nxUQH+aHv6pHdL0B7Rw892DdBx36erUfKSQ33Jy3SH1SeYjZxtGw/7nDkMNy/9/C8FPy8VNQaet1i7oV+pJ8uGUt2UqhsDmzKK0JlG8Cm8nR69kJPwsPzkvHz8iA5IgAPID7E3+WeIJjB3MuXxU2UNHe7vTelvwOcri3g7AUNq78etJPi5CQEU9JsVpwzmw+WEOrnhcU6SHu3VWSrvHFCnGxdffDoLC42dLHlyzLmpEVyorKFnWtzvy0b0reCizv4Kb+h/35nfup/yr6tjzpV2cyJ8na6+wbZf6GBldO0PH398N4t7EXXjo1iRmqY2AAv9CTtv9jImwW1sv3HMfh4Jq9Y1lsCw/o9R8uaWT8vjdunKSd3thwqdakHI/Sg/uS6DMpbuxV14ASfIfhdY4+VNx6YJmoGZsYEsP2eHFQqFV+U6Wno6qN2iI112hB5hWB/+qxYZDtzTLYJmoXxIb7MSI1QXJM786s5WKQX+6yU9mcl3/XcgRI+u9Lodpwqm43iPljeZOKB179i/dxEBlSejIvRiN/30flJ2FSeCr1BJdS2mciv7JDdm77TIjtECX1aglW1mLigM8jmxVsP5LJK0sMl9PMI+/MzecWcKG/m8YUZzM+MtMPAT9Vwoc4g83UVeiNxoWrxkPXQzq8Yrw0VtYoaDWZSozQYLQPsv9Ag+nWhd8nSZ6W3f0C8X0uflV2nqkkN15AS7okPfkO9MMNxx58+K6ao0UBGdLBsjBw1zBzn2nMHSrD09XPHFC0P7ypk0ZhIbpgYgzbQj4RojdO4KY2noF+mNHfKm0zEh/mx4NnD/HTJGP54oNRpfGtaTdwp0V3c/VAuUcFq2fySron8Mj2Hy9tlup0FlS08tiCdRVkxAHx6uYE/f17KvIwoPrrQwOMLUrgmK4qEcHkcWGfodZpTI+kFSmM2xz3CVQ+9kgm9WK7GWPpsK5q7SIrQ4O+toqixi8QIDZ9ebmTpBDtt+8Ybx7L3TC3jtaHsc6Gf9jXsW+lI5WKndz0JfAU0AHO+6Z3I7souUrgEqHXx+zDszIG52BXkfzVEdgHwEvAQkDH0c8N3cU8jWVFDF7//pJjqVnvWJD7UTzyNr8pNYM+5BsUSZkuXBY0PspO74yHqmbxiXj1aQU5yOBd1Bqf+KwBvTw9Z8DQtOZSYkAACfVT8Ztk4JmiDZb+bkhDKrlPVIrRPyIBFR2tkjHepEWpqDb00WuwLaNmkODG74aok6qoa4IhfdTRpdlvpGo0Gs6wSpfQZjtmmTXlFrHntK3F8R1MiFp7L+4U68i41Ea5QhXBlX9V0EOrvxWML0gj1d/85QhZVUA13VT15Jq+YB3aeETMoP71+LFFBapdl75FK6kKm6JEF6YQH+skgLhFqT8W5KGgJKcE9Hc1g7uVMdQfbjlXSZuoR+/tWZMezeGw4L6+eitrHi48v6Hi9sE6cE9LvYDD3Eh/sy5m6Tu7edobdZ4cx1u6enzSjfW1mhNtslpAdvnFiDJu/rORCbTvP3DaBtTOVCd2EfqTF42Jkc6BSb+KR+ancNytFsdK6cWkWu9blsm5eOq3d/RwqaeNXHxex64zObd/IcwdKaOywsOnTMnTtJl5aNYX7JJpNrnqVhD4upWsLDI13v1rAzhMVWPoGOHBFT2pUoGzOVOjtGfaqFhPNXX387XAVtsEB7pgaw3sP5dLbb2PP2Voxs7lymp056o2TVcxKj+T9c/XMTov8p1DKfk2T+qnTfId+6v+S5aZGkRoZKLKo7jmrk2WAhb0oUO1Jc5dFfK6rchO43NApMn26yj4L+6WXhwqNnyf6ob7A5q4+9l9ooFRv5o8HShSzzrp2k1s9GKEHdUpCOCfLW7h1ShwnK1rQtRidKqLpQ1npILUX5c1Gcc98cE4ybxTqKO+w98LuK6wn70oLj+86L4Ozbcor4sDVJp66boxiYJUWHci6uUmsyE4gM8qf/Q/lOkHyl0+KZeu9OS73ZyXfJfgtV+Mk6ODd/eopDl7VsymviHdOV3H/rCQmJIRR2WLmkq5DfG6LsmL45FIjiWH+lLcYqWmx60EeKdGTEBbgVCWSHqKU4FYpkYHiXi71AUr6Q2ofL3E+XKw38suPrmDs6cfSZ+XTy00cLmkhKyaACTEB/PlACVuPVcog2P82pNMoaBWtnJYoziNp9SIjVsM7p2t57kCpiOrYmV/NZ1eaePeMjkHbAIU6MzsL62QIiy2HSvH2VPHg3DQnWKPwnCqbTXxyqZFIjS95lxvF/fCpJZncMz2ZVlMPa2Yl4ufrxYZ3LrD/aqNLmJrj67dOjOW+aVonFMemvCL+kHeZKw0GHr8mnQ8u6BTH199n2EevnKalsrWb4sZODhXpxess35LP1iNl1Leb2LD7En19Vrbfl8PD89J4akkmr9w3XTxEVbWY+PSyngfnpvLR0Bi/eLSKQD8fGfOxNixAEbWhxFgr/a+0EuVYUXaFDnK0Z/KKWb3dDk189kApO09UKqKiDOZedp/RkXelha7uXm6eGE98aCC1bUaeXzlZvP/NX5Ry76xh9NA/S0tqNAepfsCCvfLjB1TZbDZnLu1vZn/BTi/rqpJ0PfC5zWZrt9lsHcDnwA0qlSoWCLLZbAVDVajXgVu/o3tyaYLomnXARk/fIL/PK2bBs4eZoA3m3YdyWTMzWQzu918YFpzbcayC3Wfr2FFQR1d3L9vuy3HKNosB/dDE+OGiTNlk1neY+evBUrYerZIt1g2Lx7BhcTpvFOh45K1z/NdHV8WJvWHxGH6+dCyvPzCTn94wVnEiC8LBjy9K51CxnjK9kfXzk+kbHBwRA6z28XJqfL+s65Ddt0BgIZhj8G8w94qO6N9vymLzwTKZw3SFn5Xi0B9ZkM5lXQevrB7PbZNi2ftgrqIGitKCFMrW3l4qvDw9eHxR2ogL3tJnxcdDxbHSFoL8vKht66bGBeZcsKeWZLL5rimK36Wm1SQ+/1K9mbM1bVyXFcMznxbz3IESl2XvkQRmBX0jja9NdI4CxCU6XKNI3AH2A8FbD7iGYwomHNbjQgM4VNLMzRMiePOB6WxcmkVGdDDr3zzHFZ2BlIhARVITsPcBJjn8XgppUyKFEBpfdxyrsDcxv1Qg9qk5mrRxVRivqjYLBdUdRLth3jL1yIUTNx8sYdfpWpY8f4y3T9sPe0rwmLRojXgoEXo5HEUwpQdoIbDad17HbdnxnKnp5EBRC/HRw5AeR0id8HdCH5eSg5A6xtgQf94XoIjHK2VzRuhZTIkMJCrIh1/cOIaOHhurt5/lUlMX5+oMvHe2gc+vNLHhmnSSwwPYdqKKJ6+Ti1X/s7DnX8P+mX7q/5RNjQth5TSty4TQU0syeXRBOv2DEOgDz985iTUzk4kJ8uPD8w1Y+qysnZXIyux4p2tHBqn5wexEVmTHo/aCHfm1fFmsE0XM3SWhtGGBIx7U9pyt41xtK7PSI/ngfAOz0yLx9PYYEuwNYEFGBPoOM5Y+qwj3vTvXnth48Z4cMqI1IqzWNJTUcYSzCf1CpXozz31e6rJfSEhkBvt589oZHau2F7D5YAkGc68YNL94uMLl/qzku1z1T1XoTezMr+bhN86Kh69gtb3S1NjVQ3yILy2dvYQGeNNssuLnaePV+7KZkRLB8ilxZMZo6O23cbyynZAAXx5blMHuM3ZCpTHRgSx1gGK6g1utm5fOvTlakRgBkBElSP2ofT4k8eR1GayZZddbUvt4cdOkWJ5YmMzYuBBOVHZwbqg6JT1UTkuJYIVEq2j3mVpxHiWFq3lkQRqhAb5Y+qxc0nWKe93Zmg62fFnOvsI6HpqXSnywv6KPGRiEt07V8cfPilk/P5kXV0/l18vGyvbt1Ch7r2eLsZelE2JlPdxp0YGUtXUTHmiHaht7rAQMaRw5jpt0PM/VttnJF14q4M0zOuJCA8RWiSs6g9hj+9hb52k19vDq/TPYuDSL/euHWywEHxc3VCl+bGE6BdXtrH/zHAXV7WIsGujrSY91kDdP1XHN2Cg+uaLnrK5TkVQmJTKQhDA19UMyK9I5KMSB0UG+6NrNsnWj1C+/M7+a658/ynMHSrj++aPsPTecAO3tG1CEAo6UbBNioGmJ4RgtA1zSddDQ1asIMxbW0SSthiB/X7r7e9GoPQkL8ONkZRsPzUshOsiXRxemMzMlzO1++F3YaKB9F7DrX/wXEAG8DPTZbLY7v9UH25Xjr7HZbBtUKlU1dhhGq8N7ngb8bDbb74b+/Z/YneVhYJMgpqhSqeYBP7PZbDe7+8xvC5v44JyOoiYj5l4rNW3dlDQZ7TjalFC23psjUm7vv1DPjRPieL9Qx7N3TKSju5/a9m5FyIS0xPxMXjFnalrJSQrn7FC5W6S3npPM8wfLXULPRoI3jWR1eiP5dR386bNS/nDbeJ75rIT56ZGkRwWwID2cWAWNm+cOlPDh+XrWzk5h7dwUDOZePjpfh66rX6Qif2RIs0KAGIE9OKxpM/LQvDSyk+w6UZY+K+drO9jw7gVFemxXMC+BIvN0uZ4vytplFKACRbU7OIlgI5WtpVZcb+BcbRv1xn6Rgv7r6OhIIQpCOX3N7ERMPYN2aMuSTP74WcmoIIYGcy/dEnFJ6Wc88dYZ7sixK9X/bdU0WjosqLxQFHocqYzuzjrMvfh5e4rP53RVK48PQU9WTY9namII5a0Wl5C1f1zQcbHB6PR7R9hiZbMJjZ8nNw1BHZ+/a7IMgiKlkAXYfqySrUcrxWfzypFybNjw8/Kix2oV4bKO5ghLKG8y8Vp+lcu1Z+mzYurpl1HDbj9WTrOpn8MlehZlRg9RQycS4u+jCPXZfaaOTbdOQBs6zNooQDNd0cVvOVRGh7nfJeW+AFn5/fJxnK7tHBLi1PLYwnQaOiyyoGFnfjUF5c1sWDSG+4cgn3PTQ5maGCbSB/9s6VjMPVY8bf109NpEmKgjNfw3tG8L7fun+Kn/afu2PkpYM+5kAqQm7J87T1Ti7+Phdp2CfW1Ea7w5WNLKhmvSRIjZv9+QxuKseMICfWQaQErC3w1tRry8vZz220aDmT1n7QykF2rbWZGjZW+hjj+smMyXRU1UtfeQd6mRGyfaKxiOe66lz8qnlxoobjZTWNNGTlI4ZU2dPLQgjZmpkU7j5A6WLV17eU/MYc3OM1gH7CQJlv4BcS9IClezZ/0st75D6ruE/+8w99Jl6ScpIpBNeUVcaegS4wlBB+/3t07kdE0714+LYv2b5zD2WPnRNclcPy6eQLWXGD/88O1zhAX4cLikhYWZkaLujgCVdFyfunYTff2w63StKFS6enoCoYFy+LFwr1LaayVYp9JrO/OrGRsVwA/fuYCXh4oV2fE0G51pyAH0bUYGrCos2EiL1lCtN/JlWSsvH6kQn/E7p2tp6OimvbufhFA1YYG+lOtN1LYZuSsnkXZLrwiLXjlNy9zUUM7UdWIdsOHpYWPApnKCDAKillWF3uhEhCXYe2dqKWsxU9TYRVOnhXkZUXyssOcKOpQ1OwAA4/FJREFU+/hfVk7m34bimFXT4wny92VvYT2b75rMRxfqWZ6t5YdD60ZK8y+s3d8uH8cvP7wqixW7ugdkbSHvrMvl3UId8SFqipuMHCtrYXZaBLqObpaMi+H+2cmK3wWgqtlITIianv4Bp/1BOg6uxsTSZ++Bjg7yo9PSx7TkMI6XtfKfN2fRbuohNsSfzu5eFmRGf+2eJPuBaYD3Cxu5fWo87w9Vrx1hxsKcrGs3cftLBUzWapiZGk5YgC/t5l7WzUvH0mfl9fxq2s39HCrR89DcNO6c7rqffBT2raB962w22y9tNlu/zWZrtNlsywFX2h3yT1WpvlCpVJcVfpYD/87/QDOwSqV6WKVSnVGpVGdaWlq+8XUM5l7+/Hkp3h42ZqWGMTUxhOvGRbNscizFjUYRkvTUkkz2rJ8lljaf+7yEdlOvYibundO1/O4fRbxz2o5s/NnSsTxz+xROlrcwLSmcmjYju9blimJt7rJ+QlZs+aTYb/T9EqI1RAb4cmeOlvcK67h+fAxdvVb+/EU5B0rbnN4vZGCkzbohAb509dlIDfPjb3dPJjMqgNChw8Ldrw6X3NfmJpAUruGRN8/JMu1//rxEsTkSXMO8QgN87QrWfr4y8VaBmS2/vNklnETKguSqbO1oLV0W3iusIylSwwU3TEXu7MXDdjjW6apWsWrwen4tq6bF89ETc7hjWoJT1lJKqiF8jpAVFSokUlP7eJERE8yvPioiIzoYtY8XarWX4iHKHaOOqzGQWmiAr/h8Wros/ONik/gcNf6+3DxZy33ZWpdVrur2brK1QeyQVGqrWkycLG/h9qnxWHr7xTm0+2ydmO1qN/UqQiJg+BBl7LFysEhPo8GM2seL7cdr+NVHV9lxokaRIEQJlpAeEyhbe/9501jajPaK1Z6zdTz/RZlT1uyWyfEcKtYzJjqIMn0nbz+Uy8PzUhVhqgLsYdG4GBncV6igTU8O5g8rJjgdlh9flOG2grpxaRb71uVyuraTsqZOXlo9mVB/XxY8e5jjlcNrWoAg5V1p4b1zdeKYLp+i5UhJM1EaH2amhLJoTCQBfl5kJ0bKYKJ3KlQu/hfsG/up/1dMSlay61QdrV3OMgGOJuwtnT1WXj1eTZjagy13T3aimjaYe6lpNfGPSw3UdliYrA2WQczaLfYgWPBJriqpAHESQgipxYYE4OkBnipIjw7mtx8XMyY6BI2fN+lR9kpTsNpH/I4FFc1UNg/v4WofLyz9g6SE+vHLm8ezt9BOXrHhnQuytS5Us1yRILV0WWTV/obOblZkx3PjpBj2X2gg71KTWDW5cUIcN79wQpHyW3pfMEwa9PGlBrYereTOrQUU6QzsLawXWTaD1F5MTgjh8NMLWTzOTiIQ7ecjwhi7++FYZZv4mWofL5ZNiRX90JHS4RhHgEo6SjeseKmA87o2NGpPnlqcwh3Zcey90OBEcHTX1pNsyisS9zalvVHpNWE/eXGIbGfAZkPt48HTS8YoVu4+vqpnZ2Ed92yzs9tFh6p5+UgFEYHeBPvZ+6nunp6IDRUHrugx9Q6wMCOCfefssM0/HChmTnIY92dreXOtvXrzw3cv0d9vpX/AhjY0UBEyKCVbcnWIArhjWiL/tjiDZ5aP465piRwvb+Y3t4xz2nOFfXx2eqQ4d1bnJosophajBY2/L2erWpwqpVIEwadX9GKseOuUePv8H5JISQpX8+iCVGJC1YT6ezNgHSAqyIfVuYl8WdzM8fI2thx2H4ukRGlQ+3gpJlmEcdiZX809204pzmuh0trV08faOSkcL2ul2dhLhL8XDV19/OKDK5S3WrD2ji4ektrPlo5FGxLA8ilxnKgcHidpZUtKxZ4QFsjt2Vou1hsJ9vNmanwIN060x8C9/QP0WgfZd85eff7T5yX/86x9gtlsNqf0mM1me2M0F7fZbNfabLYJjj9AJZACXBiqRmmxU63HOFyiHjvuXTDt0Gv1Q//v+LrSPbxis9mm2Wy2aZGRkUpvGZWFBPiyfEo8H11sormrjyevy2TD4nTFQFrj5y2W9G/PSeSBeWncmZPAmlytuLFZ+qw0d1mIDvIVNx+A1MhAZg/1HiSFD9OF69pNHLyqR+PnwW1TYrk7J87pHoUA3TEgli4qgTJVyWoNFj4vauK+mcmcr+ukoLKN6CA/dhyvVFyYQmC5bNLwvTy+KIMlE+JIjw5iRU6ibIO4XN/B9mPl6Iy9ThhctY8XN0/RUljTxp/vnDSqRnuwO4Zff3QRY0+v6NQfmjeMibZnpKSH2Hixf8QVe6IjS55g5U0mIoPUeHt5c1nXzhQXTEVKJvS5SCEALx8plx8EIocDDCmmWMr4J9zbZ1ca3TLhSOluT1a08IzEIUptNIw6UhtJTyIySE2Q2ptAHxW/XTaOnMRQevsH0EZrFPutqlpM7DxZy/q3zvPAG2dFPHaKZB3YM9PDB867su0HzgfmpckgJ4K1dFnYmV/NNWOjxETH8Yp2kcJdwJw7OhJLn5XdZ+sUoUcbFmdyz3S7mv3lBiOrthew7VgFjQaL4iE9MkjN4rExlOq7yIgO5u5XTvHKsUqX/RSuKgcbl2bZGZXev6w4V0MDfN1WHbptdomGc3VGTld38vKRCqf9SgpBSogYhnzeOS2RBZnR7DlbT2J4IKEBvlS1mGjsNMuY0BzZtP437Nv4qf9XTKpH59iDazD3int/o94kSyKFBPji6+XBosxodpys42BJm+yg886pal4+Wklfv5X7ZiZxSWdA7ePBwKCKjAh/Xrh7MmOihg/5o6FDdjThQPT4ojHcMSQwLvhKtY8XVS1msQfznhkJLJ0QSVK4hrtfLeC5A8VY+qxsyivi5aOV2FQqooPkvcv+DoeZnfnVitDcvx4s5fmD5fz1YKnIqrl4fBwbl2axdlYyd01PwNtLRUq4mtd+MEM8RDgm0hz9gLRfysdDJY7P+0OJiyC1l0gI4aiDFxc9vCYfW5julIxZPnmYRU/YVwzmXsqbTEQNNe3DcGtCs7GXzV9WcPfkOCrb+3jjVJ3MBzR3WdjyZTlz0iJlfdI2cIJsSffLu6YloBkSMH78mnSK9WZyEkPZtS6XxxeNEWF/UqvQm0ToXLOxlzJ9Jy8eLueeGfHMGxPF7z8pZvPBMgzmXq7Wd/CbZVlcbejAy9NDvJdrMmOIDtcQP9T3berpZ/38VLr7bew+o+OVoxVOSeg2Y/+oGVLBvke+/lU9O/OruTMnkRsmxCnCzoXEhOCXxmtDxEN4sL8vV3QdRGj8sfT2y2D1ArQuKVxNbnIoqREBrJqRQHp0oEwiZc3MZK42GvndP4rYfryaV45XE+rnzY3johX1Bb+JjcSEC3B9ViSfbpjP2jkp4twLUPuIz/GDc/WMnMYZNmH9W/qsbD1WydWGLqYmhrJh8TAzYnmTSfHgLiQM7piexDuFOjGBI+xrI7WofBc2IrTvf8LcQPvCgLNA9tBLhUCOzWZrV6lUXwE/Ak5hZ+17wWazfeLuc74tbOKd07VcrDMwKSGEu4dEZR0hdQJLz4ZrM1ihIDoqmMHcy0tHKkV4wSML7BA4R/X2H12TLj58d3AEgY3JEXok3M8fbrPDBFwxvAnlWoH56JbJ8XSY+51K4VLbcqgMDwaYkRJBjgMjkq7dhDZsGMq371w9f145iRZjH59dbiAxQiODkAgwgtEytQEyVpu56aE8t2yijIlw37l6HluQQo91kCiNHxGB3swbE+OWPVE6DoJj6+0f4OWjlbKxE6CAujYj4Rq1SL3+/planlqSyYLMYUy6I0TNkcGmusXoko7WYO7lli0nCFb7EB3sw8W6LvEZLRkX65YJRxiDXy3L4tf7ixRL5H89WEqXxeoSHiY1QUndHaOXYM1dFjR+3rx4uELGrKhkSvNaWAfNxl6WT45mXFywE4OlO3smr5iyli5xvIRnaezpZ2BgkAPFLTKI3c78aibFaVj/5jm8PFSszk3ghnGxpDkc/oS5E+jryUPzUrHZbNS0W1xC3ATogXRdSmGQjuYIhzpcrOen719yCe9zZ88dKKHJYCZco8bUa+VoaQvTksM4Ud7K+vlpPDA3Rfb+keCzUrjT728Zw/SUmG+kYu/C/n8p8u5o3wWzrMByJtjes7WUNJtpN1qYmxHB1SazDAItWIe5l/6BQdmaNph7OVHZym/2F7FqehxqX28yozX89P3LXD8+ms+uKMNdR4LOSc0V86gwH987U8sfPyslKyaAnyxJ47OidmanhrPh3QsiFO6e6VrWv3nOaU+v1ZvYfV7H7jM6Hl2YxkuHK1wy7LV0Wdh6tIr9Fxq4ZXIcD89PUdzfOsy97DhRRUWLmcQw/yHI7PD+psTUV99uYvOXFRwpbeHeXC3mPptsfByfGUBDuwl/X28nyLUSjF+aFDlRoudoZTuHivV2wdzCelbPTOThealsPlhG3hCr2dIJ0Tz0eqEIWZT6gJ351bx/tpbZaZHsOyff2wTfJ+yFwn7p7eXJtmNV4vcejS+v15v4tLSJZpO9HeCFe6bwxK7z/PaWsfxy/zBse++Dubx+Vse+wnpuy7a3OkQFqZ0g+S8eKqPTYiW/ooVrx0XTZRngWHkzK3O0RAep8fJQccPEOHHeKc1Rvd6E0YH9T+qPhEO80px1ZBIWrEJv5HR1G1Xt7qGzr5+sorbdwoGrTWLLhGCWPisPv3GWkiYj1gEba+ckYuq1V1wemZ9Ch6Wfc7UGpiaG8NQS91BrV7BbwXbmVxPgOUB2UjiennZafsGU1qvwrN2t+yaDmWB/X6c5oRQfOY6hUguEo791BYXvMPdi6u0nIcw1i/Qo7VtB+/5HTaVSTVOpVNsAbDZbO3bM++mhn98OvQbwGLANKAcqgLx/5n1Z+qz8+UApu76q4y+fl4obl7TRVHqS/+tQ87tjY7lgp6rbxUz2B+fr6R+w90VHBqm5d6a9mbe6xUi5BLqglH0XTKmJVXo/QuOqKxIIaVZ6VW4yD8xJkZXClapajy/KoLPHxqNvnZdly6WaE+J9r8slLcSfNlMP05LDmJMSxvY1U1k3J1lRUFWqJC81KZTjUmOXWGGYEB8qMhEKn/n2g7msmZPGsnGxtJr6eGrPZXbmV8sytw/MSSJQPbwM1D5eYqbrd7eMY8uhcqpanfV9hM1bG64RD4Dvn6llVlokP3nvklixUcrOOjLYuDpECc/1polx6Lt6GBcTLHtGIzHhCPCVGyfGKzZ/tnRZeLOglr2F9VybFcV9LljsGg1mmZL6SNmdqhZ7JlTQahmp2qWkERXo5y1+1rjYYEUGS3f2s6Vj+f2tE2VEJu2mHjR+9sBk+7FKsdpqMPey5ctyth2v5LbseCI03vQPwMtHy7iiM8iumx5jZ/O6a7oWTw8VYQHebvVETlS0O8FxHR2JsC84wqEM5l5RC2Tx2HC2rJoy6kZZodK4p7CRNqOFtbOSWT4lnrM17TyxMJ17ZiQ4iT67g88ChPj7iuumttP6XR6ivrfv0KTPRddusgf7hfWsmpFIaqRGzBqfrGiRZeJDA3xlFQwAS7+VrYcruG1qPG+faWBslIa00ADumq5lYnywEypBqHQ5aqSBfO8W7EJduyKLpLTy3j9gY2FmJMV6M32Dnrx7Wsfmg6Xclh0vMqGeKG+WwcIzYjVsPVJGQV07u8/YWQYtfVa3WXtvTw/R5310sQFvz2G/IK3g9fZb0Xf1Yuyxioxv+y/Ui7pJgs/ddapahEHHhwUSHeTLmOhABlGxYXGGDFooPDPB720+WMKOk7UyhInA+tdq7CEjKpBWY49srHaf1WEw9xIeYIe5Z8UEU9Nq5NnbJ2Iw97Pg2cNkxQWxc+109hbW8/cTVaLIukbtKfMla2Yls/VeO9TacW8T9n3Bjw7YbPh62w9R0iqGML6NLSbFZw8QHx1IkJ8XyyZE8OKqycxIiWD9/GTqDXLmQKuHSiR8SAvzpWvID0h9UEuXBeugjUMlepZPjufNgjr2FtZzb24SW49W88Tb5/mVRFx9w+IM3l2Xy725w6CnEyV6djiw/wFYrcMEClLEi5TVzh26Iy1aw925yTwyP9XJ1wlCvJY+KyV6E/svNFDbbuHv+VUytJLax4sbJsSwMDMSbZgvY2KC2HfOPibaMH/eleiOuqsCb8orYvuxSoocfJt03a+ZlYy5H94tbJTpWAnxjJeHCn8fT/RDnyOtmilBZp87UMyfvyh3QvpIEUtCXCodQ0ufVYQsG3usnKrqYOUQIsXR30rhuA/PS8bDZkNvMLPjRDW3v1Qw6taFb2L/Egcpm82WLFSjbDbbGZvN9qDkdztsNlv60M9rktfPDEEF02w22xMjaUh9W3Onviz8v/Q9P1qcwe6zOhY8e3iIunO4D6W8ycSWL8uc4GYAVXoTK6bGUdxoYLw2lH9794IssFLqcRHMMbCW3k9v/4BLWmthAUkPha6+r5TaXEktXQof2HeunrohQci3CnW8cKyKimYTLaZ+/vR5CR9fbObJPRedysiuROikvVaWPiu/3X+Vrypb+e2ycWxY7FylSI2yV6Z+tv8KB6408uTidGwDAyJG/p11uXR0W52C139camDVjHg6eqzsPqMTA2x3SuxSUUlp2dkVs95oA2JLn1UsZb97ps5JzFdKeKBkwiFNKH8/MCdJ/J2UsTDIz0fxYCQE9zsLqlmQGU1tu5GXVk91SS4g9KYJpXUlhiolc2QGUvt4kR4dyKoZCUxNCv3aUCGwO1lhTpfrjWz+soIFzx7mwwsNLJ8Sj76rh1smxxMS4Mvj16RzpqaTualh3DpFS32HiTCNmrU7zzjNw1snx9FjHaRUb+KX+4vYcqhckSa6vMnEnw+U8mWxnicWpfHYwjSne/zTZ8Usef6YTIW+TN9JeZOJkABfZqZFYuzuZVxcCLvP1MsckWOvmlQAUTr2MSEBpEVreGpJJh88Ngdvbw+RSni0zkUI1sbEaJwCZFdJj+/tf9cEOYWqVhNrZifS2NnD26dquC07nklaDbPTIlm945QMpuvYLxkbEsDs9EjyK1v45c1ZLMiKYVdhPSF+npQ1Gwn08+DarChykoLZerRSFoBKK6eOfbJgP5zsGNpbpSyS0sPInz8vobvPStTQIaRiiOpcEGD9zc3jePyadHKSIjB297Jl1RSsVivlTUb8fbzZdqyS1TMTWDY5jr+frMHQ3edSKkFYM4L0hLS6JmXs8/P24nBJC2eqO5iZGk6LsZcH5qai9vGiq7uXRxakkRkTwDWZMdz0wglxLJ9aMpY/3DqekKE+xROV7eK+3dJlEf3eifJmWU/P/gv17Cqo4bkDpXx0sVEWMOvaTDL/6evtSZvZDnP38bSRFKFh24lqzta0cfvUeLYfqyB6SFLjZGUHs1PCeHNtLhsWZ8rS7c/kFbN8Sz7P5BW7TJj99WApmVH+bL13KvEhforxwjunqvj7mTqnZy+1O6cn4+ftx7g4u7LN9eNj2Hq0mmOlzWy8IZO1M7QcL2th5XQtz90xjor2XlZtP+V0vcggNYE+niwaG827Z+2QSW8vFQE+nk6sbR9famDXqWreLtSJSd/yJpN4CDX2WLna2EWN3t5vvWbnGcbHa/joiTlkJ4Urstop+TulfmKprxOSztuP2XvKvD1VYv/d8snx/OlAqWzPX52bxG9uGc/We3M4Uqy399CmhPLF1eFEwp3TtCKU1dEaDWasViueXl6skfg2x3Vf3mQS5QOk8V1sSABrZieybHIcbxbU8veCOqfPcEwMNxrMnB/qJxd6lsVWlqhAp7jUcR6lxwRy13StCNO/0mykr29A9LFS27g0i/2PzsI6aOO1U3X89VDl12pd+Kb2LwHt+5+y7wI24ViuVipfC5NEgIjdOzORAw4QiK1HKzlZ0cL6+WncNMnerC2wyx0q0fOrZeP48bsXRRjXdVkx7D6j+0aCYpY+K5sPltFmtHDvrGQmJ4SJv3NVihYY76TfTxApFL7HnnW57CrUuRQmFV4TIHjWARt/vXsST+65xNo5Sbx2okbGUvSjxRmMiwkUIRqTtBr+eudUfH09sPTZuPtVOWvN4XI7s48rpkJBoDci0JubJsU6Mfe5KgU/d6CEJeOieGrPBeZlRHG6upU/LM/kRKWJbcer3DL0uWJKkorVfV0biZHRlWCxFF4p3NvB4iYenpfGHRIhSFeMhY7js//RWbw2VMFSgoc6CiK//WAuqVEaERr2TcwdZMAddKRab8LXV0VsSAC6dhM/33eFkia7cOZNE6M5XW1wgvg0dtgJKW7ZcoIX7pnCQ68Xiu8RhImFXre/Hap0yeL33ukaylq7ybvUyO058YpsUQB7ztTy7Gd2IcbpycF2ljF9pygeuXpmAl4eHmQnhPDikUrx/idpNcxJi5IxZQnP9vbsePx9vOmzDrBuXprT2Fv6rPzuH0XEB3txzZhYvixt5J7cVLfPRwnuKk2suJoPX9O+h/bx3fgokEORMmMC2Loqh/v/fpprxkbZe51yk1kjEeT+6Ik5+Hh6KMLDYXj/aumy8Lt/XCEnOZziRiNnatpZnZvEnNRIRbi0XRewh7tfLRAZbn990zixr25TXhGNBjM/mJPC1MRw8f4d9zxBNkDYQx0ZxQQfd6hEz/25iXh6qOjssdLebSXc35ttx6tHBUkG+NNnduj1XdMTWD4pnlXb7b7rrula2sx9FFS2sSJby9tf1fKjxcMQfkGU+MDVJv79xiwRBp0ZE8DrP5iOscdGfJgfd209ybVZ0Rh6rJypamNBZhTTkkL5yXuXRJj69BQ7BfRHFxt4ZEEa5c0mPr+qJ9DXk+vGx4jsdDHBai7UGZzY8DrMvbR29bF6xynxbxzhnNL9UxoH3D8zUZw7rsarpcuiyCIshS1XtZho6rTImHh3rcuVPTch6ePow6T7/SPzU1ny/DHmpofx8JxUkVXUEZYvXE+Yw5kxAby+ZjqvFdTyZbFdP2nl9EQsfVaOlbXQ3TcgY33dvz6X8tZu8ms6RDbeF1dN4d8/uCwK2EoFrqWiyVIT9lyBzfDu6Qk8ujDNyVfp2k2sGIJ9Cwy0xh4ri8ZG8PS1mfzq46uUNBnx8lBx94wEHpqXKrvGprwiTpa38NjCdNq6+6nvMKMN8afZ1MeuU7Uu45QinYH1uwqJ0vgREuDFxiVZMsFlsS3kRAUNXX1OUETp3uJuPUmZgJ87UEy7uZ/+gUEOl7Twh9smMiY8AJUn+Pp40mWxOq1naauHpc/K8i3HuWFcNL4+nmiD/biqV4YoV7WYOF7eygtflivCVr+F/d+B9v1fMlekBGofL1lFZ0J8sFOmYuPSLJ67fap4iCpvGhYrLNWbebOgWjypPzg3ld1ndN/4VG3otpM77Cls5OE3CsWMvqtS9Ka8Ita/eYY9Z2plCzctWp49SBxqglUSJpVCEJMiAsXskLG3n3Vzkxi02bhtqr3JNibIj8NPL2TF1Hj+9mWZmDG9aWIsbw81D34lYbpZOU3L0co2XjtRxc+WZro81KRE2j/3nhlJisx9StUiu9BfI4PWPq4fH0OI2oObJ8Zystp+iBqJoU/KlCTN1H/TQxTIK4WO5k4UWQqvbOmycLC4iXkZUfzxsxL+9NlwFtpVUCEdn59dn8EVvdFtZUgYb8dM3deBoynBzTblFXG2uo2XV03kzmz7AdDV2gO7ovzbhXVipVEbFigSgyweG05GtMaJJOSZvGKWv5jP6wU1LJ8Sz7tf1TplyoTq3P4LDS4ZNFu6LCRHBLCvsJ7adgsd3f2KWk8VehOvHqtk2dA1clMi2Lg0i5/fMEEc4xZjHztP1vDHz4qZnBDMimz7e5+6LlPWcFvdauL9Qh3z0yNpH4JANpv6FA+wah8v7pgSRWcv3L/zDF29w8+nUm9yGn/hb1zpnzlWpL+3fw0Tqs1CM35ylIabJsZxqKSZ1KggsrQh4u/Xz0uhzzogy6g/tiCVvt5+EXYk7F+RQWoemJvK2ep2vihqplRv5uOL9Vysb3OC1glrtLzFJMsoH5MwRm5cmsUTCzOZmhguy9477nl+3p6yLLsjy5rax4uNS7PYcncO2rAA/vxFBSfLW7l+bDhLxkYrQpuVzC70OewTwzXeIlnA5YYuDpe0UNtuYd85HZ9umMfctHB7EsZgFqtIte0W/vJFCSun2atbv74xix35tazaXsDr+VUsyIzCOmjj4wuNLBwThb6rl9dPVslg6hsWZ7J2lp3cZu2cFCZq7TBKc98AUYE+7Ht0Fo8tTGfzF2WiVtTKnGH+rdAAXzJi7UK6t06NV9R8E9axYxwgJXJwNV6RQWo7mVZ2PNeOi+LxRelOsOWUyEBq2kxOFUfBhCrIayeqnHyYNIYQ5uXZWgONXT1O80xq0jm8ZFws3VabTDNMILbqG7Q5CQ9Hh2uYkxnNPZPiOV/XwazUcLafqOShecMCtlKBa5VKOa4WKlHvF+ow9lhp6uphwbOHeetUjSxu0IYN+8s2U49IOjI/PRyVSsUNE2JYPiVWrP789WA5YPf5VS32vfdivZFffXSV2Slh/PCaMcwfE8GuU7Vu45TYUDXLp8SRGhnAxbouztR1KCJH1sxJY1W21im+k+4tG68fQ79lwInEzLHC9dSSsTw6P4XDJS1M1mrQdZjYf7WBN76q4438SvwZkFWXhJ5zacvH4rExTNCGMDAIyZHK2pNbDpXx+dVGuvv6XcJW/xn2fUXqa5g0a7IyR+sySys1adbHVWbeYO6l19TPe1cbRG2Wu6bZsxitRgsJ4RongoKva66aAIXr/nhxOrmpdsKI9W+eUczACFahNxIX6szA484a9Sb6sPF2oY6xUYH8/tMSvDxU3DVdy905WlEcdWd+NUdLmnjy2kwK6zrtQaGkImLqGyQuVD2qsResttnIR1caXereCNlWaeY9KVzNj68dg7eXinZTP68eqxQb9R+al8KD85xhWo7j/W0y9SM1g0ptZ341n19p4D9uGkOYpx/GQWTZYUFf6b0zdYr6VIKWhtJ3OFvdxiML00gK1fDzfefJTg532ywLrjN17kzQRHKs3AhVsclaDalRQewrrOfh+Sm8crRK8fmXN5nIr2p1mjfYbNhsNk5UG3jm0xKMPVamJYfw17unYh0YdMqweXt6oAJajX1iJcqxOmcDfBVoZIWK1Afn6lmVm4B1ANnafe5ACeX6TpIiNBwq0bNujj1TKh33fQ5/+/wdEzlZ3UF5s4llk+O4pOuSVT6fySsmJVzNnz4vc0tMYemzUt/eI5sf+9fl8s7lBpeVM+nfKjUKj5ZUYAT7viLFd1eREsyx2iz8+0ixnoggb7DZ+Ohis2yv6jD3srewjmZTv2LGF2BvYR0Fle0cKW3hN7fYdW/mpIWyZnYysRo/ggJ8ZXv0Zxvmyapdjnu2khYR2IXUP7/aTHW7xeXcdNTAy7tUz3ldl9M+JR0Lx2q91JR8bYXeyMeXG6nvsHCktIX/XDaOy7pO9hbWc9d0uzbbK8cqxCqS8LcNBjNdpn6xivLkdRmcru6gps3MtOQwBgYHyK/ooNnYy+Kx4fzHjeNd7p2WPquT9o9ACvHUdcMER477eU2zkXfO1isiJdx959FoK+48UcWVxi5FLUVBm+7flqThhY9TJUqYD0nhatbMSmHrUdfokkq9idBAb7H3W9dmcdmj2dBuQu3rPSLxiaXPSnunhUEPZ21Fx/n4p8+K2XNWJ47PaPz7M3nFXGnspLhxWB9MaZzq2ozi5+vbjLz2lU6cV6tmJIh+Jylczf0z7T3l/3XLePKr2mXf65m8Yv5xqYEbJ8baibYcyEjUPl68lV9F76CNQ8UtIsJBWI89/QNYBwZleojurLnLwpESPeWtFtk+4Yr4DOxzYnxMIO3d/RQ3GbFa+5msDeWr2k72DZFJrMxJINDPW7Z/fPzEHG564QTxIb5cOy5arEhJ17jB3MuzB0r5/KqeiEBvfrx4DBnRgW570L+mufRR3x+kRmlK8JbdZ3XfSgQXhhmV9hXW87vlWaRHBhGo9iLvit4JrvVNAlTh3tU+Xi7hZR3mXrYOsdJtuCYVH28vEXLkuBAA9p7TYe6xEuDnxYqpWqfrOZogTpqTGMIvPrgigycoBWDC/b5ypJy27n7F930TAWJ9hxmVp4db5yBc92dLx+IB7DheyU0TY2m3WPn0ciP3zkzioREOUVI2wTuzY1k/P0OR+tuVjWaTlga1eoOZQyXNBPt7c15n5IqugwnaUEXnMVqHUNlsEqGUk7Qa1s5Kori5m8KaNh5ZkMbirG+mV6ZkVS32al9pUyc3jI/l06uNvHLfdJkjvHZspCj+mRSuZvWMJLafqFJ8/luPlOHtoSIySE2wjxcnq+0N7Ztun8B7Z+uJC1bz0cUG7pqm5b6ZSUQGqZ0gma6Yg0Z7aGjustDVbRWfu5BEkTqZpRMieeraLMW5IWVyrNAbsdlUPPb2cIJj3ZwkbsvWOgXJO05Us+9cPffPsjskqVMUEkFb7p7IwdJ2UaD3pnExvHG6ziVUcSRzxzr5Nez7gxTf/UFKEK6UBteWPivPHyxjX2E9L62ewqNvnZclXVQqFWdqDG7FrgE2f1HCmZoOrh0bRau5D72DOLljkO9qz5ZCnKRQoe3HyvH28qSkyeRybiqtU127if/+x1XumJbAe2fr+PelWbJ7H83+6irpKYiPN3f1cOfWAlmAvPHGscxODnVKrpwq0/NleTsfnKtn++qJHCjpoKmrl1NVbTy5ZAyGbiufX2ngyWvHkBUX4jIp6Orw90xeEe8X1vPbW7I4r+ty+d1GOhh9XQi2lEXO8YDsDg4smOPhzRVU++skJR3fu/1YBX5eKuJC/GkwdLN6VqrL7yKt0CkdBITxcYSwv/NgLjEhyonl5i4LeVf0HCzSU9zoPE6OJvW70UG+vLsul3cL7Yfgh+elsjO/WhTA/fUt4xgbrSE1SiOD2yWFq3n7wVwx1hP2/f+6dTxeKhVflti1xvoHBmWQUFfJDCWrbTHRNwAGS4/ol6UQeOHZ/mhRGnMzImWsf5Y+K7tP12DqG2BxRhQ2YM3OM2JivdnYR3yIHwF+3rL9QnrN+ekR+Pp60WmWkx6NJFL/Le37gxR8NxUpJdrRNlOPy+yWu8qCwdxLebORx3add9qQN0mc2OGnF7L5YNk3qnC46p+RmrAxBPp68qPFGUyPC+bt8/WygFswqRO+LTuef1uc4bYaVN5k4nKj3SnHh/gyIzWCD87V88CcJJZNiiMuNMClg6htMeHn64l1YFAxsw6umcaUzNVG7ZjBE9734YV6rjZ0kV/Rwo+vyWRsbCAhCqxrSve/Ka+INqOFcI2avYX1PHltGjNSIkmNCnRZAbL0WTF09yr2bUlNeKYbbxxLaZORsTEaojW+BPl5s3Yo8zk3PZTfLpuIRu3llGESHII7Gvh6vYnXC+v44Fw9v1yWxW/2FzE9OZh1c1LQhgWMWo9hJArcTXlFfFmsZ/WMBBq6+lzi+PUdZl4rqJUdZNxd+7kDxfT0D7BgTCRP7r4o9iHNTI3A2GPF2xO8PLx4v3C471AINJQcqYD9r241kRyhvNaV7kdpbnyd6rIQGPzmlrGYegfdJjgE03eY+XtBncwpCoGNFG+va7OgDVezv7CaJvOAUzZdyRyz/9+hfX+Q4rs9SD2TV0xndw9B/r4y3yGs+6yYAOaPiRSpp6XratepaqfXlUzYSyx9Vu7fXsDtU7X0WAe4a0aSCJl1nIfSNbL9WAU91gEntECjwcypqg7O1xkIC/CiyzLIRxcbuDMngaevt89Nd5lvpaSHYYgGecVLBcSH+PJvi8eQFOZPctSw9MXX8SebD5agQsWbBbVuA2ShktRn6uNobRvNpj5sgxAa4M3qmXZ66+cOFDsdRKXm6jBxvFTPk3vsvVX/eGKOuP8r9Q853pMUopvkYk8byd46VaPYowWjS3a6O7y1dFno7B7ggde/IljtQ1dPH9vune7yOzkeyN9+KJcT5W0ydIKSP33laIWMth1G3qMF/75mTgpnazudYizHfVLo4RtNIk5p7gpJ8L3ndE6xodrHy85Od7KWD87Xc+uUeB6YlUi0A8omMyaAxxak899DvVhz0sN49o7JI8ruCPcvzBehl99o6WVqYgjlrRY+vdzIfTOTZEgdaYLecd42Npvot9nw9YDLzUa+qu0k3N+bZmOfTH5A0CUT4ujRHPYF6OU37ct2Y9/3SH0XptSrsvlgmVMvimDu1N3Bjuetbe+W0bg2G3vZ/EWpTGCvuavnG/UijEZYDexYZoHSuajJyG3bTuHtpVLElRq6e2WiayP1a6XHBIpY5O7+AWYkhvLuQ7msX5BBXGiAUy+PdOzueKWAHSeqiQsNkOFnpXSvozVXPTVKwryNBjut7IL0CP5tcQZb7pnKonHRfFVr4FBJM0dLmwFoaDXZ/37bV7x/tlbsJwA79n/9/Az2FtaTFRNAVXsPu05VuRQCFu4vv6pDkeVPMOkzNfdY2VtYzytHK6hpM1HbMYxHn5UWwZ7CeifxXINkI7rS2O4Sbx4fHUhKmB9/uXOSSP16pqaTz4taR32I2plfzfItx/nsSqPi7wWGx1K9GbW3lwzz3Nhhlj2z6NAANi7N4q0HhvHaUjif1MqbTJyv60Tj583Lh8vF75iTFM5TS8ayfl4KD89LE/uMhN4lQRtFirNflZvI/ouNLN9ynGfyilm51ZlG1dJndZpfBnOvy7k9Em29YFUtJi7rOvjVzVm0GvuYnxjqxD6lZB6eHk6ihWofL35yQ6YMb58Ra6fuV3l4MzMxhDunxvLyqiku78sR9/69/eua0KNxx7QEme+o1RvReMKK7HgeWZAuY0dblW1HF6h9vIgIUjMjMZSXVk91G/QJc1Dt48XCsVFUtVt48UgVfz1YriieKT1kGMy9+Hh5svNkLbbBAV7/wTTumW6/h9iQANpMPSxID6dvAOoN3fx0SaZ4iAJlljSwJy8d+3SFuXuwqJnHF6Zw7bhoqltNlLaY2JVf5bbnUmqCCKveYMY6AO+dtR8SH5yTyM77pzkdooTr7r/YiNGmIiUikJ0na/nT52X89csKypvsTLcCq5mSr3bFhKtrN6H2HhYcrW03uRRkltqes3W8eqySg1f1LhlyXZnjXiuwyCn18Lrr7RXM1R4mCL9fbezgxomx6Lt6WDoh1u3BUNpzdNvUeFIjNXh74NKfWvqsbDtW4UTbDq73aCG+27g0i3CNmmc+LXWKsZ47UMLKV/L55FKj+P6mzh6RKj/vcqNIma5kG5dmyWjZd+ZXc+uL+ezMr2bFVGfxZYDokACC1J6smpFAkNpTbJVwlLVZPlXLb28Zz441OaycliiOv1R258AVPW8U1Ir3I10bVS3Dvfy7TtdzrtbA/dlaVs9I4pWjVRy8qqdGb6LRYKbL0q8Yt27KK2L5qwXsKtQRHalh8fg4HsjWck1mtJP8gNAjLcTR0vniyIgomKG7/59xiHJr3x+kvqZJN0rpBneyooW/HiwRVdFHq+6+IieRR+an8qubxokT/oG5qaydkyJuQgJZg0C0EB08ukDWHWW7o90yOQ5/H28xmN11qk7x4OWKzhtwalQXGhDXzUtndbaW67Ji+PkHl3n/XIPT+EkdhNBI2WzsxWq1yg4foz0cSs3V3wj6BOLnD1GdPvKWnRZ0yfPHePFwBQnhdvap6lYThTUGdp6oZFNeEV/VGfjkUiO3TI6htNnsFDSnx2jEgGVfYT3Lp2oV1dSl9/dMXjEbFmc4UUwL42vq6RefaYCfFyuy42kz92PuGyA7LpgHsrW8uTaXlTkJTkGMNBBuNJj5709Kae7sZuu9U1k7pCElHc+7c1OI0qhZPjVRRqIxGrP0WXmroJp5GVH85wdXFINvqePrsPTJ5lVIgC+X69r52z1TuFw3TBMshcHp202KB9OL9W1MTgjG2NPP+PhQrtR38Nydk8TxDAnwJTJI7RSESa8lONKH56XyVkE1q6YnOh28wO5kHn7jrEw75rkDJRwubeGTS41EB/mRd7kRXZtRNrbusrACSUlKZCCz0iO4WN/FC4cq+fvZep6+fqzMwQvvFdgEQd4MLG0WX5gR4US4simvCKvNxg93X+L6F07yyK7zinuVwdzLh+friQ7yY/+F4QSKEjnF9/a/b/b5reWKrkNMJPxoUSqHypvZcaYOS28/kZrh/aNEbyJR0sOyfHI8szMiyE4Kd/Mpw9ZoMDsR+tiA27Nd686FBPjSZx3glzdnEqD2Zk9hA3dsLWDniUp07SbWzUsnIUzD7tM68i7r+eOBEpnuFTgHvNKgS4DzGcy9FFS08OxtE5mWFMbcjGgi/H1oMPbxiw+uYLYOuvQp0nW1Ka+In75n10wUaJVr2y1gs+Ll5cX9EjppwxDL4JYvy4kP8SU2yJf4MD+qWk38ZEk6763P5enr0kmP0cjIcJR8teMBISHcTuTx43fPU9ZsEgPoyrZuNi7N4s21ylqTYN+Xy/Um3iyoJdDP022CVtCtEvy4qySgQK6lZKOp8Dn6cOkB/L1zDewtrGfRmHBumhij6O9fOWqXtThwpYnIQG9+dn0mkYHeANS093C2uo0/3DbBqTXgq6o2Xs+vYW5GBNFBvqybmyK7X8c9WjjcPZNXTIXeHjucqe6QPbfe/gE+u9LI8smxXKgziO9PiQzkxomxtBh7WToh1m2LxvZj5SIt+7ZjFU5zUxobSu3xRWO4M0fL44vGyF5fMyuZ/Y/NYk6KvQf+vM7AhncvUFjbIXvf/TOTuFDXzoZFaahsgyJTZkF5M8/dMZGevn5igv3w9EA8vAf7+xEWqmbb8SomazWUt3Sxa4jo6XCJXjZvkyM1TrpRgm5cUKia9BiNLIHp5+3pMo6WPgupSeVX4H/OP31/kPoWJt3gfrQogy7LAJ9f1dNlseLp4eG2siC10ABf0qI1Thkc6aLeuDSL3Q/lEqHxUxQ1c2XSa7o6wYP9gCQwnYx0z6unJTgF+o7ZaqneFEBQoI8T86CSgwA5+9vtU+UZ1fp2y6gPh4K5OlBKhXlvmxpPv0o1VEEKFj9TuNc2U4/IynRHToJYCbpvVhLpQyQIjgdCsD+3BI0fK7Lj+fCcziljWNlsUrw/x7HfeqScl45UctMLJ2gz9g6xHGpF4ct189KJDtfgG+hDRqzGKZj28fQQmZkKKlsYsNppQU9WdvDplWaihxgLHeeW9ODiMyRQOZrNSdDV+kiBsU5qQuZ4/YIMmYhn3qV6wjRqnnj7POEaZ/z5prwimrv7nQ6mVS0mNn1aRlWLkZsmRPNAjpZf3zyJuRlRgDy7Jg3CHA/VZY1GkYXqhvGxVLd3O7H0CYGS1Jk+eV0m756uY+fJKpYOZVJvmRzHV7WdvHdWx95z8iqqdCxfP1HJthPVYoZY125SZJuUwpee3nNesfKtdPD19hzOXv/65rGUNdtZGLcdqxpx3UuFoW+cEEdogO/3Fap/YWvpslDbZuaFw9Xo2k28tGoKs9KiRG2Yz4ta+ORyC2unadm1Tjnw/jowt08uNckCrMcXpLLjRDUnyu36U64SMOvmpTE2OlTcWyMCvWno6mXj+xf5+EIdla2dblnaAFklSljDh0r01LSasPRZCQnwZc3sJE7WtLN25xmOl+lJjx5m/XqzoIaH5qU4+QfpuhKSezeMj2VvYT0VzcNi8LdM0so+V9CGe+VYJb+8ZRwzUiP4xQdX2HywjDunJ1PRauGxXefp7hsQEyFPLRnLjxalir7acY+VsuNa+qxcrmvnP28aS2ywmjsmxXLzxDgxgHYnlN3c1SPuJ68cLXcKdME+d945Xcv+c3W8dLSSe7YVUKQz0Ggw8+q9k5iXHkaNftjHuYsrlEz63ZR8jtR3TdGG8NwdEwkJ9GPdzrNsPlgmu9a2YxW8d7aOBRmRRGm82Xq0mif3XOSVY9WUNxl5v1DH6ZpOfr7vsshWKOzbLx0uZ+nEWM7WtPPgvBQenu+699mxuqpRe4osewLEbs2sZEICfPnFjWMZsKmc9m0l4XnHMWw0mAkP9BPn5hsFNTysMDddrU2lvTu/VC+yRroSwAb7Hj93TCRV7RbeOVPPjhN2tryxccEcKWtj+/EaXjxcweOLxrBqmlb0L0L8sn5+miwZ/+KRKtbOTJSx/kl1o1blJhAe6MOO41VOPjnE38cJoXP/rES8PT0UK90gT4xL1+H/hH/6/iD1LU0IBKclhzmVJZXU3UcyxwUiQAkAooLViuK17sTuhGu6OsFLbf2CDFblaMV7rmo2OWWANuUVcfsrBbwuKf060qf+f+ydeXwU9f3/X5N7yUHu+z6JXAKBEDkVRaNVFEVUVJRDRK221Vb67ff7bfv9ftuGr6VftaUKKvxQsSgKiJYoyk0aEAg3Sch9Z7M5Nnuw2WST+f2xmcnM7Mzs7GZz4ef5ePRRSTa7szOf63293sJDaXmzXjINY0NuJv4h0gl7Q24mPluTjdAAbxsZ6pU5idi3PschgQ+pNAOuB4+Z5CXqTk4E0HqtscF+rOS1tY+I1ZPr5Qb09vbhoekxyIj0xWuL022KsmP7ZeIfn5XI+zzusxO7PmZxrdIYYLb0obnTiP97dApq2vTQd/Wwr2MWz00HS/F/35XiWn/Hcu5hmrn/TL+ipVtOIWicB/us7UX6mHQFJlLHLE5cj62QuydGKWrIG+o/4KmODvJFZYsBSaF8edPy5oEFn4lkflxYbXPIYozwszWdqGw1YntRvWw0s7vHwtasiaXFaI1mnK/T4sDlZuwpasCdmeFsY11mAwlQeWBqXCCOvraQ/c7+Ki/2+tU6M0qb9fjr4XKUNg9EpriGiEZnQmSgivedqV6adzjlevUZ6XHmYCcW+RaLAAT7euKJWXFICvbDO0esaY/G7l4Ec8aCGExj6PsmReD+yZHQ6EzD0uiQ4BxhASokhPhhYUYYztZ04nKTAcnhfmjS3uA31DzbYCMl7ihaoxlbjlfhQm0H7p0Yhi/WZeOW6ABWnvm/vi7mOZeEpEYOrK3PzU/B3qIGPHhrLBJC/PDvXxaju9uCD56agWeniwsbMeskky2REemLxbdEYvNRa6SisFyDhBA/3gEvWjWwr+ROisLa+Sm89Vco7T/Og8LS6TH45loTls+MxTNzknCkRI1ZScEorBzwvD83L4V1GH5yuhaZEQE269ieogbEBHqjUdfNS6uLDbGm2oo5KN4/XoFHt57GztM11nsW4Y9/XtXgl19cwfYf6tlnKJShFsLNbkmLGI8NuZk8Q3pjfgl+sfsSLtd3IiXcn73287UaTI8PwoFrbfj13qv4rj+9fWN+sei5gntu4cL9bnJ7DrN3/WJxBqLG+4pKXVdpDNh/oQG3Z0SgWd8Fc7eF3Q8enBYDf3fg4emxmJk4HnlLJ7HrIbNuV7WZcGtcILavnGlXQEosys+kmr+6eAI/ghjoB4DmrNsx7GeLRaKYs9m2ExVwE8iy506KwhrB2BTD2q/Ndi/W6EwIGufN3r9tJyt5+xwzbipbDDwJf/YspzbY/KzDaEZCuD9vf1mZk4iJMYE2zviIIF8bIaINuZnYtTobHm5u+PRsHd49VsEbA5bePpsMnc/WZMPQZVXX3d2fTst9FhvzS7Dpu2tYOj0GCSEq698MomWQoxCxCRfCFCiumpOIpdNibNSSHEWswJQp4Hzt7gxMiwniiQUIm90xKG2gxnzeL+5MQduNHmhvWPDlhUa2iFKq4Sr3u3PV4MQKK4XFgpuPlKFR24XoQB+8eHsa73qYQuXf/mQC0sPHs4dbR9RllNzjcZ40Fk+IxoTYQAADzR7FChsZedKy5k6sXZCC2clhAPq9XEfKFTdN5qrz5E4Kw4a7MuHuSbHCBBvzS3C5vh2TYoNwuESNFxcm41qzeAM6wGrQvHu0Aj7enpKv2XWmFkEqT/xHf6NIRsbbzd0NYQEqyeJgpqg7JzkEhZVtPOGGC3WduDVuPF5dLP0cxJrCMhuPlBjKnnM1KG25wTaefGEhP/rIjK//uC8DGeGBNl7YyhY9+voo3vzYuSobBVVtrCqjlxuNy40G3v0qa9Lz3svUbcHbh8rQZuzGsesavLF0MqIDfUXFSYTfef+lJmw7WYm/PnYr1nCa++5dn4NxXh42hfJfXWhAg87MK/CvURvg6UHDw9PDZs7m5RfjXE0bZiSE2DTAliseZ57HlmNl8HCjEDTOGx03zFg9L1XybwCgoFSN45Xt2NvfLFgo6z5IiNgEXLtHaY1mdPf2wd/HE+aeXrb4vbJZj8c4DTjtNahVwo6CSjTqzGx7gqMlakyMDRIVqxAW4uflF8NiseDh6bGIDByHLccrcaWhA4/NjMPlRgPvPSrUBkQH+bDzTWwvqG414D++HGjAPTNxPFbmJNi8FwCb+Q5YBTAsfX0IHueJpFA/VLUasGxmovXetegRFajC0x+cwk+mRCE51A8Fle2wWCxYOi0Wl5r0KFcbeKItwr0wL78YOUlB+OUXVwbU2dZmIzHMX1RA44uiemw9XsUKUj06IwbFzQZWRCEhRIXPVs3C9h/qFAtSMUqbYo149V0WLJ8Zi8wIX1S2d2Hf+QZsfWo61Doz/uefxchKDEa5WofnF6bgd/uLbcaRlDCG2Hfbf6lJkfqu1HmCaWqu77LghYWJCB7nhZjAcWjgKPQxgkZr5yVjWdZAmwlHxUUAZZLwgFWIxNxjwdTYINw9KVrydczZLNTPE3dPioTe1IuLde34z3vToPIeh9RIf7vXued8PUqb9ZL7fuF1NY5WtPPGfpVajyA/LwT2p7TvKWrA6rkJ6LL02YgOMY2muT9j1EDFqFLr4e3lJpuFxUiV17TqcWt8MHafq8crd6TB0x2o6TChw9iDuo4beHBaDMqaDVB5ufFEXb56aQ4Aq7OQuYf6LguenROPQJU3Pi+qw6IJkdhz3lYwbRAQ1T5gaA0prdEMb093dPX0YuvxKvh709CZYXdhk1L1kzNarIe7cjRqDYgK9LVZYNStBugt4B32hPLOYtfB9Ov55V0ZOFLWig843eAZdZi8/GLUtOnx4u0pCPH14V278MBc3qyXlf3W9qu6WHr7kDs5EkmhfrzGpmLGn1KjUA7moFlQ3oJz1a0w9lC8RUhukRAqAzGywHIKUlIw9zIrIZjXs2X1nETc99cC/GpxOv73oFWl7dO1s/DTXeId4rccK0dXTy/unBAhqdrEqPdwpeefzomHoatPVlWLYdPBUhy81oTbMyKw93wD3nh4Mr6+3IyjpRoszAjDzxelIirI166q2/vHK7C1XyFJqhdbfbsBv957FZH+nlg2Mw4Hr7Zg/8Um2f4bcvdYOD80OhMsfX1oNXRj9Y5zdlWudp2pRUlTJ5ZNC8dXV9oVH1beOlQKfy839PQB7TcsNoc4qd4tN8wWJIb5K5L9ZQ5EjKqTkr9hnpGwN5aYohXDxvwSPDA5Eis54+uz1dmg3CmEBfg4fCARgRhScN0exRyQV96WgK6ePhuJcHv7AQN3PZBToOWuiwkhKjyVnYCj19V4+Y40zOp3NtWoDdh9oZ7nbKpQG/D4+3y5Z5W3G7p7+xAX4g+1Wg9dnzVdTahsd+/ECMm94K1D19meT8whXd1hhK6rj53jYnNFazTjq0tNuFTXjmB/leTh9OvLjbhc34nYQBVr0MxPD0VJkx6WXhq5kyPw7/fdwt47YYuAHQUVCBznicRQP1S3GrBk2sABf9PBUuy/2IBnbkvCXZlheOL9H5CVGIxWgxklTXpMjfVHVmIw2m9YW3LkToritYfgylDLHcLF9qytx6uw53w928eyq6cXbf299HYUVKC7D3jveBUenhaDggoNclLC+tUWY/B6bqbsuYX5bsI1T+wauT9769B1BHhRyE4MxS1xQTbfg2np8UR2PJ6blwx1p4k1Sk09Fqzcfka2L+ZQIWaki33XjfklyIj0w5nqDrQbTHgmJwGHy9qRf7kJS26Nht5kwYPTonBrvG29oqnbgs/P1dv0TdSbafb8t/tcHRICvODv44XM/vvHrA/vr5yONTuKeGu6t5ebjYR/dYse4329EOTrLevIVqISzbCzsAq12i7kX27CUzkJWDY9FsVNOry+5zJmJYbgeJkGf1o6Cb/ecwUebhTunxqNfRcasG5eEhZPjOCp4f7lYCmadV1o6uxiHSgZkb74aFX2oJ1EHIhqn6vh5vm+deg6/phfggVvHMWlei3+ebkRd6RH2dRcCJFT9ePWCT00LYa3GBm6evBFUT2+vKhGk9bIKy7dc64G287U8YpCt52sQkF5C/KWTpLcNKMCffHLxalIDg/AifJW7DxdyxZhrpufwqYceLn14da4IHx1qcXm2rt6+OlgSnonBfi4wc3dDc9/fB5bjleyP+eG0pdnxcHfx9Pm5/a61IvBqBQdLmnGO0fKsXhCNPYWNbAdyKVSFRjE6rrq2w2SqYtyYhgbcjPx6p2ZvJxo3Q0zDCard3bfxYGaqqpWg2hIXms0w2zpw87TdfjHDzWS9QRMOoOxuxcZkf7Yuz5HVJCCWeRN3Rbetb+6OAO71uZgxcw4fPPKPKRF+LEqU8euaxCooGbmy/N12MpRSAIgWrvGFF9Tbu7425FK7L/YhBa9GV9frOcpI3KNKKlUEmHx9aaDpbjvrwU4cLkZNW0Gu/UXAPDYzHi8fk8m3ChvyTktvF81rQacrdYiJTwA319TI3icB15YkIKgcQObqJgyVHiAColh/qI1W2Iwh7PoIF+bdCQxdU/uM5ITjuGi0ZlwqKQZ2htm9n6tmpOAzy824G/96VP21M4IwweTau3n7Y4JEX6iKZhKxGOY+pU95+vtKtBy10UmHem9p2exRtTmI9dxurbdJt0mJWLg71bNScCuonrcv7kQ+y81YWN+Ce5//zT2XGhglTi5ynZ+Pp4yYhY0VJ5uePH2FPZAFxHky85xKaEjRgDjqduSJFOLAWBylDVlb3vBQH3h1NjxePGOVHh6UEiPDOAdmLlGVH27AZUaI641G7H2wyIUN/NTsl5dnIGVOUl452gFztV14t7JUbjaqMWSW6Px4h2puNSgR2pYAB6fHouPV83CnqIGbDlewZubfj5udpUIxfaslxelsil1uhtmtBt62H185ZwU5KaHY+28JBRUanBbahgKKzX47U9uweu5mahvN/DOLc/NS4SXh/XsqTWaUaUxiK55UkqHOwqroTWa0aIzo1h9A5u+L0VRTRvvtRVqAyvA88qidKi8PNhI2wObC/DVxSasmZfM1uruPlfv8jQvqT1euJ+IPQ9TtwWv507AbSkhuDXWH/Gh/rjR04cjJWo8ODUaXu6An8oDlm4LShu0Np+l8vKAr48H++xfvzsN20/V8tLZmzr08Fd547vSJlZA5NMzdfBwo3Ctnl+DmBDhj8ggX54RVaU2INDXCxRsa8VaOPVxjgqB3ZEZDje6F+88MR3tehO6ui2o77iBp3IScLzMOs+3F1hTEXtpGgEqd3z10hy0GLrx2y+v4IeqVva91i9MwdFSDa9e+YnsRIQHqBQJkg0WEpFygj9/W8J61VbPScRPd13gpRHcMzESySG+OFWjlezDwW3aKucNFmvCq9GZ8P7JarZnwOq51gFT02pAu9GMdR+fZy3yFbMS8LcjFTZefzEY76Cll8ayrBh8c7UZz+Qk4dm5Saz36o2HJ0Ot68J1tQ4PzYjFlbpOLEwPw/ZTtQ73ufryfD0Cx3nil59flvRgMQ3thF6OunYD4iR6d3FRa40wdtNIDvfj3XOmVih+vAeaDL1Q68yIDVLZhI+lDDUmGiL0anKjcttOVuHdYxW86xZL9WQaFutvmNmeLytvi8eyGXEID1DxIi9M2iGXLcfK0W7swb4LDfhNbgYyI62pbmLRIaFHTMwzzfQGEfY0ycsvRpPWiOhAX/zzchMemBqN3efq8cLtqVgyJUo2IlfebECTzoivLjXj2HUNFqSHYf38ZCSFS3tN69v0CPFX4bNz9fj6Yj1mJISIjrHNR66jtw9wd4ONYhETfaxQGxDi52lzjd3dFp6XWgyt0YyOGz3YeaoK7h4eNnNa6n7tKKiAytMDIQHe+PUXVzA11h/r5qcgLlDFytPK4UgDYCV/IxU1lWrUzeXzs3UI8/fCuepWLL4lGq3GLnxQUAu1zoTMyPEoUXdi3wtzBxOZIhEpuG6P+uBEBTpMPRjv7YYOkzXqzI0CyEWXAKC+xYCl/anH/71kIv56uByLM8Pw+KwEhPp6So5fsShxebMBB6424utLjZiXFo6vLzVi2Qx+ZKC8WQ9fHzc2QvqLu9Js1uKPTtVArbM6bv57ySQU1XbgX+UarFuQgvumxAxcu0TWgBC5ucL0wJGbf8zfc3siAvZ7SwLAtXotnrGTPcDds3eeqsHWE1V45c40LJ0Ww5tnTEr2Hx+cjJSQcdhVVI+rjTrRRrliMHsWtz9hr8WC8lYTLyLH3U+YNZuJtAn3wRq1Hv8oqsfhEjVWzo5HrdYsGd3jInx2374yDz/ddQFebjRSI8fz3kMsotiktbbSeHrbaayfn4zffm3tXzk7OQwFFS349d0ZiAz0RVKYdC/AhvYu0XKMao0BEeN9bO69kggM80z1XRbMTArClidn4LNz9dh8uBy/WJyOhg4TbksOwq4ztZiVGIxarbXH6BMz4xE+jsK31zvQ0h+NFY6BSrUBUUE+6DCaccNM89LZ96/Oxvaiepv7tOlgKTzdrT3QMiN9sW5+ClIj/G3OO28dKoWnG4VOUy/2nm/A6rmJ0N6w4HBpM9bOTcEjWXG81yvpHcZFLO2yw2g1eD84WYX/WjIRmw+XISclFIWVrXhz+XT84esrNmOB+9kvLxq4P45EyBRAIlKu4vOzdTyvGg0gKzGItYIfz05EiJ8PXt97FR7ow/anZuDZWbGsOg9gHcSv7b6gyBssVpwYFqBCQpAPNi6dhIQgH3bwJ/TnczMehjVzk3G8rJW9tgXpYbLfjfEOenpQGOftjn0vzMGzc60NAxnlo+bOGwjz84L/OC/sO9+ETd+Xo1htkCx4l0JrNONGdy9Km7U8j4jw+/r7eIr2aXj4Hdt+PkJ2na7CtsJaVtCBW2g7IyEEG3IzsTAzGi8sTMXRUg12/VCHh6ZZi5V/uThDNtrFRKKEXk3GeNheUGVTRCklH7t6XiqenBGLVXNS2Pf7sLAWnUYL+1kDz8g2VeCeWyLx/IJkfPXSHDwwLQ5pUf7YUVCJo9c12FFQyXs9dxNo1hpZDyRjRJm6LfjmSrNNT5Mfqlpxpb4DiydF4YuiBtS2m/BdcTM+fz6bVStaPjMOU2L98T9LJsHH0533uamRfqhtMyI8wBvpEX6ICPBGUv+zFm7wTME0U3y9IDUU//foraJjrFZjgM7Ui49P1UJn6kUNpz9HXn4x/nb4OqsgueNUtY0HluulFt5XwFrD97cjFfiPfZfg4eGBXosF256awWsYLHa/vrzYgEZdN/52tAJUH9ho7wufXMB2jlCLHPakjMWumavuJfydVNTUnhEFAI9kxaGp4wbMfW5Y/dE5lKoNmJ0chNszIlBY2YY7MiJdkd5HcBH3To6EwWRB641efF/cjD89OBkP3Wo1NqwSwafx/okK0b/ddboK35Y2Y8M9aTj+89nISQrE7x+YAF+VF7aeKEezQdqjL2awpEb6wd0NuD0jAifLW/DaXRk26VXjx3nwIqTublahgCdmxmDb01kID1Dh1cUT8Nxcq7JdapgfT8yCqz4mlTXAxdRtwZX6Drx6VxquNHTYeK2DfL3xbHYcr2+dkA25mfh8dTbumRjFm0PC3pJi3BIbyFMvCwvwYn8nVHEFwEby3xao1gEDQkqLbomAt7ebqCy33Nw0mnts+hNyxX7EFNAYAatHt55GQXkLrw3MxvxinG/sZBVwY4P5YhFSPZSYnozMfXnh9lQE+nrjnkmRWD0vWVS0g42+97cuWbK5EF8U1eGp2fG4qjZgYUYYGjvNmBDtj8dmxOFoeTtPHpvLlVo13jxUxtuj1Voj1FojNh0swd+P8aPvUhEYRvSBq2ao8vLAK3em4f6pUShp0iP/ajP7t1caOvHpmTq8dagMz84dqIVu0prQaeqGp4c3LvZHY/VdFlys02LBG0fZPmCPvX8Kbx0qQ3SQL0806bl5idDTlGhmw6uLM/DU7AQ8PD0WJWojTpS325x3KtQGGLosCPH1ZhUItxVU47n5SbgrMxL/+22pzRlMSe8wwOrgLG824HCJGvPSwvHGt9fx52+tWUBBvt54rl9g4+6JUViQEYF9FxqxID0CyWF+WLcwlTcWmMwL5rNXZFubgTvTKsdZiCHlABqdCVtPVOD+fhnkZTNiEeTrjVcWpePXuRNYSWrKjcJD02Pw+fkmnKltx/Yf6ll1HiaseqamE+dq2kQPPYB8ShgA1Gq78KsvrqC2v3ksw9IZCViVZe0ltCwrHpNjx8PT3Q13TgjH4zNjJN5tAEaJ5pVFGTY1PitmxeOa2ojQAB8YTFYhiha9GZuPlimWemcI9PXGjW4LdGYafb29eOeJW0Xvw9+Pltv0aVCiFtakNfKUmhipT+FBMzrIl924emkat8QEiC4SYs9DSr5dazRje0EVLzWyob0LV+o78KvF6bjS0GGTqhUX7s/2nWLU/+SiJACQf6UJbx4qw9qPz+L7YjW7EGqNZjTqzPjjgRJojN288DvDpoMl+Mv35VjwxlHkX1WzP2/WdiE+eBx7z1fPTUJzZxe2HC3H87enYsvRCmsKQIgKt2dE4OF3TrMby6uLM7AwPQLfFatFU0rGj/PE/VPD8avF6TxxCq6TQSibn5dfjEe3nsI/LzeJjjGVtzu7yO+70ACVjwf7nnuKGrAsK57dSE6WaeDhDiyaEA4Pvp3HazS5vcAqx/rt1SY0aruw/2IjTlZ04EpDB57ITsRETp6+yssD90yK5I1RAKD7aOwtakD0eG+4u1OYFhcimyokhdgYEJOf56aNCFWSdhRW4+43jyP/ShOenh2vqBmw2Oc8NjsJz89Pxj/WWOXqV8xK4En8EuW+0UNUoC/umxKJvUUNCB7nidM17XjsPWtK84HLTchKDMbW41U2xlSVxoCEED+khPojZJw3dha14IkPziIpyA/NWiMiA32x9sMihxu4vnh7OpbPiMVHq7KxbCbfi82oln1wohLnqtuwZm4ijpa24OVFqQgY541nOf2ZUiOtzhWujDI31ZmB25BXrDG2yssDd02KxqbvynDXxGjR9gr3v3MKnwvaFXD3gZ2FVfi4qJ6nvCeVMigGo5hn6YVNSjT3QKqkHyTzM8YYFcpyS8Hcm78fLef1JxQ6ZNWdN/C3x29Fs9aIDqOZl+L18alq9lk8vyAFX/S3BnmoXwG3vt0o6ywFBqTra9tNyL/ShM/XZbPXvSI7AdPig7F0egymxPrjt/dnsnsl857e3m5s3z5zL42kUD98dqYeX11sQnqEHyZGBCAmeJykQVffboCH24C63bmaNmw5VoZthbUoUYs3TBZ7Lnn5xVj30TnkiZQILJ0Ww77HW99fZxvrTooZj+Uz41DVZkKHvhvhAV548NZovLIoDe8er0QfbcbU/j5jM5OC2PcYr+L3AWP2FKahr8bQgwNXaiWbNAf5esum+Pp5ArdEj8cHBVWcM28cKMAmRZebWq/y8pBVj2TKK8o0WqyVSbtkxrQwHXRWUqiobD/3b5j/Xjc/WZEzYbCQ1D4H2ZhfgsOlzVgzJwXLZsZJpkiYui1o1ZvQR1O8FL7da7Ox+3yjrNoVE478zU8yMTtuPLr6wFPhslckLkwf6zCasa2gileo62yY89MztVB5AH2UG67U63Cxvh3Pz0/B9IRgmLot7HUw90UuVM5cG/fvuJQ3G/DEB6eg77IgKzEQv71vIlIj/UULVsXYdboKVf2qQ0rSo0zdFph7ekWVhTYfLsf/PDgJqWF+SA63fheNzoSff3oeD9wag/0XG/CXR6exxgxbMNyfGgmIFzeLXYOY+p/WaEanqYcdB9YoURv+cKDYppCWSY9gVJ6Ez7xJa8Svvrgimfqx6WAJ6tpv4J5JkbinX3EoL78YXm596O5zw78qNHjtzgy89sVl3t8bunrwi92XRN+XeZbCVBbuPVk2PY5XeL5zVTae4KiL7V6TDQ9PWzWgjfklOFvTihcWpuL2CZHsz/Pyi9GmNyHEX4W95xvwp6WTsIGjlLV/fQ5gobH9rFXtavnMWCSF+SHvQAmbGvvA1Bh0GHt4ikVSzw0YWMi/vNgAww0zm9Lyy8WpqGg1OZyqJ4SrssUt1hYT7WB+9/QHp3DHhAhoTRbsPd9gV+1SozPhw1O1vM+REh1QOhcVQFL74HpBpLz8YuQkB7Pp07mTwjAjIQRbj1dJpn3tOl2FyTFBcAfwdH/62fr5CbhncjTWctQnpVRiuZ8tXO/q2w2IDfZj08S44kEJISo8PTsRW09U4oXbUzEnKVR0zQAGUsvEUp252EvzE0srlvobbprQkilR+KG6Hf++b0ABlVHecyQlVyzd1sfTXfTQ54jKnJJ0XeH3/GJdNq9nn7rDCL25D6mR4sI3zFno9owI6G6YsSI7ERNjA9l08XXzknDXLeFW0ZAOI4w9fbKNaJXct435xfiiaGAN4wpaMde48rZ4RPh5o6z1Bm/v/6SwEjVas815IC+/GAcuN+GNhybicLlV3e7Pj0xGdZsJfz1cjuRQFbKSQtDSn1YqTFtjngtzLnt4Wgy+6HcwcUsEGtoN+L60lZf6xn2m3LKAry/WoUHbxQqKvP/0RFD0OMQEW1PdmVTOMzXt2Hu+AU9kx2Hl7ER2Tpktveyz/cWdScidGCeZecFV9BSWA3x5vhbXW27g++JmPDc3BQ9nxfVnBV3HV5ca8eKCJPT00YgKHAcKfcidHCt7zhGWtOxfm42Pihqw+5ytup6c6BdgK+BiO1ZK8M/LjXg6JxFr5iVLvs4BiGof4LpNipHAZCbg0zkJWCPRh0DdbsC203XYd74BD06LwbOzYhEZIi6tDQwcikL9PLE8KxaNum7R3GKpRUc4iJl87V/vvao4Z1qOnadrUKbWY0acH3SmPtR22uY+M/flV3en2chLK2VHYTWaOwygKXdFMupcmAm49Vg5EkN8ETHeG1PjghV/NqMC9GhWHF5YmIIFbxxFZqQvJkQH2nwXOfUr7jXa29C1RjO+vNSE6816fHdNzdtYD1xqRK22y+az8680Qd/Vgz9/e92m7iUvvxjXmnQoaZI2ltQSGwNzD4TG3Mb8Ylyu78BLd6TidLXWRhULGFDPOXZdg3+7LxOz4sbDw8MdYf3zRSjTLVxUt5+tt5EKVnIokVq8mcWW2XCZ93v59mTUac24+5ZwtqZwfnooqluNyEoMRkF5K55fkIJn5ySxXjJ7KoxCuBL3EQHe2LcmG8Y+ZSIsYsgpQ8rlpn9YWAXtjR7Z+j9mQ9+YXwJdVw87BhNCVFiZk4R3j1Vgw70TcL0/pYZ7n+XmogMQQwpDoyzbpNFjx9l6/KtCg9tSwnClvgPZKWH4+FQN70DHPVCpW/QoaTPiX9Ud7MHzlUVpePNQGbuX/WxRGlp0XaJS+0LHyaers7Grv2bmjgkR/Y4L69oiXEO5h0ux+b+jsBofnKjE7++fiOQwX9HPZ/YARpZdzJkm1yZAbq1aNCEEU+KC4O/lDrWhx+a96zUG0G60XUVRBsYZ8UR2PALHebmypkMS5v4oWV+FyoqMIiBgrVX+9ReXMDE2iLc/cWXCxZw/QvLyi1HTqseaecmYkRgqec1SKo1cZ1JGpC+WTotFhJ8XUsL9MTkuCHn5xfhXuQa/uScDIf7j2OvnOqWtMvIzYbQA35eoQYNm1VZfvj0ZC1LC0OsGJITaPtcKtQEpEX7Wz+mfZ3vPN7Bjmtmfls+0beUhpElrRHNnF9Z9fB5+3u545rZEzEsJRVKEuAOgTq3HpxcaUNyoRVrkeOwpasDz85PQYuiWfba7ztSiXG3AwWvNWL8gBY2dXfj0TB1WzLaqHzLv39CmxzgfL3aNr1Qb8OHpavT09uGJWdH4vqQNelMv9l9sxLaVM7CKo4TLHSvcZy28LuEe4kyLG67h5Qp1ZxGIIQW4dpNq0hqxfOtpZCUG42RZK56bnyRpTH19sQ7xwX6obTfgJ1PjbH4vjGrtKKzGeB8PjPNyF/V4MQi9TtzNKyFEhRWzEvD+ySr85ieZKFfrZQ/OSuAWTGYlBuJPD03iGQd71+eAooAlmwsREeCDPz40UZG8tBCt0YxHtxZiXlo4LBYLls2IwyQR2VMxmAn4+wduwW/3X7M7kRgPKcOWY2XosvRB3WlGxHhvvLIoAzsKq5Ee5otXPrWVH6/VGODj7a5okkptWpsOluJCnRalzVbp3KXTY9gIyOo5iZKeT2AggiXm0alv0+PQ9TbJA3ZThxGBvt42i7rYgd3S28cuTE/OjsfBq2p2HLz92DTeItiiM8HfxxPbCiqh6y9SZRZEoSSs2D0RyuaLCa7wvodCGe9NB0txqkKDFxamIjHUD8u3nsLMxPGIDfZj+1WF+vvg64v1+MWdGchJla8plIIbEXZGMEIOqQiQMCLGoNGZsPloBbw9KABu/VLF/M2J8bS/8chk/PLzyzyp2efmJWPL8Uqe6IC9++wkxJDC0LboKG/WsxHehBAV/rEmG9FBvuyBav/FRpsouNnQzcqPA8DBq83w83IDBRrHyuVbAXDH/pPZcVj6zileLzquM4A5eIsJ5HDXDFO3BXe/eRwPT4/CjR6IOumYPeDf7p2APx4oQaifJ9bOS8bCjHAE+XqjVm3AJ0X2ey41tOnh6+OFd49X4sDlJtw7OQp7zzfg3RXTsO7j87D00li/IB4L06NlJdWV0GE0w8fT3SayrOvsgr6X38qEu76Yui2sMSuMWEmtCcJDqrNtJBj+Va7Bzzh7I9dJqKQtSJPWiHUfnUNOv/Hx8HSrlLoYco5Lxpn0vw9P5glY7Vw1G098cIrdW4XjXPjdmEO4n7c7fnpHKqbGBiI5XLodhfDnzLmMGdNKxU+4cPsoCu+58DnvPluLjwqrsW5BCn7/lbWv18rZcYgPGSfZI9DUbcH//LMYJ8o0yEoMhsrTHd9dU0tmsQjZdLAEF+q0+NmiVOw534QTZRqkhvkhd2I4yttMdrOA5KJJzhhBwkil9WfKWjw4ADGkANdvUu+fqGBTJJZMjcDLd0xASoSth0tucZX6nanbgs/OWLuYRwWOs3aknyPffZt5v73nG7B2XpJN+gaT5uRsrmhViwHvHK/AsesaPJkdCy8Pd1j6aBvFtLz8YuRfacIvF6eJNkGUg/E2/jo3A7/lNPtT0peJOwGnxPpjTkoY9py3PThy7xX33jdpjfi+WCN6WDR1W/DWoTKbaImYapCcGpZw02I2Gn2XBY/MiMHBa2r8/K503H1LBPt9mX4LYveR+Ty5qIAzjQe5XlLGO8UsTKvnJMLXyw2xwb6obzeyjQ+5h6D6dgP2nG9UpIJoL0QvRCzkb89gEdvQGVWu1+9Ow+ToYF7D539ebsTKnESs7k8J4N5DufspNibsGYKOInzW9pSJ/n6kDDe6e9l0zdzJAw0iuZ5c65wJx9WmTjR3mpARGWCViJ8Wh3ePVeD1XGtESnif7TUAVggxpDA0hhT3QC0W/fmffxbbRMG3FVRLRhGYwzs3mix07jDzgzGCmPXzSKma7UUn7CX0YWE1PN0oJISMw6JboiS/z57z9fDjOBmnxPrjL49MQ2qkP7sHWHppPDk7Dje6+2ya406PD8R7xytwz8QofHOtCW88PFX02ncUVCIlwg+/+PQSa3zueGYmm76X39/DaU9RA168IxXzU0NleygpIf9KExKD3eDj7o3z9Z0obbkhqlS3fGYsMqICcLm+E/n9Rh5zHStzEiWVRAfjqRc6uLjzXm79VZL+e+ByA/LyS5Ea5oespCA8Mj0WvX19iAr0tVnv5Zrimrot0N0w472TNfjqUiPunxKNtXMSsP1ULQxmCw5eVYueKYROaeE8kTKG5KJ1XJxxqAn7ngG2a319uwGrdpzFy3ekYuvxStw3OQrjvD0xIy5QUhWS4ZNTNdCbe/DByWpYemmsmZeA6MBx+GN/artc5pK63QB9N43USH9sPnIdOlMvfL3d8fGpWsxJCcKqOYkI8fdBtMxZSA5H+twdKVWLNoYGlDdQVggxpICh2aTeP1GBy/UdiAr0FTWI5BrUyXnSmUXqgxPlmBHrDx9Pb0yIDVR0Tczm5agUJWCbIyuE8US8fEcq/vvra5JeJGZhMnVbUN9mYjdTZlIyoXAu3KaxS6ZF23TTVoJwAkpNJKmFccuxMtHmqQxMTr7wue5ek40jZS0OGb0D97QUpyo1mJ0cimuNOsxNC8Wzc/g5vR1GMzpvdPM2/LcOlSn2fjIGl5j8uhQdRjNbH8Ys3C06E7zc3fDO8UrsLWrAQ9Nj8Pz8ZNGDF7PAikVBnEUu5G+vLkBsQxfWWGh0Jjz8biEbaX5xYQrgRmHz4XK8cmcaLH20pNFytV4r2RB5qJCrj1L6GuE60aIzYXtBjWS6Ffc+O+uBF4EYUnDNHsV15uw8XYNrjTrU9hvRK7ITbNZEJiLFjYLbq9vR6Ez4oKBaNO1OOD8OXm3GudoOXK3vwLqFKZiXFsFzBuworIa68wZ8vTyg6+JHsBmE69ZXFxtwpVGHwv40qj0cb/TG/BLozT04eFXNNsd9bXEG2vQ9eOKDgSi0mHwytwbq6HUNPj9bi4mxQaL7QWWLHo+9d5o3r4TONkfJv1yPC/V63JYcjAAfDzbtmFlPVmw7zaYhP5kdj3/fdxURAT5Q67rY1zGS4VKp/HKHVHv7P4PYvJeLbCk50DL7RVFNG2YkhuBf5dZeVXuK7Nd1CnnrUCladGaEB1izSgDrWr/vonx9utw1y5VTKHnmSiJ/ckit43n5xbjS0IF5aWFov2HB3qIG/ObedBQ3G2WN2y8vNOClhalwc6Pg4U6hpFnPi7xKnRvFykcWvHEUHm4Uls+MRVdPH9qM3YOux7c3ZnYUViM7LhC/2nvJpjE0956pvDygNZrR09snW3OlAGJIAa7ZpMS80UKvhLAIV2qiSR3amYH6wcpb0aQ141ydzqk6I+71ykVKmNcoyWW2fl/r4fPA5QZJT4AQZpP600PWAkmpw9eOwmq4Afjr4XJ2E/zpwhRE9jccVeL5VuqFkHou6g4jemFfFpr5+0ezYvFkViy2na5jjYtVs+PZXivC6xbbrKpbDXh62w/sAf7nd6bizswI0YnPvUdK0qzy8ouh4/SoUjqOpBZuoRG5a1U2lnNEIXievjY9PDw9BuUVYjySXG9qRqQv3n86S1EvMS4dRjM6jD1IDveTjOR8cKKSTWebnx7K1plx/1t4OGnSGlHaZEBhdTtbQ8IdA4DyQ4qjyDlMmM+Ueg1zb7nrmtZoxgObCxCg8oS+qwf7XpgjGu08X9PGO+gJU48dhBhSGPwexT3kvLIoDT/9x3kE+3rhVGUbHrw1Gusl6jMa2vUY5z1QByFXt8M4M15amIz56WE8545wvQCAC7UdomnR3L9ZMSsOlBuFz8/VY7zKC7qubuxel8PWIgsPboy8cUO7iSdIw+xBLToTPjpVa3NozssvRkygymbtDPL1trn2z87UolFnxtWGDqxbYDUAhYjNK0ci0I3tBozz9kSgrzeqNAbcMFvw7I6zSA5V4dGsWJv0Lu6ew0SkvumPjHEPv0xESiyVX2s0o7u3z2ZNVrr/C2tb5ea9qdvC9kuSO1Sbui1470QlPj5Vi5fvSMXbh8tFRRv8fTwHLbghjOhXthhYASl7cKNy3DOVMFonhVg2BXftFZYaAPzzg9h4q1Eb4OlBw9xH8fbl/etzRHskfnu1Cf/RH81l+mkyc8LP2x1r5yVjfmowYkSMPqEDev+6bJhpCkfKrCIaf1o6CdsLamTr8a3RLCh26IrdK2bdeGJmNEy9FAorNFg3n99PbkdhNXaeqsaDt8agU6HQkh1IHylXINUpnNudXUqK9R9rsm1Ssj44WYPvrjbjxYUpeHa2tRkZI9vcojdD3dmD2EB+DwahbLY9GI+FVFd65jt9e7UJn56ps/YpqNfKShmnRFhTG44Uq206y4vJhHP1/KUkOxlW5iTikRmxbD8rfx9PRAb5spKZSmR3lR7aw/w88frdGQjz8+T9PCLIV1Htx4bcTOxak41Qfx8UtxjY57TvfANMlj4AsLnuTQdLbaRuAQC9wNM5CThZ1goPNwrVbSYb+VRg4F5uL6jiScpKXS8jx/vIjDiUNXfi9/dnokzdqWgcScnucrvX5z04EfuvNuKBfnlUbn8iAIgOsW3yx/0u9mCkkTfmlyAsQIWHp8ciI9K3X3rdfi8xIVuOV+Kx907h/RMVkj0mVs9LZmVT754Yyd4D7n8L5VSjAn1R2qJDRvg4vPPErcgIH8czojYdLMVLn5zDv8o1Dl2vGEJZ8pU5idj/Qg7mpfCLtLljTay/B/fecr9LoK83ltwaA43ejAemxogaUeXNBvztcBlvDA7CiCK4AEZCmllb1Z1dyEkOxqnKNmQlBmPXmXrsOlNn83d5+cV46J3T2HJ8oOccIzn83Lxk3jxp0hpZ2evNxyoxzpsvNyycHyovD+jNvbxxwt0fmb85VKqGtweF3MlRUOu6kDspCuEBKpQ3D+yH+VeasJXTy0fl5YHUSH88PD2WtwcB6O87lWEj9b8hNxNzk0NsWimIXfvKOcl4fn4y/vTQZFEjCuDLlDNtHJQaUW8dKsW2f9Vi8ZsnsPlIGZq1RtR2WGXHq9pMMHX34tkZsbxeckxvuVcXT8BPJkfjZ4vSsOOZmdiQm8mb3yuyE/D7BybazHlmTfhI0Mvucl0r21bk4LUmNGqle0Fy+zHKzfsdhdV47qNzsr18GHl8lZcHogJVWJkTD6O5Bw9Nj0FBpYb9nOVZcci/Kt5WQwqxPbGyxcBbz/Lyi9k+k/aw9oE8jQ9OlNucqRgjqrbVILmvMevt24euQ2s0Q2s04/0T1vGcf6VJVKZfeH7gjrcmtQF5+cX46WdF+PCHevzqc35vUmGPRFO3he3DyciZv7gwDXuKGrC9oAqr5ybgromR+OelBlR32LZMAfgtX15YkITtP9TjkS2n0KrvwtHXFuKOCZG8vqrcfbKx1YCDVxqw7XSdaD9NLkwvLu5exz1/q7w8sHxmHP5xthFzk4Kx6eFpPCOKOSdlRo6HpY9G/hWrNP6BK42i7WAGC4lIKURJCg03TUiJ99leqPi5eYmIC1LhXJ3OoTojLnLpg0Klm3snR6NJ24XDJS2S3qOaVgO8PCj2PafE+uPNR6chOdxftlaD232dkey0930Yj5IjHjAlqLVG6LtoSWldR2DuYUygN2Ylh/Kek/C6P1mdjcfft43cMB7XdfMTQYPCje5etraIG3lhvDHMvXw9dwJmJwZJGlE7Cqvx9YV6zEgMQYA3oDPDqTQsqZqg5hY9ao1mvPTJBTZ6uG5uEuLtPBut0Ywvihqw5XilrJdSKp+/rt2AhzleMSX1cwBfRS8hRIUV2Qn44GSVZAqDVF2U1P3Yc74e6KMRG6TCrOQBo6am1YDf7LVVtnIGMa/xp6erUdlu4r23WE2Y0dyD2GA/tlGkvVoJpgBerMcOkwJSpu7EiwtTMV1CacsBSEQKrolI7T3fgBcWJOGO9Ais/vgs7p8Sjd2cSM83r8znRVLF9gduKp3QC26vfmFAmtyavq3RmeDn44nGDpOkVDlz+Fy+5V+YkxKGgkoNPn3uNtYRKFX3y3wPZ2ohxCIWztSTAo6nuNa3G/DOsSq2Nu3J2fG4VNeB5TNiMS3eFxTthUw7AktykZQqjQFJYQO/q2k1YLzKU1T4wdRtQV2rAZ8VNeJEeQsWZkRgL0dVUey7ljV3Yu2CFMxOFhfk4QpT3T81SjQyJlVjvGRzIatWd88kazsLfx9Pu+cvewg/T6iqKifnz+zl+i4L/m/5FPznl9dYBcdf3zMJqZF+rBKuWEqbsHbPx9MNnh7ueO94FSy9NN5+bAp+sXtAIOPzddnooyk8te0Hdt4y9XkAsPtMNSZHBeLpHWd5kbuZieOxcelUG2OeOZsx6emfnK7Gz+9Mxz2Totn5temRyXj3WIWifaquTW/T2od7LuswmkEB7Bk4L78YEyL9kR7mZ7d2Ky+/GOeq25CVGIoviqzKwYx6Mjd9lZs1wV3TuN/5k9PVeHhaLNpu9Ax67wWJSA0eJU3xmEkoGXUQsCE3E3vX59g8WKZp7Op5qVg8KQarZvG9UgC/gagc3G7xwsgF9zs9kZ2I5+Yl43BJi6T3KC+/GE9t+wH7LzayHsbbUsKQHO5vt4s0t/s60/TX3oBmrlWpB0wJOwoqsK2wFh+dqpRsVOcIzD1s7DRjcux4XrNf4XWnRPhj+cw4XuSG63HdeqIaC9PCsXZeMh6eHouVs+NwV2YkVrz/Azbml7DeGOZeLp0Wy3ue3Ma2zPNgGj8vviWW3wleoyyyKXewMPRR2HK0HA9Nj8H9UyKwYma8XSNqyzGrwiCTOscdK9zrb9Ia2QgU19tcpTEgLtjP5j7KXT8Dt4ln7qQotnu6lCEnbO4n9t+Mx8zUbUHegRL8Yvcl/HTXBfZz8/KLsfajs1h/O78bu1yjTimYZt4t+oFGiBqdCfEhvjaNpwN9vfFolvXevXx7CrYcr8QLO4vw1qFSvHmoHCfLNDbRZC61rQbsv9Rk4wGuUBt4jTM33D3RFUYUwUVsyM3E/vU50HZZ8MnZOsxMDMF3xc24b0o01LouPDA1BiovD3auBY7zZtfyB6fFsI4drsdYGM0UNvEUrvWM8fPzz4rYxqRvHyqX7ffERK9uSw3DF+cbsGRqNHSGLlSoDWwUZs28FMk92JnUYaHiLXMdQhrabaMMGp2Jt25Jra1izbMBq2c/PMCLjeRHjffBgowIfFpUj5qOXrtGlFwkJS+/GMu38puaL9tyCjtOVYuumyovD6h1Zvir3PFkdgK7log1vGe+66HSNryy66LkPtKs7cKLd6QiLWIc7p8SabPOcvc9bqZNVKAvHp4eC2N3L5o6zQgPUCE8QKXo/CVHZQv/8yrUertNnbnPnNnLZyYF4YMTlXhoegwWTQhBWsR4PPHBKfxQ1SratJeB2cvunRKJNmMP/H288FFhDeamheLeKZF491gFL2obF+KPhFA/3MuJ0DLnnga1AeWtJvzjbK1N5C4rIZQ1ohiHGfds9vahMjw6Ixb7XpjL9ohkzqFz0yMU71PM9Umdy/ZfasLiN09gR2E1O2a2Hq9AvZbfnFl47jpaosbhEjWeui2RjXwfvNaErccreVGuQF9vrJ6XDI3ejFVzk0XHw8qcROx7YS6eyknkfSclmTCOQiJSDmLPY6VE7nOwOFPgzY3ucOt13jp0HWeq2jEzKRjPzUvGb/dfxbHrGixID8PvH5jIflfGIxMR4AOzxYIlt0bDz8sTlr4+rO6XfVcqbiGWB6yEWo0eAeO8nKozaWg3gAZwrUnPqj2tnB2HJ7OTXCIIIDcuhIIGwhxtqcjkt1ca8B9fFtsUE4t548TGBDdypb9hRqOuW3FkU6Mz4cBVtd3c9rz8YgSrKLSbaBwuUWPtvGQsy4oXfa3WaMaHp2rw+bl6tlfTc/OSsXpeMnv9q+cmwMfDDenhfvDx8sC0hBBe3zbudxR6vYTjakdhNb670oif3ZmOrKSBw769Jp5CxJ5tY7sB/zjbwIsOCcf/kZJmvN7fADh3UhgSQvwHLYUuNlaYiBT32W45Voagcd7w83FDuJ8PXvjkAp7MjoOuqxclTVpMjA1CYbkG6xbw88qZz7jaqJPMc3e1pHs/oy4iRVHUGwDuB9ANoALAszRNa0Vedw+AtwC4A3ifpum8/p/vBJAFoAfADwDW0TTdI/eZrtijaloN2HqiipUzXjo9Gt9da2E96GkR49kI+D0TI3G6RguDqQd+Kk9MiQp0KFIvloXA1AxLNSaVgvHah/p54tnbElDeahL1IitV0BQirBNm/lZuPxWLMvzlYClUnsAt0YEwW2jMSwsVFZmQqznaUViN0kYtns5JQLCfD3tflPRk40ZSZiaOx58enMqmlgnrV22amq/Nhv84ay0ctznyA5sL8ML8BEyLC8ZXV1qw+5y0IIO9+c9dy7U3eiXFgaTex9Rtgb6rR6SGqwTna7WYFh+IVxeL17k40huMgVvfxET5pLJrajR6HC1vw9cX6/H7n0xioyvr5yfAw8PDbouZFp0Ju8/VgwKNzq5efHOlCWvmJqFF343ztW1YvyAVc9LCAdieJZk1mFuTzz3DcKOyQlGmrccrZLMvlNwnBuE8EqruimVvMfPjuXmJ+MktEdB1w2Zd0RrNWPvhGTw7Nxl17UZob1iw53wDfrk4A//7balouxWl898Z4TURiNgEMLQ9Orgokfu0h1RXZ2GjQ0dS0oQbBrOAMqHjfS/MwYErzfjmShPumRSFFdkJNn+ff6UJL92eijcETWDb9D1IjbTtZQHwB/tgVL6UFsPa/l0J9F0WhPt7wdfL3SGDQgqt0Yw2Q4+o3L0z11zerEdMsIpVmGkz9KC23YiCijacKG/BXZmR2H2uXnQh+KGqFf++7woyI8ejRN2JLStm8ArAtTfMWLK5EDGB3nh+fgpujR3Pq98RsjG/BFcaOxU1cFa3G6Az03hx1znMSwvHVxcb8WhWLF67W3yjY5SZvrnahKdmWzuOc1Mg/75iGjQ6k00jauEB4dM12fjiQiM+PVOHFxcmo0lntilIX7vjBzZN4ZeLUzE9PpT3vJSk3247WYV3j1XwNtS8/GLouyw2ktFMmozKywManQkv7DyH6YkhvLE2GOUmpr9aRmQArqt12LU2h91QWnQm6E0WpET4o7ndgO39widr5yfhWIkaE2OD4IY+fFHUhF8tTsf/HhyYv9y+Jsz6IpeSY32dsuJqBxiNhtRiAIdpmrZQFLURAGiafl3wGncA1wHcBaAewBkAj9M0fY2iqHsB5Pe/9BMAx2mafkfuM13l7PvsTA0add0oqmnDi7enoabdhL8fKceWJ6fjuY+KEOrniXnp4f0pXANNQtXM2LGzPjJe3bU7fsCLC1LgN84LqeH+UHl5YPeZapS3mlhFPW5jUntszC9BRqQfYgOtxr+crDRTTP7q4gzcPVFaLh2w3XeYg/KHK7PwtESqUX27waaR/b4XcrDtZDXc3N2Qf7kJuZOjsLeoAf/5wC2YFOnPrrtyDlVmbbo1IRgt/UbaLxan455bIhQ7CfPyi3Gupg0zEkJs9lPhQVjsYCy8H8Iziz2DTqplBdfI+8VdaXbbXwj7CkoZMEp6MSk5WwidaMzn/c+Dk1BU24E9RQ14br50CikD1wivadMjIcQfZc2dWLcwBZNjguwe7juMZnQaeqDyouDm7oawAJWNkuV3VxoxKTbIZv5ojWa829+6Q2yOclPiGQfegctNeConAWslep0KkdqnlJ7fdp6uQUOHEfdOisKkWGt0VUmbk00HS5EW7os2vQk3LDQsvTT8vN3RcaNX0ri3p0TM7PPOpu1yIKl9w4lYkSsgniIgBrcIXEhqpJ9TKWk1rQYcLlHj4WnRmBztjwaNNf3nvsnWlI97J0UjyNcbK7ITsPWpLJ4RxVzzhtxM7HhmJpZlxbMpAv/70GRsOV7JpoKI5aky6UGM8IEz6U1iaU1KqG834EJdJ3p6afy/f9ViR2EtpsQE4JPV9lMLpdhzrhbvHK/E4+/LF0wy17zqtljcd0uE6DWrtUY0a40oqGrDks0nsTG/BIvfPIGL9e2obTcgyNcD89PCsf9iI17PzbBtqqs1Yuepatw+IQKNWiNev2cCb7GyFvFa0zsbO804X98pa0RpdCZ8UVSPs9UdogWjXN46VIq3j1WhvLUTa+cl46uLjWjRm7H7XD37XbmpLRqdCV9eaISp24LHs2LxwFTr4YebIhDh74WowHG8UHy1Rs8KXEyJ9cf/PTIZvTTFjofx47xsxpXKywM/uzMde4saMCclCOWtJt7zUpJ+u73AakRxUzWYMZx/uVlUYIO5T34+npieEIKimjb88aGJeGVRGgAMSv420Ncbd0+Mwg9V7Vh8SxTvoBMeoGIPCLQbxd6/j0/VYOGECFxt6MBdmeFYPjMO+y7Ws/f7P+7LQJ+FgrrdWrj8p/wrWDo9BgEqD/YQIebB4x5qhyJVYjRA0/RBmqaZL3cKQKzIy2YBKKdpupKm6W4AuwAs6f/7A3Q/sEakxP7epTDr7aTYQJQ3dyI7ORQfnKyG9oYZR19biKsNnXhoegyem5/CSeGqR6fehLz8Ytz/zilMjfXHJ6uz8fz8gR5qYp9xqLQFy7JicbSiHat3nMNbh8pQ327AGwfLUdmiw3/cm4lnZ8Xy0gDtsW5+Em5LCUFVq4GXAiQ0okzdFuw8VY15aeH4j31XZeexUISjrs3Apjp9eWlgLjyaFYvYkIGDfmywH26NG89bB6MCfZE7ORJ7ixowXuWFvUUN0HdZ8NmZOkSMH/jbQF9vyfRjlZcH1t+eiov9qWCWXhoVLQZF5QAMG3Iz8acHp/KEOOraDKhsMaCmVY8/PjQRNW16VKj1bKkAs9+J7cPCM4v2hmzglGcwcuGmy7m7QVQIhItQDEGqPIArcsCkvnGRS6/kwjWiuJ/n7z0ggvXRqRqsnZeENXPisePpLNG9T+XlgS3HyhAV4IWXFqaz6Y4//cdFNGltxQzq2/mlGFuOV+Llz4qw/VQte9bjGto7T1XjJ1NjcOByE8L8vXHgSiM6jGZsOliKtR+ewZKpwTblHgxMGmFCiArPL0hlU7HfP1GFyhZlZy6xfUo4jxo7pAVJwvy80UtTWLXjHHYUVKCm1aCoJOPVxRmYlxaGrKQQ1LSZsPN0HUrVRjxya7TomVqYhiyEu88P0oiShRhSQ4TQm7PpYAneOlxhV3WGOci26M3Yc75eVGGEyRl3xBBICPXD8qxY9FEUfv91CT46Ww9Tt4X3WWKd0IVKhcxkYBbemCBfGxU+Rh1GuDCG+PnILoZyyG1McjCboac7hQemRsPY3YuSZoPi1C6N4P5rjWbEB/MP+kLlQe41v7VsMjq6aDy94yxPFQuwyt9vK6zF+yeqWIUZ5nls/PY6Hrg1FvdlRuHLC42obTdhY36pzaEmKtAXc9PCUVTdhumJIdjwxRXRRUVYjydc2BmYRThA5YHIAB/Jg3RjuwF6Uy++u6ZGh6Eb8+OC2Joc5vkIjZWwABUWTYjEt9fU6Oziy+8ym/2MxFA0aW+IqsFtyM3EYzNicayiHes/OcuOpc4b3aLjKispFCtmx+HZuUk2z8ueUa41mrG9oAorsmOw9anp+M29mVB5ebAbuqcHBX+VO29x1+hM7MFC5eWB1Ag/zE0Ng6G7D+aeXocMDuG4Y5By0nDh1kXmTorCmnkpeO/pWZieGIZXF2fgr4/PsNbSrMuGWmfGJ0V16LhhYQ8DZepOVhnM3uYjpWR6E7IKA9ElLjEAuFJ49f0/Y6EoyhPAUwC+GbKrA/9gmJdfjKduS0KLzozSZj0aOrrQouvCW4cr8ENlK0JVnrw5Y6Eodh1/81A59l5owNPbTuPN767j7jePW4VUBJ9hsfQhMdSPN7csPTSWTo/BpQY9DpW2IkJCtdPUbUGFmr8GcRXlls1MxKpZsZK1tCovD7y6OANfXWy0qzQrrBOOC/Fj620ig/zYmt1Qfx8s2XwS315tYv/21cUT8PLtybx1cHqCVfVP19WN5TNjcf/UKJQ06fHZuXre50rN1ZpWA7adqMTUfiPtkRnR+PJCo8NOwtRIfyydHoOEEKtU+t4LjahrNyIh1B//tvcqEkL82X2Oe4iVMkoYd7uwxkoKKWfUhtxMfLI6Gy/enm5TTyeHVB0UU8+3ITcTn68bGA/c8ePeB4frqLmfpzf3sn//xKw4xAZ5w93DA0/vOCt6H5q0Rozz8sTmo1X4739e5e1XQqEHoRofU2c6p7//mfCsx4ztbQVVuOuWSGj0Ztw/JQYUgFMVGtwzKRL7LrZjxbbT7Hty64sBax3j07MT8e7Rct59EVOUlKrjE8KdR6/dlQpTt3g2m6nbAn9vd+wtakBMoDcadd14bfcFHC5pVvQ5Qb7eSA0PYOvNjl3XICJIhSBfb2h0JraeUarOjvu9nHG+OwNJ7RsGxFIE5FRnlHZ1FsJNBxSmLWmNZhy9ruF1rf5iXTYOXW+TzB1VolQI8PspRY5XsR3VGYUY4fsPJr3J0foWhvo2PUL8Vejq6VVshIk1f9Uazfj2SiMq27sUpQdKpWI2aY34vliDvx4uxxsPT8Y/rzTjYn0HFqZHoLZdj7XzUjA9IQSAsvzeoyXN+FV/PY5QnVGIWHheOF7sqWAx6kqZkb6YEB2IvUUNWDE7Dk/PTkSQr7dsakuLzirtK9cTrFGjRzf4BwCNzoRrTTr88vPLrKLh5sdmsN5qsXGlNZrxh39eRbC/ive87KXfqrVG/PNyE9QGcbUf4We9feg6PNwAfx8vdFt62bpBU7cFW09UoqHDpLhBoVzTYUew16C4vNmAK01a/PFACZZNj4IFbg6lvCpdHxQyIql9FEV9DyBS5Fe/oWn6y/7X/AbWWqeltGDDpCjqEQD30DS9pv/fTwHIpmn6Jc5r3gNgpGn6ZxLX8ByA5wAgPj5+Rk1NjdPfh1krfn5XOgK83fC7r0p4z+fvRyt4454Zxx+cKEeLoQf7zjfgtcXpeO9EJZZOj8Unp2vZ3nbrF6bg2TlJ7Gf8232ZQF8vrjUbbcaNXOrnoWtq/FDTbldlUsk6/dah64rmVkOrAe7uFHy8PBDo641KtQFRQT68Wqslm0+y6clK0scrW/SIClQ5NQeYJqrrF6YgPTwAW45X4atLjbh/SjRevD2F991N3Ra0Gbps6ooZdbNf3p2GQyUDSrg/uzNNVCFRuMZz1zAmBf3dJ6fheU5vuH+syZY8fDPPKyPSF9ufmYloiT6VYp8NSKdkKSkFYH6+4Z40XG+5gT1FDXhxYRIWpIcrMqJM3RY0d3YhKYxfjlCh1mOctxtaDd1YveOcbAkFtw/oy7cnI6dfeIsLk5I4M3E81sxNRrSfFyJCrO1j/iWS+sq9lj9/W4Lvipvx3NwUPJwVB8AqxqA19bDnOG7NI/cecdfm3ElheO2uTNFzkzPlEuoOI7afqhVVXIwK9MWmgyUI8HZD641ezIgPxHvHKzA9McRh1TzhuafwuhpHK/jrBnP2XDUnAQ9MjbbpleqKMhsOpEYKGD5DSqy+iSlalStE5MIcZMXeS+xnzOHrydnx6OmlRSfHjoJKNOrMNpueXO6o0iK9siY9YkNUeO6jczYGIyCuhqQEsUWVm9PP4EhTPSVISW8DwOYjZQjwAqbGh2BKXDAAeQENew2Za9v0SA0PwIU6LR66NYrdGIQLo7BpH7ew1l7eNIMw13zPumzsOtfo0GK66WApvr3ahEUTIrAwPRQ/3WXbbPNSXRv2X1Szh4MnZ8UhsX+TGUyd3CeFVajR2hqxcmN408FSNGuNeConkX1egHRh98b8EhwqacafHpyE9Zw6DSnZfY3OhI9PV6PLArYh8/Pzk1mD8qe7LqC0WQ99lwUzk4Kw5ckZktcqN+4cQamgC/cAnffgRMQG+TkkvrLtZBW2HK8YbBEvMAprpACAoqhnAKwDsIim6Rsiv88B8Duapu/u//evAYCm6T/1//u3AKbBaoT12fs8V9VImXt68cDmAsxJCcXh0hasm5+CVXOTANiOe2ZNCPXzxNp5ybgtIRAnqzvx1qHruH9qNHafrbcxFLjzTd2m5xWPy81vU7cFVxu0ovVPYoceqVph7vvZM2Ty8otxuESNOyZEoE1vQoi/SvT6uI1KHTHmnC1k54onbT5ShkatCdGBKrx4exr7mi8vNuBao87mEMqtGf3VPenY9UOdTSNjLsyBefXcRCydFsPeU6EsupwoDne/eetQKYzmXhy73oLbMyJwuESNdfNT8fAM2+xVscO6kj1Aqu0JI7Zg6aWx7ZnpeO6j8zb7jxz5V5pwoU5rY8hzDb2vL9bhcqPBrmNJ3WFEL6wKkFJjdc+5GpS23LB5hoyzgTnr7SisxpW6dqyZn4y955twuESNn96RhtlJwbz35a7Zf338VrwkskdVqg04Vq7Bu8ds12Zm7jrivODW/upMvazYRUSANz5dnY1dRfXYU9SAvz1hvR59lwXP3haH5dPjUdFhxAaFTl4uTNsE5nPbdGbResYqtR6f9n++2HhSIuCiEMk9auiSBm9ypArVpbzJry6egPo2PauEZ2+DCA9Qib6X2M+46YAtejMOXlWz4cxVcxLZQfTozHh09fTiyVmxPG+6nJGzMicRj86ItWsIxYZYhRLumRSJqPE+rMFo7+/kVHa4hafzU0Oxpz8fXa0zY8EbR1kP5GAO5lIwKW5MZJC7Mb14expvctr7/A25mXj41libA+q6BWm8hbiyRQ+KopD3TRkbrn50RiyS+4u4mc8qa+5EWqTVC7VidhwsvcCnZ+qwbn6S3UWKSetgNkpfHy9e+Js7XoS9SAB+uHy8qg3JISo8ND2G3XBSIvyhNZrxztEKTIgejzszw+GvcmeNKGEu+2NZsQ7J2T+Rk4QWnQnL++8LIF2gzCBVPC22uGp0JvzzciOyEoPx/slK3r2Sus6wABWmxwezkbJ95xuwcnY8gny9EejrjazEIMQHj0NPbx+Olmrw2bl6yQPX7nP1eGhaDPZdaJCsK7CHkvnAPNvV81JRo9bjyZmx8PHykF2ThGw6WIovLzTg+fkpgzWiRiX9any/ArBAzIjq5wyANIqikgA0AHgMwBP9f78GwN2wGmF2jShXwUiJL7k1BvsvWp/Ps/1GFGA77rlrQqnagIemx+HhEH9UtRrxXXETlmfF4bNzdbz1nLuuR4T4I6A/bVVYf7NCsNcAQKepl7dmMJGrVxdn8Naftw+VQq3rtkr4L0pn/567dzLpWUwUztzTixZdF7unMOvN/DTr/vG3x2/FS/+4wF7fI9Ni2c+/e2IUrjToWGNO6eFL6R4J8CMhu8/zHVjCNcrUbQHdR/NSJ5n7mRTmh+UzY1HcrEeEvzfunRzFznnhmsGs2R5uFNqNPbjvrwV4aWEyGvsFelbMjsPymXH47GwdUsPH49XFGey+w8BdU1bmxKOksRMTosdjxawEfHy6BvPSwrHxmxJUtRrx2t0ZNp/N3V/a9D28MSK2NwIDtbPC9Tclwvpzg9nCNqYvqmnD+oWpdo0oU7cF4zzd2Ht6pFQNDzcKn52t5xl6P5kah5nxRjwicW0MTL2xXBbBlJgQ3p7OfF9m3IUHqGDqtuBKXTuC/VX4oUqLPUUN8HCjcKVBh//+upj3vqvnpULdYWTPccJ7xDjtT1W24fXcDCydFsdey47Canxxtha/uTcDiSF+7HOXG+/M39yWak1F/NXiNN5nenu7sc9zy7Fy9nd9cENChD8SIvyxdHoH+3ru+UTK4Se8n2EBKpQ363jrBvNcemmKN56enh3P+wxXq2aLQSJSTiAVDlXqTVaSuiP2XhSkm2gy6YArsq0RKaFnjzls/ucDt2ByVIBsWpWjCA+yYrVWYtjzXIpJaF5r0qGkSc/xQM3meUcYj5QSRTYl2EtxU6Im5ChiESyNzgSzpRdL3zmF39+fid9+VYwWvRm/+0kG/n6s2iEvao3aADcPmpfWIRwvYs+mXmPAF5caoDf14qtLjfjV3ROw8ZsSzEwcj9VzkpAU5o8gX2+otUa8d7IGDVoDVs9JRlygChFBvuwz2XSwBBfrOzE1drykjK1STN0W3P3mcdFmo87ywYlKts/Vogkh+M29E0XTW4TIyca26Ez4CWfuinnOmTnv4UZh+cxYPD4rXnTsyTkfnFG32n2uDuVqg3WTV5hOOARtHkZdRIqiqHIA3gDa+n90iqbp5ymKioZV5vze/tfdC+BNWOXPt9E0/Yf+n1sA1ABgkvf30DT9X3Kf6eqsCTEHgtTayKR6cX/P/L1kU26tEd8Wa3jrv9Q8YPaJDfdOwJzEIOjNfZLpf99fbcK5Wi2uNOowKToAz85JRHiACh8WVCIyUIVmrQlPz0lmX2/qtmDPuTrUarugv2HGiuxETIwNBDCg2urh5obOG10I8VeJXh+TmuSMB1vJfsONhPzy7nS88e11Nkr91vJbRT/z0DW1ZAP7nadrcLFOi6bOLrtlA5sOlsLTnWJV9LY+NZ1tAyKURRcijAx9sTYbLTe68bv9V3FnZgQiA3zw5+/KRNeCGo0Bn59vEN1fuN+JG/EQOnMkFQLVeuy92AiTuRseHh6KnakF19U4UWG9p68tTrdRIHb02Ss59ylpGXG1Xotnd5yFn7c77poYCV8vd7uqhwzMPSpvNuD3/xQvIzF1W7B8y79w3+QotJssNun4YjDnMG4rg2XTo/CzBekw0TQ7f4Xfr6xZjzTB3Bammks1ZPZwcxO9n03tBrh7uKPTaOFFv89Vt2FGYojk/XXVWRBEtc91yBWwiTUQFaJETELqveTenynqfHlROl5dnIF/rJ3NLlpMgXBMoDcu13di2Rb7haRKEVPaYTyicthT2RErPN2Qm4nf/+QW3s8Z7xS3qZ7ShshK6O2TdiRrjWa7akLOsCE3E/9YM1BUy6g4HryqxtLpMfi8qA6/uDMFB346C53mXlH1OCny8oux7P1T2PnDQGG0sChaWMTZ0KJHXn4xztZrsfNUHfYUNeDOzHDcljgeK29LQNR4Xzy/8wK2FVQDACICfeGvckewrw8KKtsQEeTLPpO3Dl1HqL8PSpr0CPX34V2bM+pvjOed22wUkBbTUMLqeclYNz8ZEQHemJ8RqciIAqQbbANWryN3nHBrM5j/Z+Z3L02jq4cWXT+Y5ppS89feeKzS2DambNKasPd8A0L9PJER6Se5JnFxVvxlLEHTdCpN03E0Td/a/7/n+3/eyBhR/f8+QNN0Ok3TKYwR1f9zj/6fMX8va0QNBcLn8udvSyTXRn8fL5u1U6hGySUvvxi//OIKb/2vbzegSWvEpmWT0aQ1sspezD7h4UahtEmP+/9eiC/ON4hec5PWiEv1HWgzdqO0WY82Yzc83d2g0Zng7gZ4e7jB3Q28cWru6UVYgA/0N8zwH+eNZzkiAS8sTMWBy83YU9QAL08PvLIozUbEIi+/GCu3n8Hus7UOj2XhPRNbx4SRkPdPVmJFdhwrVLH/UpPN3wBAc6cRYX6eeP3uDIT5efLe75srzThaqlGksPrq4gw8NTuBPT80a028dSK+3wkmxjgvd95rY8P8ETPeB7elhuHj03XoMHXj0SzbtSAvvxjLtp6ChzvY/YURNuAKZm07WYXlW/6FD06Uo6CiDdtOVPA+X2hEVaoN0BrNUHm74dXFGXgyJ0mxmhwAnKhoR02rHpufuJWnQOzoOsZ8F6lzWZN24DqUCISFenlg6fQYGLt7ETzOA8unx9h9XwbmHqVGWsW17rolAvPTQ/HyojR2PGw9UYmVtyVhnLcnOw4/OV0nue9qjWb2HMY0/V02PQoh/io89P4pfH5+4AzB3fveOlSK/1dYjbcO8dcYYSSK+8ya2qxnjCWbC/HZuTqb752XX4wl75zCtoJq1ohizilnajpxrqZN9P668iwoB4lIOYG9AjZuFMPUbYGhq4fnZVEiJsGEU39+Vzpun8CvhbYXJZFqzhoV4M3zQknVfNhDazSjp7cPnu5u2PlDLTqMPfjqUqPdgj5hqpicl4YxyKQ8ocKfMyIUg/WUc5vNyTdq5EclByOgIYdGZ8KKD05jakwQbvT04Lf3TwTdY8H2H+px54QwvPDJBVh6aeROjsDq25KQLJPaoKQH2cb8ElS36pAQOpAnvzwrDsu3nmJ7z3DzxuXuN+OB4r5mfnooL6J49LWFMHT12DT/VdrzQSpy6YpUTxf0nWCpaTXgqW0/IEDlCX1XDz58ZiaOlrfhgxOVWHJrjLUfVv93l5rfZ6taebUlwvnLvV658Sicd7vP1UFnNKPFKC6sIcdw5J//mBjKOt79F+vxP1+XiM7VTQdL0W7sFu2NJgYjNiPWb0xq3dxRWI3OG902XnYvdzcbj/GHhVX42+EK3rzu1HVh+5k6tg7xmaxYRHHG/87CKkyNC8IqkToKuTqmJq0RK7efUdQHTwh3bUsIUWFlThLePVaBvy6fjBA/X15vRSYixayfryxKk63vqm83oEJjZFOGheIPTESqqlWP1+7KwJQ4+/2LAOv5obevD1GBvnZ7+/zlYCmadV1o05vw4u2pmJ4Yirz8YowTiZZ4urvx0sKl+v5xM3m2nazEu8cq8fy8RKj715+HpsdgdX8EUshbh0rh6Uahs6sX+Zeb8NLtKViQForthbWKmoRLraFK1jFutEwsK4m7bjtabsCk49F9vXg6JwmT44LY3zn6voeuNqFMY8S2gmp2T2HGqZ+3O5bPjGVFMqTul/D7MWNYeIYQ9ndTa414v6AGX15oxJJbo7H6tgREcgworsAIdx9amROPJZsL2ffdvz4HlLsbwgNUslkWmw6WoK7DhOXToxEZ6Ms7Xw5n1gSJSDmBPQliZtDvPleHN78vs+kJZU8SlPHeXWrQY8OeKzYeA3upZmK9mlbmJGJeWpjDEqFCthwrx87TNXjvZBXeOHgdpyrbcfBaM8L8vbH/YoOkN05MUlXY36JKY40icCWVpTYG4c+Z3OjBeMoZj0hefrFszwSxqORQGFGA1dt198RIUG7AmWotvitugcFizQnecrwCDzFS3D6eskYUYL8HGRMtzb+qQU2bnvXwMD2c2ow9CPfz5EnQ7vyhVjIixniguM9kRkIQG1F87e4MvH2oHL/YfYn1am87WYntBVWKJbWFkcs2Q5fTvcrE3ttVJIT64d7JUdDozcidFIXw8SpsPlyO8aqBGjUmois2v6s0BpypbmNldh+cFoPx47zY3wtlyOXGozDiuWxGHO68JZJXi8Hct6YWg41MNZebMRJ1M6LRmVClMbJzddmMWPbZMesZ0xstI9IXf1gyGUazdC8hRgpZ2G9MozNhT1EDLL00DGYLL2rUqu9Cs7aLXYNWZMfjo1O1oh7jp3OS8Nz8JF6UxUSDHaP7zjfAJPABr8hJQkyQSnSNW5mTyJMw5+5TUYG+bB88fZcFlxo60WE0i8pCC+cCd2175jarEfXg1EgcLmvHqg9/wI6CCpwo0+Dry43InRSFny1KY+eelNw3Q2ywH+rbjZLS2iuyE/D7ByZiRkIIfrrrIt46VCb5vLhsK6hm9zm5M4BGZ8L5fgXeQ6VtWP/JBfZ8seuHOjw0LYYXNeCuBcyewVx3oK+XzZ65+2wt/t+/qjE3LRRRQT68Z2vusT1HlNRr0aLrhr+Pl7UZ8qQIVLTewP2bCxE0zkMyG4BBazTj60vNvDU0YrwKWgVGFLe3p1RWEjdi5EiErLLF2uvyaKkGu4uasOajc7zx6cj7ao1m/L9TtdhWUM3bU5hxauzuBUBh9ZxE9n4Jz2xi348Zm8IzhDA119vTg5Xy33+xEd6cMS3s+cSVs2fWk4QQFdbOS0JEkC8rtiaXZREf4ouZceNxtLzd5nzp7enOrnf3T4mGj6e77HMYDMSQchJ7E8/UbWFTZsTS+OSMIXsLLJcmtYHV1QfkU3uY9Diu8eIIWqMZvX00fL09sO98I/IvN2NSdABmJ4dAozdj1dxk0Ya8P1S18RYAblM4ZiHPyy/Guo/P4sDlBsmmfEp5dXEGvnppjkNyl8JFyo3m96XghqWVGmtK+zPIoTWa2UVW32XBxTot3j5cwvZqSQ1VsWkqUr2HuMilGHBTFBJDAniGVlygN/IemgQfDzd2TGmNZnx5oQE3zL24f3IEVs1JlPzch6bG4JtX5uGVRensoWZhWqhN899X7kzHO0crHHr+3EPSUKRaOgLjDBDSpDXy5p7KywPLZ8ZB19XN3nO5uZ4U5ofuPiDCzxP/df8tiA/0sWngmJMcgk9OVyu6Z8J0RW5TZOa+7T5Tjf93rk5R42nC6Gfn6YG03KdzBhqutxt68MDUaHh6UAj29cATWXE4U9vO633Dhdugfe/6HF6/MXN3L56cHY+l02Nw8KoaH52qBWA1QL692gyVtwf+Va7B735yC56enSDb52XNvBSe8cM9nD+aFYuoQNs9NMjX22aNE9bsivU+W5YVz6baNXeasPV4lY2Bl5dfLDoXGMfqs3OS8OIdqXjo1ljsLWrArMRgNOq68e/7ruJyfSfr1U8O92evSWjgCVmRk4xVs2J5jg8u2htmRYd2Zm9QchivbLGuYWEBKkyLC+SlDTLray9NI0DlLusQ5jpsmD0zI9IXv7p7AtoNPXjvRCXuuiUS52ra0dVNy67bpm4LPj9fh/AAL+i7uvFUTgJig3xZ42tbgW3LAGF6d6CvN4L8vODnReG/7r8FM+KD8PejFXZTv4TlGN29fWzPxFVzEmHp5af/M0bBogkh2P7srejqkc/6Sg63bfwstg8I+6GJiUox4kZi78WM03ULUhEeoEJ0kK/oXLB3vmH6rkmdIbnGC4NUzyfmOeflF6NNb8JjM+PwYWEN8q808YxXYQ8xYOCMHRviy3MCMudLppfjnZnhSI3wG9KGvCS1zwmUpvwwRdyMApejPWHsfc6X52txrdmoqM+Nq/j0hxp8UFCFeWnh+PpSI57OScATs+Lh4+luc61M2lVyqApT44MlQ8lVGgPWfXwW89LC8d21Ztw3OQp7zjc4LalsT8VNCrFUQ7lePHLpAPb6M3BTCO3B9Epp1nVZa4v8PPFv90xA5PhxrEfIVb2HgIFUAq7MupgMLaAs1UDuNUya6/KsOKxfmAKVl4dsGo7SwlHh+FeaIjoY7PU8EcrZ3/3mcQSoPNHT24tda3NsxpJaa4S3pwfv+1a36DHe17YofDDPXywlsLLFgHZjFy8NZvfqbNDuFE/owpk+JBKQ1D4MbWqfXEr5W4dK0aIzIysxGBTA6zfITaWRW1uZcf7vP5kgmkK4+2wtW9i/ZGoEksP9WeEaR/q8VLXocalJB7qPBuVGYcnUGMnXigkhiaXTXatX42zdDfztcAVykkNQWNnGu/42fY9sShMXU7cF2woqMTspGOt32qaRObs/SWFPyEC4NkgJPahbDdh+ps5mrWrRmeDfL0PNYK9HnRR//tZaFrHhnnRcbzHiSKkaq+ck49GZ8QDkzy07CqtR16rHI9NjERE4Dt9da0ZF6w3Rc0VefjH+Va7B8wtTcO9k/vjoMJrh4+kOc0+v4tQvZu6szEmAvqsXh0qa8fId6bjaqJNcd49ca8LpGq3iFL9KtR5RQSq7+5GSe898R7n3stc6wNm0bSkpf6lxWt9uwM8/vYDFk6LwUWENshKDkRgyzq7QRkO7Af+vsAaJQSrUdtq29QGsqX/na7WYFh84aGErkD5SVlyxSTm6CJq6LdB39TglYyxFhdoAmgaMPd1Y+2GRbM3LULDtZBU+PVuDn9+ZjnsmRdv8nqsqtqOwGttOVmL9/GREBPigq5dG7qQom7/hbrIJISrRXhhKGGyTUGc3CC72cnOlDtxyRgKzMB4sVtv0FXFV7yEuwmsUWwSV5CArqcsSy9MXM3CcPbRLzVlXHmiENQFMLQNTSyLWQ0POYHzrUCn0pl7sV9AcdDC54HL3YPeZapS3mrDvfANeWJCEJl03r4+bIwcRBRBDCq43pNRaI2700GztgFx9bVmT3lpPJNNvUGpt5TpbMiJ9cVdmJHafq7cxkP78bQl2n6vH1qemY+2HRWx955rbkpCksNG6qduCNw+VsfU0P1uUhiZtl00fQanrFc47U7cFNS0GPPdJEbISg3G1UYs7MiKw53wDns5JQFdPH5q1Rkm1PzE25pcgyt8NjfpeG6NlwRtHFfWUcwSpfYvZG2ICvbF+YSpujQtEeICKff2OwmocL2lGWuR4LEgLxSuf2vYEdBVi61SbvptnkNpzbAl/b+q2oMNothEyeGFnEXL6m90+PD0Gr4s8L2aNVWrIt+hMPOXkX9yVJnnYV7ca0Gzin88Gcz8H4/CTO1c42wPNHlJGGLPGCDlc0ozf7b/G9q3zcKNw/9Rou0GITQdLUNd+A8unxyAicBwv00Ju73USUiPlCsQU6uyh8vJQfKhV8n5MesHVpnZUtxrwECev1Fkjivu5pm4Lm6ojdT2r5iZh3wtzRY0ooarYypxEfPnCHPzl+3I8u+Mcfrf/quj7LsuKZ8PlD0y17YUhd80MTVqjQ2mRYgzWiALkQ+NSqRX21GWCfL2h8vLAjLjxNrUsStQilcCkQojV2YmlhCpJcbRXl7UxvwSPbj3NqyEEbOuT5NQy5TB1W/DdlUb89ieZcKP7eCp5g00h5TLOk+J9T2ZBl0vHkErradIa0aLrxv6LjYq+r7N1gfbuwbKZiVg5PRY7V2VjYUaETR+3b66pb3rlvrHMzsJKbCus5dUOGLp6JV/P1hPNSUaEnxdevzsD4X4DdXhya+s4L3e29mRhRgSezkkQrSN+7e4J+OaVeZgaF4ylnPpOpUYUAFyo7WDXwGatEW8dKsNj79mm3Eldr3Deqbw8YOk1I3dyFM7VtOORGXF4PTcT37wyDytmxePTM3XYXdSENr3JrvIaMJAKNiMxHGXNnfjPn0xAmboTZU16qLw88Mqdaaxa32fn6mXfSylS+5anuxteXJCEWcmh+Pd9V1ll1eggX3b+PzIjDnuKGvDusXJePRb30F+hNji1RnL/Rmyd4hpRYmlmwnR14b6g8vKw+e6xwX54fmEKp6yiwUaJtElr5CnQrphp20hYiFA52cONEt13NToTTH1WOXOp++kIYvdFKfbOFfZSS51Fai+QOqPeMSES906OwvfFzXg0y6pcmxrhJ5s6Clj7s756Vxpy0iNsjCglqZCugkSkHMQZC56rVCL3vva840xHb8bC3rc6GwDwVUkL3j9Z5ZRnnfncV+5Mg7sbhXK1AQevNeO+ydH4oqjeofeUSwGzd9825pfgUEkz1s5NwbKZcTa/F7tm7rUJIyiuTNni4khPAimvjDC642hEQSpEbk/NUQ7m/jHRhrcOldn1vjL3QkkKgJgnytFIGqOW+dpdaZiREGLjgWYQPvvNR65DZ+q16ZPkKm8cc+9euSMZOSlhonLpXI+xWKNjIY56SwHHUzE0OhMOXFWL3gOxfi55+cU2fdyOvrYQXT29rjCiSEQKrotINWmNKG8x4NXdl9lUuqhAX0VpRvbmpdja2tRuwHfFLYgI8IFa18Xr8SQHo7aqFK3RjJc+OYeJsUHYd76BjWzJef3t7QV5+cU4cLkJ7zw+Eb5e4+DhSfGahNpT6RVjY34J0sO8UKox26yjzmRNOJIKLuRcdatoiiFgXQOPlzZjSlwQNPpuTI8LwNTYYN49ZBTljpZqHDoPSJ1pmHWKm7ki1g/w7UPlvLQ5sTVJDK3RjN/uv4yoQF/sPS8d0VDS20mK6lYDEkNto7xMGuWfHpqIX++9isxIX6ybn4LM6PE2a6SS7zOYDBvmXMEoa76wMIW95tEKs08O5vwmPAu6IsuoHxKRchWOWvBCpRIxlHrHhT2ToiL8ERikwvsnq2T/VqqnDvdzv73ajMv1ndh/sRHjVV5scaU9b31N68B7CwvWuelacveN8eBdVxvx5+9KJfvY1LQaRO+VWJTH3iSUUyKTwtGeBFKHS2G/IUcjCmKFl4C8gIkcTASKG23IjA4Q/QwG7r1QcogW80Q5GkljimUrWm+IeqABW+9dfbsBvX0QFX1xhTeOO/bePlIJH0/xJZVZyMXUK8V4ZVEG1swR9+pL4YgxwxTytunNNveAW+QL8MUF/vv+W2y8/EOphkRwjqhAX9S2DSi+rZqbbNM/TAp781K4tjI9XvTdFkxLCFJsRAFw2Esf6OuNaQkhuNrQgb8sm8JGtuS8/nJ7AVMEr9F342BJO/5RVM8T2dhRWI1vrzbh9w/cIjoPpfbG13MnoLazB5Oj/bB39Qw8OHkgnd3RrAmumqwzzEgMld2X//ZEFiy9wMGralS1dfHuYXnzgKKcI9F7uTNNkK+3TeaKsB+goauHPYMUVLRgY36xjfqxFDtOVSPMfxz+VaHB7+6/RVYMY//6HKzMibf5nZyAzsb8Ejy65RR7Lf4+1v5eXFGK/z1YikezYlGiNqKwqkO0pvW+vxbgw4JK3hlKiLMZNoyTc8XseDw7Jx5Bvl54VGH/0JEUD2L2SWeNKLGz4FBGohiIIeUESh+ylFKJ2PutmZukaLIIFVPsTbS8/GJJ9SVVfwO4iABvTI0dj8mx4/HA1GhFSmIanUm0OaicKqDU91JyoGY+6+9Hy22+r6MhXCn1JTmkUsvkFkE5ooN82bSF+naDXUl9LqZui0uFRBglpplJQeyG+cd/FiPUX9ywcTbNTkiVxmC3FYCQ2naj5IFQbPOODfaDuxtspHoZlMxluY3FkbEnbIbLqAsxKllCIoN8XZ4uV99u4G34n56tg75rQOaa+7tGrQFbj1XwDNOEMH+eATqYtBPC0GHqtuCtw5W4WNuOjQ9NQnpEgF2Dg4vSeclNA/6wsFZUtlp4XQBQpTYoUhkV49XFGfjr4zMwJy0cgHXP+WS1/ZQ7MQOAST2emRSEpFB/Xtp0dasemw+X47raiN/uv2bz93Jjv7zZgBZdN9r0JuwoasbTnAbBgHInjqNS2lLI7cvmnl7J9Zxp8GpPUY5LfbtB9lxS08pfB6s1epi6Lbxr8PPxZM8E6xek4osicfVjIf8q17Ape5NjAjElWn6cbz9Va2OkyjlMuevjgSuN+OBEJTsGmHPMkqkR+N+Hp7BprMI9nXmPmEBvNOjMsg3WAcccfhqdib3+nYVVCBrniajx43jjWm4MubqBrXCOS7XHcZXxNpzpfFyIITWE2KsPYcjLL8bHp2uwZl4Sb7JIeX6EakFSE02qpxRDk9aIA5eb+ntANWJBagheXZyOfS9YN1CpybsxvwTn67Q2iyGDI/2pGOl2uY2bu/B+drYeC1NDbK5NGOWRokLNvyflzfx7IrW5i0WNxAxJpWzML8HPPz3PM3SVHJr3nK/H5+fqsee8c7n1Gp2JJ5fPsCE3E39cYhttEENpBE3Oc8mNzCiNpGl0Jmw7WSmZd27o6rHpPQMAL96ejpXZsQ4ZbAxKNpYNuZnYtz4HryxKk3wNYNtbJTncH3n5xZLRNWeR2pSYsSbWOZ6BOQzkTgrD5NggyWg30yzblXVmBNfBHGSr2kyo0XZB5eWBZ2fGKTI4GJTMS0faDeworMZH/yrH5iPXsetcnWiEQalxJVxz5AxDjc4k25tuQ24m/vPeW0C5Uby1JTHUX3I9tDf2UyP9EB7ghVmJ4bxDLNeZqsSJI3UwdMaBJ7Uv21vPX108Ac/NTWb3XLXWKPn53P2Mey7hPlexzBUxw+v13An4x9rZyJ0crShzQWs04+9Hyni9FWNkxqOYkWrPSch1+j49OxFbjlfyxsDruROsfck+LGL3dGFGEPMe6xem8g13jXSUWNioWYyN+SV481A5e/1hAT54/0QV6jv4vcikjAtXOUiZ9xJmNog5HnadqcWmg9ddarwpPQu6ElIjNQxIKZUA0qpmrlIUs5cHLPd7sTxVrgrQrORQ2e7YSq5NqTToYPKZ7b0XI/OtREKam98tVQ8mhdZoRqepB+O83HHfXwvwq8Xp+N+D10VlhsUQU6tyJAS+91wtSlrE5fKFn6PkfeVqcuTGr5TCnRI25pegUWvAM3OSMC0+hP35thPlUBt6kH+5CU/lJGDtvBRF7yeHUPVHrnbNkfla2aJHcrhVYvyx9wbugz1VJyX1eVLKhsLu8HvXZcPDU1oIp7zZgFUf/oCsxGAUlLdi9dwkPDd/4J4y9Q0uVH0iNVJw/R7FHPDfOlSmeK11BnvtNphajw9XZuFgaYuo2pkrWzgw/OVgKQAau87UK6ozMXVb0Ko3KWqdsKOwGp+crsYv7srA3RNtlWgBQK3WY3tR/aD3Lm6KkiP7plLeOlQKdacZEeO98coi6ayIHQUVaNR12+whWqMZBnMPb41h9jOp52pPrVX4PZXUAG86WIrztW1YvyCVjVjKIXauUFITx1yLcP3jrulzU4MwKSZI8lm16EzYVlBt8/lytVNSz545l1l6rf24vrrUiJdvT0Gdtgv5V5rw4sIUzE0NtRuhcaYeUOw9suID8Msvrg7sNy/k4EHOXnr0tYUAgP/5ZzG+u6Z2lfLrUEPkz4GRM6TkMHVbbAr7ByvhLcTeJieWR7qjsBpfnK3Fq4szsCAjgneIY3oqvLggGfPTw5AY5q+4EJRBiSy2EKUy2VJwX1verEdqpD+7MP3ugUz8bn+xQxLijhh3OwurUKvtwt6iBqy8LR6Grj5cbmjHpJggxe9xtqqV19dHifHGUKUxQHvDjHUfnx9yuXwl43cwhjF3Q1VrjbhQ3wkawH9+ec1lUqebDpbi26tNuGNCBPaebxDdWCrUBqRE+A1qviq9D0qk38VES7i9RKT6x8hdW/6VJjw5m2+YDpGwCzGk4Po9akdhNaICvPHv+646tW5I4UjhP2CNeuworIbZ3AV3dw+0GHqwjyMEMBQtHDQ6E36x+xJq2oysQ+D5BSl4dk6SU++npCWDlLCUnDNVyecwOOPAs/c5SmWim7RGXG7Q8cbS7rXZ2H2+EZ+eqcOLC5PRpDMj/0oTnpqdgDXzUnjPNSPSF+8/nYW4YPuCB0quiVl/hTgquiN2/hF7D6W9CJl19i+PTsHPP71k11HKPdfIOROEzjDh+zHnshXZ8XhqdgJ7/WLnJjmc7R8FDOxBO1dl4fPzTdh3oQEP3hqDx2bE4nhlG7adrMQrd6Zj6TSrSuKuM7UoVxvsiipxhUlGECI2MRphQp25mcE8WdXBSngLkTKimHC7cBExdVvwxdla5KSE4Q8HipGXX8wLvTJpeE/PSUZimL9NCFcJStMeuQgXA0fqM4SvTY3056U+vnusAg9PF6+jkUIu7xwYCMFrjdYw+15OLcETWTH4y6PTJIUjxN7rnaPlkkXDcuTlF+OVXUWo6pfLd+SeO4OS8cvteu8o4QEqmLot2FFQgSvNehy43IQPTlQqSl9QApPicF1tbRi5d32OzQLPrbMbzHxVUt+hNOWCm6LzRHY89l9q4o157lhTMnc25GZi+8qZPCNKrL5hKDvGE5yHST975yhf1nqwRpTS9X7zkTJsPlrB7h0rcxLx1G2p0Jp6UVjOFwJwVQsHLmEBKkyLC8Ts5BCcq2nH67kZThtRYvOFOy91N8yywlJK11rmc06UqkVTsOUEnZz9PlLpg4wRzJwTogJ90aS9wRtLAeO82Hvw92OVWDc/GU9lJ2Dr8Spe3VBGpC9uz4jAw++cEk3hEtaJcq/piew4jBNxxEnVOTNGQJNWWT2Z2F4hNCTk1kvh+ses6XNSw0XTXoVpkYlh1n6D3PorsVowe2m0zLns5UXpvOtnxoizabOOwOxBhRUtCFC544lZcQhQuSMpwlpb+8xtScg7UMLex8dmxuPVxemS9eFao3lQJRTDBYlIjRBKvNhDJeENiHs+uFb/sVI1fvn5ZdEu79yJNlhPoiOeOmAgvcmRKIDca4Veeqn0AUefxa7TVUgL94Oppw9z0yPYiNRgUyHL1J1YOz8Fs5PDFP0N17M3JdYfWx+dBn2v8o19MLhi/Iq9x47CargBiAjwxnvHK5CdEooOYw+uqzvxs0XpilI67CGX4iBsQ7BzVTZSI/2HdL46knLBNG+WGvODjXi7Ms2WA4lIYWgiUn8/Uo7/uP8WTIr0d0kkSmy9F459rdGMzUfK8eXFJt7eQQGSrR6Gonm91mhGd28f/H08nZ6bcvOFmZfbn87CMzvODiraz3zO7ekhCPTzkU3BdjTKoOT7cCMzmw6W4lipGrelhmFPEb9thLrDCJOlD4lh1jVv08Hr+OpSI+6fEo2Xbk/hPV/mvSs1ejy29bToc8/LL8bhEjVeXpSG7MRgXqSzucOInWfqeVE/qfWXiyvTHwe7XnIzgsSui/kZk6my53y9pGS71miGvqvbYbGpoUiblaPDaAYFQKPrZueBo/dxR0Elbo0P5LU3cEU0fRCQiNRoQ4kXe6gOZWKeD6HVvyAjAg9Pj0WJupP1gogVog7Wkyi22Uh5kriF/45GAaReK4wIiV2/o8pk6nYD2m704GBxK36x+zI25hdjRU4Snp+fzEawnFGs2pCbid/dP1HSiBITGeB69m5LCUNkuP+wGFHA4Mev2H1nvOyVLXo0aW9gemIITle24s7MMHy4arZLjCgAsiqKwjYEzCY+lJEZ5nqemGW/cSTTvFk45pkxZ2/u2BONsBeJJYwemIL/n0yORmKYP5q1xkGJgoit92LzNNDXG7MSg/DA1GhEBHjj/inR7M/FhA2Y98i/qh7U9+XC7Bcfnaod1NyUmy/MvLwlNtDhDAupz3kyJ5EnQlDebFW14z63wRwmmc9ZMjUC7z89g/0+3EjUp2fqMCcljI0+cyMkEUG+7OervDyQGuGHOzPDkdqfZsco/C1ID0NXTy82HSzF8x+fEz1HVLYYcLhEjfumROFyvc4m0unj5WETjZdafxmUKh1KiTZI3S9nM4S4kShhNJ97rR8W1uKZ2XGSokjMeP7sXKNDn28v0jUUbCuoxuI3T2D/5YFr7bxhxot3pCIhRIV/u09+79AazWjUmfH299ddGk0fKkhEaoQZSi+2HEw+7dJpsXhsZqxk3jUTobGXNzuYZrBcpDxJUk1r7d0/bsPhpdNiHL7XznijKlsM+Ppyo2hBNTA03iF7NTTD1U/BVcjd9x2F1ZgS7Y8/HijGiux4jFd5IjnYDwlOdo53FqbObigQG9fOeFmZ9xEbc1LRPiWiGUOwbpGIFIZ2j3K2saoYzHovN0/fPnQdlt4+tBm7ER2owou3D6hacvcTV9cEA9L7hTMw9WDCMS82BxzNsBCDWzf9aFYsIsercLFO65LnBgw095VbTzYdLMWx62rclhKGvecbsHpOIu6fGiXZFJh7L946dB1nq9uRlRiMlbMT2OeQEemL7Stn2uxDu8/WoqmzS3K/lIrGy62/SkS2nFlLgcE5zJjrem5eIu6eGInYYD9FEX6l41lsTNa3GrDzTL1spMuViF3rluOV2FPUgBWz4xA8zhuXGzplxzO3HmzRhBBsuHui3XnFiIYNISQiNVpRMimV5vo6wsuLUvHp2my8njtBNu+aWczsbUKuMKKEcu1cT1KgrzfPE8pcj9z940rUvn2ozOnrctQblRwu3btoKLxDSmpoHDGinO3votTTbeq2sJ+h0ZlE/07OC7gyJxETosZjRmII/vTNdZyu1g65ESV2jb4+Q7N8inn4ne0nw0Si/nm5EREBPjhwpZEdc2KRKCVy5qR31NijSWt0qrGq1FrArGcqLw+8cmca7rwlHP923wR2TGl0Jnx8qhYfn6oDACybwY+kcvcTV9cEA443ORfCRCu49WDc65KaA66I9qu8PLAhNxMfP5uNFxam4psrzQ4/N0B8zWKa+16sa5dt0Pzq4gzs6K/b/uqlOWi/0WPTb6lOY2DPJtx788qidLz92DS8siid9xwW3xIlug8ty4qHtzsl2etPKjtAzoklJ4EtPGc0tOkV9TH67Fy94nVPqjfShtxM7FmbDY2hh5WJVyLXrWQ8i43JvPxiLN1yCn4+bpKRLlefL4XX2tVjYe/3uRotDl5T2x3P3HqwtIjxdudVXn4x/vDPayiqaXPpd1EKMaRGOYPtai7G7nN1ePP7Mjy69TQbRndFus5gUkZ2FFbj559ekGymZuq24FBJM2YlBeNwabOizxrsBs0sTB5ulOKGeAwv3p6Ox2cM9C5iFtWhKKoe7KGBizPCIYDywzUz9lbvOINNB0vwxsHrkn8n14iQe9gY6hQzqQ3K1fMSkDZmBtNoMCxAhXsnR0Gt60LupCjJMSc3X5jrIL2jxiZRgb52G6sKn6Uja0HwOC/84euBInJmnfP0oBDg42V3nWPm+pIp4jLizuBIk3MuTC+kA5cbRJ1ejs4BZ+dIWpS1v9I9kyIVNcTlpv/Zc8ZsO1lpt0Ezs4/09vWxf5d/pQnqNgO2HCvDx2frJNdA7h7EfQ5Sh/Z1C9Pw/IJkycO+M3ua1BrJPaQ/kR2HXeca7fYxcuSZ2+uNRLtTNv09lazncuNZ7Pq4qYQfFtaix2yxSWccqn2Me63cvWtq7HhF4zkvvxhX6jvw52WTFQlx1bTqER/qj+c/Pj8iohTEkBrFuKqrORdTtwVNWhP2nrfNfZbKP1WyEUipGim9ps2Hy3GmphPnatokVexujQvCD1XtmBobpOh9AesG/e0r8/DoDPu1JWLXNJhoVmKEP8IDVDZNXeWaD3M/35Fu384eGrjYi5ZJjQOxRVyqgzkz9uakhCnykNszfoe63ov5bvouCw4Vq/u/g/J56eghSs6YcbbRoKnbwrteuWsSM165c3soogeE4eHVxRPw80Wpos4J4fqtNHJu6rZIRkxez52Af6zJVpxK9HeOwp+rcCYSxVVyXTM3Af+9ZCJWz0lEeIAKWqPZoTkw2OhtpdqAVoMZbQYzXrw9RdKZt/N0DX67/yoWvHEUe87X23XGRAX6iiqGytXZJoSokDspCiWtRozz8uTVcdk7mzAN7OUO7UG+3jYGd02LwekMCSl2FlYizM8Tr9+dgfRwX0WKqEqfuanbgsv1ndh/sVHyPaXU9zQ6k909X2o8i10fN9PouXmJ+OhsPRsFA4bmfCl1rRtyM/Hl+hysnpOEFdkJ+P0DE/HF87NFx3OVxjoHT1Z04LXdl1HZIt2oGLDez7XzU3jjUexvhtLpRwypUYqp22JXApSL0kP3nvMN0Hf1SIbRhSjZCMQO0kLjQQ7uIvCTqbE2zRCZ10yNC0R6hB/mZ4ShzdCl6PvuPF2DP+aXOLyZKV047d13qbQ77j0XvoeUR8teN3vhIluhVlZMyyAXLbMn/8rcq5cXpUmmQKi8PBAVqMJD02JQUKmx6yEfDTCpS/dPjUJJkx6fnatXHB1y9hDFNWaEi78zdW6OGj/CSJRwbstFCgmjm6ggX5u1U+wZK42cy0VM8vKL8dh7pxV5iJVK/LsSeylF902OQvsNC/56uBwmSy9vT5OaA9xD/2Cjt3n5xfjgX1XYeaoWh0o02HykQvS+CI3Zt76/jvULUySdMfvX52D59DhodCZeJEpuz96Qm4mPV83CnqIGvHO0HDe6eyTbTYjtU8yhPdTPExMi/aEWObQLDabNR67jk7N1TmVISFGlMSA22BdbjlfjF7sv4c1D5Xg0K9Ymm0PMeFOy7qm8PDA5djwrsiKVISIUuXr70HVsOV41KEfCozNiba6PyTS6e2Ik9hQ1wMONwjgvdzS26R3OclAqziHFzjP1WPzmCXxwogJvHSrjGXVcksL4hmZyuH1n6fSEENm/Gep0dCI2MQoRFnyLSYByUdKsExgo6tV3WXD3xHD86u4M2cnjSBEwt8P3kilRThX6CgslxQrfmUJcJYWipm4LnvvoHEqb9U4XMssV1Su973LS1cL3MHVbRLt9M8WaSotjByP/KhQOUToOmIOCEll/Rua4RWcSlSbmNoAGRrYhnxK5YKV/4whi4194Xxz9Hs4Yq9y5LXaIcLQZtwREbAJDv0dJrQtSz5hZC+w9Y2Ehvr3moWJIrZNSnz0YwRMpURXms8qa9PBTubGtI56Zk4ADl5pl9zQxQRd7c0cKpmG9pZfG0ukxdhuW7jxdg4t1Why7rmE/S+r+bD5yHTpTL/aeH5A1VypmwHyf3z0wEbeG+8FIU6xaHyAvFvVFUR1aDD2isu7Ce2dPsMlZtEYz/nmpAbVaM68VCVf8xBViUKZuC1oNXYqaD2t0Jrx5qNxmz3ckmqpEJEjsuQPSQlTc8TNYOXnu+PrvJRPx134HQ0SAdAPoyha9IiPK3t+4UMyGiE2MFRgvlqWXRpnaKnsqJgHK4Ignj/FMB6g8MC0hmB283LxlrtfMEU8211vjbM2OPU94U7sBTdoum/xiufdTml+u5Jq4OHLfpdLuxN5DzKOlM/XwctTthbor1Pxi2vJm+dcLEW5WUuNAGElTeXkolvVnPiM8QGXzGqFndKQb8kl9J7FG1vb+Rg7u/RxslFfqeziDnCfW2Zo6wvAjLLLnrp1Szzg8QKXoGTNzn8Fe81AxxNZJqc8ejIdZKlK0Mb8E//nlZeTlF2PFttPYf7GR/Q6pob48KXcfT3fee0qlQjobvWUa1nt6UPBXudtN22bSpbifJTbfy5sN6O2DTWq/0j2b+T4L08Ow41w9nuA0xT1f02Yj7w0MrOeTYwNRVN2GdfOScK6mjR1/YvdOTrBpMOz8oRa9fTTuzgjG1qemsUYB8zzLmw0uEYN6+1A5Hn7nlKJ1MSxAhYgAb7tRLCmURj4fnRFn89zr2w2iRgx3fsmtG1yksnPKmw288dVt6VUUCXPUiJL7m6FORx/RiBRFUa8C+DOAMJqmW0V+vxLAv/f/839omt7R//M/AHgaQBBN04rd1CMVkXLUY7vrTC3K1Qbsv9jIRirsRTWUNusErBPP3NOLwP685T1FDVg+Mxah/j6iXg1nPX/2JNPtwXi//u2+TNS3G6Ez9eJqQwcmxQY51BBUTLa0SmNAUpjzEQ6t0YwPCqoduu9iSD07U7cFXT29vKaF+VeakDspCnuKGthnJPVshqJpKvez7EXjnB0zQs/oJ2tm4/H3xKX5hwsp+WMuUh5BJfeBGUvC++mKKO9QMthm3AJIRArDE5FyZF0Y7DPmNiN1FLnmv3Ie5oZ2A4L9fOzWLnEjRcxnbVw6Ca/vucK+9+412fDwdEN0kC92nanF5XotJscG4rGZ8TbvyW0p4iqJaSk5dUbC3BmYyMS+Cw0216pkz95RWI3xPh7444GSgXX5uWz891dXkRo5Ht9cacJTsxOwZl4Kbz1fNj0KIf4q0ciG1L2rVusxTuXhEiOKuRZLL43cyRF4bXEGgny9saOwGpVqHVTenvhXuQa3pVol3519js7OmRadCZ7ubk6t60ojn8x9XjcvCS2GbtFnITa/GDl+qXWDOQ+snpuIpdNi2POuMJLFHV9D2T6EYdvJKrx7rAK/XzIREyL8B3Xmg8weNWKGFEVRcQDeBzABwAyhIUVRVDCAswCyANAAzvW/roOiqNkAagCUjYQh5cgh0ZkwsVSYXW6Rc8RoYQb9/z48Gb/64jJa9GbMTw9FSZPzKXBDhanbgjZDF/acHwjzz00Nwu/vnyyqNKSEwYapmfu38rYEPDErftAHWqXPrrJFj8fes3aHTwhR4ZnbkvDO0QrJcP5QLVSu7NEixp+/LcHuc/WscTkURqFSlMzfwaQObDpYinZjt2Rah9B4lTPcua81dVtg6OpxRcqdLC48PBJDCsPj7HPUuBkKA2Gwny11cBTrlSVldAj38Y35Jahu0yEhxF9yvbG39zvST1H4XuXNBqRG2j/ODHb/AoBKtR5+ThgozFoX6ueJeenhvPS4HYXVOFXRgimxQdheUMPef2bd2rh04LwhltLlql6UcgjXUOb7vPfUdKz9qAgtejOmxPpj08PTnBYy2nKsHO3GHlFDdShp6jAiSkENbYvOZI0KyaTeis0vqXWDOQ94uFG4f2o0mzr48LRYPPHBwGd8/Gw2e09dMYbtsb2gCu8crUBMoDdmJYeKppQ6yKg0pD4H8N8AvgSQJWJIPQ5gIU3T6/r/vQXAUZqm/8F5jWG4DSmlDSuBwXnzHI0yKYV7CJ6ZOB4zEkLYxn+h/j5O5XMrZTC1FHJeNEeo0hiwfOvA5P7HmmyHQshDbUTYg1ngXs+dgDyOR3C4Dd+hGp95+cU4XKLGmrnJeJTj9a3W6EckEqV0/jpTC8H1kCqpgwCkjW5u42kvDzeUqw02ufCDReoQ6aIDEDGkMHrreOWe8RA0Z1b02cLPrW834Nd7r7I1scz+5siBjfksZ+ozHEF4jlB6sGzSGtm6Lbn6Ens46mjh1mZyM0WmxwbwDtfC/ZHZl5h1S84pNpgomz24NbbCNZQbkRK7NkfqUrlGxfKZsXg6J3FYzgfOOOztOSgdmdebDpbC052yqWnbVlBt8xmDPYMpQWs044HNBchKDMY9EyPw7/uuuiKrZXQZUhRFLQFwB03Tr1AUVQ1xQ+o1AD40Tf9P/7//A4CJpuk/c14zrIaUPc9zhdqAFE7hpUZnwraCGqe9eYNNjZNCeAjmFhsOdlOUWnRcUcDZ2KaHh6cHK0PrbNH95iPX0dsHuLtZ+z05ylAZEUphnpGzhcyuQun4VLpBuuqQ4Eoc8cY7M3eYsfR0jnh0U8l7ctel+emhyEoIcnmRtvDgx00PdhHEkMLoNaSkkHMsMmN3qA0tLkxE6th1Dd58dCpe+fTiqFpPANtzxM5VsyU992IMNkK/+1wdytUGXG3owPrbUzEnNVz29WJp3HLPdHuBNZ1KuC8x+4CYuMFQRiiUvDfzfYSplEoFpbg4U2oxmPnBOPz0XRbMTArCnx+Zoni9H0zqrZAOoxlbj1fZ7Jdi6al5+cX4V4UGzy9Iwb2TY1zy+UI2HSzF/osNeO2uDFxt0rkiq2X4DSmKor4HECnyq98A+DcAi2ma7hxqQ4qiqOcAPAcA8fHxM2pqagbxraQ9z8LJyhgOz9yWgEdmuKZQ0pUMhZEmtei4uJZC9HPq2w2IVaCQAwzOqGM2g6Eycu0hXHSH85DiDI5ukCOZxifFUKec2IsyKYl+M+vSy4sGIlKuSi3hHvwSQlR4fn4KKjRGXg2nCyCGFMaWISXnWNxRWI0PTlRiya0x+PRMnaIx7Ao0OhPMPRaE+FtFbEbjegLYniMcvU45xVA5TN0WvHeiEj9UtmJibJDddCdHMzB2nalFTasRlxs6MTMpGK8sSkedxoCdZ+sk9wFHHGiOOlBrWg1YtsW5Gltnsk+Y/Vjp+UBsjXcmMveXg6Vo1nXxUlpHCqX75cb8YnxR5NqsCSHc5+CCrJbhV+2jafpOmqYnCf8HoBJAEoCL/UZULIAiiqKERlcDgDjOv2P7f+bodWylaTqLpumssLAw574MB7EO7GIqaYzyy47CwRluQ4WzRoBUo1g5FTulPUmU9NkQ+xymE70SVTeljSbF4DYUHAkjSkypSmhEubp54WBwpuGfs01nh5KhdoKIjSVHe9Aw69KK7AQsmxGHn92ZZrfpsxI0OhNPgfCVO9NxpVEn23CS8ONASpmSGbvjVV7sWu1MHyVHYRT+dv7QwF7LaFxPAFs1vw25mfj4WWtfISVruD0jSkpBjenl9/zCVF4DUyklNkcUeJlmtF8UNeBkeRtKmrTIyy/G0fJW2X1AaT8jZ1RLuU1pH5oW49BB2lH1Ye7+rLTdi3CNt9e0WIr1C1PsNreXuw5XomS/tJ7DGmzOYeXN8r2quArTSuA+h6EsDRh2+XOapi/TNB1O03QiTdOJAOoBTKdpulnw0m8BLKYoKoiiqCAAi/t/NuIIO7CnRPAna2qkvyLDYawh1SgWsL/ovJ47QfZgp1TOVvg5RnOPYjl0QLlRJ2Sou4DbQ8nBerRJUTva8I9hNKTfiDGcRqoz8unCvx/susMdT8zBb+m0WEUNJwk/DsTkvZmxq+vqZtfqoW64LecgG+x6orTZvRRSB1Xh/UiL8nfJGm7P4Fg2Iw4zEoIVy9NLte8QImzdsXZeCvYUNWB7QZVk814GewbvYBo2M01pHTWmTd0Wye8ufKbONF8WrvHaG2anzxhK9wvhdQ11o1opxM5hefnFeOIDaYe4M0amlOPf1Yx4Q15uah9FUVkAnqdpek3/71bBmgYIAH+gaXp7/8//F8ATAKIBNAJ4n6bp39n7LFekTciFe4UqacOhQjNcSDWKFR6inEl5c0b1jPs5zqRvOPNsRjpNRCr3HHB9+qQrsZeGotGZ4CfSlHc04YoaP2dQmrrpSBqgEuyNJ6E8vwsgqX0YW6l99hjuGilXqQty08ecqZHh4mpxKnv30tF0NFfWyHCvkVkbmD1z1ZwE3D8lelBG7XDWJss9N6nfOVuzLGx8O5gzhiMtOlzYqFYxQsEx5hzGNKCWqhN0pn5arI3QIBldYhMjhas2qZEWG5DDkVohR2EGphJ1MUcZrHDCUGwIYjibmz5YmJqDn92VhqXT4kRfM5Iyxc4y1LndrjjEjWYjFXBp53YewzyeiCGFm8uQGgmEDjIlNTVckSiu4bR6TqKNUeLj6a54bjkzL+XmnFKjzBXnE1cav1wFxMEq8w1HbbLcc7P3TJWKA8m9RnjGGIywlr3vNJyCVfackVJGJHO/HDEylTr+HYQYUoBrN6mREhuQw5HCfmelyIfAE81779EclRgpHNmQx1IUVKMz4Re7L7Fyxa72irkqSvP3I2XoNFmGvS+IIwzVhjiM44kYUiCGlCtREk3i7pnPz0+2MZy29TddfyI7HoHjvBxeT5yZl8I5x6RjOWKUOXM+Yc4Ero5uMwxH7yBXIXdoH8xa68i91RrNeP+kdfy5IqIidd3Dce5S6owUKvwJ75cjjuwhcPwTQwq4uTep+naDbIM1LiOVpqQEV3hfRpLB9MqSQ7gI3ixGJxOROnZd41IjwFVRGmYDYPqCPD4rftQaqmN8TBBDCjfHHiUch4NZE+X2A7nxLpfixrxnhdqAx9/n9LNZnY29FxttojkdRjN8PN2dXk8G0yqA2x/O0kcPWfSAOROsm5+MLccrbb5nZYsByeHOZ7qMxtYWUmh0Jjz8biECVJ7Qd/Vg97och1MsxXBkT9p8pAyhvl7Y9F2ZS3tWjuQe4WiGgyv2cBc7/odftY8wvMQG+ykqHh2Mat1Q44wqz2hiKMUeuEXdw10gOpTFmr9YnIHXFqfbFKyLUd8ur+jDZbBiDQxMUWwvTaOrhx61RhRgW7xOIAw3wrVpMGui3H5gbw2UEj/ivqdQJCo5wl9UXCDI13tQ64lQnEopXAGDtw+V4dEZsYrWSXsI11HumeDDU9VYNz+Z9z3z8ovx2HvKVHGlcFZ4aCQIC1DhvsnR0OjNuHdStOia78xaq3QMaY1mNGq78EFBFe7vF+5YNsM1oj4juUe8njsBe1/IUey8d8UervLyGJbMMRKRGgNI1T1xvXV5+cW4Ut+BdQtTMC8tQvb9RmMtjTM9G0YTw1VHM9wFooMttnYVzqaFuMoDN5ZSJscoJCIF1+9Rw+mBFq5N+17I4UUhHFkT5fYDR9ZAboqb1HsKRaLsfUexz5L6+WD3NVen7Eqto8IzAfN9hCIAn6zORkqE87XIlRo9khXKUGt0JpgtvUNW820v+2Wo1nwlc3LzkTJ0GHtworwFz81NwcNZ4nXRYwlnU0bt3a+hygISgUSkxipSPZK4nrX6dmsfq5MVHXht92VZCXCNzmRXinwk2PlD7ZiWVHZGVt1ezwQxXBVpUcJgJGddCTO+lUrcc3HV/SFG1I8PiqLeoCiqhKKoSxRF7aUoKlDidfdQFFVKUVQ5RVEbRH7/NkVRjk/2QTLckWvh2hQV6Ot0GxC5dhqOrIHcv5N6T6VGFPPZQuTus6O9iISIycs7i9w6KjwTMJGovx4p4UWSBmNEbcwvwWNbTyuKTm47UYH3C6oV94d0FCXZL17uQ3M8trcn7SisxpcXGpCTHIxda3NwL6dn6VjFGXl4Brn7NVpavpCI1ChGqu5JzMu15XilXUUTV9ZGudILwHwfSy+N3MkReG1xxpgzpBiUerEGW3jrSk+znGdu1+lqRAWq0KQ14bHsRJd8nqN8cKIcLYYe7BtB6XnCkDPqIlIURS0GcJimaQtFURsBgKbp1wWvcQdwHcBdsPZEPAPgcZqmr/X/PgvAKwAeomnarmvdVXuUI1GbwSqpiX0297MG49mXE01wdg10pVCU0vs8WsSplCqfcc8eS6ZG4KWFGTwRAEdxJGNDozOhoKINfzxQoqjm21GURAlHKhODO54SQlR45rYkvHO0YkgUbYcbZ6KrcnN8BNR0SURqLCJV9yTm5dqQm4nP10k3nXNlbZSrvQDM9/H0oBDs6z0qNhxnURqJ4noGy5qUR1gYXGVE2fPMVbeb8MvPL6O6fWRq6erbDdhyvBonrrdgwz0ZeGZWLO/3ru7KTiAw0DR9kKZpZoCdAhAr8rJZAMppmq6kabobwC4ASwDWyHoDwK+G43q5KI3aONPkUslnc3HmcMPMa7m9QG4NlFsXXLm/SN1nYV3paNnT7J0TGLhnj6hA30EZUYB8xoZaa+Tdr7AAFdoNZl4TX1e2NrEXJRzJTAzueHrlznS8c7RCURRnOPdBZz/L0eiqvYi63JgydVugGcb6fxKRGgNI9Uhy1MvlitqoofQCjBav3XAw0s19AfueuSqNAcu3clSt1mSz/UCGE6l7NVQyvSPBUPZ/GyOMuogUF4qivgLwKU3THwt+/giAezhN5J8CkE3T9EsURb0CwI2m6f+jKMownBEpBjmP7mhVUhvsvB6JdYF7n4XRjKGuUxvKGhFX92cURiffOlQKvalXtGlqi84Ec49lyPpDyp03RrpXKDNmlERxhnO8D9dnDably+5zdShXG7D3fIOrlalJRGosI7WQOGp0DKY2ivEaOVMLpJQfixEFAJmRvtjy5DRkRrr24OKIsp09z1xSmEDVagSMKEDcizqYnOuhwtlr+MvBEvx671X85eDI5nn/GKEo6nuKoq6I/G8J5zW/AWABsNOB940GsAzAXxW89jmKos5SFHVWo9E48zUkkTvAD5eSmiPzYrDzeqTWBW4kiolm7L/YgG0nq4a0Tm0oa0S0RrPLjRjueaFJa0SLrhv7LzaKRn/CA1RDZkQB8ucNMfXG4YQZT2JRHG6kZTjH+3B+liN1kMJIVJPWhL3nG4ZVmZoYUj8yxAwfeyHQtw6V4r2TNWwK2GgUqxhLVKgN+MOB63jw76fwx/zrKG92PLVPDClhEjnsbRgbcjPxjzX2U0GGGuGGOpyiG0pwpLCfuwE1tBvQrDOjtFmPZp0Z9f0F4KPBMPwxQNP0nTRNTxL535cAQFHUMwB+AmAFLZ6+0QCAK6kV2/+zaQBSAZRTFFUNYBxFUeUS17CVpuksmqazwsLCXPflFLAhNxN71+cM2fx2VPBisPN6pNcFrnPqmduS8O4xZalZzjCUrUyGoxVJVKAvwgO8ZEWmhrL1hj1Gi2OXO4bFDOeFGWGICPDGgvShXTuGe245I7Si8vJAVKAKD02LGRJnvxQktW+YGK2NZu0JUDRpjfjbkUp8d009aqTJx3jjUQDS6WrlzQakRjqe4uVIQ+abidEwFhxJQxCmRoilV+6/1HTTpCw6wKhL7aMo6h4AfwGwgKZp0VARRVEesIpNLILVgDoD4Amapq8KXjciqX0MrhaUUMJgWjUMdl6P9LrApI25Wr5cyFC0MhnOViRaoxld3RZ4i/T7GU7Bh9F6PuMiVVax83QNvrnShHsmRWFFdsKQX8dIzy0lmLot0Hf1uNqIIql9I4mrvTumbotLvFtKPFpKvEbDyXBL+g4VG3IzsXMVP9KTl1+MJz5wTu5VaUPmm43RsKAr9dSJpUYE+nrjsVlWD/bymXHw8XQfdSmLP2L+BsAfwHcURV2gKOpdwJq2R1HUAQDoF6N4CcC3AIoBfCY0okaaoRCUUMJgPNiDndfM34/U/GH2SFfKl4sxFNkhg5VsVwpzLvq0qAE+nu683w2n4MNQRN8qW1zf7UCqrGJFdgK2PpU1LEYUMDr2XHuovDyGtWUJiUgNMa727uw8XYOLdVocLdW4xGOt1KPV3GEU9RoNJ8PdjHY4ETY+/PjZbKeUkipa9EgZoVomZxiuZnpSHkdXeiKVeOqkPNTcwueh9mKPUkZdRGokcPUeNRoEJUbKg30zidGMBEMp/sSci/RdFtw/NUr0PDMcgg9DEX0bbGsTe9zMzeFHebSLRKRGCld6d0zdFnxzpRlHSzUu81gr9WhFBvmOeM7wSOe/DyWpkfyIkjNG1Mb8Ejz+nrKGh6OB4WqmJ+VxdLUnUsl4lPJQc+fWUHuxCT8ehktQQo6RWKdHoxjNWGMo93vmXDQzKUjyPDMcgg+ujr5VtvBbm1SoXVP/zOVmNaLGcrYRiUgNE67y7jARqWPXNaIe61Fu0buEm/k7ljXpnTKiRqA53aAYruuV8jgOZx0AQREkIoWh26MaO4yjQtp8OHFVZPdm3m9Gmg6jGfsvNY14BN6V0bfR0NpkNCM2n8ZIthGJSI00zkxSMS/aiuwE/P6BiaIe67Fs0TvCKJxgLsPZxodDKUs/FAzF9YrNFymPo5KmjATCzcJokDYfblwR2f2x7KkjRZCv96iIwLvSibYhNxOfrB55pdvRiNR8GuvZRiQiNUpxNL97jFj0hCFmrOVPu+p67c0XKY+j2M+HUy2KwEIiUri596ixBtlTCQTXoWQ+jfLoL4lIjSWcye8e6xY9wTWMJSMKkL5ee73NuCiZL1IeR7FI1HCpRREIY5UfQw3SSO+pNa2uV34j3PyM1rm453wD2/NKaj6N1XMrMaRGIc4u4KMhRE4gDBZHRShceeAZCulf4cbmiJFIIIxGuHPu9dwJ0N64OR0OI7Wn5uUXY9kW51phEH68jNZUVFO3BW99X4avLjYhPcIPj86IHelLcikktW8UM8rDnLKMdIO7mlYDEkIdb2xLGFkGI0LhyvniquJjYfqTvQbYP2JIah/G5h711qGyIZV7/rFR02rAsi0DrTA+XZuNxLCx09KCMDKM9lTUm6CtB0ntG4uMpkngCEPR4M4RiDdv7DIYEQpn5otUdMhVkShu+lOT1mi3ATaBMJbQ3jDz5J4bO4wjfUkuZ7hTpRJC+a0wiBFFUMJIp6La42bOmCKGFMGljHSNSU0rv49Dtcb1fRwIQ4vS3mb2sHcAGuo+VsKNLSrQd0wpKxII9lDSp2q01mwoYaRSpTbkZuLTtUT5jeAYj86IHdXGijPG3VioFSSGFMGlDEWNiSMkhPph+cxYzE8PxaNZsTxvXpP25vOW3qwM1siwdwDS6EzDEh0SeuFcZSSKMZYPrISxy4bcTOxdnyN66B+tNRtKGGlBjbEciXJV+4jRtGdLfSdTtwX17SN/2N9RWI273zyO/KvNI30pLmOsZBcRQ+omYrQUsQ9HR3I5Qv19UNKkR6i/D/uzvPxiLNlcOOonJGHwKDkADVcfK8DWC+fv4znozxIylg+shLGPVCRqLCv7jfZUKYbRcIjn4qrU/tG0Z0t9py8vNuDNQ2VY+s7IHvZN3RZ8cKISWYnByDtQgu0FVSN2La5iLGUXEUPqJmGo05QcZbgjUQxim3eT1njT5/ETBlB6AHJldEipITMUBs9YP7ASbk7GiiEix2iv68jLLx7xQzwXV6X2j6Y9W+o7mbotoPto7OVcZ13byBz2VV4e+Nld6ThZ1ooWvRnvHqsY8/uA0lrB0fA9iSF1EzBcaUpjAbHNW0keP2F0UaEenJdV6QHIVZEoJYaMIwaPI6kxN8OBlTC2kRrLUvPQValfw8FonU/17XyP/Ugd4rm4KrV/qPZsZ8ad1HdSeXmAcqPwEOc640KcT8ccbGRx6bRYrF+YclPtA/ZqBUdLJgaRP79J2Jhfgj3n67F0GpFVBsSlsBs7jMSIGgPk5RePOTllpdKuSl636WApPj1Th+Uz4xxKjx3L7RJA5M8BjM09Sijxbw9nx/dYRaMzIWyIhGXy8oux93wDHpo2+LXSlS1DHGkfIbduuXLPHsy4M3Vb0NXTK/qdTN0WtOpNgzKiXLnnjfF9QDEjIPcuuUcRQ+omokVnIkpghDFNhdqAx98f6KGyc1U2UiPHRtG10g1M7nVaoxmL3zzBfv9vXpk3YmmywwwxpDD29ihHDzM/tvE9HH3j6tr0gzrEAyPnvHLUCHeWwYy7ob7G+nYDlr4zsOd9vi570M/zx8Iw96YifaR+DBAjijDWSYng50WPFSMKUJ4CJPe6kVa9JBAcwdG00h/T+B6ulPvBHrpHqqh/OGs7nR13w3GNscH8PY8YUcoZLTWMJCJFIBBGHeXN+jFlRLkaR1JjbhJIRApjd49yNJ1oLI3vwaTmjZWUe1emCDqCoxGFwaatOZO1M1xRD1dEFglDCkntA8buJkUgEIaGH0s++RiAGFIge9RowxWpeaM95Z5ZA6s1+hHpXaV0DR5sit1g/p7sEwSQ1D4CgUDgM1oUfwgEwujDVal5o9mI4q6BI9UAWGld6WBS7Ab798SIIshBDCkCgfCjg/ReIhAIcgxF0+7RxHCugZpB1ocNtsUDaRFBGEpIat8ohoSTCYShY5gVfwjykNQ+jL096sfAaE/NGwzDsQa6UrlwsGcicqYiDAJSIwWMrU1quGRBCYQfM2RjHTUQQwpja48i3BwM5Rqo0Zlw318LWGnvr16aAwoYsr5aBMIQQmqkxhIk7YhAcAxn5wgxoggEwo+ZoVwDuemRK3MSsL2gBvf9tQAb80uG7DMJhOGGGFKjEJLPSyAoh4hGEAgEwujk9dwJ+OqlOVg2I3ZY+moRCMMNOaGPUlbmJOLRGbHEiCIQZBBGb8mcIRAIhNEFU2P28PRYtq/WzVp3RvjxQU4coxhyICQQ5GGit0zB9FiYM4Np8EkgEAhjlddzJ+DZOQnEiCLcVIz+UweBQCDIMJait65UsCIQCISxBjGiCDcbpEaKQCCMecaCEeWqBp8EAoFAIBBGB8SQIhAIhGHgZm/wSSAQCATCj43R78YlEAiEmwRSI0AgEAgEws0DiUgRCATCMEKMKAKBQCAQbg6IIUUgEAgEAoFAIBAIDkIMKQKBQCAQCAQCgUBwEGJIEQgEAoFAIBAIBIKDEEOKQCAQCCymbstIXwKBQCAQCGMCYkiNcao0hpG+BAKBcJOwo7AaC944ih2F1SN9KSMORVFvUBRVQlHUJYqi9lIUFSjxunsoiiqlKKqcoqgNnJ9TFEX9gaKo6xRFFVMU9fKwXTyBQCAQhgViSI1h8vKLsXzrKeTlF4/0pRAIhDGOqduCzYfL0aI34+9HyklkCvgOwCSapqcAuA7g18IXUBTlDmAzgFwAtwB4nKKoW/p//QyAOAATaJrOBLBrOC6aQCAQCMMHMaTGKFUaA/YUNaBFb8be8w2obNGP9CURCIQxjMrLAy/ekYqIAG+8cHsqVF4/7jaDNE0fpGmasSZPAYgVedksAOU0TVfSNN0Nq7G0pP936wH8F03Tff3v1zLU10wgEAiE4eXHvVOOYZLC/LB0egz2nm/AQ9NikBzuP9KXRCAQxjgrcxLx6IzYH70RJcIqAJ+K/DwGQB3n3/UAsvv/OwXAcoqiHgKgAfAyTdNlQ3qVBAKBQBhWyG45htmQm4lHZ8QSI4pAILiMH5MRRVHU9wAiRX71G5qmv+x/zW8AWADsdPDtvQF00TSdRVHUUgDbAMwTuYbnADwHAPHx8Q5+BIFAIBBGkh/PjnmTQowoAoFAcA6apu+U+z1FUc8A+AmARTRN0yIvaYC1Doohtv9ngDU6taf/v/cC2C5xDVsBbAWArKwssc8gEAgEwiiF1EgRCAQCgSCAoqh7APwKwAM0Td+QeNkZAGkURSVRFOUF4DEA+/t/tw/A7f3/vQBWwQoCgUAg3ESMqCFFUdSrFEXRFEWFSvx+JUVRZf3/W9n/s3EURf2zX5b2KkVRecN71QQCgUD4EfA3AP4AvqMo6gJFUe8CAEVR0RRFHQCAfjGKlwB8C6AYwGc0TV/t//s8AA9TFHUZwJ8ArBnuL0AgEAiEoWXEUvsoiooDsBhArcTvgwH8FkAWABrAOYqi9gMwA/gzTdNH+j2AhyiKyqVpOn+YLp1AIBAINzk0TadK/LwRwL2cfx8AcEDkdVoA9w3V9REIBAJh5BnJiNT/wZo2IZUTfjeA72iabqdpugPWnh730DR9g6bpIwDQLzdbBHFZWgKBQCAQCAQCgUAYEkbEkKIoagmABpqmL8q8TExWNkbwPoEA7gdwSOaznqMo6ixFUWc1Go3zF00gEAgEAoFAIBAI/QxZap+crCyAf4M1rW8w7+8B4B8A3qZpulLqdUQRiUAgEAgEAoFAILiaITOkpGRlKYqaDCAJwEWKogBrWl4RRVGzaJpu5ry0AcBCzr9jARzl/HsrgDKapt903VUTXIWp2/Kj6kdDIBAIBAJBnsoWA5LD/Ub6MggElzHsqX00TV+maTqcpulEmqYTYU3Zmy4wogCrCtJiiqKCKIoKgjWC9S0AUBT1PwDGA/jZ8F05QSk7Cqux4I2j2FFYPdKXQiAQCAQCYRSQl1+Mx947hbz84pG+FALBZYyqPlIURWVRFPU+ANA03Q7gv2Ht03EGwH/RNN1OUVQsrOmBt8AaybpAURSRlR0lmLot2Hy4HC16M/5+pBymbstIXxKBQCAQCIQRpLLFgD1FDWjRm7H3fAMq1PqRviQCwSWMeO5Vf1SK+e+z4PTaoGl6G4BtgtfXA6CG6/oIjqHy8sCLd6Ti70fK8cLtqSS9j0AgEAiEHznJ4X5YOj0Ge8834KFpMUiJ8B/pSyIQXAJF0z8e/YWsrCz67NmzI30ZPwpIjRSBQHAA4hwD2aMINz8Vaj0xoghjEck9alSl9hFuHogRRSAQCAQCgQsxogg3G8SQIhAIBAKBQCAQCAQHIYYUgUAgEAgEAoFAIDgIMaQIBAKBQCAQCAQCwUGIIUUgEAgEAoFAIBAIDkIMKQKBQCAQCAQCgUBwEGJIEQgEAoFAIBAIBIKDEEOKQCAQCAQCgUAgEByEGFIEAoFAIBAIBAKB4CDEkCIQCAQCgUAgEAgEByGGFIFAIBAIBAKBQCA4CEXT9Ehfw7BBUZQGQM0g3iIUQKuLLmesQu6BFXIfyD0AyD1gGOx9aKVp+h5XXcxYxQV7FEDGJEDuAUDuAQO5D+QeAEO4R/2oDKnBQlHUWZqms0b6OkYScg+skPtA7gFA7gEDuQ+jB/IsyD0AyD1gIPeB3ANgaO8BSe0jEAgEAoFAIBAIBAchhhSBQCAQCAQCgUAgOAgxpBxj60hfwCiA3AMr5D6QewCQe8BA7sPogTwLcg8Acg8YyH0g9wAYwntAaqQIBAKBQCAQCAQCwUFIRIpAIBAIBAKBQCAQHIQYUgqhKOoeiqJKKYoqpyhqw0hfjyuh/n979x97VV3Hcfz5ii+/Ascvy5GwgI0yciUoGwwzpoRprl9jA6VJmau0smzOQdYarD80W0vXD2laWiFZSMZoBYWaP1hAkPwGwWAKiYAZJK1CevfHeX/xcOPH9/K9fO/3e+/rsZ3dz/l8zjn38/mcz73vfe4991xpqKTHJG2StFHSFzJ/oKTfSdqWjwMyX5Luzr5YJ2lM6VgzcvttkmbUq02nS1I3SX+WtDjXh0takW19SFKPzO+Z69uzfFjpGLMyf6uky+vUlNMiqb+kBZK2SNosaXyzjQNJN+frYIOk+ZJ6NcM4kPRDSXslbSjl1ezcS7pQ0vrc525J6tgWNjbHqMZ/bwLHKHCcAsepThWnIsLLKRagG/AcMALoAawFRtW7XjVs32BgTKbPAp4FRgHfAGZm/kzgjkxfCfwGEDAOWJH5A4G/5OOATA+od/uq7IsvAQ8Ci3P958C0TN8D3JDpG4F7Mj0NeCjTo3J89ASG57jpVu92VdH+B4DrM90D6N9M4wA4F9gB9C6d/483wzgALgHGABtKeTU798DK3Fa57xX1bnOjLDhGNfx7U6kvmjpGZRscpxynOk2cqnundIUFGA8sKa3PAmbVu15nsL2/At4HbAUGZ95gYGum5wJXl7bfmuVXA3NL+cds19kXYAiwDLgUWJwvpP1AS+U4AJYA4zPdktupcmyUt+vsC9Av35xVkd804yAD1Av5BtuS4+DyZhkHwLCKAFWTc59lW0r5x2znpd3nzTGqwd+bsr5NHaOyvo5TjlOdKk750r62aR20rXZlXsPJr3xHAyuAcyLixSzaA5yT6RP1R1fvp28DtwL/zfVBwN8j4rVcL7fnaFuz/EBu35X7YDiwD/hRXjpyr6Q+NNE4iIjdwDeB54EXKc7rapprHJTV6tyfm+nKfKuNRhlvp+QY1dQxChynHKf+X13jlCdSdpSkvsDDwBcj4mC5LIrpedSlYh1A0lXA3ohYXe+61FELxVfm34+I0cAhiq/Jj2qCcTAA+BBFsH4L0Ad4f10r1Uk0+rm3zs8xquljFDhOOU6dRD3OvSdSbbMbGFpaH5J5DUNSd4oANS8iFmb2S5IGZ/lgYG/mn6g/unI/TQA+KGkn8DOKSyfuAvpLasltyu052tYs7we8TNfug13ArohYkesLKAJWM42DScCOiNgXEYeBhRRjo5nGQVmtzv3uTFfmW200yng7Iccox6jkOOU4VamuccoTqbZZBYzMO6L0oPix3qI616lm8q4k9wGbI+JbpaJFwIxMz6C4Lr01/9q8I8o44EB+rboEmCxpQH5iMjnzOr2ImBURQyJiGMX5fTQipgOPAVNys8o+aO2bKbl9ZP60vEvOcGAkxY8XO72I2AO8IOntmXUZsIkmGgcUl0qMk/TGfF209kHTjIMKNTn3WXZQ0rjs12tLx7L2c4xq8Pcmx6iC4xTgOFWpvnGq3j8a6yoLxd0/nqW4q8lt9a5Pjdt2McVXoeuAZ3K5kuIa2mXANuD3wMDcXsB3sy/WAxeVjnUdsD2XT9S7bafZHxN5/Y5IIyjeWLYDvwB6Zn6vXN+e5SNK+9+WfbOVLnZnMuAC4E85Fh6huKNNU40DYDawBdgA/ITijkYNPw6A+RTX2x+m+NT3k7U898BF2afPAd+h4sfiXtp9/hyjGvy9qdSGpo1RWX/HKcepThOnlDuamZmZmZlZG/nSPjMzMzMzsyp5ImVmZmZmZlYlT6TMzMzMzMyq5ImUmZmZmZlZlTyRMjMzMzMzq5InUmZtJGl5Pg6TdE2Nj/3l4z1XDY47R9KkKvfZKensWjy/mZl1DMcos47n25+bVUnSROCWiLiqin1aIuK1k5S/GhF9a1C9dpO0k+L/FvbXuy5mZlYdxyizjuNvpMzaSNKrmbwdeI+kZyTdLKmbpDslrZK0TtKnc/uJkp6UtIjiX8eR9Iik1ZI2SvpU5t0O9M7jzSs/V/4j952SNkhaL2lq6diPS1ogaYukeflP3JV1vl/SlEzvlDRb0po81nmZP0jS0qzTvRR/Yte6/8ckrcy6zc22js129pLUJ/c7/4x0upmZtYljlGOUdbyWelfArAuaSenTvgw2ByJirKSewNOSlua2Y4DzI2JHrl8XEX+T1BtYJenhiJgp6XMRccFxnuujFP/i/m7g7NzniSwbDbwT+CvwNDABeOoUdd8fEWMk3QjcAlwPfA14KiLmSPoAxT+FI+kdwFRgQkQclvQ9YHpE/DgD79eB3sBPI2JD27rOzMzOMMcoxyjrIJ5ImbXfZOBdrZ+qAf2AkcB/gJWlAAVwk6SPZHpobvfySY59MTA/Io4AL0n6AzAWOJjH3gUg6RlgGKcOUgvzcTVFAAS4pDUdEb+W9ErmXwZcSBEYoQhIe7NsDrAK+Bdw0yme08zM6scxyuwM8UTKrP0EfD4ilhyTWVynfqhifRIwPiL+KelxoFc7nvffpfQR2vZ6bt2nLdsLeCAiZh2nbBDQF+hO0YZDx9nGzMzqzzHKMcrOEP9Gyqx6/wDOKq0vAW6Q1B1A0tsk9TnOfv2AVzJAnQeMK5Udbt2/wpPA1Lzu+00Un8ytrEkrXvcEcE3W/QpgQOYvA6ZIenOWDZT01iybC3wVmAfcUeP6mJnZ6XOMcoyyDuJvpMyqtw44ImktcD9wF8UlC2vyx7T7gA8fZ7/fAp+RtBnYCvyxVPYDYJ2kNRExvZT/S2A8sBYI4NaI2NP6I9wamQ3Ml7QRWA48DxARmyR9BVgq6Q3AYeCzkt4LHI6IByV1A5ZLujQiHq1hnczM7PQ4RjlGWQfx7c/NzMzMzMyq5Ev7zMzMzMzMquSJlJmZmZmZWZU8kTIzMzMzM6uSJ1JmZmZmZmZV8kTKzMzMzMysSp5ImZmZmZmZVckTKTMzMzMzsyp5ImVmZmZmZlal/wGBa2+m40N8rQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "pypesto.sample.geweke_test(result)\n", "visualize.sampling_parameter_traces(\n", @@ -321,10 +619,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "e64acdf3-b4dd-4b9a-9bc3-aa5a219f937a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "visualize.sampling_scatter(result, size=[13, 6]);" ] From c92455c0c5b5766010161501d8111c42bd8ddf56 Mon Sep 17 00:00:00 2001 From: Doresic Date: Thu, 21 Dec 2023 12:22:27 +0100 Subject: [PATCH 13/31] Reduce notebook run time I reduced the notebook output and run time by tweaking some small things like numbers of starts, rerunning, removing PEtab model compilation output, and added seeds for reproducability. Changes per notebook: - `amici.ipynb` -- TODO, didn't do yet - `censored.ipynb` -- reduce n_starts 10 -> 3 - `getting_started.ipynb` -- n_starts 100 -> 10 (do we need more for a nicer waterfall plot?), added seed for reproducability, Fides verbose=logging.ERROR, create_problem verbose=False (TODO amici.swig_wrappers logging) - `hierarchical.ipynb` -- change to only create_problem with verbose=False, set a seed for reproducability - TODO rest... --- doc/example/censored.ipynb | 2 +- doc/example/getting_started.ipynb | 8720 +---------------------------- doc/example/hierarchical.ipynb | 12 +- pypesto/petab/importer.py | 5 +- 4 files changed, 70 insertions(+), 8669 deletions(-) diff --git a/doc/example/censored.ipynb b/doc/example/censored.ipynb index 54485d385..5fce1728a 100644 --- a/doc/example/censored.ipynb +++ b/doc/example/censored.ipynb @@ -174,7 +174,7 @@ " method=\"L-BFGS-B\",\n", " options={\"disp\": None, \"ftol\": 2.220446049250313e-09, \"gtol\": 1e-5},\n", ")\n", - "n_starts = 10\n", + "n_starts = 3\n", "np.random.seed(n_starts)" ] }, diff --git a/doc/example/getting_started.ipynb b/doc/example/getting_started.ipynb index 5b40e06c3..afa1610db 100644 --- a/doc/example/getting_started.ipynb +++ b/doc/example/getting_started.ipynb @@ -20,7 +20,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -28,6 +28,8 @@ }, "outputs": [], "source": [ + "import logging\n", + "\n", "import amici\n", "import matplotlib as mpl\n", "import numpy as np\n", @@ -36,6 +38,8 @@ "import pypesto.petab\n", "import pypesto.visualize as visualize\n", "\n", + "np.random.seed(1)\n", + "\n", "# increase image resolution\n", "mpl.rcParams['figure.dpi'] = 300" ] @@ -59,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -94,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -123,21 +127,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████| 10/10 [00:00<00:00, 496.47it/s]\n" - ] - } - ], + "outputs": [], "source": [ "# choose optimizer\n", "optimizer = optimize.ScipyOptimizer()\n", @@ -161,42 +157,13 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': '0',\n", - " 'x': array([0., 0.]),\n", - " 'fval': 0.0,\n", - " 'grad': array([0., 0.]),\n", - " 'hess': None,\n", - " 'res': None,\n", - " 'sres': None,\n", - " 'n_fval': 4,\n", - " 'n_grad': 4,\n", - " 'n_hess': 0,\n", - " 'n_res': 0,\n", - " 'n_sres': 0,\n", - " 'x0': array([0.6901338 , 7.17776654]),\n", - " 'fval0': 51.996617134691896,\n", - " 'history': ,\n", - " 'exitflag': 0,\n", - " 'time': 0.007859945297241211,\n", - " 'message': 'CONVERGENCE: NORM_OF_PROJECTED_GRADIENT_<=_PGTOL',\n", - " 'optimizer': \"\"}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# E.g., The best model fit was obtained by the following optimization run:\n", "result_custom_problem.optimize_result.list[0]" @@ -204,33 +171,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "[0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 0.0,\n", - " 7.523163845262637e-37,\n", - " 1.504632769052528e-36,\n", - " 1.5046327690525307e-36,\n", - " 3.0562853121379475e-36,\n", - " 3.1973446342366133e-36]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Objective function values of the different optimizer runs:\n", "result_custom_problem.optimize_result.get_for_key('fval')" @@ -274,7 +221,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -287,7 +234,7 @@ "petab_yaml = './boehm_JProteomeRes2014/boehm_JProteomeRes2014.yaml'\n", "\n", "importer = pypesto.petab.PetabImporter.from_yaml(petab_yaml)\n", - "problem = importer.create_problem()" + "problem = importer.create_problem(verbose=False)" ] }, { @@ -303,34 +250,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-17 16:01:22.984 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 158.494 and h = 1.34133e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:22.986 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 158.494: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:23.022 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 158.494 and h = 1.34133e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:23.023 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 158.494: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:23.045 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 158.494 and h = 1.34133e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:23.046 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 158.494: AMICI failed to integrate the forward problem\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 15 s, sys: 476 ms, total: 15.5 s\n", - "Wall time: 15.4 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "%%capture\n", @@ -357,48 +283,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'id': '3',\n", - " 'x': array([-1.56904695, -5. , -2.20985861, -1.78591646, 5. ,\n", - " 4.19769784, 0.693 , 0.58567634, 0.81886181, 0.49859024,\n", - " 0.107 ]),\n", - " 'fval': 138.2219738778035,\n", - " 'grad': array([ 1.02808139e-02, 5.53588489e-02, 2.03115656e-04, 1.00993749e-02,\n", - " -4.41596715e-05, -9.26213990e-03, nan, 2.11437217e-03,\n", - " 2.50708689e-03, -3.97887714e-04, nan]),\n", - " 'hess': None,\n", - " 'res': None,\n", - " 'sres': None,\n", - " 'n_fval': 191,\n", - " 'n_grad': 191,\n", - " 'n_hess': 0,\n", - " 'n_res': 0,\n", - " 'n_sres': 0,\n", - " 'x0': array([ 2.1608791 , -2.88363288, -4.61132422, 1.82016484, -1.49529658,\n", - " -1.08670354, 0.693 , -3.8208997 , 2.23962013, 0.32443793,\n", - " 0.107 ]),\n", - " 'fval0': 1950794339021.3562,\n", - " 'history': ,\n", - " 'exitflag': 0,\n", - " 'time': 2.9797539710998535,\n", - " 'message': 'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH',\n", - " 'optimizer': \"\"}" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# E.g., best model fit was obtained by the following optimization run:\n", "result.optimize_result.list[0]" @@ -406,33 +297,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "[138.2219738778035,\n", - " 149.58788363739558,\n", - " 149.58968254433313,\n", - " 150.06701061933796,\n", - " 158.809924925096,\n", - " 171.13435267913164,\n", - " 233.09982603500364,\n", - " 249.74596437461332,\n", - " 249.74599739701642,\n", - " 249.74599747333608]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Objective function values of the different optimizer runs:\n", "result.optimize_result.get_for_key('fval')" @@ -470,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -481,7 +352,7 @@ "optimizer_scipy_lbfgsb = optimize.ScipyOptimizer(method='L-BFGS-B')\n", "optimizer_scipy_powell = optimize.ScipyOptimizer(method='Powell')\n", "\n", - "optimizer_fides = optimize.FidesOptimizer()\n", + "optimizer_fides = optimize.FidesOptimizer(verbose=logging.ERROR)\n", "optimizer_pyswarm = optimize.PyswarmOptimizer()" ] }, @@ -500,8291 +371,17 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", - "Performing parallel task execution on 8 processes.\n", - "2023-02-17 16:01:41.379 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 98.8572 and h = 1.56563e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:41.379 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 98.8572: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:41.408 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 98.8572 and h = 1.56563e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:41.408 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 98.8572: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:42.689 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 191.945 and h = 2.71313e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:42.690 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 191.945: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:42.903 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 191.945 and h = 2.71313e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:42.904 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 191.945: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:43.010 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 191.945 and h = 2.71313e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:43.010 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 191.945: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:43.595 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 51.0716 and h = 3.63082e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:43.595 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 51.0716: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:43.628 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 51.0716 and h = 3.63082e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:43.628 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 51.0716: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:50.278 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:TOO_MUCH_WORK] AMICI ERROR: in module CVODES in function CVode : At t = 76.964, mxstep steps taken before reaching tout. \n", - "2023-02-17 16:01:50.278 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 76.964: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:50.564 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 140.591 and h = 1.97937e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:50.565 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 140.591: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:51.675 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 167.379 and h = 7.66589e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:51.676 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 167.379: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:51.735 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 167.379 and h = 7.66589e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:51.736 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 167.379: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:01:55.008 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 188.834 and h = 3.15716e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:01:55.009 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 188.834: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:00.240 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.6972 and h = 1.45985e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:00.240 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.6972: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:00.294 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.6972 and h = 1.45985e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:00.294 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.6972: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:07.377 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 169.229 and h = 1.90001e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:07.378 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 169.229: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:07.511 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 169.229 and h = 1.90001e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:07.512 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 169.229: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:08.616 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 92.2863 and h = 1.84593e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:08.616 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 92.2863: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:08.718 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 118.037 and h = 2.126e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:08.718 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 118.037: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:08.830 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 118.037 and h = 2.126e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:08.831 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 118.037: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:08.900 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 118.037 and h = 2.126e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:08.900 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 118.037: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:12.500 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 138.745 and h = 1.31331e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:12.500 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 138.745: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:12.732 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 138.745 and h = 1.31331e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:12.732 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 138.745: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:12.830 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 138.745 and h = 1.31331e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:12.830 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 138.745: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:13.480 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.825 and h = 1.87415e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:13.480 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.825: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:13.674 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.825 and h = 1.87415e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:13.674 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.825: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:13.761 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.825 and h = 1.87415e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:13.761 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.825: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:15.776 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 168.782 and h = 3.21943e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:15.776 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 168.782: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:16.578 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 168.806 and h = 2.98398e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:16.578 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 168.806: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:18.407 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 78.788 and h = 1.37128e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:18.407 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 78.788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:18.474 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 78.788 and h = 1.37128e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:18.474 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 78.788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:18.529 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 78.788 and h = 1.37128e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:18.530 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 78.788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:20.019 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 150.727 and h = 1.41441e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:20.020 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 150.727: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:20.112 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 150.727 and h = 1.41441e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:20.113 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 150.727: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:22.871 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 66.7204 and h = 1.90309e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:22.871 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 66.7204: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:27.514 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 13.8788 and h = 2.73008e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:27.514 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 13.8788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:27.582 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 13.8788 and h = 2.73008e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:27.583 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 13.8788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:27.608 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 13.8788 and h = 2.73008e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:27.608 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 13.8788: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:27.889 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 221.347 and h = 4.88446e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:27.889 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 221.347: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:28.730 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 97.978 and h = 2.09287e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:28.730 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 97.978: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:28.807 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 97.978 and h = 2.09287e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:28.807 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 97.978: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:02:28.886 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 97.978 and h = 2.09287e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:02:28.886 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 97.978: AMICI failed to integrate the forward problem\n", - "Performing parallel task execution on 8 processes.\n", - "Performing parallel task execution on 8 processes.\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +2.27E+04 | NaN | NaN | 1.0E+00 | 9.5E+04 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +9.53E+12 | NaN | NaN | 1.0E+00 | 4.4E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +7.39E+13 | NaN | NaN | 1.0E+00 | 3.4E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +3.54E+08 | NaN | NaN | 1.0E+00 | 1.6E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +4.33E+02 | NaN | NaN | 1.0E+00 | 6.4E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +3.21E+11 | NaN | NaN | 1.0E+00 | 1.4E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +4.32E+02 | NaN | NaN | 1.0E+00 | 6.4E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 0| +8.98E+11 | NaN | NaN | 1.0E+00 | 4.1E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +9.31E+02 | -2.2E+04 | +1.3E-01 | 1.0E+00 | 9.5E+04 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +9.41E+06 | -9.5E+12 | +8.5E-02 | 1.0E+00 | 4.4E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +4.70E+04 | -3.5E+08 | +1.0E-01 | 1.0E+00 | 1.6E+09 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +3.88E+02 | -5.4E+02 | +9.0E-01 | 2.5E-01 | 3.0E+03 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +3.05E+02 | -1.3E+02 | +5.0E-01 | 1.0E+00 | 6.4E+01 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +1.79E+06 | -7.6E+06 | +8.9E-01 | 2.5E-01 | 3.6E+07 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +3.04E+02 | -8.4E+01 | +8.9E-01 | 5.0E-01 | 4.8E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +2.34E+11 | -7.4E+13 | +8.4E-02 | 1.0E+00 | 3.4E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +8.13E+03 | -3.9E+04 | +9.0E-01 | 2.5E-01 | 2.1E+05 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +7.96E+09 | -3.1E+11 | +9.3E-02 | 1.0E+00 | 1.4E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +3.05E+02 | -1.3E+02 | +5.4E-01 | 1.0E+00 | 6.4E+01 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +2.78E+05 | -1.5E+06 | +9.0E-01 | 5.0E-01 | 6.2E+06 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +2.73E+02 | -3.2E+01 | +5.6E-01 | 1.0E+00 | 7.5E+01 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +3.48E+10 | -2.0E+11 | +8.9E-01 | 2.5E-01 | 1.1E+12 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +1.52E+03 | -6.6E+03 | +9.0E-01 | 5.0E-01 | 3.3E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +4.37E+04 | -2.3E+05 | +9.0E-01 | 1.0E+00 | 9.6E+05 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +2.58E+02 | -4.7E+01 | +8.8E-01 | 1.0E+00 | 2.3E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 5| +2.51E+02 | -2.2E+01 | +7.0E-01 | 1.0E+00 | 9.4E+01 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +1.20E+09 | -6.8E+09 | +8.9E-01 | 2.5E-01 | 3.7E+10 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 1| +2.58E+08 | -9.0E+11 | +8.9E-02 | 1.0E+00 | 4.1E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +2.68E+02 | -3.7E+01 | +8.8E-01 | 1.0E+00 | 1.9E+02 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 6| +2.51E+02 | +3.1E+00 | -1.3E+00 | 1.0E+00 | 1.7E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 3| +5.19E+09 | -3.0E+10 | +8.9E-01 | 5.0E-01 | 1.6E+11 | 7.6E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +4.18E+02 | -1.1E+03 | +9.0E-01 | 1.0E+00 | 5.3E+03 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 5| +7.03E+03 | -3.7E+04 | +9.0E-01 | 1.0E+00 | 1.5E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 7| +2.51E+02 | +3.2E+00 | -1.3E+00 | 2.5E-01 | 1.7E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 3| +2.58E+02 | +1.6E+02 | -1.0E+01 | 2.0E+00 | 3.8E+01 | 4.1E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 6| +1.28E+03 | -5.8E+03 | +9.0E-01 | 1.0E+00 | 2.4E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 8| +2.50E+02 | -1.1E+00 | +5.2E-01 | 6.2E-02 | 1.7E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 5| +2.63E+02 | -1.5E+02 | +8.8E-01 | 1.0E+00 | 8.2E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +7.78E+08 | -4.4E+09 | +8.9E-01 | 5.0E-01 | 2.4E+10 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +1.81E+08 | -1.0E+09 | +8.9E-01 | 5.0E-01 | 5.5E+09 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +2.68E+02 | +2.9E+06 | -2.2E+05 | 2.0E+00 | 3.7E+01 | 5.2E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 5| +1.17E+08 | -6.6E+08 | +8.9E-01 | 5.0E-01 | 3.6E+09 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 9| +2.50E+02 | -4.6E-02 | +2.0E-01 | 6.2E-02 | 6.5E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 7| +3.80E+02 | -9.0E+02 | +9.0E-01 | 1.0E+00 | 3.8E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 2| +3.93E+07 | -2.2E+08 | +8.9E-01 | 2.5E-01 | 1.2E+09 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 6| +2.32E+02 | -3.1E+01 | +1.4E+00 | 2.0E+00 | 1.1E+02 | 4.0E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +2.58E+02 | +1.6E+02 | -9.3E+00 | 5.0E-01 | 3.8E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 6| +1.77E+07 | -9.9E+07 | +8.9E-01 | 5.0E-01 | 5.4E+08 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 10| +2.50E+02 | -5.6E-02 | +4.5E-01 | 1.6E-02 | 4.6E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +2.76E+07 | -1.5E+08 | +8.9E-01 | 1.0E+00 | 8.3E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 8| +2.56E+02 | -1.2E+02 | +8.7E-01 | 1.0E+00 | 5.9E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 11| +2.50E+02 | -2.1E-03 | +8.3E-02 | 1.6E-02 | 2.1E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 4| +2.68E+02 | +8.2E+01 | -3.8E+00 | 5.0E-01 | 3.7E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 5| +2.50E+02 | -8.2E+00 | +7.7E-01 | 1.2E-01 | 3.8E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 7| +2.32E+02 | +2.8E+03 | -8.1E+01 | 4.0E+00 | 4.5E+01 | 1.1E+01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 7| +2.70E+06 | -1.5E+07 | +8.9E-01 | 5.0E-01 | 8.2E+07 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 12| +2.50E+02 | -9.9E-03 | +7.0E-01 | 3.9E-03 | 1.8E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 9| +2.32E+02 | -2.4E+01 | +1.6E+00 | 2.0E+00 | 7.0E+01 | 3.8E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 5| +4.23E+06 | -2.3E+07 | +8.9E-01 | 1.0E+00 | 1.3E+08 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 8| +4.12E+05 | -2.3E+06 | +8.9E-01 | 5.0E-01 | 1.2E+07 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 13| +2.50E+02 | -3.6E-05 | +2.6E-01 | 3.9E-03 | 1.3E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 8| +2.32E+02 | +2.5E+02 | -9.6E+00 | 1.0E+00 | 4.5E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 5| +2.57E+02 | -1.1E+01 | +9.5E-01 | 1.2E-01 | 3.7E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 14| +2.50E+02 | -3.8E-05 | +2.8E-01 | 3.9E-03 | 1.3E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 6| +2.50E+02 | +3.5E-01 | -5.0E-01 | 2.5E-01 | 1.0E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 9| +6.25E+04 | -3.5E+05 | +9.0E-01 | 5.0E-01 | 1.9E+06 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 3| +5.97E+06 | -3.3E+07 | +8.9E-01 | 5.0E-01 | 1.8E+08 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 15| +2.50E+02 | -3.7E-05 | +2.8E-01 | 3.9E-03 | 1.3E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 10| +2.32E+02 | +1.0E+03 | -3.9E+01 | 4.0E+00 | 4.2E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 9| +2.22E+02 | -9.9E+00 | +4.0E-01 | 2.5E-01 | 4.5E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 6| +6.50E+05 | -3.6E+06 | +9.0E-01 | 1.0E+00 | 1.9E+07 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 6| +2.57E+02 | +3.5E+00 | -4.3E-01 | 2.5E-01 | 3.0E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) 7| +2.50E+02 | +3.5E-01 | -5.0E-01 | 6.2E-02 | 1.0E+01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:05 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:05 fides(INFO) 10| +9.71E+03 | -5.3E+04 | +9.0E-01 | 5.0E-01 | 2.9E+05 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:05 fides(INFO) 16| +2.50E+02 | -3.9E-05 | +2.9E-01 | 3.9E-03 | 1.3E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 7| +9.99E+04 | -5.5E+05 | +9.0E-01 | 1.0E+00 | 3.0E+06 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +2.50E+02 | -3.8E-05 | +2.9E-01 | 3.9E-03 | 1.3E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 10| +2.18E+02 | -4.7E+00 | +6.3E-01 | 2.5E-01 | 4.8E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.32E+02 | +1.1E+02 | -5.5E+00 | 1.0E+00 | 4.2E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 11| +1.61E+03 | -8.1E+03 | +9.1E-01 | 1.0E+00 | 4.4E+04 | 5.1E-01 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) 8| +2.50E+02 | -2.9E-01 | +8.4E-01 | 1.6E-02 | 1.0E+01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 7| +2.53E+02 | -4.3E+00 | +9.3E-01 | 6.2E-02 | 3.0E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +2.50E+02 | -4.0E-05 | +3.0E-01 | 3.9E-03 | 1.3E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +2.28E+02 | -4.3E+00 | +1.7E-01 | 2.5E-01 | 4.2E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +2.50E+02 | -3.9E-05 | +3.0E-01 | 3.9E-03 | 1.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +4.30E+02 | -1.2E+03 | +9.0E-01 | 1.0E+00 | 6.6E+03 | 2.7E+00 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.17E+02 | -6.0E-01 | +6.6E-02 | 2.5E-01 | 2.1E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.23E+02 | -5.2E+00 | +8.6E-01 | 6.2E-02 | 6.5E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 8| +2.51E+02 | -1.4E+00 | +2.2E-01 | 1.2E-01 | 2.3E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 9| +2.50E+02 | +2.2E-02 | -1.7E-01 | 3.1E-02 | 4.7E+00 | 7.6E-02 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +2.50E+02 | -4.1E-05 | +3.1E-01 | 3.9E-03 | 1.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 4| +9.12E+05 | -5.1E+06 | +8.9E-01 | 5.0E-01 | 2.7E+07 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +2.50E+02 | -4.0E-05 | +3.1E-01 | 3.9E-03 | 1.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.35E+02 | -1.9E+02 | +9.3E-01 | 1.0E+00 | 1.0E+03 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 14| +2.20E+02 | -2.3E+00 | +5.9E-01 | 1.2E-01 | 3.3E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 8| +1.53E+04 | -8.5E+04 | +9.0E-01 | 1.0E+00 | 4.6E+05 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +2.14E+02 | -3.4E+00 | +8.3E-01 | 6.2E-02 | 4.6E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +2.50E+02 | -4.2E-05 | +3.3E-01 | 3.9E-03 | 1.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 10| +2.50E+02 | -6.0E-02 | +8.0E-01 | 7.8E-03 | 4.7E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 9| +2.50E+02 | -1.0E+00 | +8.3E-01 | 3.1E-02 | 2.5E+01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +2.20E+02 | +3.1E-01 | -1.7E-01 | 1.2E-01 | 1.9E+01 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 23| +2.50E+02 | -4.1E-05 | +3.3E-01 | 3.9E-03 | 1.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.12E+02 | -2.0E+00 | +7.7E-01 | 1.2E-01 | 2.1E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.50E+02 | +4.3E-04 | -2.9E-02 | 1.6E-02 | 1.7E+00 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 24| +2.50E+02 | -4.3E-05 | +3.4E-01 | 3.9E-03 | 1.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 10| +2.50E+02 | -1.5E-01 | +3.2E-01 | 6.2E-02 | 9.9E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 14| +2.14E+02 | -2.1E+01 | +7.7E-01 | 2.0E+00 | 1.4E+02 | 5.9E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 16| +2.19E+02 | -9.2E-01 | +8.5E-01 | 3.1E-02 | 1.9E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +2.50E+02 | -4.2E-05 | +3.4E-01 | 3.9E-03 | 1.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 14| +2.09E+02 | -2.5E+00 | +6.5E-01 | 2.5E-01 | 1.6E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 5| +1.41E+05 | -7.7E+05 | +9.0E-01 | 5.0E-01 | 4.2E+06 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +2.50E+02 | -8.0E-03 | +6.8E-01 | 3.9E-03 | 1.7E+00 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +2.50E+02 | -4.4E-05 | +3.5E-01 | 3.9E-03 | 1.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 9| +2.47E+03 | -1.3E+04 | +9.1E-01 | 1.0E+00 | 7.0E+04 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +2.18E+02 | -1.1E+00 | +9.1E-01 | 6.2E-02 | 1.5E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.50E+02 | +2.9E-02 | -1.4E-01 | 6.2E-02 | 5.7E+00 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 27| +2.50E+02 | -4.3E-05 | +3.5E-01 | 3.9E-03 | 1.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +2.14E+02 | +1.2E+04 | -3.9E+02 | 4.0E+00 | 5.8E+01 | 9.7E+00 | trr |0\n", - "2023-02-17 16:05:06 fides(INFO) 15| +2.02E+02 | -7.4E+00 | +1.0E+00 | 2.5E-01 | 2.9E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 28| +2.50E+02 | -4.5E-05 | +3.7E-01 | 3.9E-03 | 1.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +2.17E+02 | -1.3E+00 | +7.6E-01 | 1.2E-01 | 1.7E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 29| +2.50E+02 | -4.5E-05 | +3.7E-01 | 3.9E-03 | 1.1E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.50E+02 | -1.0E-04 | +1.0E-01 | 3.9E-03 | 4.0E-01 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +2.50E+02 | -1.1E-01 | +6.3E-01 | 1.6E-02 | 5.7E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 30| +2.50E+02 | -4.6E-05 | +3.8E-01 | 3.9E-03 | 1.1E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +2.17E+02 | +3.8E-01 | -2.1E-01 | 2.5E-01 | 1.7E+01 | 7.0E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 6| +2.19E+04 | -1.2E+05 | +9.0E-01 | 5.0E-01 | 6.4E+05 | 9.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 16| +1.88E+02 | -1.3E+01 | +1.8E-01 | 5.0E-01 | 2.3E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 16| +2.14E+02 | +3.6E+01 | -2.8E+00 | 9.1E-01 | 5.8E+01 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 14| +2.50E+02 | -4.0E-04 | +5.9E-01 | 9.8E-04 | 3.7E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 31| +2.50E+02 | -4.6E-05 | +3.8E-01 | 3.9E-03 | 1.1E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.50E+02 | -5.0E-03 | +7.3E-01 | 1.6E-02 | 5.3E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +2.16E+02 | -9.1E-01 | +5.2E-01 | 6.2E-02 | 1.7E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 10| +5.65E+02 | -1.9E+03 | +9.0E-01 | 1.0E+00 | 1.1E+04 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 32| +2.50E+02 | -4.8E-05 | +3.9E-01 | 3.9E-03 | 1.1E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +1.62E+02 | -2.6E+01 | +9.5E-01 | 1.2E-01 | 2.4E+02 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +2.15E+02 | -7.3E-01 | +7.1E-01 | 6.2E-02 | 1.6E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +2.50E+02 | -1.8E-05 | +4.9E-01 | 9.8E-04 | 6.0E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 33| +2.50E+02 | -8.5E-05 | +1.0E+00 | 3.9E-03 | 1.1E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 14| +2.50E+02 | -4.5E-03 | +6.8E-01 | 1.6E-02 | 5.8E-01 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 34| +2.50E+02 | +3.0E-03 | -1.3E+01 | 7.8E-03 | 3.4E-02 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 7| +3.57E+03 | -1.8E+04 | +9.0E-01 | 5.0E-01 | 9.9E+04 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +2.15E+02 | -2.4E-01 | +2.6E-01 | 6.2E-02 | 1.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 35| +2.50E+02 | +1.1E-03 | -1.0E+01 | 2.0E-03 | 3.4E-02 | 5.0E-03 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 16| +2.50E+02 | -1.7E-05 | +5.1E-01 | 9.8E-04 | 5.5E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +1.51E+02 | -1.2E+01 | +7.1E-01 | 2.5E-01 | 5.4E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 36| +2.50E+02 | +7.4E-05 | -2.0E+00 | 4.9E-04 | 3.4E-02 | 1.2E-03 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.58E+02 | -3.1E+02 | +9.3E-01 | 2.0E+00 | 1.7E+03 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +2.02E+02 | -1.2E+01 | +9.9E-01 | 2.3E-01 | 5.8E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +2.50E+02 | -4.3E-03 | +6.5E-01 | 1.6E-02 | 6.2E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 8| +7.19E+02 | -2.9E+03 | +8.9E-01 | 1.0E+00 | 1.5E+04 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 23| +2.14E+02 | -7.2E-01 | +6.0E-01 | 6.2E-02 | 1.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 37| +2.50E+02 | -3.1E-06 | +3.0E-01 | 1.2E-04 | 3.4E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +2.50E+02 | -1.8E-05 | +5.4E-01 | 9.8E-04 | 5.4E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +2.04E+02 | -5.3E+01 | +9.2E-01 | 2.0E+00 | 2.4E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +1.49E+02 | -1.5E+00 | +8.9E-01 | 2.5E-01 | 2.9E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 38| +2.50E+02 | -2.7E-06 | +1.0E+00 | 1.2E-04 | 1.9E-02 | 3.0E-04 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 24| +2.14E+02 | -1.0E-01 | +1.3E-01 | 6.2E-02 | 1.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 39| +2.50E+02 | -5.2E-06 | +1.0E+00 | 2.4E-04 | 8.2E-03 | 6.3E-04 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 16| +2.50E+02 | -3.8E-03 | +6.2E-01 | 1.6E-02 | 6.2E-01 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +2.50E+02 | -1.7E-05 | +5.6E-01 | 9.8E-04 | 5.0E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +2.14E+02 | -3.8E-01 | +9.0E-01 | 1.6E-02 | 1.8E+01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 9| +3.47E+02 | -3.7E+02 | +6.6E-01 | 2.0E+00 | 2.4E+03 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 40| +2.50E+02 | -9.1E-06 | +1.0E+00 | 4.9E-04 | 8.8E-03 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +1.48E+02 | -1.0E+00 | +6.6E-01 | 5.0E-01 | 3.8E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 41| +2.50E+02 | -1.9E-05 | +1.0E+00 | 9.8E-04 | 8.3E-03 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +1.89E+02 | -1.2E+01 | +7.6E-02 | 4.6E-01 | 3.2E+01 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +2.50E+02 | -3.6E-03 | +6.1E-01 | 1.6E-02 | 6.4E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +2.04E+02 | +7.8E+03 | -4.7E+02 | 2.0E+00 | 5.1E+01 | 4.2E+00 | trr |0\n", - "2023-02-17 16:05:06 fides(INFO) 19| +2.50E+02 | -1.8E-05 | +5.8E-01 | 9.8E-04 | 4.9E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 42| +2.50E+02 | -3.8E-05 | +1.0E+00 | 2.0E-03 | 1.1E-02 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +2.14E+02 | -3.5E-01 | +7.6E-01 | 3.1E-02 | 1.3E+01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 43| +2.50E+02 | -7.6E-05 | +1.0E+00 | 3.9E-03 | 1.0E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +1.48E+02 | +3.6E+01 | -1.4E+01 | 5.0E-01 | 2.5E+01 | 7.9E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 14| +1.72E+02 | -3.2E+01 | +8.5E-01 | 5.0E-01 | 5.1E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 10| +2.01E+02 | -1.5E+02 | +9.0E-01 | 2.0E+00 | 5.2E+02 | 1.8E+00 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +2.50E+02 | -3.2E-03 | +5.8E-01 | 1.6E-02 | 6.2E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +2.50E+02 | -1.7E-05 | +5.9E-01 | 9.8E-04 | 4.7E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 44| +2.50E+02 | -1.6E-04 | +1.0E+00 | 7.8E-03 | 1.4E-02 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 27| +2.13E+02 | -3.5E-01 | +6.8E-01 | 6.2E-02 | 8.4E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 45| +2.50E+02 | -3.3E-04 | +1.0E+00 | 1.6E-02 | 2.7E-02 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +1.72E+02 | +4.5E+03 | -2.0E+02 | 1.0E+00 | 1.3E+02 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 28| +2.13E+02 | -2.6E-01 | +4.4E-01 | 6.2E-02 | 1.3E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +1.48E+02 | +4.0E-01 | -4.6E-01 | 1.2E-01 | 2.5E+01 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 46| +2.50E+02 | -7.3E-04 | +1.1E+00 | 3.1E-02 | 5.6E-02 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +2.50E+02 | -1.8E-05 | +6.2E-01 | 9.8E-04 | 4.6E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +2.50E+02 | -3.1E-03 | +5.7E-01 | 1.6E-02 | 6.4E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +1.68E+02 | -2.1E+01 | +9.8E-01 | 1.1E-01 | 2.5E+02 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 47| +2.50E+02 | -1.8E-03 | +1.2E+00 | 6.2E-02 | 1.1E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 16| +1.60E+02 | -1.3E+01 | +5.5E-01 | 2.4E-01 | 1.3E+02 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 29| +2.13E+02 | +1.7E-02 | -5.8E-02 | 6.2E-02 | 8.9E+00 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 23| +1.48E+02 | -2.2E-01 | +7.3E-01 | 3.1E-02 | 2.5E+01 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 48| +2.50E+02 | -6.0E-03 | +1.5E+00 | 1.2E-01 | 2.2E-01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 11| +2.01E+02 | +7.6E+02 | -3.3E+01 | 2.0E+00 | 1.3E+02 | 2.0E+00 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +2.50E+02 | -2.7E-03 | +5.5E-01 | 1.6E-02 | 6.1E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +2.50E+02 | -1.8E-05 | +6.2E-01 | 9.8E-04 | 4.4E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 49| +2.50E+02 | -3.3E-02 | +2.1E+00 | 2.5E-01 | 4.4E-01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 30| +2.13E+02 | -2.1E-01 | +8.2E-01 | 1.6E-02 | 8.9E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +1.53E+02 | -6.8E+00 | +9.6E-01 | 2.4E-01 | 1.9E+02 | 8.8E-02 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06.633 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 142.235 and h = 2.73909e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:06.633 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 142.235: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:06 fides(INFO) 50| +2.49E+02 | -5.0E-01 | +3.7E+00 | 5.0E-01 | 8.0E-01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 24| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.1E-02 | 1.1E+01 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 23| +2.50E+02 | -1.8E-05 | +6.4E-01 | 9.8E-04 | 4.4E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 31| +2.13E+02 | -1.5E-01 | +7.0E-01 | 3.1E-02 | 6.6E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +2.50E+02 | -2.6E-03 | +5.4E-01 | 1.6E-02 | 6.2E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +1.50E+02 | -3.2E+00 | +7.8E-01 | 2.4E-01 | 5.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 12| +1.89E+02 | -1.2E+01 | +5.5E-01 | 3.2E-01 | 1.3E+02 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 51| +2.37E+02 | -1.2E+01 | +4.4E+00 | 1.0E+00 | 1.3E+00 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 32| +2.13E+02 | -1.4E-01 | +5.8E-01 | 3.1E-02 | 8.4E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +1.48E+02 | -5.5E-02 | +9.5E-01 | 7.8E-03 | 1.1E+01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 24| +2.50E+02 | -1.8E-05 | +6.4E-01 | 9.8E-04 | 4.3E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +1.50E+02 | +5.4E+01 | -1.2E+01 | 4.7E-01 | 6.5E+01 | 7.7E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +1.59E+02 | -8.8E+00 | +8.0E-01 | 2.3E-01 | 4.5E+01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +2.50E+02 | -2.3E-03 | +5.2E-01 | 1.6E-02 | 5.9E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 13| +1.82E+02 | -6.7E+00 | +9.3E-01 | 3.2E-01 | 1.2E+02 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 33| +2.12E+02 | -6.5E-02 | +3.0E-01 | 3.1E-02 | 6.3E+00 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 52| +2.28E+02 | -9.6E+00 | +5.7E-01 | 2.0E+00 | 3.1E+01 | 2.9E+00 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +2.50E+02 | -1.8E-05 | +6.5E-01 | 9.8E-04 | 4.3E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +1.48E+02 | -4.0E-02 | +9.3E-01 | 1.6E-02 | 1.3E+01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +1.49E+02 | -3.2E-01 | +1.4E-01 | 1.2E-01 | 6.5E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 53| +2.28E+02 | +4.7E+03 | -1.9E+02 | 2.0E+00 | 4.1E+01 | 3.2E+00 | trr |0\n", - "2023-02-17 16:05:06 fides(INFO) 34| +2.12E+02 | -1.4E-01 | +5.3E-01 | 3.1E-02 | 8.8E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 14| +1.82E+02 | -8.6E-01 | -1.3E-01 | 6.4E-01 | 3.2E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 23| +2.50E+02 | -2.2E-03 | +5.2E-01 | 1.6E-02 | 6.0E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +2.50E+02 | -1.8E-05 | +6.5E-01 | 9.8E-04 | 4.2E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 27| +1.48E+02 | -4.3E-02 | +9.2E-01 | 3.1E-02 | 7.4E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 35| +2.12E+02 | -4.9E-02 | +2.7E-01 | 3.1E-02 | 5.7E+00 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +1.49E+02 | -5.4E-01 | +9.3E-01 | 2.9E-02 | 3.9E+01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 27| +2.50E+02 | -1.8E-05 | +6.6E-01 | 9.8E-04 | 4.2E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 24| +2.50E+02 | -2.0E-03 | +4.9E-01 | 1.6E-02 | 5.7E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 54| +2.21E+02 | -6.9E+00 | +2.5E-01 | 3.2E-01 | 4.1E+01 | 8.2E-01 | trr |1\n", - "2023-02-17 16:05:06 fides(INFO) 15| +1.79E+02 | -2.9E+00 | +9.5E-01 | 1.6E-01 | 3.2E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 36| +2.12E+02 | -1.2E-01 | +5.0E-01 | 3.1E-02 | 8.1E+00 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 28| +1.48E+02 | -5.0E-02 | +9.3E-01 | 6.2E-02 | 8.9E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +1.48E+02 | -4.9E-01 | +6.7E-01 | 5.9E-02 | 3.5E+01 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 28| +2.50E+02 | -1.8E-05 | +6.6E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +2.50E+02 | -1.9E-03 | +5.0E-01 | 1.6E-02 | 5.8E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 55| +2.14E+02 | -6.7E+00 | +8.1E-01 | 8.0E-02 | 7.1E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:06.818 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.0543 and h = 1.00881e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:06.818 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.0543: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:06 fides(INFO) 37| +2.12E+02 | -4.5E-02 | +2.6E-01 | 3.1E-02 | 5.3E+00 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 23| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.9E-02 | 2.9E+01 | 5.4E-02 | 2d |0\n", - "2023-02-17 16:05:06.820 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 142.802 and h = 3.01856e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:06.820 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 142.802: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:06 fides(INFO) 29| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 5.5E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 16| +1.76E+02 | -2.8E+00 | +1.0E+00 | 3.2E-01 | 5.7E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 21| +1.55E+02 | -4.4E+00 | +1.1E+00 | 4.6E-01 | 1.3E+02 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 29| +2.50E+02 | -1.9E-05 | +6.7E-01 | 9.8E-04 | 4.2E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 38| +2.12E+02 | -1.0E-01 | +4.8E-01 | 3.1E-02 | 7.4E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 56| +2.13E+02 | -1.4E+00 | +8.9E-01 | 1.6E-01 | 2.1E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +2.50E+02 | -1.7E-03 | +4.7E-01 | 1.6E-02 | 5.5E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 30| +1.48E+02 | -3.0E-02 | +9.2E-01 | 3.1E-02 | 5.5E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 24| +1.48E+02 | -1.3E-01 | +9.5E-01 | 1.5E-02 | 2.9E+01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 17| +1.76E+02 | +5.8E+00 | -1.0E+00 | 6.4E-01 | 3.4E+01 | 9.5E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 39| +2.12E+02 | -4.6E-02 | +2.9E-01 | 3.1E-02 | 4.9E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 30| +2.50E+02 | -1.8E-05 | +6.7E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 25| +1.48E+02 | -1.4E-01 | +9.7E-01 | 2.9E-02 | 2.0E+01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 57| +2.13E+02 | -1.6E-01 | +8.3E-02 | 3.2E-01 | 9.5E+00 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 27| +2.50E+02 | -1.6E-03 | +4.7E-01 | 1.6E-02 | 5.6E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 31| +1.48E+02 | -4.1E-02 | +9.4E-01 | 6.2E-02 | 8.6E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 18| +1.72E+02 | -4.7E+00 | +1.0E+00 | 1.6E-01 | 3.4E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 40| +2.12E+02 | -9.2E-02 | +4.9E-01 | 3.1E-02 | 6.6E+00 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 31| +2.50E+02 | -1.9E-05 | +6.7E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 26| +1.48E+02 | -1.3E-01 | +9.2E-01 | 5.9E-02 | 2.0E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 58| +2.12E+02 | -1.1E+00 | +9.4E-01 | 8.0E-02 | 1.6E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 32| +1.48E+02 | -6.2E-02 | +9.3E-01 | 1.2E-01 | 3.4E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 28| +2.50E+02 | -1.4E-03 | +4.4E-01 | 1.6E-02 | 5.3E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 19| +1.69E+02 | -3.0E+00 | +9.6E-01 | 3.2E-01 | 7.8E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 41| +2.12E+02 | -5.1E-02 | +3.6E-01 | 3.1E-02 | 4.5E+00 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 32| +2.50E+02 | -1.8E-05 | +6.7E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 22| +1.55E+02 | -6.0E-02 | +2.0E-01 | 4.6E-01 | 1.6E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 59| +2.11E+02 | -8.6E-02 | +4.5E-01 | 1.6E-01 | 3.0E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 33| +1.48E+02 | -5.6E-02 | +5.5E-01 | 2.5E-01 | 7.0E+00 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 27| +1.48E+02 | -1.5E-01 | +8.3E-01 | 1.2E-01 | 1.2E+01 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 42| +2.12E+02 | -8.7E-02 | +5.3E-01 | 3.1E-02 | 5.9E+00 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:06.973 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 191.433 and h = 1.57281e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:06.973 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 191.433: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:06 fides(INFO) 29| +2.50E+02 | -1.3E-03 | +4.5E-01 | 1.6E-02 | 5.4E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:06 fides(INFO) 20| +1.69E+02 | +0.0E+00 | +0.0E+00 | 6.4E-01 | 3.8E+01 | 9.4E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 33| +2.50E+02 | -1.9E-05 | +6.8E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:06 fides(INFO) 34| +1.48E+02 | +1.4E-01 | -7.5E-01 | 2.5E-01 | 3.6E+00 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:06 fides(INFO) 43| +2.12E+02 | -6.0E-02 | +4.7E-01 | 3.1E-02 | 4.0E+00 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 60| +2.11E+02 | -4.1E-01 | +1.6E+00 | 1.6E-01 | 4.8E+00 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 28| +1.48E+02 | +1.2E+00 | -3.6E+00 | 2.4E-01 | 1.1E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 21| +1.64E+02 | -4.6E+00 | +8.6E-01 | 1.6E-01 | 3.8E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 30| +2.50E+02 | -1.2E-03 | +4.2E-01 | 1.6E-02 | 5.1E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 34| +2.50E+02 | -1.9E-05 | +6.7E-01 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +2.12E+02 | -8.8E-02 | +6.1E-01 | 3.1E-02 | 5.1E+00 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 35| +1.48E+02 | -5.7E-03 | +4.7E-02 | 6.2E-02 | 3.6E+00 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 22| +1.62E+02 | -2.0E+00 | +9.1E-01 | 3.2E-01 | 9.0E+01 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 29| +1.48E+02 | -2.4E-02 | +2.0E-01 | 5.9E-02 | 1.1E+01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 61| +2.10E+02 | -1.3E+00 | +1.6E+00 | 3.2E-01 | 4.7E+00 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 31| +2.50E+02 | -1.1E-03 | +4.2E-01 | 1.6E-02 | 5.2E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 35| +2.50E+02 | -2.6E-05 | +1.0E+00 | 9.8E-04 | 4.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 45| +2.12E+02 | -7.4E-02 | +6.0E-01 | 3.1E-02 | 3.6E+00 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 36| +1.48E+02 | -3.6E-02 | +9.2E-01 | 1.6E-02 | 1.2E+01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 30| +1.48E+02 | -5.9E-02 | +6.2E-01 | 1.5E-02 | 7.1E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 23| +1.62E+02 | +6.4E+01 | -1.0E+01 | 6.4E-01 | 2.8E+01 | 7.8E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 62| +2.10E+02 | +3.8E+01 | -2.7E+01 | 6.4E-01 | 8.3E+00 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 46| +2.11E+02 | -9.7E-02 | +7.2E-01 | 3.1E-02 | 4.4E+00 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 36| +2.50E+02 | -4.4E-05 | +1.0E+00 | 2.0E-03 | 1.3E-02 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 32| +2.50E+02 | -9.9E-04 | +3.9E-01 | 1.6E-02 | 5.0E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 23| +1.55E+02 | -1.6E-01 | +9.6E-01 | 1.1E-01 | 3.5E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 37| +1.48E+02 | -1.9E-02 | +6.6E-01 | 3.1E-02 | 2.4E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 31| +1.48E+02 | -2.2E-02 | +9.5E-01 | 1.5E-02 | 1.2E+01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 47| +2.11E+02 | -9.3E-02 | +7.4E-01 | 3.1E-02 | 3.2E+00 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 24| +1.60E+02 | -1.6E+00 | +4.6E-01 | 1.6E-01 | 2.8E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 37| +2.50E+02 | -5.0E-05 | +5.4E-01 | 3.9E-03 | 3.3E-02 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 38| +1.48E+02 | -6.6E-03 | +9.5E-01 | 3.1E-02 | 2.7E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 63| +2.10E+02 | -1.7E-02 | -1.9E-02 | 1.6E-01 | 8.3E+00 | 4.2E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 48| +2.11E+02 | -1.2E-01 | +8.2E-01 | 3.1E-02 | 3.8E+00 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 33| +2.50E+02 | -9.9E-05 | +5.5E-02 | 1.6E-02 | 5.1E-01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 32| +1.48E+02 | -1.0E-02 | +8.3E-01 | 2.9E-02 | 1.9E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 25| +1.59E+02 | -8.2E-01 | +9.2E-01 | 1.6E-01 | 7.7E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 49| +2.11E+02 | -2.5E-01 | +9.9E-01 | 6.2E-02 | 2.9E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 38| +2.50E+02 | -1.4E-04 | +1.0E+00 | 3.9E-03 | 1.3E-01 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 39| +1.48E+02 | -9.5E-03 | +9.5E-01 | 6.2E-02 | 9.8E-01 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 64| +2.09E+02 | -2.9E-01 | +1.1E+00 | 4.0E-02 | 8.3E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 33| +1.48E+02 | -1.6E-02 | +9.3E-01 | 5.9E-02 | 3.7E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 26| +1.59E+02 | +1.4E+00 | -9.1E-01 | 3.2E-01 | 1.4E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 34| +2.50E+02 | -1.6E-03 | +5.5E-01 | 3.9E-03 | 7.6E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 39| +2.50E+02 | -1.9E-04 | +1.0E+00 | 7.8E-03 | 3.9E-02 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 50| +2.10E+02 | -8.2E-01 | +1.3E+00 | 1.2E-01 | 4.2E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 40| +1.48E+02 | -1.5E-02 | +9.6E-01 | 1.2E-01 | 1.4E+00 | 9.6E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 65| +2.09E+02 | -2.2E-03 | -4.0E-03 | 8.0E-02 | 2.7E+00 | 2.1E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 34| +1.48E+02 | -8.3E-03 | +3.8E-01 | 1.2E-01 | 1.5E+00 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 40| +2.50E+02 | -4.0E-04 | +1.0E+00 | 1.6E-02 | 3.2E-02 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 51| +2.06E+02 | -4.2E+00 | +1.9E+00 | 2.5E-01 | 5.5E+00 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 27| +1.59E+02 | -2.9E-01 | +3.6E-01 | 8.0E-02 | 1.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 35| +2.50E+02 | -5.5E-04 | +1.0E+00 | 3.9E-03 | 3.4E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 24| +1.55E+02 | -5.3E-02 | +8.6E-01 | 2.3E-01 | 9.3E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 41| +1.48E+02 | -1.7E-02 | +8.2E-01 | 2.5E-01 | 7.1E-01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 66| +2.09E+02 | -6.0E-02 | +9.2E-01 | 2.0E-02 | 2.7E+00 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 35| +1.48E+02 | +1.5E-01 | -2.0E+00 | 1.2E-01 | 2.4E+00 | 8.4E-02 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 28| +1.59E+02 | -2.9E-01 | +9.1E-01 | 8.0E-02 | 4.7E+01 | 1.3E-01 | trr |1\n", - "2023-02-17 16:05:07 fides(INFO) 41| +2.50E+02 | -9.0E-04 | +1.1E+00 | 3.1E-02 | 7.0E-02 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 42| +1.48E+02 | +1.8E-01 | -6.0E+00 | 5.0E-01 | 1.3E+00 | 2.7E-01 | trr |0\n", - "2023-02-17 16:05:07 fides(INFO) 52| +1.82E+02 | -2.4E+01 | +1.6E+00 | 5.0E-01 | 1.7E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 36| +2.50E+02 | -4.2E-04 | +9.5E-01 | 7.8E-03 | 3.5E-02 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 36| +1.48E+02 | -7.1E-03 | +2.8E-01 | 2.9E-02 | 2.4E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 42| +2.50E+02 | -2.3E-03 | +1.2E+00 | 6.2E-02 | 1.4E-01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 29| +1.59E+02 | -5.4E-02 | +6.6E-01 | 8.0E-02 | 7.8E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:07.368 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.2556 and h = 1.10609e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:07.368 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.2556: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:07 fides(INFO) 43| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 1.3E+00 | 6.7E-02 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 67| +2.09E+02 | +1.3E-02 | -3.3E-01 | 4.0E-02 | 8.9E-01 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 53| +1.82E+02 | +4.1E+01 | -3.7E+00 | 1.0E+00 | 4.0E+01 | 2.1E+00 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 37| +2.50E+02 | -7.0E-04 | +7.1E-01 | 1.6E-02 | 1.1E-01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 37| +1.48E+02 | -5.7E-03 | +5.0E-01 | 2.9E-02 | 4.8E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 30| +1.59E+02 | -1.4E-02 | +6.0E-01 | 8.0E-02 | 6.5E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 43| +2.50E+02 | -8.0E-03 | +1.6E+00 | 1.2E-01 | 2.9E-01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 25| +1.55E+02 | -5.3E-02 | +1.1E+00 | 4.6E-01 | 1.1E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +1.48E+02 | -2.9E-03 | +7.2E-01 | 3.1E-02 | 1.3E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 54| +1.75E+02 | -7.0E+00 | +3.9E-01 | 2.5E-01 | 4.0E+01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 68| +2.09E+02 | -1.2E-02 | +8.8E-01 | 1.0E-02 | 8.9E-01 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 38| +1.48E+02 | -1.7E-03 | +3.0E-01 | 2.9E-02 | 1.1E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 38| +2.50E+02 | -9.7E-04 | +1.0E+00 | 1.6E-02 | 2.7E-01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 31| +1.59E+02 | -2.1E-03 | +1.1E-01 | 8.0E-02 | 4.2E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +2.50E+02 | -5.1E-02 | +2.4E+00 | 2.5E-01 | 5.9E-01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 45| +1.48E+02 | -1.6E-03 | +9.8E-01 | 3.1E-02 | 1.4E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 55| +1.65E+02 | -9.7E+00 | +1.1E+00 | 2.5E-01 | 9.5E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 39| +1.48E+02 | -2.7E-03 | +3.8E-01 | 2.9E-02 | 3.2E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 26| +1.55E+02 | +2.1E+01 | -4.1E+02 | 9.1E-01 | 5.1E+00 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 45| +2.49E+02 | -1.0E+00 | +4.7E+00 | 5.0E-01 | 1.3E+00 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 32| +1.59E+02 | -7.8E-03 | +7.2E-01 | 2.0E-02 | 4.0E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 39| +2.50E+02 | -1.3E-03 | +1.0E+00 | 3.1E-02 | 4.4E-02 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 69| +2.09E+02 | -7.3E-03 | +9.6E-01 | 2.0E-02 | 6.9E-01 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 46| +1.48E+02 | -2.4E-03 | +9.7E-01 | 6.2E-02 | 4.0E-01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 40| +1.48E+02 | +4.8E-04 | -1.0E-01 | 2.9E-02 | 8.6E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 56| +1.51E+02 | -1.4E+01 | +1.1E+00 | 5.0E-01 | 5.4E+01 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 33| +1.59E+02 | -2.3E-03 | +8.5E-01 | 2.0E-02 | 2.5E+00 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 70| +2.09E+02 | +9.4E-03 | -7.8E-01 | 4.0E-02 | 4.6E-01 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 47| +1.48E+02 | -3.0E-03 | +8.2E-01 | 1.2E-01 | 7.2E-01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 40| +2.50E+02 | -1.9E-03 | +9.5E-01 | 6.2E-02 | 4.8E-02 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 41| +1.48E+02 | -8.9E-04 | +4.3E-01 | 7.4E-03 | 8.6E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 57| +1.51E+02 | +6.8E+00 | -1.5E+00 | 1.0E+00 | 6.2E+01 | 1.0E+00 | trr |0\n", - "2023-02-17 16:05:07 fides(INFO) 27| +1.55E+02 | -5.3E-02 | +1.4E+00 | 2.3E-01 | 5.1E+00 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 34| +1.59E+02 | -1.0E-03 | +7.6E-01 | 4.0E-02 | 1.4E+00 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 48| +1.48E+02 | +1.4E-02 | -2.2E+00 | 2.5E-01 | 5.3E-01 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 71| +2.09E+02 | -1.4E-03 | +3.6E-01 | 1.0E-02 | 4.6E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 41| +2.50E+02 | -2.2E-03 | +1.1E+00 | 1.2E-01 | 1.9E-01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 42| +1.48E+02 | -1.1E-03 | +9.4E-01 | 7.4E-03 | 2.7E+00 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 58| +1.49E+02 | -2.4E+00 | +9.7E-01 | 1.5E-01 | 6.2E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 35| +1.59E+02 | -7.6E-04 | +5.0E-01 | 8.0E-02 | 1.5E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 59| +1.49E+02 | +1.9E+00 | -1.2E+00 | 3.1E-01 | 1.5E+01 | 4.2E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 46| +2.30E+02 | -1.9E+01 | +3.6E+00 | 1.0E+00 | 6.0E+00 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 28| +1.55E+02 | +9.0E-01 | -9.7E+00 | 4.6E-01 | 8.2E+00 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 49| +1.48E+02 | -5.8E-04 | +2.4E-01 | 6.2E-02 | 5.3E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 43| +1.48E+02 | -5.3E-04 | +9.8E-01 | 1.5E-02 | 3.1E-01 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 36| +1.59E+02 | +3.0E-03 | -1.1E+00 | 8.0E-02 | 9.1E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 72| +2.09E+02 | -3.3E-03 | +1.0E+00 | 1.0E-02 | 6.9E-01 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 42| +2.50E+02 | -8.4E-04 | +1.4E+00 | 2.5E-01 | 1.8E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 50| +1.48E+02 | -1.0E-03 | +7.4E-01 | 1.6E-02 | 1.3E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +1.48E+02 | -7.2E-04 | +9.3E-01 | 2.9E-02 | 7.2E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 60| +1.48E+02 | -4.6E-01 | +4.5E-01 | 7.7E-02 | 1.5E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 43| +2.50E+02 | -3.8E-04 | +1.3E+00 | 2.5E-01 | 4.7E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 37| +1.59E+02 | -5.2E-04 | +5.3E-01 | 2.0E-02 | 9.1E-01 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 73| +2.09E+02 | -3.0E-03 | +6.9E-01 | 2.0E-02 | 2.2E-01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 51| +1.48E+02 | -2.7E-04 | +8.9E-01 | 1.6E-02 | 2.4E-01 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 45| +1.48E+02 | -7.6E-04 | +7.2E-01 | 5.9E-02 | 2.6E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 29| +1.55E+02 | -6.0E-02 | +1.2E+00 | 1.1E-01 | 8.2E+00 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 61| +1.48E+02 | -2.5E-01 | +4.8E-01 | 7.7E-02 | 4.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +2.50E+02 | -2.1E-04 | +1.2E+00 | 2.5E-01 | 8.0E-02 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 38| +1.59E+02 | -1.2E-04 | +8.9E-01 | 2.0E-02 | 7.5E-01 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 46| +1.48E+02 | +3.8E-03 | -1.7E+00 | 5.9E-02 | 5.2E-01 | 4.3E-02 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 74| +2.09E+02 | -6.2E-03 | +1.2E+00 | 2.0E-02 | 6.9E-01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 52| +1.48E+02 | -3.5E-04 | +9.2E-01 | 3.1E-02 | 1.1E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 47| +2.24E+02 | -6.0E+00 | +5.9E-01 | 2.0E+00 | 5.6E+01 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 62| +1.48E+02 | +6.5E-02 | -1.4E-01 | 7.7E-02 | 1.1E+01 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 39| +1.59E+02 | -8.4E-05 | +7.5E-01 | 4.0E-02 | 3.7E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 45| +2.50E+02 | -8.4E-05 | +1.4E+00 | 2.5E-01 | 5.6E-02 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 47| +1.48E+02 | -2.8E-04 | +4.1E-01 | 1.5E-02 | 5.2E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 53| +1.48E+02 | -5.6E-04 | +9.9E-01 | 6.2E-02 | 3.4E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 63| +1.48E+02 | -1.3E-01 | +7.3E-01 | 1.9E-02 | 1.1E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 75| +2.09E+02 | -1.1E-02 | +1.0E+00 | 4.0E-02 | 2.8E-01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 30| +1.54E+02 | -9.4E-02 | +5.4E-01 | 2.3E-01 | 4.2E+00 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 46| +2.50E+02 | -4.5E-05 | +1.4E+00 | 2.5E-01 | 1.1E-02 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 48| +1.48E+02 | -1.2E-04 | +4.9E-01 | 1.5E-02 | 2.5E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 40| +1.59E+02 | +1.3E-04 | -9.4E-01 | 8.0E-02 | 3.1E-01 | 6.6E-03 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 54| +1.48E+02 | -6.1E-04 | +9.0E-01 | 1.2E-01 | 1.0E-01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 64| +1.48E+02 | -4.6E-02 | +9.2E-01 | 1.9E-02 | 1.4E+01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 76| +2.09E+02 | -2.8E-02 | +1.2E+00 | 8.0E-02 | 4.1E-01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 41| +1.59E+02 | -3.6E-05 | +6.4E-01 | 2.0E-02 | 3.1E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 31| +1.54E+02 | -8.5E-02 | +1.3E-01 | 2.3E-01 | 2.8E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 49| +1.48E+02 | -9.6E-05 | +5.4E-01 | 1.5E-02 | 3.0E-01 | 7.1E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 47| +2.50E+02 | -2.1E-05 | +1.4E+00 | 2.5E-01 | 5.0E-03 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 55| +1.48E+02 | +2.3E-03 | -4.7E+00 | 2.5E-01 | 2.3E-01 | 5.3E-02 | trr |0\n", - "2023-02-17 16:05:07 fides(INFO) 65| +1.48E+02 | -3.8E-02 | +9.3E-01 | 3.8E-02 | 5.5E+00 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 77| +2.09E+02 | -9.8E-02 | +1.6E+00 | 1.6E-01 | 8.6E-01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 42| +1.59E+02 | -2.2E-05 | +6.9E-01 | 2.0E-02 | 2.2E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 50| +1.48E+02 | -4.4E-05 | +3.1E-01 | 1.5E-02 | 1.5E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 48| +2.50E+02 | -1.0E-05 | +1.4E+00 | 2.5E-01 | 2.5E-03 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 56| +1.48E+02 | -3.8E-05 | +1.7E-01 | 5.7E-02 | 2.3E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 78| +2.08E+02 | -7.3E-01 | +2.7E+00 | 3.2E-01 | 1.7E+00 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 66| +1.48E+02 | -5.2E-02 | +8.7E-01 | 7.7E-02 | 5.7E+00 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 43| +1.59E+02 | -9.7E-06 | +4.7E-01 | 2.0E-02 | 1.7E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:07.915 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.426 and h = 1.34871e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:07.915 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.426: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:07 fides(INFO) 32| +1.54E+02 | -1.2E-01 | +9.6E-01 | 5.7E-02 | 2.8E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 51| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.5E-02 | 4.5E-01 | 7.8E-03 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 49| +2.50E+02 | -4.6E-06 | +1.4E+00 | 2.5E-01 | 2.9E-04 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 79| +2.05E+02 | -3.5E+00 | +9.6E-01 | 6.4E-01 | 3.7E+00 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 67| +1.48E+02 | +1.3E-01 | -2.0E+00 | 1.5E-01 | 2.4E+00 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:07 fides(INFO) 57| +1.48E+02 | -1.2E-04 | +4.9E-01 | 1.4E-02 | 1.8E-01 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 44| +1.59E+02 | -6.2E-06 | +3.7E-01 | 2.0E-02 | 1.3E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 52| +1.48E+02 | -6.5E-05 | +8.6E-01 | 3.7E-03 | 4.5E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 50| +2.50E+02 | -2.0E-06 | +1.4E+00 | 2.5E-01 | 2.6E-04 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(WARNING) Stopping as function difference 2.03E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:07 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:07 fides(INFO) 80| +2.02E+02 | -2.6E+00 | +6.2E-01 | 1.3E+00 | 4.4E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 68| +1.48E+02 | -8.2E-03 | +3.9E-01 | 3.8E-02 | 2.4E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 58| +1.48E+02 | -2.3E-05 | +9.1E-01 | 1.4E-02 | 1.8E-01 | 2.9E-03 | 2d |1\n", - "2023-02-17 16:05:07 fides(INFO) 45| +1.59E+02 | +7.7E-08 | -4.1E-03 | 2.0E-02 | 1.1E-01 | 1.1E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 33| +1.54E+02 | -4.6E-02 | +8.8E-01 | 1.1E-01 | 1.0E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 48| +2.24E+02 | +2.3E+02 | -8.9E+00 | 2.0E+00 | 5.0E+01 | 1.9E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 53| +1.48E+02 | -3.0E-05 | +7.3E-01 | 7.4E-03 | 6.1E-02 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 69| +1.48E+02 | +2.3E-02 | -5.4E-01 | 3.8E-02 | 2.7E+00 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 59| +1.48E+02 | -2.8E-05 | +8.9E-01 | 2.8E-02 | 5.1E-02 | 6.1E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 81| +2.02E+02 | +6.9E+01 | -1.0E+01 | 1.3E+00 | 2.2E+01 | 3.0E+00 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) 46| +1.59E+02 | -3.8E-06 | +5.6E-01 | 5.0E-03 | 1.1E-01 | 2.9E-04 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 54| +1.48E+02 | -2.2E-05 | +1.0E+00 | 7.4E-03 | 5.7E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 70| +1.48E+02 | -6.9E-03 | +3.8E-01 | 9.6E-03 | 2.7E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 60| +1.48E+02 | -3.5E-05 | +8.4E-01 | 5.7E-02 | 6.1E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 55| +1.48E+02 | -3.6E-05 | +1.0E+00 | 1.5E-02 | 7.9E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 47| +1.59E+02 | -4.3E-07 | +2.7E-01 | 5.0E-03 | 7.0E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:08 fides(WARNING) Stopping as function difference 4.32E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:08 fides(INFO) 82| +1.96E+02 | -6.6E+00 | +1.8E+00 | 2.7E-01 | 2.2E+01 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 34| +1.54E+02 | -4.9E-02 | +8.7E-01 | 2.3E-01 | 1.1E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 61| +1.48E+02 | +5.0E-04 | -5.9E+00 | 1.1E-01 | 3.8E-02 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 0| +2.03E+13 | NaN | NaN | 1.0E+00 | 6.9E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:08 fides(INFO) 71| +1.48E+02 | -6.8E-03 | +8.7E-01 | 9.6E-03 | 6.0E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 56| +1.48E+02 | -4.2E-05 | +8.9E-01 | 2.9E-02 | 4.2E-02 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 1| +1.32E+10 | -2.0E+13 | +1.1E-01 | 1.0E+00 | 6.9E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 83| +1.96E+02 | +5.1E+00 | -6.2E-01 | 5.3E-01 | 2.9E+01 | 1.5E+00 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) 62| +1.48E+02 | +3.7E-06 | -1.1E-01 | 2.8E-02 | 3.8E-02 | 5.8E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 72| +1.48E+02 | -3.8E-03 | +8.6E-01 | 1.9E-02 | 6.6E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 57| +1.48E+02 | -2.8E-05 | +8.4E-01 | 5.9E-02 | 5.8E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 84| +1.89E+02 | -6.6E+00 | +1.5E+00 | 1.3E-01 | 2.9E+01 | 3.6E-01 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 49| +2.01E+02 | -2.3E+01 | +1.1E+00 | 2.3E-01 | 5.0E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 63| +1.48E+02 | -8.8E-06 | +9.8E-01 | 7.1E-03 | 3.8E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 2| +2.14E+09 | -1.1E+10 | +8.8E-01 | 2.5E-01 | 6.1E+10 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 35| +1.54E+02 | -2.3E-02 | +7.8E-01 | 4.6E-01 | 4.4E+00 | 7.8E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 73| +1.48E+02 | -6.8E-03 | +8.8E-01 | 3.8E-02 | 6.9E-01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 58| +1.48E+02 | +4.4E-04 | -1.3E+01 | 1.2E-01 | 3.7E-02 | 7.8E-03 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) 64| +1.48E+02 | -3.3E-06 | +6.2E-01 | 1.4E-02 | 3.8E-02 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 85| +1.82E+02 | -7.5E+00 | +7.6E-01 | 2.7E-01 | 3.2E+01 | 7.2E-01 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 3| +3.18E+08 | -1.8E+09 | +8.9E-01 | 5.0E-01 | 9.3E+09 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 74| +1.48E+02 | +3.8E-03 | -4.3E-01 | 7.7E-02 | 7.3E-01 | 8.7E-02 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 36| +1.54E+02 | -1.3E-02 | +9.2E-01 | 9.1E-01 | 4.6E+00 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 0| +3.89E+02 | NaN | NaN | 1.0E+00 | 6.4E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:08 fides(INFO) 59| +1.48E+02 | +1.6E-05 | -1.0E+00 | 9.9E-03 | 3.7E-02 | 2.0E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 65| +1.48E+02 | -4.5E-06 | +1.1E+00 | 1.4E-02 | 2.3E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 4| +4.57E+07 | -2.7E+08 | +9.0E-01 | 5.0E-01 | 1.4E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 75| +1.48E+02 | -2.0E-03 | +7.1E-01 | 1.9E-02 | 7.3E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 37| +1.54E+02 | +6.1E-02 | -6.0E+00 | 1.8E+00 | 5.8E-01 | 7.5E-01 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 60| +1.48E+02 | -1.4E-06 | +3.2E-01 | 2.5E-03 | 3.7E-02 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:08 fides(WARNING) Stopping as function difference 1.44E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:08 fides(INFO) 86| +1.82E+02 | +1.8E+02 | -8.8E+00 | 5.3E-01 | 4.6E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 1| +2.62E+02 | -1.3E+02 | +6.5E-01 | 1.0E+00 | 6.4E+01 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 5| +6.32E+06 | -3.9E+07 | +9.0E-01 | 5.0E-01 | 2.0E+08 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 76| +1.48E+02 | +7.0E-04 | -1.9E-01 | 1.9E-02 | 4.9E-01 | 1.7E-02 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 66| +1.48E+02 | -7.6E-06 | +1.3E+00 | 2.8E-02 | 3.3E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 2| +2.38E+02 | -2.4E+01 | +6.8E-01 | 1.0E+00 | 8.7E+01 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 38| +1.54E+02 | -2.1E-03 | +5.7E-01 | 2.0E-01 | 5.8E-01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 6| +9.04E+05 | -5.4E+06 | +9.0E-01 | 5.0E-01 | 2.9E+07 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 67| +1.48E+02 | +3.6E-06 | -4.7E-01 | 5.7E-02 | 1.7E-02 | 6.1E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 77| +1.48E+02 | +4.7E-04 | -2.3E-01 | 4.8E-03 | 4.9E-01 | 7.5E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 87| +1.73E+02 | -8.4E+00 | +7.9E-01 | 1.3E-01 | 4.6E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 3| +2.38E+02 | -1.7E+01 | -3.4E-01 | 1.0E+00 | 5.5E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 7| +1.32E+05 | -7.7E+05 | +8.9E-01 | 5.0E-01 | 4.4E+06 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 78| +1.48E+02 | -5.8E-04 | +5.9E-01 | 1.2E-03 | 4.9E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 68| +1.48E+02 | -2.4E-08 | +8.8E-03 | 1.4E-02 | 1.7E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 39| +1.54E+02 | -5.8E-04 | +8.9E-01 | 2.0E-01 | 5.2E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 88| +1.66E+02 | -7.4E+00 | +7.2E-01 | 2.7E-01 | 2.7E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 69| +1.48E+02 | +4.0E-07 | -4.9E-01 | 3.5E-03 | 3.4E-02 | 4.3E-04 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 4| +2.19E+02 | -1.9E+01 | +9.9E-01 | 2.5E-01 | 5.5E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 8| +1.97E+04 | -1.1E+05 | +8.9E-01 | 5.0E-01 | 6.9E+05 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 50| +2.01E+02 | +1.3E+02 | -4.8E+00 | 4.5E-01 | 5.6E+01 | 9.4E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 40| +1.54E+02 | -8.3E-04 | +1.0E+00 | 3.9E-01 | 2.3E-01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 79| +1.48E+02 | -2.7E-04 | +9.7E-01 | 1.2E-03 | 1.1E+00 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 0| +1.94E+12 | NaN | NaN | 1.0E+00 | 8.9E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:08 fides(INFO) 89| +1.66E+02 | +8.5E+01 | -5.9E+00 | 2.7E-01 | 5.0E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 5| +2.19E+02 | +1.6E+02 | -7.7E+00 | 5.0E-01 | 4.4E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 70| +1.48E+02 | -4.4E-06 | +1.4E+01 | 8.9E-04 | 3.4E-02 | 1.1E-04 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 80| +1.48E+02 | -2.5E-04 | +9.9E-01 | 2.4E-03 | 2.0E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 9| +3.04E+03 | -1.7E+04 | +8.9E-01 | 5.0E-01 | 1.1E+05 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 41| +1.54E+02 | +1.7E-03 | -1.6E+00 | 7.9E-01 | 3.7E-01 | 2.5E-01 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 90| +1.66E+02 | +5.9E+00 | -1.0E+00 | 6.7E-02 | 5.0E+01 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 6| +2.09E+02 | -1.0E+01 | +9.3E-01 | 1.2E-01 | 4.4E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 81| +1.48E+02 | -4.5E-04 | +9.7E-01 | 4.8E-03 | 2.9E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 1| +2.04E+06 | -1.9E+12 | +8.7E-02 | 1.0E+00 | 8.9E+12 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 71| +1.48E+02 | +4.6E-06 | -1.4E+01 | 1.8E-03 | 8.1E-03 | 2.1E-04 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 10| +5.75E+02 | -2.5E+03 | +8.8E-01 | 5.0E-01 | 1.7E+04 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 7| +2.06E+02 | -2.8E+00 | +2.6E-01 | 2.5E-01 | 3.2E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 82| +1.48E+02 | -8.1E-04 | +9.6E-01 | 9.6E-03 | 2.3E-01 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 2| +3.17E+05 | -1.7E+06 | +9.0E-01 | 2.5E-01 | 9.4E+06 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 42| +1.54E+02 | -3.0E-04 | +6.2E-01 | 1.2E-01 | 3.7E-01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 72| +1.48E+02 | +5.3E-06 | -4.8E+01 | 4.4E-04 | 8.1E-03 | 5.4E-05 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 91| +1.65E+02 | -1.1E+00 | +5.7E-01 | 1.7E-02 | 5.0E+01 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 11| +2.11E+02 | -3.6E+02 | +8.6E-01 | 5.0E-01 | 2.9E+03 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 8| +1.96E+02 | -1.0E+01 | +9.7E-01 | 2.5E-01 | 7.1E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 3| +4.97E+04 | -2.7E+05 | +9.0E-01 | 5.0E-01 | 1.5E+06 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 43| +1.54E+02 | -2.9E-04 | +8.4E-01 | 1.2E-01 | 1.3E-01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 83| +1.48E+02 | -1.4E-03 | +9.8E-01 | 1.9E-02 | 3.5E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 73| +1.48E+02 | +2.9E-06 | -5.4E+01 | 1.1E-04 | 8.1E-03 | 1.6E-05 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 12| +1.67E+02 | -4.4E+01 | +6.4E-01 | 1.0E+00 | 5.1E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 4| +8.02E+03 | -4.2E+04 | +9.0E-01 | 1.0E+00 | 2.3E+05 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 51| +1.95E+02 | -6.1E+00 | +6.2E-01 | 1.1E-01 | 5.6E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 9| +1.84E+02 | -1.1E+01 | +1.1E+00 | 5.0E-01 | 4.1E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 92| +1.64E+02 | -7.7E-01 | +9.9E-01 | 1.7E-02 | 3.9E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 74| +1.48E+02 | +3.8E-06 | -9.6E+01 | 2.8E-05 | 8.1E-03 | 9.6E-06 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 84| +1.48E+02 | -1.8E-03 | +9.3E-01 | 3.8E-02 | 2.1E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 44| +1.54E+02 | -1.1E-04 | +2.8E-01 | 2.4E-01 | 5.9E-01 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 5| +1.46E+03 | -6.6E+03 | +9.0E-01 | 1.0E+00 | 3.6E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 10| +1.76E+02 | -8.0E+00 | +3.6E-01 | 1.0E+00 | 2.6E+01 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 13| +1.50E+02 | -1.6E+01 | +9.1E-01 | 1.0E+00 | 2.6E+02 | 2.7E+00 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 75| +1.48E+02 | +5.2E-06 | -1.4E+02 | 6.9E-06 | 8.1E-03 | 8.9E-06 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 85| +1.48E+02 | +6.0E-04 | -4.5E-01 | 7.7E-02 | 3.3E-01 | 4.5E-02 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) 93| +1.63E+02 | -1.2E+00 | +9.0E-01 | 3.3E-02 | 3.3E+01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 6| +4.22E+02 | -1.0E+03 | +9.0E-01 | 1.0E+00 | 5.7E+03 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 45| +1.54E+02 | +7.4E-03 | -4.7E+00 | 2.4E-01 | 1.0E-01 | 7.6E-02 | trr |0\n", - "2023-02-17 16:05:08 fides(INFO) 14| +1.49E+02 | -1.1E+00 | +2.7E-01 | 1.0E+00 | 9.6E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 86| +1.48E+02 | -4.6E-04 | +7.7E-01 | 1.8E-02 | 3.3E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 7| +2.66E+02 | -1.6E+02 | +8.9E-01 | 2.0E+00 | 9.1E+02 | 4.8E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 11| +1.72E+02 | -4.6E+00 | +7.7E-01 | 1.0E+00 | 6.3E+01 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 46| +1.54E+02 | -2.6E-04 | +4.0E-01 | 5.1E-02 | 1.0E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 76| +1.48E+02 | +5.5E-06 | -2.1E+02 | 1.7E-06 | 8.1E-03 | 4.5E-06 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 15| +1.49E+02 | +1.4E+02 | -2.4E+01 | 1.0E+00 | 6.2E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 8| +2.37E+02 | -2.8E+01 | +1.0E+00 | 4.0E+00 | 1.3E+02 | 4.8E+00 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 94| +1.62E+02 | -1.2E+00 | +8.0E-01 | 6.7E-02 | 3.7E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 87| +1.48E+02 | +1.7E-03 | -1.5E+00 | 3.6E-02 | 1.5E-01 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 52| +1.93E+02 | -1.5E+00 | +8.0E-01 | 1.1E-01 | 2.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 16| +1.49E+02 | -4.9E-01 | +1.1E-01 | 2.5E-01 | 6.2E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 12| +1.71E+02 | -4.6E-01 | +7.5E-01 | 2.0E+00 | 1.8E+01 | 2.8E+00 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 77| +1.48E+02 | +1.5E-06 | -1.9E+02 | 4.3E-07 | 8.1E-03 | 1.1E-06 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 9| +2.37E+02 | +2.0E+02 | -1.2E+01 | 4.0E+00 | 3.2E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 47| +1.54E+02 | -9.5E-05 | +4.3E-01 | 5.1E-02 | 4.2E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 88| +1.48E+02 | -1.6E-04 | +4.8E-01 | 9.0E-03 | 1.5E-01 | 9.1E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 95| +1.62E+02 | +4.1E-01 | -4.3E-01 | 1.3E-01 | 2.2E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 10| +2.37E+02 | +3.3E+01 | -3.9E+00 | 1.0E+00 | 3.2E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 78| +1.48E+02 | +4.2E-06 | -2.0E+03 | 1.1E-07 | 8.1E-03 | 2.8E-07 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 89| +1.48E+02 | -1.0E-04 | +3.4E-01 | 9.0E-03 | 2.2E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 17| +1.48E+02 | -5.8E-01 | +9.5E-01 | 6.2E-02 | 6.4E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 13| +1.71E+02 | -1.2E-02 | +9.5E-02 | 2.0E+00 | 7.1E+00 | 3.1E-01 | trr |1\n", - "2023-02-17 16:05:08 fides(INFO) 11| +2.37E+02 | +9.4E+00 | -9.6E-01 | 2.5E-01 | 3.2E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 48| +1.54E+02 | -4.6E-05 | +3.6E-01 | 5.1E-02 | 7.2E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 96| +1.61E+02 | -5.5E-01 | +1.0E+00 | 3.3E-02 | 2.2E+01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 12| +2.34E+02 | -3.9E+00 | +8.6E-01 | 6.2E-02 | 3.2E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 79| +1.48E+02 | +2.5E-06 | -4.6E+03 | 2.7E-08 | 8.1E-03 | 7.0E-08 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 90| +1.48E+02 | -9.1E-05 | +2.6E-01 | 9.0E-03 | 2.6E-01 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 18| +1.48E+02 | -3.9E-01 | +6.1E-01 | 1.2E-01 | 3.1E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 13| +2.33E+02 | -5.2E-01 | +1.1E-01 | 1.2E-01 | 2.0E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 49| +1.54E+02 | -1.1E-05 | +7.9E-02 | 5.1E-02 | 1.5E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 14| +1.71E+02 | -4.8E-02 | +5.8E-01 | 1.4E-01 | 4.7E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 97| +1.60E+02 | -1.0E+00 | +7.6E-01 | 6.7E-02 | 1.9E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 80| +1.48E+02 | +4.0E-07 | -3.0E+03 | 6.8E-09 | 8.1E-03 | 1.7E-08 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 19| +1.48E+02 | -2.0E-01 | +9.2E-01 | 1.2E-01 | 3.9E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 91| +1.48E+02 | +1.0E-04 | -2.6E-01 | 9.0E-03 | 2.1E-01 | 3.6E-03 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 14| +2.31E+02 | -1.8E+00 | +9.2E-01 | 3.1E-02 | 4.1E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 53| +1.91E+02 | -2.0E+00 | +1.2E+00 | 2.3E-01 | 1.0E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 50| +1.54E+02 | -6.4E-05 | +7.1E-01 | 1.3E-02 | 5.9E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 81| +1.48E+02 | -4.6E-07 | +1.4E+04 | 1.7E-09 | 8.1E-03 | 4.4E-09 | 2d |1\n", - "2023-02-17 16:05:08 fides(WARNING) Stopping as function difference 4.61E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:08 fides(INFO) 92| +1.48E+02 | -7.7E-05 | +6.3E-01 | 2.3E-03 | 2.1E-01 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 15| +2.29E+02 | -2.0E+00 | +8.6E-01 | 6.2E-02 | 2.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 15| +1.71E+02 | -8.8E-03 | +8.3E-01 | 1.4E-01 | 5.9E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:08 fides(INFO) 20| +1.48E+02 | -6.9E-02 | +8.0E-01 | 2.5E-01 | 1.2E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 98| +1.60E+02 | +2.9E+00 | -9.3E-01 | 1.3E-01 | 4.1E+01 | 3.4E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 16| +2.26E+02 | -3.5E+00 | +1.2E+00 | 1.2E-01 | 1.8E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 51| +1.54E+02 | -1.5E-05 | +9.4E-01 | 1.3E-02 | 1.8E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 93| +1.48E+02 | -3.0E-05 | +8.5E-01 | 2.3E-03 | 3.7E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:08 fides(INFO) 21| +1.48E+02 | +2.4E-01 | -2.3E+00 | 4.9E-01 | 1.1E+01 | 5.1E-01 | 2d |0\n", - "2023-02-17 16:05:08 fides(INFO) 17| +2.21E+02 | -4.4E+00 | +1.1E+00 | 2.5E-01 | 1.8E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 99| +1.60E+02 | -4.3E-01 | +3.8E-01 | 3.3E-02 | 4.1E+01 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 16| +1.71E+02 | -2.1E-03 | +9.9E-01 | 2.8E-01 | 1.2E+00 | 7.2E-02 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) 94| +1.48E+02 | -3.9E-05 | +9.9E-01 | 4.5E-03 | 6.3E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 18| +2.18E+02 | -3.2E+00 | +5.1E-01 | 5.0E-01 | 2.6E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 52| +1.54E+02 | -1.8E-05 | +1.1E+00 | 2.5E-02 | 3.4E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 22| +1.48E+02 | -1.8E-02 | +4.4E-01 | 1.2E-01 | 1.1E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 100| +1.59E+02 | -4.9E-01 | +1.0E+00 | 3.3E-02 | 2.8E+01 | 6.9E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 95| +1.48E+02 | -4.2E-05 | +6.7E-01 | 9.0E-03 | 1.9E-01 | 5.7E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 19| +1.98E+02 | -2.0E+01 | +1.5E+00 | 5.0E-01 | 4.0E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 17| +1.71E+02 | -4.8E-05 | +3.4E-01 | 5.6E-01 | 4.9E-01 | 5.5E-03 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) 53| +1.54E+02 | -2.5E-05 | +1.2E+00 | 5.1E-02 | 9.4E-02 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 0| +5.85E+12 | NaN | NaN | 1.0E+00 | 2.7E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:09 fides(INFO) 54| +1.91E+02 | -4.2E+00 | -3.0E-01 | 4.5E-01 | 2.5E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 23| +1.48E+02 | -7.8E-03 | +1.9E-01 | 1.2E-01 | 5.9E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 101| +1.59E+02 | -2.4E-01 | +4.7E-01 | 6.7E-02 | 8.2E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 96| +1.48E+02 | +5.6E-05 | -5.8E-01 | 9.0E-03 | 8.7E-02 | 1.7E-03 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 18| +1.71E+02 | -3.1E-06 | +3.2E-02 | 5.6E-01 | 1.8E-01 | 6.7E-03 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 20| +1.98E+02 | +1.9E+03 | -4.7E+01 | 1.0E+00 | 3.7E+01 | 2.1E+00 | trr |0\n", - "2023-02-17 16:05:09 fides(INFO) 1| +1.05E+07 | -5.9E+12 | +8.3E-02 | 1.0E+00 | 2.7E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 24| +1.48E+02 | -1.7E-02 | +8.1E-01 | 3.1E-02 | 9.3E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 54| +1.54E+02 | -9.9E-07 | +5.9E-02 | 1.0E-01 | 2.7E-02 | 4.2E-03 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) 97| +1.48E+02 | -1.2E-05 | +3.4E-01 | 2.3E-03 | 8.7E-02 | 6.5E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 2| +2.00E+06 | -8.5E+06 | +9.1E-01 | 2.5E-01 | 3.6E+07 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 21| +1.79E+02 | -1.9E+01 | +9.5E-01 | 2.5E-01 | 3.7E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 25| +1.48E+02 | -3.3E-03 | +5.9E-01 | 6.2E-02 | 2.4E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 102| +1.59E+02 | +7.1E-01 | -1.5E+00 | 6.7E-02 | 6.6E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 19| +1.71E+02 | -3.2E-05 | +3.8E-01 | 1.9E-03 | 1.9E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 55| +1.54E+02 | +5.1E-04 | -4.1E+00 | 1.2E-02 | 3.0E-02 | 1.8E-02 | trr |0\n", - "2023-02-17 16:05:09 fides(INFO) 22| +1.79E+02 | -1.4E+01 | -3.3E-02 | 5.0E-01 | 8.4E+01 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 98| +1.48E+02 | -2.4E-05 | +9.6E-01 | 2.3E-03 | 3.0E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:09.201 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 117.386 and h = 3.00881e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.202 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 117.386: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 103| +1.59E+02 | +0.0E+00 | +0.0E+00 | 1.7E-02 | 6.6E+00 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 26| +1.48E+02 | -1.6E-03 | +9.3E-01 | 6.2E-02 | 2.6E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 20| +1.71E+02 | -8.8E-06 | +3.9E-01 | 1.9E-03 | 1.4E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 3| +3.31E+05 | -1.7E+06 | +9.0E-01 | 5.0E-01 | 6.6E+06 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 99| +1.48E+02 | -7.5E-06 | +5.8E-01 | 4.5E-03 | 3.6E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 23| +1.68E+02 | -1.2E+01 | +1.0E+00 | 1.2E-01 | 8.4E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 56| +1.54E+02 | -1.4E-05 | +2.6E-01 | 2.1E-03 | 3.0E-02 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 4| +5.18E+04 | -2.8E+05 | +9.0E-01 | 1.0E+00 | 1.1E+06 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 27| +1.48E+02 | -1.6E-03 | +9.7E-01 | 1.2E-01 | 1.6E+00 | 9.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 104| +1.59E+02 | -3.3E-02 | +6.8E-01 | 4.2E-03 | 6.6E+00 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 21| +1.71E+02 | -2.4E-07 | +2.8E-02 | 1.9E-03 | 7.6E-02 | 1.8E-03 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) 5| +8.31E+03 | -4.4E+04 | +9.0E-01 | 1.0E+00 | 1.7E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 24| +1.61E+02 | -7.0E+00 | +6.1E-01 | 2.5E-01 | 6.1E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 100| +1.48E+02 | -1.2E-05 | +1.2E+00 | 4.5E-03 | 3.0E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 55| +1.89E+02 | -2.8E+00 | +9.3E-01 | 1.1E-01 | 2.5E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 57| +1.54E+02 | +6.2E-06 | -2.1E-01 | 2.1E-03 | 2.9E-02 | 4.2E-03 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 6| +1.47E+03 | -6.8E+03 | +9.0E-01 | 1.0E+00 | 2.7E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 25| +1.53E+02 | -7.2E+00 | +4.6E-01 | 2.5E-01 | 1.7E+02 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 28| +1.48E+02 | -1.2E-03 | +8.4E-01 | 2.5E-01 | 1.8E+00 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 105| +1.59E+02 | -3.4E-02 | +8.9E-01 | 4.2E-03 | 9.4E+00 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 101| +1.48E+02 | -1.4E-05 | +9.8E-01 | 9.0E-03 | 3.3E-02 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 22| +1.71E+02 | -3.9E-07 | +1.5E-01 | 1.7E-04 | 5.9E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 7| +4.09E+02 | -1.1E+03 | +9.0E-01 | 1.0E+00 | 4.3E+03 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 58| +1.54E+02 | -6.8E-06 | +7.5E-01 | 5.4E-04 | 2.9E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 29| +1.48E+02 | +1.7E-02 | -6.6E+00 | 4.9E-01 | 7.7E-01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 26| +1.49E+02 | -4.5E+00 | +9.6E-01 | 2.5E-01 | 2.1E+02 | 7.9E-01 | trr |1\n", - "2023-02-17 16:05:09 fides(INFO) 102| +1.48E+02 | +7.9E-06 | -5.8E-01 | 1.8E-02 | 2.0E-02 | 3.0E-03 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 8| +2.59E+02 | -1.5E+02 | +8.7E-01 | 1.0E+00 | 6.6E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 23| +1.71E+02 | -1.8E-06 | +3.0E+00 | 4.2E-05 | 2.8E-02 | 6.1E-05 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 106| +1.59E+02 | -4.6E-02 | +8.4E-01 | 8.3E-03 | 4.3E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 9| +2.39E+02 | -2.0E+01 | +1.1E+00 | 2.0E+00 | 8.1E+01 | 4.0E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 59| +1.54E+02 | -2.1E-06 | +6.9E-01 | 5.4E-04 | 1.7E-02 | 7.1E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 30| +1.48E+02 | +3.6E-04 | -3.1E-01 | 1.2E-01 | 7.7E-01 | 7.3E-02 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 27| +1.48E+02 | -9.4E-01 | +6.7E-01 | 5.0E-01 | 3.8E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 103| +1.48E+02 | -7.8E-06 | +1.5E+00 | 4.5E-03 | 2.0E-02 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 107| +1.59E+02 | -1.9E-02 | +7.9E-01 | 1.7E-02 | 5.2E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 31| +1.48E+02 | -3.0E-04 | +6.1E-01 | 3.1E-02 | 7.7E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 24| +1.71E+02 | -6.3E-07 | +1.2E+00 | 8.3E-05 | 1.9E-02 | 1.1E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(WARNING) Stopping as function difference 6.33E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 60| +1.54E+02 | -2.3E-06 | +1.2E+00 | 5.4E-04 | 1.2E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 10| +2.39E+02 | +2.5E+02 | -1.2E+01 | 4.0E+00 | 3.4E+01 | 1.1E+01 | trr |0\n", - "2023-02-17 16:05:09 fides(INFO) 104| +1.48E+02 | +1.9E-05 | -1.6E+00 | 9.0E-03 | 4.3E-02 | 3.8E-03 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 28| +1.48E+02 | -1.4E-02 | -3.5E-01 | 5.0E-01 | 2.5E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 11| +2.39E+02 | +6.1E+01 | -5.4E+00 | 1.0E+00 | 3.4E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 32| +1.48E+02 | -2.0E-04 | +9.4E-01 | 3.1E-02 | 1.1E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 56| +1.77E+02 | -1.2E+01 | +1.8E+00 | 2.3E-01 | 3.2E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 108| +1.59E+02 | +3.5E-02 | -1.2E+00 | 3.3E-02 | 2.7E+00 | 3.5E-02 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 105| +1.48E+02 | +7.4E-06 | -2.0E+00 | 2.3E-03 | 4.3E-02 | 9.6E-04 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 29| +1.48E+02 | -1.7E-01 | +9.4E-01 | 1.2E-01 | 2.5E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 12| +2.39E+02 | +6.7E+00 | -6.1E-01 | 2.5E-01 | 3.4E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 61| +1.54E+02 | -2.1E-06 | +1.1E+00 | 1.1E-03 | 4.2E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 33| +1.48E+02 | -1.4E-04 | +9.7E-01 | 6.2E-02 | 4.4E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 13| +2.35E+02 | -4.3E+00 | +8.8E-01 | 6.2E-02 | 3.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 106| +1.48E+02 | +4.3E-06 | -3.8E+00 | 5.6E-04 | 4.3E-02 | 2.4E-04 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 30| +1.48E+02 | -9.7E-02 | +3.7E-01 | 2.5E-01 | 9.0E+00 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 62| +1.54E+02 | -7.0E-07 | +4.7E-01 | 2.1E-03 | 6.9E-03 | 9.2E-04 | trr |1\n", - "2023-02-17 16:05:09 fides(WARNING) Stopping as function difference 6.97E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:09 fides(INFO) 109| +1.59E+02 | -9.5E-03 | +7.2E-01 | 8.3E-03 | 2.7E+00 | 8.9E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 34| +1.48E+02 | -1.1E-04 | +8.6E-01 | 1.2E-01 | 6.1E-01 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 14| +2.34E+02 | -8.2E-01 | +1.7E-01 | 1.2E-01 | 2.2E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 107| +1.48E+02 | +4.4E-06 | -9.6E+00 | 1.4E-04 | 4.3E-02 | 6.1E-05 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 31| +1.48E+02 | +7.1E-02 | -5.0E-01 | 2.5E-01 | 1.9E+01 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 0| +9.19E+02 | NaN | NaN | 1.0E+00 | 3.1E+03 | NaN | NaN |1\n", - "2023-02-17 16:05:09 fides(INFO) 15| +2.32E+02 | -1.8E+00 | +9.2E-01 | 3.1E-02 | 3.8E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 110| +1.59E+02 | -2.8E-03 | +6.2E-01 | 8.3E-03 | 1.0E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 35| +1.48E+02 | +3.6E-04 | -2.0E+00 | 2.5E-01 | 2.4E-01 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 32| +1.48E+02 | -5.4E-02 | +7.6E-01 | 6.2E-02 | 1.9E+01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 108| +1.48E+02 | +4.9E-06 | -1.7E+01 | 3.5E-05 | 4.3E-02 | 1.9E-05 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 1| +2.87E+02 | -6.3E+02 | +1.3E-01 | 1.0E+00 | 3.1E+03 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 16| +2.30E+02 | -2.0E+00 | +8.7E-01 | 6.2E-02 | 2.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 36| +1.48E+02 | -2.7E-05 | +3.7E-01 | 6.2E-02 | 2.4E-01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 111| +1.59E+02 | -1.7E-03 | +8.1E-01 | 8.3E-03 | 2.8E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 2| +2.61E+02 | -2.7E+01 | +8.9E-01 | 2.5E-01 | 7.8E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 109| +1.48E+02 | +6.0E-06 | -2.5E+01 | 8.8E-06 | 4.3E-02 | 1.1E-05 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 17| +2.27E+02 | -3.5E+00 | +1.2E+00 | 1.2E-01 | 1.8E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 33| +1.48E+02 | -2.7E-02 | +7.8E-01 | 1.2E-01 | 4.4E+00 | 9.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 37| +1.48E+02 | -1.3E-05 | +1.2E-01 | 6.2E-02 | 3.6E-01 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 18| +2.22E+02 | -4.9E+00 | +1.2E+00 | 2.5E-01 | 1.9E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 3| +2.61E+02 | +3.7E+02 | -1.9E+01 | 5.0E-01 | 3.3E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 0| +6.60E+12 | NaN | NaN | 1.0E+00 | 2.2E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 110| +1.48E+02 | +6.7E-06 | -4.9E+01 | 2.2E-06 | 4.3E-02 | 4.0E-06 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 112| +1.59E+02 | -8.3E-05 | +4.7E-01 | 8.3E-03 | 6.4E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 34| +1.48E+02 | +2.1E-02 | -6.9E-01 | 2.5E-01 | 9.4E+00 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 4| +2.51E+02 | -9.4E+00 | +8.8E-01 | 1.2E-01 | 3.3E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 38| +1.48E+02 | -3.1E-05 | +5.7E-01 | 1.5E-02 | 1.8E-01 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 19| +2.21E+02 | -1.2E+00 | +4.9E-02 | 5.0E-01 | 2.1E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:09.703 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0069 and h = 1.38872e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.703 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0069: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 111| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.5E-07 | 4.3E-02 | 1.0E-06 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 57| +1.77E+02 | +1.4E+02 | -6.5E+00 | 4.5E-01 | 4.1E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 35| +1.48E+02 | -1.2E-02 | +7.4E-01 | 6.2E-02 | 9.4E+00 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 113| +1.59E+02 | -4.1E-05 | +8.5E-01 | 8.3E-03 | 1.7E-01 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 5| +2.51E+02 | +8.2E+00 | -2.3E+00 | 2.5E-01 | 1.9E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 20| +2.13E+02 | -7.3E+00 | +7.8E-01 | 1.2E-01 | 6.6E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 1| +2.56E+09 | -6.6E+12 | +1.1E-01 | 1.0E+00 | 2.2E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 39| +1.48E+02 | -1.4E-05 | +9.5E-01 | 1.5E-02 | 3.3E-01 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 112| +1.48E+02 | +7.4E-06 | -7.0E+02 | 1.4E-07 | 4.3E-02 | 2.5E-07 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 36| +1.48E+02 | -2.9E-03 | +6.1E-01 | 6.2E-02 | 1.9E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 6| +2.50E+02 | -1.6E+00 | +6.5E-01 | 6.2E-02 | 1.9E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 21| +2.05E+02 | -8.7E+00 | +1.3E+00 | 2.5E-01 | 1.8E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 2| +4.81E+08 | -2.1E+09 | +8.8E-01 | 2.5E-01 | 9.3E+09 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 114| +1.59E+02 | -2.4E-06 | +5.2E-01 | 8.3E-03 | 4.1E-02 | 6.8E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 40| +1.48E+02 | -7.4E-06 | +5.9E-01 | 3.1E-02 | 8.1E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 113| +1.48E+02 | +5.8E-06 | -2.2E+03 | 3.4E-08 | 4.3E-02 | 6.2E-08 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 7| +2.50E+02 | -1.4E-03 | +6.7E-02 | 6.2E-02 | 1.8E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 37| +1.48E+02 | -2.2E-03 | +6.4E-01 | 6.2E-02 | 3.6E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 22| +1.94E+02 | -1.1E+01 | +9.7E-01 | 5.0E-01 | 3.2E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 3| +7.29E+07 | -4.1E+08 | +8.9E-01 | 5.0E-01 | 1.6E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 41| +1.48E+02 | -5.0E-06 | +1.0E+00 | 3.1E-02 | 1.6E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 115| +1.59E+02 | -1.3E-06 | +2.0E+00 | 8.3E-03 | 1.1E-02 | 5.1E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(WARNING) Stopping as function difference 1.35E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:09 fides(INFO) 114| +1.48E+02 | +4.0E-06 | -6.1E+03 | 8.6E-09 | 4.3E-02 | 1.6E-08 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 38| +1.48E+02 | -7.8E-04 | +5.2E-01 | 6.2E-02 | 1.0E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 8| +2.50E+02 | +9.3E-04 | -6.0E-02 | 1.6E-02 | 1.6E+00 | 4.3E-02 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 4| +1.11E+07 | -6.2E+07 | +8.9E-01 | 5.0E-01 | 2.4E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:09.842 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0854 and h = 2.28973e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.842 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0854: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 42| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 3.1E-02 | 2.1E-02 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 23| +1.94E+02 | +8.6E+00 | -1.9E+00 | 1.0E+00 | 4.2E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 9| +2.50E+02 | -8.1E-03 | +6.5E-01 | 3.9E-03 | 1.6E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 5| +1.70E+06 | -9.4E+06 | +9.0E-01 | 5.0E-01 | 3.6E+07 | 9.7E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 115| +1.48E+02 | +6.4E-06 | -3.9E+04 | 2.1E-09 | 4.3E-02 | 3.9E-09 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 39| +1.48E+02 | -5.9E-04 | +5.2E-01 | 6.2E-02 | 1.7E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 43| +1.48E+02 | -1.3E-06 | +1.2E+00 | 1.5E-02 | 3.1E-02 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:09 fides(WARNING) Stopping as function difference 1.27E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 10| +2.50E+02 | -1.5E-07 | +5.2E-03 | 3.9E-03 | 6.7E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:09.892 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.732 and h = 3.21785e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.892 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.732: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 116| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.4E-10 | 4.3E-02 | 9.8E-10 | 2d |0\n", - "2023-02-17 16:05:09.895 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 123.347 and h = 8.2092e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.895 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 123.347: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 24| +1.94E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 4.2E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 40| +1.48E+02 | -2.6E-04 | +3.5E-01 | 6.2E-02 | 6.7E-01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 11| +2.50E+02 | +3.9E-08 | -1.6E-03 | 9.8E-04 | 6.7E-02 | 2.7E-03 | 2d |0\n", - "2023-02-17 16:05:09.925 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 147.013 and h = 1.79811e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:09.925 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 147.013: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:09 fides(INFO) 25| +1.94E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 4.2E+01 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 58| +1.71E+02 | -5.8E+00 | +6.8E-01 | 1.1E-01 | 4.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 6| +2.63E+05 | -1.4E+06 | +9.0E-01 | 5.0E-01 | 5.5E+06 | 9.5E-01 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 117| +1.48E+02 | +2.3E-06 | -2.2E+05 | 1.3E-10 | 4.3E-02 | 2.4E-10 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 41| +1.48E+02 | -1.8E-04 | +2.6E-01 | 6.2E-02 | 9.3E-01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 12| +2.50E+02 | -8.1E-06 | +3.2E-01 | 2.4E-04 | 6.7E-02 | 6.4E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 26| +1.92E+02 | -1.3E+00 | +9.9E-01 | 1.6E-02 | 4.2E+01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) 118| +1.48E+02 | +2.9E-06 | -1.1E+06 | 3.4E-11 | 4.3E-02 | 6.1E-11 | 2d |0\n", - "2023-02-17 16:05:09 fides(INFO) 13| +2.50E+02 | -2.2E-06 | +2.1E-01 | 2.4E-04 | 4.2E-02 | 4.3E-04 | 2d |1\n", - "2023-02-17 16:05:09 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:09 fides(INFO) 0| +1.10E+14 | NaN | NaN | 1.0E+00 | 5.1E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:09 fides(INFO) 42| +1.48E+02 | -2.5E-05 | +3.3E-02 | 6.2E-02 | 5.0E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 27| +1.90E+02 | -2.4E+00 | +9.8E-01 | 3.1E-02 | 4.1E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 0| +5.57E+12 | NaN | NaN | 1.0E+00 | 2.6E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:10 fides(INFO) 119| +1.48E+02 | +4.2E-06 | -6.5E+06 | 8.4E-12 | 4.3E-02 | 1.5E-11 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 7| +4.11E+04 | -2.2E+05 | +9.0E-01 | 5.0E-01 | 8.6E+05 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 14| +2.50E+02 | -2.9E-06 | +7.3E-01 | 6.1E-05 | 3.2E-02 | 1.6E-04 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 43| +1.48E+02 | -2.6E-04 | +7.3E-01 | 1.6E-02 | 7.4E-01 | 7.1E-03 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 1| +1.08E+08 | -1.1E+14 | +8.2E-02 | 1.0E+00 | 5.1E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 28| +1.86E+02 | -4.3E+00 | +9.8E-01 | 6.2E-02 | 4.0E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 1| +1.50E+09 | -5.6E+12 | +8.6E-02 | 1.0E+00 | 2.6E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 15| +2.50E+02 | -1.1E-09 | +9.6E-03 | 6.1E-05 | 4.5E-03 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 120| +1.48E+02 | +1.6E-06 | -9.7E+06 | 2.1E-12 | 4.3E-02 | 3.8E-12 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 2| +2.44E+08 | -1.3E+09 | +8.9E-01 | 2.5E-01 | 6.5E+09 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 44| +1.48E+02 | -5.8E-05 | +8.4E-01 | 1.6E-02 | 3.6E-01 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 16| +2.50E+02 | -5.9E-08 | +6.6E-01 | 1.5E-05 | 4.5E-03 | 2.7E-05 | 2d |1\n", - "2023-02-17 16:05:10 fides(WARNING) Stopping as function difference 5.90E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:10 fides(INFO) 121| +1.48E+02 | +3.6E-06 | -8.8E+07 | 5.2E-13 | 4.3E-02 | 9.5E-13 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 29| +1.77E+02 | -9.0E+00 | +1.1E+00 | 1.2E-01 | 4.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 3| +3.70E+07 | -2.1E+08 | +8.9E-01 | 5.0E-01 | 1.0E+09 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 2| +1.69E+07 | -9.1E+07 | +8.9E-01 | 2.5E-01 | 3.5E+08 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 45| +1.48E+02 | -3.7E-05 | +8.4E-01 | 3.1E-02 | 3.0E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 59| +1.63E+02 | -8.4E+00 | +8.7E-01 | 1.1E-01 | 8.0E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 122| +1.48E+02 | +3.2E-06 | -3.2E+08 | 1.3E-13 | 4.3E-02 | 2.4E-13 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 30| +1.62E+02 | -1.5E+01 | +8.6E-01 | 2.5E-01 | 4.9E+01 | 5.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 4| +5.64E+06 | -3.1E+07 | +8.9E-01 | 5.0E-01 | 1.5E+08 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 46| +1.48E+02 | -4.9E-05 | +9.3E-01 | 6.2E-02 | 2.4E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 3| +2.62E+06 | -1.4E+07 | +8.9E-01 | 5.0E-01 | 5.5E+07 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 123| +1.48E+02 | +3.2E-06 | -1.3E+09 | 3.3E-14 | 4.3E-02 | 6.0E-14 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 8| +6.58E+03 | -3.5E+04 | +9.0E-01 | 5.0E-01 | 1.3E+05 | 9.2E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 31| +1.62E+02 | +7.2E+01 | -5.0E+00 | 5.0E-01 | 1.0E+02 | 9.7E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 5| +8.66E+05 | -4.8E+06 | +8.9E-01 | 5.0E-01 | 2.3E+07 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10.187 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.882 and h = 4.14838e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:10.188 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.882: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:10 fides(INFO) 47| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 2.7E-01 | 4.4E-02 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 0| +4.28E+11 | NaN | NaN | 1.0E+00 | 2.0E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:10 fides(INFO) 4| +4.31E+05 | -2.2E+06 | +8.6E-01 | 1.0E+00 | 8.6E+06 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 124| +1.48E+02 | +6.8E-06 | -1.1E+10 | 8.2E-15 | 4.3E-02 | 1.5E-14 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 6| +1.34E+05 | -7.3E+05 | +9.0E-01 | 1.0E+00 | 3.5E+06 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 32| +1.55E+02 | -7.2E+00 | +7.5E-01 | 1.2E-01 | 1.0E+02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 48| +1.48E+02 | -1.9E-05 | +7.7E-01 | 3.1E-02 | 2.7E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 7| +2.10E+04 | -1.1E+05 | +9.0E-01 | 1.0E+00 | 5.4E+05 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 9| +1.18E+03 | -5.4E+03 | +9.0E-01 | 5.0E-01 | 2.1E+04 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 125| +1.48E+02 | +4.5E-06 | -2.8E+10 | 2.0E-15 | 4.3E-02 | 3.7E-15 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 5| +6.72E+04 | -3.6E+05 | +9.0E-01 | 2.0E+00 | 1.3E+06 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 8| +3.45E+03 | -1.8E+04 | +9.0E-01 | 1.0E+00 | 8.5E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 1| +5.82E+05 | -4.3E+11 | +8.9E-02 | 1.0E+00 | 2.0E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 33| +1.53E+02 | -2.2E+00 | +1.0E+00 | 1.2E-01 | 6.5E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 49| +1.48E+02 | -2.3E-05 | +8.5E-01 | 6.2E-02 | 1.5E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 126| +1.48E+02 | +3.8E-06 | -9.7E+10 | 5.1E-16 | 4.3E-02 | 9.3E-16 | 2d |0\n", - "2023-02-17 16:05:10 fides(WARNING) Stopping as trust region radius 1.28E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:10 fides(INFO) 9| +7.11E+02 | -2.7E+03 | +9.0E-01 | 1.0E+00 | 1.3E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 2| +9.11E+04 | -4.9E+05 | +9.0E-01 | 2.5E-01 | 2.7E+06 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 10| +3.53E+02 | -8.3E+02 | +9.0E-01 | 5.0E-01 | 3.4E+03 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 6| +1.05E+04 | -5.7E+04 | +9.0E-01 | 2.0E+00 | 2.1E+05 | 9.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 34| +1.51E+02 | -1.9E+00 | +4.6E-01 | 2.5E-01 | 3.0E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 3| +1.46E+04 | -7.6E+04 | +9.0E-01 | 5.0E-01 | 4.2E+05 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 50| +1.48E+02 | +2.8E-05 | -7.2E-01 | 1.2E-01 | 2.0E-01 | 3.8E-02 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 10| +2.99E+02 | -4.1E+02 | +9.0E-01 | 1.0E+00 | 2.1E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 11| +2.39E+02 | -1.1E+02 | +9.0E-01 | 1.0E+00 | 5.2E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 4| +4.15E+03 | -1.0E+04 | +6.8E-01 | 1.0E+00 | 6.6E+04 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 11| +2.35E+02 | -6.4E+01 | +1.0E+00 | 1.0E+00 | 3.1E+02 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 7| +1.72E+03 | -8.8E+03 | +9.1E-01 | 2.0E+00 | 3.2E+04 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 35| +1.50E+02 | -6.8E-01 | +2.3E-01 | 2.5E-01 | 9.0E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 5| +8.28E+02 | -3.3E+03 | +9.0E-01 | 1.0E+00 | 1.3E+04 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 51| +1.48E+02 | -1.4E-05 | +8.8E-01 | 3.1E-02 | 2.0E-01 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 6| +3.15E+02 | -5.1E+02 | +9.0E-01 | 2.0E+00 | 2.1E+03 | 3.5E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 12| +2.20E+02 | -1.9E+01 | +1.1E+00 | 1.0E+00 | 6.1E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 36| +1.49E+02 | -7.8E-01 | +9.3E-01 | 6.2E-02 | 4.3E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 52| +1.48E+02 | -9.5E-07 | +6.4E-02 | 6.2E-02 | 9.3E-02 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 12| +2.35E+02 | +9.0E+01 | -2.0E+01 | 2.0E+00 | 2.8E+01 | 4.0E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 8| +3.86E+02 | -1.3E+03 | +9.3E-01 | 2.0E+00 | 4.9E+03 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 7| +2.51E+02 | -6.4E+01 | +8.4E-01 | 4.0E+00 | 3.2E+02 | 2.3E+00 | trr |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 60| +1.63E+02 | +2.1E+01 | -2.5E+00 | 2.3E-01 | 3.8E+01 | 4.6E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 13| +2.35E+02 | +2.0E+01 | -2.5E+00 | 5.0E-01 | 2.8E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 8| +2.34E+02 | -1.7E+01 | +5.1E+00 | 4.0E+00 | 2.5E+01 | 4.2E+00 | trr |1\n", - "2023-02-17 16:05:10 fides(INFO) 53| +1.48E+02 | -5.7E-06 | +4.3E-01 | 1.6E-02 | 1.2E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 37| +1.48E+02 | -6.9E-01 | +6.4E-01 | 1.2E-01 | 3.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 14| +2.32E+02 | -3.5E+00 | +5.1E-01 | 1.2E-01 | 2.8E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 13| +2.20E+02 | +2.1E+02 | -1.3E+01 | 2.0E+00 | 3.4E+01 | 5.8E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 0| +5.24E+12 | NaN | NaN | 1.0E+00 | 2.4E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:10 fides(INFO) 15| +2.29E+02 | -3.1E+00 | +8.1E-01 | 1.2E-01 | 2.9E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 9| +2.06E+02 | -1.8E+02 | +1.0E+00 | 2.0E+00 | 7.0E+02 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:10.466 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.274 and h = 2.21878e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:10.467 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.274: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:10 fides(INFO) 54| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 6.9E-02 | 3.8E-03 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 38| +1.48E+02 | -3.1E-01 | +7.9E-01 | 1.2E-01 | 3.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 14| +2.20E+02 | +1.6E+00 | -6.3E-01 | 5.0E-01 | 3.4E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 9| +2.34E+02 | +1.1E+04 | -3.1E+02 | 4.0E+00 | 4.9E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 16| +2.29E+02 | +2.5E-01 | -1.9E-01 | 2.5E-01 | 2.2E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 55| +1.48E+02 | -5.4E-06 | +6.4E+00 | 3.9E-03 | 6.9E-02 | 9.5E-04 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 39| +1.48E+02 | -1.1E-01 | +1.8E-01 | 2.5E-01 | 1.5E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 1| +7.80E+10 | -5.2E+12 | +8.7E-02 | 1.0E+00 | 2.4E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 15| +2.14E+02 | -5.5E+00 | +7.8E-01 | 1.2E-01 | 3.4E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 10| +2.34E+02 | +1.5E+03 | -4.5E+01 | 1.0E+00 | 4.9E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 17| +2.27E+02 | -1.8E+00 | +7.9E-01 | 6.2E-02 | 2.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 56| +1.48E+02 | +3.0E-06 | -3.9E+00 | 7.8E-03 | 5.3E-02 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 2| +1.16E+10 | -6.6E+10 | +8.9E-01 | 2.5E-01 | 3.6E+11 | 7.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 40| +1.48E+02 | -2.4E-01 | +3.2E-01 | 6.2E-02 | 2.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 18| +2.23E+02 | -3.4E+00 | +1.1E+00 | 1.2E-01 | 2.1E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 10| +2.06E+02 | +2.3E+02 | -1.4E+01 | 2.0E+00 | 8.5E+01 | 5.7E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 11| +2.17E+02 | -1.7E+01 | +6.3E-01 | 2.5E-01 | 4.9E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 16| +2.06E+02 | -8.7E+00 | +1.1E+00 | 2.5E-01 | 3.9E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 57| +1.48E+02 | +1.8E-06 | -4.1E+00 | 2.0E-03 | 5.3E-02 | 4.7E-04 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 19| +2.23E+02 | -8.5E-01 | +1.2E-01 | 2.5E-01 | 2.0E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 12| +2.16E+02 | -1.2E+00 | +5.0E-01 | 2.5E-01 | 2.4E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 41| +1.48E+02 | -9.5E-02 | +7.6E-01 | 6.2E-02 | 1.2E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 17| +2.06E+02 | -3.9E+00 | -9.5E-02 | 5.0E-01 | 3.1E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 3| +1.74E+09 | -9.9E+09 | +8.9E-01 | 5.0E-01 | 5.3E+10 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 13| +2.16E+02 | +5.5E-01 | -3.9E-01 | 2.5E-01 | 1.7E+01 | 7.3E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 20| +2.19E+02 | -3.7E+00 | +8.4E-01 | 6.2E-02 | 4.9E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 58| +1.48E+02 | +9.3E-07 | -2.6E+00 | 4.9E-04 | 5.3E-02 | 1.2E-04 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 42| +1.48E+02 | -7.8E-02 | +9.5E-01 | 1.2E-01 | 8.4E+00 | 9.7E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 11| +1.91E+02 | -1.5E+01 | +1.4E+00 | 5.0E-01 | 8.5E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 14| +2.15E+02 | -4.4E-01 | +3.1E-01 | 6.2E-02 | 1.7E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 18| +2.01E+02 | -4.8E+00 | +7.6E-01 | 1.2E-01 | 3.1E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 4| +2.61E+08 | -1.5E+09 | +8.9E-01 | 1.0E+00 | 8.0E+09 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 61| +1.63E+02 | -1.1E-01 | +9.7E-03 | 5.6E-02 | 3.8E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 21| +2.17E+02 | -2.3E+00 | +8.0E-01 | 1.2E-01 | 2.4E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 59| +1.48E+02 | -2.1E-08 | +6.2E-02 | 1.2E-04 | 5.3E-02 | 3.3E-05 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 43| +1.48E+02 | -7.0E-02 | +8.7E-01 | 2.5E-01 | 5.8E+00 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 15| +2.14E+02 | -6.6E-01 | +6.0E-01 | 6.2E-02 | 1.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 5| +3.95E+07 | -2.2E+08 | +8.9E-01 | 1.0E+00 | 1.2E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 19| +1.95E+02 | -5.8E+00 | +6.8E-01 | 2.5E-01 | 4.1E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 22| +2.14E+02 | -2.5E+00 | +5.9E-01 | 2.5E-01 | 1.9E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 60| +1.48E+02 | +2.8E-06 | -4.1E+00 | 3.1E-05 | 5.3E-02 | 3.5E-05 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 44| +1.48E+02 | +7.5E-02 | -1.7E+00 | 5.0E-01 | 3.6E+00 | 2.1E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 12| +1.91E+02 | +4.2E+01 | -2.3E+00 | 1.0E+00 | 4.9E+01 | 2.1E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 6| +6.00E+06 | -3.3E+07 | +8.9E-01 | 1.0E+00 | 1.8E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 16| +2.14E+02 | -4.9E-03 | -2.0E-02 | 6.2E-02 | 1.2E+01 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 23| +2.07E+02 | -6.8E+00 | +9.6E-01 | 2.5E-01 | 3.3E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 20| +1.89E+02 | -6.2E+00 | +3.7E-01 | 2.5E-01 | 3.9E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 61| +1.48E+02 | +3.0E-06 | -7.2E+00 | 7.6E-06 | 5.3E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 17| +2.14E+02 | -2.3E-01 | +8.6E-01 | 1.6E-02 | 1.2E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 7| +9.19E+05 | -5.1E+06 | +8.9E-01 | 1.0E+00 | 2.8E+07 | 9.5E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 45| +1.48E+02 | -1.3E-02 | +6.0E-01 | 1.0E-01 | 3.6E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 62| +1.48E+02 | +2.6E-06 | -2.1E+01 | 1.9E-06 | 5.3E-02 | 3.2E-06 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 24| +1.82E+02 | -2.5E+01 | +1.2E+00 | 5.0E-01 | 2.5E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 18| +2.14E+02 | -3.3E-01 | +9.6E-01 | 3.1E-02 | 1.0E+01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 21| +1.80E+02 | -8.9E+00 | +8.7E-01 | 2.5E-01 | 7.0E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 13| +1.78E+02 | -1.3E+01 | +7.1E-01 | 2.5E-01 | 4.9E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 8| +1.42E+05 | -7.8E+05 | +8.9E-01 | 1.0E+00 | 4.2E+06 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 46| +1.48E+02 | +2.7E-02 | -6.9E-01 | 1.0E-01 | 2.4E+00 | 9.6E-02 | 2d |0\n", - "2023-02-17 16:05:10.784 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 37.9918 and h = 5.00215e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:10.784 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 37.9918: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:10 fides(INFO) 25| +1.82E+02 | +0.0E+00 | +0.0E+00 | 1.0E+00 | 9.0E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 19| +2.13E+02 | -4.8E-01 | +8.3E-01 | 6.2E-02 | 9.0E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 63| +1.48E+02 | +6.0E-06 | -1.9E+02 | 4.8E-07 | 5.3E-02 | 8.1E-07 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 22| +1.63E+02 | -1.7E+01 | +1.1E+00 | 5.0E-01 | 6.2E+01 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 47| +1.48E+02 | -6.8E-03 | +6.0E-01 | 2.5E-02 | 2.4E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 9| +2.22E+04 | -1.2E+05 | +9.0E-01 | 1.0E+00 | 6.5E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 20| +2.13E+02 | -1.1E-01 | +2.1E-01 | 1.2E-01 | 1.1E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 26| +1.68E+02 | -1.4E+01 | +8.3E-01 | 2.5E-01 | 9.0E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 14| +1.71E+02 | -6.9E+00 | +9.9E-01 | 2.5E-01 | 1.1E+02 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 64| +1.48E+02 | +1.3E-06 | -1.6E+02 | 1.2E-07 | 5.3E-02 | 2.0E-07 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 23| +1.59E+02 | -4.3E+00 | +9.0E-01 | 1.0E+00 | 6.6E+01 | 1.7E+00 | trr |1\n", - "2023-02-17 16:05:10 fides(INFO) 48| +1.48E+02 | -3.0E-03 | +9.9E-01 | 2.5E-02 | 2.1E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 62| +1.60E+02 | -2.7E+00 | +1.0E+00 | 1.4E-02 | 1.1E+02 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 21| +2.13E+02 | -3.8E-01 | +7.0E-01 | 3.1E-02 | 1.2E+01 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 27| +1.64E+02 | -4.1E+00 | +9.1E-01 | 5.0E-01 | 7.4E+01 | 5.4E-01 | trr |1\n", - "2023-02-17 16:05:10 fides(INFO) 65| +1.48E+02 | +3.1E-06 | -1.5E+03 | 3.0E-08 | 5.3E-02 | 5.1E-08 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 22| +2.13E+02 | -1.9E-01 | +8.6E-01 | 3.1E-02 | 7.4E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 49| +1.48E+02 | -4.2E-03 | +9.5E-01 | 5.0E-02 | 1.8E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 10| +3.64E+03 | -1.9E+04 | +9.0E-01 | 1.0E+00 | 1.0E+05 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 24| +1.59E+02 | +1.6E+03 | -1.2E+02 | 1.0E+00 | 7.0E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:10.914 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.723 and h = 3.11306e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:10.914 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.723: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:10 fides(INFO) 66| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.5E-09 | 5.3E-02 | 1.3E-08 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 28| +1.64E+02 | -1.6E+00 | -3.3E-01 | 5.0E-01 | 6.9E+01 | 9.0E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 15| +1.61E+02 | -1.0E+01 | +9.0E-01 | 5.0E-01 | 7.3E+01 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 23| +2.13E+02 | -2.4E-02 | +8.0E-02 | 6.2E-02 | 6.5E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 50| +1.48E+02 | -5.6E-03 | +9.2E-01 | 1.0E-01 | 1.4E+00 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 67| +1.48E+02 | +1.9E-06 | -1.5E+04 | 1.9E-09 | 5.3E-02 | 3.2E-09 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 24| +2.13E+02 | -2.2E-01 | +8.3E-01 | 1.6E-02 | 1.2E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 29| +1.59E+02 | -5.6E+00 | +8.1E-01 | 1.2E-01 | 6.9E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 11| +7.26E+02 | -2.9E+03 | +9.0E-01 | 2.0E+00 | 1.6E+04 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 25| +1.52E+02 | -7.0E+00 | +5.3E-01 | 2.3E-01 | 7.0E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 51| +1.48E+02 | +1.6E-02 | -2.0E+00 | 2.0E-01 | 1.1E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:10 fides(INFO) 16| +1.56E+02 | -4.5E+00 | +9.9E-02 | 1.0E+00 | 7.5E+01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 25| +2.12E+02 | -9.8E-02 | +5.4E-01 | 3.1E-02 | 6.8E+00 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:10 fides(INFO) 68| +1.48E+02 | -5.6E-07 | +1.8E+04 | 4.7E-10 | 5.3E-02 | 7.9E-10 | 2d |1\n", - "2023-02-17 16:05:10 fides(WARNING) Stopping as function difference 5.55E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:10 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:10 fides(INFO) 30| +1.53E+02 | -5.5E+00 | +1.0E+00 | 2.5E-01 | 7.0E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 26| +1.49E+02 | -2.9E+00 | +9.7E-01 | 2.3E-01 | 1.4E+02 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 52| +1.48E+02 | -1.2E-03 | +4.3E-01 | 5.0E-02 | 1.1E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 26| +2.12E+02 | -2.7E-02 | +1.2E-01 | 3.1E-02 | 6.2E+00 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 31| +1.53E+02 | +2.0E+01 | -3.2E+00 | 5.0E-01 | 5.6E+01 | 7.8E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 12| +7.26E+02 | +9.4E+03 | -2.8E+01 | 2.0E+00 | 2.5E+03 | 3.8E+00 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 17| +1.53E+02 | -3.1E+00 | +1.0E+00 | 2.5E-01 | 7.8E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 63| +1.59E+02 | -1.2E+00 | +8.4E-01 | 2.8E-02 | 3.6E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 27| +1.49E+02 | +1.3E+00 | -6.9E-01 | 4.7E-01 | 3.3E+01 | 6.9E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 53| +1.48E+02 | +1.1E-03 | -2.3E-01 | 5.0E-02 | 9.6E-01 | 1.7E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 27| +2.12E+02 | -9.1E-02 | +9.0E-01 | 7.8E-03 | 8.7E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 32| +1.51E+02 | -2.6E+00 | +7.3E-01 | 1.2E-01 | 5.6E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 28| +2.12E+02 | -8.2E-02 | +7.4E-01 | 1.6E-02 | 6.4E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 54| +1.48E+02 | -9.9E-04 | +5.4E-01 | 1.3E-02 | 9.6E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:11.099 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 208.039 and h = 1.4974e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:11.099 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 208.039: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:11 fides(INFO) 13| +7.26E+02 | +0.0E+00 | +0.0E+00 | 4.3E-01 | 2.5E+03 | 8.8E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 28| +1.48E+02 | -8.3E-01 | +4.9E-01 | 1.2E-01 | 3.3E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 18| +1.53E+02 | -3.9E-01 | +1.0E+00 | 5.0E-01 | 4.0E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 33| +1.49E+02 | -1.2E+00 | +8.6E-01 | 1.2E-01 | 5.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 29| +2.12E+02 | -6.2E-02 | +8.4E-01 | 1.6E-02 | 4.5E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 55| +1.48E+02 | -7.5E-04 | +9.4E-01 | 1.3E-02 | 2.1E+00 | 5.8E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 0| +3.71E+02 | NaN | NaN | 1.0E+00 | 1.4E+02 | NaN | NaN |1\n", - "2023-02-17 16:05:11.147 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 172.17 and h = 5.26904e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:11.147 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 172.17: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:11 fides(INFO) 34| +1.49E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 3.2E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 30| +2.12E+02 | -5.4E-02 | +4.9E-01 | 3.1E-02 | 4.9E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 29| +1.48E+02 | -4.0E-01 | +4.6E-01 | 1.2E-01 | 4.7E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 14| +3.80E+02 | -3.5E+02 | +8.5E-01 | 1.1E-01 | 2.5E+03 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 19| +1.52E+02 | -8.7E-01 | +7.8E-01 | 1.0E+00 | 2.3E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 56| +1.48E+02 | -7.6E-04 | +9.4E-01 | 2.5E-02 | 4.2E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 1| +3.36E+02 | -3.5E+01 | +2.1E-02 | 1.0E+00 | 1.4E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 31| +2.12E+02 | -8.0E-03 | +3.5E-02 | 3.1E-02 | 5.3E+00 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 35| +1.49E+02 | -6.2E-01 | +7.4E-01 | 6.2E-02 | 3.2E+01 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 30| +1.48E+02 | -1.2E-01 | +7.7E-01 | 1.2E-01 | 9.1E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 57| +1.48E+02 | -4.3E-04 | +4.5E-01 | 5.0E-02 | 9.4E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 2| +2.85E+02 | -5.1E+01 | +9.0E-01 | 2.5E-01 | 1.8E+02 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 20| +1.51E+02 | -3.6E-01 | +9.9E-01 | 2.0E+00 | 3.9E+01 | 1.0E+00 | trr |1\n", - "2023-02-17 16:05:11 fides(INFO) 32| +2.12E+02 | -8.0E-02 | +8.8E-01 | 7.8E-03 | 7.6E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 15| +2.32E+02 | -1.5E+02 | +8.8E-01 | 2.1E-01 | 8.4E+02 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 36| +1.48E+02 | -3.6E-01 | +7.7E-01 | 6.2E-02 | 4.0E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 31| +1.48E+02 | -9.2E-02 | +9.3E-01 | 2.3E-01 | 5.5E+00 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 33| +2.12E+02 | -5.0E-02 | +6.1E-01 | 1.6E-02 | 5.1E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 58| +1.48E+02 | +2.6E-03 | -1.2E+00 | 5.0E-02 | 4.0E-01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 21| +1.51E+02 | +1.7E-01 | -1.2E+00 | 2.0E+00 | 4.7E+00 | 9.1E-01 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 3| +2.67E+02 | -1.8E+01 | +1.7E-01 | 5.0E-01 | 5.6E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 37| +1.48E+02 | -3.0E-01 | +4.8E-01 | 1.2E-01 | 1.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 32| +1.48E+02 | +7.5E-03 | -8.6E-02 | 4.7E-01 | 5.0E+00 | 5.5E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 59| +1.48E+02 | +1.5E-04 | -1.6E-01 | 1.3E-02 | 4.0E-01 | 5.0E-03 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 34| +2.12E+02 | -2.2E-02 | +4.2E-01 | 1.6E-02 | 3.6E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 16| +2.11E+02 | -2.1E+01 | +8.5E-01 | 4.3E-01 | 1.3E+02 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 4| +2.52E+02 | -1.5E+01 | +8.1E-01 | 1.2E-01 | 1.1E+02 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 64| +1.57E+02 | -1.7E+00 | +8.4E-01 | 5.6E-02 | 2.9E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 22| +1.51E+02 | -9.5E-02 | +1.0E+00 | 2.3E-01 | 4.7E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 38| +1.48E+02 | +1.0E+00 | -9.7E-01 | 1.2E-01 | 3.1E+01 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 35| +2.12E+02 | -4.0E-02 | +5.6E-01 | 1.6E-02 | 4.7E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 33| +1.48E+02 | -3.1E-02 | +7.6E-01 | 1.2E-01 | 5.0E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 60| +1.48E+02 | -3.9E-05 | +6.2E-02 | 3.1E-03 | 4.0E-01 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 5| +2.52E+02 | -4.8E-02 | -2.8E-02 | 2.5E-01 | 2.8E+01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 36| +2.12E+02 | -1.6E-02 | +3.3E-01 | 1.6E-02 | 3.4E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 23| +1.51E+02 | -2.1E-01 | +1.3E+00 | 4.7E-01 | 6.5E+00 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 39| +1.48E+02 | -2.6E-01 | +6.8E-01 | 3.1E-02 | 3.1E+01 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 34| +1.48E+02 | -4.3E-02 | +6.3E-01 | 2.3E-01 | 3.8E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 61| +1.48E+02 | -3.7E-04 | +9.5E-01 | 7.9E-04 | 1.8E+00 | 5.5E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 37| +2.12E+02 | -3.5E-02 | +5.2E-01 | 1.6E-02 | 4.4E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 6| +2.50E+02 | -2.0E+00 | +7.3E-01 | 6.2E-02 | 2.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 24| +1.51E+02 | -4.8E-01 | +1.0E+00 | 9.4E-01 | 9.9E+00 | 3.2E-01 | trr |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 40| +1.48E+02 | -5.2E-02 | +8.1E-01 | 3.1E-02 | 6.5E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 35| +1.48E+02 | +1.5E-01 | -1.1E+00 | 2.3E-01 | 6.3E+00 | 2.5E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 62| +1.48E+02 | -3.1E-05 | +1.0E+00 | 1.6E-03 | 1.5E-01 | 6.9E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 38| +2.12E+02 | -1.4E-02 | +2.9E-01 | 1.6E-02 | 3.2E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 17| +2.11E+02 | +1.7E+02 | -1.5E+01 | 8.5E-01 | 3.1E+01 | 1.6E+00 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 7| +2.50E+02 | -1.8E-02 | +1.2E-01 | 6.2E-02 | 5.1E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 41| +1.48E+02 | -7.6E-02 | +9.4E-01 | 6.2E-02 | 1.3E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 25| +1.51E+02 | +4.7E-01 | -4.4E-01 | 9.4E-01 | 4.8E+01 | 3.7E-01 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 36| +1.48E+02 | -2.2E-02 | +5.4E-01 | 5.8E-02 | 6.3E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 39| +2.12E+02 | -3.0E-02 | +4.8E-01 | 1.6E-02 | 4.1E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 8| +2.50E+02 | -3.2E-02 | +3.2E-01 | 1.6E-02 | 4.1E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 63| +1.48E+02 | -3.8E-05 | +9.4E-01 | 3.1E-03 | 2.3E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 42| +1.48E+02 | -5.1E-02 | +5.7E-01 | 1.2E-01 | 4.1E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 40| +2.12E+02 | -1.2E-02 | +2.7E-01 | 1.6E-02 | 3.0E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 37| +1.48E+02 | -1.1E-02 | +8.5E-01 | 5.8E-02 | 2.4E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 26| +1.50E+02 | -4.2E-01 | +9.8E-01 | 4.7E-02 | 4.8E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 64| +1.48E+02 | -5.1E-05 | +8.8E-01 | 6.3E-03 | 8.6E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 43| +1.48E+02 | +1.4E-01 | -8.1E-01 | 1.2E-01 | 5.6E+00 | 8.6E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 18| +2.08E+02 | -3.4E+00 | +4.1E-01 | 1.6E-01 | 3.1E+01 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 9| +2.50E+02 | -7.8E-03 | +1.9E-01 | 1.6E-02 | 2.7E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 41| +2.12E+02 | -2.6E-02 | +4.5E-01 | 1.6E-02 | 3.9E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 65| +1.48E+02 | -7.4E-05 | +9.6E-01 | 1.3E-02 | 1.8E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 38| +1.48E+02 | -1.4E-02 | +7.9E-01 | 1.2E-01 | 5.3E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 27| +1.50E+02 | -4.1E-01 | +8.8E-01 | 9.4E-02 | 1.3E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 65| +1.56E+02 | -1.1E+00 | +4.5E-01 | 1.1E-01 | 5.5E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 10| +2.50E+02 | -1.2E-02 | +7.4E-01 | 3.9E-03 | 2.1E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 39| +1.48E+02 | -4.5E-03 | +1.6E-01 | 2.3E-01 | 1.6E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 44| +1.48E+02 | -3.4E-02 | +5.7E-01 | 3.1E-02 | 5.6E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 42| +2.12E+02 | -1.1E-02 | +2.6E-01 | 1.6E-02 | 2.9E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 66| +1.48E+02 | -1.1E-04 | +9.1E-01 | 2.5E-02 | 8.0E-02 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 11| +2.50E+02 | +7.2E-06 | -9.2E-03 | 3.9E-03 | 3.7E-01 | 9.9E-03 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 28| +1.50E+02 | -1.2E-01 | +8.3E-01 | 1.9E-01 | 1.8E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 43| +2.12E+02 | -2.3E-02 | +4.3E-01 | 1.6E-02 | 3.6E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 45| +1.48E+02 | -1.6E-02 | +8.7E-01 | 3.1E-02 | 6.4E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 19| +1.99E+02 | -8.5E+00 | +9.5E-01 | 1.6E-01 | 9.1E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 67| +1.48E+02 | -7.9E-05 | +3.8E-01 | 5.0E-02 | 2.9E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 40| +1.48E+02 | -1.7E-02 | +3.7E-01 | 5.8E-02 | 5.5E+00 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 12| +2.50E+02 | -4.0E-04 | +5.9E-01 | 9.8E-04 | 3.7E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 29| +1.50E+02 | +5.5E-03 | -2.2E-01 | 3.8E-01 | 2.0E+00 | 9.9E-02 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 46| +1.48E+02 | -2.2E-02 | +9.1E-01 | 6.2E-02 | 2.7E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 44| +2.12E+02 | -1.1E-02 | +2.7E-01 | 1.6E-02 | 2.7E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 68| +1.48E+02 | +1.4E-03 | -1.9E+00 | 5.0E-02 | 2.0E-01 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 41| +1.48E+02 | -3.0E-03 | +3.5E-01 | 5.8E-02 | 1.4E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 13| +2.50E+02 | -3.5E-08 | +1.6E-03 | 9.8E-04 | 5.8E-02 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 45| +2.12E+02 | -2.0E-02 | +4.1E-01 | 1.6E-02 | 3.4E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 30| +1.50E+02 | -9.2E-03 | +7.6E-01 | 3.5E-02 | 2.0E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 69| +1.48E+02 | +4.1E-05 | -1.5E-01 | 1.3E-02 | 2.0E-01 | 3.6E-03 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 47| +1.48E+02 | +7.5E-03 | -2.5E-01 | 1.2E-01 | 4.2E+00 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 20| +1.94E+02 | -5.8E+00 | +5.3E-01 | 3.2E-01 | 4.2E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 42| +1.48E+02 | -3.9E-03 | +6.6E-01 | 5.8E-02 | 3.8E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 46| +2.12E+02 | -1.1E-02 | +2.8E-01 | 1.6E-02 | 2.6E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 14| +2.50E+02 | -2.2E-06 | +1.1E-01 | 2.4E-04 | 5.8E-02 | 6.4E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 70| +1.48E+02 | -2.8E-05 | +1.9E-01 | 3.1E-03 | 2.0E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 48| +1.48E+02 | -8.3E-03 | +7.3E-01 | 3.1E-02 | 4.2E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 31| +1.50E+02 | -4.4E-03 | +8.8E-01 | 7.0E-02 | 4.0E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 43| +1.48E+02 | -1.6E-03 | +7.3E-01 | 5.8E-02 | 6.7E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 47| +2.12E+02 | -1.8E-02 | +4.1E-01 | 1.6E-02 | 3.2E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 15| +2.50E+02 | -4.5E-06 | +9.0E-01 | 6.1E-05 | 5.1E-02 | 1.1E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 32| +1.50E+02 | -4.5E-04 | +7.2E-01 | 1.4E-01 | 9.6E-01 | 1.2E-02 | trr |1\n", - "2023-02-17 16:05:11 fides(INFO) 49| +1.48E+02 | -7.2E-03 | +7.7E-01 | 3.1E-02 | 1.7E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 71| +1.48E+02 | -7.9E-05 | +9.3E-01 | 7.9E-04 | 8.5E-01 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:11.838 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.1643 and h = 1.42051e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:11.838 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.1643: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:11 fides(INFO) 44| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.8E-02 | 1.5E+00 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 48| +2.12E+02 | -1.1E-02 | +3.0E-01 | 1.6E-02 | 2.5E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 50| +1.48E+02 | -4.3E-03 | +3.0E-01 | 6.2E-02 | 3.9E+00 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 16| +2.50E+02 | -3.0E-06 | +6.0E-01 | 1.2E-04 | 3.2E-02 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 49| +2.12E+02 | -1.7E-02 | +4.2E-01 | 1.6E-02 | 3.0E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 33| +1.50E+02 | +6.5E-05 | -2.0E-01 | 1.4E-01 | 5.2E-01 | 1.0E-02 | trr |0\n", - "2023-02-17 16:05:11 fides(INFO) 72| +1.48E+02 | -5.7E-06 | +1.6E+00 | 1.6E-03 | 5.8E-02 | 3.7E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 45| +1.48E+02 | -6.5E-04 | +9.4E-01 | 1.5E-02 | 1.5E+00 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 73| +1.48E+02 | -2.7E-06 | +4.9E-01 | 3.1E-03 | 5.4E-02 | 7.1E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 66| +1.55E+02 | -8.8E-01 | +4.3E-01 | 1.1E-01 | 1.9E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 21| +1.86E+02 | -7.9E+00 | +5.9E-01 | 3.2E-01 | 1.2E+02 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 17| +2.50E+02 | +1.6E-11 | -1.2E-04 | 1.2E-04 | 4.9E-03 | 3.1E-04 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:11 fides(INFO) 50| +2.12E+02 | -1.2E-02 | +3.3E-01 | 1.6E-02 | 2.3E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 51| +1.48E+02 | +1.1E-01 | -2.7E+00 | 6.2E-02 | 1.7E+00 | 5.5E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 46| +1.48E+02 | -8.0E-04 | +9.8E-01 | 2.9E-02 | 2.7E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 34| +1.50E+02 | -1.4E-04 | +8.5E-01 | 6.2E-03 | 5.2E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 74| +1.48E+02 | -6.6E-06 | +1.4E+00 | 3.1E-03 | 3.8E-02 | 6.7E-04 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 51| +2.12E+02 | -1.7E-02 | +4.4E-01 | 1.6E-02 | 2.8E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 18| +2.50E+02 | +1.6E-11 | -1.2E-04 | 3.1E-05 | 4.9E-03 | 7.8E-05 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 47| +1.48E+02 | -1.1E-03 | +9.0E-01 | 5.8E-02 | 7.6E-01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 52| +1.48E+02 | +3.0E-02 | -1.1E+00 | 1.6E-02 | 1.7E+00 | 3.2E-02 | 2d |0\n", - "2023-02-17 16:05:11 fides(INFO) 35| +1.50E+02 | -2.7E-05 | +5.7E-01 | 1.2E-02 | 1.4E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:11 fides(INFO) 75| +1.48E+02 | -7.1E-06 | +8.7E-01 | 6.3E-03 | 5.6E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 22| +1.82E+02 | -3.9E+00 | +9.2E-01 | 3.2E-01 | 1.2E+02 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 52| +2.12E+02 | -1.2E-02 | +3.7E-01 | 1.6E-02 | 2.2E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 19| +2.50E+02 | -6.4E-08 | +7.9E-01 | 7.6E-06 | 4.9E-03 | 2.0E-05 | 2d |1\n", - "2023-02-17 16:05:12 fides(WARNING) Stopping as function difference 6.38E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:12 fides(INFO) 48| +1.48E+02 | -1.4E-03 | +7.4E-01 | 1.2E-01 | 3.1E-01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 53| +1.48E+02 | -7.1E-03 | +6.3E-01 | 3.9E-03 | 1.7E+00 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 36| +1.50E+02 | -1.1E-06 | +9.1E-02 | 1.2E-02 | 1.4E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 53| +2.12E+02 | -1.6E-02 | +4.7E-01 | 1.6E-02 | 2.6E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 76| +1.48E+02 | -1.3E-05 | +9.6E-01 | 1.3E-02 | 3.3E-02 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:12.047 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.6153 and h = 1.60736e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:12.047 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.6153: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:12 fides(INFO) 49| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 1.1E+00 | 6.9E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 54| +1.48E+02 | -6.0E-04 | +9.7E-01 | 3.9E-03 | 1.6E+00 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 23| +1.81E+02 | -1.1E+00 | +9.7E-01 | 6.3E-01 | 4.5E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 54| +2.12E+02 | -1.3E-02 | +1.0E+00 | 1.6E-02 | 2.0E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 77| +1.48E+02 | -2.3E-05 | +1.1E+00 | 2.5E-02 | 6.5E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 37| +1.50E+02 | -4.6E-06 | +3.9E-01 | 3.1E-03 | 1.9E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 50| +1.48E+02 | -7.1E-04 | +7.0E-01 | 2.9E-02 | 1.1E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 55| +1.48E+02 | -8.9E-04 | +9.4E-01 | 7.8E-03 | 5.8E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 55| +2.12E+02 | -2.9E-02 | +1.0E+00 | 3.1E-02 | 1.4E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 24| +1.81E+02 | +6.2E+00 | -4.8E+00 | 1.3E+00 | 3.3E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 0| +1.34E+06 | NaN | NaN | 1.0E+00 | 6.1E+06 | NaN | NaN |1\n", - "2023-02-17 16:05:12 fides(INFO) 78| +1.48E+02 | -1.6E-05 | +6.3E-01 | 5.0E-02 | 3.3E-02 | 7.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 38| +1.50E+02 | +1.1E-07 | -3.6E-02 | 3.1E-03 | 5.1E-02 | 9.7E-04 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 56| +1.48E+02 | -1.2E-03 | +9.6E-01 | 1.6E-02 | 1.3E+00 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 56| +2.12E+02 | -2.7E-02 | +1.1E+00 | 6.2E-02 | 2.3E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 51| +1.48E+02 | -2.6E-04 | +1.0E+00 | 2.9E-02 | 2.2E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 1| +1.14E+03 | -1.3E+06 | +1.1E-01 | 1.0E+00 | 6.1E+06 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 25| +1.80E+02 | -3.4E-01 | +3.2E-01 | 3.2E-01 | 3.3E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 79| +1.48E+02 | +2.3E-04 | -3.5E+00 | 5.0E-02 | 4.3E-02 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 57| +2.11E+02 | -3.2E-02 | +2.9E-01 | 1.2E-01 | 1.1E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 39| +1.50E+02 | -1.2E-06 | +8.9E-01 | 7.8E-04 | 5.1E-02 | 2.4E-04 | 2d |1\n", - "2023-02-17 16:05:12 fides(WARNING) Stopping as function difference 1.19E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:12 fides(INFO) 2| +4.50E+02 | -6.9E+02 | +9.0E-01 | 2.5E-01 | 3.7E+03 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 57| +1.48E+02 | -2.0E-03 | +9.0E-01 | 3.1E-02 | 4.0E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 52| +1.48E+02 | -4.2E-04 | +9.7E-01 | 5.8E-02 | 1.7E-01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 3| +2.97E+02 | -1.5E+02 | +9.1E-01 | 5.0E-01 | 6.1E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 58| +2.11E+02 | -1.8E-01 | +1.4E+00 | 1.2E-01 | 6.7E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 67| +1.55E+02 | +1.3E+00 | -1.5E+00 | 1.1E-01 | 3.3E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 80| +1.48E+02 | -7.5E-07 | +3.6E-02 | 1.3E-02 | 4.3E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 53| +1.48E+02 | -6.1E-04 | +9.6E-01 | 1.2E-01 | 2.1E-01 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 26| +1.79E+02 | -1.2E+00 | +8.4E-01 | 3.2E-01 | 3.8E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 58| +1.48E+02 | -6.3E-04 | +1.6E-01 | 6.2E-02 | 1.3E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 4| +2.51E+02 | -4.7E+01 | +7.6E-01 | 1.0E+00 | 9.8E+01 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 59| +2.11E+02 | -3.6E-01 | +1.2E+00 | 2.5E-01 | 2.9E+00 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 81| +1.48E+02 | -6.3E-06 | +8.9E-01 | 3.1E-03 | 6.2E-02 | 4.4E-04 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 54| +1.48E+02 | -4.3E-04 | +7.1E-01 | 2.3E-01 | 9.3E-02 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 5| +2.27E+02 | -2.3E+01 | +4.0E+00 | 2.0E+00 | 2.5E+01 | 3.8E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 59| +1.48E+02 | +7.6E-03 | -9.4E-01 | 1.6E-02 | 9.2E-01 | 1.7E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 60| +2.07E+02 | -3.5E+00 | +2.6E+00 | 5.0E-01 | 2.8E+00 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 27| +1.79E+02 | +4.8E+00 | -5.3E+00 | 6.3E-01 | 1.9E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 82| +1.48E+02 | -2.3E-06 | +8.6E-01 | 6.3E-03 | 7.4E-02 | 5.6E-04 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 55| +1.48E+02 | +1.9E-02 | -1.2E+01 | 2.3E-01 | 2.7E-01 | 7.0E-02 | trr |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 60| +1.48E+02 | -3.8E-04 | +7.3E-02 | 3.9E-03 | 9.2E-01 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 0| +9.11E+06 | NaN | NaN | 1.0E+00 | 4.2E+07 | NaN | NaN |1\n", - "2023-02-17 16:05:12 fides(INFO) 61| +2.07E+02 | +1.0E+02 | -8.3E+00 | 1.0E+00 | 9.9E+00 | 2.6E+00 | trr |0\n", - "2023-02-17 16:05:12 fides(INFO) 6| +2.27E+02 | +1.5E+04 | -7.0E+02 | 4.0E+00 | 4.2E+01 | 1.2E+01 | trr |0\n", - "2023-02-17 16:05:12 fides(INFO) 28| +1.79E+02 | -9.2E-02 | +1.9E-01 | 1.6E-01 | 1.9E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 83| +1.48E+02 | -9.2E-07 | +6.3E-01 | 1.3E-02 | 9.9E-03 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(WARNING) Stopping as function difference 9.23E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:12 fides(INFO) 56| +1.48E+02 | +5.4E-04 | -8.0E-01 | 5.7E-02 | 2.7E-01 | 1.7E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 1| +1.64E+03 | -9.1E+06 | +1.1E-01 | 1.0E+00 | 4.2E+07 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 62| +2.01E+02 | -6.2E+00 | +1.4E+00 | 2.5E-01 | 9.9E+00 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 61| +1.48E+02 | -1.8E-03 | +9.5E-01 | 9.8E-04 | 3.9E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 7| +2.27E+02 | +2.6E+02 | -1.2E+01 | 1.0E+00 | 4.2E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 57| +1.48E+02 | -1.1E-04 | +5.4E-01 | 1.4E-02 | 2.7E-01 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 62| +1.48E+02 | -3.2E-04 | +8.2E-01 | 2.0E-03 | 5.3E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 8| +2.18E+02 | -9.3E+00 | +4.2E-01 | 2.5E-01 | 4.2E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 2| +5.48E+02 | -1.1E+03 | +9.0E-01 | 2.5E-01 | 5.9E+03 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 29| +1.79E+02 | -1.0E-01 | +9.7E-01 | 3.9E-02 | 1.9E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 63| +1.72E+02 | -2.9E+01 | +2.0E+00 | 5.0E-01 | 3.6E+01 | 8.7E-01 | trr |1\n", - "2023-02-17 16:05:12 fides(INFO) 58| +1.48E+02 | -1.8E-05 | +1.0E+00 | 1.4E-02 | 2.9E-01 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 9| +2.14E+02 | -4.2E+00 | +5.9E-01 | 2.5E-01 | 4.8E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 3| +3.02E+02 | -2.5E+02 | +9.1E-01 | 5.0E-01 | 1.0E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 63| +1.48E+02 | -2.5E-04 | +8.9E-01 | 3.9E-03 | 7.8E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 64| +1.72E+02 | +8.7E+02 | -2.7E+01 | 5.0E-01 | 4.9E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 4| +2.58E+02 | -4.4E+01 | +8.4E-01 | 1.0E+00 | 1.9E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 10| +2.13E+02 | -2.6E-01 | +1.0E-01 | 2.5E-01 | 1.5E+01 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 30| +1.79E+02 | -9.2E-02 | +8.6E-01 | 7.9E-02 | 1.6E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 68| +1.55E+02 | +9.2E-01 | -1.4E+00 | 2.8E-02 | 3.3E+01 | 7.5E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 59| +1.48E+02 | -1.7E-05 | +1.1E+00 | 2.8E-02 | 4.8E-02 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 64| +1.48E+02 | -2.2E-04 | +8.0E-01 | 7.8E-03 | 2.3E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 5| +2.58E+02 | +2.7E+03 | -2.1E+02 | 2.0E+00 | 3.1E+01 | 4.1E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 0| +1.54E+13 | NaN | NaN | 1.0E+00 | 7.1E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:12 fides(INFO) 65| +1.69E+02 | -3.0E+00 | +2.5E-01 | 1.2E-01 | 4.9E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 11| +2.12E+02 | -1.3E+00 | +7.0E-01 | 6.2E-02 | 2.4E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 60| +1.48E+02 | -1.7E-05 | +8.7E-01 | 5.7E-02 | 8.5E-02 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 6| +2.58E+02 | +4.5E+02 | -3.0E+01 | 5.0E-01 | 3.1E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 65| +1.48E+02 | -2.4E-04 | +9.0E-01 | 1.6E-02 | 3.5E-01 | 7.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 31| +1.79E+02 | -1.4E-01 | +7.6E-01 | 1.6E-01 | 1.5E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 1| +1.08E+07 | -1.5E+13 | +8.4E-02 | 1.0E+00 | 7.1E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 66| +1.60E+02 | -8.8E+00 | +7.2E-01 | 1.2E-01 | 9.3E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 12| +2.12E+02 | -5.9E-01 | +1.0E+00 | 6.2E-02 | 7.5E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 7| +2.50E+02 | -7.4E+00 | +8.2E-01 | 1.2E-01 | 3.1E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 66| +1.48E+02 | -3.3E-04 | +8.6E-01 | 3.1E-02 | 1.7E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 61| +1.48E+02 | +1.0E-04 | -2.8E+00 | 1.1E-01 | 4.4E-02 | 2.1E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 13| +2.11E+02 | -8.7E-01 | +9.4E-01 | 1.2E-01 | 7.4E+00 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 32| +1.79E+02 | +2.8E-01 | -1.5E+00 | 3.2E-01 | 1.0E+01 | 5.4E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 67| +1.53E+02 | -7.3E+00 | +6.4E-01 | 1.2E-01 | 6.9E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 8| +2.50E+02 | +4.4E-01 | -5.8E-01 | 2.5E-01 | 1.0E+01 | 6.1E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 2| +1.65E+06 | -9.1E+06 | +8.9E-01 | 2.5E-01 | 5.0E+07 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 62| +1.48E+02 | +2.2E-06 | -1.6E-01 | 2.8E-02 | 4.4E-02 | 5.2E-03 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 67| +1.48E+02 | +4.0E-04 | -5.8E-01 | 6.2E-02 | 4.1E-01 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 9| +2.50E+02 | +3.7E-01 | -5.0E-01 | 6.2E-02 | 1.0E+01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 14| +2.09E+02 | -1.7E+00 | +7.7E-01 | 2.5E-01 | 1.0E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 68| +1.51E+02 | -1.7E+00 | +3.2E-01 | 1.2E-01 | 4.7E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:12.603 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1383 and h = 1.69942e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:12.603 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1383: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:12 fides(INFO) 3| +2.56E+05 | -1.4E+06 | +9.0E-01 | 5.0E-01 | 7.7E+06 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 33| +1.79E+02 | -5.1E-02 | +6.3E-01 | 7.9E-02 | 1.0E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 63| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.1E-03 | 4.4E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:12.618 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.406 and h = 2.66838e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:12.619 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.406: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:12 fides(INFO) 68| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 4.1E-01 | 7.5E-03 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 10| +2.50E+02 | -3.0E-01 | +8.5E-01 | 1.6E-02 | 1.0E+01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 15| +2.04E+02 | -5.2E+00 | +1.0E+00 | 5.0E-01 | 1.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 4| +4.44E+04 | -2.1E+05 | +8.7E-01 | 1.0E+00 | 1.2E+06 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 69| +1.51E+02 | +1.6E+01 | -2.7E+00 | 1.2E-01 | 9.4E+01 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 11| +2.50E+02 | +3.0E-02 | -2.0E-01 | 3.1E-02 | 4.8E+00 | 7.8E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 64| +1.48E+02 | +7.2E-07 | -2.8E-01 | 1.8E-03 | 4.4E-02 | 3.4E-04 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 69| +1.48E+02 | -6.7E-05 | +9.3E-01 | 3.9E-03 | 4.1E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 5| +7.00E+03 | -3.7E+04 | +9.0E-01 | 2.0E+00 | 2.0E+05 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 34| +1.79E+02 | -6.4E-02 | +8.2E-01 | 7.9E-02 | 1.1E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 12| +2.50E+02 | -6.6E-02 | +8.1E-01 | 7.8E-03 | 4.8E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 16| +2.04E+02 | +1.6E+02 | -1.8E+01 | 1.0E+00 | 1.5E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 70| +1.48E+02 | -6.2E-05 | +8.7E-01 | 7.8E-03 | 5.2E-02 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 65| +1.48E+02 | +2.0E-06 | -1.0E+00 | 4.4E-04 | 4.4E-02 | 1.2E-04 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 70| +1.51E+02 | +4.9E-01 | -1.4E-01 | 3.1E-02 | 9.4E+01 | 8.4E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 6| +1.25E+03 | -5.7E+03 | +9.0E-01 | 2.0E+00 | 3.1E+04 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 13| +2.50E+02 | +1.2E-03 | -6.2E-02 | 1.6E-02 | 1.8E+00 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 17| +1.98E+02 | -6.1E+00 | +1.2E+00 | 2.5E-01 | 1.5E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 35| +1.79E+02 | +1.4E-02 | -2.4E-01 | 1.6E-01 | 7.4E+00 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 71| +1.50E+02 | -1.3E+00 | +8.2E-01 | 7.8E-03 | 9.4E+01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 66| +1.48E+02 | +2.5E-07 | -1.3E-01 | 1.1E-04 | 4.4E-02 | 9.0E-05 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 71| +1.48E+02 | -1.0E-04 | +9.8E-01 | 1.6E-02 | 6.5E-02 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 7| +3.67E+02 | -8.8E+02 | +9.0E-01 | 2.0E+00 | 4.9E+03 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 14| +2.50E+02 | -9.9E-03 | +7.1E-01 | 3.9E-03 | 1.8E+00 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 69| +1.55E+02 | +7.1E-02 | -1.6E-01 | 7.0E-03 | 3.3E+01 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 67| +1.48E+02 | +2.2E-06 | -1.4E+00 | 2.8E-05 | 4.4E-02 | 5.5E-05 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 18| +1.98E+02 | +2.5E+01 | -2.4E+00 | 5.0E-01 | 3.0E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 72| +1.48E+02 | -1.5E-04 | +9.8E-01 | 3.1E-02 | 1.3E-01 | 8.8E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 72| +1.49E+02 | -6.0E-01 | +9.0E-01 | 1.6E-02 | 4.2E+01 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 15| +2.50E+02 | -2.1E-05 | +8.1E-02 | 3.9E-03 | 2.0E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 8| +2.31E+02 | -1.4E+02 | +9.2E-01 | 2.0E+00 | 7.7E+02 | 4.4E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 36| +1.79E+02 | -2.2E-02 | +7.6E-01 | 3.9E-02 | 7.4E+00 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 19| +1.90E+02 | -7.6E+00 | +1.2E+00 | 1.2E-01 | 3.0E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 16| +2.50E+02 | -8.1E-05 | +3.5E-01 | 9.8E-04 | 2.0E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 68| +1.48E+02 | +2.6E-06 | -4.9E+00 | 6.9E-06 | 4.4E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 73| +1.49E+02 | -7.1E-01 | +7.0E-01 | 3.1E-02 | 2.8E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 73| +1.48E+02 | -1.1E-04 | +6.6E-01 | 6.2E-02 | 4.3E-02 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 9| +2.31E+02 | +1.1E+02 | -4.5E+00 | 4.0E+00 | 1.1E+02 | 7.3E+00 | trr |0\n", - "2023-02-17 16:05:12 fides(INFO) 17| +2.50E+02 | -6.3E-06 | +7.5E-02 | 9.8E-04 | 1.2E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:12.833 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 213.323 and h = 2.13543e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:12.833 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 213.323: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:12 fides(INFO) 37| +1.79E+02 | +0.0E+00 | +0.0E+00 | 7.9E-02 | 7.9E+00 | 3.5E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 20| +1.73E+02 | -1.7E+01 | +1.0E+00 | 2.5E-01 | 3.0E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 18| +2.50E+02 | -3.5E-05 | +7.9E-01 | 2.4E-04 | 1.1E-01 | 4.7E-04 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 74| +1.48E+02 | +3.6E-03 | -7.0E+00 | 6.2E-02 | 1.5E-01 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 69| +1.48E+02 | +4.9E-06 | -3.5E+01 | 1.7E-06 | 4.4E-02 | 3.5E-06 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 74| +1.48E+02 | -2.2E-01 | +3.5E-01 | 3.1E-02 | 3.8E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 10| +2.22E+02 | -9.6E+00 | +2.2E-01 | 6.3E-01 | 1.1E+02 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 19| +2.50E+02 | -3.1E-06 | +3.2E-01 | 4.9E-04 | 3.5E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 38| +1.79E+02 | -1.9E-02 | +1.0E+00 | 2.0E-02 | 7.9E+00 | 9.5E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 75| +1.48E+02 | -3.0E-01 | +7.4E-01 | 3.1E-02 | 3.5E+01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 70| +1.48E+02 | +2.1E-06 | -6.0E+01 | 4.3E-07 | 4.4E-02 | 8.7E-07 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 21| +1.73E+02 | +1.7E+01 | -1.4E+00 | 5.0E-01 | 1.0E+02 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 75| +1.48E+02 | +9.0E-05 | -4.7E-01 | 1.6E-02 | 1.5E-01 | 4.9E-03 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 20| +2.50E+02 | -2.9E-06 | +3.3E-01 | 4.9E-04 | 3.3E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 11| +2.09E+02 | -1.3E+01 | +1.0E+00 | 1.6E-01 | 6.5E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 76| +1.48E+02 | -7.8E-02 | +3.8E-01 | 3.1E-02 | 1.5E+01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 76| +1.48E+02 | -3.6E-05 | +6.2E-01 | 3.9E-03 | 1.5E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 71| +1.48E+02 | +3.0E-06 | -3.3E+02 | 1.1E-07 | 4.4E-02 | 2.2E-07 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) 22| +1.63E+02 | -1.1E+01 | +9.1E-01 | 1.2E-01 | 1.0E+02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 21| +2.50E+02 | -3.0E-06 | +3.5E-01 | 4.9E-04 | 3.2E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 12| +1.95E+02 | -1.4E+01 | +6.0E-01 | 3.2E-01 | 4.7E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 39| +1.79E+02 | -2.0E-02 | +9.2E-01 | 3.9E-02 | 6.4E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 22| +2.50E+02 | -2.9E-06 | +3.6E-01 | 4.9E-04 | 3.0E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 77| +1.48E+02 | -2.7E-01 | +6.9E-01 | 3.1E-02 | 1.5E+01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 77| +1.48E+02 | -6.3E-06 | +7.1E-01 | 3.9E-03 | 2.1E-01 | 4.2E-04 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 23| +1.52E+02 | -1.1E+01 | +8.0E-01 | 2.5E-01 | 5.0E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 72| +1.48E+02 | +6.4E-06 | -2.8E+03 | 2.7E-08 | 4.4E-02 | 5.4E-08 | 2d |0\n", - "2023-02-17 16:05:12 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:12 fides(INFO) 70| +1.55E+02 | -1.2E-01 | +7.8E-01 | 1.8E-03 | 3.3E+01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 13| +1.77E+02 | -1.8E+01 | +8.9E-01 | 3.2E-01 | 9.3E+01 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:12 fides(INFO) 23| +2.50E+02 | -3.0E-06 | +3.9E-01 | 4.9E-04 | 2.9E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 24| +1.52E+02 | +6.1E+00 | -1.7E+00 | 5.0E-01 | 7.6E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 78| +1.48E+02 | +3.6E+00 | -5.2E+00 | 3.1E-02 | 1.4E+01 | 6.0E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 40| +1.78E+02 | -3.1E-02 | +1.0E+00 | 7.9E-02 | 6.4E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 73| +1.48E+02 | +4.2E-06 | -7.4E+03 | 6.8E-09 | 4.4E-02 | 1.4E-08 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 78| +1.48E+02 | +2.4E-06 | -7.3E-01 | 3.9E-03 | 2.8E-02 | 5.0E-04 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 24| +2.50E+02 | -2.9E-06 | +4.0E-01 | 4.9E-04 | 2.8E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 14| +1.77E+02 | +5.3E+01 | -3.5E+00 | 6.3E-01 | 1.2E+02 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 79| +1.48E+02 | +1.1E-01 | -5.8E-01 | 7.8E-03 | 1.4E+01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 25| +2.50E+02 | -3.0E-06 | +4.2E-01 | 4.9E-04 | 2.7E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 79| +1.48E+02 | -4.3E-06 | +2.8E+00 | 9.8E-04 | 2.8E-02 | 1.4E-04 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 41| +1.78E+02 | +6.6E-03 | -2.4E-01 | 1.6E-01 | 4.1E+00 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 25| +1.50E+02 | -1.9E+00 | +7.9E-01 | 1.2E-01 | 7.6E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 26| +2.50E+02 | -2.9E-06 | +4.3E-01 | 4.9E-04 | 2.6E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 15| +1.63E+02 | -1.4E+01 | +1.0E+00 | 1.6E-01 | 1.2E+02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 74| +1.48E+02 | +1.7E-07 | -1.2E+03 | 1.7E-09 | 4.4E-02 | 3.4E-09 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 80| +1.48E+02 | -2.9E-02 | +6.0E-01 | 2.0E-03 | 1.4E+01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 80| +1.48E+02 | -1.0E-06 | +6.1E-01 | 2.0E-03 | 7.0E-02 | 2.9E-04 | 2d |1\n", - "2023-02-17 16:05:13 fides(WARNING) Stopping as function difference 1.05E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:13 fides(INFO) 27| +2.50E+02 | -3.0E-06 | +4.5E-01 | 4.9E-04 | 2.6E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 16| +1.53E+02 | -9.5E+00 | +8.3E-01 | 3.2E-01 | 6.9E+01 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 26| +1.49E+02 | -1.3E+00 | +6.4E-01 | 2.4E-01 | 3.7E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 42| +1.78E+02 | -9.1E-03 | +7.5E-01 | 3.9E-02 | 4.1E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 81| +1.48E+02 | -2.3E-02 | +9.7E-01 | 2.0E-03 | 6.0E+00 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 28| +2.50E+02 | -2.9E-06 | +4.6E-01 | 4.9E-04 | 2.5E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 75| +1.48E+02 | +2.5E-06 | -7.2E+04 | 4.2E-10 | 4.4E-02 | 8.5E-10 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 27| +1.49E+02 | +1.6E+01 | -5.5E+00 | 2.4E-01 | 2.8E+01 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 29| +2.50E+02 | -3.0E-06 | +4.8E-01 | 4.9E-04 | 2.4E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 17| +1.50E+02 | -3.6E+00 | +5.4E-01 | 6.3E-01 | 1.1E+02 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 43| +1.78E+02 | -8.1E-03 | +7.5E-01 | 3.9E-02 | 4.5E+00 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 82| +1.48E+02 | -3.2E-02 | +8.8E-01 | 3.9E-03 | 6.4E+00 | 8.4E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 76| +1.48E+02 | +3.9E-06 | -4.5E+05 | 1.1E-10 | 4.4E-02 | 2.1E-10 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 30| +2.50E+02 | -2.9E-06 | +4.8E-01 | 4.9E-04 | 2.3E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 28| +1.48E+02 | -3.0E-01 | +3.1E-01 | 5.9E-02 | 2.8E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 18| +1.50E+02 | +3.2E+00 | -1.1E+00 | 6.3E-01 | 1.1E+02 | 6.8E-01 | trr |0\n", - "2023-02-17 16:05:13 fides(INFO) 83| +1.48E+02 | -6.3E-03 | +2.3E-01 | 7.8E-03 | 7.4E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 31| +2.50E+02 | -3.0E-06 | +5.0E-01 | 4.9E-04 | 2.3E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 44| +1.78E+02 | +1.6E-03 | -1.3E-01 | 7.9E-02 | 3.3E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 77| +1.48E+02 | +2.5E-06 | -1.1E+06 | 2.6E-11 | 4.4E-02 | 5.3E-11 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 71| +1.55E+02 | -5.4E-02 | +9.5E-01 | 3.5E-03 | 1.5E+01 | 9.6E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 29| +1.48E+02 | -2.9E-01 | +8.0E-01 | 5.9E-02 | 2.2E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 84| +1.48E+02 | -1.7E-02 | +9.2E-01 | 2.0E-03 | 6.3E+00 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 19| +1.48E+02 | -1.3E+00 | +9.3E-01 | 7.1E-02 | 1.1E+02 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 32| +2.50E+02 | -2.9E-06 | +5.0E-01 | 4.9E-04 | 2.3E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 0| +4.20E+12 | NaN | NaN | 1.0E+00 | 1.9E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:13 fides(INFO) 45| +1.78E+02 | -4.2E-03 | +7.6E-01 | 2.0E-02 | 3.3E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 78| +1.48E+02 | +1.2E-06 | -2.1E+06 | 6.6E-12 | 4.4E-02 | 1.3E-11 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 30| +1.48E+02 | -8.3E-02 | +2.7E-01 | 1.2E-01 | 2.0E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 33| +2.50E+02 | -3.0E-06 | +5.2E-01 | 4.9E-04 | 2.2E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 20| +1.48E+02 | -5.8E-01 | +7.8E-01 | 1.4E-01 | 2.7E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 85| +1.48E+02 | -1.7E-02 | +6.1E-01 | 3.9E-03 | 4.6E+00 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 79| +1.48E+02 | +2.4E-06 | -1.7E+07 | 1.6E-12 | 4.4E-02 | 3.3E-12 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 1| +4.24E+06 | -4.2E+12 | +8.7E-02 | 1.0E+00 | 1.9E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 34| +2.50E+02 | -2.9E-06 | +5.2E-01 | 4.9E-04 | 2.2E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 31| +1.48E+02 | +2.7E-01 | -4.4E-01 | 1.2E-01 | 1.4E+01 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 46| +1.78E+02 | -7.1E-03 | +9.8E-01 | 3.9E-02 | 3.6E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 80| +1.48E+02 | +3.1E-06 | -8.9E+07 | 4.1E-13 | 4.4E-02 | 8.3E-13 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 35| +2.50E+02 | -3.0E-06 | +5.3E-01 | 4.9E-04 | 2.2E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 21| +1.48E+02 | -2.0E-01 | +8.9E-01 | 2.8E-01 | 3.7E+01 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 86| +1.48E+02 | -4.8E-03 | +2.6E-01 | 3.9E-03 | 7.3E+00 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 2| +6.58E+05 | -3.6E+06 | +9.0E-01 | 2.5E-01 | 2.0E+07 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:13.358 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 83.5233 and h = 1.6975e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:13.358 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 83.5233: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:13 fides(INFO) 32| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.0E-02 | 1.4E+01 | 3.4E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 36| +2.50E+02 | -2.9E-06 | +5.3E-01 | 4.9E-04 | 2.1E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 87| +1.48E+02 | -9.4E-03 | +9.6E-01 | 3.9E-03 | 7.9E+00 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 22| +1.48E+02 | +2.8E+00 | -2.5E+01 | 5.7E-01 | 6.0E+00 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 47| +1.78E+02 | -8.8E-05 | -1.8E-02 | 7.9E-02 | 2.6E+00 | 9.4E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 81| +1.48E+02 | +2.6E-06 | -3.0E+08 | 1.0E-13 | 4.4E-02 | 2.1E-13 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 37| +2.50E+02 | -3.0E-06 | +5.4E-01 | 4.9E-04 | 2.1E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 33| +1.48E+02 | -7.7E-02 | +9.2E-01 | 7.4E-03 | 1.4E+01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 3| +1.03E+05 | -5.5E+05 | +9.0E-01 | 5.0E-01 | 3.0E+06 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 38| +2.50E+02 | -2.9E-06 | +5.5E-01 | 4.9E-04 | 2.1E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 82| +1.48E+02 | +3.6E-06 | -1.7E+09 | 2.6E-14 | 4.4E-02 | 5.2E-14 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 23| +1.48E+02 | -3.8E-02 | +6.6E-01 | 1.4E-01 | 6.0E+00 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 88| +1.48E+02 | -1.4E-02 | +7.3E-01 | 7.8E-03 | 2.8E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 34| +1.48E+02 | -8.4E-02 | +8.0E-01 | 1.5E-02 | 1.5E+01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 4| +1.64E+04 | -8.7E+04 | +9.0E-01 | 1.0E+00 | 4.7E+05 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 48| +1.78E+02 | -2.9E-03 | +7.7E-01 | 2.0E-02 | 2.6E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 39| +2.50E+02 | -3.0E-06 | +5.5E-01 | 4.9E-04 | 2.1E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 24| +1.48E+02 | +9.8E-02 | -1.6E+00 | 1.4E-01 | 6.6E+00 | 9.3E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 83| +1.48E+02 | +3.4E-06 | -6.2E+09 | 6.4E-15 | 4.4E-02 | 1.3E-14 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 5| +2.79E+03 | -1.4E+04 | +9.0E-01 | 1.0E+00 | 7.5E+04 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 89| +1.48E+02 | -6.6E-03 | +3.9E-01 | 7.8E-03 | 6.7E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 25| +1.48E+02 | -1.5E-02 | +5.9E-01 | 3.5E-02 | 6.6E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 40| +2.50E+02 | -4.6E-06 | +1.0E+00 | 4.9E-04 | 2.0E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 35| +1.48E+02 | -5.7E-02 | +8.0E-01 | 3.0E-02 | 1.1E+01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 49| +1.78E+02 | -4.1E-03 | +8.1E-01 | 3.9E-02 | 2.8E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 90| +1.48E+02 | +1.6E-02 | -2.4E+00 | 7.8E-03 | 1.9E+00 | 9.1E-03 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 84| +1.48E+02 | -2.4E-07 | +1.8E+09 | 1.6E-15 | 4.4E-02 | 3.2E-15 | 2d |1\n", - "2023-02-17 16:05:13 fides(WARNING) Stopping as function difference 2.41E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:13 fides(INFO) 41| +2.50E+02 | -7.0E-06 | +1.0E+00 | 9.8E-04 | 4.2E-03 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 6| +6.31E+02 | -2.2E+03 | +9.1E-01 | 2.0E+00 | 1.2E+04 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 36| +1.48E+02 | -4.6E-02 | +9.4E-01 | 5.9E-02 | 7.7E+00 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 26| +1.48E+02 | -1.1E-02 | +4.4E-01 | 3.5E-02 | 3.6E+00 | 9.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 50| +1.78E+02 | +1.3E-02 | -1.2E+00 | 7.9E-02 | 2.0E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 42| +2.50E+02 | -1.4E-05 | +1.0E+00 | 2.0E-03 | 8.9E-03 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 91| +1.48E+02 | +9.8E-03 | -2.5E+00 | 2.0E-03 | 1.9E+00 | 4.1E-03 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 72| +1.55E+02 | -7.6E-02 | +8.2E-01 | 7.0E-03 | 7.5E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 7| +2.91E+02 | -3.4E+02 | +9.1E-01 | 2.0E+00 | 1.9E+03 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 37| +1.48E+02 | -4.3E-02 | +7.8E-01 | 1.2E-01 | 5.8E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 92| +1.48E+02 | -3.6E-04 | +2.2E-01 | 4.9E-04 | 1.9E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 27| +1.48E+02 | +7.5E-04 | -3.3E-02 | 3.5E-02 | 6.6E+00 | 3.5E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 43| +2.50E+02 | -2.8E-05 | +1.1E+00 | 3.9E-03 | 1.2E-02 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 51| +1.78E+02 | -1.9E-03 | +5.6E-01 | 2.0E-02 | 2.0E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 44| +2.50E+02 | -5.6E-05 | +1.0E+00 | 7.8E-03 | 2.1E-02 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 28| +1.48E+02 | -8.1E-03 | +8.1E-01 | 8.9E-03 | 6.6E+00 | 9.0E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 38| +1.48E+02 | +6.4E-01 | -6.7E+00 | 2.4E-01 | 2.4E+00 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 93| +1.48E+02 | -5.6E-04 | +9.9E-01 | 1.2E-04 | 3.3E+00 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 8| +2.21E+02 | -7.0E+01 | +1.3E+00 | 2.0E+00 | 2.7E+02 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 45| +2.50E+02 | -1.1E-04 | +1.0E+00 | 1.6E-02 | 3.9E-02 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 39| +1.48E+02 | +1.0E-02 | -3.2E-01 | 5.9E-02 | 2.4E+00 | 7.5E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 29| +1.48E+02 | -3.2E-03 | +9.3E-01 | 1.8E-02 | 1.4E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 52| +1.78E+02 | -1.9E-03 | +7.9E-01 | 2.0E-02 | 2.2E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 0| +2.98E+09 | NaN | NaN | 1.0E+00 | 1.4E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:13 fides(INFO) 46| +2.50E+02 | -2.3E-04 | +1.0E+00 | 3.1E-02 | 7.6E-02 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 94| +1.48E+02 | -6.8E-04 | +1.0E+00 | 2.4E-04 | 2.5E+00 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 9| +2.21E+02 | +4.6E+04 | -1.3E+03 | 2.0E+00 | 5.2E+01 | 5.8E+00 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 47| +2.50E+02 | -5.0E-04 | +1.1E+00 | 6.2E-02 | 1.5E-01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 30| +1.48E+02 | -3.8E-03 | +9.6E-01 | 3.5E-02 | 2.2E+00 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 40| +1.48E+02 | -6.9E-03 | +6.8E-01 | 1.5E-02 | 2.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 53| +1.78E+02 | -1.1E-03 | +3.1E-01 | 3.9E-02 | 1.8E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 48| +2.50E+02 | -1.4E-03 | +1.3E+00 | 1.2E-01 | 3.0E-01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 95| +1.48E+02 | -9.9E-04 | +9.4E-01 | 4.9E-04 | 1.4E+00 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 1| +4.43E+04 | -3.0E+09 | +9.3E-02 | 1.0E+00 | 1.4E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 41| +1.48E+02 | -2.9E-03 | +8.7E-01 | 1.5E-02 | 2.7E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 31| +1.48E+02 | +2.1E-03 | -4.0E-01 | 7.1E-02 | 8.2E-01 | 4.7E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 10| +2.21E+02 | +2.8E+01 | -8.8E-01 | 5.0E-01 | 5.2E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 49| +2.50E+02 | -6.8E-03 | +1.9E+00 | 2.5E-01 | 5.9E-01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 42| +1.48E+02 | -4.1E-03 | +9.5E-01 | 3.0E-02 | 1.6E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 54| +1.78E+02 | -1.1E-03 | +1.7E-01 | 3.9E-02 | 1.8E+00 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 2| +8.94E+03 | -3.5E+04 | +8.9E-01 | 2.5E-01 | 1.8E+05 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 50| +2.50E+02 | -8.2E-02 | +3.3E+00 | 5.0E-01 | 1.2E+00 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 32| +1.48E+02 | -1.2E-03 | +5.4E-01 | 1.8E-02 | 8.2E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 96| +1.48E+02 | -7.9E-04 | +7.9E-01 | 9.8E-04 | 1.1E+00 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 3| +1.58E+03 | -7.4E+03 | +9.0E-01 | 5.0E-01 | 3.1E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 11| +2.06E+02 | -1.5E+01 | +9.3E-01 | 1.2E-01 | 5.2E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 51| +2.47E+02 | -2.4E+00 | +4.8E+00 | 1.0E+00 | 2.3E+00 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 43| +1.48E+02 | -3.4E-03 | +6.6E-01 | 5.9E-02 | 1.6E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 73| +1.55E+02 | +3.3E-02 | -5.3E-01 | 1.4E-02 | 7.1E+00 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 55| +1.78E+02 | -1.5E-03 | +7.4E-01 | 9.9E-03 | 1.3E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 33| +1.48E+02 | -1.3E-03 | +4.6E-01 | 1.8E-02 | 2.0E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 4| +4.27E+02 | -1.2E+03 | +9.0E-01 | 1.0E+00 | 4.9E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 97| +1.48E+02 | -7.3E-04 | +9.8E-01 | 2.0E-03 | 2.3E+00 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 52| +2.31E+02 | -1.6E+01 | +1.8E+00 | 2.0E+00 | 8.6E+00 | 3.7E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 12| +2.01E+02 | -4.9E+00 | +2.2E-01 | 2.5E-01 | 4.3E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 5| +2.63E+02 | -1.6E+02 | +8.8E-01 | 1.0E+00 | 7.8E+02 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 44| +1.48E+02 | +2.6E-02 | -2.3E+00 | 5.9E-02 | 1.0E+00 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 34| +1.48E+02 | -2.1E-04 | +1.2E-01 | 1.8E-02 | 5.5E-01 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:13.862 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.905 and h = 3.56407e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:13.862 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.905: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:13 fides(INFO) 98| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.9E-03 | 5.3E-01 | 5.2E-03 | 2d |0\n", - "2023-02-17 16:05:13 fides(INFO) 56| +1.78E+02 | -8.2E-04 | +8.0E-01 | 9.9E-03 | 1.4E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 53| +2.31E+02 | +7.7E+00 | -1.1E+00 | 4.0E+00 | 4.1E+01 | 1.2E+00 | trr |0\n", - "2023-02-17 16:05:13 fides(INFO) 45| +1.48E+02 | -5.8E-04 | +1.4E-01 | 1.5E-02 | 1.0E+00 | 8.4E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 35| +1.48E+02 | -5.4E-04 | +7.7E-01 | 4.4E-03 | 6.2E-01 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 54| +2.30E+02 | -1.7E+00 | +7.7E-01 | 1.4E-01 | 4.1E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 99| +1.48E+02 | -1.6E-04 | +9.8E-01 | 9.8E-04 | 5.3E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 6| +2.50E+02 | -1.3E+01 | +7.5E-01 | 1.0E+00 | 1.0E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 13| +1.98E+02 | -3.5E+00 | +9.4E-01 | 6.2E-02 | 6.6E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 57| +1.78E+02 | -8.6E-04 | +9.3E-01 | 2.0E-02 | 1.3E+00 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 46| +1.48E+02 | -1.0E-03 | +8.9E-01 | 3.7E-03 | 2.1E+00 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 36| +1.48E+02 | -3.2E-04 | +6.8E-01 | 8.9E-03 | 4.2E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 55| +2.23E+02 | -6.5E+00 | +1.2E+00 | 2.8E-01 | 3.8E+01 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:13 fides(INFO) 100| +1.48E+02 | -2.6E-04 | +1.0E+00 | 2.0E-03 | 8.1E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 7| +2.44E+02 | -5.2E+00 | +6.4E+00 | 1.0E+00 | 5.5E+00 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 47| +1.48E+02 | -5.4E-04 | +7.1E-01 | 7.4E-03 | 3.9E-01 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 14| +1.94E+02 | -3.5E+00 | +9.2E-01 | 1.2E-01 | 5.1E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:13 fides(INFO) 58| +1.78E+02 | -1.2E-03 | +1.0E+00 | 3.9E-02 | 1.2E+00 | 9.6E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 56| +2.17E+02 | -6.6E+00 | +9.8E-01 | 5.5E-01 | 2.0E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 37| +1.48E+02 | -1.8E-04 | +9.1E-01 | 8.9E-03 | 2.2E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 101| +1.48E+02 | -4.1E-04 | +1.0E+00 | 3.9E-03 | 4.2E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 8| +2.16E+02 | -2.8E+01 | +2.0E+00 | 2.0E+00 | 1.5E+01 | 3.6E+00 | 2d |1\n", - "2023-02-17 16:05:14.026 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.61 and h = 3.16878e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:14.027 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.61: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:14 fides(INFO) 48| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.4E-03 | 4.4E-01 | 5.5E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 57| +2.17E+02 | +8.6E+01 | -1.7E+01 | 1.1E+00 | 3.3E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 38| +1.48E+02 | -2.4E-04 | +7.6E-01 | 1.8E-02 | 5.1E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 102| +1.48E+02 | -8.5E-04 | +1.0E+00 | 7.8E-03 | 4.6E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 59| +1.78E+02 | +1.8E-03 | -1.0E+00 | 7.9E-02 | 8.1E-01 | 7.2E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 9| +2.16E+02 | +8.9E+02 | -4.8E+01 | 4.0E+00 | 2.7E+01 | 1.2E+01 | trr |0\n", - "2023-02-17 16:05:14 fides(INFO) 58| +2.15E+02 | -2.0E+00 | +6.1E-01 | 2.5E-01 | 3.3E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 49| +1.48E+02 | -9.2E-05 | +1.0E+00 | 1.9E-03 | 4.4E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 39| +1.48E+02 | +6.3E-04 | -1.1E+00 | 3.5E-02 | 2.0E-01 | 6.4E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 10| +2.16E+02 | +2.9E+01 | -3.6E+00 | 1.0E+00 | 2.7E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 103| +1.48E+02 | -1.4E-03 | +9.5E-01 | 1.6E-02 | 2.9E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 60| +1.78E+02 | -3.3E-04 | +5.7E-01 | 2.0E-02 | 8.1E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 59| +2.12E+02 | -2.4E+00 | +4.6E-01 | 2.5E-01 | 2.7E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 50| +1.48E+02 | -1.5E-04 | +9.8E-01 | 3.7E-03 | 2.6E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 40| +1.48E+02 | -1.0E-04 | +5.6E-01 | 8.9E-03 | 2.0E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 15| +1.93E+02 | -1.1E+00 | +1.0E+00 | 2.5E-01 | 2.3E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 11| +2.16E+02 | +3.8E+00 | -6.2E-01 | 2.5E-01 | 2.7E+01 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 60| +2.12E+02 | +1.1E-01 | -1.1E-01 | 2.5E-01 | 1.4E+01 | 6.6E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 104| +1.48E+02 | -2.0E-03 | +7.5E-01 | 3.1E-02 | 3.1E-01 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 74| +1.55E+02 | -1.7E-02 | +7.7E-01 | 3.5E-03 | 7.1E+00 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 61| +1.78E+02 | +3.3E-05 | -4.2E-02 | 2.0E-02 | 8.6E-01 | 3.1E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 51| +1.48E+02 | -2.3E-04 | +9.2E-01 | 7.4E-03 | 3.9E-01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 41| +1.48E+02 | -7.7E-05 | +6.2E-01 | 8.9E-03 | 2.4E-01 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 61| +2.12E+02 | -6.5E-01 | +8.3E-01 | 6.3E-02 | 1.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 12| +2.13E+02 | -2.9E+00 | +8.4E-01 | 6.2E-02 | 2.7E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 105| +1.48E+02 | -2.8E-03 | +5.0E-01 | 6.2E-02 | 1.1E+00 | 9.7E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 52| +1.48E+02 | -3.8E-04 | +9.7E-01 | 1.5E-02 | 2.0E-01 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 62| +2.11E+02 | -8.7E-01 | +6.2E-01 | 1.3E-01 | 1.0E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 13| +2.12E+02 | -7.5E-01 | +3.3E-01 | 1.2E-01 | 1.3E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 42| +1.48E+02 | -6.0E-05 | +7.2E-01 | 8.9E-03 | 1.4E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 62| +1.78E+02 | -2.5E-04 | +8.1E-01 | 4.9E-03 | 8.6E-01 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 106| +1.48E+02 | +2.0E-03 | -5.2E-01 | 6.2E-02 | 2.4E+00 | 9.6E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 14| +2.10E+02 | -2.1E+00 | +6.9E-01 | 1.2E-01 | 2.6E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 53| +1.48E+02 | -3.9E-04 | +7.0E-01 | 3.0E-02 | 4.1E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 63| +2.10E+02 | -5.7E-01 | +6.0E-01 | 1.3E-01 | 8.3E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 43| +1.48E+02 | -4.0E-05 | +5.8E-01 | 8.9E-03 | 1.5E-01 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 16| +1.78E+02 | -1.5E+01 | +2.2E+00 | 5.0E-01 | 2.3E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 15| +2.09E+02 | -1.5E+00 | +5.4E-01 | 1.2E-01 | 1.3E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 63| +1.78E+02 | -1.9E-04 | +8.1E-01 | 9.9E-03 | 6.8E-01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 107| +1.48E+02 | -2.9E-04 | +1.3E-01 | 1.6E-02 | 2.4E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 54| +1.48E+02 | +1.5E-03 | -1.5E+00 | 3.0E-02 | 2.5E-01 | 7.4E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 64| +2.10E+02 | -2.4E-01 | +1.9E-01 | 1.3E-01 | 1.1E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 44| +1.48E+02 | -3.1E-05 | +5.4E-01 | 8.9E-03 | 1.1E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 16| +2.06E+02 | -2.5E+00 | +6.6E-01 | 1.2E-01 | 2.8E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 64| +1.78E+02 | -2.5E-04 | +6.8E-01 | 2.0E-02 | 7.5E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 65| +2.09E+02 | -5.4E-01 | +8.9E-01 | 3.1E-02 | 1.5E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 108| +1.48E+02 | -1.0E-03 | +9.3E-01 | 3.9E-03 | 2.9E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 55| +1.48E+02 | -4.0E-05 | +1.0E-01 | 7.4E-03 | 2.5E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 45| +1.48E+02 | -1.9E-05 | +3.1E-01 | 8.9E-03 | 1.3E-01 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 17| +2.04E+02 | -2.1E+00 | +7.1E-01 | 1.2E-01 | 1.8E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 66| +2.09E+02 | -2.2E-01 | +9.8E-01 | 6.3E-02 | 4.9E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 109| +1.48E+02 | +3.4E-03 | -7.9E+00 | 7.8E-03 | 2.8E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 46| +1.48E+02 | -8.9E-07 | +1.1E-02 | 8.9E-03 | 1.1E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 65| +1.78E+02 | +2.6E-04 | -3.7E-01 | 2.0E-02 | 5.4E-01 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 56| +1.48E+02 | -1.5E-04 | +8.9E-01 | 1.9E-03 | 9.1E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 18| +2.02E+02 | -1.9E+00 | +6.4E-01 | 1.2E-01 | 2.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 17| +1.78E+02 | +1.7E+01 | -1.2E+00 | 1.0E+00 | 7.0E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 67| +2.09E+02 | -1.6E-01 | +9.0E-01 | 1.3E-01 | 2.5E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 47| +1.48E+02 | -3.0E-05 | +7.1E-01 | 2.2E-03 | 1.5E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 110| +1.48E+02 | +3.3E-05 | -1.2E-01 | 1.8E-03 | 2.8E-01 | 3.1E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 57| +1.48E+02 | -6.5E-05 | +8.0E-01 | 3.7E-03 | 1.0E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 19| +2.00E+02 | -2.7E+00 | +9.3E-01 | 1.2E-01 | 1.9E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 75| +1.55E+02 | -2.8E-02 | +9.4E-01 | 7.0E-03 | 4.5E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 68| +2.09E+02 | -5.2E-02 | +8.6E-01 | 2.5E-01 | 2.9E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 66| +1.78E+02 | -1.5E-04 | +6.9E-01 | 4.9E-03 | 5.4E-01 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 20| +1.90E+02 | -1.0E+01 | +1.7E+00 | 2.5E-01 | 2.1E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 48| +1.48E+02 | -7.8E-06 | +9.6E-01 | 2.2E-03 | 8.6E-02 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 111| +1.48E+02 | -1.4E-04 | +8.9E-01 | 4.6E-04 | 2.8E-01 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 69| +2.09E+02 | -4.3E-02 | +2.1E+00 | 2.5E-01 | 1.3E+00 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 58| +1.48E+02 | -5.8E-05 | +8.5E-01 | 7.4E-03 | 8.8E-02 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 18| +1.69E+02 | -9.4E+00 | +1.2E+00 | 2.5E-01 | 7.0E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 21| +1.90E+02 | -2.0E+00 | -3.4E-01 | 5.0E-01 | 2.8E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 49| +1.48E+02 | -5.0E-06 | +6.4E-01 | 4.4E-03 | 4.3E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 67| +1.78E+02 | -1.0E-04 | +1.0E+00 | 4.9E-03 | 6.0E-01 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 112| +1.48E+02 | -5.3E-05 | +1.0E+00 | 9.2E-04 | 4.0E-01 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 59| +1.48E+02 | -7.7E-05 | +8.0E-01 | 1.5E-02 | 7.8E-02 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 70| +2.09E+02 | -1.1E-01 | +2.5E+00 | 2.5E-01 | 9.3E-01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 22| +1.80E+02 | -9.2E+00 | +1.2E+00 | 1.2E-01 | 2.8E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 50| +1.48E+02 | +3.8E-08 | -7.5E-03 | 4.4E-03 | 7.3E-02 | 8.8E-04 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 113| +1.48E+02 | -3.5E-05 | +8.3E-01 | 1.8E-03 | 1.4E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 71| +2.08E+02 | -3.1E-01 | +2.3E+00 | 2.5E-01 | 1.8E+00 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 68| +1.78E+02 | -1.2E-04 | +9.3E-01 | 9.9E-03 | 5.1E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 60| +1.48E+02 | +1.9E-04 | -9.5E-01 | 3.0E-02 | 2.2E-01 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:14.613 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 95.0225 and h = 1.18628e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:14.613 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 95.0225: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:14 fides(INFO) 23| +1.80E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 4.4E+01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 72| +2.08E+02 | +1.8E+00 | -6.1E+00 | 2.5E-01 | 4.0E+00 | 7.0E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 114| +1.48E+02 | +1.2E-04 | -3.3E+00 | 3.7E-03 | 1.3E-01 | 4.6E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 51| +1.48E+02 | -3.1E-06 | +1.7E+00 | 1.1E-03 | 7.3E-02 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 69| +1.78E+02 | -1.8E-04 | +1.0E+00 | 2.0E-02 | 5.1E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 24| +1.75E+02 | -5.4E+00 | +1.1E+00 | 6.2E-02 | 4.4E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 61| +1.48E+02 | -3.8E-05 | +6.0E-01 | 7.4E-03 | 2.2E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 115| +1.48E+02 | -1.4E-05 | +8.8E-01 | 9.2E-04 | 1.3E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 73| +2.08E+02 | -1.6E-01 | +1.2E+00 | 6.3E-02 | 4.0E+00 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 52| +1.48E+02 | +6.0E-06 | -1.3E+00 | 2.2E-03 | 2.1E-02 | 6.8E-04 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 70| +1.78E+02 | +1.2E-05 | -4.9E-02 | 3.9E-02 | 3.7E-01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 25| +1.64E+02 | -1.1E+01 | +1.0E+00 | 1.2E-01 | 4.7E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 62| +1.48E+02 | -1.9E-05 | +4.9E-01 | 7.4E-03 | 7.7E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 116| +1.48E+02 | +1.0E-04 | -4.8E+00 | 1.8E-03 | 1.2E-01 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 74| +2.08E+02 | -5.9E-01 | +7.0E-01 | 1.3E-01 | 4.8E+00 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 26| +1.57E+02 | -7.6E+00 | +4.7E-01 | 2.5E-01 | 5.9E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 71| +1.78E+02 | -6.7E-05 | +7.6E-01 | 9.9E-03 | 3.7E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 53| +1.48E+02 | -2.0E-07 | +6.1E-02 | 5.5E-04 | 2.1E-02 | 3.8E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 75| +2.06E+02 | -1.4E+00 | +1.5E+00 | 1.3E-01 | 1.0E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 63| +1.48E+02 | -2.3E-05 | +5.6E-01 | 7.4E-03 | 1.8E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 117| +1.48E+02 | +7.5E-05 | -5.2E+00 | 4.6E-04 | 1.2E-01 | 5.2E-04 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 19| +1.66E+02 | -3.0E+00 | +7.7E-01 | 5.0E-01 | 3.8E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 27| +1.50E+02 | -6.6E+00 | +9.6E-01 | 2.5E-01 | 1.7E+02 | 7.0E-01 | trr |1\n", - "2023-02-17 16:05:14.763 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.642 and h = 2.79391e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:14.763 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.642: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:14 fides(INFO) 64| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.4E-03 | 6.7E-02 | 8.8E-04 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 76| +2.03E+02 | -3.5E+00 | +1.3E+00 | 2.5E-01 | 5.5E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 54| +1.48E+02 | -1.4E-06 | +7.5E-01 | 1.4E-04 | 1.1E-01 | 6.5E-05 | 2d |1\n", - "2023-02-17 16:05:14 fides(WARNING) Stopping as function difference 1.43E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:14 fides(INFO) 118| +1.48E+02 | +6.3E-05 | -5.4E+00 | 1.1E-04 | 1.2E-01 | 1.9E-04 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 72| +1.78E+02 | +1.7E-05 | -9.9E-02 | 2.0E-02 | 4.0E-01 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 28| +1.48E+02 | -1.5E+00 | +6.2E-01 | 2.5E-01 | 5.0E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 77| +2.03E+02 | +1.6E+01 | -1.5E+00 | 5.0E-01 | 2.5E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 76| +1.55E+02 | +1.6E-01 | -3.1E+00 | 1.4E-02 | 4.2E+00 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 65| +1.48E+02 | -4.5E-06 | +3.7E-01 | 1.9E-03 | 6.7E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 119| +1.48E+02 | -1.9E-06 | +4.0E-01 | 2.9E-05 | 1.2E-01 | 5.0E-05 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 29| +1.48E+02 | -5.2E-01 | +9.7E-01 | 2.5E-01 | 4.8E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 78| +1.99E+02 | -3.4E+00 | +6.4E-01 | 1.3E-01 | 2.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 73| +1.78E+02 | -5.0E-05 | +7.9E-01 | 4.9E-03 | 4.0E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 66| +1.48E+02 | -1.1E-05 | +1.6E+00 | 1.9E-03 | 1.8E-01 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 120| +1.48E+02 | -7.8E-07 | +4.1E-01 | 2.9E-05 | 1.2E-01 | 5.0E-05 | 2d |1\n", - "2023-02-17 16:05:14 fides(WARNING) Stopping as function difference 7.82E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:14 fides(INFO) 79| +1.97E+02 | -2.9E+00 | +8.4E-01 | 1.3E-01 | 1.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 30| +1.48E+02 | -8.9E-02 | +2.0E-01 | 5.0E-01 | 1.9E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 67| +1.48E+02 | -6.2E-06 | +8.9E-01 | 3.7E-03 | 2.7E-02 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 74| +1.78E+02 | -5.3E-05 | +8.0E-01 | 9.9E-03 | 3.2E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 0| +1.31E+11 | NaN | NaN | 1.0E+00 | 6.0E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:14 fides(INFO) 68| +1.48E+02 | -1.0E-05 | +8.4E-01 | 7.4E-03 | 8.4E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 31| +1.48E+02 | +8.5E-04 | -1.1E-02 | 1.2E-01 | 1.9E+01 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 80| +1.97E+02 | +8.7E+00 | -2.4E+00 | 2.5E-01 | 1.3E+01 | 4.3E-01 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 75| +1.78E+02 | +2.1E-05 | -1.6E-01 | 2.0E-02 | 3.4E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:14 fides(INFO) 1| +1.53E+05 | -1.3E+11 | +8.8E-02 | 1.0E+00 | 6.0E+11 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 69| +1.48E+02 | -4.2E-06 | +2.3E-01 | 1.5E-02 | 3.2E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 20| +1.60E+02 | -6.0E+00 | +4.7E-01 | 1.0E+00 | 9.6E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 81| +1.96E+02 | -6.2E-01 | +5.3E-01 | 6.3E-02 | 1.3E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 32| +1.48E+02 | -1.7E-01 | +7.8E-01 | 3.1E-02 | 1.9E+01 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 76| +1.78E+02 | -3.6E-05 | +7.8E-01 | 4.9E-03 | 3.4E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:14 fides(INFO) 70| +1.48E+02 | -1.3E-05 | +5.2E-01 | 3.7E-03 | 9.4E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:14 fides(INFO) 82| +1.96E+02 | -3.9E-01 | +4.5E-01 | 6.3E-02 | 9.0E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 33| +1.48E+02 | -4.3E-02 | +2.9E-01 | 6.2E-02 | 8.1E+00 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 2| +2.41E+04 | -1.3E+05 | +9.0E-01 | 2.5E-01 | 7.0E+05 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 0| +4.56E+02 | NaN | NaN | 1.0E+00 | 3.3E+02 | NaN | NaN |1\n", - "2023-02-17 16:05:15 fides(INFO) 83| +1.95E+02 | -3.7E-01 | +6.8E-01 | 6.3E-02 | 1.0E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 3| +3.93E+03 | -2.0E+04 | +9.0E-01 | 5.0E-01 | 1.1E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 77| +1.78E+02 | -3.7E-05 | +7.0E-01 | 9.9E-03 | 2.7E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 71| +1.48E+02 | -3.5E-08 | +8.7E-03 | 3.7E-03 | 4.8E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 21| +1.56E+02 | -4.0E+00 | +9.7E-01 | 1.0E+00 | 1.2E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 34| +1.48E+02 | -2.6E-02 | +6.1E-01 | 6.2E-02 | 9.4E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 1| +3.80E+02 | -7.6E+01 | +1.2E-01 | 1.0E+00 | 3.3E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 77| +1.55E+02 | -1.0E-02 | +6.0E-01 | 3.5E-03 | 4.2E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 4| +7.99E+02 | -3.1E+03 | +9.0E-01 | 1.0E+00 | 1.7E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 72| +1.48E+02 | +4.6E-07 | -8.5E-01 | 9.3E-04 | 2.2E-02 | 1.9E-04 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 84| +1.95E+02 | -3.1E-01 | +9.2E-01 | 6.3E-02 | 7.2E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 2| +3.36E+02 | -4.5E+01 | +9.9E-01 | 2.5E-01 | 6.4E+01 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 78| +1.78E+02 | -1.3E-05 | +1.8E-01 | 9.9E-03 | 2.8E-01 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 35| +1.48E+02 | -1.2E-02 | +5.0E-01 | 6.2E-02 | 4.9E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 5| +3.19E+02 | -4.8E+02 | +9.0E-01 | 1.0E+00 | 2.7E+03 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 3| +2.66E+02 | -7.0E+01 | +8.4E-01 | 5.0E-01 | 6.2E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 73| +1.48E+02 | +1.7E-07 | -9.3E-01 | 2.3E-04 | 2.2E-02 | 4.8E-05 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 22| +1.56E+02 | +6.1E+01 | -2.3E+01 | 2.0E+00 | 4.1E+01 | 4.8E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 6| +2.51E+02 | -6.8E+01 | +8.8E-01 | 1.0E+00 | 4.1E+02 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 85| +1.95E+02 | -3.7E-01 | +8.4E-01 | 1.3E-01 | 2.8E+00 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 36| +1.48E+02 | -7.6E-03 | +3.8E-01 | 6.2E-02 | 5.9E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 79| +1.78E+02 | -3.6E-05 | +7.1E-01 | 2.5E-03 | 2.1E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 4| +2.66E+02 | +6.6E+03 | -3.4E+02 | 1.0E+00 | 3.9E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 74| +1.48E+02 | -6.6E-08 | +7.5E-01 | 5.8E-05 | 2.2E-02 | 1.3E-05 | 2d |1\n", - "2023-02-17 16:05:15 fides(WARNING) Stopping as function difference 6.57E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:15 fides(INFO) 7| +2.22E+02 | -2.9E+01 | +2.1E+00 | 2.0E+00 | 4.3E+01 | 3.6E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 37| +1.48E+02 | +4.8E-03 | -2.8E-01 | 6.2E-02 | 2.0E+00 | 5.5E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 86| +1.94E+02 | -7.3E-01 | +1.3E+00 | 2.5E-01 | 5.0E+00 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 23| +1.56E+02 | +4.9E+00 | -7.2E+00 | 5.0E-01 | 4.1E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 5| +2.50E+02 | -1.6E+01 | +7.2E-01 | 2.5E-01 | 3.9E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 80| +1.78E+02 | -1.6E-05 | +9.2E-01 | 2.5E-03 | 2.3E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 38| +1.48E+02 | -3.9E-03 | +6.1E-01 | 1.6E-02 | 2.0E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 8| +2.22E+02 | +4.5E+03 | -3.9E+02 | 4.0E+00 | 3.4E+01 | 4.6E+00 | trr |0\n", - "2023-02-17 16:05:15 fides(INFO) 6| +2.50E+02 | +5.5E-01 | -4.4E-01 | 2.5E-01 | 1.5E+01 | 6.1E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 87| +1.94E+02 | +9.1E-01 | -1.2E+00 | 5.0E-01 | 4.6E+00 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 9| +2.22E+02 | +4.8E+01 | -3.1E+00 | 3.9E-01 | 3.4E+01 | 9.8E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 39| +1.48E+02 | -2.4E-03 | +9.7E-01 | 1.6E-02 | 4.5E+00 | 6.1E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 7| +2.50E+02 | -3.7E-01 | +2.7E-01 | 6.2E-02 | 1.5E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 81| +1.78E+02 | -1.6E-05 | +9.1E-01 | 4.9E-03 | 2.0E-01 | 5.7E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 88| +1.93E+02 | -3.2E-01 | +8.6E-01 | 1.3E-01 | 4.6E+00 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 24| +1.55E+02 | -5.8E-01 | +1.1E+00 | 1.2E-01 | 4.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 8| +2.50E+02 | -1.7E-01 | +2.8E-01 | 6.2E-02 | 1.1E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 40| +1.48E+02 | -8.2E-04 | +9.4E-01 | 3.1E-02 | 1.2E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 10| +2.15E+02 | -6.9E+00 | +8.9E-01 | 9.8E-02 | 3.4E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 82| +1.78E+02 | -2.3E-05 | +9.9E-01 | 9.9E-03 | 2.0E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 9| +2.50E+02 | +7.5E-02 | -2.7E-01 | 6.2E-02 | 6.6E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 0| +9.51E+13 | NaN | NaN | 1.0E+00 | 4.4E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:15 fides(INFO) 89| +1.93E+02 | -3.5E-01 | +1.9E-01 | 2.5E-01 | 7.6E+00 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 41| +1.48E+02 | -1.1E-03 | +9.5E-01 | 6.2E-02 | 1.5E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 11| +2.15E+02 | +7.1E+00 | -1.3E+00 | 2.0E-01 | 2.3E+01 | 4.8E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 1| +9.94E+08 | -9.5E+13 | +8.3E-02 | 1.0E+00 | 4.4E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 78| +1.55E+02 | +3.9E-02 | -1.6E+00 | 3.5E-03 | 4.8E+00 | 9.1E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 10| +2.50E+02 | -1.4E-01 | +7.0E-01 | 1.6E-02 | 6.6E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 90| +1.92E+02 | -8.5E-01 | +1.3E+00 | 6.3E-02 | 1.9E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 25| +1.55E+02 | -3.0E-01 | +4.9E-01 | 2.5E-01 | 2.2E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 12| +2.13E+02 | -2.2E+00 | +8.7E-01 | 4.9E-02 | 2.3E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 42| +1.48E+02 | -2.5E-04 | +1.9E-01 | 1.2E-01 | 8.2E-01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 83| +1.78E+02 | +6.7E-06 | -1.6E-01 | 2.0E-02 | 1.6E-01 | 6.2E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 11| +2.50E+02 | -2.0E-04 | +2.5E-02 | 1.6E-02 | 1.2E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 2| +1.55E+08 | -8.4E+08 | +8.9E-01 | 2.5E-01 | 4.3E+09 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 13| +2.13E+02 | +7.7E-01 | -6.4E-01 | 9.8E-02 | 1.3E+01 | 2.5E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 91| +1.91E+02 | -1.4E+00 | +1.2E+00 | 1.3E-01 | 9.2E+00 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 43| +1.48E+02 | +5.0E-04 | -1.5E-01 | 3.1E-02 | 8.2E-01 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 84| +1.78E+02 | -1.0E-05 | +7.3E-01 | 4.9E-03 | 1.6E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 3| +2.36E+07 | -1.3E+08 | +8.9E-01 | 5.0E-01 | 6.6E+08 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 12| +2.50E+02 | -2.7E-03 | +4.2E-01 | 3.9E-03 | 1.1E+00 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 26| +1.54E+02 | -6.9E-01 | +6.5E-01 | 2.5E-01 | 4.8E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 14| +2.13E+02 | -5.2E-01 | +8.2E-01 | 2.4E-02 | 1.3E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 4| +3.63E+06 | -2.0E+07 | +8.9E-01 | 5.0E-01 | 1.0E+08 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 92| +1.86E+02 | -4.8E+00 | +1.6E+00 | 2.5E-01 | 1.1E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 44| +1.48E+02 | -6.6E-04 | +5.4E-01 | 7.8E-03 | 8.2E-01 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 13| +2.50E+02 | -9.0E-05 | +4.1E-02 | 3.9E-03 | 6.0E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 5| +5.63E+05 | -3.1E+06 | +9.0E-01 | 5.0E-01 | 1.5E+07 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 85| +1.78E+02 | -9.9E-06 | +7.6E-01 | 4.9E-03 | 1.7E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 6| +8.81E+04 | -4.7E+05 | +9.0E-01 | 5.0E-01 | 2.4E+06 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 45| +1.48E+02 | -3.3E-04 | +9.6E-01 | 7.8E-03 | 1.6E+00 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 15| +2.13E+02 | +1.8E-03 | -2.9E-02 | 4.9E-02 | 6.7E+00 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 14| +2.50E+02 | -9.1E-04 | +7.7E-01 | 9.8E-04 | 5.7E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 86| +1.78E+02 | -3.4E-06 | +1.5E-01 | 9.9E-03 | 1.4E-01 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 7| +1.40E+04 | -7.4E+04 | +9.0E-01 | 5.0E-01 | 3.7E+05 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 15| +2.50E+02 | -8.7E-07 | +7.3E-03 | 2.0E-03 | 1.4E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 16| +2.13E+02 | -1.0E-01 | +8.0E-01 | 1.2E-02 | 6.7E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 46| +1.48E+02 | -1.5E-04 | +6.8E-01 | 1.6E-02 | 3.0E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 27| +1.54E+02 | -7.9E-02 | +7.8E-01 | 2.5E-01 | 1.3E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 8| +2.39E+03 | -1.2E+04 | +9.0E-01 | 5.0E-01 | 5.9E+04 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 9| +5.58E+02 | -1.8E+03 | +9.0E-01 | 5.0E-01 | 9.4E+03 | 5.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 93| +1.86E+02 | +1.8E+02 | -2.9E+01 | 5.0E-01 | 5.7E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 16| +2.50E+02 | -5.7E-05 | +6.4E-01 | 4.9E-04 | 1.4E-01 | 8.6E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 17| +2.12E+02 | -1.4E-01 | +9.7E-01 | 2.4E-02 | 5.6E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 47| +1.48E+02 | -4.1E-05 | +4.3E-01 | 1.6E-02 | 2.4E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 87| +1.78E+02 | -1.5E-05 | +6.0E-01 | 2.5E-03 | 1.4E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 10| +2.88E+02 | -2.7E+02 | +9.0E-01 | 5.0E-01 | 1.5E+03 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 17| +2.50E+02 | -3.3E-07 | +5.3E-01 | 4.9E-04 | 7.1E-03 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(WARNING) Stopping as function difference 3.30E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:15 fides(INFO) 11| +2.50E+02 | -3.8E+01 | +8.3E-01 | 1.0E+00 | 2.3E+02 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 18| +2.12E+02 | -1.4E-01 | +6.8E-01 | 4.9E-02 | 5.5E+00 | 6.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 48| +1.48E+02 | -1.5E-05 | +8.3E-01 | 1.6E-02 | 1.2E-01 | 2.9E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 28| +1.54E+02 | -9.8E-03 | +3.6E-01 | 5.0E-01 | 4.1E+00 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 12| +2.50E+02 | -4.6E-01 | +3.9E-01 | 2.0E+00 | 1.7E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 88| +1.78E+02 | -3.8E-06 | +9.1E-01 | 2.5E-03 | 1.1E-01 | 3.0E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 94| +1.86E+02 | +2.7E+00 | -1.2E+00 | 1.3E-01 | 5.7E+01 | 3.4E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 79| +1.55E+02 | -1.3E-03 | +1.4E-01 | 8.8E-04 | 4.8E+00 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 49| +1.48E+02 | -3.8E-06 | +2.6E-01 | 3.1E-02 | 1.1E-01 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 19| +2.12E+02 | +2.0E-02 | -9.4E-02 | 4.9E-02 | 6.4E+00 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 13| +2.50E+02 | +1.4E-01 | -3.5E-01 | 2.0E+00 | 7.7E+00 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 89| +1.78E+02 | -5.9E-06 | +1.0E+00 | 4.9E-03 | 1.1E-01 | 4.1E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 29| +1.54E+02 | +1.0E-02 | -3.5E-01 | 5.0E-01 | 5.2E+00 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 14| +2.50E+02 | -1.6E-01 | +8.5E-01 | 1.1E-02 | 7.7E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 95| +1.84E+02 | -1.7E+00 | +1.5E+00 | 3.1E-02 | 5.7E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 50| +1.48E+02 | +5.1E-07 | -2.8E-02 | 3.1E-02 | 5.9E-02 | 5.0E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 20| +2.12E+02 | -1.1E-01 | +7.9E-01 | 1.2E-02 | 6.4E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 15| +2.50E+02 | +1.1E-02 | -1.4E-01 | 2.1E-02 | 3.7E+00 | 4.7E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 0| +1.65E+08 | NaN | NaN | 1.0E+00 | 7.6E+08 | NaN | NaN |1\n", - "2023-02-17 16:05:15 fides(INFO) 16| +2.50E+02 | -3.2E-02 | +8.5E-01 | 4.5E-03 | 3.7E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 90| +1.78E+02 | -7.3E-06 | +9.2E-01 | 9.9E-03 | 9.7E-02 | 7.4E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 51| +1.48E+02 | -8.3E-06 | +1.3E+00 | 7.8E-03 | 5.9E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 96| +1.79E+02 | -5.4E+00 | +8.9E-01 | 6.3E-02 | 6.0E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 21| +2.12E+02 | -4.7E-02 | +5.4E-01 | 2.4E-02 | 4.1E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 17| +2.50E+02 | +1.2E-03 | -6.3E-02 | 9.1E-03 | 1.8E+00 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 30| +1.54E+02 | -1.1E-02 | +7.8E-01 | 1.2E-01 | 5.2E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 52| +1.48E+02 | -3.3E-06 | +9.8E-01 | 1.6E-02 | 9.9E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 1| +1.45E+03 | -1.6E+08 | +1.0E-01 | 1.0E+00 | 7.6E+08 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 18| +2.50E+02 | -7.4E-03 | +8.6E-01 | 2.1E-03 | 1.8E+00 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 22| +2.12E+02 | -4.8E-02 | +3.7E-01 | 2.4E-02 | 5.9E+00 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 97| +1.70E+02 | -9.0E+00 | +9.8E-01 | 1.3E-01 | 3.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 91| +1.78E+02 | -3.8E-06 | +3.7E-01 | 2.0E-02 | 9.5E-02 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 19| +2.50E+02 | +1.4E-04 | -3.0E-02 | 4.2E-03 | 8.9E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 2| +5.27E+02 | -9.2E+02 | +9.2E-01 | 2.5E-01 | 4.8E+03 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:15.736 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1579 and h = 1.35913e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:15.736 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1579: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:15 fides(INFO) 53| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.1E-02 | 2.0E-02 | 4.7E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 20| +2.50E+02 | -1.8E-03 | +8.6E-01 | 1.0E-03 | 8.9E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 3| +2.92E+02 | -2.3E+02 | +8.9E-01 | 5.0E-01 | 9.8E+02 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 23| +2.12E+02 | -1.5E-02 | +1.2E-01 | 2.4E-02 | 4.6E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 31| +1.54E+02 | -3.4E-03 | +9.9E-01 | 2.5E-01 | 1.7E+00 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 92| +1.78E+02 | +5.7E-04 | -5.2E+00 | 2.0E-02 | 5.3E-02 | 2.6E-02 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 4| +2.59E+02 | -3.3E+01 | +8.3E-01 | 1.0E+00 | 1.4E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 21| +2.50E+02 | +1.6E-05 | -1.5E-02 | 2.1E-03 | 4.4E-01 | 5.3E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 54| +1.48E+02 | -2.6E-06 | +2.0E+00 | 7.8E-03 | 2.0E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 98| +1.70E+02 | +5.5E+00 | -4.3E-01 | 2.5E-01 | 4.4E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 24| +2.12E+02 | -5.1E-02 | +8.9E-01 | 6.1E-03 | 6.2E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 22| +2.50E+02 | -4.4E-04 | +8.6E-01 | 5.1E-04 | 4.4E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 5| +2.59E+02 | +1.3E+03 | -8.4E+01 | 2.0E+00 | 3.4E+01 | 4.2E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 32| +1.54E+02 | -3.9E-03 | +1.1E+00 | 5.0E-01 | 1.7E+00 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:15.818 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 212.759 and h = 2.82009e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:15.818 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 212.759: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:15 fides(INFO) 55| +1.48E+02 | +1.5E-06 | -8.5E-01 | 1.6E-02 | 4.7E-02 | 2.3E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 23| +2.50E+02 | +2.0E-06 | -7.3E-03 | 1.0E-03 | 2.2E-01 | 2.6E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 93| +1.78E+02 | +0.0E+00 | +0.0E+00 | 4.9E-03 | 5.3E-02 | 6.5E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 6| +2.59E+02 | +2.7E+02 | -1.7E+01 | 5.0E-01 | 3.4E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 24| +2.50E+02 | -1.1E-04 | +8.6E-01 | 2.5E-04 | 2.2E-01 | 6.5E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 99| +1.66E+02 | -3.9E+00 | +8.5E-01 | 6.3E-02 | 4.4E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 80| +1.55E+02 | -1.4E-03 | +1.0E+00 | 2.2E-04 | 4.5E+00 | 4.0E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 25| +2.12E+02 | -3.8E-02 | +6.7E-01 | 1.2E-02 | 4.4E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 56| +1.48E+02 | +2.2E-06 | -3.4E+00 | 3.9E-03 | 4.7E-02 | 5.8E-04 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 7| +2.51E+02 | -8.3E+00 | +8.3E-01 | 1.2E-01 | 3.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 33| +1.54E+02 | -1.9E-03 | +6.5E-01 | 1.0E+00 | 4.6E-01 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 25| +2.50E+02 | +2.5E-07 | -3.6E-03 | 5.0E-04 | 1.1E-01 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 94| +1.78E+02 | -5.8E-06 | +6.8E-01 | 1.2E-03 | 5.3E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 8| +2.51E+02 | +4.1E+00 | -1.5E+00 | 2.5E-01 | 1.8E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 26| +2.50E+02 | -2.7E-05 | +8.6E-01 | 1.3E-04 | 1.1E-01 | 3.2E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 26| +2.12E+02 | -2.3E-02 | +6.2E-01 | 1.2E-02 | 3.1E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 57| +1.48E+02 | +2.5E-06 | -7.1E+00 | 9.8E-04 | 4.7E-02 | 1.5E-04 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 27| +2.50E+02 | +3.1E-08 | -1.8E-03 | 2.5E-04 | 5.5E-02 | 6.5E-04 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 34| +1.54E+02 | +9.7E-02 | -7.4E+00 | 1.0E+00 | 6.1E-01 | 2.0E-01 | trr |0\n", - "2023-02-17 16:05:15 fides(INFO) 9| +2.50E+02 | -1.3E+00 | +5.9E-01 | 6.2E-02 | 1.8E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 100| +1.66E+02 | -9.8E-02 | -4.1E-02 | 1.3E-01 | 2.9E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 27| +2.12E+02 | -2.8E-02 | +6.3E-01 | 1.2E-02 | 3.8E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 95| +1.78E+02 | -1.4E-06 | +8.2E-01 | 1.2E-03 | 6.4E-02 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(WARNING) Stopping as function difference 1.45E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 10| +2.50E+02 | -1.3E-02 | +1.4E-01 | 6.2E-02 | 4.1E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 28| +2.50E+02 | -6.7E-06 | +8.6E-01 | 6.2E-05 | 5.5E-02 | 1.6E-04 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 58| +1.48E+02 | +1.1E-06 | -3.8E+00 | 2.4E-04 | 4.7E-02 | 3.8E-05 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 29| +2.50E+02 | +3.8E-09 | -9.0E-04 | 1.2E-04 | 2.7E-02 | 3.2E-04 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 35| +1.54E+02 | -2.4E-03 | +5.6E-01 | 5.1E-02 | 6.1E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 28| +2.12E+02 | -1.5E-02 | +4.4E-01 | 1.2E-02 | 2.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 11| +2.50E+02 | -3.7E-03 | +4.7E-02 | 1.6E-02 | 3.3E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 101| +1.65E+02 | -1.3E+00 | +7.3E-01 | 3.1E-02 | 2.9E+01 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:15 fides(INFO) 30| +2.50E+02 | -1.7E-06 | +8.6E-01 | 3.1E-05 | 2.7E-02 | 8.1E-05 | 2d |1\n", - "2023-02-17 16:05:15 fides(WARNING) Stopping as function difference 1.67E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:15 fides(INFO) 59| +1.48E+02 | +4.8E-06 | -1.8E+01 | 6.1E-05 | 4.7E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:15 fides(INFO) 12| +2.50E+02 | -2.0E-02 | +8.9E-01 | 3.9E-03 | 3.3E+00 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 29| +2.12E+02 | -2.6E-02 | +5.8E-01 | 1.2E-02 | 3.7E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 36| +1.54E+02 | -4.8E-04 | +1.0E+00 | 5.1E-02 | 2.1E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:15 fides(INFO) 13| +2.50E+02 | -9.9E-03 | +5.1E-01 | 7.8E-03 | 2.0E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 102| +1.63E+02 | -1.7E+00 | +9.7E-01 | 3.1E-02 | 4.7E+01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 60| +1.48E+02 | +3.8E-06 | -1.5E+01 | 1.5E-05 | 4.7E-02 | 1.1E-05 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 30| +2.12E+02 | -1.1E-02 | +3.6E-01 | 1.2E-02 | 2.7E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 14| +2.50E+02 | -1.2E-05 | +3.7E-03 | 7.8E-03 | 6.2E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 61| +1.48E+02 | +3.9E-06 | -1.5E+01 | 3.8E-06 | 4.7E-02 | 1.0E-05 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 37| +1.54E+02 | -7.3E-04 | +1.2E+00 | 1.0E-01 | 2.7E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 15| +2.50E+02 | -1.2E-03 | +6.7E-01 | 2.0E-03 | 6.4E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 31| +2.12E+02 | -2.3E-02 | +5.4E-01 | 1.2E-02 | 3.5E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 0| +1.33E+09 | NaN | NaN | 1.0E+00 | 6.1E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:16 fides(INFO) 62| +1.48E+02 | +5.4E-06 | -5.3E+01 | 9.5E-07 | 4.7E-02 | 2.5E-06 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 16| +2.50E+02 | -1.4E-05 | +9.4E-01 | 2.0E-03 | 1.1E-02 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 38| +1.54E+02 | -6.6E-05 | +8.6E-02 | 2.0E-01 | 1.3E-01 | 2.8E-02 | trr |1\n", - "2023-02-17 16:05:16 fides(INFO) 103| +1.61E+02 | -2.2E+00 | +8.5E-01 | 6.3E-02 | 2.6E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 81| +1.55E+02 | -1.9E-03 | +1.0E+00 | 4.4E-04 | 3.4E+00 | 8.2E-04 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 1| +1.40E+06 | -1.3E+09 | +9.5E-02 | 1.0E+00 | 6.1E+09 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 0| +1.62E+11 | NaN | NaN | 1.0E+00 | 7.4E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:16 fides(INFO) 32| +2.12E+02 | -9.2E-03 | +3.1E-01 | 1.2E-02 | 2.6E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 17| +2.50E+02 | -2.8E-05 | +9.4E-01 | 3.9E-03 | 1.7E-02 | 9.3E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 63| +1.48E+02 | +3.6E-06 | -1.3E+02 | 2.4E-07 | 4.7E-02 | 6.2E-07 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 18| +2.50E+02 | -5.7E-05 | +9.7E-01 | 7.8E-03 | 2.0E-02 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 39| +1.54E+02 | +4.5E-03 | -1.1E+00 | 3.1E-02 | 2.8E-01 | 6.3E-02 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 104| +1.58E+02 | -2.9E+00 | +6.1E-01 | 1.3E-01 | 4.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 2| +2.19E+05 | -1.2E+06 | +9.0E-01 | 2.5E-01 | 6.4E+06 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 64| +1.48E+02 | +4.2E-06 | -5.8E+02 | 6.0E-08 | 4.7E-02 | 1.6E-07 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 1| +1.28E+06 | -1.6E+11 | +9.1E-02 | 1.0E+00 | 7.4E+11 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 33| +2.12E+02 | -2.0E-02 | +5.0E-01 | 1.2E-02 | 3.3E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 19| +2.50E+02 | -1.2E-04 | +1.0E+00 | 1.6E-02 | 2.3E-02 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 3| +3.43E+04 | -1.8E+05 | +9.0E-01 | 5.0E-01 | 1.0E+06 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 2| +2.77E+05 | -1.0E+06 | +8.9E-01 | 2.5E-01 | 4.6E+06 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 40| +1.54E+02 | -8.6E-04 | +6.4E-01 | 7.8E-03 | 2.8E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 20| +2.50E+02 | -2.6E-04 | +1.0E+00 | 3.1E-02 | 2.5E-02 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 65| +1.48E+02 | +8.8E-07 | -4.9E+02 | 1.5E-08 | 4.7E-02 | 3.9E-08 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 34| +2.12E+02 | -8.1E-03 | +2.9E-01 | 1.2E-02 | 2.4E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 105| +1.54E+02 | -4.0E+00 | +6.8E-01 | 1.3E-01 | 3.6E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 4| +5.57E+03 | -2.9E+04 | +9.0E-01 | 5.0E-01 | 1.6E+05 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 21| +2.50E+02 | -6.0E-04 | +1.1E+00 | 6.2E-02 | 2.9E-02 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 3| +4.42E+04 | -2.3E+05 | +9.0E-01 | 5.0E-01 | 8.5E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:16.222 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0112 and h = 1.07533e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:16.223 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0112: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:16 fides(INFO) 66| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.7E-09 | 4.7E-02 | 9.7E-09 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 22| +2.50E+02 | -1.6E-03 | +1.2E+00 | 1.2E-01 | 3.1E-02 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 41| +1.54E+02 | -1.2E-04 | +6.7E-01 | 7.8E-03 | 8.3E-02 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 35| +2.12E+02 | -1.7E-02 | +4.6E-01 | 1.2E-02 | 3.1E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 5| +1.06E+03 | -4.5E+03 | +9.0E-01 | 5.0E-01 | 2.5E+04 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 4| +7.14E+03 | -3.7E+04 | +9.0E-01 | 1.0E+00 | 1.3E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 67| +1.48E+02 | +3.4E-06 | -3.0E+04 | 9.3E-10 | 4.7E-02 | 2.4E-09 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 23| +2.50E+02 | -5.9E-03 | +1.5E+00 | 2.5E-01 | 3.8E-02 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 5| +1.28E+03 | -5.9E+03 | +9.0E-01 | 1.0E+00 | 2.1E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 6| +3.55E+02 | -7.0E+02 | +9.0E-01 | 5.0E-01 | 3.9E+03 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 36| +2.12E+02 | -7.5E-03 | +2.8E-01 | 1.2E-02 | 2.3E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 106| +1.54E+02 | +5.3E+00 | -1.0E+00 | 1.3E-01 | 5.8E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 42| +1.54E+02 | -4.8E-05 | +2.0E-01 | 7.8E-03 | 1.1E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 24| +2.50E+02 | -3.9E-02 | +2.2E+00 | 5.0E-01 | 4.4E-02 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 6| +3.74E+02 | -9.1E+02 | +9.0E-01 | 1.0E+00 | 3.4E+03 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 68| +1.48E+02 | +3.9E-06 | -1.4E+05 | 2.3E-10 | 4.7E-02 | 6.1E-10 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 7| +2.50E+02 | -1.1E+02 | +9.0E-01 | 1.0E+00 | 6.1E+02 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 37| +2.12E+02 | -1.5E-02 | +4.4E-01 | 1.2E-02 | 2.9E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 7| +2.55E+02 | -1.2E+02 | +8.7E-01 | 1.0E+00 | 5.3E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 43| +1.54E+02 | -1.0E-04 | +7.9E-01 | 1.9E-03 | 7.6E-02 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 69| +1.48E+02 | +3.7E-07 | -5.2E+04 | 5.8E-11 | 4.7E-02 | 1.5E-10 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 25| +2.49E+02 | -8.1E-01 | +4.2E+00 | 1.0E+00 | 1.7E-01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 38| +2.12E+02 | -7.2E-03 | +2.8E-01 | 1.2E-02 | 2.2E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 8| +2.41E+02 | -8.4E+00 | +7.4E-01 | 2.0E+00 | 7.8E+01 | 5.9E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 107| +1.54E+02 | +1.2E+00 | -4.8E-01 | 3.1E-02 | 5.8E+01 | 7.7E-02 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 8| +2.39E+02 | -1.5E+01 | +1.2E+00 | 2.0E+00 | 5.9E+01 | 3.9E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 26| +2.32E+02 | -1.7E+01 | +5.1E+00 | 2.0E+00 | 2.5E+00 | 3.9E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 82| +1.55E+02 | -2.1E-03 | +9.8E-01 | 8.8E-04 | 1.7E+00 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 44| +1.54E+02 | -5.7E-05 | +5.3E-01 | 3.9E-03 | 1.0E-01 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 70| +1.48E+02 | +4.8E-06 | -2.7E+06 | 1.5E-11 | 4.7E-02 | 3.8E-11 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 9| +2.33E+02 | -8.3E+00 | +6.4E-01 | 2.0E+00 | 1.4E+01 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 39| +2.12E+02 | -1.3E-02 | +4.2E-01 | 1.2E-02 | 2.7E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 9| +2.39E+02 | +6.9E+01 | -1.4E+01 | 4.0E+00 | 3.0E+01 | 6.6E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 108| +1.54E+02 | -5.2E-01 | +5.3E-01 | 7.9E-03 | 5.8E+01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 45| +1.54E+02 | -4.4E-05 | +4.6E-01 | 3.9E-03 | 5.3E-02 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 71| +1.48E+02 | +4.2E-06 | -9.5E+06 | 3.6E-12 | 4.7E-02 | 9.5E-12 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 27| +2.32E+02 | +2.5E+02 | -1.5E+01 | 4.0E+00 | 3.4E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 40| +2.12E+02 | -7.0E-03 | +2.8E-01 | 1.2E-02 | 2.1E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 10| +2.39E+02 | +1.5E+01 | -1.6E+00 | 1.0E+00 | 3.0E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 28| +2.32E+02 | +1.9E+01 | -1.8E+00 | 1.0E+00 | 3.4E+01 | 3.0E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 72| +1.48E+02 | +2.0E-06 | -1.8E+07 | 9.1E-13 | 4.7E-02 | 2.4E-12 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 11| +2.39E+02 | +1.2E+01 | -1.4E+00 | 2.5E-01 | 3.0E+01 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 46| +1.54E+02 | -2.3E-05 | +2.1E-01 | 3.9E-03 | 7.2E-02 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 109| +1.53E+02 | -2.4E-01 | +9.9E-01 | 7.9E-03 | 2.4E+01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 41| +2.12E+02 | -1.2E-02 | +4.1E-01 | 1.2E-02 | 2.5E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 29| +2.32E+02 | +8.3E+00 | -8.0E-01 | 2.5E-01 | 3.4E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 12| +2.36E+02 | -3.5E+00 | +8.4E-01 | 6.2E-02 | 3.0E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 73| +1.48E+02 | -2.3E-06 | +8.4E+07 | 2.3E-13 | 4.7E-02 | 5.9E-13 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 30| +2.28E+02 | -4.1E+00 | +8.7E-01 | 6.2E-02 | 3.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 42| +2.12E+02 | -6.9E-03 | +3.0E-01 | 1.2E-02 | 2.0E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 13| +2.35E+02 | -1.2E+00 | +3.5E-01 | 1.2E-01 | 1.8E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 47| +1.54E+02 | -3.3E-05 | +8.3E-01 | 9.7E-04 | 4.4E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 31| +2.28E+02 | -3.7E-01 | +6.1E-02 | 1.2E-01 | 2.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 74| +1.48E+02 | +1.5E-07 | -2.7E+06 | 4.5E-13 | 4.7E-02 | 1.2E-12 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 110| +1.53E+02 | -3.9E-01 | +9.0E-01 | 1.6E-02 | 1.9E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 14| +2.31E+02 | -3.8E+00 | +7.4E-01 | 1.2E-01 | 3.9E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 43| +2.12E+02 | -1.1E-02 | +4.2E-01 | 1.2E-02 | 2.4E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 32| +2.26E+02 | -1.8E+00 | +9.2E-01 | 3.1E-02 | 4.1E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 48| +1.54E+02 | -2.4E-05 | +5.7E-01 | 1.9E-03 | 6.1E-02 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 75| +1.48E+02 | +6.5E-06 | -4.7E+08 | 1.1E-13 | 4.7E-02 | 3.0E-13 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 10| +2.13E+02 | -1.9E+01 | +7.5E-01 | 2.0E+00 | 3.3E+01 | 4.2E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 15| +2.29E+02 | -1.6E+00 | +4.5E-01 | 1.2E-01 | 2.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 33| +2.24E+02 | -1.9E+00 | +8.4E-01 | 6.2E-02 | 2.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 44| +2.12E+02 | -7.0E-03 | +3.2E-01 | 1.2E-02 | 1.9E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 16| +2.26E+02 | -3.5E+00 | +7.4E-01 | 1.2E-01 | 3.7E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 76| +1.48E+02 | +8.7E-06 | -2.5E+09 | 2.8E-14 | 4.7E-02 | 7.4E-14 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 111| +1.52E+02 | -5.8E-01 | +9.4E-01 | 3.1E-02 | 2.7E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 49| +1.54E+02 | -1.9E-05 | +6.5E-01 | 1.9E-03 | 4.1E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 34| +2.21E+02 | -3.0E+00 | +1.1E+00 | 1.2E-01 | 1.7E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 17| +2.24E+02 | -1.4E+00 | +4.0E-01 | 1.2E-01 | 2.0E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 45| +2.12E+02 | -1.0E-02 | +4.3E-01 | 1.2E-02 | 2.2E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 83| +1.55E+02 | -1.3E-03 | +9.9E-01 | 1.8E-03 | 1.6E+00 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 11| +2.13E+02 | +2.1E+02 | -1.6E+01 | 2.0E+00 | 4.2E+01 | 4.5E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 77| +1.48E+02 | +5.7E-06 | -6.6E+09 | 7.1E-15 | 4.7E-02 | 1.9E-14 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 35| +2.19E+02 | -2.2E+00 | +8.5E-01 | 2.5E-01 | 1.8E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 18| +2.21E+02 | -3.2E+00 | +7.2E-01 | 1.2E-01 | 3.6E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 50| +1.54E+02 | -1.3E-05 | +4.5E-01 | 1.9E-03 | 3.6E-02 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 46| +2.12E+02 | -1.9E-02 | +9.6E-01 | 1.2E-02 | 1.8E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 36| +2.18E+02 | -8.3E-01 | +7.5E-02 | 5.0E-01 | 2.4E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 78| +1.48E+02 | +5.4E-06 | -2.5E+10 | 1.8E-15 | 4.7E-02 | 4.6E-15 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 112| +1.52E+02 | -7.8E-01 | +9.4E-01 | 6.3E-02 | 1.6E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 19| +2.20E+02 | -8.3E-01 | +2.6E-01 | 1.2E-01 | 1.9E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 37| +2.14E+02 | -3.7E+00 | +6.3E-01 | 1.2E-01 | 4.5E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 51| +1.54E+02 | -9.0E-06 | +2.6E-01 | 1.9E-03 | 3.7E-02 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 47| +2.12E+02 | +3.2E-02 | -8.1E-01 | 2.4E-02 | 1.2E+00 | 5.0E-02 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 79| +1.48E+02 | +4.8E-06 | -8.8E+10 | 4.4E-16 | 4.7E-02 | 1.2E-15 | 2d |0\n", - "2023-02-17 16:05:16 fides(WARNING) Stopping as trust region radius 1.11E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:16 fides(INFO) 38| +2.13E+02 | -1.1E+00 | +5.1E-01 | 1.2E-01 | 1.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 20| +2.18E+02 | -2.7E+00 | +6.5E-01 | 1.2E-01 | 3.5E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 48| +2.12E+02 | -5.5E-03 | +5.2E-01 | 6.1E-03 | 1.2E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 52| +1.54E+02 | -7.7E-06 | +2.0E-01 | 1.9E-03 | 2.8E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 39| +2.11E+02 | -2.1E+00 | +7.8E-01 | 1.2E-01 | 2.4E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 113| +1.51E+02 | -4.7E-01 | +5.7E-01 | 1.3E-01 | 1.7E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 21| +2.16E+02 | -1.1E+00 | +4.0E-01 | 1.2E-01 | 1.7E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 49| +2.12E+02 | -7.4E-03 | +9.4E-01 | 6.1E-03 | 1.3E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 53| +1.54E+02 | -9.0E-06 | +8.1E-01 | 4.9E-04 | 3.4E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 40| +2.07E+02 | -3.4E+00 | +8.4E-01 | 2.5E-01 | 1.2E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 22| +2.13E+02 | -3.0E+00 | +7.8E-01 | 1.2E-01 | 2.8E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:16.826 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 106.88 and h = 5.48355e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:16.826 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 106.88: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:16 fides(INFO) 12| +2.13E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 4.2E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 41| +1.94E+02 | -1.4E+01 | +1.5E+00 | 5.0E-01 | 2.6E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 50| +2.12E+02 | +1.7E-03 | -1.1E-01 | 1.2E-02 | 1.1E+00 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 54| +1.54E+02 | -6.0E-06 | +5.0E-01 | 9.7E-04 | 2.3E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 23| +2.06E+02 | -7.7E+00 | +1.2E+00 | 2.5E-01 | 1.6E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 84| +1.55E+02 | -2.7E-03 | +1.0E+00 | 3.5E-03 | 1.4E+00 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:16 fides(INFO) 0| +1.85E+12 | NaN | NaN | 1.0E+00 | 8.5E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:16 fides(INFO) 51| +2.12E+02 | -3.0E-03 | +6.5E-01 | 3.1E-03 | 1.1E+00 | 6.3E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 42| +1.94E+02 | +5.1E+03 | -1.7E+02 | 1.0E+00 | 2.9E+01 | 2.2E+00 | trr |0\n", - "2023-02-17 16:05:16 fides(INFO) 1| +2.10E+09 | -1.8E+12 | +8.5E-02 | 1.0E+00 | 8.5E+12 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 24| +1.95E+02 | -1.0E+01 | +5.5E-01 | 5.0E-01 | 3.5E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 55| +1.54E+02 | -4.6E-06 | +4.7E-01 | 9.7E-04 | 1.7E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 114| +1.51E+02 | -1.6E-01 | +3.1E-01 | 1.3E-01 | 2.1E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 52| +2.12E+02 | -3.7E-03 | +1.0E+00 | 3.1E-03 | 1.0E+00 | 6.3E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 43| +1.77E+02 | -1.7E+01 | +1.0E+00 | 2.5E-01 | 2.9E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 2| +3.18E+08 | -1.8E+09 | +8.9E-01 | 2.5E-01 | 9.7E+09 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 25| +1.75E+02 | -2.1E+01 | +8.6E-01 | 5.0E-01 | 6.0E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 56| +1.54E+02 | -2.9E-06 | +2.9E-01 | 9.7E-04 | 2.0E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 44| +1.77E+02 | -1.2E+01 | -2.0E-01 | 5.0E-01 | 9.9E+01 | 9.3E-01 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 53| +2.12E+02 | -7.0E-03 | +1.0E+00 | 6.1E-03 | 9.4E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 3| +4.90E+07 | -2.7E+08 | +8.9E-01 | 5.0E-01 | 1.5E+09 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:16 fides(INFO) 26| +1.75E+02 | +6.0E+04 | -1.8E+03 | 1.0E+00 | 1.0E+02 | 2.2E+00 | 2d |0\n", - "2023-02-17 16:05:16 fides(INFO) 4| +7.48E+06 | -4.2E+07 | +8.9E-01 | 1.0E+00 | 2.2E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 57| +1.54E+02 | -3.3E-06 | +3.1E-01 | 9.7E-04 | 1.4E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 45| +1.66E+02 | -1.1E+01 | +9.8E-01 | 1.2E-01 | 9.9E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 54| +2.12E+02 | -1.2E-02 | +9.9E-01 | 1.2E-02 | 8.8E-01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 27| +1.63E+02 | -1.2E+01 | +3.7E-01 | 2.5E-01 | 1.0E+02 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 5| +1.15E+06 | -6.3E+06 | +9.0E-01 | 1.0E+00 | 3.4E+07 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 13| +2.06E+02 | -7.3E+00 | +8.5E-01 | 1.2E-01 | 4.2E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 58| +1.54E+02 | -6.0E-07 | +6.4E-02 | 9.7E-04 | 1.5E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 6| +1.78E+05 | -9.7E+05 | +9.0E-01 | 1.0E+00 | 5.2E+06 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 46| +1.52E+02 | -1.4E+01 | +7.9E-01 | 2.5E-01 | 5.9E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 115| +1.51E+02 | -5.6E-03 | +1.3E-02 | 1.3E-01 | 1.6E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 55| +2.12E+02 | -2.5E-02 | +1.0E+00 | 2.4E-02 | 8.6E-01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 28| +1.51E+02 | -1.2E+01 | +9.6E-01 | 2.5E-01 | 2.5E+02 | 3.5E-01 | trr |1\n", - "2023-02-17 16:05:17 fides(INFO) 7| +2.79E+04 | -1.5E+05 | +9.0E-01 | 1.0E+00 | 8.1E+05 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 8| +4.54E+03 | -2.3E+04 | +9.0E-01 | 1.0E+00 | 1.3E+05 | 7.8E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 85| +1.55E+02 | -3.3E-03 | +1.0E+00 | 7.0E-03 | 1.9E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 47| +1.51E+02 | -1.3E+00 | +2.1E-01 | 5.0E-01 | 1.1E+02 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 59| +1.54E+02 | -3.0E-06 | +8.6E-01 | 2.4E-04 | 1.1E-02 | 5.2E-04 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 56| +2.12E+02 | -5.2E-02 | +1.1E+00 | 4.9E-02 | 8.0E-01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 29| +1.51E+02 | +5.9E-01 | -2.9E-01 | 2.5E-01 | 5.0E+01 | 3.4E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 9| +8.89E+02 | -3.7E+03 | +9.0E-01 | 1.0E+00 | 2.0E+04 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 57| +2.11E+02 | -1.3E-01 | +1.2E+00 | 9.8E-02 | 1.0E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 48| +1.48E+02 | -2.4E+00 | +8.2E-01 | 1.0E-01 | 6.8E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 10| +3.24E+02 | -5.7E+02 | +9.1E-01 | 1.0E+00 | 3.1E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 30| +1.50E+02 | -1.3E+00 | +8.1E-01 | 6.2E-02 | 5.0E+01 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 60| +1.54E+02 | -2.0E-06 | +5.0E-01 | 4.9E-04 | 2.5E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 11| +2.37E+02 | -8.7E+01 | +9.3E-01 | 1.0E+00 | 4.7E+02 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 58| +2.11E+02 | -5.4E-01 | +1.7E+00 | 2.0E-01 | 2.1E+00 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 116| +1.51E+02 | -1.1E-01 | +9.8E-01 | 1.1E-02 | 7.6E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 49| +1.48E+02 | +1.3E+00 | -1.0E+00 | 2.1E-01 | 2.4E+01 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 31| +1.49E+02 | -7.1E-01 | +6.6E-01 | 1.2E-01 | 4.5E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 61| +1.54E+02 | -9.9E-07 | +4.4E-01 | 4.9E-04 | 9.8E-03 | 8.5E-04 | 2d |1\n", - "2023-02-17 16:05:17 fides(WARNING) Stopping as function difference 9.90E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:17.202 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 148.098 and h = 1.74276e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:17.202 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 148.098: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 59| +2.07E+02 | -3.4E+00 | +2.0E+00 | 3.9E-01 | 5.3E+00 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 50| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.1E-02 | 2.4E+01 | 7.1E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 12| +2.37E+02 | -3.1E+01 | -7.3E-01 | 2.0E+00 | 5.8E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 32| +1.49E+02 | +9.6E-02 | -1.4E-01 | 1.2E-01 | 3.0E+01 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 60| +2.07E+02 | +9.3E+01 | -1.7E+01 | 7.8E-01 | 1.9E+01 | 2.0E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 13| +2.24E+02 | -1.2E+01 | +9.6E-01 | 5.0E-01 | 5.8E+01 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 61| +2.05E+02 | -2.5E+00 | +9.4E-01 | 2.0E-01 | 1.9E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 33| +1.48E+02 | -7.1E-01 | +8.5E-01 | 3.1E-02 | 3.0E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 51| +1.48E+02 | -3.1E-01 | +8.5E-01 | 1.3E-02 | 2.4E+01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 14| +1.90E+02 | -1.6E+01 | +1.2E+00 | 2.5E-01 | 3.8E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 62| +1.99E+02 | -5.8E+00 | +3.0E-01 | 3.9E-01 | 3.2E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:17.296 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 87.7814 and h = 9.97554e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:17.296 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 87.7814: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:17 fides(INFO) 52| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.6E-02 | 2.4E+01 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 117| +1.51E+02 | -4.8E-02 | +9.2E-01 | 2.3E-02 | 5.6E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 34| +1.48E+02 | -2.2E-01 | +9.6E-01 | 6.2E-02 | 2.2E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 14| +2.24E+02 | -3.2E+00 | -2.8E+00 | 1.0E+00 | 3.1E+01 | 1.6E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 0| +8.25E+04 | NaN | NaN | 1.0E+00 | 3.8E+05 | NaN | NaN |1\n", - "2023-02-17 16:05:17 fides(INFO) 53| +1.48E+02 | -1.0E-01 | +9.5E-01 | 6.4E-03 | 2.4E+01 | 8.8E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 63| +1.93E+02 | -6.6E+00 | +1.4E-01 | 3.9E-01 | 8.1E+01 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:17.330 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.4894 and h = 1.16646e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:17.330 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.4894: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:17 fides(INFO) 35| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 1.2E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 1| +3.63E+02 | -8.2E+04 | +1.1E-01 | 1.0E+00 | 3.8E+05 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 15| +2.20E+02 | -4.9E+00 | +3.7E-01 | 2.5E-01 | 3.1E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 36| +1.48E+02 | -1.2E-01 | +9.0E-01 | 3.1E-02 | 1.2E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 64| +1.81E+02 | -1.2E+01 | +7.5E-01 | 9.8E-02 | 9.0E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 2| +3.32E+02 | -3.2E+01 | +9.2E-01 | 2.5E-01 | 5.0E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 54| +1.48E+02 | -1.2E-01 | +7.6E-01 | 1.3E-02 | 1.3E+01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 118| +1.51E+02 | -6.4E-03 | +1.5E+00 | 2.3E-02 | 1.2E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 16| +2.06E+02 | -1.3E+01 | +8.9E-01 | 2.5E-01 | 7.9E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 86| +1.55E+02 | -4.1E-03 | +3.6E-01 | 1.4E-02 | 1.0E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 65| +1.81E+02 | +1.6E+00 | -1.2E-01 | 2.0E-01 | 4.8E+01 | 5.1E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 3| +2.77E+02 | -5.5E+01 | +9.8E-01 | 5.0E-01 | 3.7E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 37| +1.48E+02 | -1.1E-01 | +9.5E-01 | 6.2E-02 | 2.1E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 4| +2.77E+02 | +6.4E+03 | -2.6E+02 | 1.0E+00 | 4.2E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 55| +1.48E+02 | -6.5E-02 | +9.1E-01 | 2.6E-02 | 1.9E+01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 66| +1.75E+02 | -5.4E+00 | +9.3E-01 | 4.9E-02 | 4.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 17| +1.79E+02 | -2.8E+01 | +1.1E+00 | 5.0E-01 | 3.2E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 38| +1.48E+02 | -9.0E-02 | +8.3E-01 | 1.2E-01 | 6.6E+00 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 5| +2.54E+02 | -2.2E+01 | +9.0E-01 | 2.5E-01 | 4.2E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 56| +1.48E+02 | -2.7E-02 | +5.2E-01 | 5.1E-02 | 4.7E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 67| +1.67E+02 | -8.4E+00 | +1.0E+00 | 9.8E-02 | 4.1E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 18| +1.79E+02 | +1.8E+03 | -8.2E+01 | 1.0E+00 | 1.2E+02 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 6| +2.54E+02 | +7.1E+01 | -7.5E+00 | 5.0E-01 | 2.8E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:17.483 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 125.418 and h = 8.51644e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:17.484 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 125.418: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:17 fides(INFO) 119| +1.51E+02 | +0.0E+00 | +0.0E+00 | 2.3E-02 | 1.5E+00 | 4.8E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 39| +1.48E+02 | +1.2E+00 | -5.1E+00 | 2.5E-01 | 7.7E+00 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 7| +2.50E+02 | -4.2E+00 | +5.5E-01 | 1.2E-01 | 2.8E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 57| +1.48E+02 | -1.1E-02 | +2.6E-01 | 5.1E-02 | 9.1E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 68| +1.62E+02 | -4.6E+00 | +4.8E-01 | 2.0E-01 | 4.1E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 40| +1.48E+02 | +1.3E-03 | -2.5E-02 | 6.2E-02 | 7.7E+00 | 7.9E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 15| +1.90E+02 | +4.9E+01 | -2.8E+00 | 5.0E-01 | 4.8E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 19| +1.60E+02 | -1.8E+01 | +8.4E-01 | 2.5E-01 | 1.2E+02 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 8| +2.50E+02 | -1.6E-01 | +2.4E-01 | 1.2E-01 | 1.2E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 58| +1.48E+02 | +8.0E-02 | -1.2E+00 | 5.1E-02 | 2.2E+00 | 5.7E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 69| +1.51E+02 | -1.2E+01 | +7.2E-01 | 2.0E-01 | 6.9E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 20| +1.51E+02 | -9.0E+00 | +8.8E-01 | 5.0E-01 | 1.2E+02 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 9| +2.50E+02 | -9.9E-02 | +2.6E-01 | 3.1E-02 | 7.6E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 41| +1.48E+02 | -2.0E-02 | +7.7E-01 | 1.6E-02 | 7.7E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 10| +2.50E+02 | -3.4E-02 | +1.9E-01 | 3.1E-02 | 5.7E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 59| +1.48E+02 | -5.2E-04 | +4.9E-03 | 1.3E-02 | 2.2E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 70| +1.51E+02 | +2.7E+02 | -7.7E+00 | 2.0E-01 | 8.4E+01 | 5.1E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 120| +1.51E+02 | -5.6E-03 | +1.1E+00 | 5.7E-03 | 1.5E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 21| +1.50E+02 | -1.9E+00 | +3.7E-01 | 1.0E+00 | 9.2E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 42| +1.48E+02 | -1.3E-02 | +7.5E-01 | 3.1E-02 | 3.5E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 11| +2.50E+02 | -5.3E-02 | +7.6E-01 | 7.8E-03 | 4.2E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 71| +1.51E+02 | +1.2E+01 | -1.2E+00 | 4.9E-02 | 8.4E+01 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 60| +1.48E+02 | -8.1E-03 | +9.6E-01 | 3.2E-03 | 6.6E+00 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 12| +2.50E+02 | +1.6E-04 | -2.7E-02 | 1.6E-02 | 1.0E+00 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 22| +1.50E+02 | +9.7E+01 | -1.7E+01 | 1.0E+00 | 7.2E+01 | 1.9E+00 | trr |0\n", - "2023-02-17 16:05:17 fides(INFO) 43| +1.48E+02 | -1.5E-02 | +8.0E-01 | 6.2E-02 | 4.6E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 87| +1.55E+02 | -1.2E-02 | +1.1E+00 | 1.4E-02 | 6.1E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 13| +2.50E+02 | -1.8E-03 | +3.1E-01 | 3.9E-03 | 1.0E+00 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 72| +1.49E+02 | -1.3E+00 | +4.8E-01 | 1.2E-02 | 8.4E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 61| +1.48E+02 | -5.4E-03 | +8.5E-01 | 6.4E-03 | 2.1E+00 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 44| +1.48E+02 | +4.5E-02 | -1.5E+00 | 1.2E-01 | 1.9E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 23| +1.50E+02 | +2.6E+00 | -9.5E-01 | 2.2E-01 | 7.2E+01 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:17.680 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 148.597 and h = 1.48907e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:17.680 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 148.597: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:17 fides(INFO) 121| +1.51E+02 | +0.0E+00 | +0.0E+00 | 1.1E-02 | 6.2E-01 | 2.4E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 14| +2.50E+02 | -2.2E-04 | +8.8E-02 | 3.9E-03 | 6.6E-01 | 7.3E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 73| +1.49E+02 | -5.1E-01 | +9.6E-01 | 1.2E-02 | 2.2E+01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 62| +1.48E+02 | -5.5E-03 | +7.8E-01 | 1.3E-02 | 2.6E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 45| +1.48E+02 | -6.4E-03 | +5.3E-01 | 3.1E-02 | 1.9E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 15| +2.50E+02 | -9.5E-04 | +7.9E-01 | 9.8E-04 | 5.9E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 24| +1.49E+02 | -8.5E-01 | +7.4E-01 | 5.6E-02 | 7.2E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 74| +1.48E+02 | -3.2E-01 | +5.1E-01 | 2.4E-02 | 1.9E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 16| +1.80E+02 | -9.8E+00 | +1.0E+00 | 1.2E-01 | 4.8E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 16| +2.50E+02 | +8.8E-07 | -5.1E-03 | 2.0E-03 | 1.8E-01 | 4.9E-03 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 63| +1.48E+02 | -2.1E-03 | +5.0E-01 | 2.6E-02 | 8.1E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 46| +1.48E+02 | -5.4E-03 | +6.0E-01 | 3.1E-02 | 3.7E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 25| +1.48E+02 | -8.8E-01 | +6.9E-01 | 5.6E-02 | 2.2E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 17| +2.50E+02 | -8.9E-05 | +5.8E-01 | 4.9E-04 | 1.8E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 75| +1.48E+02 | -3.1E-01 | +5.0E-01 | 2.4E-02 | 4.2E+01 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 18| +2.50E+02 | -7.5E-09 | +1.1E-03 | 4.9E-04 | 3.3E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 64| +1.48E+02 | -1.5E-03 | +7.8E-01 | 2.6E-02 | 9.6E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 47| +1.48E+02 | -2.6E-03 | +4.2E-01 | 3.1E-02 | 9.2E-01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 26| +1.48E+02 | -9.9E-02 | +9.3E-01 | 5.6E-02 | 2.5E+01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 76| +1.48E+02 | -1.6E-01 | +2.8E-01 | 2.4E-02 | 3.0E+01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 122| +1.51E+02 | -3.0E-03 | +1.0E+00 | 2.8E-03 | 6.2E-01 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 19| +2.50E+02 | -2.1E-06 | +3.4E-01 | 1.2E-04 | 3.3E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:17 fides(WARNING) Stopping as function difference 2.08E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:17 fides(INFO) 65| +1.48E+02 | -1.5E-03 | +6.6E-01 | 5.1E-02 | 5.0E-01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 77| +1.48E+02 | +1.9E-01 | -3.5E-01 | 2.4E-02 | 1.5E+01 | 5.6E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 48| +1.48E+02 | -1.7E-03 | +2.6E-01 | 3.1E-02 | 1.8E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 27| +1.48E+02 | -6.9E-02 | +8.6E-01 | 1.1E-01 | 1.3E+01 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 49| +1.48E+02 | -3.8E-04 | +5.7E-02 | 3.1E-02 | 4.7E-01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 66| +1.48E+02 | +1.0E-03 | -2.9E-01 | 5.1E-02 | 6.0E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 28| +1.48E+02 | +5.3E-02 | -7.0E-01 | 2.2E-01 | 1.2E+01 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 78| +1.48E+02 | -1.1E-01 | +7.5E-01 | 6.1E-03 | 1.5E+01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 88| +1.55E+02 | -1.1E-02 | +8.7E-01 | 2.8E-02 | 1.3E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 50| +1.48E+02 | -1.8E-03 | +7.2E-01 | 7.8E-03 | 1.6E+00 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 67| +1.48E+02 | -7.1E-04 | +6.3E-01 | 1.3E-02 | 6.0E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 79| +1.48E+02 | -8.6E-02 | +7.4E-01 | 6.1E-03 | 1.9E+01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 123| +1.51E+02 | -5.0E-03 | +1.0E+00 | 5.7E-03 | 7.4E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 17| +1.67E+02 | -1.3E+01 | +1.1E+00 | 2.5E-01 | 7.2E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 29| +1.48E+02 | -2.5E-02 | +7.2E-01 | 5.6E-02 | 1.2E+01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 0| +3.21E+02 | NaN | NaN | 1.0E+00 | 6.2E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:17 fides(INFO) 51| +1.48E+02 | -5.0E-04 | +8.4E-01 | 7.8E-03 | 8.3E-01 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 80| +1.48E+02 | -6.5E-02 | +7.7E-01 | 6.1E-03 | 1.1E+01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:17 fides(INFO) 30| +1.48E+02 | -1.4E-02 | +7.1E-01 | 5.6E-02 | 6.4E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 68| +1.48E+02 | -4.2E-04 | +9.6E-01 | 1.3E-02 | 9.6E-01 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 1| +3.21E+02 | +4.3E+02 | -5.6E+00 | 1.0E+00 | 6.2E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:17 fides(INFO) 52| +1.48E+02 | -4.8E-04 | +9.1E-01 | 1.6E-02 | 3.7E-01 | 6.7E-03 | 2d |1\n", - "2023-02-17 16:05:17 fides(INFO) 81| +1.48E+02 | -5.7E-02 | +7.3E-01 | 1.2E-02 | 1.1E+01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 69| +1.48E+02 | -5.7E-04 | +9.7E-01 | 2.6E-02 | 2.2E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 31| +1.48E+02 | -9.6E-03 | +6.6E-01 | 5.6E-02 | 7.1E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 82| +1.48E+02 | -5.1E-02 | +9.4E-01 | 1.2E-02 | 8.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 53| +1.48E+02 | -6.3E-04 | +8.3E-01 | 3.1E-02 | 4.0E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 2| +2.80E+02 | -4.0E+01 | +9.6E-01 | 2.5E-01 | 6.2E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 70| +1.48E+02 | -6.9E-04 | +8.2E-01 | 5.1E-02 | 4.2E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 32| +1.48E+02 | -5.2E-03 | +5.1E-01 | 5.6E-02 | 3.7E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 83| +1.48E+02 | +2.1E-03 | -4.7E-02 | 2.4E-02 | 5.7E+00 | 4.9E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 124| +1.51E+02 | -9.3E-03 | +1.0E+00 | 1.1E-02 | 6.5E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 71| +1.48E+02 | +4.6E-03 | -3.1E+00 | 1.0E-01 | 2.6E-01 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 3| +2.54E+02 | -2.6E+01 | +2.9E-01 | 5.0E-01 | 5.4E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 54| +1.48E+02 | +8.5E-04 | -6.5E-01 | 6.2E-02 | 2.8E-01 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 33| +1.48E+02 | -3.3E-03 | +3.4E-01 | 5.6E-02 | 4.6E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 84| +1.48E+02 | -1.1E-02 | +4.4E-01 | 6.1E-03 | 5.7E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 72| +1.48E+02 | -6.8E-05 | +1.2E-01 | 2.6E-02 | 2.6E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 55| +1.48E+02 | -2.2E-04 | +4.7E-01 | 1.6E-02 | 2.8E-01 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 34| +1.48E+02 | -8.4E-04 | +8.3E-02 | 5.6E-02 | 2.3E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 4| +2.28E+02 | -2.7E+01 | +3.4E-01 | 5.0E-01 | 1.6E+02 | 1.3E+00 | trr |1\n", - "2023-02-17 16:05:18 fides(INFO) 85| +1.48E+02 | -1.9E-02 | +7.3E-01 | 6.1E-03 | 1.0E+01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 73| +1.48E+02 | -2.6E-04 | +7.4E-01 | 6.4E-03 | 6.1E-01 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 89| +1.55E+02 | -3.5E-03 | +3.7E-01 | 5.6E-02 | 1.2E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 35| +1.48E+02 | -3.9E-03 | +7.8E-01 | 1.4E-02 | 3.9E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 56| +1.48E+02 | -2.6E-04 | +5.1E-01 | 1.6E-02 | 7.1E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 5| +2.10E+02 | -1.8E+01 | +8.4E-01 | 5.0E-01 | 1.2E+02 | 9.0E-01 | trr |1\n", - "2023-02-17 16:05:18 fides(INFO) 125| +1.51E+02 | -1.5E-02 | +9.6E-01 | 2.3E-02 | 5.4E-01 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 36| +1.48E+02 | -8.9E-04 | +5.7E-01 | 2.8E-02 | 1.3E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 86| +1.48E+02 | -6.1E-03 | +8.1E-01 | 6.1E-03 | 3.6E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 74| +1.48E+02 | -6.5E-05 | +8.5E-01 | 6.4E-03 | 1.4E-01 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 18| +1.59E+02 | -7.6E+00 | +9.5E-01 | 5.0E-01 | 6.4E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 57| +1.48E+02 | -9.3E-05 | +2.6E-01 | 1.6E-02 | 2.3E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 6| +2.10E+02 | +3.0E+00 | -1.1E+00 | 1.0E+00 | 1.7E+01 | 2.6E+00 | trr |0\n", - "2023-02-17 16:05:18 fides(INFO) 87| +1.48E+02 | -6.2E-03 | +9.5E-01 | 1.2E-02 | 2.9E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 75| +1.48E+02 | -7.0E-05 | +8.8E-01 | 1.3E-02 | 5.9E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 37| +1.48E+02 | -4.7E-04 | +8.7E-01 | 2.8E-02 | 1.2E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 58| +1.48E+02 | -1.4E-04 | +2.8E-01 | 1.6E-02 | 6.3E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 88| +1.48E+02 | -5.2E-03 | +6.7E-01 | 2.4E-02 | 1.8E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:18.263 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.9411 and h = 2.74506e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:18.263 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.9411: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:18 fides(INFO) 76| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.6E-02 | 1.4E-01 | 6.6E-03 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 7| +2.10E+02 | +3.5E+00 | -8.7E-01 | 2.5E-01 | 1.7E+01 | 6.7E-01 | trr |0\n", - "2023-02-17 16:05:18 fides(INFO) 38| +1.48E+02 | -5.7E-04 | +9.4E-01 | 5.6E-02 | 8.1E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 59| +1.48E+02 | +4.7E-05 | -1.0E-01 | 1.6E-02 | 2.5E-01 | 4.0E-03 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 8| +2.07E+02 | -2.5E+00 | +1.0E+00 | 6.2E-02 | 1.7E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 89| +1.48E+02 | -5.3E-03 | +9.7E-01 | 2.4E-02 | 1.4E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 126| +1.51E+02 | -8.3E-03 | +6.3E-01 | 4.5E-02 | 4.6E-01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 77| +1.48E+02 | -3.5E-05 | +1.1E+00 | 6.4E-03 | 1.4E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 39| +1.48E+02 | -2.7E-04 | +5.7E-01 | 1.1E-01 | 1.1E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 9| +2.06E+02 | -1.6E+00 | +9.2E-01 | 1.2E-01 | 1.2E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 60| +1.48E+02 | -9.4E-05 | +5.6E-01 | 3.9E-03 | 2.5E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 78| +1.48E+02 | -5.3E-05 | +9.6E-01 | 1.3E-02 | 7.7E-02 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 90| +1.48E+02 | -7.1E-03 | +1.0E+00 | 4.9E-02 | 5.7E-01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 40| +1.48E+02 | +7.1E-03 | -3.8E+00 | 1.1E-01 | 3.3E-01 | 4.1E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 61| +1.48E+02 | -6.1E-05 | +9.4E-01 | 3.9E-03 | 6.2E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 10| +2.00E+02 | -5.3E+00 | +1.7E+00 | 2.5E-01 | 1.4E+01 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 91| +1.48E+02 | -1.3E-02 | +1.1E+00 | 9.8E-02 | 9.1E-01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 79| +1.48E+02 | -8.0E-05 | +9.8E-01 | 2.6E-02 | 8.9E-02 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 41| +1.48E+02 | +4.1E-05 | -6.7E-02 | 2.8E-02 | 3.3E-01 | 1.0E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 62| +1.48E+02 | -4.5E-05 | +1.0E+00 | 7.8E-03 | 7.3E-02 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 11| +2.00E+02 | +1.7E+01 | -1.7E+00 | 5.0E-01 | 2.0E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 80| +1.48E+02 | -8.7E-05 | +1.0E+00 | 5.1E-02 | 4.7E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 19| +1.58E+02 | -1.4E+00 | +1.0E+00 | 1.0E+00 | 7.7E+01 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 92| +1.48E+02 | -9.6E-03 | +3.9E-01 | 2.0E-01 | 2.3E+00 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 42| +1.48E+02 | -1.2E-04 | +6.2E-01 | 7.0E-03 | 3.3E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 90| +1.55E+02 | +1.5E-03 | -2.4E-01 | 5.6E-02 | 2.7E+00 | 2.9E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 127| +1.51E+02 | -3.0E-03 | +9.3E-01 | 4.5E-02 | 4.5E-01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 12| +1.93E+02 | -6.9E+00 | +1.3E+00 | 1.2E-01 | 2.0E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 63| +1.48E+02 | -5.4E-05 | +7.4E-01 | 1.6E-02 | 2.2E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 81| +1.48E+02 | +2.1E-05 | -8.9E-01 | 1.0E-01 | 6.7E-02 | 7.8E-03 | trr |0\n", - "2023-02-17 16:05:18 fides(INFO) 43| +1.48E+02 | -7.2E-05 | +9.8E-01 | 7.0E-03 | 7.4E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 93| +1.48E+02 | -9.5E-03 | +7.6E-01 | 2.0E-01 | 5.4E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 64| +1.48E+02 | -1.5E-05 | +2.0E-01 | 1.6E-02 | 8.4E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 13| +1.73E+02 | -2.0E+01 | +1.1E+00 | 2.5E-01 | 3.5E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 82| +1.48E+02 | -4.9E-06 | +4.6E-01 | 1.8E-02 | 6.7E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 65| +1.48E+02 | -4.3E-05 | +6.9E-01 | 3.9E-03 | 2.9E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 94| +1.48E+02 | -5.2E-03 | +1.5E+00 | 2.0E-01 | 1.6E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 44| +1.48E+02 | -2.0E-05 | +8.0E-01 | 1.4E-02 | 2.1E-01 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 14| +1.68E+02 | -5.9E+00 | +3.9E-01 | 5.0E-01 | 7.4E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 83| +1.48E+02 | +1.2E-05 | -7.1E-01 | 1.8E-02 | 2.7E-02 | 3.9E-03 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 95| +1.48E+02 | -1.4E-03 | +5.1E-01 | 2.0E-01 | 7.2E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 45| +1.48E+02 | -2.4E-05 | +9.3E-01 | 2.8E-02 | 2.8E-01 | 5.8E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 66| +1.48E+02 | -4.5E-06 | +4.3E-01 | 3.9E-03 | 5.4E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 128| +1.51E+02 | -3.9E-04 | +1.4E+00 | 4.5E-02 | 4.1E-01 | 7.7E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 84| +1.48E+02 | -3.8E-06 | +7.6E-01 | 4.5E-03 | 2.7E-02 | 9.9E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 15| +1.60E+02 | -7.7E+00 | +8.1E-01 | 5.0E-01 | 1.3E+02 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 67| +1.48E+02 | -1.0E-05 | +1.6E+00 | 3.9E-03 | 2.2E-02 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 46| +1.48E+02 | -2.3E-05 | +7.7E-01 | 5.6E-02 | 1.6E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 96| +1.48E+02 | -8.4E-04 | +9.8E-01 | 2.0E-01 | 4.1E-01 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:18.650 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 183.579 and h = 4.55724e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:18.650 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 183.579: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:18 fides(INFO) 16| +1.60E+02 | +0.0E+00 | +0.0E+00 | 1.0E+00 | 5.0E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 85| +1.48E+02 | -6.5E-07 | +2.4E-01 | 9.0E-03 | 3.3E-02 | 8.6E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 47| +1.48E+02 | +3.8E-04 | -4.8E+00 | 1.1E-01 | 1.4E-01 | 2.1E-02 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 97| +1.48E+02 | -2.0E-04 | +1.7E+00 | 2.0E-01 | 2.4E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 68| +1.48E+02 | -1.4E-05 | +1.3E+00 | 7.8E-03 | 6.2E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 86| +1.48E+02 | -1.4E-06 | +2.3E+00 | 2.2E-03 | 1.7E-02 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(WARNING) Stopping as function difference 1.44E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:18 fides(INFO) 17| +1.56E+02 | -3.5E+00 | +9.4E-01 | 2.3E-01 | 5.0E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 20| +1.58E+02 | +1.5E+00 | -8.6E+00 | 2.0E+00 | 4.7E+01 | 6.1E+00 | trr |0\n", - "2023-02-17 16:05:18 fides(INFO) 69| +1.48E+02 | -9.0E-06 | +5.7E-01 | 1.6E-02 | 2.4E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 48| +1.48E+02 | -8.7E-07 | +2.8E-02 | 2.8E-02 | 1.4E-01 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 98| +1.48E+02 | -9.9E-04 | +1.5E+00 | 2.0E-01 | 1.8E-01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 129| +1.51E+02 | -1.2E-04 | +9.9E-01 | 4.5E-02 | 9.1E-02 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 18| +1.49E+02 | -6.9E+00 | +1.3E+00 | 4.6E-01 | 5.0E+01 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 70| +1.48E+02 | -1.0E-05 | +9.8E-01 | 1.6E-02 | 4.7E-02 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 49| +1.48E+02 | -1.5E-05 | +5.9E-01 | 7.0E-03 | 1.5E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 99| +1.48E+02 | -1.2E-03 | +1.4E+00 | 2.0E-01 | 2.6E-01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 19| +1.49E+02 | +2.1E+01 | -9.6E+00 | 9.2E-01 | 5.3E+01 | 8.2E-01 | trr |0\n", - "2023-02-17 16:05:18 fides(INFO) 71| +1.48E+02 | +7.4E-05 | -3.6E+00 | 3.1E-02 | 2.6E-02 | 2.3E-03 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 91| +1.55E+02 | -2.5E-03 | +6.8E-01 | 2.8E-03 | 2.7E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 50| +1.48E+02 | -4.1E-06 | +1.4E+00 | 7.0E-03 | 1.1E-01 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 100| +1.48E+02 | -7.8E-04 | +1.4E+00 | 2.0E-01 | 3.0E-01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:18.802 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 68.1343 and h = 1.67073e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:18.803 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 68.1343: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 20| +1.49E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 5.3E+01 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 51| +1.48E+02 | -5.2E-07 | +2.2E-01 | 1.4E-02 | 7.3E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 72| +1.48E+02 | +2.5E-05 | -2.1E+00 | 7.8E-03 | 2.6E-02 | 9.4E-04 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 0| +3.01E+13 | NaN | NaN | 1.0E+00 | 1.4E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 130| +1.51E+02 | -4.7E-06 | +1.1E+00 | 4.5E-02 | 9.5E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 21| +1.49E+02 | -5.5E-01 | +9.1E-01 | 2.9E-02 | 5.3E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 101| +1.48E+02 | -3.8E-04 | +1.4E+00 | 2.0E-01 | 1.5E-01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 52| +1.48E+02 | -1.1E-06 | +9.4E-01 | 3.5E-03 | 7.1E-02 | 4.6E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(WARNING) Stopping as function difference 1.13E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:18 fides(INFO) 73| +1.48E+02 | +1.7E-05 | -1.9E+00 | 2.0E-03 | 2.6E-02 | 7.1E-04 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 1| +1.44E+07 | -3.0E+13 | +8.2E-02 | 1.0E+00 | 1.4E+14 | 3.2E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 2| +2.20E+06 | -1.2E+07 | +8.9E-01 | 2.5E-01 | 6.6E+07 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 22| +1.48E+02 | -5.6E-01 | +9.4E-01 | 5.8E-02 | 1.4E+01 | 9.7E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 74| +1.48E+02 | +1.6E-05 | -2.0E+00 | 4.9E-04 | 2.6E-02 | 6.6E-04 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 3| +3.38E+05 | -1.9E+06 | +8.9E-01 | 5.0E-01 | 1.0E+07 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 102| +1.48E+02 | -1.8E-04 | +1.4E+00 | 2.0E-01 | 2.6E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 4| +5.60E+04 | -2.8E+05 | +8.7E-01 | 1.0E+00 | 1.6E+06 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 21| +1.56E+02 | -1.6E+00 | +9.1E-01 | 5.0E-01 | 4.7E+01 | 1.3E+00 | trr |1\n", - "2023-02-17 16:05:18 fides(INFO) 23| +1.48E+02 | +4.3E-01 | -9.2E-01 | 1.2E-01 | 7.4E+00 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:18 fides(INFO) 5| +8.88E+03 | -4.7E+04 | +9.0E-01 | 1.0E+00 | 2.4E+05 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 75| +1.48E+02 | -2.6E-06 | +4.8E-01 | 1.2E-04 | 2.6E-02 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 103| +1.48E+02 | -1.1E-04 | +1.4E+00 | 2.0E-01 | 3.4E-02 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 6| +1.56E+03 | -7.3E+03 | +9.0E-01 | 1.0E+00 | 3.8E+04 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 7| +4.27E+02 | -1.1E+03 | +9.0E-01 | 1.0E+00 | 5.9E+03 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 24| +1.48E+02 | -9.2E-02 | +6.4E-01 | 2.9E-02 | 7.4E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 76| +1.48E+02 | -2.1E-07 | +2.3E-01 | 1.2E-04 | 8.4E-02 | 3.4E-05 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 131| +1.51E+02 | -6.9E-07 | +1.3E+00 | 4.5E-02 | 2.8E-02 | 1.9E-04 | 2d |1\n", - "2023-02-17 16:05:18 fides(WARNING) Stopping as function difference 6.90E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:18 fides(INFO) 104| +1.48E+02 | -5.9E-05 | +1.5E+00 | 3.9E-01 | 4.0E-02 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) 8| +2.64E+02 | -1.6E+02 | +8.9E-01 | 1.0E+00 | 9.2E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:18 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:18 fides(INFO) 0| +1.63E+13 | NaN | NaN | 1.0E+00 | 7.5E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:19 fides(INFO) 77| +1.48E+02 | -2.0E-06 | +2.0E+01 | 3.1E-05 | 1.5E-02 | 1.3E-05 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 9| +2.33E+02 | -3.1E+01 | +1.2E+00 | 1.0E+00 | 1.2E+02 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 25| +1.48E+02 | -1.3E-01 | +9.4E-01 | 2.9E-02 | 4.3E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 1| +1.16E+07 | -1.6E+13 | +8.4E-02 | 1.0E+00 | 7.5E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 105| +1.48E+02 | -2.8E-05 | +1.3E+00 | 3.9E-01 | 1.3E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 92| +1.55E+02 | -8.4E-04 | +9.3E-01 | 2.8E-03 | 1.3E+00 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 26| +1.48E+02 | -1.5E-01 | +8.7E-01 | 5.8E-02 | 5.6E+00 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 10| +2.33E+02 | +6.2E+01 | -1.3E+01 | 2.0E+00 | 2.2E+01 | 6.0E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 78| +1.48E+02 | +3.4E-07 | -2.4E+00 | 6.1E-05 | 2.4E-02 | 1.6E-05 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 2| +1.77E+06 | -9.8E+06 | +8.9E-01 | 2.5E-01 | 5.3E+07 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 106| +1.48E+02 | -1.5E-05 | +1.3E+00 | 3.9E-01 | 8.3E-03 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 11| +2.33E+02 | +2.3E+00 | -6.2E-01 | 5.0E-01 | 2.2E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 3| +2.75E+05 | -1.5E+06 | +9.0E-01 | 5.0E-01 | 8.2E+06 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 27| +1.48E+02 | -7.8E-02 | +2.0E-01 | 1.2E-01 | 3.8E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 107| +1.48E+02 | -1.3E-05 | +2.0E+00 | 3.9E-01 | 1.5E-02 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 79| +1.48E+02 | +2.0E-06 | -2.1E+01 | 1.5E-05 | 2.4E-02 | 8.3E-06 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 12| +2.32E+02 | -8.6E-01 | +1.8E-01 | 1.2E-01 | 2.2E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 4| +4.30E+04 | -2.3E+05 | +9.0E-01 | 1.0E+00 | 1.3E+06 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:19.102 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 82.3892 and h = 4.26295e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:19.102 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 82.3892: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:19 fides(INFO) 28| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.9E-02 | 1.3E+01 | 5.4E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 13| +2.30E+02 | -1.8E+00 | +9.1E-01 | 3.1E-02 | 3.9E+01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 0| +6.04E+09 | NaN | NaN | 1.0E+00 | 2.2E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:19 fides(INFO) 108| +1.48E+02 | +1.4E-06 | -6.3E-01 | 3.9E-01 | 5.6E-03 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 22| +1.54E+02 | -2.6E+00 | +1.0E+00 | 5.0E-01 | 8.5E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 14| +2.28E+02 | -1.9E+00 | +8.6E-01 | 6.2E-02 | 2.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 5| +6.90E+03 | -3.6E+04 | +9.0E-01 | 1.0E+00 | 2.0E+05 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 1| +2.23E+07 | -6.0E+09 | +1.2E-01 | 1.0E+00 | 2.2E+10 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 80| +1.48E+02 | +1.0E-06 | -1.2E+01 | 3.8E-06 | 2.4E-02 | 6.6E-06 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 29| +1.48E+02 | -7.8E-02 | +9.0E-01 | 7.2E-03 | 1.3E+01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 109| +1.48E+02 | +2.0E-06 | -1.5E+00 | 6.6E-02 | 5.6E-03 | 5.7E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 15| +2.25E+02 | -3.3E+00 | +1.1E+00 | 1.2E-01 | 1.8E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 2| +3.45E+06 | -1.9E+07 | +8.9E-01 | 2.5E-01 | 1.0E+08 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:19.180 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 82.9518 and h = 2.54562e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:19 fides(INFO) 81| +1.48E+02 | -1.2E-07 | +3.7E+00 | 9.5E-07 | 2.4E-02 | 1.7E-06 | 2d |1\n", - "2023-02-17 16:05:19.180 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 82.9518: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:19 fides(WARNING) Stopping as function difference 1.25E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 30| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.4E-02 | 5.8E+00 | 2.6E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 16| +2.22E+02 | -3.0E+00 | +1.0E+00 | 2.5E-01 | 2.0E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 110| +1.48E+02 | +3.0E-06 | -7.9E+00 | 1.7E-02 | 5.6E-03 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 3| +5.50E+05 | -2.9E+06 | +9.0E-01 | 5.0E-01 | 1.6E+07 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 31| +1.48E+02 | -3.2E-02 | +9.3E-01 | 3.6E-03 | 5.8E+00 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 17| +2.22E+02 | +4.3E+00 | -1.1E+00 | 5.0E-01 | 2.1E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 6| +1.26E+03 | -5.6E+03 | +9.0E-01 | 1.0E+00 | 3.1E+04 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 93| +1.55E+02 | -9.2E-04 | +7.3E-01 | 5.6E-03 | 3.2E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 4| +9.46E+04 | -4.6E+05 | +8.9E-01 | 1.0E+00 | 2.4E+06 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 111| +1.48E+02 | +2.3E-06 | -2.3E+01 | 4.1E-03 | 5.6E-03 | 3.5E-03 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 32| +1.48E+02 | -3.9E-02 | +8.8E-01 | 7.2E-03 | 4.9E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 18| +2.21E+02 | -5.4E-01 | +8.7E-02 | 1.2E-01 | 2.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 5| +1.50E+04 | -8.0E+04 | +9.0E-01 | 2.0E+00 | 3.8E+05 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 112| +1.48E+02 | +4.4E-06 | -1.5E+02 | 1.0E-03 | 5.6E-03 | 8.8E-04 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 19| +2.20E+02 | -1.7E+00 | +8.8E-01 | 3.1E-02 | 3.9E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 6| +2.54E+03 | -1.3E+04 | +9.0E-01 | 2.0E+00 | 6.0E+04 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 7| +3.71E+02 | -8.8E+02 | +9.0E-01 | 1.0E+00 | 4.9E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 33| +1.48E+02 | -4.7E-02 | +7.9E-01 | 1.4E-02 | 5.2E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 20| +2.18E+02 | -1.3E+00 | +7.5E-01 | 6.2E-02 | 2.5E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 113| +1.48E+02 | +1.9E-06 | -1.8E+02 | 2.6E-04 | 5.6E-03 | 2.2E-04 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 7| +5.75E+02 | -2.0E+03 | +9.0E-01 | 2.0E+00 | 9.6E+03 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 0| +2.54E+08 | NaN | NaN | 1.0E+00 | 1.2E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:19 fides(INFO) 23| +1.53E+02 | -4.4E-01 | +1.0E+00 | 1.0E+00 | 3.4E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:19.338 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 142.009 and h = 2.78692e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:19 fides(INFO) 8| +2.31E+02 | -1.4E+02 | +9.2E-01 | 1.0E+00 | 7.8E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:19.339 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 142.009: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:19 fides(INFO) 34| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.9E-02 | 2.5E+00 | 4.4E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 21| +2.17E+02 | -1.0E+00 | +4.8E-01 | 1.2E-01 | 1.5E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 114| +1.48E+02 | +2.9E-06 | -4.5E+02 | 6.5E-05 | 5.6E-03 | 5.5E-05 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 1| +4.19E+03 | -2.5E+08 | +1.0E-01 | 1.0E+00 | 1.2E+09 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 8| +2.83E+02 | -2.9E+02 | +8.9E-01 | 2.0E+00 | 1.5E+03 | 4.2E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 22| +2.16E+02 | -1.5E+00 | +5.7E-01 | 1.2E-01 | 3.0E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 9| +2.31E+02 | -7.6E+00 | -1.7E-01 | 2.0E+00 | 1.1E+02 | 4.0E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 35| +1.48E+02 | -1.3E-02 | +8.5E-01 | 7.2E-03 | 2.5E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 115| +1.48E+02 | +3.9E-06 | -7.4E+02 | 1.6E-05 | 5.6E-03 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 9| +2.34E+02 | -5.0E+01 | +1.1E+00 | 4.0E+00 | 2.2E+02 | 3.3E+00 | trr |1\n", - "2023-02-17 16:05:19 fides(INFO) 23| +2.16E+02 | +4.5E-01 | -3.4E-01 | 1.2E-01 | 1.5E+01 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 2| +1.07E+03 | -3.1E+03 | +9.1E-01 | 2.5E-01 | 1.3E+04 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 36| +1.48E+02 | -1.6E-02 | +8.0E-01 | 1.4E-02 | 2.6E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 116| +1.48E+02 | +4.0E-06 | -7.9E+02 | 4.0E-06 | 5.6E-03 | 3.9E-06 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 10| +2.34E+02 | +9.9E+02 | -4.3E+01 | 4.0E+00 | 3.4E+01 | 1.2E+01 | trr |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 10| +2.08E+02 | -2.3E+01 | +9.7E-01 | 5.0E-01 | 1.1E+02 | 9.7E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 24| +2.15E+02 | -6.5E-01 | +8.1E-01 | 3.1E-02 | 1.5E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 3| +4.00E+02 | -6.7E+02 | +9.1E-01 | 5.0E-01 | 2.7E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 37| +1.48E+02 | -2.3E-02 | +8.8E-01 | 2.9E-02 | 1.8E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 25| +2.14E+02 | -8.9E-01 | +9.9E-01 | 6.2E-02 | 1.2E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 117| +1.48E+02 | +4.0E-06 | -8.0E+02 | 1.0E-06 | 5.6E-03 | 1.9E-06 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 11| +1.66E+02 | -4.2E+01 | +9.2E-01 | 9.9E-01 | 4.1E+01 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 11| +2.34E+02 | +6.6E+01 | -5.4E+00 | 1.0E+00 | 3.4E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 4| +2.93E+02 | -1.1E+02 | +8.8E-01 | 1.0E+00 | 4.5E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 12| +2.34E+02 | +2.7E+00 | -3.2E-01 | 2.5E-01 | 3.4E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 26| +2.14E+02 | -7.9E-01 | +7.4E-01 | 1.2E-01 | 1.2E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 38| +1.48E+02 | -4.1E-02 | +7.8E-01 | 5.8E-02 | 2.5E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 118| +1.48E+02 | +1.7E-07 | -6.1E+01 | 2.5E-07 | 5.6E-03 | 6.3E-07 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 94| +1.55E+02 | -5.9E-04 | +9.5E-01 | 5.6E-03 | 9.6E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 5| +2.57E+02 | -3.6E+01 | +8.3E-01 | 2.0E+00 | 6.1E+01 | 3.7E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 12| +1.66E+02 | +4.4E+01 | -2.9E+00 | 2.0E+00 | 1.7E+02 | 1.3E+00 | trr |0\n", - "2023-02-17 16:05:19 fides(INFO) 27| +2.14E+02 | +4.2E-01 | -3.2E-01 | 1.2E-01 | 1.4E+01 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 13| +2.29E+02 | -4.4E+00 | +9.0E-01 | 6.2E-02 | 3.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 24| +1.53E+02 | -8.0E-01 | +5.0E-01 | 2.0E+00 | 3.9E+01 | 8.7E-01 | trr |1\n", - "2023-02-17 16:05:19 fides(INFO) 6| +2.57E+02 | +4.7E+01 | -1.1E+01 | 4.0E+00 | 3.5E+01 | 1.1E+01 | trr |0\n", - "2023-02-17 16:05:19 fides(INFO) 39| +1.48E+02 | +1.5E-01 | -1.8E+00 | 1.2E-01 | 1.7E+00 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 14| +2.26E+02 | -3.0E+00 | +5.2E-01 | 1.2E-01 | 2.6E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 28| +2.13E+02 | -6.3E-01 | +7.9E-01 | 3.1E-02 | 1.4E+01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 119| +1.48E+02 | +6.6E-07 | -8.0E+02 | 6.3E-08 | 5.6E-03 | 1.6E-07 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 7| +2.57E+02 | +1.8E+02 | -1.1E+01 | 9.4E-01 | 3.5E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 13| +1.56E+02 | -1.1E+01 | +9.8E-01 | 1.2E-01 | 1.7E+02 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 25| +1.53E+02 | +7.1E+01 | -7.4E+01 | 2.0E+00 | 1.6E+01 | 5.5E+00 | trr |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 40| +1.48E+02 | -9.7E-03 | +3.9E-01 | 2.9E-02 | 1.7E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 15| +2.22E+02 | -4.1E+00 | +8.2E-01 | 1.2E-01 | 3.8E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 29| +2.12E+02 | -4.7E-01 | +7.7E-01 | 6.2E-02 | 8.9E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 8| +2.57E+02 | +8.1E-01 | -1.4E-01 | 2.3E-01 | 3.5E+01 | 6.2E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 120| +1.48E+02 | +4.5E-06 | -2.1E+04 | 1.6E-08 | 5.6E-03 | 3.9E-08 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 14| +1.52E+02 | -3.8E+00 | +3.0E-01 | 2.4E-01 | 6.1E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 16| +2.19E+02 | -3.8E+00 | +4.6E-01 | 2.5E-01 | 2.4E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 41| +1.48E+02 | +7.5E-03 | -2.4E-01 | 2.9E-02 | 1.5E+00 | 4.2E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 9| +2.53E+02 | -4.7E+00 | +9.2E-01 | 5.9E-02 | 3.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 121| +1.48E+02 | +4.3E-06 | -7.9E+04 | 4.0E-09 | 5.6E-03 | 9.9E-09 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 30| +2.12E+02 | -4.6E-01 | +5.7E-01 | 1.2E-01 | 1.2E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 10| +2.51E+02 | -1.7E+00 | +2.9E-01 | 1.2E-01 | 2.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 17| +2.12E+02 | -7.1E+00 | +7.3E-01 | 2.5E-01 | 5.7E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 26| +1.52E+02 | -5.0E-01 | +8.9E-01 | 5.0E-01 | 1.6E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 42| +1.48E+02 | -5.7E-03 | +4.9E-01 | 7.2E-03 | 1.5E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 31| +2.12E+02 | -2.4E-01 | +2.0E-01 | 1.2E-01 | 1.1E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 122| +1.48E+02 | +1.7E-08 | -1.3E+03 | 9.9E-10 | 5.6E-03 | 2.5E-09 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 15| +1.49E+02 | -3.3E+00 | +9.6E-01 | 2.4E-01 | 1.8E+02 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 18| +2.10E+02 | -1.2E+00 | +6.9E-02 | 2.5E-01 | 2.5E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 11| +2.50E+02 | -7.6E-01 | +3.8E-01 | 1.2E-01 | 2.2E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 27| +1.52E+02 | +4.7E+01 | -1.8E+02 | 1.0E+00 | 2.4E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 32| +2.11E+02 | -6.6E-01 | +7.8E-01 | 3.1E-02 | 1.8E+01 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 12| +2.50E+02 | +2.4E-01 | -3.5E-01 | 1.2E-01 | 1.0E+01 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 123| +1.48E+02 | +5.2E-07 | -1.6E+05 | 2.5E-10 | 5.6E-03 | 6.2E-10 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 16| +1.49E+02 | +8.2E+00 | -5.7E+00 | 4.8E-01 | 3.0E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 43| +1.48E+02 | -4.2E-03 | +8.9E-01 | 7.2E-03 | 4.1E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 19| +2.05E+02 | -5.1E+00 | +8.5E-01 | 6.2E-02 | 6.3E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 13| +2.50E+02 | -3.4E-01 | +5.9E-01 | 2.9E-02 | 1.0E+01 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 33| +2.11E+02 | -4.2E-01 | +8.2E-01 | 6.2E-02 | 7.7E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 124| +1.48E+02 | +4.5E-07 | -5.3E+05 | 6.2E-11 | 5.6E-03 | 1.5E-10 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 17| +1.48E+02 | -5.2E-01 | +5.3E-01 | 1.2E-01 | 3.0E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 28| +1.52E+02 | -1.9E-01 | +1.0E+00 | 2.5E-01 | 2.4E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 20| +2.02E+02 | -3.3E+00 | +7.9E-01 | 1.2E-01 | 3.1E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 44| +1.48E+02 | -3.9E-03 | +9.1E-01 | 1.4E-02 | 4.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 14| +2.50E+02 | -9.7E-04 | +4.9E-02 | 2.9E-02 | 1.8E+00 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 34| +2.10E+02 | -3.5E-01 | +4.0E-01 | 1.2E-01 | 7.9E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 125| +1.48E+02 | +4.6E-06 | -2.2E+07 | 1.5E-11 | 5.6E-03 | 3.9E-11 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 15| +2.50E+02 | -9.4E-04 | +5.5E-02 | 7.3E-03 | 1.7E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 18| +1.48E+02 | -2.8E-01 | +8.6E-01 | 1.2E-01 | 2.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 45| +1.48E+02 | -7.6E-03 | +9.4E-01 | 2.9E-02 | 5.2E-01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 21| +1.94E+02 | -8.3E+00 | +1.3E+00 | 2.5E-01 | 1.8E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 29| +1.52E+02 | +1.9E+00 | -5.6E+00 | 5.0E-01 | 1.5E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 35| +2.09E+02 | -9.0E-01 | +6.7E-01 | 1.2E-01 | 1.5E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 95| +1.55E+02 | -6.7E-04 | +1.3E+00 | 1.1E-02 | 1.6E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 16| +2.50E+02 | -4.3E-03 | +9.0E-01 | 1.8E-03 | 1.6E+00 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 126| +1.48E+02 | +5.1E-06 | -9.7E+07 | 3.9E-12 | 5.6E-03 | 9.6E-12 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 46| +1.48E+02 | -5.5E-03 | +6.3E-01 | 5.8E-02 | 8.4E-01 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 19| +1.48E+02 | +1.9E-01 | -6.3E-01 | 2.4E-01 | 8.0E+00 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 17| +2.50E+02 | -3.0E-03 | +6.2E-01 | 3.7E-03 | 1.0E+00 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 22| +1.94E+02 | +3.1E+01 | -2.3E+00 | 5.0E-01 | 3.7E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 36| +2.09E+02 | -3.3E-01 | +3.2E-01 | 1.2E-01 | 8.2E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 30| +1.52E+02 | -3.1E-01 | +9.7E-01 | 1.2E-01 | 1.5E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 127| +1.48E+02 | -1.7E-08 | +1.3E+06 | 9.7E-13 | 5.6E-03 | 2.4E-12 | 2d |1\n", - "2023-02-17 16:05:19 fides(WARNING) Stopping as function difference 1.67E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:19 fides(INFO) 47| +1.48E+02 | +2.1E-01 | -7.9E+00 | 5.8E-02 | 9.7E-01 | 8.8E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 20| +1.48E+02 | -2.8E-02 | +9.8E-02 | 6.0E-02 | 8.0E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 18| +2.50E+02 | -2.0E-06 | +3.5E-02 | 3.7E-03 | 9.8E-02 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 23| +1.84E+02 | -9.8E+00 | +1.2E+00 | 1.2E-01 | 3.7E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 37| +2.08E+02 | -9.1E-01 | +6.3E-01 | 1.2E-01 | 1.6E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 48| +1.48E+02 | +1.0E-02 | -8.1E-01 | 1.4E-02 | 9.7E-01 | 2.4E-02 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 19| +2.50E+02 | -9.4E-07 | +1.6E-02 | 9.2E-04 | 9.8E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 24| +1.67E+02 | -1.7E+01 | +8.6E-01 | 2.5E-01 | 4.5E+01 | 5.5E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 38| +2.08E+02 | -5.2E-01 | +4.6E-01 | 1.2E-01 | 8.6E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 21| +1.48E+02 | -8.0E-02 | +8.6E-01 | 1.5E-02 | 1.4E+01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 31| +1.52E+02 | -6.6E-02 | +1.2E-01 | 2.5E-01 | 6.1E+00 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 20| +2.50E+02 | -2.8E-05 | +6.5E-01 | 2.3E-04 | 9.7E-02 | 6.0E-04 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 49| +1.48E+02 | -1.7E-03 | +3.6E-01 | 3.6E-03 | 9.7E-01 | 7.1E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 25| +1.67E+02 | +7.7E+01 | -4.4E+00 | 5.0E-01 | 1.3E+02 | 9.0E-01 | 2d |0\n", - "2023-02-17 16:05:19 fides(INFO) 39| +2.06E+02 | -1.4E+00 | +7.7E-01 | 1.2E-01 | 1.6E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 22| +1.48E+02 | -5.0E-02 | +7.1E-01 | 3.0E-02 | 5.9E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 21| +2.50E+02 | -1.4E-07 | +5.9E-01 | 2.3E-04 | 4.1E-03 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:19 fides(WARNING) Stopping as function difference 1.37E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:19 fides(INFO) 32| +1.51E+02 | -1.0E-01 | +9.6E-01 | 6.2E-02 | 2.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 50| +1.48E+02 | -1.3E-03 | +9.2E-01 | 3.6E-03 | 3.0E+00 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 26| +1.55E+02 | -1.1E+01 | +9.2E-01 | 1.2E-01 | 1.3E+02 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 40| +2.03E+02 | -3.1E+00 | +1.2E+00 | 2.5E-01 | 1.0E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 23| +1.48E+02 | -1.2E-02 | +8.1E-01 | 3.0E-02 | 6.3E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 51| +1.48E+02 | -4.9E-04 | +9.2E-01 | 7.2E-03 | 2.8E-01 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 41| +1.81E+02 | -2.3E+01 | +1.5E+00 | 5.0E-01 | 2.1E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 24| +1.48E+02 | -7.0E-03 | +7.9E-01 | 6.0E-02 | 1.6E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 33| +1.51E+02 | -9.7E-02 | +1.0E+00 | 1.2E-01 | 1.1E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 96| +1.55E+02 | -1.3E-03 | +1.1E+00 | 2.2E-02 | 3.0E-01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 27| +1.52E+02 | -3.2E+00 | +4.0E-01 | 2.5E-01 | 6.4E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:19 fides(INFO) 0| +3.19E+13 | NaN | NaN | 1.0E+00 | 1.1E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:19 fides(INFO) 52| +1.48E+02 | -9.3E-04 | +9.5E-01 | 1.4E-02 | 2.6E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 1| +3.54E+10 | -3.2E+13 | +1.1E-01 | 1.0E+00 | 1.1E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 25| +1.48E+02 | -7.8E-03 | +9.6E-01 | 1.2E-01 | 2.7E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:19 fides(INFO) 28| +1.48E+02 | -4.0E+00 | +7.6E-01 | 2.5E-01 | 7.2E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 42| +1.81E+02 | +8.1E+03 | -2.3E+02 | 1.0E+00 | 8.1E+01 | 2.2E+00 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 2| +5.33E+09 | -3.0E+10 | +8.9E-01 | 2.5E-01 | 1.6E+11 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 0| +4.83E+06 | NaN | NaN | 1.0E+00 | 1.7E+07 | NaN | NaN |1\n", - "2023-02-17 16:05:20 fides(INFO) 53| +1.48E+02 | -4.7E-04 | +4.8E-01 | 2.9E-02 | 2.0E-01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 34| +1.51E+02 | -1.9E-01 | +1.1E+00 | 2.5E-01 | 1.1E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 26| +1.48E+02 | -1.0E-02 | +9.4E-01 | 2.4E-01 | 9.7E-01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 3| +8.04E+08 | -4.5E+09 | +8.9E-01 | 5.0E-01 | 2.4E+10 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 29| +1.48E+02 | +1.0E+01 | -8.3E+00 | 5.0E-01 | 4.5E+01 | 6.2E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 43| +1.67E+02 | -1.4E+01 | +5.2E-01 | 2.5E-01 | 8.1E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 4| +1.22E+08 | -6.8E+08 | +8.9E-01 | 5.0E-01 | 3.7E+09 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 54| +1.48E+02 | +1.7E-02 | -4.2E+00 | 2.9E-02 | 3.8E-01 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 27| +1.48E+02 | -5.8E-03 | +5.0E-01 | 4.8E-01 | 1.2E+00 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 1| +1.28E+04 | -4.8E+06 | +1.4E-01 | 1.0E+00 | 1.7E+07 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 5| +1.86E+07 | -1.0E+08 | +8.9E-01 | 5.0E-01 | 5.6E+08 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 30| +1.48E+02 | -1.8E-01 | +2.6E-01 | 1.2E-01 | 4.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 6| +2.87E+06 | -1.6E+07 | +8.9E-01 | 5.0E-01 | 8.6E+07 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 55| +1.48E+02 | +1.3E-03 | -7.8E-01 | 7.2E-03 | 3.8E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 28| +1.48E+02 | +4.0E-01 | -9.0E+00 | 4.8E-01 | 1.4E+00 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 44| +1.57E+02 | -9.8E+00 | +9.7E-01 | 2.5E-01 | 1.6E+02 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 2| +2.26E+03 | -1.1E+04 | +9.0E-01 | 2.5E-01 | 5.8E+04 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 31| +1.48E+02 | -2.7E-01 | +4.4E-01 | 1.2E-01 | 2.3E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 35| +1.51E+02 | -3.5E-01 | +1.4E+00 | 5.0E-01 | 1.1E+01 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 7| +4.44E+05 | -2.4E+06 | +9.0E-01 | 1.0E+00 | 1.3E+07 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 56| +1.48E+02 | -9.3E-05 | +1.2E-01 | 1.8E-03 | 3.8E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 29| +1.48E+02 | +1.1E-02 | -5.7E-01 | 1.2E-01 | 1.4E+00 | 7.4E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 45| +1.57E+02 | +2.2E+01 | -2.4E+00 | 5.0E-01 | 4.8E+01 | 9.0E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 3| +6.12E+02 | -1.6E+03 | +8.5E-01 | 5.0E-01 | 9.1E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 8| +6.95E+04 | -3.7E+05 | +9.0E-01 | 1.0E+00 | 2.0E+06 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 32| +1.48E+02 | +2.6E-01 | -6.8E-01 | 1.2E-01 | 1.5E+01 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 36| +1.51E+02 | +2.5E+00 | -4.0E+00 | 1.0E+00 | 1.0E+01 | 1.4E+00 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 4| +2.63E+02 | -3.5E+02 | +9.2E-01 | 1.0E+00 | 1.5E+03 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 57| +1.48E+02 | -3.2E-04 | +9.4E-01 | 4.5E-04 | 1.7E+00 | 6.7E-04 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 9| +1.11E+04 | -5.8E+04 | +9.0E-01 | 1.0E+00 | 3.2E+05 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 30| +1.48E+02 | -3.2E-03 | +5.3E-01 | 3.0E-02 | 1.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 46| +1.53E+02 | -3.8E+00 | +7.6E-01 | 1.2E-01 | 4.8E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 5| +2.12E+02 | -5.1E+01 | +8.7E-01 | 1.0E+00 | 1.8E+02 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 33| +1.48E+02 | -8.0E-02 | +6.2E-01 | 3.1E-02 | 1.5E+01 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 97| +1.55E+02 | -2.7E-03 | +1.0E+00 | 4.5E-02 | 3.4E-01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 10| +1.93E+03 | -9.2E+03 | +9.0E-01 | 1.0E+00 | 5.0E+04 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 58| +1.48E+02 | -3.7E-05 | +8.7E-01 | 9.0E-04 | 1.5E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 31| +1.48E+02 | -9.8E-04 | +9.6E-01 | 3.0E-02 | 2.6E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 34| +1.48E+02 | -5.9E-02 | +9.7E-01 | 3.1E-02 | 2.0E+01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 37| +1.50E+02 | -4.3E-01 | +1.1E+00 | 2.4E-01 | 1.0E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 11| +4.92E+02 | -1.4E+03 | +9.1E-01 | 1.0E+00 | 7.9E+03 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 47| +1.51E+02 | -2.3E+00 | +6.2E-01 | 2.5E-01 | 6.9E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 32| +1.48E+02 | -4.7E-04 | +9.4E-01 | 6.0E-02 | 3.0E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 6| +2.12E+02 | +2.6E+02 | -1.2E+01 | 2.0E+00 | 6.4E+01 | 5.9E+00 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 59| +1.48E+02 | -3.2E-05 | +7.2E-01 | 1.8E-03 | 2.2E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:20.266 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0723 and h = 2.40154e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.267 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0723: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) 35| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 1.1E+01 | 4.3E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 12| +2.72E+02 | -2.2E+02 | +9.0E-01 | 1.0E+00 | 1.3E+03 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 7| +1.99E+02 | -1.3E+01 | +6.2E-01 | 5.0E-01 | 6.4E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 60| +1.48E+02 | -2.0E-05 | +9.7E-01 | 1.8E-03 | 7.1E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 48| +1.49E+02 | -1.6E+00 | +8.2E-01 | 2.5E-01 | 3.4E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 38| +1.50E+02 | -5.7E-01 | +8.6E-01 | 4.7E-01 | 1.0E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 33| +1.48E+02 | -4.8E-04 | +6.8E-01 | 1.2E-01 | 6.7E-01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 8| +1.74E+02 | -2.6E+01 | +1.1E+00 | 5.0E-01 | 6.9E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 13| +2.42E+02 | -3.0E+01 | +9.2E-01 | 2.0E+00 | 1.8E+02 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 36| +1.48E+02 | -1.9E-02 | +9.6E-01 | 1.6E-02 | 1.1E+01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 61| +1.48E+02 | -3.3E-05 | +9.9E-01 | 3.6E-03 | 8.7E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 9| +1.74E+02 | +1.6E+03 | -7.8E+01 | 1.0E+00 | 5.5E+01 | 1.9E+00 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 39| +1.50E+02 | -1.8E-01 | +6.6E-01 | 9.5E-01 | 1.6E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 49| +1.49E+02 | +2.9E+00 | -2.1E+00 | 5.0E-01 | 2.9E+01 | 7.0E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 14| +2.31E+02 | -1.2E+01 | +1.7E+00 | 2.0E+00 | 1.5E+01 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 34| +1.48E+02 | +1.2E-03 | -1.0E+00 | 1.2E-01 | 3.1E-01 | 4.8E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 37| +1.48E+02 | -3.0E-02 | +9.8E-01 | 3.1E-02 | 1.2E+01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 10| +1.64E+02 | -9.2E+00 | +4.7E-01 | 2.5E-01 | 5.5E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 62| +1.48E+02 | -3.8E-05 | +1.0E+00 | 7.2E-03 | 4.1E-02 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 35| +1.48E+02 | -1.4E-04 | +3.2E-01 | 3.0E-02 | 3.1E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 50| +1.49E+02 | -3.2E-01 | +5.7E-01 | 1.2E-01 | 2.9E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 15| +2.31E+02 | +7.6E+01 | -9.5E+00 | 4.0E+00 | 1.8E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:20.371 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 169.86 and h = 3.38851e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.371 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 169.86: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 40| +1.50E+02 | +0.0E+00 | +0.0E+00 | 9.5E-01 | 4.9E+00 | 1.2E-01 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 11| +1.53E+02 | -1.1E+01 | +8.7E-01 | 2.5E-01 | 1.4E+02 | 5.7E-01 | trr |1\n", - "2023-02-17 16:05:20 fides(INFO) 38| +1.48E+02 | -2.8E-02 | +9.4E-01 | 6.2E-02 | 8.8E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 12| +1.53E+02 | +1.6E+01 | -2.6E+00 | 2.5E-01 | 1.0E+02 | 4.8E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 63| +1.48E+02 | +8.3E-05 | -4.8E+00 | 1.4E-02 | 2.5E-02 | 2.6E-03 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 16| +2.23E+02 | -7.1E+00 | +1.5E+00 | 1.0E+00 | 1.8E+01 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 36| +1.48E+02 | -2.0E-04 | +7.1E-01 | 3.0E-02 | 9.1E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 39| +1.48E+02 | -2.8E-02 | +7.5E-01 | 1.2E-01 | 7.4E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 51| +1.48E+02 | -4.1E-01 | +8.9E-01 | 1.2E-01 | 2.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 41| +1.50E+02 | -1.4E-02 | +8.3E-01 | 5.6E-02 | 4.9E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 98| +1.55E+02 | -6.4E-03 | +1.1E+00 | 8.9E-02 | 3.8E-01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 13| +1.49E+02 | -4.1E+00 | +8.9E-01 | 6.2E-02 | 1.0E+02 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 64| +1.48E+02 | +3.4E-06 | -4.5E-01 | 2.1E-03 | 2.5E-02 | 6.6E-04 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 37| +1.48E+02 | -7.0E-05 | +8.9E-01 | 3.0E-02 | 1.5E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 42| +1.50E+02 | -4.2E-03 | +8.2E-01 | 1.1E-01 | 2.0E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 40| +1.48E+02 | +5.1E-01 | -5.3E+00 | 2.5E-01 | 4.6E+00 | 2.8E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 17| +2.23E+02 | +7.1E+02 | -4.1E+01 | 2.0E+00 | 4.3E+01 | 4.5E+00 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 14| +1.48E+02 | -1.4E+00 | +4.5E-01 | 1.2E-01 | 3.6E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 52| +1.48E+02 | -4.3E-01 | +9.3E-01 | 2.5E-01 | 2.0E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 65| +1.48E+02 | +2.7E-06 | -1.2E+00 | 5.2E-04 | 2.5E-02 | 1.7E-04 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 15| +1.47E+02 | -7.8E-01 | +9.0E-01 | 1.2E-01 | 6.5E+01 | 8.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 38| +1.48E+02 | -1.0E-04 | +8.9E-01 | 6.0E-02 | 2.1E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 41| +1.48E+02 | +3.7E-03 | -1.2E-01 | 6.2E-02 | 4.6E+00 | 6.9E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 18| +2.04E+02 | -2.0E+01 | +9.7E-01 | 5.0E-01 | 4.3E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 53| +1.48E+02 | +3.2E-01 | -6.9E-01 | 5.0E-01 | 1.5E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 43| +1.50E+02 | -1.3E-03 | +9.6E-01 | 2.3E-01 | 9.1E-01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 16| +1.46E+02 | -4.7E-01 | +7.8E-01 | 2.5E-01 | 1.7E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 66| +1.48E+02 | +2.9E-06 | -3.5E+00 | 1.3E-04 | 2.5E-02 | 5.5E-05 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 54| +1.48E+02 | -1.1E-01 | +6.7E-01 | 1.2E-01 | 1.5E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 42| +1.48E+02 | -7.8E-03 | +7.4E-01 | 1.6E-02 | 4.6E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 19| +2.04E+02 | -9.9E+00 | -1.7E+00 | 1.0E+00 | 6.2E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 39| +1.48E+02 | -2.5E-05 | +1.5E-01 | 1.2E-01 | 1.1E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 44| +1.50E+02 | -1.2E-04 | +6.0E-01 | 4.5E-01 | 6.7E-01 | 3.3E-02 | trr |1\n", - "2023-02-17 16:05:20 fides(INFO) 17| +1.46E+02 | +7.2E+00 | -7.9E+00 | 5.0E-01 | 4.2E+01 | 4.1E-01 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 67| +1.48E+02 | +1.3E-06 | -3.1E+00 | 3.2E-05 | 2.5E-02 | 3.0E-05 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 43| +1.48E+02 | -3.4E-03 | +8.9E-01 | 1.6E-02 | 3.5E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 40| +1.48E+02 | -4.7E-05 | +1.3E-01 | 3.0E-02 | 2.5E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 55| +1.48E+02 | -1.0E-01 | +4.5E-01 | 1.2E-01 | 1.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 20| +1.85E+02 | -1.8E+01 | +9.9E-01 | 2.5E-01 | 6.2E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 18| +1.46E+02 | -3.0E-01 | +5.9E-01 | 5.2E-02 | 4.2E+01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 99| +1.55E+02 | -1.5E-02 | +1.1E+00 | 1.8E-01 | 1.2E+00 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 45| +1.50E+02 | +7.4E-05 | -4.2E-01 | 4.5E-01 | 1.1E-01 | 9.1E-03 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 19| +1.46E+02 | +3.2E-01 | -1.4E+00 | 5.2E-02 | 1.0E+01 | 9.8E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 68| +1.48E+02 | +4.7E-06 | -1.6E+01 | 8.1E-06 | 2.5E-02 | 2.1E-05 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 44| +1.48E+02 | -3.0E-03 | +9.2E-01 | 3.1E-02 | 3.2E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 56| +1.48E+02 | +1.0E-03 | -1.4E-02 | 1.2E-01 | 2.1E+01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 41| +1.48E+02 | -5.8E-05 | +7.2E-01 | 7.5E-03 | 2.3E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 21| +1.74E+02 | -1.2E+01 | +1.5E-01 | 5.0E-01 | 5.0E+01 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 20| +1.46E+02 | -4.0E-02 | +5.6E-01 | 1.3E-02 | 1.0E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 69| +1.48E+02 | +5.0E-06 | -4.4E+01 | 2.0E-06 | 2.5E-02 | 5.4E-06 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 46| +1.50E+02 | -5.6E-05 | +7.1E-01 | 1.1E-02 | 1.1E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 42| +1.48E+02 | -1.7E-05 | +7.9E-01 | 7.5E-03 | 2.3E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 57| +1.48E+02 | -9.3E-02 | +8.0E-01 | 3.1E-02 | 2.1E+01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 45| +1.48E+02 | -3.3E-03 | +8.3E-01 | 6.2E-02 | 2.5E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 22| +1.51E+02 | -2.2E+01 | +9.1E-01 | 1.2E-01 | 2.9E+02 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 21| +1.46E+02 | -5.9E-02 | +9.1E-01 | 1.3E-02 | 7.7E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 43| +1.48E+02 | -6.8E-06 | +3.7E-01 | 1.5E-02 | 6.3E-02 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 47| +1.50E+02 | -1.7E-05 | +1.1E+00 | 1.1E-02 | 2.5E-01 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 70| +1.48E+02 | +5.2E-06 | -1.7E+02 | 5.1E-07 | 2.5E-02 | 1.3E-06 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 46| +1.48E+02 | +1.7E-02 | -2.3E+00 | 1.2E-01 | 2.0E+00 | 8.2E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 23| +1.49E+02 | -2.8E+00 | +8.9E-01 | 2.5E-01 | 7.8E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 58| +1.48E+02 | -4.0E-02 | +7.7E-01 | 6.2E-02 | 3.8E+00 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 100| +1.55E+02 | +9.5E-01 | -3.5E+01 | 3.6E-01 | 7.2E+00 | 9.0E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 22| +1.46E+02 | -7.1E-02 | +9.0E-01 | 2.6E-02 | 1.3E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 23| +1.46E+02 | +4.0E-02 | -3.5E-01 | 5.2E-02 | 4.5E+00 | 8.6E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 71| +1.48E+02 | +6.4E-06 | -8.1E+02 | 1.3E-07 | 2.5E-02 | 3.4E-07 | 2d |0\n", - "2023-02-17 16:05:20.687 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 144.786 and h = 1.83601e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.687 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 144.786: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) 44| +1.48E+02 | -9.4E-06 | +5.6E-01 | 1.5E-02 | 2.3E-01 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 24| +1.49E+02 | +2.3E+00 | -1.8E+00 | 5.0E-01 | 4.3E+01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 48| +1.50E+02 | -7.6E-06 | +8.9E-01 | 2.2E-02 | 7.1E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 59| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 7.8E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 47| +1.48E+02 | -1.0E-03 | +3.8E-01 | 3.1E-02 | 2.0E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 24| +1.46E+02 | -3.0E-02 | +7.4E-01 | 1.3E-02 | 4.5E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 45| +1.48E+02 | -5.4E-06 | +1.5E+00 | 1.5E-02 | 3.2E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 25| +1.48E+02 | -5.5E-01 | +7.1E-01 | 1.2E-01 | 4.3E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 49| +1.50E+02 | -1.4E-06 | +5.6E-01 | 4.3E-02 | 7.3E-02 | 6.6E-04 | trr |1\n", - "2023-02-17 16:05:20 fides(WARNING) Stopping as function difference 1.40E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 60| +1.48E+02 | -1.9E-02 | +9.7E-01 | 3.1E-02 | 7.8E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 25| +1.46E+02 | -3.0E-02 | +8.3E-01 | 1.3E-02 | 8.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 48| +1.48E+02 | -2.4E-04 | +8.1E-02 | 3.1E-02 | 1.5E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 72| +1.48E+02 | +5.9E-06 | -3.0E+03 | 3.2E-08 | 2.5E-02 | 8.4E-08 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 26| +1.46E+02 | -2.7E-02 | +5.9E-01 | 2.6E-02 | 3.9E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 46| +1.48E+02 | -4.8E-06 | +8.4E-01 | 3.0E-02 | 3.2E-02 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 61| +1.48E+02 | -2.6E-02 | +7.4E-01 | 6.2E-02 | 2.8E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 26| +1.48E+02 | -1.0E-01 | +3.2E-01 | 1.2E-01 | 2.6E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 49| +1.48E+02 | -1.2E-03 | +7.9E-01 | 7.8E-03 | 1.9E+00 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 27| +1.46E+02 | -6.5E-03 | +5.1E-02 | 2.6E-02 | 3.4E+00 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 47| +1.48E+02 | -1.0E-05 | +1.3E+00 | 6.0E-02 | 2.2E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 27| +1.48E+02 | -2.0E-01 | +3.4E-01 | 1.2E-01 | 2.1E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 101| +1.55E+02 | -1.2E-02 | +8.2E-01 | 8.9E-02 | 7.2E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 50| +1.48E+02 | -4.1E-04 | +6.0E-01 | 1.6E-02 | 8.3E-01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 73| +1.48E+02 | +2.7E-06 | -5.5E+03 | 7.9E-09 | 2.5E-02 | 2.1E-08 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 62| +1.48E+02 | -2.6E-02 | +7.2E-01 | 6.2E-02 | 8.3E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 28| +1.46E+02 | -3.5E-02 | +8.3E-01 | 6.5E-03 | 9.0E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:20.822 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.271 and h = 1.32109e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.822 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.271: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) 28| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 2.6E+01 | 3.4E-01 | 2d |0\n", - "2023-02-17 16:05:20.824 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0627 and h = 1.71793e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.824 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0627: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) 48| +1.48E+02 | +2.9E-05 | -2.9E+00 | 1.2E-01 | 2.1E-02 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 74| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.0E-09 | 2.5E-02 | 5.2E-09 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 29| +1.46E+02 | -1.6E-02 | +4.7E-01 | 1.3E-02 | 2.2E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 51| +1.48E+02 | -1.9E-04 | +9.1E-01 | 1.6E-02 | 7.3E-01 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 63| +1.48E+02 | -1.5E-02 | +6.0E-01 | 6.2E-02 | 1.7E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 30| +1.46E+02 | -8.6E-03 | +6.4E-01 | 1.3E-02 | 2.5E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 0| +1.14E+14 | NaN | NaN | 1.0E+00 | 5.2E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:20 fides(INFO) 49| +1.48E+02 | +1.5E-06 | -4.3E-01 | 3.0E-02 | 2.1E-02 | 5.0E-03 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 29| +1.48E+02 | -1.8E-01 | +7.1E-01 | 3.1E-02 | 2.6E+01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 52| +1.48E+02 | -2.4E-04 | +9.8E-01 | 3.1E-02 | 5.8E-01 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 75| +1.48E+02 | +6.1E-06 | -2.0E+05 | 5.0E-10 | 2.5E-02 | 1.3E-09 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 31| +1.46E+02 | -2.3E-03 | +1.4E-01 | 1.3E-02 | 2.2E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 64| +1.48E+02 | -2.0E-02 | +6.5E-01 | 6.2E-02 | 7.4E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 30| +1.48E+02 | -1.1E-02 | +2.7E-01 | 3.1E-02 | 3.2E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:20 fides(INFO) 50| +1.48E+02 | +6.8E-07 | -6.8E-01 | 7.5E-03 | 2.1E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 76| +1.48E+02 | +3.5E-06 | -4.5E+05 | 1.2E-10 | 2.5E-02 | 3.3E-10 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 1| +6.16E+07 | -1.1E+14 | +8.2E-02 | 1.0E+00 | 5.2E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 53| +1.48E+02 | -2.0E-04 | +8.3E-01 | 6.2E-02 | 5.2E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 32| +1.46E+02 | -4.8E-03 | +7.4E-01 | 3.2E-03 | 1.8E+00 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:20.925 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 86.193 and h = 1.72762e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:20.925 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 86.193: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:20 fides(INFO) 65| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 1.4E+00 | 5.2E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 51| +1.48E+02 | +2.2E-06 | -7.3E+00 | 1.9E-03 | 2.1E-02 | 3.2E-04 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 31| +1.48E+02 | -1.7E-02 | +7.5E-01 | 3.1E-02 | 9.8E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 77| +1.48E+02 | +2.6E-06 | -1.3E+06 | 3.1E-11 | 2.5E-02 | 8.2E-11 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 33| +1.46E+02 | -3.1E-03 | +8.2E-01 | 3.2E-03 | 2.9E+00 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 54| +1.48E+02 | +4.9E-03 | -1.1E+01 | 1.2E-01 | 2.3E-01 | 4.1E-02 | trr |0\n", - "2023-02-17 16:05:20 fides(INFO) 2| +9.41E+06 | -5.2E+07 | +8.9E-01 | 2.5E-01 | 2.8E+08 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 66| +1.48E+02 | -4.0E-03 | +6.3E-01 | 1.6E-02 | 1.4E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 32| +1.48E+02 | -4.0E-03 | +6.8E-01 | 3.1E-02 | 1.9E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 34| +1.46E+02 | -4.0E-03 | +9.9E-01 | 6.5E-03 | 1.5E+00 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 52| +1.48E+02 | +4.0E-06 | -3.4E+01 | 4.7E-04 | 2.1E-02 | 7.9E-05 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 78| +1.48E+02 | +4.4E-08 | -9.1E+04 | 7.7E-12 | 2.5E-02 | 2.0E-11 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 102| +1.55E+02 | +3.2E-01 | -5.2E+00 | 1.8E-01 | 5.9E+00 | 3.7E-01 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 67| +1.48E+02 | -5.2E-03 | +9.5E-01 | 1.6E-02 | 4.9E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 55| +1.48E+02 | +1.4E-04 | -6.3E-01 | 3.0E-02 | 2.3E-01 | 1.0E-02 | 2d |0\n", - "2023-02-17 16:05:20 fides(INFO) 35| +1.46E+02 | -5.8E-03 | +8.9E-01 | 1.3E-02 | 1.6E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:20 fides(INFO) 3| +1.45E+06 | -8.0E+06 | +8.9E-01 | 5.0E-01 | 4.3E+07 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 53| +1.48E+02 | +4.1E-07 | -5.7E+00 | 1.2E-04 | 2.1E-02 | 2.1E-05 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 33| +1.48E+02 | -1.0E-03 | +6.5E-01 | 3.1E-02 | 2.0E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 36| +1.46E+02 | +4.0E-02 | -2.7E+00 | 2.6E-02 | 1.8E+00 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 56| +1.48E+02 | -4.8E-05 | +5.9E-01 | 7.6E-03 | 2.3E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 68| +1.48E+02 | -5.3E-03 | +9.9E-01 | 3.1E-02 | 7.2E-01 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 79| +1.48E+02 | +5.2E-06 | -4.3E+07 | 1.9E-12 | 2.5E-02 | 5.1E-12 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 54| +1.48E+02 | +1.5E-06 | -2.5E+01 | 2.9E-05 | 2.1E-02 | 7.5E-06 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 57| +1.48E+02 | -2.3E-05 | +9.9E-01 | 7.6E-03 | 3.4E-01 | 9.9E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 37| +1.46E+02 | -1.2E-03 | +2.5E-01 | 6.5E-03 | 1.8E+00 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 34| +1.48E+02 | -9.9E-06 | +6.5E-03 | 3.1E-02 | 4.8E-01 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 4| +2.35E+05 | -1.2E+06 | +8.8E-01 | 1.0E+00 | 6.7E+06 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 69| +1.48E+02 | -8.6E-03 | +9.2E-01 | 6.2E-02 | 1.8E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 80| +1.48E+02 | +8.5E-07 | -2.8E+07 | 4.8E-13 | 2.5E-02 | 1.3E-12 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 38| +1.46E+02 | -3.3E-03 | +4.5E-01 | 6.5E-03 | 1.1E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 55| +1.48E+02 | +1.8E-06 | -3.0E+01 | 7.3E-06 | 2.1E-02 | 5.7E-06 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 35| +1.48E+02 | -6.9E-04 | +7.4E-01 | 7.8E-03 | 1.4E+00 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 58| +1.48E+02 | -1.9E-05 | +1.1E+00 | 1.5E-02 | 1.3E-01 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 39| +1.46E+02 | -3.8E-04 | +7.1E-02 | 6.5E-03 | 2.5E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 70| +1.48E+02 | -1.3E-02 | +8.6E-01 | 1.2E-01 | 8.4E-01 | 9.7E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 81| +1.48E+02 | +7.9E-07 | -1.1E+08 | 1.2E-13 | 2.5E-02 | 3.2E-13 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 103| +1.55E+02 | +3.8E-02 | -1.3E+00 | 4.5E-02 | 5.9E+00 | 9.2E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 56| +1.48E+02 | -5.9E-07 | +1.0E+01 | 1.8E-06 | 2.1E-02 | 4.8E-06 | 2d |1\n", - "2023-02-17 16:05:21 fides(WARNING) Stopping as function difference 5.86E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 40| +1.46E+02 | -1.7E-03 | +8.7E-01 | 1.6E-03 | 8.0E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 36| +1.48E+02 | -1.1E-04 | +7.6E-01 | 7.8E-03 | 3.1E-01 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 59| +1.48E+02 | -1.1E-05 | +5.4E-01 | 3.0E-02 | 1.8E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 5| +2.86E+04 | -2.1E+05 | +8.8E-01 | 2.0E+00 | 1.0E+06 | 3.7E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 71| +1.48E+02 | +1.8E-02 | -5.8E-01 | 2.5E-01 | 3.0E+00 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 82| +1.48E+02 | +3.6E-06 | -1.9E+09 | 3.0E-14 | 2.5E-02 | 8.0E-14 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 41| +1.46E+02 | -1.3E-03 | +6.2E-01 | 3.2E-03 | 7.2E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 37| +1.48E+02 | -8.5E-05 | +8.3E-01 | 1.6E-02 | 2.0E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 60| +1.48E+02 | +1.0E-04 | -1.9E+00 | 3.0E-02 | 8.2E-02 | 4.3E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 72| +1.48E+02 | -6.5E-03 | +6.5E-01 | 6.2E-02 | 3.0E+00 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 42| +1.46E+02 | -7.8E-04 | +9.8E-01 | 3.2E-03 | 9.5E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 83| +1.48E+02 | +4.0E-06 | -8.4E+09 | 7.6E-15 | 2.5E-02 | 2.0E-14 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 38| +1.48E+02 | -9.7E-05 | +8.8E-01 | 3.1E-02 | 3.3E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 61| +1.48E+02 | +4.5E-07 | -2.4E-02 | 7.6E-03 | 8.2E-02 | 1.1E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 43| +1.46E+02 | -1.2E-03 | +1.0E+00 | 6.5E-03 | 6.6E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 73| +1.48E+02 | -4.4E-03 | +6.8E-01 | 6.2E-02 | 8.8E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 84| +1.48E+02 | +4.2E-06 | -3.6E+10 | 1.9E-15 | 2.5E-02 | 5.0E-15 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 39| +1.48E+02 | +1.3E-04 | -6.9E-01 | 6.2E-02 | 1.3E-01 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 62| +1.48E+02 | -5.9E-06 | +6.7E-01 | 1.9E-03 | 8.2E-02 | 3.4E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 44| +1.46E+02 | -1.6E-03 | +8.3E-01 | 1.3E-02 | 5.3E-01 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 74| +1.48E+02 | -4.4E-03 | +6.7E-01 | 6.2E-02 | 2.7E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 6| +4.64E+03 | -2.4E+04 | +9.0E-01 | 2.0E+00 | 1.3E+05 | 3.3E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 0| +2.13E+14 | NaN | NaN | 1.0E+00 | 9.8E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:21 fides(INFO) 85| +1.48E+02 | +4.1E-06 | -1.4E+11 | 4.7E-16 | 2.5E-02 | 1.3E-15 | 2d |0\n", - "2023-02-17 16:05:21 fides(WARNING) Stopping as trust region radius 1.18E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:21 fides(INFO) 45| +1.46E+02 | +6.0E-02 | -8.1E+00 | 2.6E-02 | 8.6E-01 | 4.3E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 40| +1.48E+02 | -3.5E-05 | +6.0E-01 | 1.6E-02 | 1.3E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 63| +1.48E+02 | -7.1E-06 | +1.8E+00 | 1.9E-03 | 1.7E-01 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 75| +1.48E+02 | -2.8E-03 | +6.2E-01 | 6.2E-02 | 7.4E-01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 104| +1.55E+02 | +2.6E-03 | -1.4E-01 | 1.1E-02 | 5.9E+00 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 46| +1.46E+02 | +1.8E-03 | -7.5E-01 | 6.5E-03 | 8.6E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 7| +9.19E+02 | -3.7E+03 | +8.9E-01 | 2.0E+00 | 2.0E+04 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 64| +1.48E+02 | +1.4E-06 | -3.1E-01 | 3.8E-03 | 3.7E-02 | 4.5E-04 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 41| +1.48E+02 | -3.0E-05 | +7.2E-01 | 1.6E-02 | 2.0E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 47| +1.46E+02 | -4.0E-04 | +6.0E-01 | 1.6E-03 | 8.6E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 1| +1.48E+11 | -2.1E+14 | +8.2E-02 | 1.0E+00 | 9.8E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 76| +1.48E+02 | -2.8E-03 | +5.6E-01 | 6.2E-02 | 2.2E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 8| +9.19E+02 | -1.4E+02 | -2.0E-01 | 2.0E+00 | 3.3E+03 | 2.2E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 65| +1.48E+02 | +2.1E-06 | -5.0E-01 | 9.5E-04 | 3.7E-02 | 3.1E-04 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 42| +1.48E+02 | -2.1E-05 | +6.9E-01 | 1.6E-02 | 9.4E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 48| +1.46E+02 | -1.9E-04 | +9.8E-01 | 1.6E-03 | 3.7E-01 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 2| +2.20E+10 | -1.3E+11 | +8.9E-01 | 2.5E-01 | 6.8E+11 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 77| +1.48E+02 | -1.4E-03 | +3.5E-01 | 6.2E-02 | 6.9E-01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 49| +1.46E+02 | -3.0E-04 | +1.0E+00 | 3.2E-03 | 6.2E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 9| +3.33E+02 | -5.9E+02 | +9.0E-01 | 2.1E-01 | 3.3E+03 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 43| +1.48E+02 | -1.2E-05 | +5.2E-01 | 1.6E-02 | 9.8E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 66| +1.48E+02 | -1.4E-07 | +3.5E-02 | 2.4E-04 | 3.7E-02 | 2.9E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 0| +1.86E+09 | NaN | NaN | 1.0E+00 | 8.6E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:21 fides(INFO) 50| +1.46E+02 | -5.1E-04 | +9.6E-01 | 6.5E-03 | 2.8E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 78| +1.48E+02 | -1.7E-03 | +3.0E-01 | 6.2E-02 | 2.3E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 3| +3.28E+09 | -1.9E+10 | +8.9E-01 | 5.0E-01 | 1.0E+11 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 10| +2.28E+02 | -1.0E+02 | +8.6E-01 | 4.3E-01 | 5.4E+02 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 1| +2.20E+06 | -1.9E+09 | +9.9E-02 | 1.0E+00 | 8.6E+09 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 44| +1.48E+02 | -1.5E-05 | +6.7E-01 | 1.6E-02 | 7.9E-02 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 51| +1.46E+02 | +7.0E-04 | -9.2E-01 | 1.3E-02 | 8.4E-01 | 6.3E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 67| +1.48E+02 | -8.6E-07 | +5.3E+00 | 5.9E-05 | 3.3E-02 | 1.2E-05 | 2d |1\n", - "2023-02-17 16:05:21 fides(WARNING) Stopping as function difference 8.62E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:21 fides(INFO) 105| +1.55E+02 | -8.5E-03 | +6.0E-01 | 2.8E-03 | 5.9E+00 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 79| +1.48E+02 | +1.1E-03 | -2.0E-01 | 6.2E-02 | 8.1E-01 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 11| +1.88E+02 | -4.1E+01 | +1.6E+00 | 8.5E-01 | 1.3E+02 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 4| +4.92E+08 | -2.8E+09 | +8.9E-01 | 5.0E-01 | 1.5E+10 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 52| +1.46E+02 | -1.7E-04 | +6.4E-01 | 3.2E-03 | 8.4E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 45| +1.48E+02 | -1.1E-06 | +4.7E-02 | 1.6E-02 | 7.5E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 2| +3.47E+05 | -1.9E+06 | +9.0E-01 | 2.5E-01 | 1.0E+07 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 80| +1.48E+02 | -7.4E-04 | +2.9E-01 | 1.6E-02 | 8.1E-01 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 12| +1.88E+02 | +1.8E+02 | -1.0E+01 | 8.5E-01 | 4.7E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 53| +1.46E+02 | -1.7E-05 | +4.3E-02 | 3.2E-03 | 1.7E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 5| +7.40E+07 | -4.2E+08 | +8.9E-01 | 5.0E-01 | 2.3E+09 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 46| +1.48E+02 | -1.1E-05 | +1.1E+00 | 3.9E-03 | 8.3E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 81| +1.48E+02 | -1.4E-03 | +9.3E-01 | 1.6E-02 | 3.1E+00 | 8.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 3| +5.82E+04 | -2.9E+05 | +9.0E-01 | 5.0E-01 | 1.6E+06 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 54| +1.46E+02 | -1.8E-04 | +7.2E-01 | 8.1E-04 | 6.2E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 13| +1.72E+02 | -1.6E+01 | +8.2E-01 | 2.1E-01 | 4.7E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 4| +1.27E+04 | -4.5E+04 | +7.8E-01 | 1.0E+00 | 2.5E+05 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 6| +1.12E+07 | -6.3E+07 | +8.9E-01 | 5.0E-01 | 3.4E+08 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 55| +1.46E+02 | -4.9E-05 | +7.7E-01 | 8.1E-04 | 1.8E-01 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 47| +1.48E+02 | +1.1E-06 | -2.4E-01 | 7.8E-03 | 5.0E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 14| +1.61E+02 | -1.0E+01 | +5.5E-01 | 4.3E-01 | 5.5E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 82| +1.48E+02 | -4.8E-04 | +9.7E-01 | 3.1E-02 | 2.5E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 5| +2.16E+03 | -1.1E+04 | +9.0E-01 | 2.0E+00 | 4.1E+04 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 56| +1.46E+02 | -5.8E-05 | +1.0E+00 | 1.6E-03 | 2.9E-01 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 48| +1.48E+02 | +4.3E-07 | -2.9E-01 | 2.0E-03 | 5.0E-02 | 3.3E-04 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 0| +9.42E+04 | NaN | NaN | 1.0E+00 | 4.3E+05 | NaN | NaN |1\n", - "2023-02-17 16:05:21 fides(INFO) 6| +5.05E+02 | -1.7E+03 | +9.0E-01 | 2.0E+00 | 6.5E+03 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 7| +1.68E+06 | -9.5E+06 | +8.9E-01 | 5.0E-01 | 5.1E+07 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 83| +1.48E+02 | -7.6E-04 | +9.7E-01 | 6.2E-02 | 2.2E-01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 15| +1.52E+02 | -9.5E+00 | +8.4E-01 | 4.3E-01 | 8.5E+01 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 57| +1.46E+02 | -1.3E-04 | +9.6E-01 | 3.2E-03 | 1.2E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 7| +2.69E+02 | -2.4E+02 | +8.8E-01 | 2.0E+00 | 1.0E+03 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 49| +1.48E+02 | +2.5E-06 | -4.3E+00 | 4.9E-04 | 5.0E-02 | 8.3E-05 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 1| +4.02E+02 | -9.4E+04 | +1.2E-01 | 1.0E+00 | 4.3E+05 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 8| +2.44E+02 | -2.5E+01 | +9.0E-01 | 2.0E+00 | 1.4E+02 | 4.3E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 106| +1.55E+02 | -1.7E-03 | +4.9E-01 | 2.8E-03 | 3.7E+00 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 58| +1.46E+02 | -2.9E-05 | +1.6E-01 | 6.5E-03 | 6.3E-01 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 16| +1.52E+02 | +2.5E+00 | -4.9E-01 | 8.5E-01 | 5.2E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 8| +2.49E+05 | -1.4E+06 | +9.0E-01 | 5.0E-01 | 7.7E+06 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 84| +1.48E+02 | -1.0E-03 | +9.5E-01 | 1.2E-01 | 2.7E-01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 2| +3.63E+02 | -3.9E+01 | +9.5E-01 | 2.5E-01 | 5.8E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 50| +1.48E+02 | +1.3E-06 | -3.5E+00 | 1.2E-04 | 5.0E-02 | 2.5E-05 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 59| +1.46E+02 | -9.3E-05 | +2.7E-01 | 1.6E-03 | 1.9E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 3| +2.89E+02 | -7.4E+01 | +9.9E-01 | 5.0E-01 | 5.2E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 17| +1.46E+02 | -6.0E+00 | +9.8E-01 | 2.1E-01 | 5.2E+01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 9| +3.71E+04 | -2.1E+05 | +9.0E-01 | 5.0E-01 | 1.1E+06 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 60| +1.46E+02 | -3.1E-05 | +2.5E-01 | 1.6E-03 | 2.7E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 85| +1.48E+02 | -6.7E-04 | +8.8E-01 | 2.5E-01 | 1.1E-01 | 8.8E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 51| +1.48E+02 | +2.4E-06 | -7.9E+00 | 3.1E-05 | 5.0E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 4| +2.89E+02 | +3.2E+03 | -6.9E+01 | 1.0E+00 | 4.9E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 18| +1.42E+02 | -4.2E+00 | +5.2E-01 | 4.3E-01 | 5.2E+01 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 61| +1.46E+02 | -5.1E-06 | +5.1E-02 | 1.6E-03 | 1.2E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 86| +1.48E+02 | +7.0E-04 | -8.6E+00 | 5.0E-01 | 2.1E-01 | 2.3E-02 | trr |0\n", - "2023-02-17 16:05:21 fides(INFO) 5| +2.60E+02 | -2.9E+01 | +9.1E-01 | 2.5E-01 | 4.9E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 10| +5.74E+03 | -3.1E+04 | +9.0E-01 | 5.0E-01 | 1.7E+05 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 52| +1.48E+02 | -2.4E-06 | +8.4E+00 | 7.6E-06 | 5.0E-02 | 1.3E-05 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 62| +1.46E+02 | -2.6E-05 | +8.0E-01 | 4.0E-04 | 1.6E-01 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 87| +1.48E+02 | +1.8E-05 | -4.6E-01 | 3.8E-02 | 2.1E-01 | 5.9E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 6| +2.60E+02 | +3.1E+02 | -1.7E+01 | 5.0E-01 | 3.4E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 11| +1.04E+03 | -4.7E+03 | +9.0E-01 | 5.0E-01 | 2.6E+04 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 19| +1.40E+02 | -1.5E+00 | +5.0E-01 | 4.3E-01 | 2.9E+01 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 9| +2.44E+02 | -6.9E+00 | -1.6E+00 | 4.0E+00 | 1.8E+01 | 5.3E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 63| +1.46E+02 | -1.6E-05 | +5.8E-01 | 8.1E-04 | 7.3E-02 | 7.1E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 53| +1.48E+02 | +8.9E-07 | -2.2E+00 | 1.5E-05 | 4.0E-02 | 2.2E-05 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 7| +2.51E+02 | -9.0E+00 | +8.5E-01 | 1.2E-01 | 3.4E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 88| +1.48E+02 | -8.8E-06 | +5.9E-01 | 9.4E-03 | 2.1E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 12| +3.35E+02 | -7.1E+02 | +9.0E-01 | 5.0E-01 | 4.1E+03 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 10| +2.43E+02 | -1.2E+00 | +3.1E-02 | 1.0E+00 | 1.8E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 64| +1.46E+02 | -1.0E-05 | +9.0E-01 | 8.1E-04 | 7.2E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 107| +1.55E+02 | -1.4E-03 | +1.0E+00 | 2.8E-03 | 2.7E+00 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 8| +2.51E+02 | +5.4E+00 | -1.8E+00 | 2.5E-01 | 1.8E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 54| +1.48E+02 | +3.1E-06 | -2.0E+01 | 3.8E-06 | 4.0E-02 | 5.6E-06 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 20| +1.40E+02 | +3.8E-01 | -3.1E-01 | 4.3E-01 | 1.7E+01 | 7.8E-01 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 11| +2.36E+02 | -6.8E+00 | +7.5E-01 | 2.5E-01 | 5.1E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 65| +1.46E+02 | -1.5E-05 | +9.4E-01 | 1.6E-03 | 1.6E-01 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 89| +1.48E+02 | -4.1E-06 | +1.2E+00 | 9.4E-03 | 6.7E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 9| +2.50E+02 | -1.3E+00 | +5.9E-01 | 6.2E-02 | 1.8E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 55| +1.48E+02 | +7.0E-06 | -1.6E+02 | 9.5E-07 | 4.0E-02 | 1.4E-06 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 13| +2.29E+02 | -1.1E+02 | +9.1E-01 | 5.0E-01 | 6.3E+02 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 66| +1.46E+02 | -1.3E-05 | +4.4E-01 | 3.2E-03 | 6.0E-02 | 7.6E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 12| +2.30E+02 | -6.2E+00 | +5.1E-01 | 5.0E-01 | 2.2E+01 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 21| +1.40E+02 | -3.0E-01 | +6.9E-01 | 1.1E-01 | 1.7E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 10| +2.50E+02 | -1.2E-02 | +1.3E-01 | 6.2E-02 | 4.0E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 90| +1.48E+02 | -4.1E-06 | +8.8E-01 | 1.9E-02 | 6.2E-02 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 56| +1.48E+02 | +3.7E-06 | -3.4E+02 | 2.4E-07 | 4.0E-02 | 3.5E-07 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 67| +1.46E+02 | +1.6E-04 | -1.2E+00 | 3.2E-03 | 2.0E-01 | 3.4E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 13| +2.16E+02 | -1.4E+01 | +8.9E-01 | 5.0E-01 | 6.9E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 11| +2.50E+02 | +6.8E-03 | -1.1E-01 | 1.6E-02 | 3.2E+00 | 4.1E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 91| +1.48E+02 | -3.6E-06 | +6.4E-01 | 3.8E-02 | 4.7E-02 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 14| +1.99E+02 | -3.0E+01 | +1.2E+00 | 5.0E-01 | 9.7E+01 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 68| +1.46E+02 | -1.8E-05 | +4.8E-01 | 8.1E-04 | 2.0E-01 | 8.5E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 57| +1.48E+02 | +2.6E-06 | -9.5E+02 | 6.0E-08 | 4.0E-02 | 8.7E-08 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 12| +2.50E+02 | -2.4E-02 | +8.5E-01 | 3.9E-03 | 3.2E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 22| +1.40E+02 | -2.6E-01 | +9.3E-01 | 1.1E-01 | 2.2E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 69| +1.46E+02 | -4.8E-06 | +7.2E-01 | 8.1E-04 | 5.7E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 92| +1.48E+02 | -3.3E-06 | +8.1E-01 | 3.8E-02 | 3.1E-02 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 14| +1.56E+02 | -6.0E+01 | +1.6E+00 | 1.0E+00 | 3.9E+01 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 58| +1.48E+02 | +2.7E-06 | -4.0E+03 | 1.5E-08 | 4.0E-02 | 2.2E-08 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 13| +2.50E+02 | +8.1E-04 | -5.6E-02 | 7.8E-03 | 1.6E+00 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 15| +1.99E+02 | +3.0E+01 | -3.5E+00 | 1.0E+00 | 5.7E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 70| +1.46E+02 | -4.4E-06 | +8.6E-01 | 8.1E-04 | 6.3E-02 | 2.4E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 108| +1.55E+02 | -1.2E-03 | +9.4E-01 | 5.6E-03 | 1.1E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 93| +1.48E+02 | +3.3E-04 | -8.6E+00 | 7.5E-02 | 3.7E-02 | 1.2E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 15| +1.56E+02 | +1.1E+02 | -8.8E+00 | 2.0E+00 | 1.0E+02 | 9.6E-01 | trr |0\n", - "2023-02-17 16:05:21 fides(INFO) 14| +2.50E+02 | -5.9E-03 | +8.5E-01 | 2.0E-03 | 1.6E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 23| +1.39E+02 | -3.7E-01 | +9.2E-01 | 2.1E-01 | 1.3E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 59| +1.48E+02 | +2.1E-06 | -1.2E+04 | 3.7E-09 | 4.0E-02 | 5.4E-09 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 71| +1.46E+02 | -5.9E-06 | +7.5E-01 | 1.6E-03 | 5.1E-02 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 15| +2.50E+02 | +7.7E-05 | -2.5E-02 | 3.9E-03 | 7.4E-01 | 1.0E-02 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 16| +1.52E+02 | -4.2E+00 | +9.5E-01 | 1.2E-01 | 1.0E+02 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 16| +1.81E+02 | -1.8E+01 | +1.1E+00 | 2.5E-01 | 5.7E+01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 72| +1.46E+02 | -5.3E-06 | +5.5E-01 | 1.6E-03 | 5.0E-02 | 5.7E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 94| +1.48E+02 | +1.0E-05 | -6.8E-01 | 1.9E-02 | 3.7E-02 | 2.9E-03 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:21 fides(INFO) 60| +1.48E+02 | +4.7E-06 | -1.1E+05 | 9.3E-10 | 4.0E-02 | 1.4E-09 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 16| +2.50E+02 | -1.3E-03 | +8.3E-01 | 9.8E-04 | 7.4E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 17| +1.50E+02 | -2.0E+00 | +3.0E-01 | 2.3E-01 | 4.8E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 73| +1.46E+02 | +9.2E-06 | -4.0E-01 | 1.6E-03 | 5.9E-02 | 9.5E-04 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 24| +1.39E+02 | -4.8E-01 | +8.3E-01 | 4.3E-01 | 1.1E+01 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 95| +1.48E+02 | -5.7E-06 | +1.4E+00 | 4.7E-03 | 3.7E-02 | 7.4E-04 | 2d |1\n", - "2023-02-17 16:05:21 fides(INFO) 61| +1.48E+02 | +1.6E-06 | -1.5E+05 | 2.3E-10 | 4.0E-02 | 3.4E-10 | 2d |0\n", - "2023-02-17 16:05:21 fides(INFO) 17| +2.50E+02 | +4.9E-06 | -8.7E-03 | 2.0E-03 | 3.2E-01 | 5.0E-03 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 74| +1.46E+02 | -4.1E-06 | +6.5E-01 | 4.0E-04 | 5.9E-02 | 2.5E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 17| +1.67E+02 | -1.4E+01 | +8.9E-01 | 5.0E-01 | 5.8E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +2.50E+02 | -2.7E-04 | +8.0E-01 | 4.9E-04 | 3.2E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +1.48E+02 | -1.6E+00 | +6.1E-01 | 2.3E-01 | 1.2E+02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 96| +1.48E+02 | +3.9E-06 | -6.2E+00 | 9.4E-03 | 2.9E-02 | 9.1E-04 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 75| +1.46E+02 | -3.8E-07 | +2.3E-01 | 4.0E-04 | 6.0E-02 | 5.3E-05 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 62| +1.48E+02 | +3.3E-06 | -1.2E+06 | 5.8E-11 | 4.0E-02 | 8.5E-11 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 25| +1.39E+02 | +4.3E+00 | -5.8E+00 | 8.5E-01 | 1.2E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:22.041 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.735 and h = 9.95199e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.041 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.735: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 19| +2.50E+02 | -2.6E-07 | +4.0E-03 | 9.8E-04 | 1.0E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 19| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.3E-01 | 2.6E+01 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:22.048 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.486 and h = 4.04049e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.048 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.486: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 97| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.4E-03 | 2.9E-02 | 2.3E-04 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 76| +1.46E+02 | -1.5E-06 | +3.3E+00 | 1.0E-04 | 4.1E-02 | 1.9E-05 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 63| +1.48E+02 | +3.0E-06 | -4.5E+06 | 1.5E-11 | 4.0E-02 | 2.1E-11 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 20| +2.50E+02 | -2.9E-05 | +7.8E-01 | 2.4E-04 | 1.0E-01 | 4.4E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +1.59E+02 | -7.7E+00 | +1.0E+00 | 1.0E+00 | 1.1E+02 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 109| +1.55E+02 | -2.1E-03 | +9.3E-01 | 1.1E-02 | 1.7E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 77| +1.46E+02 | -8.5E-07 | +1.0E+00 | 2.0E-04 | 4.6E-02 | 3.6E-05 | 2d |1\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as function difference 8.51E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 20| +1.48E+02 | -2.1E-01 | +8.8E-01 | 5.8E-02 | 2.6E+01 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +2.50E+02 | -2.3E-07 | +4.5E-02 | 4.9E-04 | 2.9E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 64| +1.48E+02 | +6.6E-06 | -3.9E+07 | 3.6E-12 | 4.0E-02 | 5.3E-12 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 98| +1.48E+02 | +5.9E-06 | -3.8E+01 | 5.9E-04 | 2.9E-02 | 5.7E-05 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 26| +1.39E+02 | -1.5E-02 | +3.4E-02 | 2.1E-01 | 1.2E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +1.48E+02 | -1.6E-01 | +7.1E-01 | 1.2E-01 | 2.0E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 99| +1.48E+02 | +3.9E-06 | -3.0E+01 | 1.5E-04 | 2.9E-02 | 1.7E-05 | 2d |0\n", - "2023-02-17 16:05:22.130 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0572 and h = 1.54133e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.131 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0572: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 22| +2.50E+02 | -4.9E-07 | +1.0E-01 | 1.2E-04 | 2.8E-02 | 3.2E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 65| +1.48E+02 | +0.0E+00 | +0.0E+00 | 9.1E-13 | 4.0E-02 | 1.3E-12 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 23| +2.50E+02 | -1.1E-06 | +9.0E-01 | 3.1E-05 | 2.5E-02 | 5.5E-05 | 2d |1\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as function difference 1.13E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:22 fides(INFO) 22| +1.48E+02 | -3.6E-02 | +2.4E-01 | 1.2E-01 | 1.7E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 19| +1.59E+02 | +1.8E+02 | -1.6E+02 | 2.0E+00 | 5.3E+01 | 5.5E+00 | trr |0\n", - "2023-02-17 16:05:22 fides(INFO) 66| +1.48E+02 | +3.3E-06 | -3.1E+08 | 2.3E-13 | 4.0E-02 | 3.3E-13 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 100| +1.48E+02 | +5.2E-06 | -4.3E+01 | 3.7E-05 | 2.9E-02 | 9.3E-06 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 27| +1.39E+02 | -1.3E-01 | +6.0E-01 | 5.3E-02 | 8.7E+00 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 110| +1.55E+02 | -5.9E-03 | +1.0E+00 | 2.2E-02 | 1.2E+00 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 23| +1.48E+02 | -9.7E-02 | +4.3E-01 | 2.9E-02 | 1.1E+01 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 67| +1.48E+02 | +3.5E-06 | -1.3E+09 | 5.7E-14 | 4.0E-02 | 8.3E-14 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 101| +1.48E+02 | -7.2E-07 | +6.0E+00 | 9.2E-06 | 2.9E-02 | 8.6E-06 | 2d |1\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as function difference 7.25E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 0| +1.05E+12 | NaN | NaN | 1.0E+00 | 4.6E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 20| +1.58E+02 | -1.4E+00 | +1.0E+00 | 5.0E-01 | 5.3E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 68| +1.48E+02 | +4.8E-07 | -7.4E+08 | 1.4E-14 | 4.0E-02 | 2.1E-14 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 24| +1.48E+02 | -3.9E-02 | +6.7E-01 | 2.9E-02 | 5.8E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 1| +2.02E+10 | -1.0E+12 | +9.0E-02 | 1.0E+00 | 4.6E+12 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 28| +1.39E+02 | -9.6E-02 | +9.6E-01 | 5.3E-02 | 2.9E+01 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 0| +8.11E+12 | NaN | NaN | 1.0E+00 | 2.8E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:22 fides(INFO) 69| +1.48E+02 | +4.7E-06 | -2.9E+10 | 3.6E-15 | 4.0E-02 | 5.2E-15 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 2| +3.02E+09 | -1.7E+10 | +8.9E-01 | 2.5E-01 | 9.3E+10 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 25| +1.48E+02 | -9.3E-03 | +8.9E-01 | 2.9E-02 | 7.5E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 1| +3.54E+10 | -8.1E+12 | +1.1E-01 | 1.0E+00 | 2.8E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 3| +4.53E+08 | -2.6E+09 | +8.9E-01 | 5.0E-01 | 1.4E+10 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:22.287 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.9304 and h = 2.14384e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.287 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.9304: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 26| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.8E-02 | 2.0E+00 | 4.0E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 70| +1.48E+02 | +3.6E-06 | -8.7E+10 | 8.9E-16 | 4.0E-02 | 1.3E-15 | 2d |0\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as trust region radius 2.22E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:22 fides(INFO) 2| +5.99E+09 | -2.9E+10 | +8.8E-01 | 2.5E-01 | 1.4E+11 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 29| +1.39E+02 | -4.5E-02 | +7.2E-01 | 1.1E-01 | 3.4E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 111| +1.55E+02 | -3.6E-03 | +2.9E-01 | 4.5E-02 | 1.0E+00 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +1.54E+02 | -3.7E+00 | +1.0E+00 | 1.0E+00 | 6.3E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 0| +3.21E+07 | NaN | NaN | 1.0E+00 | 1.5E+08 | NaN | NaN |1\n", - "2023-02-17 16:05:22 fides(INFO) 4| +6.84E+07 | -3.8E+08 | +8.9E-01 | 5.0E-01 | 2.1E+09 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 27| +1.48E+02 | -6.2E-03 | +7.5E-01 | 1.5E-02 | 2.0E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 3| +8.99E+08 | -5.1E+09 | +8.9E-01 | 5.0E-01 | 2.1E+10 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 5| +1.04E+07 | -5.8E+07 | +8.9E-01 | 5.0E-01 | 3.1E+08 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 4| +1.36E+08 | -7.6E+08 | +8.9E-01 | 5.0E-01 | 3.2E+09 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 30| +1.39E+02 | +1.1E-02 | -1.3E-01 | 1.1E-01 | 4.2E+00 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 28| +1.48E+02 | -4.2E-03 | +9.5E-01 | 2.9E-02 | 5.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 1| +1.89E+03 | -3.2E+07 | +1.1E-01 | 1.0E+00 | 1.5E+08 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 6| +1.58E+06 | -8.8E+06 | +8.9E-01 | 5.0E-01 | 4.8E+07 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 5| +2.07E+07 | -1.2E+08 | +8.9E-01 | 5.0E-01 | 4.8E+08 | 8.5E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 2| +6.31E+02 | -1.3E+03 | +9.1E-01 | 2.5E-01 | 6.5E+03 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 29| +1.48E+02 | -2.8E-03 | +8.5E-01 | 5.8E-02 | 1.3E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 3| +3.08E+02 | -3.2E+02 | +9.0E-01 | 5.0E-01 | 1.3E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 6| +3.16E+06 | -1.7E+07 | +8.9E-01 | 5.0E-01 | 7.3E+07 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 22| +1.54E+02 | +2.2E+00 | -3.0E+01 | 2.0E+00 | 5.7E+01 | 6.3E+00 | trr |0\n", - "2023-02-17 16:05:22 fides(INFO) 7| +2.44E+05 | -1.3E+06 | +8.9E-01 | 5.0E-01 | 7.3E+06 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 31| +1.39E+02 | -1.9E-02 | +7.2E-01 | 2.7E-02 | 4.2E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 4| +2.56E+02 | -5.2E+01 | +8.6E-01 | 1.0E+00 | 2.0E+02 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 0| +3.56E+11 | NaN | NaN | 1.0E+00 | 1.6E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:22 fides(INFO) 7| +4.86E+05 | -2.7E+06 | +8.9E-01 | 5.0E-01 | 1.1E+07 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 30| +1.48E+02 | -1.3E-03 | +3.4E-01 | 1.2E-01 | 2.6E+00 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 8| +3.80E+04 | -2.1E+05 | +9.0E-01 | 5.0E-01 | 1.1E+06 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 112| +1.55E+02 | -2.6E-03 | +2.1E-01 | 4.5E-02 | 2.8E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 5| +2.56E+02 | +6.0E+02 | -5.2E+01 | 2.0E+00 | 3.0E+01 | 2.5E+00 | trr |0\n", - "2023-02-17 16:05:22 fides(INFO) 8| +7.55E+04 | -4.1E+05 | +9.0E-01 | 5.0E-01 | 1.7E+06 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 1| +3.50E+05 | -3.6E+11 | +8.7E-02 | 1.0E+00 | 1.6E+12 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 9| +6.10E+03 | -3.2E+04 | +9.0E-01 | 5.0E-01 | 1.7E+05 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 9| +1.19E+04 | -6.4E+04 | +9.0E-01 | 5.0E-01 | 2.7E+05 | 7.8E-01 | 2d |1\n", - "2023-02-17 16:05:22.461 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2575 and h = 1.54213e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.461 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2575: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 6| +2.56E+02 | +9.4E+01 | -7.3E+00 | 3.7E-01 | 3.0E+01 | 9.5E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 31| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 8.9E-01 | 6.5E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 32| +1.39E+02 | -1.3E-02 | +9.9E-01 | 2.7E-02 | 4.0E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 2| +5.43E+04 | -3.0E+05 | +9.0E-01 | 2.5E-01 | 1.6E+06 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 7| +2.50E+02 | -5.5E+00 | +8.5E-01 | 9.3E-02 | 3.0E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 10| +2.01E+03 | -9.9E+03 | +9.0E-01 | 5.0E-01 | 4.1E+04 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 10| +1.14E+03 | -5.0E+03 | +9.0E-01 | 5.0E-01 | 2.7E+04 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 8| +2.50E+02 | +1.6E+00 | -9.5E-01 | 1.9E-01 | 1.4E+01 | 3.7E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 32| +1.48E+02 | +2.9E-03 | -5.7E-01 | 2.9E-02 | 8.9E-01 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 11| +4.82E+02 | -1.5E+03 | +9.0E-01 | 5.0E-01 | 6.4E+03 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 23| +1.53E+02 | -7.1E-01 | +1.0E+00 | 5.0E-01 | 5.7E+01 | 1.6E+00 | trr |1\n", - "2023-02-17 16:05:22 fides(INFO) 3| +8.67E+03 | -4.6E+04 | +9.0E-01 | 5.0E-01 | 2.5E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 33| +1.38E+02 | -2.4E-02 | +9.7E-01 | 5.3E-02 | 3.0E+00 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 9| +2.50E+02 | -8.0E-01 | +6.2E-01 | 4.6E-02 | 1.4E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 11| +3.71E+02 | -7.7E+02 | +9.0E-01 | 5.0E-01 | 4.3E+03 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 12| +2.60E+02 | -2.2E+02 | +8.9E-01 | 5.0E-01 | 9.9E+02 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 4| +1.54E+03 | -7.1E+03 | +9.0E-01 | 1.0E+00 | 3.9E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 10| +2.50E+02 | -3.1E-02 | +6.2E-01 | 4.6E-02 | 2.1E+00 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 11| +2.50E+02 | -3.3E-02 | +6.6E-01 | 4.6E-02 | 1.8E+00 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 33| +1.48E+02 | +1.3E-03 | -3.4E-01 | 7.3E-03 | 8.9E-01 | 9.9E-03 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 12| +2.59E+02 | -1.1E+02 | +8.8E-01 | 5.0E-01 | 6.6E+02 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 13| +2.37E+02 | -2.4E+01 | +8.6E-01 | 5.0E-01 | 1.3E+02 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 12| +2.50E+02 | -4.3E-02 | +7.5E-01 | 4.6E-02 | 2.0E+00 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 5| +4.32E+02 | -1.1E+03 | +9.0E-01 | 1.0E+00 | 6.1E+03 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 113| +1.55E+02 | -6.3E-03 | +1.0E+00 | 1.1E-02 | 5.7E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 34| +1.48E+02 | -1.5E-03 | +6.4E-01 | 1.8E-03 | 8.9E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 13| +2.50E+02 | -9.2E+00 | +7.3E-01 | 5.0E-01 | 8.8E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 34| +1.38E+02 | -4.0E-02 | +9.5E-01 | 1.1E-01 | 4.8E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 13| +2.49E+02 | -4.7E-02 | +7.7E-01 | 4.6E-02 | 1.7E+00 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 14| +2.14E+02 | -2.2E+01 | +1.2E+00 | 1.0E+00 | 2.0E+01 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 6| +2.67E+02 | -1.6E+02 | +8.8E-01 | 1.0E+00 | 9.6E+02 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 14| +2.49E+02 | -1.3E-01 | +1.0E+00 | 9.3E-02 | 1.9E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 7| +2.50E+02 | -1.7E+01 | +7.9E-01 | 1.0E+00 | 1.3E+02 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 35| +1.48E+02 | -3.8E-04 | +9.6E-01 | 1.8E-03 | 1.8E+00 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 15| +2.49E+02 | -4.1E-01 | +1.3E+00 | 1.9E-01 | 2.0E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 14| +2.50E+02 | +2.3E-02 | -1.5E-01 | 5.0E-01 | 5.1E+00 | 6.6E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 15| +2.14E+02 | +3.3E+02 | -3.4E+01 | 2.0E+00 | 3.7E+01 | 6.2E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 8| +2.50E+02 | -1.4E-01 | -1.0E+00 | 2.0E+00 | 3.8E+00 | 4.9E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 35| +1.38E+02 | -6.2E-02 | +8.9E-01 | 2.1E-01 | 2.3E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 16| +2.47E+02 | -2.3E+00 | +1.9E+00 | 3.7E-01 | 2.8E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 15| +2.50E+02 | -6.3E-02 | +8.5E-01 | 6.4E-03 | 5.1E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 36| +1.48E+02 | -2.0E-04 | +5.0E-01 | 3.6E-03 | 3.0E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 16| +2.14E+02 | +2.2E+00 | -3.4E-01 | 5.0E-01 | 3.7E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 17| +2.40E+02 | -6.7E+00 | +1.8E+00 | 7.4E-01 | 7.3E+00 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 9| +2.50E+02 | -8.6E-03 | +1.0E-01 | 5.0E-01 | 3.8E+00 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 24| +1.52E+02 | -1.9E+00 | +9.2E-01 | 1.0E+00 | 4.7E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 16| +2.50E+02 | +2.3E-03 | -6.4E-02 | 1.3E-02 | 2.5E+00 | 3.1E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 17| +2.09E+02 | -5.2E+00 | +6.9E-01 | 1.2E-01 | 3.7E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +2.36E+02 | -4.1E+00 | +1.3E+00 | 1.5E+00 | 1.4E+01 | 4.4E+00 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 36| +1.38E+02 | +5.4E-02 | -6.1E-01 | 4.3E-01 | 4.7E+00 | 4.4E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 10| +2.50E+02 | +5.7E-03 | -9.2E-02 | 1.2E-01 | 3.3E+00 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 37| +1.48E+02 | -1.8E-04 | +9.6E-01 | 3.6E-03 | 1.1E+00 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 25| +1.52E+02 | +5.7E+01 | -6.0E+02 | 2.0E+00 | 3.9E+01 | 5.8E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 17| +2.50E+02 | -1.4E-02 | +8.5E-01 | 3.0E-03 | 2.5E+00 | 7.7E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +2.06E+02 | -2.6E+00 | +5.9E-01 | 1.2E-01 | 3.1E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 11| +2.50E+02 | +5.8E-03 | -9.2E-02 | 3.1E-02 | 3.3E+00 | 7.5E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 114| +1.55E+02 | -1.9E-03 | +1.2E+00 | 2.2E-02 | 1.1E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 19| +2.36E+02 | +9.5E+01 | -9.3E+00 | 3.0E+00 | 2.4E+01 | 8.5E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 12| +2.50E+02 | -3.3E-02 | +6.7E-01 | 7.8E-03 | 3.3E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 38| +1.48E+02 | -7.9E-05 | +9.6E-01 | 7.3E-03 | 1.5E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +2.50E+02 | +2.5E-04 | -2.9E-02 | 6.0E-03 | 1.2E+00 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 37| +1.38E+02 | -2.0E-02 | +6.4E-01 | 1.1E-01 | 4.7E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 19| +2.06E+02 | -8.1E-01 | +2.2E-01 | 1.2E-01 | 2.8E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 13| +2.50E+02 | -2.2E-06 | +1.8E-02 | 7.8E-03 | 1.5E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 20| +2.36E+02 | +9.2E+00 | -1.7E+00 | 7.4E-01 | 2.4E+01 | 2.1E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 19| +2.50E+02 | -3.4E-03 | +8.5E-01 | 1.5E-03 | 1.2E+00 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 39| +1.48E+02 | -1.2E-04 | +9.2E-01 | 1.5E-02 | 2.8E-01 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 14| +2.50E+02 | -9.8E-07 | +7.6E-03 | 2.0E-03 | 1.5E-01 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 20| +2.04E+02 | -1.3E+00 | +8.4E-01 | 3.1E-02 | 2.9E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +2.36E+02 | +3.0E+00 | -6.5E-01 | 1.9E-01 | 2.4E+01 | 4.5E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 20| +2.50E+02 | +2.5E-05 | -1.2E-02 | 2.9E-03 | 6.1E-01 | 7.3E-03 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 38| +1.38E+02 | +9.9E-02 | -1.4E+00 | 1.1E-01 | 1.7E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 15| +2.50E+02 | -5.2E-05 | +5.1E-01 | 4.9E-04 | 1.5E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 40| +1.48E+02 | -2.0E-04 | +9.7E-01 | 2.9E-02 | 1.2E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 22| +2.34E+02 | -2.0E+00 | +8.8E-01 | 4.6E-02 | 2.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +2.04E+02 | -9.6E-02 | +1.7E-01 | 6.2E-02 | 1.4E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 21| +2.50E+02 | -8.4E-04 | +8.5E-01 | 7.4E-04 | 6.1E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 16| +2.50E+02 | -3.3E-07 | +1.2E-02 | 4.9E-04 | 6.6E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 115| +1.55E+02 | -6.8E-04 | +2.8E-01 | 2.2E-02 | 1.1E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 23| +2.32E+02 | -1.8E+00 | +7.1E-01 | 9.3E-02 | 1.8E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 41| +1.48E+02 | -2.8E-04 | +8.2E-01 | 5.8E-02 | 3.4E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 22| +2.50E+02 | -1.8E-05 | +3.2E-02 | 1.5E-03 | 3.0E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 22| +2.04E+02 | -2.9E-01 | +8.3E-01 | 1.6E-02 | 1.4E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 39| +1.38E+02 | -7.3E-03 | +3.5E-01 | 2.7E-02 | 1.7E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 17| +2.50E+02 | -1.2E-05 | +7.5E-01 | 1.2E-04 | 6.6E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 24| +2.30E+02 | -2.1E+00 | +7.9E-01 | 9.3E-02 | 2.5E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 42| +1.48E+02 | -2.1E-04 | +4.1E-01 | 1.2E-01 | 1.7E-01 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 23| +2.50E+02 | -1.7E-04 | +8.8E-01 | 3.7E-04 | 2.9E-01 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 18| +2.50E+02 | -7.4E-08 | +6.3E-02 | 2.4E-04 | 1.4E-02 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 23| +2.04E+02 | -7.7E-02 | +6.5E-01 | 3.1E-02 | 6.0E+00 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 25| +2.30E+02 | -2.2E-01 | -7.7E-02 | 1.9E-01 | 1.9E+01 | 4.3E-01 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:22 fides(INFO) 40| +1.38E+02 | -8.6E-03 | +8.7E-01 | 2.7E-02 | 5.2E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 19| +2.50E+02 | -4.4E-07 | +4.3E-01 | 6.1E-05 | 1.4E-02 | 1.2E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as function difference 4.40E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:22 fides(INFO) 24| +2.50E+02 | -7.4E-05 | +4.8E-01 | 7.4E-04 | 1.7E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 43| +1.48E+02 | +2.0E-03 | -1.1E+00 | 1.2E-01 | 5.7E-01 | 5.6E-02 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 24| +2.04E+02 | -3.4E-02 | +4.1E-01 | 3.1E-02 | 4.5E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 26| +2.29E+02 | -1.3E+00 | +8.9E-01 | 4.6E-02 | 1.9E+01 | 9.9E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 25| +2.50E+02 | +2.5E-08 | -1.1E-03 | 7.4E-04 | 6.5E-02 | 7.7E-04 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 41| +1.38E+02 | -9.0E-03 | +8.7E-01 | 5.3E-02 | 1.2E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 25| +2.04E+02 | -5.4E-02 | +6.3E-01 | 3.1E-02 | 4.4E+00 | 9.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 27| +2.26E+02 | -2.5E+00 | +1.1E+00 | 9.3E-02 | 1.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 116| +1.55E+02 | -3.1E-03 | +1.5E+00 | 2.2E-02 | 2.6E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 44| +1.48E+02 | -2.7E-04 | +5.1E-01 | 2.9E-02 | 5.7E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 26| +2.50E+02 | -9.4E-06 | +8.5E-01 | 8.1E-05 | 6.5E-02 | 2.0E-04 | 2d |1\n", - "2023-02-17 16:05:22.937 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 176.485 and h = 1.21791e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:22.937 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 176.485: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:22 fides(INFO) 26| +2.04E+02 | -4.1E-02 | +6.5E-01 | 3.1E-02 | 3.2E+00 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 26| +1.52E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 3.9E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:22 fides(INFO) 28| +2.24E+02 | -2.5E+00 | +6.5E-01 | 1.9E-01 | 1.8E+01 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 42| +1.38E+02 | -1.0E-02 | +4.5E-01 | 1.1E-01 | 1.6E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 27| +2.50E+02 | -4.3E-07 | +7.1E-02 | 1.6E-04 | 3.2E-02 | 3.6E-04 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 45| +1.48E+02 | -6.2E-05 | +9.4E-01 | 2.9E-02 | 1.8E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 29| +2.20E+02 | -3.2E+00 | +6.5E-01 | 1.9E-01 | 3.8E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 27| +2.04E+02 | -5.0E-02 | +7.9E-01 | 3.1E-02 | 2.9E+00 | 9.3E-02 | 2d |1\n", - "2023-02-17 16:05:22 fides(INFO) 28| +2.50E+02 | -1.9E-06 | +8.5E-01 | 4.0E-05 | 3.0E-02 | 8.7E-05 | 2d |1\n", - "2023-02-17 16:05:22 fides(WARNING) Stopping as function difference 1.85E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:22 fides(INFO) 46| +1.48E+02 | -8.3E-05 | +9.3E-01 | 5.8E-02 | 8.7E-02 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 43| +1.38E+02 | +2.4E-02 | -4.4E-01 | 1.1E-01 | 1.9E+00 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 28| +2.03E+02 | -1.0E-01 | +1.0E+00 | 6.2E-02 | 2.3E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 30| +2.20E+02 | -1.4E-01 | -3.0E-02 | 1.9E-01 | 1.9E+01 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 0| +2.00E+11 | NaN | NaN | 1.0E+00 | 1.1E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:23 fides(INFO) 47| +1.48E+02 | -9.9E-05 | +8.3E-01 | 1.2E-01 | 7.9E-02 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 29| +2.03E+02 | -2.9E-01 | +1.2E+00 | 1.2E-01 | 2.1E+00 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 31| +2.19E+02 | -1.3E+00 | +8.0E-01 | 4.6E-02 | 1.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 44| +1.38E+02 | -9.8E-03 | +6.3E-01 | 2.7E-02 | 1.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 48| +1.48E+02 | +1.9E-03 | -6.7E+00 | 2.3E-01 | 7.6E-02 | 7.1E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 117| +1.55E+02 | -2.6E-03 | +1.3E+00 | 2.2E-02 | 1.2E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 30| +2.02E+02 | -1.2E+00 | +1.6E+00 | 2.5E-01 | 2.8E+00 | 7.5E-01 | 2d |1\n", - "2023-02-17 16:05:23.071 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 104.324 and h = 1.41477e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.071 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 104.324: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) 27| +1.52E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 3.9E+01 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 1| +1.25E+05 | -2.0E+11 | +8.0E-02 | 1.0E+00 | 1.1E+12 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 32| +2.17E+02 | -2.0E+00 | +1.1E+00 | 9.3E-02 | 1.7E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 0| +1.23E+09 | NaN | NaN | 1.0E+00 | 6.1E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:23 fides(INFO) 45| +1.38E+02 | -4.0E-03 | +9.7E-01 | 2.7E-02 | 2.4E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 49| +1.48E+02 | +3.2E-05 | -3.0E-01 | 5.8E-02 | 7.6E-02 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 31| +1.94E+02 | -7.9E+00 | +2.2E+00 | 5.0E-01 | 3.7E+00 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 33| +2.15E+02 | -2.1E+00 | +8.2E-01 | 1.9E-01 | 1.6E+01 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 50| +1.48E+02 | -1.7E-05 | +5.5E-01 | 1.5E-02 | 7.6E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 34| +2.11E+02 | -3.7E+00 | +8.1E-01 | 3.7E-01 | 2.3E+01 | 9.5E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 32| +1.94E+02 | +4.8E+02 | -2.6E+01 | 1.0E+00 | 2.5E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 2| +5.32E+04 | -7.2E+04 | +9.6E-01 | 2.5E-01 | 5.9E+05 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 1| +5.39E+04 | -1.2E+09 | +8.5E-02 | 1.0E+00 | 6.1E+09 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 46| +1.38E+02 | -6.8E-03 | +9.8E-01 | 5.3E-02 | 1.0E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 51| +1.48E+02 | -6.5E-06 | +8.5E-01 | 1.5E-02 | 5.4E-02 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 35| +1.95E+02 | -1.7E+01 | +6.7E-01 | 7.4E-01 | 2.2E+01 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 33| +1.79E+02 | -1.5E+01 | +1.5E+00 | 2.5E-01 | 2.5E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 28| +1.51E+02 | -2.0E-01 | +1.0E+00 | 3.1E-02 | 3.9E+01 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 118| +1.55E+02 | -1.6E-03 | +1.3E+00 | 4.5E-02 | 4.2E-01 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 2| +8.35E+03 | -4.6E+04 | +9.0E-01 | 2.5E-01 | 2.7E+05 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 52| +1.48E+02 | -9.5E-06 | +7.9E-01 | 2.9E-02 | 2.8E-02 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 47| +1.38E+02 | -1.2E-02 | +9.5E-01 | 1.1E-01 | 1.2E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 36| +1.95E+02 | +7.6E+01 | -2.6E+00 | 7.4E-01 | 1.1E+02 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 34| +1.79E+02 | +3.4E+01 | -2.2E+00 | 5.0E-01 | 4.6E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 37| +1.78E+02 | -1.7E+01 | +8.4E-01 | 1.9E-01 | 1.1E+02 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 53| +1.48E+02 | -1.7E-05 | +9.4E-01 | 5.8E-02 | 4.8E-02 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 3| +1.39E+03 | -7.0E+03 | +9.1E-01 | 5.0E-01 | 4.2E+04 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 3| +1.25E+04 | -4.1E+04 | +9.4E-01 | 5.0E-01 | 1.9E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 35| +1.69E+02 | -1.1E+01 | +1.1E+00 | 1.2E-01 | 4.6E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 48| +1.38E+02 | -1.3E-02 | +5.7E-01 | 2.1E-01 | 1.2E+00 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 38| +1.66E+02 | -1.2E+01 | +5.0E-01 | 3.7E-01 | 4.7E+01 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 54| +1.48E+02 | -1.7E-05 | +9.6E-01 | 1.2E-01 | 1.8E-02 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 119| +1.55E+02 | -1.1E-03 | +1.6E+00 | 4.5E-02 | 4.4E-01 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 36| +1.55E+02 | -1.4E+01 | +8.2E-01 | 2.5E-01 | 6.2E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 4| +3.46E+02 | -1.0E+03 | +9.1E-01 | 1.0E+00 | 6.8E+03 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 39| +1.59E+02 | -6.7E+00 | +3.1E-01 | 3.7E-01 | 1.2E+02 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 55| +1.48E+02 | +5.0E-04 | -1.2E+01 | 2.3E-01 | 4.5E-02 | 2.3E-02 | trr |0\n", - "2023-02-17 16:05:23 fides(INFO) 49| +1.38E+02 | +3.6E-01 | -3.5E+00 | 2.1E-01 | 1.4E+00 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 37| +1.49E+02 | -5.6E+00 | +7.6E-01 | 5.0E-01 | 1.1E+02 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 40| +1.51E+02 | -8.9E+00 | +9.5E-01 | 3.7E-01 | 2.3E+02 | 3.2E-01 | trr |1\n", - "2023-02-17 16:05:23 fides(INFO) 56| +1.48E+02 | +1.9E-05 | -1.0E+00 | 3.7E-02 | 4.5E-02 | 5.8E-03 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 5| +1.68E+02 | -1.8E+02 | +1.0E+00 | 1.0E+00 | 1.2E+03 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 29| +1.51E+02 | -3.0E-01 | +1.0E+00 | 6.2E-02 | 2.3E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 50| +1.38E+02 | +5.8E-03 | -2.0E-01 | 5.3E-02 | 1.4E+00 | 4.8E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 41| +1.51E+02 | +2.8E+00 | -1.1E+00 | 3.7E-01 | 4.8E+01 | 5.7E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 38| +1.49E+02 | +3.5E+00 | -1.7E+00 | 1.0E+00 | 5.2E+01 | 7.7E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 57| +1.48E+02 | -1.9E-06 | +3.4E-01 | 9.3E-03 | 4.5E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 120| +1.55E+02 | -1.7E-03 | +1.3E+00 | 4.5E-02 | 6.1E-01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 6| +1.68E+02 | -3.3E+00 | -2.8E-02 | 1.0E+00 | 2.0E+02 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:23.374 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 77.935 and h = 1.24774e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.374 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 77.935: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) 39| +1.49E+02 | +0.0E+00 | +0.0E+00 | 1.7E-01 | 5.2E+01 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 42| +1.49E+02 | -1.3E+00 | +7.7E-01 | 8.8E-02 | 4.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 51| +1.38E+02 | -5.3E-03 | +5.0E-01 | 1.3E-02 | 1.4E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 58| +1.48E+02 | +4.1E-07 | -6.7E-01 | 9.3E-03 | 4.6E-02 | 1.4E-03 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 4| +2.02E+03 | -1.0E+04 | +9.0E-01 | 1.0E+00 | 4.4E+04 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 43| +1.49E+02 | +8.8E-01 | -6.9E-01 | 1.8E-01 | 3.0E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 40| +1.49E+02 | -5.5E-01 | +8.8E-01 | 4.2E-02 | 5.2E+01 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 7| +1.58E+02 | -1.1E+01 | +8.9E-01 | 2.1E-01 | 2.0E+02 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 59| +1.48E+02 | +3.4E-07 | -9.7E-01 | 2.3E-03 | 4.6E-02 | 3.4E-04 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 52| +1.38E+02 | -4.2E-03 | +9.7E-01 | 1.3E-02 | 6.4E+00 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 44| +1.49E+02 | -3.3E-01 | +7.0E-01 | 4.4E-02 | 3.0E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 41| +1.48E+02 | -4.1E-01 | +9.6E-01 | 8.3E-02 | 4.1E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 8| +1.52E+02 | -5.7E+00 | +8.5E-01 | 4.3E-01 | 6.1E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 121| +1.55E+02 | -4.6E-03 | +1.2E+00 | 8.9E-02 | 7.5E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 60| +1.48E+02 | +8.9E-07 | -3.2E+00 | 5.8E-04 | 4.6E-02 | 8.6E-05 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 42| +1.48E+02 | -4.1E-01 | +8.1E-01 | 1.7E-01 | 3.2E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 45| +1.48E+02 | -4.8E-01 | +7.5E-01 | 4.4E-02 | 2.3E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 53| +1.38E+02 | -1.8E-03 | +9.8E-01 | 2.7E-02 | 6.1E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 61| +1.48E+02 | +3.9E-07 | -1.5E+00 | 1.5E-04 | 4.6E-02 | 2.4E-05 | 2d |0\n", - "2023-02-17 16:05:23.546 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 133.055 and h = 1.26071e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.546 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 133.055: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 30| +1.51E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 9.4E+00 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 46| +1.48E+02 | +4.8E-02 | -1.0E-01 | 8.8E-02 | 2.6E+01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 43| +1.48E+02 | +9.7E-02 | -2.0E-01 | 3.3E-01 | 2.9E+01 | 3.7E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 9| +1.50E+02 | -2.2E+00 | +1.0E+00 | 8.5E-01 | 3.9E+01 | 8.0E-01 | trr |1\n", - "2023-02-17 16:05:23 fides(INFO) 54| +1.38E+02 | -3.5E-03 | +9.7E-01 | 5.3E-02 | 8.2E-01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 47| +1.48E+02 | -1.7E-01 | +8.0E-01 | 2.2E-02 | 2.6E+01 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 62| +1.48E+02 | -1.0E-06 | +4.0E+00 | 3.6E-05 | 4.6E-02 | 1.2E-05 | 2d |1\n", - "2023-02-17 16:05:23 fides(WARNING) Stopping as function difference 1.03E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:23 fides(INFO) 5| +4.14E+02 | -1.6E+03 | +9.0E-01 | 1.0E+00 | 6.8E+03 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 122| +1.55E+02 | -1.0E-02 | +1.1E+00 | 1.8E-01 | 1.4E+00 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 44| +1.48E+02 | -1.8E-01 | +8.0E-01 | 8.3E-02 | 2.9E+01 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 10| +1.50E+02 | -4.8E-02 | +2.5E-01 | 8.5E-01 | 8.1E+00 | 1.0E+00 | trr |1\n", - "2023-02-17 16:05:23 fides(INFO) 55| +1.38E+02 | -5.4E-03 | +8.0E-01 | 1.1E-01 | 7.0E-01 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 48| +1.48E+02 | -2.3E-01 | +9.6E-01 | 4.4E-02 | 9.2E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 45| +1.48E+02 | -1.7E-01 | +8.5E-01 | 1.7E-01 | 1.7E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 123| +1.55E+02 | +6.4E-01 | -5.7E+01 | 3.6E-01 | 6.7E+00 | 9.0E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 49| +1.48E+02 | -1.6E-01 | +5.9E-01 | 8.8E-02 | 1.4E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 56| +1.38E+02 | +2.1E-01 | -6.3E+00 | 2.1E-01 | 6.0E-01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 11| +1.50E+02 | -3.8E-02 | +9.0E-01 | 1.7E-01 | 1.3E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 46| +1.48E+02 | +1.0E-01 | -4.8E-01 | 3.3E-01 | 1.8E+01 | 3.3E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 50| +1.48E+02 | +2.8E-01 | -5.9E-01 | 8.8E-02 | 6.2E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 31| +1.51E+02 | -1.4E-02 | +1.0E+00 | 3.1E-02 | 9.4E+00 | 8.9E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 47| +1.48E+02 | -7.1E-02 | +7.5E-01 | 8.3E-02 | 1.8E+01 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 57| +1.38E+02 | +6.1E-03 | -6.6E-01 | 5.3E-02 | 6.0E-01 | 4.0E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 0| +3.10E+09 | NaN | NaN | 1.0E+00 | 1.4E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:23 fides(INFO) 12| +1.50E+02 | -1.9E-02 | +8.5E-01 | 3.4E-01 | 2.0E+00 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 124| +1.55E+02 | -6.6E-03 | +9.7E-01 | 8.9E-02 | 6.7E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:23.765 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.64 and h = 4.19393e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.765 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.64: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) 48| +1.48E+02 | +0.0E+00 | +0.0E+00 | 8.3E-02 | 8.8E+00 | 6.9E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 51| +1.48E+02 | -1.1E-01 | +6.1E-01 | 2.2E-02 | 6.2E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 13| +1.50E+02 | -5.3E-03 | +3.6E-01 | 6.8E-01 | 1.7E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 58| +1.38E+02 | -1.4E-03 | +5.2E-01 | 1.3E-02 | 6.0E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 52| +1.48E+02 | -4.2E-02 | +8.8E-01 | 2.2E-02 | 9.9E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 1| +1.92E+04 | -3.1E+09 | +9.3E-02 | 1.0E+00 | 1.4E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 49| +1.48E+02 | -2.9E-02 | +8.6E-01 | 2.1E-02 | 8.8E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 125| +1.55E+02 | -2.5E-02 | +3.9E-01 | 1.8E-01 | 1.0E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 14| +1.50E+02 | +2.2E-03 | -2.6E-01 | 6.8E-01 | 3.5E+00 | 2.8E-01 | trr |0\n", - "2023-02-17 16:05:23 fides(INFO) 53| +1.48E+02 | -6.1E-02 | +9.5E-01 | 4.4E-02 | 2.4E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 50| +1.48E+02 | -2.5E-02 | +9.6E-01 | 4.2E-02 | 1.2E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 2| +3.81E+03 | -1.5E+04 | +9.1E-01 | 2.5E-01 | 6.4E+04 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 59| +1.38E+02 | -1.1E-03 | +9.5E-01 | 1.3E-02 | 2.1E+00 | 8.8E-03 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 6| +1.81E+02 | -2.3E+02 | +8.9E-01 | 1.0E+00 | 1.0E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 54| +1.48E+02 | -7.4E-02 | +9.0E-01 | 8.8E-02 | 4.8E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 3| +8.29E+02 | -3.0E+03 | +9.1E-01 | 5.0E-01 | 1.2E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 15| +1.50E+02 | -3.8E-03 | +8.1E-01 | 1.2E-01 | 3.5E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 51| +1.48E+02 | -1.9E-02 | +9.5E-01 | 8.3E-02 | 5.1E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:23 fides(INFO) 60| +1.38E+02 | -1.6E-03 | +9.7E-01 | 2.7E-02 | 4.5E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 126| +1.55E+02 | +3.1E-01 | -3.2E+00 | 1.8E-01 | 1.2E+01 | 3.4E-01 | 2d |0\n", - "2023-02-17 16:05:23.931 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 87.6304 and h = 1.05166e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.932 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 87.6304: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) 32| +1.51E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 4.6E+00 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 4| +8.29E+02 | +5.6E+03 | -3.0E+01 | 1.0E+00 | 2.0E+03 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 16| +1.50E+02 | -7.9E-04 | +7.6E-01 | 2.4E-01 | 9.1E-01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 55| +1.48E+02 | +3.2E-02 | -5.2E-01 | 1.8E-01 | 1.5E+00 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 52| +1.48E+02 | -1.9E-02 | +8.1E-01 | 1.7E-01 | 5.9E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 5| +3.55E+02 | -4.7E+02 | +8.8E-01 | 2.5E-01 | 2.0E+03 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:23.973 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 146.739 and h = 3.74372e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:23.973 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 146.739: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:23 fides(INFO) 56| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.8E-02 | 1.5E+00 | 5.0E-02 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 61| +1.38E+02 | -2.6E-03 | +9.1E-01 | 5.3E-02 | 1.8E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:23 fides(INFO) 53| +1.48E+02 | +4.3E-02 | -1.3E+00 | 3.3E-01 | 2.9E+00 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:23 fides(INFO) 17| +1.50E+02 | -1.6E-04 | +8.4E-01 | 4.7E-01 | 5.0E-01 | 1.7E-01 | trr |1\n", - "2023-02-17 16:05:24 fides(INFO) 127| +1.55E+02 | -3.7E-02 | +7.2E-01 | 4.2E-02 | 1.2E+01 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 6| +2.54E+02 | -1.0E+02 | +8.0E-01 | 5.0E-01 | 3.6E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 57| +1.48E+02 | -1.1E-02 | +6.4E-01 | 9.6E-03 | 1.5E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 54| +1.48E+02 | -5.9E-03 | +4.5E-01 | 8.3E-02 | 2.9E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 62| +1.38E+02 | +1.8E-03 | -3.4E-01 | 1.1E-01 | 4.0E-01 | 6.8E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 7| +1.53E+02 | -2.7E+01 | +8.6E-01 | 1.0E+00 | 1.5E+02 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 18| +1.50E+02 | +5.0E-05 | -4.1E-01 | 4.7E-01 | 3.0E-01 | 7.7E-03 | trr |0\n", - "2023-02-17 16:05:24 fides(INFO) 7| +2.49E+02 | -4.7E+00 | +6.1E-01 | 1.0E+00 | 4.7E+01 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 55| +1.48E+02 | -3.9E-03 | +2.8E-01 | 8.3E-02 | 3.9E+00 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 58| +1.48E+02 | -7.2E-03 | +8.7E-01 | 9.6E-03 | 3.6E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:24.073 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.1719 and h = 1.21884e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.073 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.1719: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 33| +1.51E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 4.6E+00 | 3.8E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 128| +1.55E+02 | -2.4E-02 | +1.9E-01 | 4.2E-02 | 1.1E+01 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 19| +1.50E+02 | -4.8E-05 | +8.1E-01 | 1.4E-02 | 3.0E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 8| +2.43E+02 | -5.9E+00 | +1.1E+00 | 1.0E+00 | 1.6E+01 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 63| +1.38E+02 | -1.0E-03 | +5.1E-01 | 2.7E-02 | 4.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 56| +1.48E+02 | +6.8E-03 | -4.1E-01 | 8.3E-02 | 1.9E+00 | 5.1E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 59| +1.48E+02 | -6.9E-03 | +7.8E-01 | 1.9E-02 | 5.3E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 9| +2.27E+02 | -1.6E+01 | +1.6E+00 | 2.0E+00 | 2.4E+01 | 4.5E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 20| +1.50E+02 | -1.1E-05 | +5.6E-01 | 2.9E-02 | 7.4E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 8| +1.52E+02 | -7.5E-01 | +9.6E-01 | 1.0E+00 | 4.2E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 64| +1.38E+02 | -1.5E-03 | +4.7E-01 | 2.7E-02 | 1.9E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 60| +1.48E+02 | -1.4E-02 | +8.8E-01 | 3.8E-02 | 1.2E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 57| +1.48E+02 | -1.5E-03 | +1.6E-01 | 2.1E-02 | 1.9E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 129| +1.54E+02 | -7.7E-02 | +1.1E+00 | 1.0E-02 | 2.3E+01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 10| +2.27E+02 | +4.8E+03 | -1.3E+02 | 4.0E+00 | 3.3E+01 | 7.2E+00 | trr |0\n", - "2023-02-17 16:05:24 fides(INFO) 21| +1.50E+02 | -5.6E-07 | +7.6E-02 | 2.9E-02 | 7.5E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 9| +1.52E+02 | +8.3E+01 | -9.5E+01 | 2.0E+00 | 1.9E+01 | 4.7E+00 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 58| +1.48E+02 | -4.3E-03 | +9.4E-01 | 5.2E-03 | 5.9E+00 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 61| +1.48E+02 | +6.8E-03 | -3.8E-01 | 7.7E-02 | 6.1E-01 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 65| +1.38E+02 | -1.0E-03 | +4.3E-01 | 2.7E-02 | 4.5E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 22| +1.50E+02 | -2.7E-06 | +4.9E-01 | 7.1E-03 | 1.9E-02 | 8.7E-04 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 11| +2.27E+02 | +4.6E+01 | -4.0E+00 | 7.1E-01 | 3.3E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 130| +1.54E+02 | -3.0E-02 | +1.1E+00 | 2.1E-02 | 3.5E+00 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 62| +1.48E+02 | -3.8E-03 | +6.9E-01 | 1.9E-02 | 6.1E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 59| +1.48E+02 | -6.3E-04 | +8.8E-01 | 1.0E-02 | 5.0E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:24.245 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 78.9074 and h = 1.0834e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.246 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 78.9074: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 34| +1.51E+02 | +0.0E+00 | +0.0E+00 | 3.9E-03 | 4.6E+00 | 8.9E-03 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 10| +1.52E+02 | +2.3E+00 | -1.1E+01 | 5.0E-01 | 1.9E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 66| +1.38E+02 | -8.5E-04 | +3.4E-01 | 2.7E-02 | 6.9E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 12| +2.22E+02 | -5.2E+00 | +4.5E-01 | 1.8E-01 | 3.3E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 23| +1.50E+02 | -2.8E-08 | +4.4E-02 | 7.1E-03 | 2.2E-02 | 4.1E-04 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 60| +1.48E+02 | -6.7E-04 | +8.4E-01 | 2.1E-02 | 6.4E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 63| +1.48E+02 | -2.7E-03 | +3.8E-01 | 1.9E-02 | 6.8E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 13| +2.14E+02 | -7.9E+00 | +9.1E-01 | 1.8E-01 | 4.9E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 131| +1.54E+02 | -4.3E-02 | +1.0E+00 | 4.2E-02 | 3.0E+00 | 9.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 61| +1.48E+02 | -7.4E-04 | +9.1E-01 | 4.2E-02 | 2.8E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 64| +1.48E+02 | -4.7E-03 | +5.2E-01 | 1.9E-02 | 3.0E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 24| +1.50E+02 | -1.5E-07 | +6.6E-01 | 1.8E-03 | 1.2E-02 | 1.4E-04 | 2d |1\n", - "2023-02-17 16:05:24 fides(WARNING) Stopping as function difference 1.50E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:24 fides(INFO) 67| +1.38E+02 | -9.3E-04 | +3.2E-01 | 2.7E-02 | 5.1E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 14| +1.95E+02 | -1.9E+01 | +1.3E+00 | 3.5E-01 | 2.8E+01 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 11| +1.52E+02 | -1.4E-01 | +9.4E-01 | 1.2E-01 | 1.9E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 65| +1.48E+02 | -2.7E-03 | +6.2E-01 | 1.9E-02 | 6.9E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 62| +1.48E+02 | -1.1E-03 | +9.6E-01 | 8.3E-02 | 3.9E-01 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 132| +1.54E+02 | -7.1E-02 | +8.4E-01 | 8.3E-02 | 5.1E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 15| +1.95E+02 | -8.5E+00 | -3.6E-01 | 7.1E-01 | 5.5E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 68| +1.38E+02 | -5.6E-04 | +1.7E-01 | 2.7E-02 | 7.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 63| +1.48E+02 | -1.3E-03 | +9.2E-01 | 1.7E-01 | 2.4E-01 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 66| +1.48E+02 | -1.3E-03 | +3.5E-01 | 1.9E-02 | 6.8E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 16| +1.83E+02 | -1.2E+01 | +1.0E+00 | 1.8E-01 | 5.5E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 12| +1.52E+02 | -2.0E-01 | +7.0E-01 | 2.5E-01 | 2.1E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 67| +1.48E+02 | +5.7E-03 | -9.3E-01 | 1.9E-02 | 6.4E-01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 69| +1.38E+02 | -8.7E-04 | +7.9E-01 | 6.7E-03 | 6.0E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 0| +1.22E+14 | NaN | NaN | 1.0E+00 | 5.6E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:24 fides(INFO) 64| +1.48E+02 | +1.8E-03 | -1.7E+00 | 3.3E-01 | 1.5E-01 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:24.455 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 111.961 and h = 1.39991e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.456 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 111.961: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 133| +1.54E+02 | -5.6E-02 | +8.6E-01 | 1.7E-01 | 1.0E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 35| +1.51E+02 | +0.0E+00 | +0.0E+00 | 9.8E-04 | 4.6E+00 | 2.2E-03 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 65| +1.48E+02 | -1.2E-04 | +2.7E-01 | 8.3E-02 | 1.5E-01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 68| +1.48E+02 | -9.4E-04 | +4.7E-01 | 4.8E-03 | 6.4E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 17| +1.67E+02 | -1.6E+01 | +6.3E-01 | 3.5E-01 | 5.7E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 70| +1.38E+02 | -6.7E-04 | +7.7E-01 | 1.3E-02 | 8.1E-01 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 13| +1.52E+02 | -1.9E-01 | +8.0E-01 | 2.5E-01 | 1.9E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 1| +7.10E+07 | -1.2E+14 | +8.3E-02 | 1.0E+00 | 5.6E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 18| +1.54E+02 | -1.3E+01 | +9.7E-01 | 3.5E-01 | 2.2E+02 | 9.7E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 69| +1.48E+02 | -5.4E-04 | +8.7E-01 | 4.8E-03 | 1.3E+00 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 66| +1.48E+02 | +1.3E-03 | -7.9E-01 | 8.3E-02 | 5.7E-01 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 134| +1.54E+02 | -1.9E-02 | +6.2E-01 | 1.7E-01 | 2.8E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 2| +1.09E+07 | -6.0E+07 | +8.9E-01 | 2.5E-01 | 3.3E+08 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 71| +1.38E+02 | -8.8E-04 | +9.9E-01 | 2.7E-02 | 3.1E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 19| +1.54E+02 | +6.4E+00 | -9.7E-01 | 7.1E-01 | 7.2E+01 | 9.7E-01 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 67| +1.48E+02 | -3.0E-04 | +6.2E-01 | 2.1E-02 | 5.7E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 70| +1.48E+02 | -5.7E-04 | +9.2E-01 | 9.6E-03 | 1.8E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 3| +1.68E+06 | -9.2E+06 | +8.9E-01 | 5.0E-01 | 5.0E+07 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 14| +1.52E+02 | -2.2E-01 | +6.7E-01 | 5.0E-01 | 2.5E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:24.587 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.6128 and h = 1.23616e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.587 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.6128: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 36| +1.51E+02 | +0.0E+00 | +0.0E+00 | 2.4E-04 | 4.6E+00 | 5.6E-04 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 72| +1.38E+02 | -1.7E-03 | +9.8E-01 | 5.3E-02 | 3.0E-01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 71| +1.48E+02 | -1.0E-03 | +8.9E-01 | 1.9E-02 | 2.1E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:24.596 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.536 and h = 2.82335e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.596 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.536: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 68| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.1E-02 | 5.3E-02 | 7.1E-03 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 20| +1.51E+02 | -3.6E+00 | +8.2E-01 | 1.8E-01 | 7.2E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 135| +1.54E+02 | -2.3E-02 | +1.0E+00 | 1.7E-01 | 1.2E+01 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 4| +2.61E+05 | -1.4E+06 | +9.0E-01 | 5.0E-01 | 7.7E+06 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 69| +1.48E+02 | -1.4E-05 | +1.1E+00 | 5.2E-03 | 5.3E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 5| +4.12E+04 | -2.2E+05 | +9.0E-01 | 5.0E-01 | 1.2E+06 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 72| +1.48E+02 | +1.4E-03 | -1.1E+00 | 3.8E-02 | 2.3E-01 | 4.4E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 21| +1.50E+02 | -1.2E+00 | +4.4E-01 | 3.5E-01 | 7.7E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 73| +1.38E+02 | -2.9E-03 | +9.1E-01 | 1.1E-01 | 4.1E-01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 6| +6.71E+03 | -3.4E+04 | +9.0E-01 | 5.0E-01 | 1.9E+05 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 70| +1.48E+02 | -2.2E-05 | +9.2E-01 | 1.0E-02 | 1.0E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:24.659 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.443 and h = 3.24687e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.659 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.443: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 73| +1.48E+02 | +0.0E+00 | +0.0E+00 | 9.6E-03 | 2.3E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 136| +1.54E+02 | -1.3E-02 | +1.6E+00 | 1.7E-01 | 4.5E+00 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 22| +1.49E+02 | -3.9E-01 | +7.7E-02 | 3.5E-01 | 4.4E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 15| +1.51E+02 | -2.6E-01 | +1.2E+00 | 5.0E-01 | 1.4E+01 | 6.9E-01 | trr |1\n", - "2023-02-17 16:05:24 fides(INFO) 7| +1.25E+03 | -5.5E+03 | +9.0E-01 | 5.0E-01 | 3.0E+04 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 74| +1.38E+02 | +4.9E-02 | -5.3E+00 | 2.1E-01 | 2.9E-01 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 71| +1.48E+02 | -3.9E-05 | +9.4E-01 | 2.1E-02 | 5.5E-02 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 74| +1.48E+02 | -9.3E-05 | +8.1E-01 | 2.4E-03 | 2.3E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 8| +3.91E+02 | -8.6E+02 | +9.0E-01 | 5.0E-01 | 4.8E+03 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 23| +1.48E+02 | -9.5E-01 | +5.1E-01 | 8.9E-02 | 5.4E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 16| +1.51E+02 | -1.2E-01 | +8.4E-01 | 1.0E+00 | 7.2E+00 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:24.718 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 94.2389 and h = 1.24166e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.718 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 94.2389: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 37| +1.51E+02 | +0.0E+00 | +0.0E+00 | 6.1E-05 | 4.6E+00 | 1.4E-04 | 2d |0\n", - "2023-02-17 16:05:24.727 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.508 and h = 2.16248e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.728 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.508: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 72| +1.48E+02 | +0.0E+00 | +0.0E+00 | 4.2E-02 | 8.9E-02 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 75| +1.38E+02 | +1.4E-03 | -5.2E-01 | 5.3E-02 | 2.9E-01 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 75| +1.48E+02 | -1.6E-04 | +9.4E-01 | 4.8E-03 | 8.1E-02 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 9| +2.62E+02 | -1.3E+02 | +8.9E-01 | 5.0E-01 | 7.6E+02 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 24| +1.48E+02 | -2.6E-01 | +9.1E-01 | 8.9E-02 | 2.8E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 137| +1.54E+02 | -1.6E-02 | +1.2E+00 | 1.7E-01 | 1.7E+00 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 76| +1.48E+02 | -2.8E-04 | +9.8E-01 | 9.6E-03 | 1.8E-01 | 9.1E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 17| +1.51E+02 | -3.1E-01 | +1.5E+00 | 2.0E+00 | 7.5E+00 | 3.3E-01 | trr |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 10| +2.50E+02 | -1.2E+01 | +7.8E-01 | 5.0E-01 | 1.1E+02 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 73| +1.48E+02 | -2.4E-05 | +1.2E+00 | 1.0E-02 | 8.9E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 76| +1.38E+02 | -3.9E-04 | +4.8E-01 | 1.3E-02 | 2.9E-01 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 25| +1.48E+02 | -3.1E-01 | +8.7E-01 | 1.8E-01 | 2.9E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 77| +1.48E+02 | -2.8E-04 | +8.3E-01 | 1.9E-02 | 8.6E-02 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 11| +2.50E+02 | -2.3E-04 | +3.8E-02 | 1.0E+00 | 9.9E-01 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 74| +1.48E+02 | -3.3E-05 | +9.7E-01 | 2.1E-02 | 5.8E-02 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 138| +1.54E+02 | -5.5E-03 | +1.5E+00 | 1.7E-01 | 1.5E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 18| +1.51E+02 | -4.8E-01 | +9.4E-01 | 2.0E+00 | 9.3E+00 | 4.2E-01 | trr |1\n", - "2023-02-17 16:05:24 fides(INFO) 26| +1.48E+02 | +3.8E-01 | -1.6E+00 | 3.5E-01 | 1.4E+01 | 3.3E-01 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 12| +2.50E+02 | +1.4E-04 | -2.8E-02 | 2.5E-01 | 9.4E-01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:24.822 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2018 and h = 1.60887e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.822 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2018: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 78| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.8E-02 | 1.8E-01 | 1.2E-02 | trr |0\n", - "2023-02-17 16:05:24 fides(INFO) 77| +1.38E+02 | -5.1E-04 | +8.1E-01 | 1.3E-02 | 1.2E+00 | 6.7E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 75| +1.48E+02 | -5.3E-05 | +9.5E-01 | 4.2E-02 | 7.3E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 19| +1.51E+02 | +1.9E+00 | -1.3E+00 | 2.0E+00 | 4.8E+01 | 5.6E-01 | trr |0\n", - "2023-02-17 16:05:24 fides(INFO) 27| +1.48E+02 | -4.3E-02 | +5.1E-01 | 8.9E-02 | 1.4E+01 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 13| +2.50E+02 | +1.5E-04 | -3.1E-02 | 6.2E-02 | 9.4E-01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 78| +1.38E+02 | -6.3E-04 | +9.5E-01 | 2.7E-02 | 1.9E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 79| +1.48E+02 | +1.3E-04 | -5.2E-01 | 6.2E-03 | 1.8E-01 | 3.0E-03 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 38| +1.51E+02 | -1.5E-04 | +1.0E+00 | 1.5E-05 | 4.6E+00 | 3.5E-05 | 2d |1\n", - "2023-02-17 16:05:24.882 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.542 and h = 2.33234e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.882 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.542: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 76| +1.48E+02 | +0.0E+00 | +0.0E+00 | 8.3E-02 | 4.4E-02 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 139| +1.54E+02 | -5.3E-03 | +1.4E+00 | 1.7E-01 | 1.0E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 28| +1.48E+02 | -4.4E-02 | +4.8E-01 | 8.9E-02 | 1.2E+01 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 20| +1.50E+02 | -7.5E-01 | +8.6E-01 | 8.9E-02 | 4.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 14| +2.50E+02 | +1.5E-04 | -3.1E-02 | 1.6E-02 | 9.4E-01 | 3.8E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 80| +1.48E+02 | -4.6E-05 | +6.3E-01 | 1.5E-03 | 1.8E-01 | 7.9E-04 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 77| +1.48E+02 | -2.1E-05 | +9.8E-01 | 2.1E-02 | 4.4E-02 | 5.7E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 79| +1.38E+02 | -9.6E-04 | +6.9E-01 | 5.3E-02 | 2.3E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 29| +1.48E+02 | -2.1E-02 | +2.8E-01 | 8.9E-02 | 1.3E+01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 15| +2.50E+02 | -1.1E-03 | +2.1E-01 | 3.9E-03 | 9.4E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 81| +1.48E+02 | -1.7E-05 | +1.0E+00 | 1.5E-03 | 1.6E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 21| +1.50E+02 | -2.0E-01 | +6.3E-01 | 1.8E-01 | 1.2E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 78| +1.48E+02 | -3.4E-05 | +9.7E-01 | 4.2E-02 | 6.2E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 140| +1.54E+02 | -3.3E-03 | +1.4E+00 | 1.7E-01 | 7.0E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 80| +1.38E+02 | +3.6E-03 | -9.5E-01 | 5.3E-02 | 3.3E-01 | 2.7E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:24 fides(INFO) 16| +2.50E+02 | -1.1E-03 | +8.7E-01 | 9.8E-04 | 7.3E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 30| +1.48E+02 | +3.0E-02 | -3.0E-01 | 8.9E-02 | 6.8E+00 | 7.7E-02 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 82| +1.48E+02 | -2.5E-05 | +9.9E-01 | 3.1E-03 | 5.1E-02 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 79| +1.48E+02 | -4.2E-05 | +9.6E-01 | 8.3E-02 | 3.2E-02 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 22| +1.50E+02 | -1.2E-02 | +9.6E-01 | 1.8E-01 | 3.8E+00 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:24.994 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 94.9704 and h = 1.35144e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:24.994 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 94.9704: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:24 fides(INFO) 39| +1.51E+02 | +0.0E+00 | +0.0E+00 | 3.1E-05 | 4.5E+00 | 7.0E-05 | 2d |0\n", - "2023-02-17 16:05:24 fides(INFO) 31| +1.48E+02 | -2.3E-02 | +2.8E-01 | 2.2E-02 | 6.8E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:24 fides(INFO) 17| +2.50E+02 | -3.1E-04 | +3.4E-01 | 2.0E-03 | 4.0E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 81| +1.38E+02 | -5.1E-04 | +5.1E-01 | 1.3E-02 | 3.3E-01 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 83| +1.48E+02 | -3.0E-05 | +8.6E-01 | 6.2E-03 | 1.1E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 80| +1.48E+02 | -1.2E-05 | +4.7E-01 | 1.7E-01 | 4.8E-02 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 23| +1.50E+02 | +3.0E-02 | -8.8E-01 | 3.6E-01 | 1.2E+00 | 1.4E-01 | trr |0\n", - "2023-02-17 16:05:25 fides(INFO) 32| +1.48E+02 | -2.0E-02 | +8.0E-01 | 2.2E-02 | 8.7E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 141| +1.54E+02 | -1.8E-03 | +1.4E+00 | 1.7E-01 | 7.8E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 18| +2.50E+02 | +2.5E-06 | -8.0E-03 | 2.0E-03 | 2.4E-01 | 4.8E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 84| +1.48E+02 | +1.5E-04 | -2.3E+00 | 1.2E-02 | 5.4E-02 | 3.0E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 81| +1.48E+02 | +2.1E-03 | -1.3E+01 | 1.7E-01 | 6.4E-02 | 2.1E-02 | trr |0\n", - "2023-02-17 16:05:25 fides(INFO) 82| +1.38E+02 | -3.2E-04 | +8.1E-01 | 1.3E-02 | 4.4E-01 | 6.1E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 33| +1.48E+02 | -5.4E-03 | +5.8E-01 | 4.4E-02 | 2.6E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 24| +1.50E+02 | -3.2E-03 | +8.6E-01 | 1.7E-02 | 1.2E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 19| +2.50E+02 | -1.6E-04 | +7.2E-01 | 4.9E-04 | 2.4E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 85| +1.48E+02 | -2.4E-06 | +1.0E-01 | 3.1E-03 | 5.4E-02 | 8.1E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 82| +1.48E+02 | +8.3E-05 | -1.2E+00 | 1.5E-02 | 6.4E-02 | 5.2E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 34| +1.48E+02 | -1.9E-03 | +8.6E-01 | 4.4E-02 | 3.0E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 20| +2.50E+02 | +2.0E-09 | -4.6E-04 | 4.9E-04 | 2.9E-02 | 1.2E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 83| +1.38E+02 | -5.3E-04 | +8.8E-01 | 2.7E-02 | 1.8E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 25| +1.50E+02 | -4.6E-03 | +9.1E-01 | 3.3E-02 | 1.4E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 142| +1.54E+02 | -1.0E-03 | +1.4E+00 | 1.7E-01 | 2.3E-01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 86| +1.48E+02 | -9.0E-06 | +8.1E-01 | 7.7E-04 | 1.3E-01 | 7.8E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 35| +1.48E+02 | -1.3E-03 | +5.8E-01 | 8.9E-02 | 7.3E-01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 83| +1.48E+02 | -7.3E-06 | +3.4E-01 | 3.7E-03 | 6.4E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:25.130 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 137.357 and h = 1.59524e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.130 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 137.357: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 40| +1.51E+02 | +0.0E+00 | +0.0E+00 | 7.6E-06 | 4.5E+00 | 1.7E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 21| +2.50E+02 | -8.2E-07 | +1.7E-01 | 1.2E-04 | 2.9E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 84| +1.38E+02 | -3.0E-04 | +1.8E-01 | 5.3E-02 | 2.2E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 87| +1.48E+02 | -6.9E-06 | +8.7E-01 | 1.5E-03 | 2.7E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 26| +1.50E+02 | -5.2E-03 | +8.9E-01 | 6.6E-02 | 1.7E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 36| +1.48E+02 | -6.0E-04 | +6.7E-01 | 8.9E-02 | 1.6E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 84| +1.48E+02 | -1.8E-06 | +7.3E-01 | 3.7E-03 | 1.3E-01 | 2.7E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 22| +2.50E+02 | -1.1E-06 | +8.8E-01 | 3.1E-05 | 2.3E-02 | 5.9E-05 | 2d |1\n", - "2023-02-17 16:05:25 fides(WARNING) Stopping as function difference 1.08E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:25 fides(INFO) 88| +1.48E+02 | -6.1E-06 | +9.5E-01 | 3.1E-03 | 2.1E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 37| +1.48E+02 | +3.6E-04 | -4.3E-01 | 8.9E-02 | 3.6E-01 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 85| +1.48E+02 | -2.1E-06 | +3.2E+01 | 3.7E-03 | 1.1E-02 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 27| +1.50E+02 | -2.7E-03 | +6.6E-01 | 1.3E-01 | 9.8E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 143| +1.54E+02 | -6.4E-04 | +1.3E+00 | 3.3E-01 | 5.7E-01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 85| +1.38E+02 | -8.7E-04 | +3.8E-01 | 1.3E-02 | 4.6E-01 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 89| +1.48E+02 | -4.8E-06 | +5.7E-01 | 6.2E-03 | 3.0E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 86| +1.48E+02 | +2.4E-06 | -4.6E+01 | 7.5E-03 | 7.2E-03 | 4.1E-04 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 28| +1.50E+02 | -2.5E-04 | +1.6E-01 | 1.3E-01 | 1.1E+00 | 2.6E-02 | trr |1\n", - "2023-02-17 16:05:25 fides(INFO) 38| +1.48E+02 | -1.9E-04 | +7.0E-01 | 2.2E-02 | 3.6E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 86| +1.38E+02 | -3.5E-04 | +5.9E-01 | 1.3E-02 | 7.5E-01 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 90| +1.48E+02 | +2.6E-04 | -6.1E+00 | 6.2E-03 | 3.6E-02 | 3.9E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 144| +1.54E+02 | -3.3E-04 | +1.3E+00 | 3.3E-01 | 1.9E-01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 87| +1.48E+02 | -2.2E-06 | +1.0E+02 | 1.9E-03 | 7.2E-03 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 29| +1.50E+02 | -8.3E-04 | +5.2E-01 | 3.0E-02 | 1.8E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 41| +1.51E+02 | -1.8E-05 | +9.7E-01 | 1.9E-06 | 4.5E+00 | 4.3E-06 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 39| +1.48E+02 | -6.3E-05 | +9.4E-01 | 2.2E-02 | 5.1E-01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 0| +1.23E+14 | NaN | NaN | 1.0E+00 | 5.6E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:25 fides(INFO) 87| +1.38E+02 | -2.5E-04 | +7.5E-01 | 1.3E-02 | 1.9E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 91| +1.48E+02 | +9.5E-05 | -3.5E+00 | 1.5E-03 | 3.6E-02 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 88| +1.48E+02 | -2.4E-07 | +7.6E+00 | 3.7E-03 | 5.7E-03 | 2.0E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(WARNING) Stopping as function difference 2.41E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 30| +1.50E+02 | -7.7E-05 | +5.7E-01 | 3.0E-02 | 7.1E-01 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 40| +1.48E+02 | -5.6E-05 | +8.4E-01 | 4.4E-02 | 2.2E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 1| +9.94E+11 | -1.2E+14 | +8.2E-02 | 1.0E+00 | 5.6E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 92| +1.48E+02 | +2.8E-05 | -1.5E+00 | 3.9E-04 | 3.6E-02 | 8.5E-04 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 145| +1.54E+02 | -1.5E-04 | +1.4E+00 | 3.3E-01 | 1.8E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 88| +1.38E+02 | -4.1E-04 | +7.3E-01 | 2.7E-02 | 2.6E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 41| +1.48E+02 | -6.0E-05 | +7.6E-01 | 8.9E-02 | 2.7E-01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 31| +1.50E+02 | +3.6E-05 | -4.1E-01 | 3.0E-02 | 6.1E-02 | 6.7E-03 | trr |0\n", - "2023-02-17 16:05:25 fides(INFO) 2| +1.47E+11 | -8.5E+11 | +8.9E-01 | 2.5E-01 | 4.6E+12 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 93| +1.48E+02 | -1.7E-06 | +2.5E-01 | 9.6E-05 | 3.6E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 3| +2.19E+10 | -1.3E+11 | +8.9E-01 | 5.0E-01 | 6.8E+11 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 146| +1.54E+02 | -9.2E-05 | +1.4E+00 | 3.3E-01 | 9.7E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 42| +1.48E+02 | +6.0E-04 | -3.3E+00 | 1.8E-01 | 1.3E-01 | 5.3E-02 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 32| +1.50E+02 | -2.9E-05 | +7.7E-01 | 6.3E-03 | 6.1E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 89| +1.38E+02 | -1.5E-04 | +1.6E-01 | 2.7E-02 | 2.0E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 4| +3.26E+09 | -1.9E+10 | +8.9E-01 | 5.0E-01 | 1.0E+11 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:25.403 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 85.3984 and h = 1.13613e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.403 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 85.3984: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) 42| +1.51E+02 | +0.0E+00 | +0.0E+00 | 3.8E-06 | 4.5E+00 | 8.7E-06 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 5| +4.90E+08 | -2.8E+09 | +8.9E-01 | 5.0E-01 | 1.5E+10 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 94| +1.48E+02 | -3.3E-06 | +1.2E+01 | 2.4E-05 | 4.5E-02 | 1.3E-05 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 43| +1.48E+02 | -5.1E-06 | +7.7E-02 | 4.4E-02 | 1.3E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 6| +7.38E+07 | -4.2E+08 | +8.9E-01 | 5.0E-01 | 2.3E+09 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 0| +2.27E+09 | NaN | NaN | 1.0E+00 | 1.0E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:25 fides(INFO) 147| +1.54E+02 | -4.9E-05 | +1.4E+00 | 3.3E-01 | 2.1E-02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 33| +1.50E+02 | -1.1E-05 | +8.4E-01 | 1.3E-02 | 1.4E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 90| +1.38E+02 | -4.6E-04 | +6.0E-01 | 6.7E-03 | 3.9E-01 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 95| +1.48E+02 | +4.0E-06 | -2.2E+01 | 4.8E-05 | 1.4E-02 | 2.8E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 7| +1.12E+07 | -6.3E+07 | +8.9E-01 | 5.0E-01 | 3.4E+08 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 44| +1.48E+02 | -3.1E-05 | +7.5E-01 | 1.1E-02 | 2.4E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 8| +1.70E+06 | -9.5E+06 | +8.9E-01 | 5.0E-01 | 5.1E+07 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 34| +1.50E+02 | -2.3E-06 | +5.8E-01 | 2.5E-02 | 7.8E-02 | 7.5E-04 | trr |1\n", - "2023-02-17 16:05:25 fides(INFO) 96| +1.48E+02 | +2.7E-06 | -1.6E+01 | 1.2E-05 | 1.4E-02 | 2.5E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 148| +1.54E+02 | -2.6E-05 | +1.4E+00 | 3.3E-01 | 9.4E-03 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 1| +1.35E+06 | -2.3E+09 | +9.7E-02 | 1.0E+00 | 1.0E+10 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 45| +1.48E+02 | -9.5E-06 | +7.1E-01 | 2.2E-02 | 6.8E-02 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 9| +2.61E+05 | -1.4E+06 | +8.9E-01 | 5.0E-01 | 7.8E+06 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 97| +1.48E+02 | +3.7E-06 | -4.7E+01 | 3.0E-06 | 1.4E-02 | 7.1E-06 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 91| +1.38E+02 | -1.1E-04 | +9.8E-01 | 6.7E-03 | 3.1E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 2| +2.13E+05 | -1.1E+06 | +9.0E-01 | 2.5E-01 | 6.4E+06 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 35| +1.50E+02 | +2.5E-06 | -5.7E-01 | 2.5E-02 | 2.0E-02 | 1.5E-03 | trr |0\n", - "2023-02-17 16:05:25 fides(INFO) 149| +1.54E+02 | -1.4E-05 | +1.4E+00 | 3.3E-01 | 9.2E-03 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 10| +4.03E+04 | -2.2E+05 | +9.0E-01 | 5.0E-01 | 1.2E+06 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 46| +1.48E+02 | -6.7E-06 | +1.2E+00 | 2.2E-02 | 5.2E-02 | 5.8E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 3| +3.59E+04 | -1.8E+05 | +9.0E-01 | 5.0E-01 | 9.9E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 98| +1.48E+02 | +2.4E-06 | -1.1E+02 | 7.5E-07 | 1.4E-02 | 1.8E-06 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 92| +1.38E+02 | -2.4E-04 | +1.0E+00 | 1.3E-02 | 1.3E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 36| +1.50E+02 | -1.6E-06 | +8.4E-01 | 4.0E-04 | 2.0E-02 | 3.8E-04 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 47| +1.48E+02 | -5.3E-06 | +6.6E-01 | 4.4E-02 | 5.8E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 4| +5.68E+03 | -3.0E+04 | +9.0E-01 | 1.0E+00 | 1.6E+05 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 11| +6.40E+03 | -3.4E+04 | +9.0E-01 | 5.0E-01 | 1.8E+05 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:25.573 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 114.461 and h = 1.42314e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.573 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 114.461: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 150| +1.54E+02 | -7.9E-06 | +1.4E+00 | 3.3E-01 | 5.7E-03 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 43| +1.51E+02 | +0.0E+00 | +0.0E+00 | 9.5E-07 | 4.5E+00 | 2.2E-06 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 99| +1.48E+02 | +3.0E-06 | -5.4E+02 | 1.9E-07 | 1.4E-02 | 4.4E-07 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 5| +1.04E+03 | -4.6E+03 | +9.0E-01 | 1.0E+00 | 2.4E+04 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:25.590 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 168.763 and h = 2.32709e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.590 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 168.763: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) 93| +1.38E+02 | -4.2E-04 | +9.8E-01 | 2.7E-02 | 4.6E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 37| +1.50E+02 | +0.0E+00 | +0.0E+00 | 7.9E-04 | 1.1E-02 | 3.5E-04 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 48| +1.48E+02 | -7.0E-06 | +1.2E+00 | 4.4E-02 | 3.3E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 12| +1.18E+03 | -5.2E+03 | +9.0E-01 | 5.0E-01 | 2.9E+04 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 6| +3.31E+02 | -7.1E+02 | +9.0E-01 | 1.0E+00 | 3.9E+03 | 7.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 151| +1.54E+02 | -4.2E-06 | +1.3E+00 | 3.3E-01 | 3.9E-03 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 100| +1.48E+02 | -5.3E-08 | +3.8E+01 | 4.7E-08 | 1.4E-02 | 1.1E-07 | 2d |1\n", - "2023-02-17 16:05:25 fides(WARNING) Stopping as function difference 5.31E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:25 fides(INFO) 13| +3.70E+02 | -8.1E+02 | +9.0E-01 | 5.0E-01 | 4.5E+03 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 38| +1.50E+02 | -1.7E-07 | +8.8E-01 | 2.0E-04 | 1.1E-02 | 8.9E-05 | 2d |1\n", - "2023-02-17 16:05:25 fides(WARNING) Stopping as function difference 1.74E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:25 fides(INFO) 94| +1.38E+02 | -6.3E-04 | +7.2E-01 | 5.3E-02 | 1.1E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 49| +1.48E+02 | +4.9E-06 | -5.2E-01 | 8.9E-02 | 3.7E-02 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 7| +2.31E+02 | -1.0E+02 | +8.7E-01 | 1.0E+00 | 6.2E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 14| +2.52E+02 | -1.2E+02 | +8.8E-01 | 5.0E-01 | 6.9E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 152| +1.54E+02 | -2.1E-06 | +1.3E+00 | 3.3E-01 | 2.0E-03 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 50| +1.48E+02 | +7.5E-07 | -2.5E-01 | 2.2E-02 | 3.7E-02 | 4.6E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 95| +1.38E+02 | +9.1E-03 | -2.5E+00 | 5.3E-02 | 2.5E-01 | 2.5E-02 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 15| +2.40E+02 | -1.2E+01 | +7.3E-01 | 1.0E+00 | 9.1E+01 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 8| +2.04E+02 | -2.7E+01 | +1.1E+00 | 2.0E+00 | 1.1E+02 | 3.7E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 153| +1.54E+02 | -6.1E-07 | +8.8E-01 | 3.3E-01 | 1.2E-03 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:25 fides(WARNING) Stopping as function difference 6.15E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:25 fides(INFO) 51| +1.48E+02 | +2.3E-07 | -2.4E-01 | 5.5E-03 | 3.7E-02 | 1.1E-03 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 16| +2.32E+02 | -8.6E+00 | +1.1E+00 | 1.0E+00 | 1.7E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 96| +1.38E+02 | -1.4E-04 | +1.4E-01 | 1.3E-02 | 2.5E-01 | 6.3E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 9| +2.04E+02 | +1.3E+03 | -5.7E+01 | 4.0E+00 | 4.6E+01 | 4.4E+00 | trr |0\n", - "2023-02-17 16:05:25.751 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 95.8575 and h = 1.32106e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.752 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 95.8575: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) 44| +1.51E+02 | +0.0E+00 | +0.0E+00 | 2.4E-07 | 4.5E+00 | 5.4E-07 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 17| +2.22E+02 | -9.5E+00 | +2.3E-01 | 2.0E+00 | 4.5E+01 | 4.4E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 52| +1.48E+02 | +3.1E-06 | -8.0E+00 | 1.4E-03 | 3.7E-02 | 2.8E-04 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 0| +7.56E+07 | NaN | NaN | 1.0E+00 | 3.5E+08 | NaN | NaN |1\n", - "2023-02-17 16:05:25 fides(INFO) 18| +2.05E+02 | -1.7E+01 | +9.8E-01 | 5.0E-01 | 5.7E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 53| +1.48E+02 | +9.8E-07 | -4.0E+00 | 3.5E-04 | 3.7E-02 | 7.2E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 10| +2.04E+02 | +2.9E+01 | -1.7E+00 | 6.1E-01 | 4.6E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 97| +1.38E+02 | -2.1E-04 | +8.1E-01 | 3.3E-03 | 4.0E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 0| +2.91E+12 | NaN | NaN | 1.0E+00 | 1.3E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:25 fides(INFO) 54| +1.48E+02 | +3.3E-06 | -1.6E+01 | 8.7E-05 | 3.7E-02 | 2.1E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 1| +1.15E+03 | -7.6E+07 | +1.0E-01 | 1.0E+00 | 3.5E+08 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 19| +2.05E+02 | +3.0E+02 | -1.6E+01 | 1.0E+00 | 3.0E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 11| +1.95E+02 | -9.4E+00 | +9.2E-01 | 1.5E-01 | 4.6E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 98| +1.38E+02 | -1.5E-04 | +7.3E-01 | 6.7E-03 | 1.9E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 55| +1.48E+02 | +1.8E-06 | -9.2E+00 | 2.2E-05 | 3.7E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 2| +4.22E+02 | -7.3E+02 | +8.9E-01 | 2.5E-01 | 3.0E+03 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 20| +1.93E+02 | -1.3E+01 | +1.1E+00 | 2.5E-01 | 3.0E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 0| +2.93E+04 | NaN | NaN | 1.0E+00 | 1.3E+05 | NaN | NaN |1\n", - "2023-02-17 16:05:25 fides(INFO) 1| +1.31E+08 | -2.9E+12 | +8.7E-02 | 1.0E+00 | 1.3E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 12| +1.84E+02 | -1.1E+01 | +7.4E-01 | 3.0E-01 | 5.2E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 3| +2.61E+02 | -1.6E+02 | +8.8E-01 | 5.0E-01 | 5.4E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 99| +1.38E+02 | -8.6E-05 | +9.8E-01 | 6.7E-03 | 1.3E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 56| +1.48E+02 | +1.6E-06 | -8.3E+00 | 5.4E-06 | 3.7E-02 | 9.9E-06 | 2d |0\n", - "2023-02-17 16:05:25.897 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 100 and h = 1.26382e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.897 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 100: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) 45| +1.51E+02 | +0.0E+00 | +0.0E+00 | 6.0E-08 | 4.5E+00 | 1.4E-07 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 21| +1.93E+02 | -1.0E+01 | -3.3E-02 | 5.0E-01 | 4.8E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 1| +4.30E+02 | -2.9E+04 | +1.2E-01 | 1.0E+00 | 1.3E+05 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 4| +2.55E+02 | -6.8E+00 | +2.9E-01 | 1.0E+00 | 7.7E+01 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 57| +1.48E+02 | +3.5E-07 | -4.8E+00 | 1.4E-06 | 3.7E-02 | 2.5E-06 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 13| +1.77E+02 | -7.0E+00 | +4.1E-01 | 3.0E-01 | 8.9E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 22| +1.84E+02 | -8.9E+00 | +1.1E+00 | 1.2E-01 | 4.8E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 5| +2.42E+02 | -1.3E+01 | +1.2E+00 | 1.0E+00 | 5.6E+01 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 2| +2.22E+07 | -1.1E+08 | +8.9E-01 | 2.5E-01 | 5.9E+08 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:25 fides(INFO) 100| +1.38E+02 | -1.7E-04 | +9.9E-01 | 1.3E-02 | 7.1E-02 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 2| +3.86E+02 | -4.4E+01 | +9.8E-01 | 2.5E-01 | 6.2E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:25.952 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1525 and h = 2.26931e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:25.952 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1525: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:25 fides(INFO) 58| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.4E-07 | 3.7E-02 | 6.2E-07 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 14| +1.69E+02 | -8.1E+00 | +8.9E-01 | 3.0E-01 | 1.1E+02 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 6| +2.42E+02 | +3.1E+02 | -1.0E+01 | 2.0E+00 | 2.7E+01 | 3.9E+00 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 23| +1.68E+02 | -1.5E+01 | +9.7E-01 | 2.5E-01 | 5.6E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 3| +3.17E+02 | -6.8E+01 | +8.3E-01 | 5.0E-01 | 5.8E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 59| +1.48E+02 | +2.8E-08 | -5.6E+00 | 8.5E-08 | 3.7E-02 | 1.5E-07 | 2d |0\n", - "2023-02-17 16:05:25 fides(INFO) 101| +1.38E+02 | -3.3E-04 | +9.9E-01 | 2.7E-02 | 1.3E-01 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:25 fides(INFO) 7| +2.13E+02 | -2.9E+01 | +1.5E+00 | 5.0E-01 | 2.7E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 15| +1.63E+02 | -5.7E+00 | +4.1E-01 | 6.1E-01 | 5.5E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 3| +4.98E+06 | -1.7E+07 | +9.0E-01 | 5.0E-01 | 9.1E+07 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 24| +1.55E+02 | -1.3E+01 | +6.9E-01 | 5.0E-01 | 7.3E+01 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 4| +2.26E+02 | -9.2E+01 | +8.5E-01 | 1.0E+00 | 8.9E+01 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 60| +1.48E+02 | +3.1E-06 | -2.4E+03 | 2.1E-08 | 3.7E-02 | 3.9E-08 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 16| +1.58E+02 | -5.4E+00 | +9.5E-01 | 6.1E-01 | 1.7E+02 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 8| +1.99E+02 | -1.5E+01 | +1.2E+00 | 1.0E+00 | 5.0E+01 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 102| +1.38E+02 | -6.2E-04 | +9.6E-01 | 5.3E-02 | 7.0E-02 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 25| +1.53E+02 | -2.5E+00 | +2.0E-01 | 5.0E-01 | 5.7E+01 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 61| +1.48E+02 | +1.3E-06 | -4.1E+03 | 5.3E-09 | 3.7E-02 | 9.7E-09 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 4| +8.59E+05 | -4.1E+06 | +9.0E-01 | 1.0E+00 | 1.6E+07 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 17| +1.58E+02 | +1.8E+01 | -3.5E+00 | 1.2E+00 | 3.4E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 5| +2.26E+02 | +5.5E+02 | -2.1E+01 | 2.0E+00 | 2.7E+01 | 5.1E+00 | trr |0\n", - "2023-02-17 16:05:26 fides(INFO) 103| +1.38E+02 | +2.9E-03 | -2.1E+00 | 1.1E-01 | 8.4E-02 | 3.3E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 26| +1.48E+02 | -4.2E+00 | +6.1E-01 | 1.1E-01 | 6.8E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 46| +1.51E+02 | -1.3E-07 | +8.7E-01 | 1.5E-08 | 4.5E+00 | 3.4E-08 | 2d |1\n", - "2023-02-17 16:05:26 fides(WARNING) Stopping as function difference 1.29E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:26 fides(INFO) 18| +1.55E+02 | -3.1E+00 | +8.8E-01 | 2.1E-01 | 3.4E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 9| +1.99E+02 | +5.3E+04 | -1.3E+03 | 2.0E+00 | 5.0E+01 | 4.7E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 62| +1.48E+02 | +5.8E-07 | -7.3E+03 | 1.3E-09 | 3.7E-02 | 2.4E-09 | 2d |0\n", - "2023-02-17 16:05:26.114 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 201.089 and h = 7.23664e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:26.115 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 201.089: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:26 fides(INFO) 27| +1.48E+02 | +1.3E+00 | -9.5E-01 | 1.1E-01 | 3.1E+01 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 6| +2.26E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 2.7E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 5| +1.43E+05 | -7.2E+05 | +9.0E-01 | 2.0E+00 | 2.8E+06 | 2.3E+00 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 63| +1.48E+02 | +2.9E-06 | -1.5E+05 | 3.3E-10 | 3.7E-02 | 6.0E-10 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 104| +1.38E+02 | -1.3E-04 | +3.0E-01 | 2.7E-02 | 8.4E-02 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 19| +1.50E+02 | -4.7E+00 | +1.1E+00 | 4.3E-01 | 6.0E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 10| +1.61E+02 | -3.7E+01 | +1.1E+00 | 5.0E-01 | 5.0E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 7| +2.21E+02 | -4.9E+00 | +8.1E-01 | 1.2E-01 | 2.7E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 28| +1.48E+02 | -4.9E-01 | +6.8E-01 | 2.8E-02 | 3.1E+01 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 64| +1.48E+02 | +2.7E-06 | -5.3E+05 | 8.3E-11 | 3.7E-02 | 1.5E-10 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 20| +1.50E+02 | +4.7E-01 | -2.8E-01 | 8.5E-01 | 1.5E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 8| +2.08E+02 | -1.2E+01 | +1.4E+00 | 2.5E-01 | 2.0E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 105| +1.38E+02 | +4.1E-03 | -2.1E+00 | 2.7E-02 | 2.1E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 11| +1.56E+02 | -4.6E+00 | +9.5E-01 | 1.0E+00 | 9.1E+01 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 29| +1.48E+02 | -2.1E-01 | +9.2E-01 | 2.8E-02 | 2.5E+01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 6| +2.37E+04 | -1.2E+05 | +9.0E-01 | 2.0E+00 | 4.7E+05 | 3.6E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 65| +1.48E+02 | +2.7E-06 | -2.2E+06 | 2.1E-11 | 3.7E-02 | 3.8E-11 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 21| +1.49E+02 | -1.1E+00 | +8.3E-01 | 1.3E-01 | 1.5E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 9| +2.08E+02 | -1.5E+00 | -1.2E-01 | 5.0E-01 | 3.3E+01 | 8.1E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 0| +1.12E+14 | NaN | NaN | 1.0E+00 | 5.2E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:26 fides(INFO) 12| +1.56E+02 | +7.5E+01 | -7.0E+00 | 2.0E+00 | 8.2E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 106| +1.38E+02 | +6.0E-04 | -7.9E-01 | 6.7E-03 | 2.1E-01 | 5.7E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 30| +1.48E+02 | -8.9E-02 | +8.5E-01 | 5.7E-02 | 1.0E+01 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 66| +1.48E+02 | +2.9E-06 | -9.2E+06 | 5.2E-12 | 3.7E-02 | 9.4E-12 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 10| +2.02E+02 | -6.1E+00 | +9.3E-01 | 1.2E-01 | 3.3E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 1| +8.83E+08 | -1.1E+14 | +8.3E-02 | 1.0E+00 | 5.2E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 22| +1.49E+02 | +2.3E+01 | -9.5E+00 | 2.6E-01 | 1.0E+01 | 4.8E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 13| +1.51E+02 | -5.3E+00 | +7.2E-01 | 1.9E-01 | 8.2E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 31| +1.48E+02 | +4.5E-01 | -5.9E+00 | 1.1E-01 | 6.9E+00 | 3.2E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 107| +1.38E+02 | +6.3E-05 | -1.6E-01 | 1.7E-03 | 2.1E-01 | 3.1E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 67| +1.48E+02 | +2.1E-06 | -2.7E+07 | 1.3E-12 | 3.7E-02 | 2.4E-12 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 11| +1.93E+02 | -9.1E+00 | +9.1E-01 | 2.5E-01 | 4.4E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:26.296 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 93.9924 and h = 1.89086e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:26.296 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 93.9924: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:26 fides(INFO) 23| +1.49E+02 | +1.5E-02 | -4.4E-02 | 6.5E-02 | 1.0E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 14| +1.51E+02 | +0.0E+00 | +0.0E+00 | 1.9E-01 | 9.0E+01 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 32| +1.48E+02 | +4.8E-04 | -1.8E-02 | 2.8E-02 | 6.9E+00 | 7.9E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 108| +1.38E+02 | -1.1E-04 | +7.5E-01 | 4.2E-04 | 2.1E-01 | 8.8E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 68| +1.48E+02 | +2.5E-06 | -1.3E+08 | 3.2E-13 | 3.7E-02 | 5.9E-13 | 2d |0\n", - "2023-02-17 16:05:26.333 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 149.627 and h = 2.79486e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:26.333 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 149.627: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:26 fides(INFO) 24| +1.49E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 1.0E+01 | 3.1E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 15| +1.50E+02 | -1.4E+00 | +9.4E-01 | 4.7E-02 | 9.0E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 12| +1.81E+02 | -1.2E+01 | +8.2E-01 | 5.0E-01 | 5.6E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 2| +1.73E+08 | -7.1E+08 | +8.9E-01 | 2.5E-01 | 3.8E+09 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 7| +3.98E+03 | -2.0E+04 | +9.1E-01 | 4.0E+00 | 7.7E+04 | 1.9E+00 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 33| +1.48E+02 | -1.0E-02 | +8.0E-01 | 7.1E-03 | 6.9E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 69| +1.48E+02 | +2.7E-06 | -5.5E+08 | 8.1E-14 | 3.7E-02 | 1.5E-13 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 25| +1.49E+02 | -5.8E-02 | +9.4E-01 | 4.1E-03 | 1.0E+01 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 109| +1.38E+02 | -2.5E-05 | +9.9E-01 | 4.2E-04 | 4.1E-01 | 1.6E-04 | 2d |1\n", - "2023-02-17 16:05:26.373 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 110.183 and h = 2.72528e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:26 fides(INFO) 16| +1.49E+02 | -7.5E-01 | +6.9E-01 | 9.4E-02 | 4.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:26.374 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 110.183: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:26 fides(INFO) 13| +1.81E+02 | +0.0E+00 | +0.0E+00 | 1.0E+00 | 7.6E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 34| +1.48E+02 | -6.1E-03 | +9.6E-01 | 1.4E-02 | 3.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 3| +3.61E+07 | -1.4E+08 | +9.1E-01 | 5.0E-01 | 6.1E+08 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 70| +1.48E+02 | +2.6E-06 | -2.2E+09 | 2.0E-14 | 3.7E-02 | 3.7E-14 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 26| +1.49E+02 | -9.6E-02 | +9.2E-01 | 8.1E-03 | 9.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 110| +1.38E+02 | +1.9E-05 | -1.5E-01 | 8.3E-04 | 1.2E-01 | 1.8E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 8| +3.98E+03 | +2.9E+07 | -4.3E+04 | 4.0E+00 | 1.2E+04 | 4.0E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 17| +1.48E+02 | -6.0E-01 | +9.6E-01 | 9.4E-02 | 5.3E+01 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 14| +1.76E+02 | -5.2E+00 | +7.9E-01 | 2.5E-01 | 7.6E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 35| +1.48E+02 | -9.4E-03 | +8.1E-01 | 2.8E-02 | 3.3E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 71| +1.48E+02 | +1.6E-06 | -5.3E+09 | 5.0E-15 | 3.7E-02 | 9.2E-15 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 4| +5.59E+06 | -3.1E+07 | +8.9E-01 | 1.0E+00 | 1.2E+08 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 27| +1.49E+02 | -1.1E-01 | +6.8E-01 | 1.6E-02 | 9.8E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 18| +1.48E+02 | -4.1E-01 | +6.9E-01 | 1.9E-01 | 3.0E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 15| +1.73E+02 | -2.9E+00 | +8.6E-01 | 5.0E-01 | 5.1E+01 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 111| +1.38E+02 | -3.0E-05 | +7.7E-01 | 2.1E-04 | 1.2E-01 | 4.6E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 36| +1.48E+02 | +2.9E-02 | -4.5E+00 | 5.7E-02 | 5.5E-01 | 7.5E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 72| +1.48E+02 | +8.8E-07 | -1.2E+10 | 1.3E-15 | 3.7E-02 | 2.3E-15 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 28| +1.49E+02 | -9.7E-02 | +9.9E-01 | 1.6E-02 | 6.3E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 5| +8.13E+05 | -4.8E+06 | +9.0E-01 | 2.0E+00 | 1.8E+07 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 19| +1.48E+02 | +2.1E-01 | -3.7E-01 | 1.9E-01 | 3.0E+01 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 37| +1.48E+02 | +2.2E-04 | -1.1E-01 | 1.4E-02 | 5.5E-01 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 73| +1.48E+02 | +2.8E-06 | -1.4E+11 | 3.2E-16 | 3.7E-02 | 5.8E-16 | 2d |0\n", - "2023-02-17 16:05:26 fides(WARNING) Stopping as trust region radius 7.88E-17 is smaller than machine precision.\n", - "2023-02-17 16:05:26 fides(INFO) 16| +1.72E+02 | -9.8E-01 | +8.3E-01 | 1.0E+00 | 2.6E+01 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 112| +1.38E+02 | -9.3E-06 | +1.0E+00 | 4.2E-04 | 1.9E-01 | 1.3E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 20| +1.48E+02 | -1.8E-01 | +7.7E-01 | 4.7E-02 | 3.0E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 29| +1.48E+02 | -1.4E-01 | +9.6E-01 | 3.2E-02 | 1.2E+01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 38| +1.48E+02 | -4.4E-04 | +7.1E-01 | 3.5E-03 | 5.5E-01 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 6| +1.19E+05 | -6.9E+05 | +9.0E-01 | 2.0E+00 | 2.8E+06 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 113| +1.38E+02 | -1.2E-05 | +9.3E-01 | 8.3E-04 | 7.9E-02 | 2.6E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 30| +1.48E+02 | -2.6E-01 | +9.8E-01 | 6.5E-02 | 5.8E+00 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 21| +1.48E+02 | -1.0E-01 | +8.8E-01 | 9.4E-02 | 1.6E+01 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 17| +1.71E+02 | -6.7E-01 | +7.0E-01 | 2.0E+00 | 1.5E+01 | 1.0E+00 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 39| +1.48E+02 | +7.8E-05 | -1.8E-01 | 3.5E-03 | 5.8E-01 | 9.0E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 9| +8.88E+02 | -3.1E+03 | +8.6E-01 | 7.6E-01 | 1.2E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 7| +1.79E+04 | -1.0E+05 | +9.0E-01 | 2.0E+00 | 4.3E+05 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 22| +1.48E+02 | -7.5E-02 | +7.7E-01 | 1.9E-01 | 1.6E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 31| +1.48E+02 | -2.5E-01 | +7.2E-01 | 1.3E-01 | 1.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 114| +1.38E+02 | -2.0E-05 | +9.2E-01 | 1.7E-03 | 1.1E-01 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 40| +1.48E+02 | -1.1E-04 | +7.4E-01 | 8.8E-04 | 5.8E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 18| +1.71E+02 | -8.7E-02 | +4.2E-01 | 2.0E+00 | 2.1E+01 | 2.5E-01 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 23| +1.48E+02 | +1.0E+00 | -5.4E+00 | 3.8E-01 | 7.1E+00 | 3.7E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 8| +2.80E+03 | -1.5E+04 | +9.0E-01 | 2.0E+00 | 6.7E+04 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 32| +1.48E+02 | +5.2E-01 | -1.0E+00 | 1.3E-01 | 5.8E+00 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 0| +7.69E+12 | NaN | NaN | 1.0E+00 | 3.5E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:26 fides(INFO) 115| +1.38E+02 | -3.9E-05 | +1.0E+00 | 3.3E-03 | 7.0E-02 | 9.6E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 41| +1.48E+02 | -2.5E-05 | +7.3E-01 | 8.8E-04 | 2.7E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 24| +1.48E+02 | -9.4E-03 | +8.2E-02 | 9.4E-02 | 7.1E+00 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 19| +1.71E+02 | -2.1E-03 | +1.3E-02 | 2.0E+00 | 6.5E+00 | 4.3E-01 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 33| +1.48E+02 | -9.3E-02 | +3.4E-01 | 3.2E-02 | 5.8E+00 | 6.9E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 9| +5.59E+02 | -2.2E+03 | +9.1E-01 | 2.0E+00 | 1.1E+04 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 42| +1.48E+02 | -1.5E-05 | +1.1E+00 | 8.8E-04 | 2.2E-01 | 3.7E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 116| +1.38E+02 | -7.0E-05 | +9.8E-01 | 6.7E-03 | 7.5E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 25| +1.48E+02 | -3.0E-02 | +8.9E-01 | 2.4E-02 | 8.6E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 34| +1.48E+02 | -9.3E-02 | +5.2E-01 | 3.2E-02 | 6.4E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 1| +8.84E+10 | -7.6E+12 | +8.6E-02 | 1.0E+00 | 3.5E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 10| +2.39E+02 | -3.2E+02 | +9.2E-01 | 2.0E+00 | 1.7E+03 | 5.7E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 20| +1.71E+02 | -5.0E-02 | +6.7E-01 | 1.5E-01 | 7.6E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 43| +1.48E+02 | -1.3E-05 | +8.4E-01 | 1.8E-03 | 1.8E-01 | 2.2E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 26| +1.48E+02 | -1.0E-02 | +2.2E-01 | 4.7E-02 | 3.3E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 35| +1.48E+02 | -4.0E-02 | +9.7E-01 | 3.2E-02 | 5.5E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 117| +1.38E+02 | -1.4E-04 | +9.9E-01 | 1.3E-02 | 6.5E-02 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 44| +1.48E+02 | -8.8E-06 | +6.8E-01 | 3.5E-03 | 1.4E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 21| +1.71E+02 | -5.9E-03 | +8.5E-01 | 1.5E-01 | 4.6E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 11| +2.39E+02 | +7.4E+01 | -1.7E+00 | 4.0E+00 | 2.7E+02 | 8.5E+00 | trr |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 10| +2.85E+02 | -6.0E+02 | +9.2E-01 | 1.5E+00 | 2.6E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 36| +1.48E+02 | -6.2E-02 | +9.5E-01 | 6.5E-02 | 2.1E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 2| +1.32E+10 | -7.5E+10 | +8.9E-01 | 2.5E-01 | 4.1E+11 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 27| +1.48E+02 | -1.3E-02 | +9.3E-01 | 1.2E-02 | 1.0E+01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 118| +1.38E+02 | -2.7E-04 | +1.0E+00 | 2.7E-02 | 6.5E-02 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 45| +1.48E+02 | +2.4E-04 | -4.5E+00 | 3.5E-03 | 5.8E-02 | 6.0E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 22| +1.71E+02 | -3.4E-05 | +7.4E-02 | 2.9E-01 | 3.5E-01 | 1.5E-02 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 12| +1.96E+02 | -4.3E+01 | +9.1E-01 | 7.1E-01 | 2.7E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:26.755 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.898 and h = 2.79382e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:26 fides(INFO) 37| +1.48E+02 | -6.4E-02 | +7.6E-01 | 1.3E-01 | 3.7E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:26.755 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.898: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:26 fides(INFO) 28| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.4E-02 | 9.6E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 3| +1.99E+09 | -1.1E+10 | +8.9E-01 | 5.0E-01 | 6.1E+10 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 46| +1.48E+02 | +2.3E-05 | -9.3E-01 | 8.8E-04 | 5.8E-02 | 1.5E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 13| +1.96E+02 | +2.8E+01 | -2.9E+00 | 1.4E+00 | 6.6E+01 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 119| +1.38E+02 | -4.7E-04 | +9.0E-01 | 5.3E-02 | 6.5E-02 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 4| +3.02E+08 | -1.7E+09 | +8.9E-01 | 5.0E-01 | 9.2E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 29| +1.48E+02 | -6.8E-04 | +8.6E-01 | 5.9E-03 | 9.6E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 38| +1.48E+02 | +7.6E-01 | -5.7E+00 | 2.6E-01 | 2.5E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 23| +1.71E+02 | -3.2E-06 | +7.1E-03 | 2.9E-02 | 4.0E-01 | 3.5E-03 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) 47| +1.48E+02 | -3.7E-06 | +2.9E-01 | 2.2E-04 | 5.8E-02 | 4.0E-04 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 5| +4.59E+07 | -2.6E+08 | +8.9E-01 | 5.0E-01 | 1.4E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 14| +1.84E+02 | -1.2E+01 | +1.0E+00 | 3.5E-01 | 6.6E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 30| +1.48E+02 | -5.9E-04 | +8.9E-01 | 1.2E-02 | 1.6E+00 | 6.7E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 120| +1.38E+02 | +1.4E-02 | -6.7E+00 | 1.1E-01 | 1.0E-01 | 3.3E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 11| +2.85E+02 | +3.6E+02 | -7.6E+00 | 1.5E+00 | 3.1E+02 | 2.2E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 39| +1.48E+02 | +4.4E-03 | -7.9E-02 | 5.5E-02 | 2.5E+00 | 4.2E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 24| +1.71E+02 | +1.4E-06 | -3.8E-03 | 2.8E-03 | 3.8E-01 | 3.8E-03 | trr |0\n", - "2023-02-17 16:05:26 fides(INFO) 48| +1.48E+02 | +2.7E-07 | -1.8E-01 | 2.2E-04 | 8.8E-02 | 1.5E-04 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 15| +1.83E+02 | -1.2E+00 | +1.6E-01 | 7.1E-01 | 1.9E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 6| +7.05E+06 | -3.9E+07 | +8.9E-01 | 5.0E-01 | 2.1E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 31| +1.48E+02 | -4.6E-04 | +8.6E-01 | 2.4E-02 | 6.7E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 40| +1.48E+02 | -1.3E-02 | +5.9E-01 | 1.4E-02 | 2.5E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 121| +1.38E+02 | +4.9E-04 | -8.2E-01 | 2.7E-02 | 1.0E-01 | 8.3E-03 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 49| +1.48E+02 | -5.0E-06 | +4.4E+00 | 5.5E-05 | 8.8E-02 | 4.3E-05 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 16| +1.79E+02 | -3.4E+00 | +8.2E-01 | 1.8E-01 | 5.0E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 25| +1.71E+02 | -1.7E-04 | +7.8E-01 | 4.6E-04 | 3.8E-01 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 7| +1.09E+06 | -6.0E+06 | +9.0E-01 | 1.0E+00 | 3.2E+07 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 32| +1.48E+02 | -3.8E-04 | +9.3E-01 | 4.7E-02 | 1.1E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 41| +1.48E+02 | -8.9E-03 | +9.5E-01 | 1.4E-02 | 6.2E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 50| +1.48E+02 | +5.8E-06 | -1.8E+01 | 1.1E-04 | 2.2E-02 | 6.4E-05 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 17| +1.71E+02 | -8.0E+00 | +1.6E+00 | 3.5E-01 | 1.5E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 33| +1.48E+02 | -4.0E-04 | +8.7E-01 | 9.4E-02 | 4.7E-01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 8| +1.70E+05 | -9.2E+05 | +9.0E-01 | 1.0E+00 | 5.0E+06 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 26| +1.71E+02 | -3.4E-05 | +4.9E-01 | 9.3E-04 | 2.3E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 122| +1.38E+02 | -7.0E-05 | +4.2E-01 | 6.7E-03 | 1.0E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 42| +1.48E+02 | -9.9E-03 | +9.7E-01 | 2.7E-02 | 9.7E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 51| +1.48E+02 | +2.8E-06 | -1.4E+01 | 2.8E-05 | 2.2E-02 | 2.2E-05 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 18| +1.71E+02 | +3.0E+02 | -2.0E+01 | 7.1E-01 | 2.1E+01 | 1.6E+00 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 34| +1.48E+02 | +7.5E-04 | -1.0E+00 | 1.9E-01 | 6.7E-01 | 8.2E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 9| +2.68E+04 | -1.4E+05 | +9.0E-01 | 1.0E+00 | 7.8E+05 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 43| +1.48E+02 | -1.2E-02 | +7.9E-01 | 5.5E-02 | 2.6E+00 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 52| +1.48E+02 | +3.9E-06 | -2.4E+01 | 6.9E-06 | 2.2E-02 | 1.5E-05 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) 19| +1.62E+02 | -9.5E+00 | +1.2E+00 | 1.8E-01 | 2.1E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 123| +1.38E+02 | -8.0E-05 | +7.7E-01 | 6.7E-03 | 3.9E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 12| +2.47E+02 | -3.8E+01 | +7.3E-01 | 2.0E-01 | 3.1E+02 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 27| +1.71E+02 | -7.2E-07 | +3.4E-02 | 9.3E-04 | 7.9E-02 | 7.6E-04 | trr |1\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 10| +4.40E+03 | -2.2E+04 | +9.0E-01 | 1.0E+00 | 1.2E+05 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 35| +1.48E+02 | -1.6E-04 | +6.1E-01 | 4.7E-02 | 6.7E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 44| +1.48E+02 | +3.3E-02 | -1.1E+00 | 1.1E-01 | 1.1E+00 | 6.7E-02 | 2d |0\n", - "2023-02-17 16:05:26 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:26 fides(INFO) 20| +1.53E+02 | -8.3E+00 | +5.2E-01 | 3.5E-01 | 3.1E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:26 fides(INFO) 53| +1.48E+02 | +1.4E-06 | -1.6E+01 | 1.7E-06 | 2.2E-02 | 4.9E-06 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 124| +1.38E+02 | -1.1E-04 | +9.2E-01 | 1.3E-02 | 6.8E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 11| +8.75E+02 | -3.5E+03 | +9.1E-01 | 1.0E+00 | 1.9E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 28| +1.71E+02 | -9.9E-06 | +6.4E-01 | 1.4E-04 | 7.1E-02 | 3.4E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 36| +1.48E+02 | -8.3E-05 | +4.2E-01 | 4.7E-02 | 2.9E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 45| +1.48E+02 | -3.6E-03 | +3.1E-01 | 2.7E-02 | 1.1E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 12| +3.28E+02 | -5.5E+02 | +9.1E-01 | 1.0E+00 | 3.0E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 54| +1.48E+02 | +4.5E-06 | -1.7E+02 | 4.3E-07 | 2.2E-02 | 1.2E-06 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 21| +1.51E+02 | -2.4E+00 | +9.6E-01 | 3.5E-01 | 1.2E+02 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 125| +1.38E+02 | -1.1E-04 | +3.9E-01 | 2.7E-02 | 7.3E-02 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 37| +1.48E+02 | -7.3E-05 | +3.1E-01 | 4.7E-02 | 5.2E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 13| +2.49E+02 | -7.9E+01 | +9.0E-01 | 2.0E+00 | 4.7E+02 | 1.3E+00 | trr |1\n", - "2023-02-17 16:05:27 fides(INFO) 29| +1.71E+02 | -8.2E-07 | +8.0E+00 | 1.4E-04 | 2.1E-02 | 2.3E-05 | trr |1\n", - "2023-02-17 16:05:27 fides(WARNING) Stopping as function difference 8.24E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:27 fides(INFO) 46| +1.48E+02 | -6.3E-03 | +4.6E-01 | 2.7E-02 | 3.9E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 55| +1.48E+02 | +7.1E-07 | -1.1E+02 | 1.1E-07 | 2.2E-02 | 3.1E-07 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 22| +1.51E+02 | +4.0E-01 | -2.8E-01 | 7.1E-01 | 6.6E+01 | 8.1E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 38| +1.48E+02 | +4.8E-05 | -1.8E-01 | 4.7E-02 | 2.2E-01 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 126| +1.38E+02 | +1.9E-03 | -1.4E+00 | 2.7E-02 | 2.1E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 14| +2.29E+02 | -1.9E+01 | +1.5E+00 | 2.0E+00 | 5.4E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 47| +1.48E+02 | -2.5E-03 | +3.4E-01 | 2.7E-02 | 9.6E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 56| +1.48E+02 | +3.7E-06 | -2.2E+03 | 2.7E-08 | 2.2E-02 | 7.7E-08 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 23| +1.49E+02 | -1.6E+00 | +7.3E-01 | 1.8E-01 | 6.6E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 39| +1.48E+02 | -5.2E-05 | +4.6E-01 | 1.2E-02 | 2.2E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 15| +2.29E+02 | +6.1E+02 | -3.0E+01 | 2.0E+00 | 3.3E+01 | 3.5E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 13| +2.19E+02 | -2.8E+01 | +8.4E-01 | 2.0E-01 | 3.5E+02 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 127| +1.38E+02 | -1.5E-04 | +4.1E-01 | 6.7E-03 | 2.1E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 24| +1.48E+02 | -9.6E-01 | +9.8E-01 | 1.8E-01 | 6.9E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 48| +1.48E+02 | -3.0E-03 | +3.5E-01 | 2.7E-02 | 2.3E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 16| +2.08E+02 | -2.2E+01 | +7.3E-01 | 5.0E-01 | 3.3E+01 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 40| +1.48E+02 | -4.7E-05 | +8.9E-01 | 1.2E-02 | 6.1E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 57| +1.48E+02 | +4.0E-06 | -9.6E+03 | 6.7E-09 | 2.2E-02 | 1.9E-08 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 49| +1.48E+02 | +1.4E-03 | -1.9E-01 | 2.7E-02 | 8.8E-01 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 25| +1.48E+02 | -6.1E-01 | +8.2E-01 | 3.5E-01 | 2.9E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 0| +2.51E+02 | NaN | NaN | 1.0E+00 | 2.5E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:27 fides(INFO) 128| +1.38E+02 | -6.6E-05 | +6.4E-01 | 6.7E-03 | 1.8E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:27.170 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0041 and h = 1.08836e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:27 fides(INFO) 17| +1.74E+02 | -3.4E+01 | +1.1E+00 | 5.0E-01 | 9.3E+01 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:27.171 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0041: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:27 fides(INFO) 41| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.4E-02 | 8.5E-02 | 7.8E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 58| +1.48E+02 | +3.8E-06 | -3.6E+04 | 1.7E-09 | 2.2E-02 | 4.8E-09 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 50| +1.48E+02 | -1.3E-03 | +4.4E-01 | 6.8E-03 | 8.8E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 26| +1.48E+02 | +4.7E+01 | -3.5E+01 | 7.1E-01 | 2.0E+01 | 1.7E+00 | trr |0\n", - "2023-02-17 16:05:27 fides(INFO) 42| +1.48E+02 | -1.7E-05 | +1.0E+00 | 5.9E-03 | 8.5E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 1| +2.51E+02 | +2.0E+04 | -8.4E+02 | 1.0E+00 | 2.5E+01 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 129| +1.38E+02 | -5.4E-05 | +8.4E-01 | 6.7E-03 | 7.3E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 59| +1.48E+02 | +5.9E-06 | -2.3E+05 | 4.2E-10 | 2.2E-02 | 1.2E-09 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 18| +1.74E+02 | +6.4E+03 | -2.0E+02 | 1.0E+00 | 6.8E+01 | 2.0E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 51| +1.48E+02 | -1.3E-03 | +9.3E-01 | 6.8E-03 | 2.7E+00 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 27| +1.48E+02 | +1.3E+00 | -2.3E+00 | 1.8E-01 | 2.0E+01 | 4.2E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 43| +1.48E+02 | -1.3E-05 | +9.4E-01 | 1.2E-02 | 2.3E-01 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 2| +2.51E+02 | +2.4E+01 | -1.9E+00 | 2.5E-01 | 2.5E+01 | 6.1E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 19| +1.56E+02 | -1.7E+01 | +8.1E-01 | 2.5E-01 | 6.8E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 130| +1.38E+02 | -1.0E-04 | +9.2E-01 | 1.3E-02 | 7.2E-02 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 28| +1.48E+02 | -9.1E-02 | +4.6E-01 | 4.4E-02 | 2.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 52| +1.48E+02 | -8.8E-04 | +9.8E-01 | 1.4E-02 | 2.9E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 14| +2.17E+02 | -2.3E+00 | +5.5E-01 | 4.1E-01 | 1.8E+02 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 60| +1.48E+02 | +2.3E-06 | -3.4E+05 | 1.1E-10 | 2.2E-02 | 3.0E-10 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 44| +1.48E+02 | -1.3E-05 | +8.1E-01 | 2.4E-02 | 4.7E-02 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 131| +1.38E+02 | -8.7E-05 | +3.3E-01 | 2.7E-02 | 6.9E-02 | 6.1E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 20| +1.53E+02 | -3.2E+00 | +9.5E-01 | 5.0E-01 | 1.1E+02 | 1.6E+00 | trr |1\n", - "2023-02-17 16:05:27 fides(INFO) 53| +1.48E+02 | -1.4E-03 | +9.6E-01 | 2.7E-02 | 4.2E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 29| +1.48E+02 | -4.4E-02 | +4.4E-01 | 4.4E-02 | 8.9E+00 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 3| +2.49E+02 | -2.2E+00 | +6.1E-01 | 6.2E-02 | 2.5E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 61| +1.48E+02 | +3.9E-06 | -2.4E+06 | 2.6E-11 | 2.2E-02 | 7.5E-11 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 45| +1.48E+02 | -1.6E-05 | +5.7E-01 | 4.7E-02 | 1.4E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 54| +1.48E+02 | -1.7E-03 | +9.2E-01 | 5.5E-02 | 2.8E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 21| +1.53E+02 | +7.2E-01 | -2.2E-01 | 1.0E+00 | 5.8E+01 | 7.4E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 30| +1.48E+02 | -2.2E-02 | +5.1E-01 | 4.4E-02 | 5.5E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 132| +1.38E+02 | +1.2E-02 | -5.8E+00 | 2.7E-02 | 1.7E-01 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 4| +2.49E+02 | -1.8E-01 | +6.1E-01 | 6.2E-02 | 5.3E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 62| +1.48E+02 | +4.5E-06 | -1.1E+07 | 6.6E-12 | 2.2E-02 | 1.9E-11 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 46| +1.48E+02 | +1.8E-05 | -4.3E-01 | 4.7E-02 | 6.3E-02 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 15| +2.16E+02 | -6.6E-01 | +7.3E-01 | 4.1E-01 | 1.6E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 22| +1.50E+02 | -2.6E+00 | +7.4E-01 | 2.5E-01 | 5.8E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 55| +1.48E+02 | +6.0E-03 | -4.5E+00 | 1.1E-01 | 1.3E-01 | 8.0E-02 | trr |0\n", - "2023-02-17 16:05:27 fides(INFO) 31| +1.48E+02 | -4.7E-03 | +7.7E-01 | 4.4E-02 | 2.8E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 63| +1.48E+02 | +4.3E-06 | -4.2E+07 | 1.6E-12 | 2.2E-02 | 4.7E-12 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 133| +1.38E+02 | +3.1E-03 | -3.3E+00 | 6.7E-03 | 1.7E-01 | 9.3E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 47| +1.48E+02 | -4.0E-06 | +2.3E-01 | 1.2E-02 | 6.3E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 5| +2.49E+02 | -2.8E-01 | +7.8E-01 | 6.2E-02 | 5.7E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 56| +1.48E+02 | -1.2E-04 | +2.0E-01 | 2.7E-02 | 1.3E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 23| +1.49E+02 | -1.4E+00 | +9.5E-01 | 2.5E-01 | 8.6E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 64| +1.48E+02 | +4.0E-06 | -1.6E+08 | 4.1E-13 | 2.2E-02 | 1.2E-12 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 32| +1.48E+02 | -2.6E-03 | +4.8E-01 | 8.9E-02 | 2.2E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 134| +1.38E+02 | +1.1E-04 | -2.6E-01 | 1.7E-03 | 1.7E-01 | 3.3E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 48| +1.48E+02 | -6.0E-06 | +7.3E-01 | 2.9E-03 | 2.5E-01 | 8.3E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 24| +1.48E+02 | -6.4E-01 | +9.9E-01 | 5.0E-01 | 3.4E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 65| +1.48E+02 | +4.5E-06 | -6.9E+08 | 1.0E-13 | 2.2E-02 | 2.9E-13 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 16| +2.16E+02 | -5.6E-02 | -4.4E-01 | 4.1E-01 | 6.3E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 57| +1.48E+02 | -2.0E-04 | +2.5E-01 | 6.8E-03 | 3.9E-01 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 6| +2.48E+02 | -6.8E-01 | +1.1E+00 | 1.2E-01 | 5.1E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 33| +1.48E+02 | +1.3E-02 | -1.0E+00 | 8.9E-02 | 1.0E+00 | 9.4E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 49| +1.48E+02 | -3.0E-06 | +3.2E+00 | 2.9E-03 | 1.8E-02 | 8.2E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 58| +1.48E+02 | -1.6E-04 | +8.9E-01 | 1.7E-03 | 1.1E+00 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 25| +1.48E+02 | -4.2E-01 | +6.1E-01 | 1.0E+00 | 3.0E+01 | 9.1E-01 | trr |1\n", - "2023-02-17 16:05:27 fides(INFO) 135| +1.38E+02 | -8.8E-05 | +7.3E-01 | 4.2E-04 | 1.7E-01 | 8.4E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 66| +1.48E+02 | +4.6E-06 | -2.9E+09 | 2.6E-14 | 2.2E-02 | 7.3E-14 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 34| +1.48E+02 | -2.0E-03 | +4.5E-01 | 2.2E-02 | 1.0E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 7| +2.45E+02 | -3.4E+00 | +1.6E+00 | 2.5E-01 | 8.1E+00 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 50| +1.48E+02 | +2.1E-07 | -1.4E-01 | 5.9E-03 | 1.7E-02 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 59| +1.48E+02 | -5.6E-05 | +8.7E-01 | 3.4E-03 | 1.0E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 26| +1.48E+02 | +1.1E+01 | -1.8E+01 | 1.0E+00 | 1.1E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 17| +2.16E+02 | -3.1E-01 | +1.0E+00 | 9.4E-02 | 6.3E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 67| +1.48E+02 | +4.1E-06 | -1.0E+10 | 6.4E-15 | 2.2E-02 | 1.8E-14 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 136| +1.38E+02 | -1.0E-05 | +9.1E-01 | 4.2E-04 | 1.9E-01 | 1.3E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 35| +1.48E+02 | -9.6E-04 | +8.6E-01 | 2.2E-02 | 2.5E+00 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 51| +1.48E+02 | +5.1E-07 | -1.2E+00 | 1.5E-03 | 1.7E-02 | 4.0E-04 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 60| +1.48E+02 | -5.0E-05 | +8.3E-01 | 6.8E-03 | 1.2E-01 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 27| +1.48E+02 | -9.3E-02 | +2.3E-01 | 1.5E-01 | 1.1E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 68| +1.48E+02 | +4.0E-06 | -4.0E+10 | 1.6E-15 | 2.2E-02 | 4.6E-15 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 36| +1.48E+02 | -6.1E-04 | +8.1E-01 | 4.4E-02 | 4.8E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 8| +2.19E+02 | -2.6E+01 | +2.2E+00 | 5.0E-01 | 1.7E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 137| +1.38E+02 | -1.5E-05 | +8.9E-01 | 8.3E-04 | 9.0E-02 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 52| +1.48E+02 | -2.6E-07 | +1.9E+00 | 3.7E-04 | 1.7E-02 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(WARNING) Stopping as function difference 2.58E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:27 fides(INFO) 61| +1.48E+02 | -7.9E-05 | +9.1E-01 | 1.4E-02 | 8.3E-02 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 28| +1.48E+02 | -7.1E-02 | +6.8E-01 | 3.7E-02 | 1.9E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:27.544 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2248 and h = 8.49905e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:27.544 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2248: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:27 fides(INFO) 37| +1.48E+02 | +0.0E+00 | +0.0E+00 | 8.9E-02 | 1.2E+00 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 69| +1.48E+02 | +3.9E-06 | -1.5E+11 | 4.0E-16 | 2.2E-02 | 1.1E-15 | 2d |0\n", - "2023-02-17 16:05:27 fides(WARNING) Stopping as trust region radius 1.00E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:27 fides(INFO) 18| +2.16E+02 | -4.3E-02 | -8.6E-02 | 1.9E-01 | 3.2E+00 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 138| +1.38E+02 | -2.0E-05 | +9.4E-01 | 1.7E-03 | 1.7E-01 | 3.9E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 38| +1.48E+02 | -2.5E-04 | +8.3E-01 | 2.2E-02 | 1.2E+00 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 62| +1.48E+02 | +3.9E-05 | -2.8E-01 | 2.7E-02 | 1.8E-01 | 1.4E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 29| +1.48E+02 | -1.9E-02 | +7.6E-01 | 3.7E-02 | 3.9E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 139| +1.38E+02 | -2.5E-05 | +8.7E-01 | 3.3E-03 | 6.6E-02 | 7.2E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 63| +1.48E+02 | -3.2E-05 | +7.2E-01 | 6.8E-03 | 1.8E-01 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 39| +1.48E+02 | -1.2E-04 | +6.4E-01 | 4.4E-02 | 2.7E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 30| +1.48E+02 | -9.9E-03 | +9.2E-01 | 7.3E-02 | 7.7E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 9| +2.19E+02 | +2.1E+02 | -9.7E+00 | 1.0E+00 | 5.0E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 19| +2.16E+02 | -3.8E-02 | +1.1E+00 | 4.7E-02 | 3.2E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 140| +1.38E+02 | -4.7E-05 | +1.0E+00 | 6.7E-03 | 8.1E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 64| +1.48E+02 | -2.2E-05 | +6.9E-01 | 6.8E-03 | 6.6E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 40| +1.48E+02 | +3.4E-05 | -1.3E-01 | 4.4E-02 | 3.9E-01 | 9.7E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 31| +1.48E+02 | +2.0E-03 | -4.7E-01 | 1.5E-01 | 1.1E+00 | 6.4E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 0| +1.95E+08 | NaN | NaN | 1.0E+00 | 9.0E+08 | NaN | NaN |1\n", - "2023-02-17 16:05:27 fides(INFO) 65| +1.48E+02 | -2.0E-05 | +5.7E-01 | 6.8E-03 | 1.9E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 41| +1.48E+02 | -6.6E-05 | +7.8E-01 | 1.1E-02 | 3.9E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 32| +1.48E+02 | -1.7E-03 | +7.5E-01 | 3.7E-02 | 1.1E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 0| +2.66E+08 | NaN | NaN | 1.0E+00 | 1.2E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:27 fides(INFO) 141| +1.38E+02 | -9.1E-05 | +9.9E-01 | 1.3E-02 | 6.1E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 10| +2.05E+02 | -1.4E+01 | +6.8E-01 | 2.5E-01 | 5.0E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 1| +4.07E+04 | -1.9E+08 | +9.8E-02 | 1.0E+00 | 9.0E+08 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 1| +2.73E+05 | -2.7E+08 | +1.0E-01 | 1.0E+00 | 1.2E+09 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 42| +1.48E+02 | -4.0E-05 | +9.3E-01 | 2.2E-02 | 1.9E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 66| +1.48E+02 | -8.2E-06 | +3.1E-01 | 6.8E-03 | 6.1E-02 | 9.2E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 20| +2.15E+02 | -8.7E-02 | +1.1E+00 | 9.4E-02 | 1.9E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 33| +1.48E+02 | -4.0E-05 | +2.0E-02 | 7.3E-02 | 2.0E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 142| +1.38E+02 | -1.8E-04 | +9.8E-01 | 2.7E-02 | 6.5E-02 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 2| +4.40E+04 | -2.3E+05 | +9.0E-01 | 2.5E-01 | 1.2E+06 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 2| +7.17E+03 | -3.4E+04 | +9.0E-01 | 2.5E-01 | 1.8E+05 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 43| +1.48E+02 | -5.0E-05 | +9.1E-01 | 4.4E-02 | 1.7E-01 | 9.9E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 34| +1.48E+02 | -1.0E-03 | +3.6E-01 | 1.8E-02 | 5.7E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 67| +1.48E+02 | -1.5E-05 | +4.3E-01 | 6.8E-03 | 1.6E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 3| +7.74E+03 | -3.6E+04 | +9.0E-01 | 5.0E-01 | 2.0E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 11| +2.01E+02 | -5.0E+00 | +7.0E-01 | 2.5E-01 | 4.0E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 143| +1.38E+02 | +2.2E-04 | -6.8E-01 | 5.3E-02 | 6.5E-02 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 3| +1.60E+03 | -5.6E+03 | +9.1E-01 | 5.0E-01 | 2.9E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 21| +2.15E+02 | +4.3E-01 | -3.7E+00 | 1.9E-01 | 1.0E+01 | 4.4E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 4| +1.72E+03 | -6.0E+03 | +9.1E-01 | 1.0E+00 | 3.1E+04 | 6.8E-01 | trr |1\n", - "2023-02-17 16:05:27 fides(INFO) 44| +1.48E+02 | -5.3E-06 | +7.9E-02 | 8.9E-02 | 1.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 35| +1.48E+02 | -2.9E-04 | +5.8E-01 | 1.8E-02 | 1.2E+00 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 68| +1.48E+02 | +9.2E-06 | -2.6E-01 | 6.8E-03 | 6.7E-02 | 1.0E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 4| +1.60E+03 | +3.5E+04 | -8.3E+01 | 1.0E+00 | 5.0E+03 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 5| +4.61E+02 | -1.3E+03 | +9.0E-01 | 1.0E+00 | 5.3E+03 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 45| +1.48E+02 | +2.0E-04 | -7.1E-01 | 2.2E-02 | 1.1E-01 | 9.5E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 144| +1.38E+02 | -6.1E-05 | +5.7E-01 | 1.3E-02 | 6.5E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 36| +1.48E+02 | -9.1E-05 | +5.8E-01 | 1.8E-02 | 2.9E-01 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 69| +1.48E+02 | -4.2E-06 | +3.3E-01 | 1.7E-03 | 6.7E-02 | 3.4E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 6| +2.62E+02 | -2.0E+02 | +9.0E-01 | 2.0E+00 | 8.5E+02 | 3.9E+00 | trr |1\n", - "2023-02-17 16:05:27 fides(INFO) 46| +1.48E+02 | -3.2E-05 | +3.6E-01 | 5.5E-03 | 1.1E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 12| +1.93E+02 | -7.4E+00 | +1.1E+00 | 2.5E-01 | 3.8E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 37| +1.48E+02 | -7.0E-05 | +7.1E-01 | 1.8E-02 | 3.1E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 22| +2.15E+02 | -6.4E-02 | +1.1E+00 | 4.7E-02 | 1.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 5| +5.17E+02 | -1.1E+03 | +9.1E-01 | 2.5E-01 | 5.0E+03 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 70| +1.48E+02 | -4.4E-06 | +8.2E-01 | 1.7E-03 | 1.8E-01 | 3.6E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 7| +2.38E+02 | -2.4E+01 | +1.0E+00 | 4.0E+00 | 1.1E+02 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 145| +1.38E+02 | +7.2E-05 | -2.9E-01 | 1.3E-02 | 1.4E-01 | 3.7E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 47| +1.48E+02 | -1.6E-05 | +9.2E-01 | 5.5E-03 | 3.7E-01 | 6.5E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 6| +3.08E+02 | -2.1E+02 | +6.8E-01 | 5.0E-01 | 1.0E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 38| +1.48E+02 | -5.1E-05 | +7.0E-01 | 1.8E-02 | 2.8E-01 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 71| +1.48E+02 | -6.4E-06 | +1.8E+00 | 3.4E-03 | 2.1E-02 | 6.6E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 8| +2.38E+02 | +3.4E+01 | -6.3E+00 | 4.0E+00 | 2.4E+01 | 5.0E+00 | trr |0\n", - "2023-02-17 16:05:27 fides(INFO) 48| +1.48E+02 | -6.9E-07 | +3.5E-01 | 1.1E-02 | 3.3E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(WARNING) Stopping as function difference 6.90E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:27 fides(INFO) 146| +1.38E+02 | -4.5E-05 | +6.7E-01 | 3.3E-03 | 1.4E-01 | 9.2E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 7| +2.51E+02 | -5.7E+01 | +8.4E-01 | 5.0E-01 | 2.8E+02 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 23| +2.15E+02 | -1.2E-01 | +7.2E-01 | 9.4E-02 | 6.2E+00 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 9| +2.38E+02 | +1.6E+00 | -3.9E-01 | 6.9E-01 | 2.4E+01 | 2.0E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 39| +1.48E+02 | -4.4E-05 | +7.4E-01 | 1.8E-02 | 2.0E-01 | 2.9E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 13| +1.81E+02 | -1.2E+01 | +8.0E-01 | 5.0E-01 | 3.3E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 72| +1.48E+02 | -4.6E-06 | +7.9E-01 | 6.8E-03 | 5.6E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 8| +2.50E+02 | -1.1E+00 | +3.8E-01 | 1.0E+00 | 2.6E+01 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 40| +1.48E+02 | -2.9E-05 | +5.3E-01 | 1.8E-02 | 2.7E-01 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 73| +1.48E+02 | +6.7E-07 | -7.7E-02 | 1.4E-02 | 2.2E-02 | 1.5E-03 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 147| +1.38E+02 | -2.1E-05 | +9.9E-01 | 3.3E-03 | 7.8E-02 | 6.0E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:27 fides(INFO) 10| +2.38E+02 | +1.3E+00 | -3.2E-01 | 1.7E-01 | 2.4E+01 | 4.3E-01 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 24| +2.15E+02 | -1.6E-01 | +7.4E-01 | 9.4E-02 | 2.9E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 41| +1.48E+02 | -1.4E-05 | +2.8E-01 | 1.8E-02 | 1.4E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 148| +1.38E+02 | -4.1E-05 | +1.0E+00 | 6.7E-03 | 5.9E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 74| +1.48E+02 | -3.8E-06 | +1.3E+00 | 3.4E-03 | 2.2E-02 | 3.8E-04 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 11| +2.36E+02 | -1.9E+00 | +8.7E-01 | 4.3E-02 | 2.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 14| +1.81E+02 | -6.7E-01 | -5.0E-01 | 1.0E+00 | 1.5E+02 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:27 fides(INFO) 42| +1.48E+02 | -4.4E-06 | +5.4E-02 | 1.8E-02 | 2.5E-01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 9| +2.50E+02 | -2.9E-01 | +2.9E-02 | 1.0E+00 | 1.2E+01 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:27 fides(INFO) 75| +1.48E+02 | +1.6E-06 | -3.0E-01 | 6.8E-03 | 6.5E-02 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 149| +1.38E+02 | -7.9E-05 | +9.9E-01 | 1.3E-02 | 6.8E-02 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 25| +2.15E+02 | -1.7E-01 | +9.8E-01 | 9.4E-02 | 3.2E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 12| +2.34E+02 | -2.2E+00 | +9.0E-01 | 8.6E-02 | 1.9E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 0| +1.64E+11 | NaN | NaN | 1.0E+00 | 7.4E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:28 fides(INFO) 43| +1.48E+02 | -1.9E-05 | +3.6E-01 | 4.6E-03 | 1.3E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 76| +1.48E+02 | +1.3E-06 | -7.2E-01 | 1.7E-03 | 6.5E-02 | 4.1E-04 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 150| +1.38E+02 | -1.5E-04 | +1.0E+00 | 2.7E-02 | 5.9E-02 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 10| +2.49E+02 | -8.7E-01 | +5.1E-01 | 2.5E-01 | 1.7E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 13| +2.30E+02 | -3.6E+00 | +9.8E-01 | 1.7E-01 | 2.4E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:28.049 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.543 and h = 3.35461e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:28.049 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.543: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:28 fides(INFO) 44| +1.48E+02 | +0.0E+00 | +0.0E+00 | 4.6E-03 | 3.8E-01 | 7.2E-04 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 26| +2.15E+02 | -1.5E-01 | +3.9E-01 | 1.9E-01 | 1.9E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 1| +3.11E+09 | -1.6E+11 | +8.7E-02 | 1.0E+00 | 7.4E+11 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 77| +1.48E+02 | +1.7E-06 | -2.1E+00 | 4.3E-04 | 6.5E-02 | 1.0E-04 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 15| +1.69E+02 | -1.2E+01 | +9.8E-01 | 2.5E-01 | 1.5E+02 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 151| +1.38E+02 | -2.5E-04 | +8.5E-01 | 5.3E-02 | 6.2E-02 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 45| +1.48E+02 | -1.7E-05 | +9.9E-01 | 1.1E-03 | 3.8E-01 | 2.0E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 14| +2.28E+02 | -2.3E+00 | +2.9E-01 | 3.4E-01 | 2.2E+01 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 78| +1.48E+02 | -2.3E-06 | +3.9E+00 | 1.1E-04 | 6.5E-02 | 3.0E-05 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 2| +4.64E+08 | -2.6E+09 | +8.9E-01 | 2.5E-01 | 1.4E+10 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 15| +2.18E+02 | -9.5E+00 | +8.3E-01 | 3.4E-01 | 5.4E+01 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 46| +1.48E+02 | -3.4E-06 | +1.0E+00 | 2.3E-03 | 4.0E-02 | 3.6E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 11| +2.48E+02 | -6.8E-01 | +4.8E-01 | 2.5E-01 | 1.0E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 152| +1.38E+02 | +3.9E-02 | -1.9E+01 | 1.1E-01 | 9.7E-02 | 3.4E-02 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 27| +2.15E+02 | -1.9E-01 | +1.1E+00 | 1.9E-01 | 3.5E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 16| +1.69E+02 | +8.9E+00 | -1.6E+00 | 5.0E-01 | 4.4E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 79| +1.48E+02 | +4.9E-06 | -2.7E+01 | 2.1E-04 | 6.9E-03 | 5.2E-05 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 16| +1.99E+02 | -1.9E+01 | +4.2E-01 | 6.9E-01 | 2.5E+01 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 47| +1.48E+02 | -6.6E-06 | +2.6E+00 | 4.6E-03 | 1.0E-01 | 6.3E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 3| +6.92E+07 | -3.9E+08 | +8.9E-01 | 5.0E-01 | 2.1E+09 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 153| +1.38E+02 | +1.9E-03 | -3.0E+00 | 2.7E-02 | 9.7E-02 | 8.6E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 80| +1.48E+02 | +5.6E-06 | -7.7E+01 | 5.3E-05 | 6.9E-03 | 1.8E-05 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 12| +2.47E+02 | -1.4E+00 | +7.4E-01 | 2.5E-01 | 1.5E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 17| +1.99E+02 | +3.2E+01 | -2.2E+00 | 6.9E-01 | 1.5E+02 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 48| +1.48E+02 | +2.7E-06 | -7.8E-01 | 9.2E-03 | 2.6E-02 | 1.2E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 28| +2.14E+02 | -1.7E-01 | +1.4E-01 | 3.8E-01 | 1.4E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 154| +1.38E+02 | +5.5E-07 | -6.5E-03 | 6.7E-03 | 9.7E-02 | 2.2E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 4| +1.04E+07 | -5.9E+07 | +8.9E-01 | 5.0E-01 | 3.2E+08 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 81| +1.48E+02 | +3.1E-06 | -6.9E+01 | 1.3E-05 | 6.9E-03 | 1.2E-05 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 17| +1.65E+02 | -3.7E+00 | +8.2E-01 | 1.2E-01 | 4.4E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 18| +1.77E+02 | -2.1E+01 | +9.9E-01 | 1.7E-01 | 1.5E+02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 49| +1.48E+02 | +1.4E-06 | -6.6E-01 | 2.3E-03 | 2.6E-02 | 3.3E-04 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 13| +2.46E+02 | -1.4E+00 | +6.1E-01 | 2.5E-01 | 1.2E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 82| +1.48E+02 | +3.2E-07 | -1.0E+01 | 3.3E-06 | 6.9E-03 | 7.3E-06 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 155| +1.38E+02 | -3.3E-05 | +7.0E-01 | 1.7E-03 | 9.7E-02 | 6.3E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 5| +1.56E+06 | -8.8E+06 | +8.9E-01 | 5.0E-01 | 4.8E+07 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 29| +2.14E+02 | -9.0E-02 | +1.1E+00 | 9.4E-02 | 3.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 50| +1.48E+02 | -1.0E-06 | +5.9E-01 | 5.7E-04 | 2.6E-02 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(WARNING) Stopping as function difference 1.02E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:28 fides(INFO) 19| +1.59E+02 | -1.9E+01 | +6.8E-01 | 3.4E-01 | 5.9E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 14| +2.43E+02 | -2.7E+00 | +7.9E-01 | 2.5E-01 | 2.1E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 6| +2.38E+05 | -1.3E+06 | +8.9E-01 | 5.0E-01 | 7.2E+06 | 9.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 156| +1.38E+02 | -1.4E-05 | +9.4E-01 | 1.7E-03 | 2.4E-01 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 83| +1.48E+02 | -4.8E-08 | +4.5E+00 | 8.3E-07 | 6.9E-03 | 1.8E-06 | 2d |1\n", - "2023-02-17 16:05:28 fides(WARNING) Stopping as function difference 4.76E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:28 fides(INFO) 18| +1.55E+02 | -9.5E+00 | +1.1E+00 | 2.5E-01 | 8.3E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +1.59E+02 | +4.1E+01 | -3.7E+00 | 3.4E-01 | 1.8E+02 | 5.7E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 7| +3.68E+04 | -2.0E+05 | +8.9E-01 | 5.0E-01 | 1.1E+06 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 15| +2.38E+02 | -4.9E+00 | +6.5E-01 | 5.0E-01 | 1.9E+01 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 30| +2.14E+02 | -9.2E-02 | +9.5E-01 | 1.9E-01 | 3.3E+00 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 157| +1.38E+02 | -1.6E-05 | +9.6E-01 | 3.3E-03 | 6.0E-02 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 8| +5.70E+03 | -3.1E+04 | +9.0E-01 | 5.0E-01 | 1.7E+05 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 21| +1.51E+02 | -8.1E+00 | +8.9E-01 | 7.6E-02 | 1.8E+02 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 19| +1.55E+02 | -2.7E-01 | -1.1E-02 | 5.0E-01 | 5.1E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 16| +2.29E+02 | -9.2E+00 | +9.1E-01 | 5.0E-01 | 4.7E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 158| +1.38E+02 | -3.1E-05 | +1.0E+00 | 6.7E-03 | 6.2E-02 | 9.3E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 9| +9.75E+02 | -4.7E+03 | +9.1E-01 | 5.0E-01 | 2.6E+04 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 22| +1.49E+02 | -2.0E+00 | +9.2E-01 | 1.5E-01 | 5.5E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 31| +2.14E+02 | -1.3E-01 | +7.0E-01 | 3.8E-01 | 5.3E+00 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 17| +2.29E+02 | +2.5E+01 | -3.2E+00 | 1.0E+00 | 2.9E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 0| +2.62E+11 | NaN | NaN | 1.0E+00 | 9.5E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 10| +7.50E+02 | -2.2E+02 | +9.7E-02 | 5.0E-01 | 3.8E+03 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 159| +1.38E+02 | -5.8E-05 | +9.6E-01 | 1.3E-02 | 5.8E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 23| +1.49E+02 | +1.6E+00 | -1.6E+00 | 3.0E-01 | 3.7E+01 | 4.1E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 0| +1.18E+11 | NaN | NaN | 1.0E+00 | 5.4E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +1.55E+02 | -9.1E-01 | +9.5E-01 | 9.3E-02 | 5.1E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 18| +2.29E+02 | -1.8E-01 | -1.2E-01 | 2.5E-01 | 2.9E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 32| +2.14E+02 | -1.0E-01 | +1.2E+00 | 3.8E-01 | 1.3E+01 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 11| +2.94E+02 | -4.6E+02 | +9.2E-01 | 1.2E-01 | 3.6E+03 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 24| +1.48E+02 | -3.3E-01 | +7.0E-01 | 7.6E-02 | 3.7E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 1| +1.03E+09 | -2.6E+11 | +1.1E-01 | 1.0E+00 | 9.5E+11 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 1| +2.17E+08 | -1.2E+11 | +9.2E-02 | 1.0E+00 | 5.4E+11 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 160| +1.38E+02 | -3.8E-05 | +2.4E-01 | 2.7E-02 | 6.0E-02 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 12| +1.83E+02 | -1.1E+02 | +8.5E-01 | 2.5E-01 | 9.1E+02 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 19| +2.26E+02 | -3.1E+00 | +8.9E-01 | 6.2E-02 | 2.9E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 25| +1.48E+02 | -3.2E-01 | +5.8E-01 | 7.6E-02 | 2.8E+01 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 21| +1.54E+02 | -2.6E-01 | +8.2E-01 | 1.9E-01 | 1.3E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 2| +1.56E+08 | -8.7E+08 | +8.9E-01 | 2.5E-01 | 4.7E+09 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 33| +2.14E+02 | -1.2E-01 | +5.8E-01 | 7.5E-01 | 7.1E+00 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 2| +3.32E+07 | -1.8E+08 | +8.9E-01 | 2.5E-01 | 1.0E+09 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 13| +1.72E+02 | -1.1E+01 | +7.6E-01 | 5.0E-01 | 1.5E+02 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 161| +1.38E+02 | +8.5E-05 | -2.1E-01 | 6.7E-03 | 1.5E-01 | 3.5E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +2.20E+02 | -5.6E+00 | +1.1E+00 | 1.2E-01 | 2.9E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 3| +2.38E+07 | -1.3E+08 | +8.9E-01 | 5.0E-01 | 7.2E+08 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 3| +5.10E+06 | -2.8E+07 | +8.9E-01 | 5.0E-01 | 1.5E+08 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 26| +1.48E+02 | -6.1E-02 | +1.6E-01 | 7.6E-02 | 1.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 22| +1.54E+02 | -1.4E-01 | +8.6E-01 | 3.7E-01 | 2.2E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 34| +2.14E+02 | -5.4E-02 | +1.4E+00 | 7.5E-01 | 1.1E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 14| +1.71E+02 | -6.7E-01 | +6.7E-01 | 1.0E+00 | 1.3E+01 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 162| +1.38E+02 | -7.4E-05 | +6.9E-01 | 1.7E-03 | 1.5E-01 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 21| +2.09E+02 | -1.1E+01 | +1.2E+00 | 2.5E-01 | 3.0E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 4| +3.66E+06 | -2.0E+07 | +8.9E-01 | 1.0E+00 | 1.1E+08 | 9.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 27| +1.48E+02 | -1.1E-01 | +8.0E-01 | 1.9E-02 | 2.2E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 4| +7.91E+05 | -4.3E+06 | +8.9E-01 | 1.0E+00 | 2.3E+07 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 23| +1.54E+02 | -1.2E-02 | +5.1E-01 | 7.4E-01 | 3.6E+00 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 5| +5.66E+05 | -3.1E+06 | +9.0E-01 | 1.0E+00 | 1.7E+07 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 22| +2.09E+02 | -1.8E+00 | -5.4E-01 | 5.0E-01 | 3.1E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 28| +1.48E+02 | -7.0E-02 | +6.5E-01 | 3.8E-02 | 1.4E+01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 5| +1.24E+05 | -6.7E+05 | +9.0E-01 | 2.0E+00 | 3.6E+06 | 3.9E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 163| +1.38E+02 | -1.7E-05 | +8.1E-01 | 1.7E-03 | 2.0E-01 | 3.5E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 35| +2.14E+02 | -7.3E-02 | +7.5E-01 | 7.5E-01 | 3.2E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 15| +1.71E+02 | -1.3E-01 | +6.7E-01 | 1.0E+00 | 1.1E+01 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 6| +8.84E+04 | -4.8E+05 | +9.0E-01 | 1.0E+00 | 2.6E+06 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 6| +1.96E+04 | -1.0E+05 | +9.0E-01 | 4.0E+00 | 5.6E+05 | 1.6E+00 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 164| +1.38E+02 | -1.4E-05 | +8.9E-01 | 3.3E-03 | 6.0E-02 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 29| +1.48E+02 | -3.8E-02 | +5.8E-01 | 3.8E-02 | 1.0E+01 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 23| +2.03E+02 | -6.0E+00 | +9.5E-01 | 1.2E-01 | 3.1E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 24| +1.54E+02 | +1.5E-01 | -5.1E+00 | 7.4E-01 | 1.8E+00 | 3.3E-01 | trr |0\n", - "2023-02-17 16:05:28 fides(INFO) 7| +1.40E+04 | -7.4E+04 | +9.0E-01 | 1.0E+00 | 4.0E+05 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 7| +3.28E+03 | -1.6E+04 | +9.0E-01 | 4.0E+00 | 8.8E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 16| +1.71E+02 | -2.8E-02 | +8.1E-01 | 1.0E+00 | 4.7E+00 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 36| +2.14E+02 | -1.6E-02 | +1.4E+00 | 7.5E-01 | 4.7E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 30| +1.48E+02 | -3.1E-02 | +6.4E-01 | 3.8E-02 | 1.1E+01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 24| +1.89E+02 | -1.5E+01 | +1.1E+00 | 2.5E-01 | 4.0E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 8| +2.35E+03 | -1.2E+04 | +9.1E-01 | 1.0E+00 | 6.3E+04 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 8| +7.03E+02 | -2.6E+03 | +9.0E-01 | 4.0E+00 | 1.4E+04 | 1.2E+00 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 25| +1.54E+02 | -4.9E-03 | +8.4E-01 | 9.0E-02 | 1.8E+00 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 31| +1.48E+02 | -1.9E-02 | +3.9E-01 | 3.8E-02 | 5.1E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 165| +1.38E+02 | -2.5E-05 | +9.2E-01 | 6.7E-03 | 6.2E-02 | 8.1E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 9| +5.18E+02 | -1.8E+03 | +9.2E-01 | 1.0E+00 | 9.9E+03 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 17| +1.71E+02 | -3.2E-03 | +5.4E-01 | 2.0E+00 | 1.8E+00 | 1.1E+00 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 25| +1.89E+02 | -5.0E+00 | -3.4E-01 | 5.0E-01 | 4.5E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 37| +2.14E+02 | -1.0E-02 | +1.0E+00 | 7.5E-01 | 2.2E+00 | 7.6E-03 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 9| +3.02E+02 | -4.0E+02 | +9.1E-01 | 4.0E+00 | 2.2E+03 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 10| +2.23E+02 | -3.0E+02 | +1.0E+00 | 1.0E+00 | 1.4E+03 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 32| +1.48E+02 | -6.2E-03 | +1.7E-01 | 3.8E-02 | 3.7E+00 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 26| +1.54E+02 | -4.2E-03 | +6.5E-01 | 1.8E-01 | 1.8E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 26| +1.79E+02 | -9.6E+00 | +1.0E+00 | 1.2E-01 | 4.5E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 166| +1.38E+02 | -4.1E-05 | +7.2E-01 | 1.3E-02 | 5.9E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 18| +1.71E+02 | -1.2E-04 | +4.8E-02 | 2.0E+00 | 1.5E+00 | 1.4E-02 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 11| +1.99E+02 | -2.4E+01 | +9.8E-01 | 1.0E+00 | 1.4E+02 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 10| +2.21E+02 | -8.1E+01 | +1.2E+00 | 4.0E+00 | 3.3E+02 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 38| +2.14E+02 | -3.0E-04 | +9.4E-01 | 7.5E-01 | 2.2E+00 | 1.4E-03 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 33| +1.48E+02 | -5.0E-03 | +7.4E-01 | 9.5E-03 | 1.3E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 27| +1.59E+02 | -2.0E+01 | +1.0E+00 | 2.5E-01 | 5.8E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 167| +1.38E+02 | +3.5E-04 | -1.9E+00 | 1.3E-02 | 7.7E-02 | 3.8E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 27| +1.54E+02 | -1.4E-03 | +3.8E-01 | 1.8E-01 | 7.8E-01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 19| +1.71E+02 | -7.2E-06 | +2.1E-03 | 3.0E-02 | 1.1E+00 | 1.4E-02 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 12| +1.99E+02 | +4.4E+02 | -2.4E+01 | 2.0E+00 | 3.8E+01 | 5.9E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 34| +1.48E+02 | -2.3E-03 | +7.8E-01 | 9.5E-03 | 2.1E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 28| +1.59E+02 | +3.0E+01 | -2.1E+00 | 5.0E-01 | 7.3E+01 | 8.5E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 11| +2.21E+02 | +1.1E+03 | -3.7E+01 | 4.0E+00 | 2.9E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 39| +2.14E+02 | -4.6E-06 | +1.7E-01 | 7.5E-01 | 2.1E+00 | 5.5E-04 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 28| +1.54E+02 | +2.0E-04 | -6.0E-02 | 1.8E-01 | 8.8E-01 | 9.4E-02 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 168| +1.38E+02 | +1.7E-05 | -3.1E-01 | 3.3E-03 | 7.7E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +1.71E+02 | -5.6E-04 | +2.6E-01 | 5.2E-03 | 1.1E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 12| +2.21E+02 | +7.1E+00 | -1.2E+00 | 1.0E+00 | 2.9E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 13| +1.94E+02 | -4.5E+00 | +2.2E-01 | 5.0E-01 | 3.8E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 35| +1.48E+02 | -9.7E-04 | +5.6E-01 | 1.9E-02 | 5.3E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 29| +1.53E+02 | -6.7E+00 | +8.1E-01 | 1.2E-01 | 7.3E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 169| +1.38E+02 | -9.1E-08 | +2.9E-03 | 8.3E-04 | 7.7E-02 | 7.0E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 13| +2.21E+02 | +3.5E+00 | -4.7E-01 | 2.5E-01 | 2.9E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 36| +1.48E+02 | -8.1E-04 | +5.1E-01 | 1.9E-02 | 1.0E+00 | 5.7E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 40| +2.14E+02 | +1.2E-07 | -1.1E-02 | 3.8E-04 | 2.1E+00 | 5.3E-04 | trr |0\n", - "2023-02-17 16:05:28 fides(INFO) 29| +1.54E+02 | -1.0E-03 | +8.0E-01 | 4.5E-02 | 8.8E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 30| +1.50E+02 | -2.8E+00 | +9.0E-01 | 2.5E-01 | 7.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 14| +1.85E+02 | -9.2E+00 | +8.7E-01 | 1.2E-01 | 8.4E+01 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 21| +1.71E+02 | -3.1E-04 | +3.0E-01 | 5.2E-03 | 9.2E-01 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 37| +1.48E+02 | -4.7E-04 | +3.1E-01 | 1.9E-02 | 5.6E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 170| +1.38E+02 | -1.2E-05 | +9.8E-01 | 2.1E-04 | 3.7E-01 | 6.8E-05 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 31| +1.50E+02 | +4.9E+00 | -1.6E+00 | 5.0E-01 | 4.5E+01 | 5.3E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 14| +2.18E+02 | -3.3E+00 | +8.5E-01 | 6.2E-02 | 2.9E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 15| +1.75E+02 | -1.0E+01 | +1.3E+00 | 2.5E-01 | 3.6E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 30| +1.54E+02 | -6.0E-04 | +8.2E-01 | 9.0E-02 | 4.2E-01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 22| +1.71E+02 | -7.9E-06 | +1.7E-02 | 5.2E-03 | 6.1E-01 | 5.8E-03 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 15| +2.16E+02 | -1.6E+00 | +5.3E-01 | 1.2E-01 | 1.8E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 38| +1.48E+02 | +2.2E-04 | -1.2E-01 | 1.9E-02 | 5.9E-01 | 6.9E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 41| +2.14E+02 | -4.4E-06 | +8.1E-01 | 6.5E-05 | 2.1E+00 | 1.4E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 32| +1.49E+02 | -1.2E+00 | +7.2E-01 | 1.2E-01 | 4.5E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 16| +1.75E+02 | +1.1E+01 | -1.4E+00 | 5.0E-01 | 2.4E+01 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 171| +1.38E+02 | -3.4E-06 | +1.1E+00 | 4.2E-04 | 6.3E-02 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 31| +1.54E+02 | -8.5E-04 | +1.1E+00 | 1.8E-01 | 3.8E-01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 16| +2.14E+02 | -2.7E+00 | +7.2E-01 | 1.2E-01 | 2.9E+01 | 2.7E-01 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 39| +1.48E+02 | -3.9E-04 | +6.1E-01 | 4.8E-03 | 5.9E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 23| +1.71E+02 | -1.4E-04 | +7.8E-01 | 5.8E-04 | 5.0E-01 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 33| +1.48E+02 | -4.1E-01 | +5.5E-01 | 1.2E-01 | 4.4E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 17| +1.69E+02 | -6.2E+00 | +1.0E+00 | 1.2E-01 | 2.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 42| +2.14E+02 | -5.0E-07 | +1.8E-01 | 1.3E-04 | 2.1E+00 | 2.4E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 17| +2.11E+02 | -2.5E+00 | +6.5E-01 | 1.2E-01 | 1.8E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 172| +1.38E+02 | -3.1E-06 | +8.8E-01 | 8.3E-04 | 6.2E-02 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 32| +1.54E+02 | +6.3E-04 | -7.2E-01 | 3.6E-01 | 3.1E-01 | 8.3E-02 | trr |0\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 40| +1.48E+02 | -2.2E-04 | +9.6E-01 | 4.8E-03 | 1.0E+00 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 24| +1.71E+02 | -7.0E-05 | +5.8E-01 | 1.2E-03 | 3.1E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 34| +1.48E+02 | -3.8E-01 | +3.7E-01 | 1.2E-01 | 2.5E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 18| +1.60E+02 | -8.5E+00 | +8.5E-01 | 2.5E-01 | 5.6E+01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 18| +2.08E+02 | -3.3E+00 | +7.6E-01 | 1.2E-01 | 2.7E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 173| +1.38E+02 | -3.5E-06 | +5.5E-01 | 1.7E-03 | 6.6E-02 | 1.9E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 41| +1.48E+02 | -3.0E-04 | +7.9E-01 | 9.5E-03 | 2.6E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 33| +1.54E+02 | -3.0E-04 | +8.0E-01 | 6.8E-02 | 3.1E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 43| +2.14E+02 | -9.1E-07 | +8.2E-01 | 3.2E-05 | 2.1E+00 | 7.5E-05 | 2d |1\n", - "2023-02-17 16:05:28 fides(WARNING) Stopping as function difference 9.13E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:28 fides(INFO) 25| +1.71E+02 | -1.7E-06 | +5.8E-02 | 1.2E-03 | 1.4E-01 | 1.7E-03 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 35| +1.48E+02 | +7.8E-02 | -1.1E-01 | 1.2E-01 | 1.2E+01 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 19| +2.07E+02 | -5.6E-01 | +4.8E-02 | 2.5E-01 | 1.8E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 19| +1.55E+02 | -4.8E+00 | +7.9E-01 | 5.0E-01 | 7.0E+01 | 1.1E+00 | trr |1\n", - "2023-02-17 16:05:28 fides(INFO) 174| +1.38E+02 | -8.5E-06 | +1.4E+00 | 1.7E-03 | 6.0E-02 | 1.9E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 42| +1.48E+02 | -9.6E-05 | +1.7E-01 | 1.9E-02 | 6.1E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +2.03E+02 | -4.2E+00 | +8.2E-01 | 6.2E-02 | 5.4E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 26| +1.71E+02 | -8.6E-06 | +6.6E-01 | 1.6E-04 | 1.2E-01 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 36| +1.48E+02 | -2.3E-01 | +7.6E-01 | 3.1E-02 | 1.2E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) 34| +1.54E+02 | -4.5E-04 | +9.8E-01 | 1.4E-01 | 1.6E-01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:28 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:28 fides(INFO) 20| +1.55E+02 | +9.5E+01 | -1.1E+01 | 5.0E-01 | 1.0E+02 | 8.3E-01 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 43| +1.48E+02 | +8.2E-05 | -1.4E-01 | 4.8E-03 | 3.2E-01 | 3.7E-03 | 2d |0\n", - "2023-02-17 16:05:28 fides(INFO) 21| +2.01E+02 | -2.5E+00 | +8.8E-01 | 1.2E-01 | 2.0E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 37| +1.48E+02 | -7.8E-02 | +3.5E-01 | 6.2E-02 | 8.4E+00 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 175| +1.38E+02 | -1.2E-05 | +9.7E-01 | 3.3E-03 | 6.1E-02 | 3.7E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 35| +1.54E+02 | +2.2E-03 | -2.9E+00 | 2.7E-01 | 1.7E-01 | 4.6E-02 | trr |0\n", - "2023-02-17 16:05:29 fides(INFO) 21| +1.50E+02 | -5.4E+00 | +7.7E-01 | 1.2E-01 | 1.0E+02 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 27| +1.71E+02 | -3.9E-06 | +7.3E-01 | 1.6E-04 | 8.2E-02 | 4.3E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 22| +1.91E+02 | -9.1E+00 | +1.5E+00 | 2.5E-01 | 1.5E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 44| +1.48E+02 | -7.1E-05 | +1.9E-01 | 1.2E-03 | 3.2E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 38| +1.48E+02 | -3.2E-02 | +7.9E-01 | 6.2E-02 | 8.4E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 36| +1.54E+02 | -1.8E-04 | +5.5E-01 | 4.0E-02 | 1.7E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 22| +1.48E+02 | -1.6E+00 | +9.7E-01 | 2.3E-01 | 6.7E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 176| +1.38E+02 | -2.4E-05 | +9.9E-01 | 6.7E-03 | 6.0E-02 | 7.3E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 28| +1.71E+02 | +3.9E-07 | -4.3E+00 | 1.6E-04 | 1.2E-02 | 8.0E-05 | trr |0\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 0| +6.60E+09 | NaN | NaN | 1.0E+00 | 3.1E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:29 fides(INFO) 45| +1.48E+02 | -2.1E-04 | +9.7E-01 | 3.0E-04 | 1.4E+00 | 3.5E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 23| +1.83E+02 | -9.0E+00 | +3.6E-01 | 5.0E-01 | 4.0E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 39| +1.48E+02 | -3.0E-02 | +8.4E-01 | 1.2E-01 | 3.2E+00 | 8.6E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 23| +1.48E+02 | +4.3E+00 | -2.3E+00 | 4.7E-01 | 2.0E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 177| +1.38E+02 | -4.6E-05 | +1.0E+00 | 1.3E-02 | 5.9E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 46| +1.48E+02 | -8.0E-06 | +8.6E-01 | 6.0E-04 | 1.0E-01 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 37| +1.54E+02 | -1.1E-04 | +7.1E-01 | 4.0E-02 | 2.1E-01 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 29| +1.71E+02 | -8.7E-07 | +1.4E+01 | 7.2E-06 | 1.2E-02 | 2.0E-05 | 2d |1\n", - "2023-02-17 16:05:29 fides(WARNING) Stopping as function difference 8.74E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 40| +1.48E+02 | -1.8E-02 | +9.1E-01 | 2.5E-01 | 7.6E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 1| +1.51E+04 | -6.6E+09 | +9.3E-02 | 1.0E+00 | 3.1E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 24| +1.68E+02 | -1.5E+01 | +4.4E-01 | 5.0E-01 | 1.5E+02 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 24| +1.48E+02 | -2.4E-01 | +1.5E-01 | 1.2E-01 | 2.0E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 47| +1.48E+02 | -1.1E-05 | +9.5E-01 | 1.2E-03 | 1.2E-01 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 38| +1.54E+02 | -1.7E-04 | +1.0E+00 | 4.0E-02 | 7.8E-02 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 41| +1.48E+02 | +3.3E-02 | -4.0E+00 | 5.0E-01 | 1.2E+00 | 2.3E-01 | trr |0\n", - "2023-02-17 16:05:29 fides(INFO) 178| +1.38E+02 | -8.5E-05 | +9.6E-01 | 2.7E-02 | 6.0E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 25| +1.53E+02 | -1.4E+01 | +9.8E-01 | 5.0E-01 | 2.3E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 25| +1.48E+02 | -5.3E-01 | +9.5E-01 | 2.9E-02 | 7.0E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 2| +2.53E+03 | -1.3E+04 | +9.0E-01 | 2.5E-01 | 6.9E+04 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 48| +1.48E+02 | -1.8E-05 | +9.1E-01 | 2.4E-03 | 5.7E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 42| +1.48E+02 | -1.2E-03 | +3.0E-01 | 1.1E-01 | 1.2E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 39| +1.54E+02 | +1.2E-04 | -6.4E-01 | 7.9E-02 | 2.0E-01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 179| +1.38E+02 | +1.3E-03 | -6.4E+00 | 5.3E-02 | 6.0E-02 | 7.7E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 26| +1.53E+02 | +3.2E+02 | -3.1E+01 | 1.0E+00 | 6.2E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 26| +1.48E+02 | -3.4E-02 | +5.9E-01 | 5.9E-02 | 5.9E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 49| +1.48E+02 | -3.0E-05 | +1.0E+00 | 4.8E-03 | 1.1E-01 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 43| +1.48E+02 | +9.5E-03 | -8.5E-01 | 1.1E-01 | 1.2E+00 | 5.1E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 3| +6.21E+02 | -1.9E+03 | +8.4E-01 | 5.0E-01 | 1.1E+04 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 40| +1.54E+02 | -5.4E-05 | +7.3E-01 | 2.0E-02 | 2.0E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 27| +1.52E+02 | -1.1E+00 | +8.3E-02 | 2.5E-01 | 6.2E+01 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 27| +1.48E+02 | -2.3E-02 | +9.6E-01 | 5.9E-02 | 1.1E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 180| +1.38E+02 | +3.9E-05 | -6.4E-01 | 1.3E-02 | 6.0E-02 | 2.0E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 50| +1.48E+02 | -4.5E-05 | +9.5E-01 | 9.5E-03 | 5.2E-02 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 44| +1.48E+02 | -2.0E-03 | +5.8E-01 | 2.7E-02 | 1.2E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 4| +2.36E+02 | -3.8E+02 | +9.2E-01 | 1.0E+00 | 1.8E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 0| +5.61E+08 | NaN | NaN | 1.0E+00 | 2.6E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:29 fides(INFO) 28| +1.50E+02 | -2.1E+00 | +9.6E-01 | 6.2E-02 | 1.1E+02 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 28| +1.48E+02 | -1.8E-02 | +9.0E-01 | 1.2E-01 | 3.7E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 51| +1.48E+02 | -5.8E-05 | +8.2E-01 | 1.9E-02 | 1.5E-01 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 41| +1.54E+02 | -3.1E-05 | +9.9E-01 | 2.0E-02 | 1.2E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 45| +1.48E+02 | -4.5E-04 | +9.8E-01 | 2.7E-02 | 1.0E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 181| +1.38E+02 | -7.5E-06 | +4.3E-01 | 3.3E-03 | 6.0E-02 | 5.3E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 29| +1.49E+02 | -1.3E+00 | +6.3E-01 | 1.2E-01 | 5.7E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 29| +1.48E+02 | -8.7E-03 | +3.6E-01 | 2.3E-01 | 6.4E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 42| +1.54E+02 | -4.7E-05 | +1.1E+00 | 4.0E-02 | 7.7E-02 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 5| +1.91E+02 | -4.6E+01 | +9.0E-01 | 1.0E+00 | 2.2E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 52| +1.48E+02 | +2.7E-04 | -2.1E+00 | 3.8E-02 | 7.7E-02 | 4.2E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 46| +1.48E+02 | -5.9E-04 | +9.7E-01 | 5.4E-02 | 5.5E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 182| +1.38E+02 | -1.4E-05 | +7.1E-01 | 3.3E-03 | 1.1E-01 | 5.2E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 30| +1.48E+02 | -7.0E-01 | +9.6E-01 | 1.2E-01 | 6.6E+01 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 1| +3.31E+06 | -5.6E+08 | +9.3E-02 | 1.0E+00 | 2.6E+09 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 30| +1.48E+02 | +1.2E-01 | -1.8E+00 | 2.3E-01 | 2.7E+00 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 6| +1.91E+02 | +2.4E+03 | -1.7E+02 | 2.0E+00 | 8.4E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 53| +1.48E+02 | -1.1E-05 | +2.4E-01 | 9.5E-03 | 7.7E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 47| +1.48E+02 | -7.4E-04 | +9.4E-01 | 1.1E-01 | 6.0E-01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 43| +1.54E+02 | -2.6E-05 | +6.6E-01 | 7.9E-02 | 3.3E-02 | 5.6E-03 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 31| +1.48E+02 | -2.1E-01 | +6.2E-01 | 2.5E-01 | 2.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 183| +1.38E+02 | -9.0E-06 | +5.6E-01 | 3.3E-03 | 6.0E-02 | 4.8E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 31| +1.48E+02 | +2.1E-02 | -5.5E-01 | 5.9E-02 | 2.7E+00 | 5.7E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 54| +1.48E+02 | -2.1E-05 | +8.9E-01 | 2.4E-03 | 1.9E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 7| +1.82E+02 | -8.3E+00 | +4.1E-01 | 3.4E-01 | 8.4E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 48| +1.48E+02 | +2.9E-03 | -3.3E+00 | 2.2E-01 | 3.3E-01 | 7.3E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 44| +1.54E+02 | +7.6E-04 | -3.9E+00 | 7.9E-02 | 4.7E-02 | 2.2E-02 | trr |0\n", - "2023-02-17 16:05:29 fides(INFO) 32| +1.48E+02 | +9.3E-01 | -1.5E+00 | 2.5E-01 | 1.4E+01 | 3.5E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 2| +5.08E+05 | -2.8E+06 | +8.9E-01 | 2.5E-01 | 1.5E+07 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 32| +1.48E+02 | +5.0E-03 | -1.8E-01 | 1.5E-02 | 2.7E+00 | 2.6E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 55| +1.48E+02 | -5.0E-06 | +4.1E-01 | 4.8E-03 | 3.3E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 184| +1.38E+02 | -1.1E-05 | +7.4E-01 | 3.3E-03 | 6.7E-02 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 49| +1.48E+02 | -9.9E-05 | +2.9E-01 | 5.4E-02 | 3.3E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 45| +1.54E+02 | -2.4E-05 | +2.8E-01 | 2.6E-03 | 4.7E-02 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 8| +1.76E+02 | -6.8E+00 | +9.1E-01 | 3.4E-01 | 1.4E+02 | 6.9E-01 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 33| +1.48E+02 | -9.9E-02 | +5.2E-01 | 6.2E-02 | 1.4E+01 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 56| +1.48E+02 | -8.0E-06 | +1.0E+00 | 4.8E-03 | 2.4E-02 | 9.2E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 50| +1.48E+02 | +6.2E-04 | -6.9E-01 | 5.4E-02 | 3.7E-01 | 1.5E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 33| +1.48E+02 | -1.1E-02 | +7.8E-01 | 3.7E-03 | 2.7E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 185| +1.38E+02 | -1.0E-05 | +7.5E-01 | 3.3E-03 | 5.9E-02 | 4.3E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 46| +1.54E+02 | +7.5E-06 | -1.5E-01 | 2.6E-03 | 3.9E-02 | 5.2E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 34| +1.48E+02 | -2.1E-02 | +9.4E-01 | 6.2E-02 | 6.9E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 51| +1.48E+02 | -1.5E-04 | +5.0E-01 | 1.3E-02 | 3.7E-01 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 57| +1.48E+02 | -5.0E-06 | +2.9E-01 | 9.5E-03 | 1.2E-01 | 2.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 34| +1.48E+02 | -1.4E-03 | +9.6E-01 | 7.3E-03 | 2.8E+00 | 6.0E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 9| +1.74E+02 | -1.6E+00 | +7.8E-01 | 3.4E-01 | 5.5E+01 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 3| +7.83E+04 | -4.3E+05 | +9.0E-01 | 5.0E-01 | 2.4E+06 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 186| +1.38E+02 | -1.1E-05 | +3.9E-01 | 6.7E-03 | 6.3E-02 | 8.3E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 35| +1.48E+02 | -8.4E-03 | +5.5E-01 | 1.2E-01 | 6.1E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 47| +1.54E+02 | -1.1E-05 | +7.6E-01 | 6.5E-04 | 3.9E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 35| +1.48E+02 | -2.5E-03 | +6.0E-01 | 1.5E-02 | 1.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:29.462 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.596 and h = 3.12958e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.463 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.596: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 52| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.3E-02 | 6.4E-01 | 3.5E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 58| +1.48E+02 | +1.8E-04 | -3.9E+00 | 9.5E-03 | 4.7E-02 | 2.1E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 10| +1.73E+02 | -7.7E-01 | +9.5E-01 | 6.9E-01 | 4.2E+01 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 187| +1.38E+02 | +5.3E-05 | -6.2E-01 | 6.7E-03 | 7.1E-02 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 36| +1.48E+02 | -2.4E-03 | +9.5E-01 | 1.5E-02 | 4.4E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 36| +1.48E+02 | +6.4E-02 | -1.6E+00 | 1.2E-01 | 2.7E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 48| +1.54E+02 | -5.0E-06 | +4.5E-01 | 1.3E-03 | 3.3E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 59| +1.48E+02 | +1.4E-04 | -3.5E+00 | 2.4E-03 | 4.7E-02 | 1.7E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 53| +1.48E+02 | -5.8E-05 | +1.0E+00 | 3.4E-03 | 6.4E-01 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 4| +1.05E+04 | -6.8E+04 | +9.2E-01 | 1.0E+00 | 3.6E+05 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 11| +1.73E+02 | +1.0E+00 | -1.1E+01 | 1.4E+00 | 3.5E+01 | 4.2E+00 | trr |0\n", - "2023-02-17 16:05:29 fides(INFO) 37| +1.48E+02 | -9.3E-04 | +9.9E-01 | 2.9E-02 | 5.3E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 37| +1.48E+02 | -2.3E-03 | +1.3E-01 | 3.1E-02 | 2.7E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 188| +1.38E+02 | -1.2E-05 | +5.2E-01 | 1.7E-03 | 7.1E-02 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 60| +1.48E+02 | +7.4E-05 | -2.1E+00 | 6.0E-04 | 4.7E-02 | 1.4E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 49| +1.54E+02 | -3.5E-06 | +2.4E-01 | 1.3E-03 | 1.9E-02 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 54| +1.48E+02 | -6.1E-05 | +8.6E-01 | 6.7E-03 | 1.6E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 38| +1.48E+02 | -1.1E-03 | +8.8E-01 | 5.9E-02 | 1.4E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 12| +1.73E+02 | -3.5E-01 | +8.9E-01 | 3.4E-01 | 3.5E+01 | 3.5E-01 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 38| +1.48E+02 | -5.6E-03 | +8.4E-01 | 7.8E-03 | 5.9E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 61| +1.48E+02 | -9.7E-06 | +7.4E-01 | 1.5E-04 | 4.7E-02 | 3.4E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 55| +1.48E+02 | -3.7E-05 | +1.0E+00 | 1.3E-02 | 3.6E-01 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 50| +1.54E+02 | -3.9E-06 | +8.2E-01 | 3.2E-04 | 3.4E-02 | 6.5E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 189| +1.38E+02 | -6.8E-06 | +1.4E+00 | 1.7E-03 | 1.0E-01 | 1.4E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 5| +1.58E+03 | -8.9E+03 | +9.1E-01 | 1.0E+00 | 5.1E+04 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 39| +1.48E+02 | -1.4E-03 | +7.7E-01 | 1.2E-01 | 3.9E-01 | 8.9E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 62| +1.48E+02 | -1.4E-06 | +2.5E+00 | 1.5E-04 | 4.0E-02 | 4.1E-05 | 2d |1\n", - "2023-02-17 16:05:29 fides(WARNING) Stopping as function difference 1.39E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:29 fides(INFO) 39| +1.48E+02 | -6.2E-04 | +5.2E-01 | 1.6E-02 | 9.1E-01 | 7.7E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 13| +1.72E+02 | -5.0E-01 | +9.4E-01 | 3.4E-01 | 3.3E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 56| +1.48E+02 | -3.2E-05 | +8.2E-01 | 2.7E-02 | 8.1E-02 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 51| +1.54E+02 | -2.7E-06 | +5.6E-01 | 6.5E-04 | 1.7E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 190| +1.38E+02 | -8.2E-06 | +9.3E-01 | 3.3E-03 | 5.6E-02 | 2.7E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 40| +1.48E+02 | +4.2E-03 | -8.8E-01 | 2.3E-01 | 1.3E+00 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 40| +1.48E+02 | -2.9E-04 | +8.9E-01 | 1.6E-02 | 1.1E+00 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 57| +1.48E+02 | -2.9E-06 | +4.1E-02 | 5.4E-02 | 2.0E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 6| +3.68E+02 | -1.2E+03 | +9.1E-01 | 1.0E+00 | 7.7E+03 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 14| +1.72E+02 | -5.4E-01 | +8.9E-01 | 6.9E-01 | 2.5E+01 | 2.3E-01 | trr |1\n", - "2023-02-17 16:05:29.645 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2931 and h = 1.46524e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.646 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2931: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 191| +1.38E+02 | -1.7E-05 | +9.7E-01 | 6.7E-03 | 5.7E-02 | 5.4E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 41| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.1E-02 | 5.5E-01 | 6.7E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 41| +1.48E+02 | -9.1E-04 | +5.9E-01 | 5.9E-02 | 1.3E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 52| +1.54E+02 | -3.4E-06 | +7.2E-01 | 6.5E-04 | 1.3E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 58| +1.48E+02 | +1.1E-04 | -6.7E-01 | 1.3E-02 | 1.3E-01 | 3.6E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 42| +1.48E+02 | -2.0E-04 | +9.3E-01 | 7.8E-03 | 5.5E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 42| +1.48E+02 | -4.5E-04 | +7.8E-01 | 5.9E-02 | 3.3E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 15| +1.71E+02 | -4.7E-01 | +6.5E-01 | 6.9E-01 | 3.4E+01 | 4.7E-01 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 192| +1.38E+02 | -3.2E-05 | +9.5E-01 | 1.3E-02 | 5.6E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 53| +1.54E+02 | -1.3E-06 | +3.1E-01 | 6.5E-04 | 3.5E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 59| +1.48E+02 | +3.1E-05 | -3.2E-01 | 3.4E-03 | 1.3E-01 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(WARNING) Stopping as function difference 1.26E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:29 fides(INFO) 7| +1.81E+02 | -1.9E+02 | +1.1E+00 | 1.0E+00 | 1.1E+03 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 0| +4.31E+02 | NaN | NaN | 1.0E+00 | 6.4E+01 | NaN | NaN |1\n", - "2023-02-17 16:05:29 fides(INFO) 43| +1.48E+02 | -6.0E-04 | +7.4E-01 | 1.2E-01 | 5.4E-01 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 16| +1.71E+02 | -8.5E-02 | +3.1E-01 | 6.9E-01 | 2.2E+01 | 1.6E-01 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 43| +1.48E+02 | -1.5E-04 | +9.8E-01 | 1.6E-02 | 9.1E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:29.722 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.674 and h = 2.5308e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.722 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.674: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 60| +1.48E+02 | +0.0E+00 | +0.0E+00 | 8.4E-04 | 1.3E-01 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 193| +1.38E+02 | -5.4E-05 | +8.1E-01 | 2.7E-02 | 5.6E-02 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 1| +2.68E+02 | -1.6E+02 | +8.8E-01 | 1.0E+00 | 6.4E+01 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 44| +1.48E+02 | +1.2E-04 | -1.3E-01 | 1.2E-01 | 2.9E-01 | 6.7E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 44| +1.48E+02 | -9.5E-05 | +9.2E-01 | 3.1E-02 | 3.0E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 61| +1.48E+02 | -3.1E-05 | +6.7E-01 | 2.1E-04 | 1.3E-01 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 2| +2.68E+02 | +6.4E+00 | -4.0E-01 | 2.0E+00 | 6.9E+01 | 5.3E+00 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 17| +1.71E+02 | -6.9E-03 | +3.4E-02 | 6.9E-01 | 8.4E+00 | 2.1E-01 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 194| +1.38E+02 | +1.7E-02 | -2.5E+01 | 5.3E-02 | 7.3E-02 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 45| +1.48E+02 | -1.5E-04 | +4.0E-01 | 2.9E-02 | 2.9E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 3| +2.56E+02 | -1.2E+01 | +3.6E-01 | 5.0E-01 | 6.9E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 45| +1.48E+02 | -4.2E-05 | +4.1E-01 | 6.2E-02 | 3.9E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 62| +1.48E+02 | -7.6E-06 | +1.0E+00 | 2.1E-04 | 2.4E-01 | 7.2E-05 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 18| +1.71E+02 | -6.8E-02 | +6.9E-01 | 5.3E-02 | 1.1E+01 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:29.791 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.178 and h = 3.56278e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.792 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.178: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 46| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.9E-02 | 1.0E+00 | 1.7E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 4| +2.56E+02 | -7.5E-01 | -8.6E-03 | 5.0E-01 | 5.0E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 195| +1.38E+02 | +9.0E-04 | -3.9E+00 | 1.3E-02 | 7.3E-02 | 5.1E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 63| +1.48E+02 | -1.3E-06 | +7.7E-02 | 4.2E-04 | 6.3E-02 | 5.6E-04 | 2d |1\n", - "2023-02-17 16:05:29.809 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2503 and h = 4.35246e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.810 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2503: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 46| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 1.7E-01 | 2.7E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 0| +1.96E+13 | NaN | NaN | 1.0E+00 | 9.0E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:29 fides(INFO) 19| +1.71E+02 | -6.8E-03 | +8.7E-01 | 5.3E-02 | 4.4E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 5| +2.50E+02 | -6.6E+00 | +7.1E-01 | 1.2E-01 | 5.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 47| +1.48E+02 | -1.6E-04 | +9.5E-01 | 7.3E-03 | 1.0E+00 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:29.840 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.3921 and h = 2.28356e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.840 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.3921: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 47| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 1.7E-01 | 7.1E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 196| +1.38E+02 | +1.2E-05 | -2.0E-01 | 3.3E-03 | 7.3E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 1| +6.60E+10 | -2.0E+13 | +8.5E-02 | 1.0E+00 | 9.0E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 64| +1.48E+02 | -1.3E-05 | +8.6E-01 | 1.1E-04 | 3.5E-01 | 8.5E-05 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 6| +2.50E+02 | +9.0E-03 | -1.0E-01 | 1.2E-01 | 3.9E+00 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 48| +1.48E+02 | -6.7E-05 | +9.9E-01 | 1.5E-02 | 8.9E-02 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 20| +1.71E+02 | -7.8E-05 | +1.6E-01 | 1.1E-01 | 3.3E-01 | 2.2E-02 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 48| +1.48E+02 | +7.9E-05 | -5.5E-01 | 3.9E-03 | 1.7E-01 | 2.5E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 197| +1.38E+02 | -1.0E-05 | +6.2E-01 | 8.3E-04 | 7.3E-02 | 3.5E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 8| +1.81E+02 | +1.4E+03 | -5.6E+01 | 1.0E+00 | 6.2E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 65| +1.48E+02 | +6.1E-07 | -2.6E+00 | 2.1E-04 | 2.3E-02 | 4.3E-05 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 7| +2.50E+02 | +9.1E-03 | -1.1E-01 | 3.1E-02 | 3.9E+00 | 7.9E-02 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 2| +9.84E+09 | -5.6E+10 | +8.9E-01 | 2.5E-01 | 3.0E+11 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 49| +1.48E+02 | -1.0E-04 | +9.3E-01 | 2.9E-02 | 1.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 49| +1.48E+02 | +3.9E-05 | -3.4E-01 | 9.8E-04 | 1.7E-01 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 8| +2.50E+02 | -4.4E-02 | +7.3E-01 | 7.8E-03 | 3.9E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:29.904 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.594 and h = 2.06348e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.904 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.594: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 66| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.3E-05 | 2.3E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 198| +1.38E+02 | -3.1E-06 | +8.4E-01 | 8.3E-04 | 1.1E-01 | 1.1E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 21| +1.71E+02 | -7.4E-06 | +1.8E-02 | 7.8E-03 | 2.4E-01 | 1.5E-02 | trr |1\n", - "2023-02-17 16:05:29 fides(INFO) 3| +1.47E+09 | -8.4E+09 | +8.9E-01 | 5.0E-01 | 4.5E+10 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 50| +1.48E+02 | -1.7E-04 | +9.9E-01 | 5.9E-02 | 1.1E-01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 9| +2.50E+02 | -1.5E-05 | +2.5E-03 | 7.8E-03 | 8.8E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 50| +1.48E+02 | -4.0E-05 | +7.2E-01 | 2.4E-04 | 1.7E-01 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 67| +1.48E+02 | -8.0E-07 | +7.1E+00 | 1.3E-05 | 2.3E-02 | 9.6E-06 | 2d |1\n", - "2023-02-17 16:05:29 fides(WARNING) Stopping as function difference 7.96E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:29.943 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 142.796 and h = 9.16043e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:29.943 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 142.796: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:29 fides(INFO) 9| +1.81E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 6.2E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:29 fides(INFO) 199| +1.38E+02 | -3.5E-06 | +9.6E-01 | 1.7E-03 | 5.6E-02 | 1.2E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 22| +1.71E+02 | -1.0E-04 | +8.5E-01 | 1.4E-03 | 2.6E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 10| +2.50E+02 | -2.0E-03 | +7.4E-01 | 2.0E-03 | 8.8E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 51| +1.48E+02 | -2.4E-04 | +9.6E-01 | 1.2E-01 | 1.6E-01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 4| +2.21E+08 | -1.3E+09 | +8.9E-01 | 5.0E-01 | 6.8E+09 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 51| +1.48E+02 | -3.7E-06 | +1.4E+00 | 2.4E-04 | 1.2E-01 | 7.4E-05 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 11| +2.50E+02 | -4.5E-06 | +7.9E-03 | 2.0E-03 | 3.0E-01 | 3.6E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:29 fides(INFO) 200| +1.38E+02 | -6.0E-06 | +8.9E-01 | 3.3E-03 | 5.7E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 23| +1.71E+02 | -8.5E-05 | +7.9E-01 | 2.8E-03 | 2.5E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 5| +3.34E+07 | -1.9E+08 | +8.9E-01 | 5.0E-01 | 1.0E+09 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 52| +1.48E+02 | -1.7E-04 | +6.7E-01 | 2.3E-01 | 1.1E-01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:29 fides(INFO) 52| +1.48E+02 | -4.6E-06 | +7.7E-01 | 4.9E-04 | 5.9E-02 | 2.5E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.50E+02 | -2.4E-04 | +7.8E-01 | 4.9E-04 | 3.0E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 53| +1.48E+02 | +6.6E-03 | -7.8E+00 | 2.3E-01 | 1.1E-01 | 8.2E-02 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 201| +1.38E+02 | -1.2E-05 | +9.3E-01 | 6.7E-03 | 5.6E-02 | 4.4E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 53| +1.48E+02 | -3.2E-06 | +7.1E-01 | 9.8E-04 | 1.7E-01 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 6| +5.07E+06 | -2.8E+07 | +8.9E-01 | 5.0E-01 | 1.5E+08 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.50E+02 | +7.8E-08 | -1.9E-03 | 9.8E-04 | 8.6E-02 | 2.5E-03 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +1.74E+02 | -6.7E+00 | +1.0E+00 | 6.2E-02 | 6.2E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 24| +1.71E+02 | +2.2E-07 | -2.3E-02 | 5.6E-03 | 5.8E-02 | 2.9E-03 | trr |0\n", - "2023-02-17 16:05:30 fides(INFO) 54| +1.48E+02 | +1.6E-04 | -5.2E-01 | 5.9E-02 | 1.1E-01 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +2.28E+12 | NaN | NaN | 1.0E+00 | 1.0E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.50E+02 | -2.0E-05 | +5.5E-01 | 2.4E-04 | 8.6E-02 | 6.3E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 54| +1.48E+02 | -1.5E-06 | +3.4E-01 | 9.8E-04 | 2.9E-02 | 4.2E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +7.72E+05 | -4.3E+06 | +8.9E-01 | 5.0E-01 | 2.3E+07 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 202| +1.38E+02 | +8.6E-05 | -2.1E+00 | 1.3E-02 | 5.8E-02 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 25| +1.71E+02 | -2.5E-06 | +5.2E-01 | 5.4E-04 | 5.8E-02 | 7.3E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 55| +1.48E+02 | -5.5E-05 | +6.3E-01 | 1.5E-02 | 1.1E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.50E+02 | -1.5E-09 | +5.4E-04 | 2.4E-04 | 2.1E-02 | 5.7E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 1| +1.65E+06 | -2.3E+12 | +8.4E-02 | 1.0E+00 | 1.0E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 55| +1.48E+02 | -2.0E-06 | +2.7E+00 | 9.8E-04 | 1.6E-02 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +1.17E+05 | -6.5E+05 | +9.0E-01 | 5.0E-01 | 3.5E+06 | 7.8E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 203| +1.38E+02 | -3.9E-07 | +2.2E-02 | 3.3E-03 | 5.8E-02 | 5.5E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 26| +1.71E+02 | -1.8E-06 | +1.2E+00 | 5.4E-04 | 2.4E-02 | 7.2E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.50E+02 | -1.2E-06 | +5.3E-01 | 6.1E-05 | 2.1E-02 | 1.6E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(WARNING) Stopping as function difference 1.18E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:30 fides(INFO) 56| +1.48E+02 | -9.7E-06 | +9.7E-01 | 1.5E-02 | 1.6E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 2| +2.54E+05 | -1.4E+06 | +8.9E-01 | 2.5E-01 | 7.6E+06 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 56| +1.48E+02 | -1.7E-06 | +1.4E+00 | 2.0E-03 | 3.8E-02 | 3.2E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +1.64E+02 | -1.1E+01 | +1.0E+00 | 1.2E-01 | 7.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 9| +1.73E+04 | -1.0E+05 | +9.0E-01 | 5.0E-01 | 5.4E+05 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +4.01E+04 | -2.1E+05 | +9.0E-01 | 5.0E-01 | 1.2E+06 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 204| +1.38E+02 | -8.3E-06 | +7.3E-01 | 8.3E-04 | 1.1E-01 | 2.8E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 57| +1.48E+02 | -7.1E-06 | +8.7E-01 | 2.9E-02 | 2.3E-02 | 8.4E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 57| +1.48E+02 | +1.9E-06 | -1.0E+00 | 3.9E-03 | 1.5E-02 | 6.1E-04 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 27| +1.71E+02 | -1.8E-07 | +2.2E+00 | 1.1E-03 | 7.0E-03 | 1.4E-04 | trr |1\n", - "2023-02-17 16:05:30 fides(WARNING) Stopping as function difference 1.82E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:30 fides(INFO) 58| +1.48E+02 | -1.0E-05 | +8.7E-01 | 5.9E-02 | 5.2E-02 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 4| +6.41E+03 | -3.4E+04 | +9.0E-01 | 1.0E+00 | 1.8E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.55E+03 | -1.5E+04 | +9.1E-01 | 5.0E-01 | 7.9E+04 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 58| +1.48E+02 | -6.8E-07 | +1.3E+00 | 9.8E-04 | 1.5E-02 | 1.5E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(WARNING) Stopping as function difference 6.84E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:30 fides(INFO) 205| +1.38E+02 | +2.7E-07 | -1.3E-01 | 8.3E-04 | 5.9E-02 | 9.0E-05 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 59| +1.48E+02 | -7.1E-07 | +4.5E-02 | 1.2E-01 | 1.8E-02 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 5| +1.18E+03 | -5.2E+03 | +9.0E-01 | 1.0E+00 | 2.8E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +1.58E+05 | NaN | NaN | 1.0E+00 | 7.3E+05 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +4.97E+02 | -2.1E+03 | +9.2E-01 | 5.0E-01 | 1.1E+04 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 206| +1.38E+02 | -4.1E-07 | +6.9E-01 | 2.1E-04 | 5.9E-02 | 2.4E-05 | 2d |1\n", - "2023-02-17 16:05:30 fides(WARNING) Stopping as function difference 4.10E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:30 fides(INFO) 6| +3.76E+02 | -8.1E+02 | +9.0E-01 | 1.0E+00 | 4.5E+03 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 60| +1.48E+02 | +3.6E-05 | -6.2E-01 | 2.9E-02 | 6.8E-02 | 5.8E-03 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 1| +4.19E+02 | -1.6E+05 | +1.2E-01 | 1.0E+00 | 7.3E+05 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +2.56E+02 | -1.2E+02 | +8.9E-01 | 2.0E+00 | 6.9E+02 | 2.8E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +2.21E+05 | NaN | NaN | 1.0E+00 | 1.0E+06 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) 61| +1.48E+02 | +2.9E-06 | -1.4E-01 | 7.3E-03 | 6.8E-02 | 1.5E-03 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.27E+02 | -2.9E+01 | +1.5E+00 | 2.0E+00 | 8.4E+01 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +1.56E+02 | -7.7E+00 | +9.1E-01 | 2.5E-01 | 4.1E+01 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 2| +3.80E+02 | -3.9E+01 | +9.6E-01 | 2.5E-01 | 5.6E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 1| +6.40E+02 | -2.2E+05 | +1.2E-01 | 1.0E+00 | 1.0E+06 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.28E+02 | -2.7E+02 | +9.4E-01 | 5.0E-01 | 1.7E+03 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 62| +1.48E+02 | -2.5E-06 | +2.5E-01 | 1.8E-03 | 6.8E-02 | 4.4E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +3.65E+13 | NaN | NaN | 1.0E+00 | 1.7E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +3.05E+02 | -7.6E+01 | +1.0E+00 | 5.0E-01 | 5.3E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 2| +2.99E+02 | -3.4E+02 | +9.0E-01 | 2.5E-01 | 1.9E+03 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.27E+02 | +1.7E+02 | -1.3E+01 | 2.0E+00 | 2.9E+01 | 5.1E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 1| +2.37E+07 | -3.7E+13 | +8.4E-02 | 1.0E+00 | 1.7E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:30.345 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1296 and h = 1.64695e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:30.345 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1296: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:30 fides(INFO) 63| +1.48E+02 | +0.0E+00 | +0.0E+00 | 4.6E-04 | 2.1E-01 | 9.3E-05 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 2| +3.64E+06 | -2.0E+07 | +8.9E-01 | 2.5E-01 | 1.1E+08 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +2.52E+02 | -4.6E+01 | +8.5E-01 | 5.0E-01 | 2.9E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.27E+02 | +9.6E+00 | -1.1E+00 | 5.0E-01 | 2.9E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 4| +3.05E+02 | +1.3E+03 | -2.5E+01 | 1.0E+00 | 5.2E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 64| +1.48E+02 | -9.4E-06 | +1.7E+00 | 1.1E-04 | 2.1E-01 | 5.4E-05 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +5.63E+05 | -3.1E+06 | +9.0E-01 | 5.0E-01 | 1.7E+07 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 4| +2.50E+02 | -1.8E+00 | +4.6E-01 | 1.0E+00 | 3.3E+01 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +1.90E+02 | -3.8E+01 | +1.1E+00 | 5.0E-01 | 3.0E+02 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +2.24E+02 | -3.8E+00 | +5.5E-01 | 1.2E-01 | 2.9E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 4| +8.80E+04 | -4.7E+05 | +9.0E-01 | 1.0E+00 | 2.6E+06 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 5| +2.71E+02 | -3.4E+01 | +9.7E-01 | 2.5E-01 | 5.2E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 65| +1.48E+02 | -1.7E-06 | +1.0E+00 | 2.3E-04 | 2.7E-02 | 1.3E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 5| +2.49E+02 | -8.0E-01 | +3.2E-01 | 1.0E+00 | 1.2E+01 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.21E+02 | -2.6E+00 | +7.8E-01 | 1.2E-01 | 2.8E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +3.47E+09 | NaN | NaN | 1.0E+00 | 1.6E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) 5| +1.40E+04 | -7.4E+04 | +9.0E-01 | 2.0E+00 | 4.0E+05 | 4.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 6| +2.41E+03 | -1.2E+04 | +9.0E-01 | 4.0E+00 | 6.4E+04 | 1.6E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.20E+02 | -1.0E+00 | +1.8E-01 | 2.5E-01 | 2.0E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 6| +2.71E+02 | +6.3E+01 | -2.0E+00 | 5.0E-01 | 4.7E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 6| +2.31E+02 | -1.8E+01 | +3.1E+00 | 1.0E+00 | 1.8E+01 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 66| +1.48E+02 | +6.5E-07 | -4.6E-01 | 4.6E-04 | 1.1E-01 | 8.1E-05 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 1| +9.62E+04 | -3.5E+09 | +9.3E-02 | 1.0E+00 | 1.6E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +5.71E+02 | -1.8E+03 | +9.0E-01 | 4.0E+00 | 1.0E+04 | 1.3E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +1.52E+02 | -4.1E+00 | +8.1E-01 | 5.0E-01 | 6.9E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 14| +1.83E+02 | -7.2E+00 | +1.3E-01 | 1.0E+00 | 6.8E+01 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +2.57E+02 | -1.4E+01 | +9.3E-01 | 1.2E-01 | 4.7E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 67| +1.48E+02 | -1.1E-06 | +7.9E-01 | 1.1E-04 | 1.1E-01 | 3.2E-05 | 2d |1\n", - "2023-02-17 16:05:30 fides(WARNING) Stopping as function difference 1.10E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.17E+02 | -3.4E+00 | +8.3E-01 | 6.2E-02 | 4.6E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.87E+02 | -2.8E+02 | +9.0E-01 | 4.0E+00 | 1.6E+03 | 1.7E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +2.31E+02 | +4.8E+02 | -2.3E+01 | 2.0E+00 | 4.2E+01 | 3.3E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 2| +2.00E+04 | -7.6E+04 | +9.0E-01 | 2.5E-01 | 4.1E+05 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.14E+02 | -2.4E+00 | +8.0E-01 | 1.2E-01 | 2.2E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.57E+02 | +4.1E+00 | -3.7E-01 | 2.5E-01 | 3.5E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.36E+02 | -5.1E+01 | +1.0E+00 | 4.0E+00 | 2.4E+02 | 3.6E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 14| +1.51E+02 | -7.1E-01 | +9.7E-01 | 1.0E+00 | 3.9E+01 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.31E+02 | +4.3E+01 | -2.0E+00 | 5.0E-01 | 4.2E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.13E+02 | -1.5E+00 | +2.6E-01 | 2.5E-01 | 1.9E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +4.16E+03 | -1.6E+04 | +9.1E-01 | 5.0E-01 | 7.0E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.52E+02 | -4.9E+00 | +9.1E-01 | 6.2E-02 | 3.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.36E+02 | +1.2E+02 | -1.1E+01 | 4.0E+00 | 2.7E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 17| +2.06E+02 | -6.8E+00 | +7.3E-01 | 2.5E-01 | 4.5E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.21E+02 | -9.8E+00 | +8.4E-01 | 1.2E-01 | 4.2E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +1.51E+02 | +2.3E+02 | -1.3E+02 | 2.0E+00 | 2.1E+01 | 5.0E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 4| +4.16E+03 | +2.1E+04 | -1.9E+01 | 1.0E+00 | 1.3E+04 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 18| +2.02E+02 | -3.6E+00 | +4.2E-01 | 2.5E-01 | 2.3E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +2.36E+02 | +1.1E+01 | -1.6E+00 | 1.0E+00 | 2.7E+01 | 3.0E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 15| +1.74E+02 | -9.1E+00 | +1.1E+00 | 2.5E-01 | 1.1E+02 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.51E+02 | -5.6E-01 | +8.0E-02 | 1.2E-01 | 2.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 0| +4.48E+11 | NaN | NaN | 1.0E+00 | 2.1E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.08E+02 | -1.3E+01 | +9.7E-01 | 2.5E-01 | 3.5E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 1| +6.61E+05 | -4.5E+11 | +9.0E-02 | 1.0E+00 | 2.1E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +1.51E+02 | +2.5E+00 | -7.2E+00 | 5.0E-01 | 2.1E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 2| +1.04E+05 | -5.6E+05 | +9.0E-01 | 2.5E-01 | 3.0E+06 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +1.91E+02 | -1.1E+01 | +9.8E-01 | 2.5E-01 | 5.1E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 5| +9.03E+02 | -3.3E+03 | +8.9E-01 | 2.5E-01 | 1.3E+04 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.36E+02 | +1.2E+01 | -1.7E+00 | 2.5E-01 | 2.7E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 11| +2.50E+02 | -1.3E+00 | +8.7E-01 | 3.1E-02 | 2.9E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +1.96E+02 | -1.2E+01 | +1.1E+00 | 5.0E-01 | 4.4E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +1.51E+02 | -2.6E-01 | +1.1E+00 | 1.2E-01 | 2.1E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 3| +1.66E+04 | -8.7E+04 | +9.0E-01 | 5.0E-01 | 4.8E+05 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 20| +1.60E+02 | -3.1E+01 | +1.1E+00 | 5.0E-01 | 3.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 4| +2.84E+03 | -1.4E+04 | +9.0E-01 | 1.0E+00 | 7.5E+04 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.33E+02 | -3.0E+00 | +8.3E-01 | 6.2E-02 | 2.7E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.49E+02 | -5.2E-01 | +5.0E-01 | 6.2E-02 | 1.5E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 6| +3.44E+02 | -5.6E+02 | +8.3E-01 | 5.0E-01 | 2.3E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +1.96E+02 | +5.5E+02 | -3.7E+01 | 1.0E+00 | 2.8E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 5| +6.37E+02 | -2.2E+03 | +9.0E-01 | 2.0E+00 | 1.2E+04 | 3.6E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 21| +1.60E+02 | +1.1E+01 | -1.4E+00 | 1.0E+00 | 7.4E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 6| +2.97E+02 | -3.4E+02 | +9.0E-01 | 4.0E+00 | 1.9E+03 | 1.2E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.31E+02 | -1.5E+00 | +5.1E-01 | 1.2E-01 | 1.7E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 18| +1.51E+02 | -3.1E-01 | +4.8E-01 | 2.5E-01 | 2.3E+01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.49E+02 | -8.7E-02 | +3.0E-01 | 6.2E-02 | 5.5E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +2.51E+02 | -4.5E+01 | +8.5E-01 | 4.0E+00 | 3.0E+02 | 1.9E+00 | trr |1\n", - "2023-02-17 16:05:30 fides(INFO) 7| +2.52E+02 | -9.2E+01 | +8.4E-01 | 1.0E+00 | 3.8E+02 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +1.90E+02 | -6.9E+00 | +7.1E-01 | 2.5E-01 | 2.8E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 22| +1.53E+02 | -6.1E+00 | +8.1E-01 | 2.1E-01 | 7.4E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.36E+02 | -1.5E+01 | +2.5E+00 | 4.0E+00 | 3.1E+01 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.29E+02 | -2.5E+00 | +6.4E-01 | 1.2E-01 | 3.3E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 8| +2.50E+02 | -1.7E+00 | +4.4E-01 | 2.0E+00 | 3.2E+01 | 4.8E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.49E+02 | -1.5E-01 | +5.0E-01 | 6.2E-02 | 6.3E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +1.50E+02 | -5.3E-01 | +6.8E-01 | 2.5E-01 | 3.6E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +1.61E+02 | -1.3E+01 | +7.9E-01 | 5.0E-01 | 1.8E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.50E+02 | +1.4E-01 | -6.4E-01 | 2.0E+00 | 1.3E+01 | 4.5E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 14| +1.78E+02 | -1.2E+01 | +7.8E-01 | 2.5E-01 | 4.4E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 23| +1.49E+02 | -4.1E+00 | +1.0E+00 | 4.1E-01 | 8.8E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.29E+02 | -6.7E-03 | -6.1E-02 | 1.2E-01 | 1.9E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 9| +2.36E+02 | +5.5E+02 | -2.5E+01 | 4.0E+00 | 4.0E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.50E+02 | +4.2E-01 | -3.8E-01 | 5.0E-01 | 1.3E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 24| +1.49E+02 | +4.1E+00 | -2.1E+00 | 8.2E-01 | 3.2E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.49E+02 | -1.2E-01 | +4.8E-01 | 6.2E-02 | 4.7E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +2.28E+02 | -9.9E-01 | +8.9E-01 | 3.1E-02 | 1.9E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 20| +1.50E+02 | -2.7E-01 | +8.7E-01 | 2.5E-01 | 2.2E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 10| +2.36E+02 | +1.1E+02 | -5.7E+00 | 1.0E+00 | 4.0E+01 | 3.0E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 11| +2.50E+02 | +4.2E-01 | -3.8E-01 | 1.2E-01 | 1.3E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 15| +1.78E+02 | +1.8E+01 | -1.4E+00 | 5.0E-01 | 1.4E+02 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 25| +1.49E+02 | -7.4E-01 | +7.0E-01 | 1.3E-01 | 3.2E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 11| +2.35E+02 | -1.8E+00 | +3.1E-02 | 2.5E-01 | 4.0E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 18| +2.26E+02 | -1.3E+00 | +9.6E-01 | 6.2E-02 | 1.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.49E+02 | -1.6E-01 | +5.9E-01 | 6.2E-02 | 5.5E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.50E+02 | -5.6E-01 | +6.9E-01 | 3.1E-02 | 1.3E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 21| +1.50E+02 | -1.7E-01 | +8.5E-01 | 5.0E-01 | 1.8E+01 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 16| +1.65E+02 | -1.2E+01 | +9.1E-01 | 1.2E-01 | 1.4E+02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 12| +2.28E+02 | -6.2E+00 | +8.7E-01 | 6.2E-02 | 7.4E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 26| +1.49E+02 | +1.0E+00 | -1.1E+00 | 1.3E-01 | 2.6E+01 | 2.1E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 19| +2.24E+02 | -2.0E+00 | +1.0E+00 | 1.2E-01 | 1.7E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.50E+02 | +1.7E-04 | -3.3E-02 | 3.1E-02 | 9.4E-01 | 7.1E-02 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 17| +2.49E+02 | -1.4E-01 | +5.4E-01 | 6.2E-02 | 4.6E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 13| +2.25E+02 | -3.4E+00 | +6.6E-01 | 1.2E-01 | 3.9E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 27| +1.48E+02 | -2.0E-01 | +6.3E-01 | 3.4E-02 | 2.6E+01 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 22| +1.50E+02 | -2.2E-02 | +6.3E-01 | 1.0E+00 | 7.7E+00 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +1.59E+02 | -1.6E+00 | +1.2E-01 | 1.0E+00 | 9.8E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +1.58E+02 | -7.5E+00 | +6.2E-01 | 2.5E-01 | 4.3E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 20| +2.24E+02 | +7.3E-01 | -3.6E-01 | 2.5E-01 | 1.8E+01 | 7.2E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.50E+02 | +1.7E-04 | -3.3E-02 | 7.8E-03 | 9.4E-01 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 14| +2.25E+02 | -5.0E-02 | -4.1E-02 | 1.2E-01 | 1.9E+01 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 18| +2.49E+02 | -1.8E-01 | +6.1E-01 | 6.2E-02 | 5.9E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.50E+02 | -2.6E-03 | +7.2E-01 | 2.0E-03 | 9.4E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 28| +1.48E+02 | -2.4E-01 | +9.2E-01 | 3.4E-02 | 1.9E+01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 15| +2.24E+02 | -9.3E-01 | +8.8E-01 | 3.1E-02 | 1.9E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 21| +2.23E+02 | -1.1E+00 | +6.2E-01 | 6.2E-02 | 1.8E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 23| +1.50E+02 | +3.1E-01 | -5.5E+00 | 1.0E+00 | 8.4E-01 | 6.0E-01 | trr |0\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.50E+02 | +3.7E-07 | -4.0E-03 | 2.0E-03 | 1.3E-01 | 4.5E-03 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 16| +2.23E+02 | -1.5E+00 | +1.0E+00 | 6.2E-02 | 1.7E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 29| +1.48E+02 | -1.7E-01 | +8.2E-01 | 6.7E-02 | 2.4E+01 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 18| +1.54E+02 | -4.1E+00 | +8.4E-01 | 2.5E-01 | 1.0E+02 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 22| +2.22E+02 | -1.1E+00 | +8.6E-01 | 6.2E-02 | 1.7E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +2.48E+02 | -1.6E-01 | +5.0E-01 | 6.2E-02 | 5.4E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +2.50E+02 | -3.4E-05 | +3.6E-01 | 4.9E-04 | 1.3E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 17| +2.20E+02 | -2.5E+00 | +1.1E+00 | 1.2E-01 | 1.8E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 24| +1.50E+02 | -9.6E-03 | +7.0E-01 | 1.5E-01 | 8.4E-01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 23| +2.22E+02 | -4.0E-01 | +2.1E-01 | 1.2E-01 | 1.3E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 30| +1.48E+02 | -1.7E-01 | +5.7E-01 | 1.3E-01 | 8.5E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 18| +2.20E+02 | +3.6E-01 | -2.5E-01 | 2.5E-01 | 1.7E+01 | 7.3E-01 | 2d |0\n", - "2023-02-17 16:05:30 fides(INFO) 18| +2.50E+02 | -1.3E-07 | +3.4E-03 | 4.9E-04 | 7.7E-02 | 9.5E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +1.53E+02 | -9.0E-01 | +1.1E+00 | 5.0E-01 | 2.3E+01 | 7.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 18| +1.57E+02 | -2.5E+00 | +9.3E-01 | 1.0E-01 | 9.0E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:30 fides(INFO) 20| +2.48E+02 | -2.3E-01 | +5.8E-01 | 6.2E-02 | 7.3E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 24| +2.21E+02 | -1.2E+00 | +8.7E-01 | 3.1E-02 | 2.9E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +2.50E+02 | -1.6E-05 | +8.0E-01 | 1.2E-04 | 7.7E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 19| +2.19E+02 | -9.3E-01 | +6.1E-01 | 6.2E-02 | 1.7E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 25| +1.50E+02 | -1.6E-03 | +6.7E-01 | 1.5E-01 | 1.6E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:30 fides(INFO) 31| +1.48E+02 | +7.5E-01 | -1.3E+00 | 1.3E-01 | 1.8E+01 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 20| +2.50E+02 | +1.2E-09 | -3.8E-04 | 2.4E-04 | 2.5E-02 | 5.7E-04 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 25| +2.20E+02 | -8.9E-01 | +7.1E-01 | 6.2E-02 | 1.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 20| +2.18E+02 | -1.1E+00 | +8.1E-01 | 6.2E-02 | 2.0E+01 | 9.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 20| +1.52E+02 | -4.3E-01 | +9.7E-01 | 1.0E+00 | 1.7E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 32| +1.48E+02 | -1.1E-01 | +5.7E-01 | 3.4E-02 | 1.8E+01 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 21| +2.48E+02 | -1.7E-01 | +4.1E-01 | 6.2E-02 | 6.5E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 26| +1.50E+02 | -5.0E-04 | +1.0E+00 | 1.5E-01 | 6.9E-01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 21| +2.50E+02 | -1.9E-06 | +6.5E-01 | 6.1E-05 | 2.5E-02 | 1.5E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(WARNING) Stopping as function difference 1.86E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:31 fides(INFO) 21| +2.18E+02 | -1.1E-01 | -1.6E-02 | 1.2E-01 | 1.4E+01 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 26| +2.19E+02 | -4.6E-01 | +4.8E-01 | 6.2E-02 | 1.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 21| +1.52E+02 | +1.4E+02 | -2.2E+02 | 2.0E+00 | 1.4E+01 | 5.2E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 33| +1.48E+02 | -3.9E-02 | +9.1E-01 | 3.4E-02 | 4.2E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 27| +1.50E+02 | -6.0E-04 | +1.1E+00 | 2.9E-01 | 4.8E-01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 22| +2.18E+02 | -5.6E-01 | +8.8E-01 | 3.1E-02 | 1.4E+01 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 19| +1.55E+02 | -1.4E+00 | +9.8E-01 | 2.0E-01 | 8.1E+01 | 3.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 27| +2.19E+02 | -7.9E-01 | +6.5E-01 | 6.2E-02 | 1.7E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 22| +1.52E+02 | -1.5E-01 | +3.0E-01 | 5.0E-01 | 1.4E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 22| +2.48E+02 | -3.1E-01 | +5.6E-01 | 6.2E-02 | 9.4E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 23| +2.16E+02 | -1.1E+00 | +1.0E+00 | 6.2E-02 | 1.4E+01 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +1.48E+02 | -4.8E-02 | +8.4E-01 | 6.7E-02 | 1.0E+01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 28| +1.50E+02 | -1.5E-04 | +8.2E-01 | 5.9E-01 | 2.9E-01 | 2.6E-01 | trr |1\n", - "2023-02-17 16:05:31 fides(INFO) 23| +1.52E+02 | -6.4E-01 | +5.2E-01 | 5.0E-01 | 2.3E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 28| +2.18E+02 | -3.0E-01 | +3.4E-01 | 6.2E-02 | 1.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 24| +2.16E+02 | -3.9E-01 | +2.1E-01 | 1.2E-01 | 1.3E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 35| +1.48E+02 | -4.7E-02 | +6.4E-01 | 1.3E-01 | 2.3E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +1.50E+02 | +4.7E-04 | -3.2E+00 | 5.9E-01 | 3.3E-01 | 1.5E-02 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 23| +2.48E+02 | -1.9E-01 | +3.4E-01 | 6.2E-02 | 7.7E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +2.17E+02 | -7.2E-01 | +6.0E-01 | 6.2E-02 | 1.7E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 25| +2.15E+02 | -1.1E+00 | +8.5E-01 | 3.1E-02 | 2.8E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 24| +1.51E+02 | -1.1E+00 | +8.7E-01 | 5.0E-01 | 2.0E+01 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 20| +1.55E+02 | +4.0E-01 | -7.4E-01 | 4.0E-01 | 2.1E+01 | 8.1E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 0| +2.59E+04 | NaN | NaN | 1.0E+00 | 9.1E+04 | NaN | NaN |1\n", - "2023-02-17 16:05:31 fides(INFO) 36| +1.48E+02 | +1.9E-01 | -1.3E+00 | 1.3E-01 | 3.7E+00 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 30| +2.17E+02 | -1.7E-01 | +2.0E-01 | 6.2E-02 | 1.0E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 26| +2.14E+02 | -5.0E-01 | +5.4E-01 | 6.2E-02 | 1.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 30| +1.50E+02 | -3.1E-05 | +4.5E-01 | 1.9E-02 | 3.3E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 24| +2.47E+02 | -4.2E-01 | +5.7E-01 | 6.2E-02 | 1.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 25| +1.51E+02 | +2.8E+00 | -1.7E+00 | 1.0E+00 | 9.0E+00 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 31| +2.17E+02 | -3.9E-01 | +9.0E-01 | 1.6E-02 | 1.7E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +1.48E+02 | -2.1E-02 | +5.1E-01 | 3.4E-02 | 3.7E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 27| +2.14E+02 | +3.1E-03 | -3.1E-02 | 6.2E-02 | 1.1E+01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 1| +3.75E+02 | -2.6E+04 | +1.6E-01 | 1.0E+00 | 9.1E+04 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 31| +1.50E+02 | -6.2E-06 | +2.9E-01 | 1.9E-02 | 8.7E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 26| +1.50E+02 | -4.1E-01 | +6.1E-01 | 2.5E-01 | 9.0E+00 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 32| +2.17E+02 | -3.9E-01 | +7.9E-01 | 3.1E-02 | 1.2E+01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 28| +2.14E+02 | -2.7E-01 | +8.7E-01 | 1.6E-02 | 1.1E+01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 38| +1.48E+02 | -9.7E-03 | +9.2E-01 | 3.4E-02 | 1.6E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 2| +2.83E+02 | -9.2E+01 | +8.9E-01 | 2.5E-01 | 5.4E+02 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 25| +2.47E+02 | -2.2E-01 | +3.1E-01 | 6.2E-02 | 9.0E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 21| +1.55E+02 | -5.6E-01 | +8.6E-01 | 1.0E-01 | 2.1E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +2.14E+02 | -2.7E-01 | +7.9E-01 | 3.1E-02 | 9.4E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 33| +2.16E+02 | -5.8E-01 | +8.9E-01 | 6.2E-02 | 7.8E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 32| +1.50E+02 | +4.8E-08 | -4.0E-03 | 1.9E-02 | 5.6E-02 | 2.3E-03 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 3| +2.58E+02 | -2.5E+01 | +7.7E-01 | 5.0E-01 | 8.1E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 39| +1.48E+02 | -1.4E-02 | +9.3E-01 | 6.7E-02 | 2.1E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 27| +1.50E+02 | -1.8E-01 | +7.7E-01 | 2.5E-01 | 1.2E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +2.16E+02 | -3.3E-01 | +5.0E-01 | 1.2E-01 | 9.8E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 30| +2.14E+02 | -3.0E-01 | +5.8E-01 | 6.2E-02 | 1.1E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 26| +2.46E+02 | -5.9E-01 | +6.0E-01 | 6.2E-02 | 1.5E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 4| +2.58E+02 | +1.1E+02 | -6.5E+00 | 1.0E+00 | 4.0E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 35| +2.16E+02 | +1.9E-01 | -2.3E-01 | 1.2E-01 | 1.1E+01 | 3.5E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 33| +1.50E+02 | -3.2E-06 | +7.8E-01 | 4.7E-03 | 5.6E-02 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 40| +1.48E+02 | -1.6E-02 | +7.7E-01 | 1.3E-01 | 1.0E+00 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 28| +1.50E+02 | -2.1E-01 | +6.2E-01 | 5.0E-01 | 4.7E+00 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 31| +2.14E+02 | +1.2E-01 | -1.9E-01 | 6.2E-02 | 1.1E+01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 22| +1.54E+02 | -4.5E-01 | +6.0E-01 | 2.0E-01 | 4.9E+01 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 36| +2.15E+02 | -4.1E-01 | +6.8E-01 | 3.1E-02 | 1.1E+01 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 5| +2.57E+02 | -1.5E+00 | +1.0E-02 | 2.5E-01 | 4.0E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 41| +1.48E+02 | +3.6E-01 | -7.2E+00 | 2.7E-01 | 1.4E+00 | 2.6E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 32| +2.13E+02 | -2.7E-01 | +8.5E-01 | 1.6E-02 | 1.1E+01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +1.50E+02 | -7.5E-07 | +3.3E-01 | 9.5E-03 | 1.0E-02 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(WARNING) Stopping as function difference 7.48E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:31 fides(INFO) 27| +2.46E+02 | -3.1E-01 | +3.6E-01 | 6.2E-02 | 1.0E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +1.50E+02 | -1.2E-01 | +6.6E-01 | 5.0E-01 | 4.0E+00 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +2.15E+02 | -2.6E-01 | +9.4E-01 | 3.1E-02 | 6.6E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 6| +2.52E+02 | -5.5E+00 | +8.5E-01 | 6.2E-02 | 6.6E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 33| +2.13E+02 | -1.5E-01 | +5.5E-01 | 3.1E-02 | 7.8E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 42| +1.48E+02 | +6.2E-03 | -3.4E-01 | 6.7E-02 | 1.4E+00 | 6.6E-02 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 23| +1.54E+02 | -2.0E-01 | +8.9E-01 | 2.0E-01 | 2.7E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 38| +2.15E+02 | -1.4E-01 | +3.4E-01 | 6.2E-02 | 6.1E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 7| +2.50E+02 | -1.2E+00 | +4.0E-01 | 1.2E-01 | 2.8E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 30| +1.50E+02 | -6.3E-02 | +7.3E-01 | 5.0E-01 | 7.0E+00 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +2.13E+02 | -2.0E-01 | +6.4E-01 | 3.1E-02 | 9.9E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 43| +1.48E+02 | -3.6E-03 | +6.9E-01 | 1.7E-02 | 1.4E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 28| +2.45E+02 | -7.9E-01 | +6.6E-01 | 6.2E-02 | 1.8E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 39| +2.15E+02 | -3.2E-01 | +4.3E-01 | 6.2E-02 | 1.3E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 35| +2.13E+02 | -9.1E-02 | +3.8E-01 | 3.1E-02 | 7.1E+00 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 8| +2.50E+02 | +3.4E-01 | -3.3E-01 | 1.2E-01 | 1.3E+01 | 2.8E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 24| +1.54E+02 | -2.9E-02 | +9.1E-01 | 4.0E-01 | 5.9E+00 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 44| +1.48E+02 | -1.3E-03 | +9.1E-01 | 1.7E-02 | 5.8E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 40| +2.15E+02 | +1.3E-02 | -5.0E-02 | 6.2E-02 | 8.2E+00 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 31| +1.50E+02 | -1.4E-02 | +7.5E-01 | 5.0E-01 | 2.7E+00 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 36| +2.13E+02 | -1.7E-01 | +5.7E-01 | 3.1E-02 | 9.8E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 0| +1.98E+12 | NaN | NaN | 1.0E+00 | 9.2E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +2.45E+02 | -5.1E-01 | +5.1E-01 | 6.2E-02 | 1.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 9| +2.50E+02 | -5.2E-01 | +6.9E-01 | 3.1E-02 | 1.3E+01 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 41| +2.14E+02 | -2.0E-01 | +8.1E-01 | 1.6E-02 | 8.2E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 45| +1.48E+02 | -2.2E-03 | +9.8E-01 | 3.4E-02 | 7.0E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +2.13E+02 | -4.2E-02 | +1.9E-01 | 3.1E-02 | 6.4E+00 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 25| +1.54E+02 | -7.2E-03 | +5.1E-01 | 8.0E-01 | 4.4E+00 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 1| +1.26E+06 | -2.0E+12 | +8.4E-02 | 1.0E+00 | 9.2E+12 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 32| +1.50E+02 | -3.6E-03 | +7.3E-01 | 1.0E+00 | 1.0E+00 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 10| +2.50E+02 | -3.9E-03 | +9.2E-02 | 3.1E-02 | 2.7E+00 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 42| +2.14E+02 | -1.4E-01 | +7.0E-01 | 3.1E-02 | 5.2E+00 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 38| +2.13E+02 | -1.0E-01 | +9.1E-01 | 7.8E-03 | 9.6E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 46| +1.48E+02 | -3.1E-03 | +9.4E-01 | 6.7E-02 | 5.8E-01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 30| +2.44E+02 | -9.4E-01 | +7.3E-01 | 6.2E-02 | 2.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 2| +1.83E+05 | -1.1E+06 | +9.0E-01 | 2.5E-01 | 6.0E+06 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 39| +2.12E+02 | -9.9E-02 | +7.8E-01 | 1.6E-02 | 7.3E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 43| +2.14E+02 | -1.5E-01 | +5.9E-01 | 3.1E-02 | 7.3E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 26| +1.54E+02 | +3.7E-01 | -1.2E+01 | 8.0E-01 | 1.1E+00 | 5.0E-01 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 33| +1.50E+02 | +1.0E-04 | -2.0E-01 | 1.0E+00 | 5.6E-01 | 7.4E-01 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 11| +2.50E+02 | -1.3E-02 | +4.3E-01 | 7.8E-03 | 2.3E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 3| +2.56E+04 | -1.6E+05 | +9.0E-01 | 5.0E-01 | 9.0E+05 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 47| +1.48E+02 | -2.5E-03 | +8.0E-01 | 1.3E-01 | 5.2E-01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 40| +2.12E+02 | -1.4E-01 | +8.8E-01 | 3.1E-02 | 5.2E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 44| +2.14E+02 | -7.0E-02 | +3.1E-01 | 3.1E-02 | 5.7E+00 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 31| +2.43E+02 | -7.3E-01 | +6.4E-01 | 6.2E-02 | 1.3E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 27| +1.54E+02 | -5.4E-03 | +7.0E-01 | 1.3E-01 | 1.1E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 12| +2.50E+02 | -3.1E-04 | +4.0E-02 | 7.8E-03 | 1.1E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 4| +3.89E+03 | -2.2E+04 | +9.0E-01 | 5.0E-01 | 1.4E+05 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 48| +1.48E+02 | +8.2E-02 | -1.4E+01 | 2.7E-01 | 2.9E-01 | 1.4E-01 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 41| +2.12E+02 | -4.7E-02 | +3.2E-01 | 6.2E-02 | 5.9E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 45| +2.14E+02 | -1.6E-01 | +5.6E-01 | 3.1E-02 | 8.2E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +1.50E+02 | -2.0E-04 | +7.7E-01 | 2.2E-01 | 5.6E-01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 5| +7.42E+02 | -3.1E+03 | +9.0E-01 | 5.0E-01 | 2.2E+04 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 13| +2.50E+02 | -3.1E-03 | +7.4E-01 | 2.0E-03 | 1.0E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 49| +1.48E+02 | +3.1E-03 | -1.1E+00 | 3.9E-02 | 2.9E-01 | 3.4E-02 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 46| +2.14E+02 | -5.6E-02 | +2.8E-01 | 3.1E-02 | 5.3E+00 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 32| +2.42E+02 | -9.5E-01 | +7.7E-01 | 6.2E-02 | 2.0E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 42| +2.12E+02 | +3.4E-02 | -1.5E-01 | 6.2E-02 | 6.3E+00 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 28| +1.54E+02 | -5.8E-04 | +6.8E-01 | 1.3E-01 | 3.7E-01 | 9.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 35| +1.50E+02 | -7.7E-05 | +9.2E-01 | 4.3E-01 | 2.5E-01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 6| +2.75E+02 | -4.7E+02 | +9.0E-01 | 5.0E-01 | 3.5E+03 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 14| +2.50E+02 | +1.1E-06 | -6.0E-03 | 2.0E-03 | 1.8E-01 | 4.2E-03 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 47| +2.14E+02 | -1.5E-01 | +5.3E-01 | 3.1E-02 | 8.0E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 43| +2.12E+02 | -1.2E-01 | +6.9E-01 | 1.6E-02 | 6.3E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 50| +1.48E+02 | -3.3E-04 | +4.1E-01 | 9.8E-03 | 2.9E-01 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 7| +2.05E+02 | -7.0E+01 | +9.3E-01 | 5.0E-01 | 5.4E+02 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 29| +1.54E+02 | -2.3E-04 | +1.1E+00 | 1.3E-01 | 4.2E-01 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 33| +2.41E+02 | -8.7E-01 | +4.6E-01 | 1.2E-01 | 1.3E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 15| +2.50E+02 | -9.7E-05 | +5.8E-01 | 4.9E-04 | 1.8E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 44| +2.12E+02 | -6.4E-02 | +9.8E-01 | 1.6E-02 | 4.1E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 48| +2.14E+02 | -4.6E-02 | +2.4E-01 | 3.1E-02 | 5.2E+00 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 36| +1.50E+02 | -2.2E-05 | +8.7E-01 | 8.7E-01 | 1.9E-01 | 2.4E-01 | trr |1\n", - "2023-02-17 16:05:31 fides(INFO) 51| +1.48E+02 | -1.3E-04 | +8.5E-01 | 9.8E-03 | 8.1E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 45| +2.12E+02 | -6.6E-02 | +6.7E-01 | 3.1E-02 | 3.7E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 16| +2.50E+02 | -8.5E-09 | +1.2E-03 | 4.9E-04 | 3.3E-02 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 49| +2.13E+02 | -8.7E-02 | +9.0E-01 | 7.8E-03 | 7.8E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 30| +1.54E+02 | -3.4E-04 | +1.1E+00 | 2.5E-01 | 1.7E-01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +1.50E+02 | +6.1E-06 | -4.5E-01 | 8.7E-01 | 5.5E-02 | 6.1E-03 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 8| +2.05E+02 | +1.8E+02 | -1.8E+01 | 1.0E+00 | 9.1E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 34| +2.39E+02 | -1.9E+00 | +9.3E-01 | 1.2E-01 | 1.2E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 52| +1.48E+02 | -8.1E-05 | +8.2E-01 | 2.0E-02 | 1.1E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 46| +2.12E+02 | -2.9E-02 | +2.3E-01 | 3.1E-02 | 5.5E+00 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 50| +2.13E+02 | -8.5E-02 | +7.7E-01 | 1.6E-02 | 5.5E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 9| +1.94E+02 | -1.0E+01 | +1.1E+00 | 2.5E-01 | 9.1E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 17| +2.50E+02 | -1.7E-06 | +2.8E-01 | 1.2E-04 | 3.2E-02 | 3.2E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(WARNING) Stopping as function difference 1.72E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:31 fides(INFO) 38| +1.50E+02 | -5.2E-06 | +8.5E-01 | 1.9E-02 | 5.5E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 53| +1.48E+02 | +6.9E-05 | -3.9E-01 | 3.9E-02 | 1.6E-01 | 5.4E-03 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 31| +1.54E+02 | -5.0E-04 | +1.6E+00 | 5.1E-01 | 1.7E-01 | 2.1E-01 | trr |1\n", - "2023-02-17 16:05:31 fides(INFO) 47| +2.12E+02 | -5.5E-02 | +8.1E-01 | 7.8E-03 | 4.7E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 51| +2.13E+02 | -1.0E-01 | +7.1E-01 | 3.1E-02 | 3.9E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 10| +1.94E+02 | +1.3E+01 | -2.6E+00 | 5.0E-01 | 3.1E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 39| +1.50E+02 | -1.3E-06 | +6.0E-01 | 3.7E-02 | 2.5E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(WARNING) Stopping as function difference 1.28E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:31 fides(INFO) 54| +1.48E+02 | -3.9E-05 | +7.0E-01 | 9.8E-03 | 1.6E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 48| +2.12E+02 | -1.1E-02 | +2.9E-01 | 1.6E-02 | 3.0E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 35| +2.39E+02 | +2.7E+00 | -6.9E-01 | 2.5E-01 | 1.2E+01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 32| +1.54E+02 | -1.9E-04 | +1.5E+00 | 5.1E-01 | 1.0E-01 | 5.0E-03 | trr |1\n", - "2023-02-17 16:05:31 fides(INFO) 52| +2.13E+02 | -7.9E-02 | +4.2E-01 | 3.1E-02 | 6.2E+00 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 11| +1.89E+02 | -5.4E+00 | +1.0E+00 | 1.2E-01 | 3.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 55| +1.48E+02 | -2.8E-05 | +7.7E-01 | 9.8E-03 | 1.4E-01 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 49| +2.12E+02 | -3.4E-02 | +4.8E-01 | 1.6E-02 | 4.5E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 53| +2.13E+02 | -2.6E-02 | +1.3E-01 | 3.1E-02 | 5.3E+00 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 36| +2.38E+02 | -9.4E-01 | +7.2E-01 | 6.2E-02 | 1.2E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 33| +1.54E+02 | +3.4E-04 | -2.4E+00 | 5.1E-01 | 7.8E-02 | 1.6E-02 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 12| +1.76E+02 | -1.3E+01 | +1.4E+00 | 2.5E-01 | 1.9E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 50| +2.12E+02 | -4.7E-03 | +1.0E-01 | 1.6E-02 | 3.1E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 0| +1.57E+13 | NaN | NaN | 1.0E+00 | 5.8E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:31 fides(INFO) 56| +1.48E+02 | -3.1E-05 | +6.2E-01 | 2.0E-02 | 8.4E-02 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 54| +2.13E+02 | -8.6E-02 | +8.9E-01 | 7.8E-03 | 7.6E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 1| +4.16E+10 | -1.6E+13 | +1.0E-01 | 1.0E+00 | 5.8E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 51| +2.12E+02 | -2.2E-02 | +9.0E-01 | 3.9E-03 | 4.3E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 13| +1.63E+02 | -1.2E+01 | +4.7E-01 | 5.0E-01 | 6.1E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 57| +1.48E+02 | +4.0E-05 | -4.4E-01 | 2.0E-02 | 1.7E-01 | 7.6E-03 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 55| +2.13E+02 | -7.2E-02 | +7.1E-01 | 1.6E-02 | 5.2E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 34| +1.54E+02 | -3.3E-05 | +5.3E-01 | 1.8E-03 | 7.8E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +2.37E+02 | -9.8E-01 | +8.0E-01 | 6.2E-02 | 1.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 2| +6.23E+09 | -3.5E+10 | +8.9E-01 | 2.5E-01 | 1.9E+11 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 52| +2.12E+02 | -2.0E-02 | +7.2E-01 | 7.8E-03 | 3.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 0| +6.30E+11 | NaN | NaN | 1.0E+00 | 2.6E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:31 fides(INFO) 56| +2.13E+02 | -5.4E-02 | +7.6E-01 | 1.6E-02 | 3.5E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 3| +9.33E+08 | -5.3E+09 | +8.9E-01 | 5.0E-01 | 2.9E+10 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 14| +1.63E+02 | -2.2E+00 | -6.9E-02 | 5.0E-01 | 1.4E+02 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 58| +1.48E+02 | -1.9E-05 | +6.9E-01 | 4.9E-03 | 1.7E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 4| +1.41E+08 | -7.9E+08 | +8.9E-01 | 5.0E-01 | 4.3E+09 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 35| +1.54E+02 | -2.2E-05 | +4.6E-01 | 1.8E-03 | 4.6E-02 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 53| +2.12E+02 | -1.5E-02 | +8.3E-01 | 7.8E-03 | 2.4E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 1| +9.99E+09 | -6.2E+11 | +9.9E-02 | 1.0E+00 | 2.6E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 38| +2.35E+02 | -2.1E+00 | +1.0E+00 | 1.2E-01 | 1.5E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 5| +2.13E+07 | -1.2E+08 | +8.9E-01 | 5.0E-01 | 6.4E+08 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 57| +2.13E+02 | -4.7E-02 | +4.1E-01 | 3.1E-02 | 4.3E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 15| +1.58E+02 | -4.9E+00 | +9.8E-01 | 1.2E-01 | 1.4E+02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 59| +1.48E+02 | -7.7E-06 | +1.2E+00 | 4.9E-03 | 4.8E-02 | 8.9E-04 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 2| +1.49E+09 | -8.5E+09 | +8.9E-01 | 2.5E-01 | 4.6E+10 | 7.5E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 54| +2.12E+02 | -1.2E-02 | +4.2E-01 | 1.6E-02 | 2.6E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 6| +3.24E+06 | -1.8E+07 | +8.9E-01 | 5.0E-01 | 9.7E+07 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 58| +2.13E+02 | +1.6E-02 | -1.0E-01 | 3.1E-02 | 5.3E+00 | 7.5E-02 | trr |0\n", - "2023-02-17 16:05:31 fides(INFO) 36| +1.54E+02 | -2.1E-05 | +4.1E-01 | 1.8E-03 | 5.1E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 16| +1.57E+02 | -1.5E+00 | +3.8E-01 | 2.5E-01 | 6.9E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 7| +4.97E+05 | -2.7E+06 | +8.9E-01 | 5.0E-01 | 1.5E+07 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 3| +2.25E+08 | -1.3E+09 | +8.9E-01 | 5.0E-01 | 6.9E+09 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 60| +1.48E+02 | -9.4E-06 | +9.5E-01 | 9.8E-03 | 6.0E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 55| +2.12E+02 | -2.9E-03 | +5.7E-02 | 1.6E-02 | 2.9E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 59| +2.13E+02 | -7.0E-02 | +8.5E-01 | 7.8E-03 | 5.3E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 8| +7.70E+04 | -4.2E+05 | +9.0E-01 | 5.0E-01 | 2.3E+06 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 4| +3.41E+07 | -1.9E+08 | +8.9E-01 | 1.0E+00 | 1.0E+09 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 17| +1.52E+02 | -4.8E+00 | +8.1E-01 | 2.5E-01 | 7.9E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 39| +2.34E+02 | -1.3E+00 | +2.5E-01 | 2.5E-01 | 1.3E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 37| +1.54E+02 | -1.1E-05 | +2.7E-01 | 1.8E-03 | 3.5E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 61| +1.48E+02 | -1.1E-05 | +8.1E-01 | 2.0E-02 | 3.6E-02 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 56| +2.12E+02 | -2.0E-02 | +8.9E-01 | 3.9E-03 | 3.8E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 9| +1.21E+04 | -6.5E+04 | +9.0E-01 | 5.0E-01 | 3.5E+05 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 60| +2.13E+02 | -3.8E-02 | +5.5E-01 | 1.6E-02 | 3.5E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 5| +5.17E+06 | -2.9E+07 | +8.9E-01 | 2.0E+00 | 1.6E+08 | 8.0E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:31 fides(INFO) 10| +2.08E+03 | -1.0E+04 | +9.0E-01 | 1.0E+00 | 5.5E+04 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 57| +2.12E+02 | -1.4E-02 | +6.3E-01 | 7.8E-03 | 2.7E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 38| +1.54E+02 | -1.4E-05 | +3.0E-01 | 1.8E-03 | 4.1E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 18| +1.50E+02 | -2.0E+00 | +7.7E-01 | 5.0E-01 | 3.8E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 62| +1.48E+02 | +2.3E-05 | -1.4E+00 | 3.9E-02 | 4.1E-02 | 7.0E-03 | 2d |0\n", - "2023-02-17 16:05:31 fides(INFO) 11| +5.14E+02 | -1.6E+03 | +9.0E-01 | 1.0E+00 | 8.6E+03 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 6| +7.88E+05 | -4.4E+06 | +8.9E-01 | 2.0E+00 | 2.4E+07 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 61| +2.13E+02 | -5.5E-02 | +6.7E-01 | 1.6E-02 | 4.4E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:31 fides(INFO) 58| +2.12E+02 | -6.6E-03 | +4.9E-01 | 7.8E-03 | 1.9E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 12| +2.76E+02 | -2.4E+02 | +9.0E-01 | 2.0E+00 | 1.3E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 40| +2.29E+02 | -4.7E+00 | +9.8E-01 | 2.5E-01 | 3.4E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 63| +1.48E+02 | -6.0E-06 | +1.1E+00 | 9.8E-03 | 4.1E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 7| +1.21E+05 | -6.7E+05 | +9.0E-01 | 2.0E+00 | 3.6E+06 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 62| +2.13E+02 | -3.4E-02 | +5.3E-01 | 1.6E-02 | 3.3E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 39| +1.54E+02 | -5.5E-06 | +1.5E-01 | 1.8E-03 | 2.8E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 59| +2.12E+02 | -1.1E-02 | +5.8E-01 | 7.8E-03 | 2.4E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 13| +2.29E+02 | -4.7E+01 | +1.2E+00 | 2.0E+00 | 1.9E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 19| +1.50E+02 | -2.3E-01 | +2.2E-01 | 1.0E+00 | 3.0E+01 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 8| +1.88E+04 | -1.0E+05 | +9.0E-01 | 2.0E+00 | 5.5E+05 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 64| +1.48E+02 | +2.3E-05 | -1.7E+00 | 2.0E-02 | 3.1E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 63| +2.13E+02 | -5.2E-02 | +6.4E-01 | 1.6E-02 | 4.4E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 41| +2.23E+02 | -6.6E+00 | +7.2E-01 | 5.0E-01 | 2.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 60| +2.12E+02 | -4.4E-03 | +3.3E-01 | 7.8E-03 | 1.9E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 9| +3.09E+03 | -1.6E+04 | +9.0E-01 | 2.0E+00 | 8.5E+04 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 40| +1.54E+02 | -9.6E-06 | +8.3E-01 | 4.5E-04 | 3.4E-02 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 64| +2.13E+02 | -2.9E-02 | +4.7E-01 | 1.6E-02 | 3.2E+00 | 3.3E-02 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 65| +1.48E+02 | -1.8E-07 | +3.5E-02 | 4.9E-03 | 3.1E-02 | 3.8E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 14| +2.29E+02 | +1.4E+02 | -1.6E+01 | 2.0E+00 | 3.1E+01 | 6.1E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 61| +2.12E+02 | -1.0E-02 | +5.5E-01 | 7.8E-03 | 2.4E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 20| +1.50E+02 | -1.9E-01 | +5.3E-01 | 1.1E-01 | 6.9E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 10| +6.63E+02 | -2.4E+03 | +9.0E-01 | 2.0E+00 | 1.3E+04 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 42| +2.23E+02 | +8.6E+01 | -6.9E+00 | 5.0E-01 | 3.5E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 15| +2.29E+02 | +1.4E+01 | -1.6E+00 | 5.0E-01 | 3.1E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 41| +1.54E+02 | -6.5E-06 | +4.4E-01 | 9.0E-04 | 2.3E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 65| +2.13E+02 | -5.0E-02 | +6.2E-01 | 1.6E-02 | 4.4E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 62| +2.12E+02 | -3.3E-03 | +2.7E-01 | 7.8E-03 | 1.7E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 11| +2.93E+02 | -3.7E+02 | +9.0E-01 | 2.0E+00 | 2.1E+03 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 66| +1.48E+02 | -5.5E-06 | +2.3E+00 | 1.2E-03 | 1.0E-01 | 2.7E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 66| +2.13E+02 | -2.5E-02 | +4.2E-01 | 1.6E-02 | 3.1E+00 | 3.4E-02 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 16| +2.25E+02 | -3.9E+00 | +5.3E-01 | 1.2E-01 | 3.1E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 43| +2.23E+02 | +2.0E+00 | -3.7E-01 | 1.2E-01 | 3.5E+01 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 12| +2.43E+02 | -5.0E+01 | +8.8E-01 | 2.0E+00 | 3.1E+02 | 5.0E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 42| +1.54E+02 | -4.7E-06 | +4.2E-01 | 9.0E-04 | 1.7E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 17| +2.23E+02 | -2.1E+00 | +7.1E-01 | 1.2E-01 | 2.6E+01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 67| +1.48E+02 | +3.7E-06 | -3.2E+00 | 2.4E-03 | 1.2E-02 | 4.0E-04 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 63| +2.12E+02 | -9.0E-03 | +5.1E-01 | 7.8E-03 | 2.3E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 67| +2.12E+02 | -5.1E-02 | +6.3E-01 | 1.6E-02 | 4.4E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 18| +2.23E+02 | +1.0E-01 | -8.0E-02 | 1.2E-01 | 2.1E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 68| +1.48E+02 | +3.3E-06 | -9.3E+00 | 6.1E-04 | 1.2E-02 | 1.0E-04 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 43| +1.54E+02 | -3.0E-06 | +2.6E-01 | 9.0E-04 | 2.0E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 64| +2.12E+02 | -2.6E-03 | +2.3E-01 | 7.8E-03 | 1.6E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 68| +2.12E+02 | -2.2E-02 | +3.9E-01 | 1.6E-02 | 3.0E+00 | 3.4E-02 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 21| +1.50E+02 | -1.3E-01 | +8.2E-01 | 1.1E-01 | 1.7E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 44| +2.21E+02 | -1.6E+00 | +8.1E-01 | 3.1E-02 | 3.5E+01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 13| +2.38E+02 | -5.3E+00 | +7.2E-01 | 4.0E+00 | 3.5E+01 | 4.4E+00 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 19| +2.22E+02 | -1.1E+00 | +8.8E-01 | 3.1E-02 | 2.1E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 65| +2.12E+02 | -5.3E-03 | +9.0E-01 | 2.0E-03 | 2.1E+00 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 69| +1.48E+02 | +4.7E-06 | -3.2E+01 | 1.5E-04 | 1.2E-02 | 3.0E-05 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 69| +2.12E+02 | -5.1E-02 | +6.3E-01 | 1.6E-02 | 4.3E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 44| +1.54E+02 | -3.8E-06 | +3.3E-01 | 9.0E-04 | 1.4E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 14| +2.38E+02 | +7.8E+01 | -6.4E+00 | 4.0E+00 | 2.2E+01 | 9.9E+00 | trr |0\n", - "2023-02-17 16:05:32 fides(INFO) 66| +2.12E+02 | -5.2E-03 | +7.6E-01 | 3.9E-03 | 1.7E+00 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 20| +2.20E+02 | -1.2E+00 | +8.6E-01 | 6.2E-02 | 1.7E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 45| +2.18E+02 | -2.6E+00 | +7.3E-01 | 6.2E-02 | 4.6E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 22| +1.50E+02 | -5.4E-02 | +5.6E-01 | 2.2E-01 | 5.4E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 70| +1.48E+02 | +5.5E-06 | -5.8E+01 | 3.8E-05 | 1.2E-02 | 1.6E-05 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 70| +2.12E+02 | -2.0E-02 | +3.8E-01 | 1.6E-02 | 2.8E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 67| +2.12E+02 | -6.3E-03 | +7.2E-01 | 7.8E-03 | 1.3E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 21| +2.19E+02 | -1.8E+00 | +8.4E-01 | 1.2E-01 | 2.0E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 45| +1.54E+02 | -4.9E-07 | +5.4E-02 | 9.0E-04 | 1.6E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 46| +2.16E+02 | -1.9E+00 | +8.6E-01 | 6.2E-02 | 4.8E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 71| +2.12E+02 | -5.0E-02 | +6.2E-01 | 1.6E-02 | 4.3E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:32.264 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.574 and h = 2.93244e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:32.264 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.574: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:32 fides(INFO) 71| +1.48E+02 | +0.0E+00 | +0.0E+00 | 9.6E-06 | 1.2E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 68| +2.12E+02 | -3.9E-03 | +3.5E-01 | 7.8E-03 | 1.7E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 22| +2.19E+02 | +8.3E-01 | -3.6E-01 | 2.5E-01 | 1.9E+01 | 7.2E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 23| +1.50E+02 | -1.1E-02 | +8.8E-01 | 2.2E-01 | 4.1E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 46| +1.54E+02 | -2.9E-06 | +8.7E-01 | 2.2E-04 | 1.1E-02 | 4.9E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 72| +2.12E+02 | -2.8E-02 | +5.4E-01 | 1.6E-02 | 2.8E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 15| +2.38E+02 | +2.5E+01 | -5.3E+00 | 9.2E-01 | 2.2E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 69| +2.12E+02 | -2.3E-03 | +1.9E-01 | 7.8E-03 | 1.6E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 23| +2.17E+02 | -1.2E+00 | +6.1E-01 | 6.2E-02 | 1.9E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 47| +2.14E+02 | -2.2E+00 | +9.6E-01 | 1.2E-01 | 3.1E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 72| +1.48E+02 | +2.8E-06 | -6.0E+01 | 2.4E-06 | 1.2E-02 | 5.2E-06 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 73| +2.12E+02 | -4.1E-02 | +6.2E-01 | 1.6E-02 | 3.7E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 24| +1.50E+02 | -8.9E-03 | +9.2E-01 | 4.5E-01 | 3.6E+00 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 47| +1.54E+02 | -2.3E-06 | +5.4E-01 | 4.5E-04 | 2.7E-02 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 24| +2.16E+02 | -1.0E+00 | +8.6E-01 | 6.2E-02 | 1.8E+01 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 70| +2.12E+02 | -4.8E-03 | +8.9E-01 | 2.0E-03 | 1.9E+00 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 48| +2.11E+02 | -3.0E+00 | +8.7E-01 | 2.5E-01 | 3.3E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 73| +1.48E+02 | +6.7E-06 | -4.9E+02 | 6.0E-07 | 1.2E-02 | 1.3E-06 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 74| +2.12E+02 | -2.8E-02 | +5.2E-01 | 1.6E-02 | 2.8E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 16| +2.38E+02 | +3.1E+00 | -8.3E-01 | 2.3E-01 | 2.2E+01 | 5.6E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 25| +2.16E+02 | -1.4E-01 | +3.3E-02 | 1.2E-01 | 1.4E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 71| +2.12E+02 | -3.8E-03 | +6.9E-01 | 3.9E-03 | 1.4E+00 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 25| +1.50E+02 | -3.7E-03 | +5.2E-01 | 9.0E-01 | 7.0E-01 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 48| +1.54E+02 | -1.2E-06 | +4.9E-01 | 4.5E-04 | 9.6E-03 | 9.5E-04 | 2d |1\n", - "2023-02-17 16:05:32 fides(WARNING) Stopping as function difference 1.19E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:32 fides(INFO) 75| +2.12E+02 | -4.0E-02 | +6.2E-01 | 1.6E-02 | 3.6E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 74| +1.48E+02 | +1.4E-05 | -4.0E+03 | 1.5E-07 | 1.2E-02 | 3.2E-07 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 49| +2.11E+02 | +1.6E+01 | -5.2E+00 | 5.0E-01 | 4.0E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 26| +2.15E+02 | -1.2E+00 | +8.7E-01 | 3.1E-02 | 3.1E+01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 72| +2.12E+02 | -2.6E-03 | +6.5E-01 | 3.9E-03 | 1.1E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 17| +2.36E+02 | -1.9E+00 | +8.2E-01 | 5.8E-02 | 2.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 76| +2.12E+02 | -2.7E-02 | +5.2E-01 | 1.6E-02 | 2.7E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 26| +1.50E+02 | +8.2E-03 | -2.7E+00 | 9.0E-01 | 1.5E+00 | 1.2E-01 | trr |0\n", - "2023-02-17 16:05:32 fides(INFO) 73| +2.12E+02 | -3.2E-03 | +6.7E-01 | 3.9E-03 | 1.3E+00 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 27| +2.14E+02 | -8.0E-01 | +6.6E-01 | 6.2E-02 | 1.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 75| +1.48E+02 | +1.5E-05 | -1.6E+04 | 3.7E-08 | 1.2E-02 | 8.1E-08 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 50| +2.09E+02 | -1.9E+00 | +1.1E+00 | 1.2E-01 | 4.0E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 77| +2.12E+02 | -3.9E-02 | +6.2E-01 | 1.6E-02 | 3.5E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 28| +2.14E+02 | -2.5E-01 | +2.8E-01 | 6.2E-02 | 1.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 74| +2.12E+02 | -2.0E-03 | +5.4E-01 | 3.9E-03 | 1.0E+00 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 18| +2.33E+02 | -2.9E+00 | +1.2E+00 | 1.2E-01 | 1.6E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 76| +1.48E+02 | +8.1E-06 | -3.6E+04 | 9.3E-09 | 1.2E-02 | 2.0E-08 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 27| +1.50E+02 | -8.3E-04 | +5.5E-01 | 7.9E-02 | 1.5E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 51| +2.05E+02 | -4.8E+00 | +8.5E-01 | 2.5E-01 | 3.2E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 78| +2.12E+02 | -2.8E-02 | +5.3E-01 | 1.6E-02 | 2.7E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 75| +2.12E+02 | -3.0E-03 | +6.3E-01 | 3.9E-03 | 1.2E+00 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 29| +2.13E+02 | -7.1E-01 | +5.8E-01 | 6.2E-02 | 1.9E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 79| +2.12E+02 | -3.9E-02 | +6.3E-01 | 1.6E-02 | 3.4E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 77| +1.48E+02 | +4.7E-06 | -8.4E+04 | 2.3E-09 | 1.2E-02 | 5.0E-09 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 28| +1.50E+02 | -1.7E-04 | +3.6E-01 | 7.9E-02 | 4.4E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 52| +1.90E+02 | -1.5E+01 | +6.2E-01 | 5.0E-01 | 1.1E+02 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 30| +2.13E+02 | -6.8E-02 | +6.2E-02 | 6.2E-02 | 1.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 76| +2.12E+02 | -1.7E-03 | +4.8E-01 | 3.9E-03 | 9.9E-01 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 0| +7.90E+12 | NaN | NaN | 1.0E+00 | 3.6E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 80| +2.12E+02 | -2.8E-02 | +5.5E-01 | 1.6E-02 | 2.6E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 78| +1.48E+02 | +7.0E-06 | -5.0E+05 | 5.8E-10 | 1.2E-02 | 1.3E-09 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 31| +2.13E+02 | -4.1E-01 | +9.0E-01 | 1.6E-02 | 2.0E+01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 19| +2.30E+02 | -2.6E+00 | +9.5E-01 | 2.3E-01 | 1.6E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 29| +1.50E+02 | -2.5E-05 | +8.4E-02 | 7.9E-02 | 4.0E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 77| +2.12E+02 | -2.7E-03 | +6.0E-01 | 3.9E-03 | 1.2E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 53| +1.90E+02 | +6.1E+01 | -4.4E+00 | 5.0E-01 | 5.1E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 81| +2.12E+02 | -5.6E-02 | +1.0E+00 | 1.6E-02 | 3.3E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 32| +2.12E+02 | -3.7E-01 | +7.5E-01 | 3.1E-02 | 1.4E+01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 1| +3.35E+08 | -7.9E+12 | +8.5E-02 | 1.0E+00 | 3.6E+13 | 3.0E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 79| +1.48E+02 | +3.2E-06 | -9.2E+05 | 1.5E-10 | 1.2E-02 | 3.2E-10 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 78| +2.12E+02 | -1.5E-03 | +4.4E-01 | 3.9E-03 | 9.3E-01 | 8.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 30| +1.50E+02 | -8.0E-05 | +7.1E-01 | 2.0E-02 | 9.9E-02 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 54| +1.87E+02 | -2.3E+00 | +3.4E-01 | 1.2E-01 | 5.1E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 82| +2.12E+02 | -8.3E-02 | +7.8E-01 | 3.1E-02 | 2.2E+00 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 33| +2.12E+02 | -4.2E-01 | +7.5E-01 | 6.2E-02 | 8.8E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 20| +2.25E+02 | -5.8E+00 | +1.0E+00 | 4.6E-01 | 2.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 79| +2.12E+02 | -2.5E-03 | +5.7E-01 | 3.9E-03 | 1.1E+00 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 2| +5.08E+07 | -2.8E+08 | +8.9E-01 | 2.5E-01 | 1.5E+09 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 34| +2.12E+02 | -2.1E-01 | +3.8E-01 | 6.2E-02 | 1.3E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 83| +2.12E+02 | -1.8E-01 | +1.0E+00 | 6.2E-02 | 3.2E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 31| +1.50E+02 | -1.2E-05 | +7.5E-01 | 2.0E-02 | 1.7E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 55| +1.85E+02 | -2.3E+00 | +6.9E-01 | 1.2E-01 | 3.8E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 80| +1.48E+02 | +3.5E-06 | -4.0E+06 | 3.6E-11 | 1.2E-02 | 7.9E-11 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 80| +2.12E+02 | -1.3E-03 | +4.1E-01 | 3.9E-03 | 8.9E-01 | 9.3E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 3| +7.21E+06 | -4.4E+07 | +9.0E-01 | 5.0E-01 | 2.3E+08 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 21| +2.20E+02 | -4.1E+00 | +2.9E-01 | 9.2E-01 | 2.3E+01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 35| +2.12E+02 | +1.1E-01 | -2.1E-01 | 6.2E-02 | 9.5E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 84| +2.11E+02 | -2.7E-01 | +9.6E-01 | 1.2E-01 | 2.2E+00 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 32| +1.50E+02 | -2.2E-06 | +8.6E-01 | 2.0E-02 | 3.6E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 81| +2.12E+02 | -2.2E-03 | +5.4E-01 | 3.9E-03 | 1.1E+00 | 7.7E-03 | 2d |1\n", - "2023-02-17 16:05:32.617 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.317 and h = 3.91293e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:32.617 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.317: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:32 fides(INFO) 81| +1.48E+02 | +0.0E+00 | +0.0E+00 | 9.1E-12 | 1.2E-02 | 2.0E-11 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 56| +1.77E+02 | -8.4E+00 | +6.6E-01 | 1.2E-01 | 5.5E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 36| +2.12E+02 | -2.3E-01 | +8.4E-01 | 1.6E-02 | 9.5E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 85| +2.10E+02 | -1.2E+00 | +1.4E+00 | 2.5E-01 | 4.8E+00 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 82| +2.12E+02 | -1.2E-03 | +3.9E-01 | 3.9E-03 | 8.5E-01 | 9.6E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 4| +1.01E+06 | -6.2E+06 | +9.0E-01 | 5.0E-01 | 3.3E+07 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 22| +2.20E+02 | -5.1E+00 | -4.0E-01 | 9.2E-01 | 6.9E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 33| +1.50E+02 | -1.8E-06 | +8.9E-01 | 3.9E-02 | 4.4E-02 | 6.3E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 82| +1.48E+02 | +5.5E-06 | -1.0E+08 | 2.3E-12 | 1.2E-02 | 4.9E-12 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 37| +2.11E+02 | -1.4E-01 | +5.8E-01 | 3.1E-02 | 7.1E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 57| +1.73E+02 | -3.1E+00 | +5.8E-01 | 1.2E-01 | 4.6E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 86| +2.10E+02 | +2.5E+01 | -1.0E+01 | 5.0E-01 | 8.4E+00 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 83| +2.12E+02 | -2.0E-03 | +5.2E-01 | 3.9E-03 | 1.0E+00 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 38| +2.11E+02 | -1.6E-01 | +5.7E-01 | 3.1E-02 | 9.6E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 5| +1.44E+05 | -8.7E+05 | +9.0E-01 | 5.0E-01 | 4.9E+06 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 87| +2.10E+02 | -6.9E-01 | +8.2E-01 | 1.2E-01 | 8.4E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 34| +1.50E+02 | -1.8E-06 | +1.1E+00 | 7.9E-02 | 3.4E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 84| +2.12E+02 | -1.1E-03 | +3.8E-01 | 3.9E-03 | 8.2E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 58| +1.71E+02 | -2.9E+00 | +4.1E-01 | 1.2E-01 | 4.3E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 23| +2.07E+02 | -1.3E+01 | +8.3E-01 | 2.3E-01 | 6.9E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 39| +2.11E+02 | -6.7E-02 | +2.8E-01 | 3.1E-02 | 6.8E+00 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 83| +1.48E+02 | +1.4E-05 | -9.9E+08 | 5.7E-13 | 1.2E-02 | 1.2E-12 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 85| +2.12E+02 | -1.8E-03 | +5.0E-01 | 3.9E-03 | 9.7E-01 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 88| +2.10E+02 | +8.6E+00 | -4.0E+00 | 2.5E-01 | 6.7E+00 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 59| +1.71E+02 | +2.7E+01 | -2.7E+00 | 1.2E-01 | 4.9E+01 | 3.3E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 6| +2.12E+04 | -1.2E+05 | +9.0E-01 | 5.0E-01 | 7.5E+05 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 35| +1.50E+02 | +2.5E-06 | -2.2E+00 | 1.6E-01 | 1.5E-02 | 8.4E-03 | trr |0\n", - "2023-02-17 16:05:32 fides(INFO) 40| +2.11E+02 | -1.6E-01 | +5.3E-01 | 3.1E-02 | 9.8E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 86| +2.12E+02 | -1.1E-03 | +3.7E-01 | 3.9E-03 | 7.8E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 24| +1.83E+02 | -2.4E+01 | +1.0E+00 | 4.6E-01 | 3.3E+01 | 9.6E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 89| +2.10E+02 | +3.0E-01 | -4.3E-01 | 6.2E-02 | 6.7E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 84| +1.48E+02 | +5.3E-06 | -1.6E+09 | 1.4E-13 | 1.2E-02 | 3.1E-13 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 87| +2.12E+02 | -1.7E-03 | +4.8E-01 | 3.9E-03 | 9.2E-01 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 41| +2.11E+02 | -3.4E-02 | +1.6E-01 | 3.1E-02 | 6.2E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 60| +1.70E+02 | -3.8E-01 | +1.0E-01 | 3.1E-02 | 4.9E+01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 7| +3.31E+03 | -1.8E+04 | +9.0E-01 | 5.0E-01 | 1.2E+05 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 90| +2.09E+02 | -1.3E-01 | +6.3E-01 | 1.6E-02 | 6.7E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 36| +1.50E+02 | -1.7E-07 | +3.1E-01 | 2.3E-02 | 1.5E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(WARNING) Stopping as function difference 1.68E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:32 fides(INFO) 25| +1.83E+02 | +3.9E+02 | -1.7E+01 | 9.2E-01 | 1.2E+02 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 85| +1.48E+02 | +4.3E-06 | -5.0E+09 | 3.6E-14 | 1.2E-02 | 7.7E-14 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 88| +2.12E+02 | -1.0E-03 | +3.7E-01 | 3.9E-03 | 7.5E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 42| +2.11E+02 | -9.6E-02 | +9.0E-01 | 7.8E-03 | 9.5E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 91| +2.09E+02 | -5.5E-02 | +1.0E+00 | 1.6E-02 | 1.9E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 61| +1.69E+02 | -8.6E-01 | +1.0E+00 | 7.8E-03 | 6.9E+01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 8| +6.40E+02 | -2.7E+03 | +9.0E-01 | 5.0E-01 | 1.9E+04 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 89| +2.12E+02 | -1.5E-03 | +4.7E-01 | 3.9E-03 | 8.8E-01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 26| +1.70E+02 | -1.3E+01 | +8.7E-01 | 2.3E-01 | 1.2E+02 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 86| +1.48E+02 | +2.5E-06 | -1.1E+10 | 8.9E-15 | 1.2E-02 | 1.9E-14 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 43| +2.11E+02 | -9.0E-02 | +7.5E-01 | 1.6E-02 | 7.2E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 92| +2.09E+02 | -1.1E-01 | +1.1E+00 | 3.1E-02 | 1.4E+00 | 8.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 62| +1.68E+02 | -1.1E+00 | +1.0E+00 | 1.6E-02 | 5.1E+01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 27| +1.56E+02 | -1.4E+01 | +1.0E+00 | 4.6E-01 | 8.3E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 44| +2.11E+02 | -1.1E-01 | +7.6E-01 | 3.1E-02 | 5.0E+00 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 9| +2.48E+02 | -3.9E+02 | +8.9E-01 | 5.0E-01 | 3.0E+03 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 90| +2.12E+02 | -1.0E-03 | +3.8E-01 | 3.9E-03 | 7.3E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 93| +2.09E+02 | -2.0E-01 | +1.1E+00 | 6.2E-02 | 1.6E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 45| +2.11E+02 | -6.0E-02 | +3.5E-01 | 6.2E-02 | 6.6E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 0| +6.35E+09 | NaN | NaN | 1.0E+00 | 2.9E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:32 fides(INFO) 87| +1.48E+02 | +1.1E-05 | -2.0E+11 | 2.2E-15 | 1.2E-02 | 4.8E-15 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 91| +2.12E+02 | -1.4E-03 | +4.6E-01 | 3.9E-03 | 8.4E-01 | 8.8E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 28| +1.56E+02 | +1.8E+01 | -2.7E+00 | 9.2E-01 | 9.3E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 63| +1.67E+02 | -1.1E+00 | +9.9E-01 | 3.1E-02 | 2.6E+01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 94| +2.09E+02 | -5.6E-01 | +1.3E+00 | 1.2E-01 | 3.5E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 10| +1.84E+02 | -6.4E+01 | +9.0E-01 | 5.0E-01 | 5.2E+02 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 1| +1.83E+04 | -6.4E+09 | +9.6E-02 | 1.0E+00 | 2.9E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 46| +2.11E+02 | +4.8E-03 | -3.8E-02 | 6.2E-02 | 6.0E+00 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 92| +2.12E+02 | -2.2E-03 | +1.0E+00 | 3.9E-03 | 7.0E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 88| +1.48E+02 | +6.8E-06 | -5.1E+11 | 5.6E-16 | 1.2E-02 | 1.2E-15 | 2d |0\n", - "2023-02-17 16:05:32 fides(WARNING) Stopping as trust region radius 1.39E-16 is smaller than machine precision.\n", - "2023-02-17 16:05:32 fides(INFO) 2| +3.15E+03 | -1.5E+04 | +9.0E-01 | 2.5E-01 | 8.3E+04 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 29| +1.51E+02 | -4.4E+00 | +8.7E-01 | 2.1E-01 | 9.3E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 95| +2.06E+02 | -2.5E+00 | +1.9E+00 | 2.5E-01 | 7.4E+00 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 64| +1.66E+02 | -1.5E+00 | +9.8E-01 | 6.2E-02 | 2.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 47| +2.11E+02 | -1.0E-01 | +6.7E-01 | 1.6E-02 | 6.0E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 93| +2.12E+02 | -3.6E-03 | +9.0E-01 | 7.8E-03 | 5.2E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 3| +7.50E+02 | -2.4E+03 | +9.0E-01 | 5.0E-01 | 1.3E+04 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 11| +1.69E+02 | -1.5E+01 | +1.0E+00 | 5.0E-01 | 2.0E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 96| +1.98E+02 | -8.5E+00 | +6.2E-01 | 5.0E-01 | 1.4E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:32 fides(INFO) 30| +1.51E+02 | +4.7E-01 | -1.9E-01 | 4.2E-01 | 5.9E+01 | 5.4E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 4| +3.61E+02 | -3.9E+02 | +9.0E-01 | 1.0E+00 | 2.1E+03 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 48| +2.11E+02 | -6.1E-02 | +9.3E-01 | 1.6E-02 | 4.3E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 94| +2.12E+02 | -1.3E-03 | +5.8E-01 | 1.6E-02 | 6.0E-01 | 8.8E-03 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 65| +1.62E+02 | -3.3E+00 | +7.9E-01 | 1.2E-01 | 2.3E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 97| +1.98E+02 | +5.1E-01 | -1.2E-01 | 5.0E-01 | 6.8E+01 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:32 fides(INFO) 5| +2.64E+02 | -9.7E+01 | +9.2E-01 | 2.0E+00 | 3.3E+02 | 4.8E+00 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 49| +2.10E+02 | -2.6E-02 | +3.1E-01 | 3.1E-02 | 3.8E+00 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 31| +1.50E+02 | -1.1E+00 | +8.4E-01 | 1.1E-01 | 5.9E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:32 fides(INFO) 95| +2.12E+02 | -3.1E-03 | +8.9E-01 | 1.6E-02 | 7.0E-01 | 1.4E-02 | trr |1\n", - "2023-02-17 16:05:32 fides(INFO) 66| +1.60E+02 | -2.1E+00 | +1.2E-01 | 2.5E-01 | 5.4E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 6| +2.64E+02 | +1.6E+03 | -1.1E+02 | 4.0E+00 | 4.8E+01 | 9.8E+00 | trr |0\n", - "2023-02-17 16:05:33.009 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 193.843 and h = 3.10514e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33 fides(INFO) 12| +1.66E+02 | -3.1E+00 | +6.3E-01 | 1.0E+00 | 5.1E+01 | 4.0E-01 | trr |1\n", - "2023-02-17 16:05:33.009 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 193.843: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 50| +2.10E+02 | -5.4E-02 | +3.1E-01 | 3.1E-02 | 6.7E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 98| +1.98E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 6.8E+01 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 32| +1.49E+02 | -1.1E+00 | +6.4E-01 | 2.1E-01 | 4.0E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 0| +3.27E+09 | NaN | NaN | 1.0E+00 | 1.5E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:33 fides(INFO) 7| +2.64E+02 | +1.7E+02 | -8.1E+00 | 1.0E+00 | 4.8E+01 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 96| +2.12E+02 | -3.0E-03 | +8.3E-01 | 1.6E-02 | 6.4E-01 | 1.7E-02 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 67| +1.57E+02 | -3.6E+00 | +7.7E-01 | 6.2E-02 | 1.2E+02 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 8| +2.52E+02 | -1.2E+01 | +5.7E-01 | 2.5E-01 | 4.8E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 51| +2.10E+02 | -5.6E-03 | +2.7E-02 | 3.1E-02 | 4.9E+00 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 99| +1.94E+02 | -3.6E+00 | +1.0E+00 | 3.1E-02 | 6.8E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 1| +1.38E+04 | -3.3E+09 | +9.8E-02 | 1.0E+00 | 1.5E+10 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 33| +1.49E+02 | +2.8E-01 | -2.0E-01 | 2.1E-01 | 5.9E+01 | 2.8E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 97| +2.12E+02 | -3.2E-03 | +9.8E-01 | 1.6E-02 | 7.1E-01 | 1.5E-02 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 9| +2.51E+02 | -5.9E-01 | +1.5E-01 | 2.5E-01 | 2.7E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 68| +1.57E+02 | +2.0E-01 | -2.9E-01 | 1.2E-01 | 2.8E+01 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 52| +2.10E+02 | -7.1E-02 | +8.6E-01 | 7.8E-03 | 6.9E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:33.067 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 57.1283 and h = 7.30369e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33.067 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 57.1283: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 100| +1.94E+02 | +0.0E+00 | +0.0E+00 | 6.2E-02 | 6.4E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 98| +2.12E+02 | -2.4E-03 | +8.6E-01 | 1.6E-02 | 4.5E-01 | 2.3E-02 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 10| +2.50E+02 | -1.2E+00 | +6.1E-01 | 6.2E-02 | 2.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 2| +2.98E+03 | -1.1E+04 | +8.9E-01 | 2.5E-01 | 5.4E+04 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 13| +1.59E+02 | -6.5E+00 | +1.0E+00 | 1.0E+00 | 1.5E+02 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 34| +1.48E+02 | -6.5E-01 | +8.4E-01 | 5.3E-02 | 5.9E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 11| +2.50E+02 | -1.4E-02 | +1.5E-01 | 6.2E-02 | 4.0E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 53| +2.10E+02 | -3.3E-02 | +5.1E-01 | 1.6E-02 | 4.4E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 101| +1.92E+02 | -1.7E+00 | +1.0E+00 | 1.6E-02 | 6.4E+01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 99| +2.12E+02 | -3.5E-03 | +8.7E-01 | 3.1E-02 | 6.8E-01 | 2.9E-02 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 69| +1.56E+02 | -3.2E-01 | +7.2E-01 | 3.1E-02 | 2.8E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 12| +2.50E+02 | +9.1E-05 | -7.4E-03 | 1.6E-02 | 3.2E+00 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 35| +1.48E+02 | -2.7E-01 | +7.1E-01 | 1.1E-01 | 1.7E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 3| +6.53E+02 | -2.3E+03 | +9.0E-01 | 5.0E-01 | 9.7E+03 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 54| +2.10E+02 | -1.5E-02 | +2.9E-01 | 1.6E-02 | 3.3E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 13| +2.50E+02 | -2.4E-02 | +8.6E-01 | 3.9E-03 | 3.2E+00 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 102| +1.89E+02 | -3.4E+00 | +9.9E-01 | 3.1E-02 | 6.3E+01 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 100| +2.12E+02 | -2.3E-03 | +6.3E-01 | 3.1E-02 | 7.1E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 36| +1.48E+02 | -2.0E-01 | +8.9E-01 | 1.1E-01 | 2.2E+01 | 9.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 70| +1.56E+02 | -2.7E-01 | +7.6E-01 | 3.1E-02 | 1.5E+01 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 55| +2.10E+02 | -3.2E-02 | +4.9E-01 | 1.6E-02 | 4.4E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 101| +2.12E+02 | -7.8E-05 | +1.3E-02 | 3.1E-02 | 4.8E-01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 14| +2.50E+02 | -9.3E-04 | +5.2E-02 | 7.8E-03 | 1.7E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 4| +2.90E+02 | -3.6E+02 | +8.9E-01 | 1.0E+00 | 1.6E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 103| +1.82E+02 | -6.4E+00 | +9.9E-01 | 6.2E-02 | 6.0E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 15| +2.50E+02 | -4.8E-03 | +8.9E-01 | 2.0E-03 | 1.6E+00 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 37| +1.48E+02 | -1.6E-01 | +8.1E-01 | 2.1E-01 | 5.7E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 5| +2.49E+02 | -4.1E+01 | +8.3E-01 | 1.0E+00 | 2.3E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 56| +2.10E+02 | -1.3E-02 | +2.7E-01 | 1.6E-02 | 3.1E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 102| +2.12E+02 | -4.0E-03 | +8.9E-01 | 7.8E-03 | 1.1E+00 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 71| +1.56E+02 | +1.4E-01 | -5.0E-01 | 6.2E-02 | 6.1E+00 | 1.3E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 16| +2.50E+02 | -2.4E-03 | +5.0E-01 | 3.9E-03 | 9.6E-01 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 104| +1.72E+02 | -1.0E+01 | +1.0E+00 | 1.2E-01 | 5.4E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 17| +2.50E+02 | -1.6E-04 | +2.1E-01 | 3.9E-03 | 3.2E-01 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 6| +2.35E+02 | -1.5E+01 | +3.9E+00 | 2.0E+00 | 1.2E+01 | 3.9E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 57| +2.10E+02 | -2.7E-02 | +4.5E-01 | 1.6E-02 | 4.1E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 103| +2.12E+02 | -4.4E-03 | +8.0E-01 | 1.6E-02 | 3.7E-01 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 38| +1.48E+02 | +4.2E+00 | -8.7E+00 | 4.2E-01 | 3.2E+00 | 4.8E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 72| +1.56E+02 | -1.1E-01 | +8.6E-01 | 1.6E-02 | 6.1E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 18| +2.50E+02 | -3.1E-04 | +6.8E-01 | 9.8E-04 | 3.2E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 105| +1.65E+02 | -7.2E+00 | +4.9E-01 | 2.5E-01 | 4.9E+01 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 104| +2.12E+02 | -8.0E-03 | +1.1E+00 | 3.1E-02 | 5.8E-01 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 58| +2.10E+02 | -1.2E-02 | +2.6E-01 | 1.6E-02 | 3.0E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 19| +2.50E+02 | -6.7E-05 | +9.8E-01 | 9.8E-04 | 3.0E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 14| +1.59E+02 | -1.8E+00 | -4.6E-01 | 2.0E+00 | 3.8E+01 | 2.5E+00 | trr |0\n", - "2023-02-17 16:05:33 fides(INFO) 39| +1.48E+02 | +5.4E-02 | -3.5E-01 | 1.1E-01 | 3.2E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 73| +1.56E+02 | -5.4E-02 | +6.5E-01 | 3.1E-02 | 3.4E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 7| +2.35E+02 | +4.0E+02 | -1.8E+01 | 4.0E+00 | 3.5E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:33.259 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 68.9213 and h = 1.14762e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33.259 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 68.9213: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) 106| +1.65E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 1.3E+02 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 20| +2.50E+02 | -7.7E-05 | +8.7E-01 | 2.0E-03 | 4.1E-02 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 105| +2.12E+02 | -2.0E-02 | +1.1E+00 | 6.2E-02 | 3.6E-01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 59| +2.10E+02 | -2.4E-02 | +4.3E-01 | 1.6E-02 | 3.9E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 8| +2.35E+02 | +3.4E+01 | -3.0E+00 | 1.0E+00 | 3.5E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 21| +2.50E+02 | -1.6E-04 | +8.3E-01 | 3.9E-03 | 6.9E-02 | 9.1E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 40| +1.48E+02 | -3.1E-02 | +6.8E-01 | 2.6E-02 | 3.2E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 106| +2.12E+02 | -7.4E-02 | +1.4E+00 | 1.2E-01 | 6.9E-01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 60| +2.10E+02 | -1.1E-02 | +2.6E-01 | 1.6E-02 | 2.9E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 22| +2.50E+02 | -3.1E-04 | +8.6E-01 | 7.8E-03 | 9.6E-02 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 74| +1.56E+02 | -8.2E-02 | +1.1E+00 | 3.1E-02 | 3.8E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 107| +1.61E+02 | -4.2E+00 | +3.4E-01 | 6.2E-02 | 1.3E+02 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 9| +2.35E+02 | +5.7E+00 | -5.3E-01 | 2.5E-01 | 3.5E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 41| +1.48E+02 | -1.2E-02 | +9.6E-01 | 2.6E-02 | 3.4E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 23| +2.50E+02 | -6.5E-04 | +9.2E-01 | 1.6E-02 | 1.1E-01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 107| +2.11E+02 | -4.3E-01 | +2.1E+00 | 2.5E-01 | 1.7E+00 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 10| +2.30E+02 | -4.4E+00 | +8.8E-01 | 6.2E-02 | 3.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 61| +2.10E+02 | -2.1E-02 | +4.2E-01 | 1.6E-02 | 3.6E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 24| +2.50E+02 | -1.4E-03 | +9.8E-01 | 3.1E-02 | 1.3E-01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 108| +1.54E+02 | -7.0E+00 | +9.0E-01 | 6.2E-02 | 1.5E+02 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 75| +1.56E+02 | -1.7E-01 | +1.2E+00 | 6.2E-02 | 2.6E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 42| +1.48E+02 | -1.9E-02 | +9.5E-01 | 5.3E-02 | 1.5E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 108| +2.11E+02 | +8.4E+00 | -8.0E+00 | 5.0E-01 | 4.3E+00 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 25| +2.50E+02 | -3.2E-03 | +1.1E+00 | 6.2E-02 | 1.4E-01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 11| +2.29E+02 | -1.1E+00 | +2.1E-01 | 1.2E-01 | 2.3E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 62| +2.10E+02 | -1.1E-02 | +2.7E-01 | 1.6E-02 | 2.7E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 109| +1.52E+02 | -2.0E+00 | +3.1E-01 | 1.2E-01 | 5.2E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 26| +2.50E+02 | -8.3E-03 | +1.2E+00 | 1.2E-01 | 1.6E-01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 12| +2.28E+02 | -1.7E+00 | +9.2E-01 | 3.1E-02 | 3.9E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 76| +1.55E+02 | -3.3E-01 | +1.0E+00 | 1.2E-01 | 5.5E+00 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 109| +2.11E+02 | -6.0E-01 | +1.4E+00 | 1.2E-01 | 4.3E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 63| +2.10E+02 | -1.9E-02 | +4.1E-01 | 1.6E-02 | 3.4E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 43| +1.48E+02 | -3.2E-02 | +9.6E-01 | 1.1E-01 | 2.1E+00 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 15| +1.56E+02 | -3.0E+00 | +1.0E+00 | 5.0E-01 | 3.8E+01 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 27| +2.50E+02 | -2.8E-02 | +1.4E+00 | 2.5E-01 | 1.8E-01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 13| +2.26E+02 | -1.9E+00 | +8.6E-01 | 6.2E-02 | 2.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 110| +1.52E+02 | +9.5E-01 | -2.4E-01 | 1.2E-01 | 7.2E+01 | 2.8E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 110| +2.11E+02 | +1.5E+00 | -1.5E+00 | 2.5E-01 | 3.6E+00 | 6.7E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 28| +2.50E+02 | -1.6E-01 | +2.0E+00 | 5.0E-01 | 2.7E-01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 64| +2.10E+02 | -1.1E-02 | +2.9E-01 | 1.6E-02 | 2.6E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 44| +1.48E+02 | -3.8E-02 | +8.5E-01 | 2.1E-01 | 1.1E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 14| +2.23E+02 | -3.2E+00 | +1.1E+00 | 1.2E-01 | 1.8E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 77| +1.55E+02 | -5.7E-01 | +6.7E-01 | 2.5E-01 | 2.2E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 29| +2.47E+02 | -2.1E+00 | +3.2E+00 | 1.0E+00 | 5.1E-01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 111| +1.50E+02 | -1.5E+00 | +5.5E-01 | 3.1E-02 | 7.2E+01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 65| +2.10E+02 | -1.8E-02 | +4.2E-01 | 1.6E-02 | 3.2E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 111| +2.10E+02 | -3.6E-01 | +1.0E+00 | 6.2E-02 | 3.6E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 45| +1.48E+02 | +2.0E-01 | -3.7E+00 | 4.2E-01 | 1.7E+00 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 15| +2.20E+02 | -3.0E+00 | +1.0E+00 | 2.5E-01 | 1.9E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 30| +2.29E+02 | -1.8E+01 | +3.6E+00 | 2.0E+00 | 6.4E+00 | 3.7E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 112| +2.10E+02 | +2.5E-01 | -5.3E-01 | 1.2E-01 | 2.0E+00 | 3.3E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 66| +2.10E+02 | -1.2E-02 | +3.2E-01 | 1.6E-02 | 2.4E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 112| +1.49E+02 | -1.0E+00 | +5.1E-01 | 3.1E-02 | 7.3E+01 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 46| +1.48E+02 | -6.6E-03 | +2.8E-01 | 1.1E-01 | 1.7E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 16| +2.19E+02 | -9.4E-01 | +1.1E-04 | 5.0E-01 | 2.0E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 78| +1.54E+02 | -9.4E-01 | +1.4E+00 | 2.5E-01 | 3.1E+01 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 31| +2.29E+02 | +1.9E+02 | -1.3E+01 | 4.0E+00 | 2.7E+01 | 1.2E+01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 113| +2.10E+02 | -1.2E-01 | +8.6E-01 | 3.1E-02 | 2.0E+00 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 17| +2.12E+02 | -6.7E+00 | +7.7E-01 | 1.2E-01 | 6.3E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 67| +2.10E+02 | -1.7E-02 | +4.3E-01 | 1.6E-02 | 3.0E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 47| +1.48E+02 | +4.7E-02 | -7.8E-01 | 1.1E-01 | 2.3E+00 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 113| +1.49E+02 | -4.6E-01 | +6.7E-01 | 3.1E-02 | 3.7E+01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 32| +2.29E+02 | +5.4E+00 | -1.3E+00 | 1.0E+00 | 2.7E+01 | 2.9E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 114| +2.10E+02 | +1.7E-01 | -1.4E+00 | 6.2E-02 | 1.2E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 18| +2.03E+02 | -8.4E+00 | +1.3E+00 | 2.5E-01 | 1.6E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 68| +2.10E+02 | -1.2E-02 | +3.6E-01 | 1.6E-02 | 2.3E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 114| +1.48E+02 | -2.7E-01 | +6.1E-01 | 3.1E-02 | 2.5E+01 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 33| +2.29E+02 | +6.5E+00 | -9.9E-01 | 2.5E-01 | 2.7E+01 | 6.5E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 48| +1.48E+02 | -9.9E-03 | +5.8E-01 | 2.6E-02 | 2.3E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 115| +2.10E+02 | -3.1E-02 | +7.8E-01 | 1.6E-02 | 1.2E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 69| +2.10E+02 | -1.7E-02 | +4.6E-01 | 1.6E-02 | 2.8E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 19| +1.77E+02 | -2.6E+01 | +8.4E-01 | 5.0E-01 | 3.1E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 34| +2.26E+02 | -2.9E+00 | +8.2E-01 | 6.2E-02 | 2.7E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 16| +1.52E+02 | -4.3E+00 | +9.0E-01 | 1.0E+00 | 6.9E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 115| +1.48E+02 | -2.6E-01 | +4.1E-01 | 3.1E-02 | 2.4E+01 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 49| +1.48E+02 | -2.2E-03 | +9.7E-01 | 2.6E-02 | 1.0E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 116| +2.10E+02 | -3.2E-03 | +5.8E-02 | 3.1E-02 | 1.4E+00 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 35| +2.25E+02 | -1.6E+00 | +5.6E-01 | 1.2E-01 | 1.8E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 79| +1.52E+02 | -2.0E+00 | +1.2E+00 | 2.5E-01 | 4.5E+01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 20| +1.77E+02 | +1.3E+02 | -6.6E+00 | 1.0E+00 | 1.2E+02 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 70| +2.10E+02 | -1.3E-02 | +4.0E-01 | 1.6E-02 | 2.1E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 50| +1.48E+02 | -3.6E-03 | +9.6E-01 | 5.3E-02 | 4.1E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 116| +1.48E+02 | -1.2E-01 | +3.0E-01 | 3.1E-02 | 2.0E+01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 36| +2.22E+02 | -2.3E+00 | +6.6E-01 | 1.2E-01 | 3.3E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 17| +1.52E+02 | +1.8E+02 | -7.2E+02 | 2.0E+00 | 3.9E+01 | 5.7E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 117| +2.10E+02 | -3.4E-02 | +1.0E+00 | 7.8E-03 | 3.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 21| +1.61E+02 | -1.6E+01 | +8.7E-01 | 2.5E-01 | 1.2E+02 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 71| +2.10E+02 | -1.6E-02 | +5.0E-01 | 1.6E-02 | 2.5E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 117| +1.48E+02 | -1.9E-01 | +4.4E-01 | 3.1E-02 | 3.2E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 37| +2.22E+02 | -2.2E-01 | +6.1E-03 | 1.2E-01 | 1.8E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 118| +2.10E+02 | -1.7E-02 | +1.1E+00 | 1.6E-02 | 9.4E-01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 51| +1.48E+02 | -5.1E-03 | +9.5E-01 | 1.1E-01 | 7.8E-01 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 72| +2.10E+02 | -1.3E-02 | +4.6E-01 | 1.6E-02 | 2.0E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 38| +2.20E+02 | -1.7E+00 | +8.8E-01 | 3.1E-02 | 4.1E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 119| +2.10E+02 | -3.2E-02 | +1.1E+00 | 3.1E-02 | 4.9E-01 | 8.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 22| +1.58E+02 | -3.4E+00 | +9.4E-01 | 5.0E-01 | 8.1E+01 | 2.5E-01 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 118| +1.48E+02 | -1.5E-01 | +6.8E-01 | 3.1E-02 | 2.3E+01 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 52| +1.48E+02 | -4.2E-03 | +8.8E-01 | 2.1E-01 | 2.5E-01 | 9.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 73| +2.10E+02 | -1.7E-02 | +5.4E-01 | 1.6E-02 | 2.3E+00 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 39| +2.19E+02 | -1.4E+00 | +7.6E-01 | 6.2E-02 | 2.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 120| +2.10E+02 | -7.6E-02 | +1.2E+00 | 6.2E-02 | 7.9E-01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 23| +1.58E+02 | +2.3E+01 | -2.0E+00 | 5.0E-01 | 6.3E+01 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 119| +1.48E+02 | -1.3E-02 | +9.6E-02 | 3.1E-02 | 9.5E+00 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 74| +2.10E+02 | -2.2E-02 | +8.2E-01 | 1.6E-02 | 1.8E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 53| +1.48E+02 | +5.1E-03 | -5.4E+00 | 4.2E-01 | 6.0E-01 | 3.2E-02 | trr |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 40| +2.18E+02 | -1.3E+00 | +6.3E-01 | 1.2E-01 | 1.5E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 80| +1.52E+02 | +2.0E+00 | -2.0E+00 | 5.0E-01 | 1.3E+01 | 4.0E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 121| +2.10E+02 | -2.1E-01 | +1.4E+00 | 1.2E-01 | 1.5E+00 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 18| +1.51E+02 | -2.5E-01 | +1.0E+00 | 5.0E-01 | 3.9E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 75| +2.10E+02 | +5.0E-04 | -1.5E-02 | 3.1E-02 | 1.8E+00 | 8.5E-02 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 24| +1.53E+02 | -5.5E+00 | +7.9E-01 | 1.2E-01 | 6.3E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 120| +1.48E+02 | -5.1E-02 | +7.8E-01 | 7.8E-03 | 1.5E+01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 41| +2.16E+02 | -1.4E+00 | +5.9E-01 | 1.2E-01 | 2.7E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 54| +1.48E+02 | -3.6E-05 | +8.2E-02 | 4.3E-02 | 6.0E-01 | 8.1E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 122| +2.10E+02 | -1.2E-01 | +9.6E-02 | 2.5E-01 | 2.9E+00 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 76| +2.10E+02 | -1.2E-02 | +6.1E-01 | 7.8E-03 | 1.8E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 25| +1.49E+02 | -3.1E+00 | +9.8E-01 | 2.5E-01 | 7.7E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 42| +2.16E+02 | +1.5E-01 | -1.5E-01 | 1.2E-01 | 1.4E+01 | 3.2E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 19| +1.51E+02 | +3.2E+00 | -2.2E+01 | 1.0E+00 | 3.2E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 123| +2.09E+02 | -7.1E-01 | +1.0E+00 | 6.2E-02 | 6.6E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 121| +1.48E+02 | -1.2E-02 | +4.5E-01 | 1.6E-02 | 3.9E+00 | 4.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 55| +1.48E+02 | -2.5E-04 | +5.2E-01 | 1.1E-02 | 2.8E-01 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 77| +2.10E+02 | -9.6E-03 | +1.0E+00 | 7.8E-03 | 1.4E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 43| +2.16E+02 | -6.4E-01 | +8.1E-01 | 3.1E-02 | 1.4E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 26| +1.49E+02 | +1.0E+01 | -3.8E+00 | 5.0E-01 | 4.1E+01 | 5.4E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 124| +2.08E+02 | -6.2E-01 | +1.2E+00 | 1.2E-01 | 4.4E+00 | 3.0E-01 | trr |1\n", - "2023-02-17 16:05:33 fides(INFO) 122| +1.48E+02 | +2.6E-03 | -4.2E-01 | 1.6E-02 | 4.5E+00 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 56| +1.48E+02 | -3.8E-05 | +9.3E-01 | 1.1E-02 | 2.9E-01 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 81| +1.51E+02 | -3.0E-01 | +5.1E-01 | 4.2E-02 | 1.3E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 78| +2.10E+02 | -1.9E-02 | +9.2E-01 | 1.6E-02 | 1.2E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 44| +2.15E+02 | -9.0E-01 | +1.0E+00 | 6.2E-02 | 1.2E+01 | 9.3E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 27| +1.49E+02 | -9.2E-01 | +6.2E-01 | 1.2E-01 | 4.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 125| +2.07E+02 | -7.8E-01 | +4.3E-01 | 2.5E-01 | 5.8E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:33.825 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.2839 and h = 1.60631e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33.825 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.2839: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) 123| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.0E-03 | 4.5E+00 | 5.4E-03 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 79| +2.10E+02 | -4.0E-02 | +1.0E+00 | 3.1E-02 | 1.3E+00 | 8.9E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 57| +1.48E+02 | -5.9E-05 | +1.1E+00 | 2.1E-02 | 8.0E-02 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 20| +1.51E+02 | -3.6E-01 | +1.0E+00 | 2.5E-01 | 3.2E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 45| +2.14E+02 | -9.4E-01 | +8.3E-01 | 1.2E-01 | 1.2E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:33.840 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.3559 and h = 3.61223e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33.840 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.3559: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) 28| +1.49E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 4.1E+01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 80| +2.10E+02 | -8.6E-02 | +1.1E+00 | 6.2E-02 | 1.2E+00 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 46| +2.14E+02 | -3.7E-01 | +1.4E-01 | 2.5E-01 | 1.3E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 126| +2.05E+02 | -2.3E+00 | +8.8E-01 | 2.5E-01 | 3.5E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 124| +1.48E+02 | -2.3E-03 | +7.2E-01 | 4.9E-04 | 4.5E+00 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 21| +1.51E+02 | +8.5E-01 | -3.0E+00 | 5.0E-01 | 3.0E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 29| +1.48E+02 | -3.0E-01 | +8.8E-01 | 3.1E-02 | 4.1E+01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 81| +2.10E+02 | -2.1E-01 | +1.2E+00 | 1.2E-01 | 1.5E+00 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 47| +2.12E+02 | -1.5E+00 | +6.9E-01 | 6.2E-02 | 2.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 127| +2.05E+02 | +4.3E+00 | -5.9E-01 | 5.0E-01 | 3.1E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 125| +1.48E+02 | -4.1E-04 | +9.8E-01 | 4.9E-04 | 1.2E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 48| +2.11E+02 | -5.6E-01 | +9.0E-01 | 6.2E-02 | 7.2E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 82| +2.09E+02 | -5.2E-01 | +1.1E+00 | 2.5E-01 | 2.9E+00 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 82| +1.51E+02 | -3.2E-01 | +8.7E-01 | 4.2E-02 | 1.6E+01 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 30| +1.48E+02 | -2.3E-01 | +8.7E-01 | 6.2E-02 | 2.4E+01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 128| +2.04E+02 | -1.4E+00 | +2.6E-01 | 1.2E-01 | 3.1E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 49| +2.10E+02 | -1.2E+00 | +1.0E+00 | 1.2E-01 | 1.0E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 126| +1.48E+02 | -4.1E-04 | +9.0E-01 | 9.8E-04 | 7.5E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 22| +1.51E+02 | -2.5E-01 | +1.1E+00 | 1.2E-01 | 3.0E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 83| +2.09E+02 | +1.6E+00 | -4.9E+00 | 5.0E-01 | 7.9E+00 | 8.8E-01 | 2d |0\n", - "2023-02-17 16:05:33 fides(INFO) 129| +2.01E+02 | -2.9E+00 | +1.4E+00 | 1.2E-01 | 2.3E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 31| +1.48E+02 | -2.1E-01 | +9.6E-01 | 1.2E-01 | 2.6E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 127| +1.48E+02 | -5.3E-04 | +1.0E+00 | 2.0E-03 | 8.9E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 50| +2.06E+02 | -4.3E+00 | +1.3E+00 | 2.5E-01 | 9.8E+00 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 84| +2.09E+02 | -3.8E-01 | +1.3E+00 | 1.2E-01 | 7.9E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:33 fides(INFO) 130| +1.93E+02 | -7.4E+00 | +1.2E+00 | 2.5E-01 | 1.8E+01 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 58| +1.48E+02 | -5.7E-05 | +8.0E-01 | 4.3E-02 | 1.2E-01 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 23| +1.50E+02 | -6.3E-01 | +1.3E+00 | 2.5E-01 | 1.6E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:33 fides(INFO) 32| +1.48E+02 | -1.6E-01 | +8.7E-01 | 2.5E-01 | 1.3E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:33.989 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.0831 and h = 2.15215e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:33.989 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.0831: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:33 fides(INFO) 128| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.9E-03 | 5.3E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 85| +2.09E+02 | +5.0E+00 | -2.1E+00 | 2.5E-01 | 6.4E+00 | 6.7E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 51| +1.84E+02 | -2.2E+01 | +1.4E+00 | 5.0E-01 | 2.4E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 59| +1.48E+02 | +2.4E-04 | -3.0E+00 | 8.5E-02 | 4.8E-02 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 83| +1.51E+02 | -2.7E-01 | +1.1E+00 | 8.5E-02 | 2.0E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 131| +1.93E+02 | +9.2E+01 | -6.7E+00 | 5.0E-01 | 5.0E+01 | 1.3E+00 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 129| +1.48E+02 | -2.6E-04 | +1.0E+00 | 9.8E-04 | 5.3E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 33| +1.48E+02 | +8.8E-01 | -5.0E+00 | 5.0E-01 | 8.7E+00 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 86| +2.08E+02 | -3.4E-01 | +3.6E-01 | 6.2E-02 | 6.4E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 24| +1.50E+02 | +4.2E-01 | -1.1E+00 | 5.0E-01 | 2.0E+01 | 9.8E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 52| +1.84E+02 | +3.1E+02 | -1.3E+01 | 1.0E+00 | 3.1E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 60| +1.48E+02 | -1.5E-05 | +4.8E-01 | 2.1E-02 | 4.8E-02 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 130| +1.48E+02 | -4.7E-04 | +1.0E+00 | 2.0E-03 | 4.8E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 34| +1.48E+02 | -2.2E-02 | +2.7E-01 | 1.2E-01 | 8.7E+00 | 6.9E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 87| +2.08E+02 | -7.9E-01 | +1.3E+00 | 6.2E-02 | 1.0E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 132| +1.88E+02 | -4.9E+00 | +8.5E-01 | 1.2E-01 | 5.0E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 53| +1.68E+02 | -1.6E+01 | +1.3E+00 | 2.5E-01 | 3.1E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 61| +1.48E+02 | +4.6E-05 | -6.0E-01 | 2.1E-02 | 9.3E-02 | 3.1E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 88| +2.06E+02 | -1.5E+00 | +1.0E+00 | 1.2E-01 | 5.4E+00 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 25| +1.50E+02 | -1.9E-01 | +8.9E-01 | 1.2E-01 | 2.0E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 131| +1.48E+02 | -6.7E-04 | +1.0E+00 | 3.9E-03 | 4.8E-01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 133| +1.77E+02 | -1.1E+01 | +5.5E-01 | 2.5E-01 | 7.0E+01 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 35| +1.48E+02 | +1.5E-01 | -8.7E-01 | 1.2E-01 | 4.8E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 84| +1.51E+02 | +5.3E-03 | -8.8E-02 | 1.7E-01 | 4.7E+00 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 89| +2.03E+02 | -3.1E+00 | +1.0E+00 | 2.5E-01 | 1.2E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 54| +1.68E+02 | +1.4E+01 | -1.4E+00 | 5.0E-01 | 4.5E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 62| +1.48E+02 | -1.5E-05 | +6.2E-01 | 5.3E-03 | 9.3E-02 | 8.1E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 132| +1.48E+02 | -1.2E-03 | +7.6E-01 | 7.8E-03 | 3.1E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 134| +1.71E+02 | -6.0E+00 | +4.0E-01 | 2.5E-01 | 7.0E+01 | 3.7E-01 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 36| +1.48E+02 | -2.7E-02 | +5.4E-01 | 2.9E-02 | 4.8E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 26| +1.50E+02 | -1.8E-01 | +6.7E-01 | 2.5E-01 | 1.3E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 90| +2.03E+02 | +2.6E+01 | -5.3E+00 | 5.0E-01 | 4.2E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 55| +1.59E+02 | -8.5E+00 | +1.0E+00 | 1.2E-01 | 4.5E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 63| +1.48E+02 | -3.2E-06 | +6.1E-01 | 5.3E-03 | 1.4E-01 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 37| +1.48E+02 | -9.3E-03 | +9.8E-01 | 2.9E-02 | 7.3E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 135| +1.58E+02 | -1.3E+01 | +1.1E+00 | 2.5E-01 | 1.1E+02 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 133| +1.48E+02 | -2.4E-03 | +1.1E+00 | 1.6E-02 | 1.6E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 91| +2.01E+02 | -1.8E+00 | +1.1E+00 | 1.2E-01 | 4.2E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 64| +1.48E+02 | -4.0E-06 | +1.0E+00 | 5.3E-03 | 2.9E-02 | 6.8E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 27| +1.50E+02 | -1.6E-01 | +8.2E-01 | 2.5E-01 | 1.8E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 38| +1.48E+02 | -8.7E-03 | +9.5E-01 | 5.9E-02 | 3.6E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 134| +1.48E+02 | -2.7E-03 | +9.5E-01 | 3.1E-02 | 6.9E-01 | 9.1E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 56| +1.52E+02 | -7.3E+00 | +4.1E-01 | 2.5E-01 | 7.5E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 136| +1.58E+02 | +3.0E+02 | -1.7E+01 | 5.0E-01 | 5.6E+01 | 1.3E+00 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 85| +1.51E+02 | -5.1E-02 | +9.5E-01 | 1.1E-02 | 4.7E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 92| +2.01E+02 | +4.2E-01 | -1.6E-01 | 2.5E-01 | 5.3E+01 | 6.7E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 65| +1.48E+02 | -6.0E-06 | +1.2E+00 | 1.1E-02 | 8.5E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:34.214 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 87.9354 and h = 3.13883e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.215 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 87.9354: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 135| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.3E-02 | 3.9E-01 | 2.4E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 39| +1.48E+02 | -1.1E-02 | +9.2E-01 | 1.2E-01 | 3.9E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 57| +1.49E+02 | -3.2E+00 | +9.6E-01 | 2.5E-01 | 1.8E+02 | 3.3E-01 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 137| +1.51E+02 | -7.0E+00 | +8.7E-01 | 1.2E-01 | 5.6E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:34.229 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 171.577 and h = 4.79171e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.229 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 171.577: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 28| +1.50E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 1.3E+01 | 7.9E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 66| +1.48E+02 | -2.5E-06 | +3.6E-01 | 2.1E-02 | 2.2E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 93| +1.98E+02 | -3.1E+00 | +1.4E+00 | 6.2E-02 | 5.3E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:34.243 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.485 and h = 3.02269e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.243 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.485: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 136| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.0E-03 | 3.9E-01 | 5.9E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 40| +1.48E+02 | +3.8E-02 | -2.4E+00 | 2.4E-01 | 2.1E+00 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 58| +1.49E+02 | +2.5E-02 | -6.7E-02 | 2.5E-01 | 3.6E+01 | 2.6E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 138| +1.51E+02 | +2.6E+01 | -4.4E+00 | 2.5E-01 | 2.9E+01 | 5.4E-01 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 137| +1.48E+02 | -2.2E-04 | +1.3E+00 | 5.1E-04 | 3.9E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 94| +1.85E+02 | -1.3E+01 | +8.7E-01 | 1.2E-01 | 5.3E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 41| +1.48E+02 | -2.2E-03 | +3.7E-01 | 5.9E-02 | 2.1E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 67| +1.48E+02 | +2.4E-05 | -1.2E+00 | 2.1E-02 | 6.1E-02 | 4.2E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 59| +1.48E+02 | -9.4E-01 | +6.2E-01 | 6.2E-02 | 3.6E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 139| +1.51E+02 | +2.2E+00 | -7.1E-01 | 6.2E-02 | 2.9E+01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 29| +1.50E+02 | -4.7E-02 | +1.1E+00 | 1.2E-01 | 1.3E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 138| +1.48E+02 | -1.4E-04 | +9.0E-01 | 1.0E-03 | 3.1E-01 | 2.9E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 42| +1.48E+02 | +7.8E-04 | -8.4E-02 | 5.9E-02 | 2.1E+00 | 2.5E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 68| +1.48E+02 | -5.0E-06 | +8.5E-01 | 5.3E-03 | 6.1E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 95| +1.85E+02 | +1.1E+01 | -5.7E-01 | 2.5E-01 | 4.4E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 140| +1.50E+02 | -7.8E-01 | +7.2E-01 | 1.6E-02 | 2.9E+01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 60| +1.48E+02 | -1.2E-01 | +5.3E-01 | 6.2E-02 | 2.1E+01 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 86| +1.51E+02 | -3.4E-02 | +8.2E-01 | 2.2E-02 | 4.0E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 139| +1.48E+02 | -1.4E-04 | +9.4E-01 | 2.0E-03 | 6.3E-01 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 43| +1.48E+02 | -2.4E-03 | +6.7E-01 | 1.5E-02 | 2.1E+00 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 69| +1.48E+02 | +2.7E-06 | -1.5E+00 | 1.1E-02 | 2.0E-02 | 8.0E-04 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 96| +1.78E+02 | -6.7E+00 | +9.9E-01 | 6.2E-02 | 4.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 141| +1.49E+02 | -6.6E-01 | +7.9E-01 | 1.6E-02 | 3.9E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 61| +1.48E+02 | -4.4E-02 | +3.7E-01 | 6.2E-02 | 4.7E+00 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 30| +1.50E+02 | -8.0E-02 | +1.1E+00 | 2.5E-01 | 9.0E+00 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 140| +1.48E+02 | -7.6E-05 | +9.2E-01 | 4.1E-03 | 1.6E-01 | 8.2E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 70| +1.48E+02 | -1.8E-07 | +3.3E-01 | 2.7E-03 | 2.0E-02 | 2.0E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(WARNING) Stopping as function difference 1.75E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:34 fides(INFO) 44| +1.48E+02 | -1.3E-03 | +9.7E-01 | 1.5E-02 | 2.6E+00 | 5.7E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 97| +1.68E+02 | -1.0E+01 | +7.8E-01 | 1.2E-01 | 4.6E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 62| +1.48E+02 | -2.8E-02 | +6.4E-01 | 6.2E-02 | 9.5E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 142| +1.49E+02 | -5.3E-01 | +8.5E-01 | 3.1E-02 | 2.9E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 141| +1.48E+02 | -2.0E-06 | +2.8E-01 | 4.1E-03 | 4.6E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 31| +1.50E+02 | -9.7E-03 | +8.3E-01 | 5.0E-01 | 1.7E+00 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 98| +1.68E+02 | +5.6E+00 | -6.0E-01 | 2.5E-01 | 8.1E+01 | 5.1E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 45| +1.48E+02 | -1.4E-03 | +9.5E-01 | 2.9E-02 | 1.2E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 143| +1.48E+02 | -4.9E-01 | +5.0E-01 | 6.2E-02 | 2.6E+01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 63| +1.48E+02 | -1.1E-02 | +8.1E-01 | 6.2E-02 | 3.5E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 142| +1.48E+02 | -3.4E-06 | +1.7E+00 | 4.1E-03 | 2.9E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 32| +1.50E+02 | -3.3E-03 | +8.7E-01 | 1.0E+00 | 1.4E+00 | 1.1E+00 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 46| +1.48E+02 | -1.2E-03 | +7.6E-01 | 5.9E-02 | 1.8E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 87| +1.51E+02 | +3.9E-03 | -4.8E-01 | 4.4E-02 | 5.0E+00 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 99| +1.63E+02 | -4.6E+00 | +9.4E-01 | 6.2E-02 | 8.1E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 144| +1.48E+02 | -2.2E-02 | +1.2E-02 | 6.2E-02 | 5.3E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 64| +1.48E+02 | -1.2E-02 | +8.5E-01 | 1.2E-01 | 4.7E+00 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 143| +1.48E+02 | +4.7E-07 | -8.7E+00 | 4.1E-03 | 5.6E-03 | 4.0E-04 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 100| +1.59E+02 | -4.3E+00 | +6.8E-01 | 1.2E-01 | 5.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 0| +3.57E+10 | NaN | NaN | 1.0E+00 | 1.6E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:34 fides(INFO) 33| +1.50E+02 | -3.0E-04 | +2.6E-01 | 2.0E+00 | 1.8E+00 | 2.2E-01 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 47| +1.48E+02 | +4.6E-03 | -1.6E+00 | 1.2E-01 | 7.2E-01 | 3.2E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 65| +1.48E+02 | +4.4E-03 | -3.2E-01 | 2.5E-01 | 1.6E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 145| +1.48E+02 | -2.3E-01 | +7.6E-01 | 1.6E-02 | 2.7E+01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 144| +1.48E+02 | -2.7E-06 | +1.0E+02 | 3.5E-05 | 5.6E-03 | 9.9E-05 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 1| +9.25E+07 | -3.6E+10 | +9.0E-02 | 1.0E+00 | 1.6E+11 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 48| +1.48E+02 | -3.9E-04 | +3.5E-01 | 2.9E-02 | 7.2E-01 | 8.2E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 101| +1.54E+02 | -5.0E+00 | +6.0E-01 | 1.2E-01 | 7.3E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 34| +1.50E+02 | -2.1E-05 | +2.1E-02 | 2.0E+00 | 6.3E-01 | 7.8E-03 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 66| +1.48E+02 | -3.3E-03 | +6.8E-01 | 6.2E-02 | 1.6E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 146| +1.48E+02 | -2.9E-01 | +7.0E-01 | 3.1E-02 | 2.6E+01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 2| +1.42E+07 | -7.8E+07 | +8.9E-01 | 2.5E-01 | 4.3E+08 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 145| +1.48E+02 | +4.4E-06 | -2.5E+02 | 7.0E-05 | 2.1E-03 | 1.9E-04 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 3| +2.21E+06 | -1.2E+07 | +8.9E-01 | 5.0E-01 | 6.5E+07 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 49| +1.48E+02 | -4.3E-04 | +3.5E-01 | 2.9E-02 | 1.3E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 88| +1.51E+02 | -3.1E-03 | +5.4E-01 | 2.1E-03 | 5.0E+00 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 67| +1.48E+02 | -2.3E-03 | +7.4E-01 | 6.2E-02 | 2.1E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 102| +1.54E+02 | +3.8E+01 | -3.4E+00 | 1.2E-01 | 6.3E+01 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 35| +1.50E+02 | +1.6E-05 | -1.7E-02 | 1.7E-02 | 1.0E+00 | 6.4E-03 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 146| +1.48E+02 | +2.9E-06 | -3.8E+02 | 1.7E-05 | 2.1E-03 | 4.7E-05 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 147| +1.48E+02 | +2.9E-02 | -1.3E-01 | 3.1E-02 | 1.9E+01 | 3.7E-02 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 68| +1.48E+02 | -1.4E-03 | +6.5E-01 | 6.2E-02 | 8.2E-01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 4| +3.72E+05 | -1.8E+06 | +8.7E-01 | 1.0E+00 | 1.0E+07 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 103| +1.53E+02 | -5.3E-01 | +1.6E-01 | 3.1E-02 | 6.3E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 50| +1.48E+02 | +1.6E-05 | -1.7E-02 | 2.9E-02 | 5.0E-01 | 8.3E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 36| +1.50E+02 | -3.8E-04 | +7.9E-01 | 1.9E-03 | 1.0E+00 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 148| +1.48E+02 | -5.6E-02 | +6.9E-01 | 7.2E-03 | 1.9E+01 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 147| +1.48E+02 | +4.4E-06 | -1.7E+03 | 4.4E-06 | 2.1E-03 | 1.2E-05 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 5| +5.76E+04 | -3.1E+05 | +9.0E-01 | 2.0E+00 | 1.6E+06 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 69| +1.48E+02 | -1.1E-03 | +5.8E-01 | 6.2E-02 | 1.5E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 51| +1.48E+02 | -2.3E-04 | +5.3E-01 | 7.3E-03 | 5.0E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 104| +1.52E+02 | -1.0E+00 | +9.8E-01 | 7.8E-03 | 7.3E+01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 37| +1.50E+02 | -8.4E-05 | +7.0E-01 | 3.7E-03 | 4.0E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 148| +1.48E+02 | +3.9E-06 | -3.1E+03 | 1.1E-06 | 2.1E-03 | 2.9E-06 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 149| +1.48E+02 | -3.8E-02 | +9.5E-01 | 7.2E-03 | 1.3E+01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 6| +8.73E+03 | -4.9E+04 | +9.1E-01 | 2.0E+00 | 2.4E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 7| +1.35E+03 | -7.4E+03 | +9.2E-01 | 2.0E+00 | 3.6E+04 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 52| +1.48E+02 | -2.0E-04 | +9.5E-01 | 7.3E-03 | 1.2E+00 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 70| +1.48E+02 | +2.4E-04 | -1.2E-01 | 6.2E-02 | 5.1E-01 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 89| +1.51E+02 | -1.1E-03 | +9.9E-01 | 2.1E-03 | 2.5E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 150| +1.48E+02 | -4.4E-02 | +7.2E-01 | 1.4E-02 | 9.4E+00 | 1.5E-02 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 105| +1.52E+02 | -5.8E-01 | +9.3E-01 | 1.6E-02 | 3.5E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 149| +1.48E+02 | +1.5E-06 | -1.8E+03 | 2.7E-07 | 2.1E-03 | 7.0E-07 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 38| +1.50E+02 | -6.5E-07 | +9.2E-02 | 3.7E-03 | 1.4E-01 | 8.8E-04 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 8| +3.49E+02 | -1.0E+03 | +9.2E-01 | 2.0E+00 | 5.1E+03 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 53| +1.48E+02 | -1.6E-04 | +9.0E-01 | 1.5E-02 | 2.1E-01 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:34.687 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.782 and h = 3.80467e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.687 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.782: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 71| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 5.1E-01 | 7.7E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 151| +1.48E+02 | -5.6E-02 | +8.8E-01 | 1.4E-02 | 2.0E+01 | 6.5E-03 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 106| +1.51E+02 | -9.8E-01 | +8.8E-01 | 3.1E-02 | 3.1E+01 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 9| +2.27E+02 | -1.2E+02 | +9.2E-01 | 2.0E+00 | 7.5E+02 | 1.9E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 150| +1.48E+02 | +3.1E-06 | -9.7E+03 | 6.8E-08 | 2.1E-03 | 1.7E-07 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 39| +1.50E+02 | +1.8E-06 | -2.3E-01 | 9.3E-04 | 3.1E-02 | 1.2E-03 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 54| +1.48E+02 | -9.1E-05 | +5.3E-01 | 2.9E-02 | 4.0E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 72| +1.48E+02 | -1.6E-04 | +2.2E-01 | 3.9E-03 | 5.1E-01 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 10| +2.27E+02 | +5.9E+01 | -4.5E+00 | 2.0E+00 | 1.2E+02 | 3.0E+00 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 152| +1.48E+02 | -2.0E-02 | +9.5E-01 | 1.4E-02 | 7.9E+00 | 6.2E-03 | trr |1\n", - "2023-02-17 16:05:34.726 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.665 and h = 2.87681e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.726 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.665: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 151| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.7E-08 | 2.1E-03 | 4.3E-08 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 107| +1.51E+02 | -8.2E-02 | +4.8E-02 | 6.2E-02 | 3.8E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 40| +1.50E+02 | -3.0E-06 | +7.7E-01 | 1.6E-04 | 3.1E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 11| +2.00E+02 | -2.7E+01 | +1.0E+00 | 5.0E-01 | 1.2E+02 | 7.9E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 55| +1.48E+02 | +5.0E-05 | -2.5E-01 | 2.9E-02 | 1.6E-01 | 6.0E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 73| +1.48E+02 | -4.7E-04 | +9.6E-01 | 9.8E-04 | 2.0E+00 | 6.7E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 153| +1.48E+02 | -1.9E-02 | +9.3E-01 | 1.4E-02 | 9.5E+00 | 5.4E-03 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 152| +1.48E+02 | +5.5E-06 | -2.4E+05 | 4.3E-09 | 2.1E-03 | 1.1E-08 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 12| +2.00E+02 | +7.4E+02 | -2.4E+01 | 1.0E+00 | 5.5E+01 | 2.0E+00 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 108| +1.50E+02 | -5.9E-01 | +9.1E-01 | 1.6E-02 | 4.8E+01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 41| +1.50E+02 | -6.4E-08 | +5.8E-02 | 3.1E-04 | 6.1E-02 | 4.8E-04 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 56| +1.48E+02 | -1.9E-05 | +2.0E-01 | 7.3E-03 | 1.6E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 74| +1.48E+02 | -4.6E-05 | +1.0E+00 | 2.0E-03 | 2.3E-01 | 9.9E-04 | 2d |1\n", - "2023-02-17 16:05:34.794 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 81.0378 and h = 4.97343e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.794 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 81.0378: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 154| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.4E-02 | 7.2E+00 | 7.4E-03 | trr |0\n", - "2023-02-17 16:05:34 fides(INFO) 13| +1.81E+02 | -1.9E+01 | +8.5E-01 | 2.5E-01 | 5.5E+01 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 153| +1.48E+02 | +3.0E-06 | -5.3E+05 | 1.1E-09 | 2.1E-03 | 2.7E-09 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 109| +1.49E+02 | -7.0E-01 | +6.7E-01 | 3.1E-02 | 3.2E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 90| +1.51E+02 | -8.2E-04 | +1.4E+00 | 4.3E-03 | 3.5E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 42| +1.50E+02 | -2.9E-07 | +5.0E-01 | 7.8E-05 | 6.1E-03 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 57| +1.48E+02 | -4.8E-05 | +1.0E+00 | 1.8E-03 | 5.9E-01 | 5.7E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(WARNING) Stopping as function difference 2.88E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:34.819 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.558 and h = 2.71512e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.819 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.558: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 75| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.9E-03 | 2.5E-01 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 155| +1.48E+02 | -8.0E-03 | +9.9E-01 | 1.0E-03 | 7.2E+00 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 14| +1.70E+02 | -1.2E+01 | +7.0E-02 | 5.0E-01 | 5.3E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 154| +1.48E+02 | +4.5E-06 | -3.2E+06 | 2.7E-10 | 2.1E-03 | 6.7E-10 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 110| +1.49E+02 | -8.1E-02 | +8.5E-02 | 3.1E-02 | 3.5E+01 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 15| +1.53E+02 | -1.7E+01 | +7.8E-01 | 1.2E-01 | 3.3E+02 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 76| +1.48E+02 | -2.6E-05 | +9.5E-01 | 9.8E-04 | 2.5E-01 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 58| +1.48E+02 | -1.4E-05 | +1.2E+00 | 3.7E-03 | 4.6E-02 | 1.0E-03 | 2d |1\n", - "2023-02-17 16:05:34.854 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0766 and h = 1.15392e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.854 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0766: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 155| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.7E-11 | 2.1E-03 | 1.7E-10 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 156| +1.48E+02 | -8.1E-03 | +9.7E-01 | 2.0E-03 | 4.3E+00 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 111| +1.49E+02 | -4.0E-01 | +8.0E-01 | 7.8E-03 | 5.3E+01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 16| +1.51E+02 | -2.4E+00 | +4.9E-01 | 2.5E-01 | 1.2E+02 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 77| +1.48E+02 | -3.2E-05 | +9.4E-01 | 2.0E-03 | 2.0E-01 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 59| +1.48E+02 | -1.3E-05 | +7.5E-01 | 7.3E-03 | 5.0E-02 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 156| +1.48E+02 | +1.5E-06 | -1.7E+07 | 1.7E-11 | 2.1E-03 | 4.2E-11 | 2d |0\n", - "2023-02-17 16:05:34.885 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 224.204 and h = 2.042e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:34.885 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 224.204: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:34 fides(INFO) 91| +1.51E+02 | +0.0E+00 | +0.0E+00 | 8.6E-03 | 3.0E-01 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 157| +1.48E+02 | -1.0E-02 | +9.7E-01 | 4.1E-03 | 7.7E+00 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 17| +1.49E+02 | -1.7E+00 | +6.7E-01 | 2.5E-01 | 4.6E+01 | 5.0E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 78| +1.48E+02 | -5.7E-05 | +9.2E-01 | 3.9E-03 | 1.5E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 112| +1.49E+02 | -2.4E-01 | +8.9E-01 | 1.6E-02 | 1.2E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 60| +1.48E+02 | -2.2E-05 | +9.2E-01 | 1.5E-02 | 3.7E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 157| +1.48E+02 | +5.0E-06 | -2.3E+08 | 4.2E-12 | 2.1E-03 | 1.1E-11 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 0| +8.21E+10 | NaN | NaN | 1.0E+00 | 3.8E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:34 fides(INFO) 18| +1.49E+02 | -4.0E-01 | +4.2E-01 | 2.5E-01 | 3.5E+01 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 158| +1.48E+02 | -6.8E-03 | +9.8E-01 | 8.1E-03 | 4.1E+00 | 4.9E-03 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) 79| +1.48E+02 | -9.5E-05 | +9.7E-01 | 7.8E-03 | 2.4E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 113| +1.49E+02 | -1.6E-01 | +6.4E-01 | 3.1E-02 | 1.7E+01 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 1| +1.49E+05 | -8.2E+10 | +9.2E-02 | 1.0E+00 | 3.8E+11 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 19| +1.49E+02 | +5.7E-01 | -4.4E-01 | 2.5E-01 | 2.0E+01 | 4.3E-01 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 158| +1.48E+02 | +2.6E-06 | -4.8E+08 | 1.0E-12 | 2.1E-03 | 2.6E-12 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 61| +1.48E+02 | -3.6E-05 | +1.0E+00 | 2.9E-02 | 7.3E-02 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 159| +1.48E+02 | -9.2E-03 | +9.8E-01 | 8.1E-03 | 6.5E+00 | 4.6E-03 | trr |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 20| +1.48E+02 | -3.8E-01 | +7.3E-01 | 6.2E-02 | 2.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 2| +2.36E+04 | -1.2E+05 | +9.0E-01 | 2.5E-01 | 6.8E+05 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 62| +1.48E+02 | -4.2E-05 | +9.5E-01 | 5.9E-02 | 4.2E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 114| +1.49E+02 | +4.0E+00 | -6.2E+00 | 3.1E-02 | 9.9E+00 | 8.1E-02 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 80| +1.48E+02 | -1.6E-04 | +9.7E-01 | 1.6E-02 | 1.1E-01 | 7.3E-03 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) 159| +1.48E+02 | +5.8E-06 | -4.2E+09 | 2.6E-13 | 2.1E-03 | 6.6E-13 | 2d |0\n", - "2023-02-17 16:05:34 fides(INFO) 21| +1.48E+02 | -1.1E-01 | +7.5E-01 | 6.2E-02 | 1.8E+01 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:34 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:34 fides(INFO) 160| +1.48E+02 | -6.5E-03 | +9.9E-01 | 8.1E-03 | 4.8E+00 | 5.4E-03 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 3| +3.99E+03 | -2.0E+04 | +9.0E-01 | 5.0E-01 | 1.1E+05 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 63| +1.48E+02 | +3.0E-04 | -5.9E+00 | 1.2E-01 | 3.1E-02 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 22| +1.48E+02 | -6.1E-02 | +8.7E-01 | 6.2E-02 | 1.2E+01 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 115| +1.49E+02 | +9.3E-02 | -5.0E-01 | 7.8E-03 | 9.9E+00 | 2.1E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 81| +1.48E+02 | -2.5E-04 | +9.5E-01 | 3.1E-02 | 2.6E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 92| +1.51E+02 | -3.7E-04 | +1.0E+00 | 2.1E-03 | 3.0E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 4| +8.79E+02 | -3.1E+03 | +9.0E-01 | 1.0E+00 | 1.7E+04 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 161| +1.48E+02 | -8.2E-03 | +9.9E-01 | 8.1E-03 | 4.9E+00 | 7.2E-03 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 23| +1.48E+02 | -1.1E-01 | +9.5E-01 | 1.2E-01 | 1.1E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 64| +1.48E+02 | +2.6E-06 | -1.2E-01 | 2.9E-02 | 3.1E-02 | 5.4E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 160| +1.48E+02 | +1.3E-06 | -3.9E+09 | 6.5E-14 | 2.1E-03 | 1.6E-13 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 5| +3.46E+02 | -5.3E+02 | +9.2E-01 | 2.0E+00 | 2.7E+03 | 1.9E+00 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 82| +1.48E+02 | -3.9E-04 | +9.3E-01 | 6.2E-02 | 1.5E-01 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 162| +1.48E+02 | -8.6E-03 | +1.0E+00 | 8.1E-03 | 5.3E+00 | 9.9E-03 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 116| +1.49E+02 | -3.1E-02 | +6.4E-01 | 2.0E-03 | 9.9E+00 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 24| +1.48E+02 | -9.6E-02 | +6.6E-01 | 2.5E-01 | 1.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 6| +2.60E+02 | -8.6E+01 | +8.9E-01 | 2.0E+00 | 4.0E+02 | 5.5E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 65| +1.48E+02 | -2.7E-06 | +4.7E-01 | 7.3E-03 | 3.1E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 161| +1.48E+02 | +2.2E-06 | -2.6E+10 | 1.6E-14 | 2.1E-03 | 4.1E-14 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 83| +1.48E+02 | -1.5E-04 | +2.1E-01 | 1.2E-01 | 4.3E-01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 25| +1.48E+02 | +2.4E-01 | -7.0E-01 | 2.5E-01 | 7.1E+00 | 2.9E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 163| +1.48E+02 | -8.3E-03 | +1.0E+00 | 8.1E-03 | 4.7E+00 | 8.9E-03 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 117| +1.49E+02 | -3.0E-02 | +9.2E-01 | 2.0E-03 | 9.7E+00 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 7| +2.25E+02 | -3.5E+01 | +5.1E-01 | 4.0E+00 | 5.5E+01 | 8.5E+00 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 66| +1.48E+02 | -6.6E-07 | +2.7E-01 | 7.3E-03 | 2.2E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as function difference 6.59E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:35 fides(INFO) 84| +1.48E+02 | +6.5E-04 | -4.5E-01 | 3.1E-02 | 4.0E-01 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 26| +1.48E+02 | -6.0E-02 | +3.7E-01 | 6.2E-02 | 7.1E+00 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 162| +1.48E+02 | +1.3E-06 | -5.8E+10 | 4.1E-15 | 2.1E-03 | 1.0E-14 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 8| +2.25E+02 | +4.9E+02 | -3.4E+01 | 4.0E+00 | 3.9E+01 | 9.4E+00 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) 164| +1.48E+02 | -1.6E-02 | +1.0E+00 | 1.6E-02 | 3.1E+00 | 3.0E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 27| +1.48E+02 | -7.2E-02 | +8.4E-01 | 6.2E-02 | 1.9E+01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 118| +1.48E+02 | -4.0E-02 | +8.7E-01 | 3.9E-03 | 7.1E+00 | 8.9E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 85| +1.48E+02 | -2.2E-06 | +8.4E-04 | 7.8E-03 | 4.0E-01 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 163| +1.48E+02 | +1.7E-06 | -3.1E+11 | 1.0E-15 | 2.1E-03 | 2.6E-15 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 28| +1.48E+02 | -7.0E-02 | +1.0E+00 | 1.2E-01 | 3.1E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 93| +1.51E+02 | -6.9E-04 | +1.0E+00 | 4.3E-03 | 3.7E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 9| +2.25E+02 | +1.9E+01 | -1.7E+00 | 8.0E-01 | 3.9E+01 | 1.9E+00 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) 165| +1.48E+02 | -1.1E-02 | +9.8E-01 | 3.3E-02 | 2.5E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 119| +1.48E+02 | -5.0E-02 | +9.3E-01 | 7.8E-03 | 1.0E+01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 86| +1.48E+02 | -4.2E-04 | +9.5E-01 | 2.0E-03 | 1.9E+00 | 8.7E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 29| +1.47E+02 | -1.6E-01 | +1.2E+00 | 2.5E-01 | 7.0E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 164| +1.48E+02 | +1.6E-07 | -1.2E+11 | 2.5E-16 | 2.1E-03 | 6.4E-16 | 2d |0\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as trust region radius 6.35E-17 is smaller than machine precision.\n", - "2023-02-17 16:05:35 fides(INFO) 166| +1.48E+02 | -1.7E-02 | +9.4E-01 | 6.5E-02 | 2.3E+00 | 8.8E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 10| +2.21E+02 | -4.6E+00 | +3.4E-01 | 2.0E-01 | 3.9E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 87| +1.48E+02 | -2.9E-05 | +8.6E-01 | 3.9E-03 | 1.6E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 30| +1.47E+02 | +2.7E-02 | -1.4E-01 | 5.0E-01 | 4.2E+00 | 3.8E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 120| +1.48E+02 | -5.9E-02 | +8.5E-01 | 1.6E-02 | 6.9E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 167| +1.48E+02 | +1.5E-02 | -1.8E+00 | 1.3E-01 | 9.2E-01 | 7.1E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 31| +1.47E+02 | -8.3E-02 | +7.9E-01 | 1.2E-01 | 4.2E+00 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 0| +1.15E+13 | NaN | NaN | 1.0E+00 | 5.3E+13 | NaN | NaN |1\n", - "2023-02-17 16:05:35 fides(INFO) 11| +2.18E+02 | -2.9E+00 | +4.8E-01 | 2.0E-01 | 3.4E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 88| +1.48E+02 | -3.2E-05 | +8.7E-01 | 7.8E-03 | 1.9E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 121| +1.48E+02 | -1.8E-01 | +8.8E-01 | 3.1E-02 | 7.7E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 32| +1.47E+02 | -1.2E-01 | +3.1E-01 | 2.5E-01 | 7.4E+00 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 168| +1.48E+02 | -1.9E-03 | +5.3E-01 | 2.2E-02 | 9.2E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 1| +6.42E+06 | -1.2E+13 | +8.3E-02 | 1.0E+00 | 5.3E+13 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 12| +2.18E+02 | -2.4E-01 | +1.8E-02 | 2.0E-01 | 2.2E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 94| +1.51E+02 | -1.7E-03 | +1.1E+00 | 8.6E-03 | 2.1E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:35.268 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.52 and h = 3.50065e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:35.268 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.52: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:35 fides(INFO) 89| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.6E-02 | 7.9E-02 | 4.9E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 33| +1.47E+02 | +8.0E+00 | -4.5E+00 | 2.5E-01 | 1.8E+01 | 3.5E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 122| +1.48E+02 | -3.2E-02 | +1.0E-01 | 6.2E-02 | 4.7E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 13| +2.15E+02 | -2.4E+00 | +8.3E-01 | 5.0E-02 | 3.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 169| +1.48E+02 | -9.4E-04 | +7.6E-01 | 2.2E-02 | 1.2E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 2| +9.82E+05 | -5.4E+06 | +8.9E-01 | 2.5E-01 | 3.0E+07 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 34| +1.47E+02 | -1.5E-01 | +2.6E-01 | 6.2E-02 | 1.8E+01 | 8.8E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 90| +1.48E+02 | -8.6E-06 | +8.2E-01 | 3.9E-03 | 7.9E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 123| +1.48E+02 | -2.3E-01 | +9.9E-01 | 1.6E-02 | 4.0E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 14| +2.15E+02 | -6.2E-01 | +4.3E-01 | 1.0E-01 | 1.4E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 35| +1.47E+02 | -1.4E-01 | +7.0E-01 | 6.2E-02 | 9.2E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 3| +1.52E+05 | -8.3E+05 | +8.9E-01 | 5.0E-01 | 4.5E+06 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 170| +1.48E+02 | +1.6E-03 | -1.8E+00 | 4.4E-02 | 2.6E-01 | 3.0E-02 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 0| +6.05E+13 | NaN | NaN | 1.0E+00 | 2.8E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:35 fides(INFO) 91| +1.48E+02 | -1.4E-05 | +9.9E-01 | 7.8E-03 | 1.0E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 124| +1.48E+02 | -9.1E-02 | +9.3E-01 | 3.1E-02 | 3.9E+00 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 36| +1.47E+02 | -1.2E-01 | +8.4E-01 | 6.2E-02 | 1.3E+01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 15| +2.15E+02 | -8.5E-02 | +3.3E-02 | 1.0E-01 | 1.6E+01 | 2.5E-01 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 4| +3.14E+04 | -1.2E+05 | +7.2E-01 | 1.0E+00 | 7.0E+05 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 1| +3.62E+07 | -6.0E+13 | +8.3E-02 | 1.0E+00 | 2.8E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 171| +1.48E+02 | +7.3E-05 | -1.6E-01 | 7.2E-03 | 2.6E-01 | 6.3E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 37| +1.47E+02 | -2.2E-01 | +9.7E-01 | 1.2E-01 | 9.1E+00 | 9.2E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 92| +1.48E+02 | -2.2E-05 | +1.1E+00 | 1.6E-02 | 5.0E-02 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 125| +1.48E+02 | -1.1E-01 | +8.6E-01 | 6.2E-02 | 4.3E+00 | 9.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 2| +5.55E+06 | -3.1E+07 | +8.9E-01 | 2.5E-01 | 1.7E+08 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 16| +2.14E+02 | -7.3E-01 | +8.6E-01 | 2.5E-02 | 2.0E+01 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 5| +5.08E+03 | -2.6E+04 | +9.0E-01 | 1.0E+00 | 1.1E+05 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 172| +1.48E+02 | +4.1E-05 | -1.4E-01 | 1.8E-03 | 2.6E-01 | 2.3E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 38| +1.47E+02 | +3.5E+00 | -5.9E+00 | 2.5E-01 | 7.2E+00 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 6| +9.65E+02 | -4.1E+03 | +9.0E-01 | 1.0E+00 | 1.8E+04 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 17| +2.14E+02 | -2.7E-01 | +4.7E-01 | 5.0E-02 | 1.0E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 126| +1.48E+02 | -1.7E-02 | +7.5E-02 | 1.2E-01 | 4.0E+00 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 3| +8.57E+05 | -4.7E+06 | +9.0E-01 | 5.0E-01 | 2.6E+07 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 93| +1.48E+02 | -2.7E-05 | +8.9E-01 | 3.1E-02 | 9.1E-02 | 8.9E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 39| +1.47E+02 | -9.7E-02 | +3.6E-01 | 6.2E-02 | 7.2E+00 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 7| +3.34E+02 | -6.3E+02 | +9.0E-01 | 1.0E+00 | 2.8E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 173| +1.48E+02 | -9.0E-05 | +4.9E-01 | 4.5E-04 | 2.6E-01 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 4| +1.34E+05 | -7.2E+05 | +9.0E-01 | 1.0E+00 | 3.9E+06 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 18| +2.14E+02 | -8.5E-02 | +1.9E-01 | 5.0E-02 | 8.3E+00 | 1.2E-01 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 127| +1.48E+02 | -8.9E-02 | +8.5E-01 | 3.1E-02 | 6.1E+00 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 95| +1.51E+02 | -3.0E-03 | +1.0E+00 | 1.7E-02 | 5.7E-01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 40| +1.46E+02 | -1.7E-01 | +4.8E-01 | 6.2E-02 | 1.4E+01 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 94| +1.48E+02 | -4.4E-05 | +1.0E+00 | 6.2E-02 | 4.6E-02 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 5| +1.34E+05 | +1.0E+09 | -7.0E+04 | 2.0E+00 | 6.1E+05 | 5.5E+00 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 8| +2.52E+02 | -8.2E+01 | +8.6E-01 | 1.0E+00 | 4.3E+02 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 41| +1.46E+02 | -9.1E-02 | +5.9E-01 | 6.2E-02 | 1.5E+01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 19| +2.13E+02 | -1.9E-01 | +8.8E-01 | 1.3E-02 | 9.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 174| +1.48E+02 | -5.7E-05 | +7.7E-01 | 4.5E-04 | 7.2E-01 | 5.3E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 6| +2.12E+04 | -1.1E+05 | +9.0E-01 | 5.0E-01 | 6.1E+05 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 128| +1.48E+02 | -4.6E-02 | +9.8E-01 | 6.2E-02 | 9.7E+00 | 6.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 42| +1.46E+02 | -6.1E-02 | +2.8E-01 | 6.2E-02 | 6.9E+00 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 95| +1.48E+02 | +3.0E-05 | -5.7E-01 | 1.2E-01 | 6.7E-02 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 9| +2.29E+02 | -2.4E+01 | +2.0E+00 | 2.0E+00 | 4.6E+01 | 4.7E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 20| +2.13E+02 | -1.5E-01 | +6.9E-01 | 2.5E-02 | 6.1E+00 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 7| +3.53E+03 | -1.8E+04 | +9.0E-01 | 1.0E+00 | 9.7E+04 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 129| +1.48E+02 | -9.9E-03 | +1.1E+00 | 6.2E-02 | 1.1E+00 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 175| +1.48E+02 | -3.2E-05 | +1.4E+00 | 8.9E-04 | 1.3E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 43| +1.46E+02 | +2.9E-01 | -8.8E-01 | 6.2E-02 | 7.6E+00 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 8| +7.48E+02 | -2.8E+03 | +9.0E-01 | 2.0E+00 | 1.5E+04 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 96| +1.48E+02 | -9.6E-06 | +5.5E-01 | 3.1E-02 | 6.7E-02 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 21| +2.13E+02 | -9.9E-02 | +6.4E-01 | 2.5E-02 | 4.7E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 10| +2.29E+02 | +6.1E+03 | -1.7E+02 | 4.0E+00 | 4.3E+01 | 1.1E+01 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 130| +1.48E+02 | -3.1E-03 | +3.2E-01 | 6.2E-02 | 1.1E+00 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 44| +1.46E+02 | -9.1E-02 | +7.1E-01 | 1.6E-02 | 7.6E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 9| +3.13E+02 | -4.3E+02 | +9.0E-01 | 2.0E+00 | 2.4E+03 | 1.9E+00 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 176| +1.48E+02 | -3.3E-05 | +9.8E-01 | 1.8E-03 | 1.0E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 22| +2.13E+02 | -1.1E-01 | +6.0E-01 | 2.5E-02 | 5.2E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 97| +1.48E+02 | -1.9E-06 | +1.1E-01 | 3.1E-02 | 4.4E-02 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 11| +2.29E+02 | +8.1E+01 | -4.2E+00 | 1.0E+00 | 4.3E+01 | 2.7E+00 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 10| +2.53E+02 | -6.0E+01 | +8.6E-01 | 2.0E+00 | 3.8E+02 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 45| +1.46E+02 | -4.7E-02 | +9.3E-01 | 1.6E-02 | 1.6E+01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:35.557 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0211 and h = 1.41573e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:35.558 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0211: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:35 fides(INFO) 131| +1.48E+02 | -5.1E-03 | +1.0E+00 | 6.2E-02 | 3.1E+00 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 177| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.6E-03 | 8.1E-02 | 4.6E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 11| +2.50E+02 | -2.9E+00 | +5.6E-01 | 2.0E+00 | 4.4E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 12| +2.21E+02 | -7.8E+00 | +3.3E-01 | 2.5E-01 | 4.3E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 23| +2.13E+02 | -8.3E-02 | +5.3E-01 | 2.5E-02 | 5.2E+00 | 5.1E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 98| +1.48E+02 | -9.3E-06 | +8.2E-01 | 7.8E-03 | 1.3E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 96| +1.51E+02 | -5.7E-03 | +1.0E+00 | 3.4E-02 | 4.4E-01 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 46| +1.46E+02 | -7.3E-02 | +1.0E+00 | 3.1E-02 | 4.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 132| +1.48E+02 | -4.2E-04 | +9.5E-01 | 6.2E-02 | 3.6E-01 | 9.4E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 12| +2.50E+02 | +2.0E-02 | -4.4E-02 | 2.0E+00 | 1.3E+01 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 178| +1.48E+02 | -1.5E-05 | +1.0E+00 | 8.9E-04 | 8.1E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 24| +2.13E+02 | -8.0E-02 | +4.7E-01 | 2.5E-02 | 5.0E+00 | 5.5E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 13| +2.15E+02 | -6.1E+00 | +6.8E-01 | 2.5E-01 | 4.7E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 47| +1.46E+02 | -8.9E-02 | +1.0E+00 | 6.2E-02 | 1.4E+01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 99| +1.48E+02 | -4.1E-06 | +8.8E-01 | 1.6E-02 | 2.3E-02 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 13| +2.50E+02 | -3.3E-01 | +8.5E-01 | 1.7E-02 | 1.3E+01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 133| +1.48E+02 | -7.9E-05 | +1.2E+00 | 6.2E-02 | 1.1E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 48| +1.46E+02 | -8.9E-02 | +8.7E-01 | 1.2E-01 | 3.2E+00 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 25| +2.13E+02 | -8.4E-02 | +5.0E-01 | 2.5E-02 | 5.6E+00 | 5.1E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 14| +2.10E+02 | -4.9E+00 | +9.9E-01 | 2.5E-01 | 1.6E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 14| +2.50E+02 | -5.2E-02 | +2.2E-01 | 3.3E-02 | 6.6E+00 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 179| +1.48E+02 | -2.1E-05 | +9.1E-01 | 1.8E-03 | 5.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 100| +1.48E+02 | -3.7E-06 | +6.9E-01 | 3.1E-02 | 1.7E-02 | 5.7E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 49| +1.46E+02 | +2.2E+01 | -5.4E+01 | 2.5E-01 | 3.0E+00 | 4.8E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 15| +2.50E+02 | -5.9E-02 | +7.9E-01 | 8.3E-03 | 4.8E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 134| +1.48E+02 | -1.3E-05 | +1.3E+00 | 6.2E-02 | 2.3E-02 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 15| +1.91E+02 | -1.9E+01 | +1.2E+00 | 5.0E-01 | 2.3E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 26| +2.13E+02 | -5.5E-02 | +3.6E-01 | 2.5E-02 | 4.7E+00 | 5.7E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 180| +1.48E+02 | -3.3E-05 | +1.0E+00 | 3.6E-03 | 4.0E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 16| +2.50E+02 | -9.4E-04 | +4.7E-02 | 1.7E-02 | 1.8E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 50| +1.46E+02 | +4.5E-01 | -2.9E+00 | 6.2E-02 | 3.0E+00 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 101| +1.48E+02 | +4.8E-06 | -9.0E-01 | 3.1E-02 | 4.7E-02 | 5.3E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 135| +1.48E+02 | +2.4E-06 | -2.5E+01 | 6.2E-02 | 7.1E-03 | 3.0E-04 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 27| +2.13E+02 | -9.0E-02 | +5.1E-01 | 2.5E-02 | 6.0E+00 | 5.1E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 17| +2.50E+02 | -7.3E-03 | +7.9E-01 | 2.7E-03 | 1.7E+00 | 6.7E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 16| +1.91E+02 | +4.3E+03 | -9.5E+01 | 1.0E+00 | 5.8E+01 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 51| +1.46E+02 | -1.0E-02 | +2.3E-01 | 1.6E-02 | 3.0E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:35.696 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 87.9877 and h = 3.2767e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:35.696 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 87.9877: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:35 fides(INFO) 181| +1.48E+02 | -1.5E-05 | +5.8E-01 | 7.2E-03 | 3.6E-02 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 102| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.8E-03 | 4.7E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 97| +1.51E+02 | -9.5E-03 | +1.1E+00 | 6.8E-02 | 4.7E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 18| +2.50E+02 | -4.5E-06 | +9.3E-04 | 5.4E-03 | 6.0E-01 | 7.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 136| +1.48E+02 | +1.8E-06 | -3.7E+01 | 5.8E-05 | 7.1E-03 | 7.4E-05 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 28| +2.13E+02 | -4.0E-02 | +2.9E-01 | 2.5E-02 | 4.3E+00 | 5.9E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 52| +1.46E+02 | -5.1E-03 | +8.4E-01 | 3.9E-03 | 3.3E+00 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 17| +1.68E+02 | -2.3E+01 | +9.4E-01 | 2.5E-01 | 5.8E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 103| +1.48E+02 | -9.8E-07 | +1.6E+00 | 2.0E-03 | 4.7E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as function difference 9.82E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:35 fides(INFO) 19| +2.50E+02 | -8.2E-04 | +8.3E-01 | 8.6E-04 | 6.0E-01 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 182| +1.48E+02 | -2.8E-07 | +2.0E-02 | 7.2E-03 | 5.3E-02 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 29| +2.13E+02 | -9.0E-02 | +5.0E-01 | 2.5E-02 | 6.1E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 53| +1.46E+02 | -4.9E-03 | +8.0E-01 | 7.8E-03 | 3.3E+00 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 137| +1.48E+02 | +9.8E-07 | -4.8E+01 | 1.4E-05 | 7.1E-03 | 1.9E-05 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 20| +2.50E+02 | -4.6E-05 | +9.2E-02 | 1.7E-03 | 2.9E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 183| +1.48E+02 | -6.0E-06 | +8.3E-01 | 1.2E-03 | 5.0E-02 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 18| +1.68E+02 | +1.1E+00 | -2.2E-01 | 5.0E-01 | 5.4E+01 | 8.6E-01 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) 54| +1.46E+02 | -7.7E-03 | +9.1E-01 | 1.6E-02 | 2.2E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 30| +2.13E+02 | -2.3E-02 | +1.6E-01 | 2.5E-02 | 4.2E+00 | 5.9E-02 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 21| +2.50E+02 | -1.8E-04 | +7.9E-01 | 4.3E-04 | 2.6E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 138| +1.48E+02 | +7.6E-07 | -6.1E+01 | 3.6E-06 | 7.1E-03 | 5.3E-06 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 22| +2.50E+02 | -5.3E-08 | +8.2E-04 | 8.6E-04 | 9.4E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 55| +1.46E+02 | -1.1E-03 | +7.2E-02 | 3.1E-02 | 3.7E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 184| +1.48E+02 | -3.7E-06 | +3.3E+00 | 2.4E-03 | 4.0E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 19| +1.64E+02 | -4.1E+00 | +8.8E-01 | 7.5E-02 | 5.4E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 31| +2.12E+02 | -6.4E-02 | +9.1E-01 | 6.3E-03 | 6.5E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 98| +1.51E+02 | -1.1E-02 | +1.4E+00 | 1.4E-01 | 6.9E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 23| +2.50E+02 | -2.1E-05 | +8.2E-01 | 1.4E-04 | 9.4E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 139| +1.48E+02 | -6.1E-07 | +7.1E+01 | 9.0E-07 | 7.1E-03 | 1.8E-06 | 2d |1\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as function difference 6.06E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:35 fides(INFO) 56| +1.46E+02 | -8.6E-03 | +5.1E-01 | 7.8E-03 | 2.0E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 32| +2.12E+02 | -6.5E-02 | +7.8E-01 | 1.3E-02 | 4.6E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 24| +2.50E+02 | -1.0E-08 | +8.5E-04 | 2.8E-04 | 4.2E-02 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 185| +1.48E+02 | +2.3E-06 | -1.4E+01 | 2.4E-03 | 7.6E-03 | 1.1E-04 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 20| +1.54E+02 | -9.2E+00 | +7.7E-01 | 1.5E-01 | 7.6E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 57| +1.46E+02 | -2.8E-03 | +9.7E-01 | 7.8E-03 | 2.7E+00 | 3.0E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 0| +2.95E+13 | NaN | NaN | 1.0E+00 | 1.4E+14 | NaN | NaN |1\n", - "2023-02-17 16:05:35 fides(INFO) 33| +2.12E+02 | -1.1E-01 | +9.3E-01 | 2.5E-02 | 2.9E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 25| +2.50E+02 | -4.4E-06 | +8.1E-01 | 6.4E-05 | 4.2E-02 | 1.6E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 186| +1.48E+02 | +2.4E-06 | -2.7E+01 | 1.2E-04 | 7.6E-03 | 3.1E-05 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 58| +1.46E+02 | -3.5E-03 | +9.5E-01 | 1.6E-02 | 1.5E+00 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:35.868 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 143.128 and h = 1.38416e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:35.868 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 143.128: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:35 fides(INFO) 21| +1.54E+02 | +0.0E+00 | +0.0E+00 | 3.0E-01 | 8.2E+01 | 3.2E-01 | trr |0\n", - "2023-02-17 16:05:35 fides(INFO) 26| +2.50E+02 | -3.3E-10 | +1.6E-04 | 1.3E-04 | 1.7E-02 | 2.0E-04 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 59| +1.46E+02 | +8.1E-03 | -1.1E+00 | 3.1E-02 | 1.3E+00 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:35 fides(INFO) 34| +2.12E+02 | -2.6E-02 | +1.9E-01 | 5.0E-02 | 3.7E+00 | 1.2E-01 | trr |1\n", - "2023-02-17 16:05:35 fides(INFO) 187| +1.48E+02 | -1.4E-06 | +2.7E+01 | 2.9E-05 | 7.6E-03 | 1.3E-05 | 2d |1\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as function difference 1.37E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:35 fides(INFO) 27| +2.50E+02 | -6.9E-07 | +8.2E-01 | 2.5E-05 | 1.7E-02 | 6.0E-05 | 2d |1\n", - "2023-02-17 16:05:35 fides(WARNING) Stopping as function difference 6.87E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:35 fides(INFO) 22| +1.53E+02 | -1.9E+00 | +9.5E-01 | 2.6E-02 | 8.2E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 60| +1.46E+02 | -1.4E-03 | +6.0E-01 | 7.8E-03 | 1.3E+00 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 1| +2.16E+07 | -2.9E+13 | +8.5E-02 | 1.0E+00 | 1.4E+14 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 35| +2.12E+02 | -1.3E-01 | +8.2E-01 | 1.2E-02 | 6.7E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 61| +1.46E+02 | -1.0E-03 | +3.6E-01 | 7.8E-03 | 1.7E+00 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 2| +3.33E+06 | -1.8E+07 | +8.9E-01 | 2.5E-01 | 9.9E+07 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 23| +1.50E+02 | -2.1E+00 | +8.4E-01 | 5.2E-02 | 4.6E+01 | 9.9E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 36| +2.12E+02 | -3.0E-02 | +2.9E-01 | 2.4E-02 | 3.3E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 62| +1.46E+02 | -2.5E-03 | +3.8E-01 | 7.8E-03 | 9.7E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 99| +1.51E+02 | -9.1E-03 | +1.6E+00 | 1.4E-01 | 1.2E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:35 fides(INFO) 0| +5.21E+07 | NaN | NaN | 1.0E+00 | 2.4E+08 | NaN | NaN |1\n", - "2023-02-17 16:05:35 fides(INFO) 3| +5.18E+05 | -2.8E+06 | +9.0E-01 | 5.0E-01 | 1.5E+07 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 24| +1.49E+02 | -1.3E+00 | +9.6E-01 | 1.0E-01 | 4.8E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 37| +2.12E+02 | -1.1E-01 | +6.0E-01 | 2.4E-02 | 6.1E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 63| +1.46E+02 | -1.2E-03 | +1.7E-01 | 7.8E-03 | 1.9E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 1| +2.41E+04 | -5.2E+07 | +1.1E-01 | 1.0E+00 | 2.4E+08 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:35 fides(INFO) 4| +8.27E+04 | -4.4E+05 | +8.9E-01 | 1.0E+00 | 2.4E+06 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 64| +1.46E+02 | -1.1E-03 | +7.3E-01 | 2.0E-03 | 8.2E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 0| +2.15E+10 | NaN | NaN | 1.0E+00 | 9.9E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:36 fides(INFO) 25| +1.49E+02 | -1.1E-01 | -3.1E-02 | 2.1E-01 | 3.0E+01 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 5| +1.32E+04 | -6.9E+04 | +9.0E-01 | 2.0E+00 | 3.7E+05 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 38| +2.12E+02 | -5.4E-02 | +4.5E-01 | 2.4E-02 | 3.5E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 2| +4.83E+03 | -1.9E+04 | +8.9E-01 | 2.5E-01 | 1.0E+05 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 65| +1.46E+02 | -3.9E-04 | +7.8E-01 | 2.0E-03 | 9.7E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 1| +3.42E+04 | -2.2E+10 | +9.0E-02 | 1.0E+00 | 9.9E+10 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 6| +2.26E+03 | -1.1E+04 | +9.0E-01 | 2.0E+00 | 5.9E+04 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 3| +9.58E+02 | -3.9E+03 | +8.9E-01 | 5.0E-01 | 1.7E+04 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 39| +2.12E+02 | -1.1E-01 | +6.4E-01 | 2.4E-02 | 5.5E+00 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 26| +1.49E+02 | -6.0E-01 | +7.5E-01 | 5.2E-02 | 3.0E+01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 2| +5.57E+03 | -2.9E+04 | +9.0E-01 | 2.5E-01 | 1.6E+05 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 66| +1.46E+02 | -2.4E-04 | +8.1E-01 | 3.9E-03 | 4.6E-01 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 7| +5.46E+02 | -1.7E+03 | +9.0E-01 | 2.0E+00 | 9.4E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 4| +3.30E+02 | -6.3E+02 | +8.9E-01 | 1.0E+00 | 2.7E+03 | 1.6E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 0| +2.85E+08 | NaN | NaN | 1.0E+00 | 1.2E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 40| +2.12E+02 | -8.0E-02 | +6.0E-01 | 2.4E-02 | 3.6E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 3| +1.06E+03 | -4.5E+03 | +9.0E-01 | 5.0E-01 | 2.4E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 8| +2.84E+02 | -2.6E+02 | +9.0E-01 | 2.0E+00 | 1.5E+03 | 1.7E+00 | trr |1\n", - "2023-02-17 16:05:36 fides(INFO) 67| +1.46E+02 | -3.6E-04 | +7.3E-01 | 7.8E-03 | 4.5E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 27| +1.48E+02 | -3.5E-01 | +9.6E-01 | 5.2E-02 | 4.2E+01 | 4.4E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 5| +2.53E+02 | -7.7E+01 | +8.5E-01 | 1.0E+00 | 4.0E+02 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 1| +6.78E+06 | -2.8E+08 | +1.1E-01 | 1.0E+00 | 1.2E+09 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 4| +3.57E+02 | -7.0E+02 | +9.0E-01 | 1.0E+00 | 3.9E+03 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 41| +2.12E+02 | -1.2E-01 | +7.1E-01 | 2.4E-02 | 5.0E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 68| +1.46E+02 | +6.4E-05 | -8.7E-02 | 7.8E-03 | 5.6E-01 | 4.7E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 100| +1.51E+02 | -1.5E-02 | +1.4E+00 | 1.4E-01 | 1.2E+00 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 6| +2.42E+02 | -1.1E+01 | +9.0E-01 | 2.0E+00 | 4.3E+01 | 4.0E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 9| +2.48E+02 | -3.5E+01 | +8.7E-01 | 2.0E+00 | 2.3E+02 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 28| +1.48E+02 | -3.3E-01 | +8.4E-01 | 1.0E-01 | 1.9E+01 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 5| +2.57E+02 | -9.9E+01 | +8.8E-01 | 1.0E+00 | 6.0E+02 | 2.6E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 42| +2.12E+02 | -1.1E-01 | +7.0E-01 | 2.4E-02 | 3.7E+00 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 2| +1.04E+06 | -5.7E+06 | +8.9E-01 | 2.5E-01 | 3.1E+07 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 69| +1.46E+02 | -1.6E-04 | +7.5E-01 | 2.0E-03 | 5.6E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 10| +2.29E+02 | -1.9E+01 | +3.0E+00 | 2.0E+00 | 1.6E+01 | 3.4E+00 | trr |1\n", - "2023-02-17 16:05:36 fides(INFO) 7| +2.42E+02 | +3.2E+01 | -6.3E+00 | 4.0E+00 | 2.8E+01 | 9.0E+00 | trr |0\n", - "2023-02-17 16:05:36 fides(INFO) 6| +2.47E+02 | -1.1E+01 | +7.1E-01 | 2.0E+00 | 7.7E+01 | 4.3E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 29| +1.48E+02 | -1.6E-01 | +5.6E-01 | 2.1E-01 | 3.0E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 43| +2.11E+02 | -1.4E-01 | +7.8E-01 | 2.4E-02 | 4.9E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 8| +2.42E+02 | +9.1E+00 | -1.2E+00 | 8.5E-01 | 2.8E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 70| +1.46E+02 | -1.9E-04 | +9.7E-01 | 3.9E-03 | 3.4E-01 | 9.2E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 3| +1.61E+05 | -8.8E+05 | +8.9E-01 | 5.0E-01 | 4.8E+06 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 7| +2.35E+02 | -1.1E+01 | +1.6E+00 | 2.0E+00 | 1.2E+01 | 3.1E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 71| +1.46E+02 | -2.9E-04 | +1.0E+00 | 7.8E-03 | 5.7E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 44| +2.11E+02 | -2.1E-01 | +7.2E-01 | 4.8E-02 | 3.9E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 9| +2.42E+02 | +7.0E+00 | -1.0E+00 | 2.1E-01 | 2.8E+01 | 5.4E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 30| +1.48E+02 | +4.6E-01 | -1.0E+00 | 2.1E-01 | 9.8E+00 | 2.1E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 4| +2.51E+04 | -1.4E+05 | +9.0E-01 | 1.0E+00 | 7.4E+05 | 9.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 45| +2.11E+02 | -3.2E-01 | +6.7E-01 | 4.8E-02 | 7.9E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 10| +2.40E+02 | -2.8E+00 | +8.5E-01 | 5.3E-02 | 2.8E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 11| +2.29E+02 | +1.7E+02 | -2.0E+01 | 2.0E+00 | 3.1E+01 | 6.1E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 8| +2.35E+02 | +2.0E+01 | -3.9E+00 | 4.0E+00 | 1.9E+01 | 6.9E+00 | trr |0\n", - "2023-02-17 16:05:36 fides(INFO) 72| +1.46E+02 | -4.4E-04 | +8.1E-01 | 1.6E-02 | 2.4E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 101| +1.51E+02 | -1.8E-02 | +1.3E+00 | 2.7E-01 | 7.5E-01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 11| +2.39E+02 | -5.3E-01 | +1.9E-01 | 1.1E-01 | 1.6E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 73| +1.46E+02 | +1.8E-02 | -8.2E+00 | 3.1E-02 | 9.4E-01 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 31| +1.48E+02 | -9.8E-02 | +3.4E-01 | 5.2E-02 | 9.8E+00 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 12| +2.29E+02 | +1.6E+01 | -1.7E+00 | 5.0E-01 | 3.1E+01 | 1.4E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 46| +2.11E+02 | -3.0E-01 | +5.8E-01 | 4.8E-02 | 6.4E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 9| +2.30E+02 | -5.3E+00 | +1.3E+00 | 5.8E-01 | 1.9E+01 | 1.7E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 5| +4.09E+03 | -2.1E+04 | +9.0E-01 | 1.0E+00 | 1.1E+05 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 12| +2.38E+02 | -1.3E+00 | +9.3E-01 | 2.7E-02 | 3.3E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 74| +1.46E+02 | +5.1E-04 | -6.9E-01 | 7.8E-03 | 9.4E-01 | 7.0E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 13| +2.25E+02 | -4.0E+00 | +5.4E-01 | 1.2E-01 | 3.1E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 32| +1.48E+02 | -8.1E-02 | +5.7E-01 | 5.2E-02 | 1.4E+01 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 47| +2.10E+02 | -4.7E-01 | +7.3E-01 | 4.8E-02 | 9.3E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 13| +2.36E+02 | -1.6E+00 | +8.8E-01 | 5.3E-02 | 2.4E+01 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 10| +2.30E+02 | +1.0E+02 | -1.0E+01 | 1.2E+00 | 2.6E+01 | 3.2E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 75| +1.46E+02 | -1.7E-04 | +6.9E-01 | 2.0E-03 | 9.4E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 14| +2.22E+02 | -2.3E+00 | +7.6E-01 | 1.2E-01 | 2.6E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 6| +8.23E+02 | -3.3E+03 | +9.0E-01 | 1.0E+00 | 1.8E+04 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 14| +2.33E+02 | -2.9E+00 | +1.2E+00 | 1.1E-01 | 1.6E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 48| +2.10E+02 | -4.7E-01 | +7.2E-01 | 4.8E-02 | 7.1E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 33| +1.48E+02 | -5.8E-03 | +9.3E-01 | 5.2E-02 | 3.2E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 76| +1.46E+02 | -3.1E-05 | +9.7E-01 | 2.0E-03 | 1.2E-01 | 3.6E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 15| +2.22E+02 | -5.6E-02 | -5.7E-02 | 2.5E-01 | 2.0E+01 | 7.0E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 11| +2.30E+02 | +2.6E+00 | -4.7E-01 | 2.9E-01 | 2.6E+01 | 7.4E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 15| +2.30E+02 | -3.5E+00 | +8.4E-01 | 2.1E-01 | 1.7E+01 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 77| +1.46E+02 | -4.6E-05 | +9.4E-01 | 3.9E-03 | 2.3E-01 | 6.8E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 49| +2.09E+02 | -5.9E-01 | +8.2E-01 | 4.8E-02 | 9.3E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36.303 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.9015 and h = 7.61275e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.304 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.9015: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) 34| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.0E-01 | 1.7E+00 | 5.2E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 16| +2.21E+02 | -1.4E+00 | +6.8E-01 | 6.2E-02 | 2.0E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 12| +2.27E+02 | -2.9E+00 | +7.8E-01 | 7.2E-02 | 2.6E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 7| +3.14E+02 | -5.1E+02 | +9.1E-01 | 1.0E+00 | 2.8E+03 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 16| +2.25E+02 | -4.4E+00 | +7.3E-01 | 4.3E-01 | 3.9E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 78| +1.46E+02 | -6.6E-05 | +8.2E-01 | 7.8E-03 | 1.3E-01 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 50| +2.08E+02 | -6.4E-01 | +6.0E-01 | 9.6E-02 | 7.2E+00 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 17| +2.20E+02 | -1.4E+00 | +9.6E-01 | 6.2E-02 | 2.0E+01 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 102| +1.51E+02 | -2.4E-03 | +1.0E+00 | 2.7E-01 | 5.9E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 35| +1.48E+02 | -4.0E-03 | +9.1E-01 | 2.6E-02 | 1.7E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 13| +2.23E+02 | -4.2E+00 | +1.2E+00 | 1.4E-01 | 2.0E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 79| +1.46E+02 | +9.6E-04 | -3.1E+00 | 1.6E-02 | 1.3E-01 | 7.5E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 17| +2.22E+02 | -3.2E+00 | +3.5E-01 | 4.3E-01 | 2.3E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 18| +2.18E+02 | -1.7E+00 | +7.2E-01 | 1.2E-01 | 1.6E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 51| +2.07E+02 | -1.0E+00 | +7.3E-01 | 9.6E-02 | 1.4E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 80| +1.46E+02 | -7.0E-06 | +7.6E-02 | 3.9E-03 | 1.3E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 36| +1.48E+02 | -3.5E-03 | +9.6E-01 | 5.2E-02 | 3.8E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 14| +2.17E+02 | -5.5E+00 | +1.1E+00 | 2.9E-01 | 1.9E+01 | 6.9E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 18| +2.14E+02 | -8.1E+00 | +7.4E-01 | 4.3E-01 | 5.5E+01 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 19| +2.16E+02 | -1.6E+00 | +5.9E-01 | 1.2E-01 | 2.6E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 8| +2.20E+02 | -9.4E+01 | +1.1E+00 | 1.0E+00 | 4.0E+02 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 52| +2.07E+02 | -7.1E-01 | +7.6E-01 | 9.6E-02 | 7.0E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 81| +1.46E+02 | -3.8E-05 | +6.6E-01 | 9.8E-04 | 1.7E-01 | 7.2E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 19| +1.99E+02 | -1.5E+01 | +1.1E+00 | 4.3E-01 | 2.5E+01 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 20| +2.16E+02 | -2.7E-01 | +7.9E-02 | 1.2E-01 | 1.7E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 37| +1.48E+02 | -3.9E-03 | +9.0E-01 | 1.0E-01 | 9.8E-01 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 15| +2.02E+02 | -1.6E+01 | +1.0E+00 | 5.8E-01 | 2.8E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 82| +1.46E+02 | -1.1E-05 | +9.3E-01 | 9.8E-04 | 1.4E-01 | 1.9E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 53| +2.05E+02 | -2.0E+00 | +1.1E+00 | 1.9E-01 | 9.8E+00 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 103| +1.51E+02 | -3.1E-04 | +1.7E+00 | 2.7E-01 | 3.0E-01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 21| +2.15E+02 | -1.3E+00 | +8.8E-01 | 3.1E-02 | 3.2E+01 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 16| +2.02E+02 | +3.0E+03 | -1.0E+02 | 1.2E+00 | 6.6E+01 | 2.2E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 20| +1.99E+02 | +1.6E+02 | -7.8E+00 | 8.5E-01 | 4.6E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 83| +1.46E+02 | -1.5E-05 | +1.1E+00 | 2.0E-03 | 9.8E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 38| +1.48E+02 | +7.4E-03 | -1.9E+00 | 2.1E-01 | 2.5E+00 | 9.8E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 54| +1.93E+02 | -1.2E+01 | +2.2E+00 | 3.9E-01 | 1.1E+01 | 9.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 22| +2.14E+02 | -1.0E+00 | +7.3E-01 | 6.2E-02 | 2.0E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 21| +1.85E+02 | -1.3E+01 | +1.1E+00 | 2.1E-01 | 4.6E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 84| +1.46E+02 | -2.4E-05 | +9.5E-01 | 3.9E-03 | 9.2E-02 | 5.2E-04 | 2d |1\n", - "2023-02-17 16:05:36.482 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 115.442 and h = 1.5043e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.482 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 115.442: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) 17| +2.02E+02 | +0.0E+00 | +0.0E+00 | 2.9E-01 | 6.6E+01 | 5.9E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 39| +1.48E+02 | -9.4E-04 | +5.3E-01 | 5.2E-02 | 2.5E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 23| +2.13E+02 | -6.4E-01 | +6.7E-01 | 6.2E-02 | 1.2E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 55| +1.93E+02 | +1.3E+03 | -3.9E+01 | 7.7E-01 | 4.3E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 9| +2.20E+02 | +8.5E+00 | -1.0E+00 | 1.0E+00 | 2.8E+01 | 1.9E+00 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 85| +1.46E+02 | -3.4E-05 | +7.8E-01 | 7.8E-03 | 1.1E-01 | 7.7E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 18| +1.94E+02 | -8.0E+00 | +9.8E-01 | 7.2E-02 | 6.6E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 40| +1.48E+02 | +6.4E-04 | -3.5E-01 | 5.2E-02 | 6.1E-01 | 1.9E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 22| +1.62E+02 | -2.3E+01 | +5.2E-01 | 4.3E-01 | 4.7E+01 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 24| +2.12E+02 | -7.7E-01 | +7.2E-01 | 6.2E-02 | 1.6E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 86| +1.46E+02 | +1.1E-03 | -5.3E+00 | 1.6E-02 | 6.9E-02 | 7.5E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 56| +1.73E+02 | -2.0E+01 | +1.1E+00 | 1.9E-01 | 4.3E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 19| +1.81E+02 | -1.3E+01 | +9.6E-01 | 1.4E-01 | 5.7E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 87| +1.46E+02 | +2.5E-05 | -3.9E-01 | 3.9E-03 | 6.9E-02 | 1.9E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 25| +2.12E+02 | -5.1E-01 | +5.7E-01 | 6.2E-02 | 1.1E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 41| +1.48E+02 | -3.0E-04 | +3.2E-01 | 1.3E-02 | 6.1E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 23| +1.49E+02 | -1.3E+01 | +9.4E-01 | 4.3E-01 | 2.8E+02 | 5.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 57| +1.73E+02 | -2.0E+00 | -1.8E-01 | 3.9E-01 | 4.8E+01 | 8.7E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 104| +1.51E+02 | -7.1E-04 | +1.4E+00 | 2.7E-01 | 1.3E-01 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 88| +1.46E+02 | -1.2E-05 | +6.5E-01 | 9.8E-04 | 6.9E-02 | 5.2E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 26| +2.11E+02 | -6.9E-01 | +6.8E-01 | 6.2E-02 | 1.6E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36.568 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 100.881 and h = 9.40868e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.568 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 100.881: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) 42| +1.48E+02 | -5.0E-04 | +8.8E-01 | 1.3E-02 | 1.9E+00 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 10| +2.20E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 2.8E+01 | 6.0E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 20| +1.72E+02 | -8.7E+00 | +3.1E-01 | 2.9E-01 | 5.3E+01 | 5.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 24| +1.48E+02 | -7.3E-01 | +4.7E-01 | 8.5E-01 | 8.0E+01 | 8.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 58| +1.64E+02 | -8.9E+00 | +9.9E-01 | 9.6E-02 | 4.8E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 89| +1.46E+02 | -4.6E-06 | +8.3E-01 | 9.8E-04 | 1.4E-01 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 27| +2.11E+02 | -4.5E-01 | +5.4E-01 | 6.2E-02 | 9.8E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 21| +1.72E+02 | -1.8E+00 | -7.1E-02 | 2.9E-01 | 2.4E+02 | 4.7E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 43| +1.48E+02 | -1.1E-04 | +9.0E-01 | 2.6E-02 | 2.1E-01 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 25| +1.48E+02 | +8.2E+00 | -5.3E+00 | 8.5E-01 | 1.7E+01 | 4.4E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 90| +1.46E+02 | -1.8E-05 | +7.8E-01 | 2.0E-03 | 5.1E-02 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 28| +2.10E+02 | -6.3E-01 | +6.5E-01 | 6.2E-02 | 1.5E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 59| +1.54E+02 | -1.0E+01 | +7.2E-01 | 1.9E-01 | 5.2E+01 | 4.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 26| +1.48E+02 | -4.9E-01 | +5.8E-01 | 8.3E-02 | 1.7E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 91| +1.46E+02 | +1.4E-05 | -3.1E-01 | 3.9E-03 | 2.1E-01 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 44| +1.48E+02 | -1.4E-04 | +9.9E-01 | 5.2E-02 | 3.7E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 22| +1.56E+02 | -1.6E+01 | +9.7E-01 | 7.2E-02 | 2.4E+02 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 29| +2.10E+02 | -3.8E-01 | +4.6E-01 | 6.2E-02 | 9.0E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 60| +1.50E+02 | -3.9E+00 | +6.4E-01 | 1.9E-01 | 1.1E+02 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 92| +1.46E+02 | -1.2E-05 | +7.8E-01 | 9.8E-04 | 2.1E-01 | 4.0E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 27| +1.48E+02 | -7.2E-02 | +8.4E-01 | 8.3E-02 | 7.9E+00 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 45| +1.48E+02 | -1.5E-04 | +9.1E-01 | 1.0E-01 | 1.3E-01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 23| +1.51E+02 | -4.9E+00 | +8.6E-01 | 1.4E-01 | 6.7E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 30| +2.09E+02 | -5.9E-01 | +6.1E-01 | 6.2E-02 | 1.4E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 105| +1.51E+02 | -5.2E-04 | +1.5E+00 | 2.7E-01 | 4.6E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 93| +1.46E+02 | -6.2E-06 | +9.8E-01 | 2.0E-03 | 6.2E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 61| +1.50E+02 | +1.2E+00 | -5.5E-01 | 1.9E-01 | 4.1E+01 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 28| +1.48E+02 | -2.9E-02 | +8.3E-01 | 1.7E-01 | 5.7E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 11| +2.17E+02 | -3.3E+00 | +8.6E-01 | 6.2E-02 | 2.8E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 24| +1.49E+02 | -2.3E+00 | +9.2E-01 | 2.9E-01 | 8.5E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 31| +2.09E+02 | -2.9E-01 | +3.5E-01 | 6.2E-02 | 8.5E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 46| +1.48E+02 | +1.1E-03 | -6.5E+00 | 2.1E-01 | 1.4E-01 | 4.4E-02 | trr |0\n", - "2023-02-17 16:05:36 fides(INFO) 94| +1.46E+02 | -8.5E-06 | +1.0E+00 | 3.9E-03 | 4.7E-02 | 2.5E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 62| +1.49E+02 | -1.0E+00 | +8.2E-01 | 4.8E-02 | 4.1E+01 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:36.723 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0685 and h = 1.43602e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.724 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0685: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) 47| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.0E-02 | 1.4E-01 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 29| +1.48E+02 | +1.5E-02 | -2.0E+00 | 3.3E-01 | 1.3E+00 | 1.4E-01 | trr |0\n", - "2023-02-17 16:05:36 fides(INFO) 95| +1.46E+02 | -6.0E-06 | +3.4E-01 | 7.8E-03 | 5.4E-02 | 9.0E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 32| +2.08E+02 | -5.8E-01 | +5.7E-01 | 6.2E-02 | 1.5E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 25| +1.49E+02 | +2.7E+00 | -1.4E+00 | 5.8E-01 | 4.4E+01 | 5.4E-01 | 2d |0\n", - "2023-02-17 16:05:36.750 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.9736 and h = 9.3318e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.751 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.9736: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 30| +1.48E+02 | +0.0E+00 | +0.0E+00 | 4.5E-02 | 1.3E+00 | 3.4E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 48| +1.48E+02 | -1.6E-05 | +7.3E-01 | 1.3E-02 | 1.4E-01 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 96| +1.46E+02 | +5.3E-04 | -3.8E+00 | 7.8E-03 | 5.6E-02 | 4.6E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 63| +1.48E+02 | -5.9E-01 | +8.9E-01 | 9.6E-02 | 3.7E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 106| +1.51E+02 | -4.1E-04 | +1.4E+00 | 2.7E-01 | 3.9E-01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 33| +2.08E+02 | -2.1E-01 | +2.5E-01 | 6.2E-02 | 8.4E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 26| +1.48E+02 | -8.5E-01 | +7.3E-01 | 1.4E-01 | 4.4E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 97| +1.46E+02 | +5.1E-06 | -1.3E-01 | 2.0E-03 | 5.6E-02 | 1.2E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 31| +1.48E+02 | -1.1E-03 | +9.2E-01 | 1.1E-02 | 1.3E+00 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 49| +1.48E+02 | -8.1E-06 | +8.5E-01 | 1.3E-02 | 8.1E-02 | 2.8E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 34| +2.08E+02 | -3.7E-01 | +8.9E-01 | 1.6E-02 | 1.5E+01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 64| +1.48E+02 | +1.1E+00 | -2.2E+00 | 1.9E-01 | 1.7E+01 | 3.0E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 27| +1.48E+02 | -2.4E-01 | +4.3E-01 | 1.4E-01 | 3.8E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 98| +1.46E+02 | -8.5E-06 | +7.8E-01 | 4.9E-04 | 5.6E-02 | 3.2E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 35| +2.07E+02 | -3.5E-01 | +7.8E-01 | 3.1E-02 | 8.6E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 32| +1.48E+02 | -1.3E-03 | +9.6E-01 | 2.3E-02 | 8.0E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 99| +1.46E+02 | -2.1E-06 | +4.7E-01 | 9.8E-04 | 1.2E-01 | 1.9E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 50| +1.48E+02 | -1.3E-05 | +8.9E-01 | 2.5E-02 | 8.6E-02 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 12| +2.13E+02 | -4.1E+00 | +8.7E-01 | 1.2E-01 | 2.1E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 65| +1.48E+02 | -7.9E-02 | +4.5E-01 | 4.8E-02 | 1.7E+01 | 7.4E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 28| +1.48E+02 | -3.4E-01 | +4.6E-01 | 1.4E-01 | 2.4E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 36| +2.07E+02 | -4.3E-01 | +7.5E-01 | 6.2E-02 | 5.2E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 100| +1.46E+02 | -1.6E-06 | +1.1E+00 | 9.8E-04 | 3.4E-02 | 8.0E-05 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 51| +1.48E+02 | -2.1E-05 | +1.1E+00 | 5.0E-02 | 5.5E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 66| +1.48E+02 | -9.3E-02 | +3.0E-01 | 4.8E-02 | 1.5E+01 | 6.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 33| +1.48E+02 | -1.8E-03 | +9.3E-01 | 4.5E-02 | 7.7E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 29| +1.48E+02 | +5.7E-02 | -1.4E-01 | 1.4E-01 | 8.4E+00 | 2.0E-01 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 101| +1.46E+02 | -3.4E-06 | +1.3E+00 | 2.0E-03 | 5.1E-02 | 1.0E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 37| +2.07E+02 | -2.2E-01 | +2.3E-01 | 1.2E-01 | 1.2E+01 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 107| +1.51E+02 | -1.8E-04 | +1.4E+00 | 2.7E-01 | 1.4E-01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 52| +1.48E+02 | +8.1E-06 | -3.6E-01 | 1.0E-01 | 5.7E-02 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 67| +1.48E+02 | -1.2E-01 | +4.4E-01 | 4.8E-02 | 2.2E+01 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 34| +1.48E+02 | +3.4E-03 | -1.6E+00 | 9.0E-02 | 5.1E-01 | 6.3E-02 | 2d |0\n", - "2023-02-17 16:05:36.908 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 92.147 and h = 1.45023e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:36.908 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 92.147: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:36 fides(INFO) 102| +1.46E+02 | -4.4E-06 | +9.2E-01 | 3.9E-03 | 3.3E-02 | 1.8E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 30| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.6E-02 | 8.4E+00 | 5.1E-02 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 38| +2.06E+02 | -7.6E-01 | +8.4E-01 | 3.1E-02 | 1.5E+01 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 103| +1.46E+02 | -6.4E-06 | +7.2E-01 | 7.8E-03 | 3.7E-02 | 5.6E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 53| +1.48E+02 | -5.4E-06 | +7.0E-01 | 2.5E-02 | 5.7E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 68| +1.48E+02 | -4.2E-03 | +3.1E-02 | 4.8E-02 | 6.7E+00 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 35| +1.48E+02 | -3.8E-04 | +5.0E-01 | 2.3E-02 | 5.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 39| +2.05E+02 | -7.9E-01 | +1.3E+00 | 6.2E-02 | 6.2E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 31| +1.48E+02 | -6.6E-02 | +8.6E-01 | 9.0E-03 | 8.4E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 13| +2.07E+02 | -6.6E+00 | +9.3E-01 | 2.5E-01 | 3.4E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 104| +1.46E+02 | +3.6E-04 | -6.0E+00 | 7.8E-03 | 4.1E-02 | 3.6E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 54| +1.48E+02 | -3.0E-06 | +3.6E-01 | 2.5E-02 | 3.8E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 40| +2.05E+02 | -3.2E-01 | +2.5E-01 | 1.2E-01 | 9.9E+00 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 69| +1.48E+02 | -4.3E-02 | +7.4E-01 | 1.2E-02 | 5.4E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 36| +1.48E+02 | +2.3E-04 | -1.9E-01 | 2.3E-02 | 4.6E-01 | 6.1E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 32| +1.48E+02 | -7.1E-02 | +8.8E-01 | 1.8E-02 | 1.4E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 105| +1.46E+02 | +1.0E-05 | -5.9E-01 | 2.0E-03 | 4.1E-02 | 9.1E-04 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 106| +1.46E+02 | -2.5E-06 | +5.3E-01 | 4.9E-04 | 4.1E-02 | 2.4E-04 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 55| +1.48E+02 | +1.3E-06 | -7.4E-02 | 2.5E-02 | 8.9E-02 | 4.4E-03 | 2d |0\n", - "2023-02-17 16:05:36 fides(INFO) 41| +2.03E+02 | -1.5E+00 | +8.0E-01 | 1.2E-01 | 2.2E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:36 fides(INFO) 70| +1.48E+02 | -2.3E-02 | +8.2E-01 | 1.2E-02 | 8.6E+00 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 33| +1.48E+02 | -5.4E-02 | +6.1E-01 | 3.6E-02 | 5.4E+00 | 4.7E-02 | 2d |1\n", - "2023-02-17 16:05:36 fides(INFO) 37| +1.48E+02 | -2.9E-04 | +6.6E-01 | 5.6E-03 | 4.6E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 108| +1.51E+02 | -7.8E-05 | +1.4E+00 | 2.7E-01 | 3.0E-02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 107| +1.46E+02 | -5.4E-07 | +8.7E-01 | 4.9E-04 | 5.0E-02 | 3.3E-05 | 2d |1\n", - "2023-02-17 16:05:37 fides(WARNING) Stopping as function difference 5.41E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:37 fides(INFO) 42| +2.01E+02 | -2.1E+00 | +1.0E+00 | 2.5E-01 | 1.1E+01 | 7.3E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 34| +1.48E+02 | -1.5E-02 | +7.8E-01 | 3.6E-02 | 9.3E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 56| +1.48E+02 | -1.0E-06 | +1.8E-01 | 6.3E-03 | 8.9E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 71| +1.48E+02 | -9.8E-03 | +5.5E-01 | 2.4E-02 | 2.6E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 38| +1.48E+02 | -1.6E-04 | +9.8E-01 | 5.6E-03 | 7.9E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 14| +1.96E+02 | -1.0E+01 | +3.0E-01 | 5.0E-01 | 2.9E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:37.056 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.455 and h = 1.39738e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.056 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.455: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 35| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.2E-02 | 2.6E+00 | 4.1E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 43| +1.89E+02 | -1.2E+01 | +1.8E+00 | 5.0E-01 | 1.8E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 57| +1.48E+02 | -2.3E-06 | +6.4E+00 | 1.6E-03 | 2.0E-02 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 39| +1.48E+02 | -2.4E-04 | +9.3E-01 | 1.1E-02 | 2.2E-01 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:37.066 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.258 and h = 3.96555e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.066 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.258: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 72| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.4E-02 | 2.4E+00 | 2.5E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 36| +1.48E+02 | -4.0E-03 | +9.2E-01 | 1.8E-02 | 2.6E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 58| +1.48E+02 | -3.9E-06 | +7.9E+00 | 3.1E-03 | 2.2E-02 | 4.5E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 44| +1.89E+02 | +1.7E+03 | -6.6E+01 | 1.0E+00 | 3.0E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 40| +1.48E+02 | -1.6E-04 | +5.7E-01 | 2.3E-02 | 5.8E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 109| +1.51E+02 | -4.8E-05 | +1.4E+00 | 2.7E-01 | 7.1E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 73| +1.48E+02 | -3.4E-03 | +9.5E-01 | 6.0E-03 | 2.4E+00 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 59| +1.48E+02 | +2.5E-06 | -3.2E+00 | 6.3E-03 | 1.5E-02 | 8.9E-04 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 37| +1.48E+02 | -3.4E-03 | +9.6E-01 | 3.6E-02 | 4.5E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 41| +1.48E+02 | +6.4E-04 | -1.2E+00 | 2.3E-02 | 2.1E-01 | 5.7E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 45| +1.73E+02 | -1.6E+01 | +1.1E+00 | 2.5E-01 | 3.0E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 74| +1.48E+02 | -4.7E-03 | +9.6E-01 | 1.2E-02 | 2.3E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 15| +1.96E+02 | +4.8E+00 | -5.1E-01 | 5.0E-01 | 1.9E+02 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 0| +1.22E+10 | NaN | NaN | 1.0E+00 | 4.7E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 60| +1.48E+02 | +1.3E-06 | -4.3E+00 | 1.6E-03 | 1.5E-02 | 2.2E-04 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 38| +1.48E+02 | -2.8E-03 | +9.2E-01 | 7.2E-02 | 1.8E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 42| +1.48E+02 | -1.9E-05 | +8.4E-02 | 5.6E-03 | 2.1E-01 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 75| +1.48E+02 | -6.1E-03 | +9.4E-01 | 2.4E-02 | 1.8E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 1| +1.13E+08 | -1.2E+10 | +1.1E-01 | 1.0E+00 | 4.7E+10 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 46| +1.73E+02 | +7.8E-01 | -5.9E-01 | 5.0E-01 | 1.1E+02 | 9.2E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 61| +1.48E+02 | +2.9E-07 | -1.6E+00 | 3.9E-04 | 1.5E-02 | 5.9E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 43| +1.48E+02 | -9.0E-05 | +9.2E-01 | 1.4E-03 | 7.8E-01 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 2| +1.71E+07 | -9.6E+07 | +8.9E-01 | 2.5E-01 | 5.2E+08 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 39| +1.48E+02 | -1.9E-03 | +7.9E-01 | 1.4E-01 | 2.8E+00 | 7.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 76| +1.48E+02 | -8.8E-03 | +8.6E-01 | 4.8E-02 | 1.5E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 3| +2.61E+06 | -1.4E+07 | +8.9E-01 | 5.0E-01 | 7.9E+07 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 62| +1.48E+02 | +3.0E-06 | -2.0E+01 | 9.8E-05 | 1.5E-02 | 2.5E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 44| +1.48E+02 | -2.9E-05 | +7.9E-01 | 2.8E-03 | 8.0E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 40| +1.48E+02 | +2.2E-02 | -4.4E+00 | 2.9E-01 | 9.5E-01 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 110| +1.51E+02 | -1.6E-05 | +1.1E+00 | 2.7E-01 | 5.2E-02 | 1.4E-01 | trr |1\n", - "2023-02-17 16:05:37 fides(INFO) 47| +1.62E+02 | -1.1E+01 | +9.3E-01 | 1.2E-01 | 1.1E+02 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 77| +1.48E+02 | +2.9E-02 | -2.2E+00 | 9.6E-02 | 9.2E-01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 16| +1.77E+02 | -1.9E+01 | +1.0E+00 | 1.2E-01 | 1.9E+02 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 4| +4.00E+05 | -2.2E+06 | +8.9E-01 | 5.0E-01 | 1.2E+07 | 9.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 63| +1.48E+02 | +2.7E-06 | -1.9E+01 | 2.5E-05 | 1.5E-02 | 2.1E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 45| +1.48E+02 | -2.5E-05 | +7.8E-01 | 5.6E-03 | 1.0E-01 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 41| +1.48E+02 | +1.9E-04 | -9.1E-02 | 7.2E-02 | 9.5E-01 | 3.6E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 5| +6.19E+04 | -3.4E+05 | +9.0E-01 | 5.0E-01 | 1.8E+06 | 9.0E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 78| +1.48E+02 | -1.5E-03 | +3.4E-01 | 2.4E-02 | 9.2E-01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 48| +1.52E+02 | -1.0E+01 | +8.2E-01 | 2.5E-01 | 5.7E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 6| +9.77E+03 | -5.2E+04 | +9.0E-01 | 5.0E-01 | 2.8E+05 | 9.3E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 46| +1.48E+02 | -2.9E-05 | +7.8E-01 | 1.1E-02 | 5.4E-02 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 64| +1.48E+02 | +1.6E-06 | -1.4E+01 | 6.1E-06 | 1.5E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 42| +1.48E+02 | -6.2E-04 | +6.2E-01 | 1.8E-02 | 9.5E-01 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:37.289 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 125.179 and h = 9.55388e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.289 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 125.179: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 49| +1.52E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 9.7E+01 | 6.9E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 7| +1.68E+03 | -8.1E+03 | +9.0E-01 | 1.0E+00 | 4.4E+04 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 79| +1.48E+02 | +1.1E-02 | -1.2E+00 | 2.4E-02 | 9.5E-01 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 47| +1.48E+02 | -3.3E-05 | +5.7E-01 | 2.3E-02 | 1.4E-01 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 65| +1.48E+02 | +3.2E-06 | -7.9E+01 | 1.5E-06 | 1.5E-02 | 3.3E-06 | 2d |0\n", - "2023-02-17 16:05:37.307 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.78 and h = 2.75474e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.307 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.78: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 43| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.8E-02 | 1.8E+00 | 7.5E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 17| +1.68E+02 | -9.1E+00 | +4.9E-01 | 2.5E-01 | 6.8E+01 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 8| +4.19E+02 | -1.3E+03 | +9.2E-01 | 2.0E+00 | 6.8E+03 | 9.3E-01 | trr |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 80| +1.48E+02 | -4.9E-04 | +1.3E-01 | 6.0E-03 | 9.5E-01 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 50| +1.50E+02 | -2.0E+00 | +9.0E-01 | 1.2E-01 | 9.7E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 48| +1.48E+02 | +5.2E-04 | -2.5E+00 | 2.3E-02 | 1.0E-01 | 6.4E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 9| +2.28E+02 | -1.9E+02 | +9.8E-01 | 2.0E+00 | 9.6E+02 | 8.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 111| +1.51E+02 | +7.4E-08 | -6.8E-01 | 2.7E-01 | 3.3E-02 | 8.2E-06 | g |0\n", - "2023-02-17 16:05:37 fides(INFO) 44| +1.48E+02 | -3.8E-04 | +9.6E-01 | 4.5E-03 | 1.8E+00 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 66| +1.48E+02 | +5.5E-06 | -5.2E+02 | 3.8E-07 | 1.5E-02 | 8.2E-07 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 10| +2.15E+02 | -1.2E+01 | +8.0E-01 | 2.0E+00 | 9.8E+01 | 5.8E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 81| +1.48E+02 | -1.2E-03 | +9.4E-01 | 1.5E-03 | 3.0E+00 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 49| +1.48E+02 | +1.3E-05 | -1.7E-01 | 5.6E-03 | 1.0E-01 | 1.8E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 51| +1.49E+02 | -1.0E+00 | +8.9E-01 | 2.5E-01 | 4.2E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 45| +1.48E+02 | -2.4E-04 | +9.3E-01 | 9.0E-03 | 4.9E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 67| +1.48E+02 | +1.8E-06 | -6.8E+02 | 9.6E-08 | 1.5E-02 | 2.1E-07 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 82| +1.48E+02 | -4.5E-04 | +8.2E-01 | 3.0E-03 | 3.5E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 50| +1.48E+02 | -9.8E-06 | +2.6E-01 | 1.4E-03 | 1.0E-01 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 52| +1.49E+02 | +1.5E-01 | -1.6E-01 | 5.0E-01 | 3.8E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 46| +1.48E+02 | -1.4E-04 | +9.7E-01 | 1.8E-02 | 1.0E+00 | 7.4E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 68| +1.48E+02 | +3.0E-06 | -4.4E+03 | 2.4E-08 | 1.5E-02 | 5.1E-08 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 83| +1.48E+02 | -5.2E-04 | +8.1E-01 | 6.0E-03 | 6.5E-01 | 7.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 51| +1.48E+02 | -1.8E-05 | +7.9E-01 | 1.4E-03 | 4.2E-01 | 4.0E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 11| +2.15E+02 | +3.2E+02 | -1.9E+01 | 4.0E+00 | 2.5E+01 | 1.1E+01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 53| +1.48E+02 | -5.3E-01 | +7.7E-01 | 1.2E-01 | 3.8E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 47| +1.48E+02 | -7.6E-05 | +9.3E-01 | 3.6E-02 | 2.6E-01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 69| +1.48E+02 | +2.5E-06 | -1.5E+04 | 6.0E-09 | 1.5E-02 | 1.3E-08 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 84| +1.48E+02 | -6.6E-04 | +9.1E-01 | 1.2E-02 | 1.9E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 52| +1.48E+02 | -4.5E-06 | +1.7E+00 | 2.8E-03 | 3.0E-02 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 54| +1.48E+02 | -3.7E-01 | +7.3E-01 | 2.5E-01 | 3.4E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:37.474 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 159.303 and h = 7.75438e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.474 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 159.303: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 12| +2.00E+02 | -1.6E+01 | +1.4E+00 | 1.0E+00 | 2.5E+01 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 112| +1.51E+02 | +0.0E+00 | +0.0E+00 | 1.1E-06 | 3.3E-02 | 2.0E-06 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 18| +1.57E+02 | -1.1E+01 | +9.8E-01 | 2.5E-01 | 1.6E+02 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 48| +1.48E+02 | -5.7E-05 | +9.4E-01 | 7.2E-02 | 4.9E-01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 85| +1.48E+02 | -1.0E-03 | +8.6E-01 | 2.4E-02 | 2.6E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 53| +1.48E+02 | -4.1E-06 | +1.0E+00 | 5.6E-03 | 2.6E-02 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 55| +1.48E+02 | +2.4E-02 | -7.7E-02 | 2.5E-01 | 2.1E+01 | 2.5E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 70| +1.48E+02 | +2.6E-06 | -6.0E+04 | 1.5E-09 | 1.5E-02 | 3.2E-09 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 13| +2.00E+02 | +1.0E+03 | -3.7E+01 | 2.0E+00 | 6.8E+01 | 5.4E+00 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 49| +1.48E+02 | +7.9E-06 | -1.4E-01 | 1.4E-01 | 1.3E-01 | 5.0E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 86| +1.48E+02 | +2.8E-03 | -1.7E+00 | 4.8E-02 | 2.2E-01 | 5.1E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 54| +1.48E+02 | -5.6E-06 | +9.8E-01 | 1.1E-02 | 2.3E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 71| +1.48E+02 | +2.3E-06 | -2.2E+05 | 3.7E-10 | 1.5E-02 | 8.0E-10 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 56| +1.48E+02 | -2.2E-01 | +5.8E-01 | 6.2E-02 | 2.1E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:37.540 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.636 and h = 2.24351e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.540 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.636: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 50| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.6E-02 | 1.3E-01 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 87| +1.48E+02 | -2.4E-04 | +4.6E-01 | 1.2E-02 | 2.2E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 14| +1.80E+02 | -2.0E+01 | +1.0E+00 | 5.0E-01 | 6.8E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 55| +1.48E+02 | -5.4E-06 | +8.2E-01 | 2.3E-02 | 1.8E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 72| +1.48E+02 | +1.3E-06 | -4.9E+05 | 9.4E-11 | 1.5E-02 | 2.0E-10 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 57| +1.48E+02 | -1.5E-01 | +8.9E-01 | 6.2E-02 | 3.3E+01 | 6.4E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 51| +1.48E+02 | -1.2E-05 | +1.0E+00 | 9.0E-03 | 1.3E-01 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 113| +1.51E+02 | -3.4E-08 | +2.6E+00 | 2.8E-07 | 3.3E-02 | 5.1E-07 | 2d |1\n", - "2023-02-17 16:05:37 fides(WARNING) Stopping as function difference 3.41E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:37 fides(INFO) 88| +1.48E+02 | +4.4E-03 | -3.4E+00 | 1.2E-02 | 2.3E-01 | 1.0E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 73| +1.48E+02 | +2.3E-06 | -3.5E+06 | 2.3E-11 | 1.5E-02 | 5.0E-11 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 56| +1.48E+02 | +4.5E-04 | -1.3E+01 | 4.5E-02 | 3.3E-02 | 7.0E-03 | trr |0\n", - "2023-02-17 16:05:37 fides(INFO) 15| +1.80E+02 | +3.8E+01 | -2.6E+00 | 1.0E+00 | 6.9E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 58| +1.48E+02 | -5.0E-02 | +9.0E-01 | 1.2E-01 | 6.5E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 52| +1.48E+02 | -9.2E-06 | +8.9E-01 | 1.8E-02 | 2.2E-01 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 89| +1.48E+02 | +1.6E-03 | -1.8E+00 | 3.0E-03 | 2.3E-01 | 6.7E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 57| +1.48E+02 | +2.1E-05 | -1.3E+00 | 8.4E-03 | 3.3E-02 | 1.8E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 16| +1.77E+02 | -2.5E+00 | +1.5E-01 | 2.5E-01 | 6.9E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 59| +1.48E+02 | -3.1E-02 | +7.5E-01 | 2.5E-01 | 1.0E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 53| +1.48E+02 | -1.5E-05 | +1.2E+00 | 3.6E-02 | 8.0E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 74| +1.48E+02 | -5.7E-07 | +3.5E+06 | 5.8E-12 | 1.5E-02 | 1.3E-11 | 2d |1\n", - "2023-02-17 16:05:37 fides(WARNING) Stopping as function difference 5.75E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:37 fides(INFO) 19| +1.55E+02 | -1.5E+00 | +9.5E-01 | 5.0E-01 | 3.9E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 58| +1.48E+02 | +6.4E-06 | -1.2E+00 | 2.1E-03 | 3.3E-02 | 4.7E-04 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 90| +1.48E+02 | -1.7E-04 | +5.1E-01 | 7.5E-04 | 2.3E-01 | 1.7E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 17| +1.68E+02 | -9.1E+00 | +9.2E-01 | 6.2E-02 | 1.4E+02 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 54| +1.48E+02 | -6.6E-06 | +5.9E-01 | 7.2E-02 | 1.5E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 60| +1.48E+02 | +6.0E-02 | -1.3E+00 | 2.5E-01 | 2.7E+00 | 1.7E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 59| +1.48E+02 | +1.0E-06 | -4.7E-01 | 5.3E-04 | 3.3E-02 | 1.5E-04 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 91| +1.48E+02 | -2.9E-05 | +9.3E-01 | 7.5E-04 | 3.0E-01 | 7.1E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 61| +1.48E+02 | -4.7E-03 | +2.4E-01 | 6.2E-02 | 2.7E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 55| +1.48E+02 | -6.9E-06 | +1.1E+00 | 7.2E-02 | 3.7E-02 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 18| +1.66E+02 | -2.4E+00 | +5.5E-01 | 1.2E-01 | 5.0E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 0| +2.13E+11 | NaN | NaN | 1.0E+00 | 8.4E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:37 fides(INFO) 92| +1.48E+02 | -3.3E-05 | +8.2E-01 | 1.5E-03 | 1.2E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 60| +1.48E+02 | +2.5E-06 | -1.9E+00 | 1.3E-04 | 3.3E-02 | 7.8E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 62| +1.48E+02 | -6.6E-03 | +9.0E-01 | 1.6E-02 | 6.3E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 1| +2.64E+09 | -2.1E+11 | +1.0E-01 | 1.0E+00 | 8.4E+11 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 56| +1.48E+02 | +1.7E-05 | -2.5E+00 | 1.4E-01 | 6.0E-02 | 3.4E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 61| +1.48E+02 | +7.0E-07 | -6.6E-01 | 3.3E-05 | 3.3E-02 | 6.1E-05 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 93| +1.48E+02 | -6.3E-05 | +1.0E+00 | 3.0E-03 | 1.5E-01 | 2.6E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 0| +4.07E+10 | NaN | NaN | 1.0E+00 | 1.9E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:37 fides(INFO) 19| +1.62E+02 | -3.3E+00 | +9.2E-01 | 1.2E-01 | 1.0E+02 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 2| +3.98E+08 | -2.2E+09 | +8.9E-01 | 2.5E-01 | 1.2E+10 | 6.7E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 57| +1.48E+02 | -2.3E-06 | +9.2E-01 | 3.6E-02 | 6.0E-02 | 8.5E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 63| +1.48E+02 | -2.2E-03 | +7.7E-01 | 3.1E-02 | 8.7E-01 | 2.3E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 1| +8.32E+04 | -4.1E+10 | +9.3E-02 | 1.0E+00 | 1.9E+11 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 62| +1.48E+02 | -4.2E-08 | +8.9E-02 | 8.2E-06 | 3.3E-02 | 1.7E-05 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 94| +1.48E+02 | -9.4E-05 | +9.4E-01 | 6.0E-03 | 8.3E-02 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 20| +1.61E+02 | -1.2E+00 | +7.5E-01 | 2.5E-01 | 3.9E+01 | 5.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 2| +1.33E+04 | -7.0E+04 | +9.0E-01 | 2.5E-01 | 3.8E+05 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 64| +1.48E+02 | -2.2E-03 | +9.3E-01 | 6.2E-02 | 9.4E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 58| +1.48E+02 | +5.7E-06 | -1.1E+00 | 7.2E-02 | 1.4E-02 | 1.6E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 63| +1.48E+02 | +1.5E-06 | -1.2E+01 | 2.1E-06 | 2.6E-02 | 5.3E-06 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 95| +1.48E+02 | -1.5E-04 | +9.7E-01 | 1.2E-02 | 1.3E-01 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 3| +6.02E+07 | -3.4E+08 | +8.9E-01 | 5.0E-01 | 1.8E+09 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 21| +1.59E+02 | -2.1E+00 | +1.2E+00 | 5.0E-01 | 5.6E+01 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 3| +2.29E+03 | -1.1E+04 | +9.0E-01 | 5.0E-01 | 6.0E+04 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 65| +1.48E+02 | -3.2E-03 | +9.0E-01 | 1.2E-01 | 7.4E-01 | 7.8E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 59| +1.48E+02 | -1.4E-06 | +9.4E-01 | 1.8E-02 | 1.4E-02 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(WARNING) Stopping as function difference 1.40E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:37 fides(INFO) 4| +9.00E+06 | -5.1E+07 | +8.9E-01 | 1.0E+00 | 2.8E+08 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 64| +1.48E+02 | +1.0E-07 | -3.0E+00 | 5.1E-07 | 2.6E-02 | 1.3E-06 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 4| +5.52E+02 | -1.7E+03 | +9.0E-01 | 5.0E-01 | 9.6E+03 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 96| +1.48E+02 | -7.9E-05 | +4.5E-01 | 2.4E-02 | 5.5E-02 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:37.842 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 182.65 and h = 3.35302e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.842 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 182.65: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 22| +1.59E+02 | +0.0E+00 | +0.0E+00 | 1.0E+00 | 2.4E+01 | 1.8E+00 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 5| +1.28E+06 | -7.7E+06 | +9.0E-01 | 1.0E+00 | 4.1E+07 | 9.2E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 66| +1.48E+02 | +2.0E-03 | -4.1E-01 | 2.5E-01 | 7.8E-01 | 1.5E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 5| +2.84E+02 | -2.7E+02 | +9.0E-01 | 1.0E+00 | 1.5E+03 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 97| +1.48E+02 | +4.4E-03 | -5.0E+00 | 2.4E-02 | 1.8E-01 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 65| +1.48E+02 | +1.6E-06 | -1.9E+02 | 1.3E-07 | 2.6E-02 | 3.3E-07 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 23| +1.57E+02 | -2.0E+00 | +1.1E+00 | 2.5E-01 | 2.4E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 6| +1.59E+05 | -1.1E+06 | +9.2E-01 | 1.0E+00 | 5.9E+06 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 20| +1.53E+02 | -2.1E+00 | +4.9E-01 | 1.0E+00 | 3.8E+01 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 67| +1.48E+02 | -1.1E-03 | +7.0E-01 | 6.2E-02 | 7.8E-01 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 6| +2.51E+02 | -3.3E+01 | +8.5E-01 | 2.0E+00 | 2.3E+02 | 4.7E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 98| +1.48E+02 | +8.5E-05 | -2.8E-01 | 6.0E-03 | 1.8E-01 | 5.8E-03 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 66| +1.48E+02 | +1.6E-06 | -7.3E+02 | 3.2E-08 | 2.6E-02 | 8.3E-08 | 2d |0\n", - "2023-02-17 16:05:37.902 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 99.1786 and h = 1.85395e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:37.902 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 99.1786: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:37 fides(INFO) 24| +1.57E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 3.9E+01 | 7.0E-01 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 7| +1.73E+04 | -1.4E+05 | +9.4E-01 | 1.0E+00 | 7.7E+05 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 68| +1.48E+02 | -5.4E-04 | +3.6E-01 | 6.2E-02 | 4.7E-01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 7| +2.33E+02 | -1.7E+01 | +8.8E+00 | 4.0E+00 | 1.9E+01 | 6.8E+00 | trr |1\n", - "2023-02-17 16:05:37 fides(INFO) 99| +1.48E+02 | -5.8E-05 | +5.8E-01 | 1.5E-03 | 1.8E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 25| +1.56E+02 | -1.3E+00 | +1.1E+00 | 1.2E-01 | 3.9E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 8| +2.36E+03 | -1.5E+04 | +9.1E-01 | 1.0E+00 | 1.0E+05 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 0| +4.92E+02 | NaN | NaN | 1.0E+00 | 5.7E+02 | NaN | NaN |1\n", - "2023-02-17 16:05:37 fides(INFO) 69| +1.48E+02 | -6.5E-04 | +2.4E-01 | 6.2E-02 | 1.5E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 67| +1.48E+02 | +3.5E-07 | -6.5E+02 | 8.0E-09 | 2.6E-02 | 2.1E-08 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 8| +2.33E+02 | +4.2E+04 | -1.5E+03 | 4.0E+00 | 4.9E+01 | 1.2E+01 | trr |0\n", - "2023-02-17 16:05:37 fides(INFO) 100| +1.48E+02 | -1.8E-05 | +7.8E-01 | 1.5E-03 | 3.8E-01 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 9| +4.73E+02 | -1.9E+03 | +9.0E-01 | 1.0E+00 | 1.7E+04 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 26| +1.53E+02 | -2.9E+00 | +1.1E+00 | 2.5E-01 | 1.7E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 1| +3.93E+02 | -9.9E+01 | +9.5E-02 | 1.0E+00 | 5.7E+02 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 70| +1.48E+02 | -2.9E-04 | +2.1E-01 | 1.6E-02 | 5.8E-01 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 68| +1.48E+02 | +6.4E-06 | -4.8E+04 | 2.0E-09 | 2.6E-02 | 5.2E-09 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 9| +2.33E+02 | +1.7E+03 | -5.1E+01 | 9.5E-01 | 4.9E+01 | 2.5E+00 | 2d |0\n", - "2023-02-17 16:05:37 fides(INFO) 101| +1.48E+02 | -7.5E-06 | +1.2E+00 | 3.0E-03 | 3.4E-02 | 7.8E-04 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 2| +3.48E+02 | -4.5E+01 | +9.9E-01 | 2.5E-01 | 6.3E+01 | 7.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) 27| +1.49E+02 | -3.5E+00 | +1.0E+00 | 5.0E-01 | 4.1E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:37 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:37 fides(INFO) 10| +2.06E+02 | -2.7E+02 | +9.0E-01 | 1.0E+00 | 2.8E+03 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 71| +1.48E+02 | -6.4E-04 | +9.5E-01 | 3.9E-03 | 2.3E+00 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 10| +2.15E+02 | -1.9E+01 | +7.0E-01 | 2.4E-01 | 4.9E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 69| +1.48E+02 | +3.9E-06 | -1.2E+05 | 5.0E-10 | 2.6E-02 | 1.3E-09 | 2d |0\n", - "2023-02-17 16:05:38.015 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 86.6153 and h = 1.31e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:38.015 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 86.6153: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:38 fides(INFO) 102| +1.48E+02 | -6.9E-06 | +7.3E-01 | 6.0E-03 | 6.1E-02 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 28| +1.49E+02 | +0.0E+00 | +0.0E+00 | 1.0E+00 | 1.3E+01 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 11| +1.69E+02 | -3.6E+01 | +8.7E-01 | 1.0E+00 | 4.8E+02 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 3| +2.89E+02 | -5.9E+01 | +7.0E-01 | 5.0E-01 | 6.1E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 11| +2.15E+02 | +2.0E+00 | -4.5E-01 | 2.4E-01 | 2.8E+01 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 21| +1.52E+02 | -7.1E-01 | +9.7E-01 | 1.0E+00 | 7.1E+01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 72| +1.48E+02 | -7.3E-05 | +9.1E-01 | 7.8E-03 | 1.5E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 70| +1.48E+02 | +2.3E-06 | -2.8E+05 | 1.3E-10 | 2.6E-02 | 3.2E-10 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 12| +1.61E+02 | -8.0E+00 | +1.2E+00 | 1.0E+00 | 9.2E+01 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 103| +1.48E+02 | +1.4E-05 | -8.5E-01 | 6.0E-03 | 3.6E-02 | 7.7E-04 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 29| +1.49E+02 | -6.6E-01 | +6.7E-01 | 1.4E-01 | 1.3E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 4| +2.66E+02 | -2.3E+01 | +8.8E-01 | 5.0E-01 | 8.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 12| +2.12E+02 | -2.5E+00 | +8.1E-01 | 5.9E-02 | 2.8E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 13| +1.61E+02 | +1.6E+01 | -2.8E+00 | 2.0E+00 | 2.8E+01 | 2.3E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 22| +1.52E+02 | +3.8E+01 | -2.1E+02 | 2.0E+00 | 8.5E+00 | 5.0E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 73| +1.48E+02 | -1.1E-04 | +9.2E-01 | 1.6E-02 | 1.2E-01 | 7.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 71| +1.48E+02 | +1.9E-06 | -9.3E+05 | 3.1E-11 | 2.6E-02 | 8.1E-11 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 30| +1.49E+02 | +1.7E+00 | -1.5E+00 | 1.4E-01 | 1.5E+01 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 104| +1.48E+02 | +1.3E-06 | -2.2E-01 | 1.5E-03 | 3.6E-02 | 2.6E-04 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 14| +1.57E+02 | -4.8E+00 | +1.1E+00 | 3.0E-01 | 2.8E+01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 13| +2.12E+02 | +1.4E-01 | -2.4E-01 | 1.2E-01 | 9.4E+00 | 3.5E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 5| +2.66E+02 | +3.3E+03 | -1.7E+02 | 1.0E+00 | 3.9E+01 | 2.6E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 74| +1.48E+02 | -1.7E-04 | +9.6E-01 | 3.1E-02 | 1.1E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 72| +1.48E+02 | +1.0E-07 | -1.9E+05 | 7.9E-12 | 2.6E-02 | 2.0E-11 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 31| +1.48E+02 | -2.1E-01 | +5.9E-01 | 3.5E-02 | 1.5E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 15| +1.50E+02 | -6.1E+00 | +6.9E-01 | 6.0E-01 | 3.6E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 6| +2.53E+02 | -1.3E+01 | +6.0E-01 | 2.5E-01 | 3.9E+01 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 105| +1.48E+02 | -6.5E-07 | +2.4E-01 | 3.8E-04 | 3.6E-02 | 1.3E-04 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 14| +2.12E+02 | -3.2E-01 | +6.4E-01 | 3.0E-02 | 9.4E+00 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 16| +1.50E+02 | +1.2E+01 | -3.4E+00 | 6.0E-01 | 1.0E+02 | 8.8E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 75| +1.48E+02 | -2.6E-04 | +9.7E-01 | 6.2E-02 | 1.4E-01 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 23| +1.52E+02 | +6.7E-03 | -5.3E-01 | 5.0E-01 | 8.5E+00 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 73| +1.48E+02 | +1.5E-06 | -1.2E+07 | 2.0E-12 | 2.6E-02 | 5.1E-12 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 32| +1.48E+02 | -1.1E-01 | +9.5E-01 | 3.5E-02 | 9.5E+00 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 15| +2.12E+02 | -6.5E-02 | +5.8E-01 | 3.0E-02 | 2.6E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 7| +2.50E+02 | -2.6E+00 | +5.9E-01 | 2.5E-01 | 4.0E+01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 106| +1.48E+02 | -2.5E-06 | +1.7E+00 | 9.4E-05 | 1.1E-01 | 3.3E-05 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 17| +1.49E+02 | -1.7E+00 | +6.7E-01 | 1.5E-01 | 1.0E+02 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 76| +1.48E+02 | -3.3E-04 | +9.6E-01 | 1.2E-01 | 1.3E-01 | 4.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 74| +1.48E+02 | +1.2E-05 | -3.7E+08 | 4.9E-13 | 2.6E-02 | 1.3E-12 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 33| +1.48E+02 | -1.9E-01 | +9.8E-01 | 7.1E-02 | 9.3E+00 | 6.6E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 16| +2.12E+02 | +7.9E-03 | -7.3E-02 | 3.0E-02 | 4.1E+00 | 8.2E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 18| +1.48E+02 | -6.2E-01 | +9.1E-01 | 1.5E-01 | 3.1E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 8| +2.50E+02 | +1.9E-01 | -3.9E-01 | 2.5E-01 | 8.6E+00 | 6.6E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 107| +1.48E+02 | +2.9E-06 | -3.5E+00 | 1.9E-04 | 1.6E-02 | 1.0E-04 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 17| +2.12E+02 | -5.2E-02 | +8.2E-01 | 7.4E-03 | 4.1E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 19| +1.48E+02 | -3.4E-01 | +4.3E-01 | 3.0E-01 | 2.2E+01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 77| +1.48E+02 | -1.9E-04 | +8.5E-01 | 2.5E-01 | 5.8E-02 | 7.7E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 34| +1.48E+02 | -2.3E-01 | +8.7E-01 | 1.4E-01 | 8.7E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 9| +2.50E+02 | +1.9E-01 | -3.9E-01 | 6.2E-02 | 8.6E+00 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 75| +1.48E+02 | -9.9E-07 | +1.2E+08 | 1.2E-13 | 2.6E-02 | 3.2E-13 | 2d |1\n", - "2023-02-17 16:05:38 fides(WARNING) Stopping as function difference 9.94E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 20| +1.47E+02 | -3.5E-01 | +2.9E-01 | 3.0E-01 | 2.8E+01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 18| +2.12E+02 | -7.5E-03 | +2.3E-01 | 1.5E-02 | 1.6E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 108| +1.48E+02 | +1.8E-06 | -2.5E+00 | 4.7E-05 | 1.6E-02 | 8.8E-05 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 78| +1.48E+02 | +5.4E-05 | -6.7E+00 | 5.0E-01 | 9.4E-02 | 1.4E-02 | trr |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 10| +2.50E+02 | -2.3E-01 | +7.9E-01 | 1.6E-02 | 8.6E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 35| +1.48E+02 | +1.5E-01 | -4.3E-01 | 2.8E-01 | 6.8E+00 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 21| +1.47E+02 | +8.2E+00 | -4.5E+00 | 3.0E-01 | 1.9E+01 | 4.1E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 19| +2.12E+02 | -1.9E-02 | +8.9E-01 | 3.7E-03 | 3.0E+00 | 7.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 109| +1.48E+02 | -1.3E-07 | +4.1E-01 | 1.2E-05 | 1.6E-02 | 2.5E-05 | 2d |1\n", - "2023-02-17 16:05:38 fides(WARNING) Stopping as function difference 1.33E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:38 fides(INFO) 22| +1.47E+02 | -1.2E-01 | +2.0E-01 | 7.6E-02 | 1.9E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 79| +1.48E+02 | -2.9E-06 | +6.9E-01 | 2.9E-02 | 9.4E-02 | 3.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 36| +1.48E+02 | -1.0E-01 | +7.1E-01 | 7.1E-02 | 6.8E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 11| +2.50E+02 | +3.2E-03 | -7.4E-02 | 3.1E-02 | 2.9E+00 | 8.1E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 24| +1.52E+02 | -7.9E-02 | +1.1E+00 | 1.2E-01 | 8.5E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 20| +2.12E+02 | -1.3E-02 | +6.4E-01 | 7.4E-03 | 1.8E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 23| +1.47E+02 | -2.2E-01 | +8.8E-01 | 1.9E-02 | 1.9E+01 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 12| +2.50E+02 | -2.2E-02 | +5.9E-01 | 7.8E-03 | 2.9E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 80| +1.48E+02 | +2.3E-05 | -1.3E+00 | 2.9E-02 | 3.1E-02 | 3.8E-03 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 37| +1.48E+02 | -2.9E-02 | +2.2E-01 | 7.1E-02 | 9.0E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 21| +2.12E+02 | -2.8E-03 | +3.3E-01 | 7.4E-03 | 8.1E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 24| +1.47E+02 | -1.4E-01 | +9.8E-01 | 3.8E-02 | 3.3E+01 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 81| +1.48E+02 | -2.9E-06 | +5.4E-01 | 7.3E-03 | 3.1E-02 | 9.7E-04 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 13| +2.50E+02 | -1.2E-04 | +2.3E-02 | 7.8E-03 | 9.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 38| +1.48E+02 | -6.1E-02 | +6.2E-01 | 1.8E-02 | 5.7E+00 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 22| +2.12E+02 | -4.2E-03 | +3.3E-01 | 7.4E-03 | 1.4E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 25| +1.47E+02 | -1.7E-01 | +9.8E-01 | 7.6E-02 | 1.0E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 25| +1.52E+02 | -1.3E-01 | +1.1E+00 | 2.5E-01 | 1.6E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 82| +1.48E+02 | +1.2E-06 | -2.3E+00 | 7.3E-03 | 4.7E-02 | 7.2E-04 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 23| +2.12E+02 | -8.3E-04 | +9.7E-02 | 7.4E-03 | 1.1E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 39| +1.48E+02 | -4.0E-02 | +9.7E-01 | 1.8E-02 | 1.6E+01 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 14| +2.50E+02 | -2.3E-03 | +6.8E-01 | 2.0E-03 | 8.7E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 26| +1.47E+02 | -1.9E-01 | +9.4E-01 | 1.5E-01 | 2.4E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 24| +2.12E+02 | -3.5E-03 | +8.5E-01 | 1.9E-03 | 1.2E+00 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 83| +1.48E+02 | +2.3E-06 | -6.7E+00 | 1.8E-03 | 4.7E-02 | 1.8E-04 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 27| +1.46E+02 | -2.1E-01 | +5.4E-01 | 3.0E-01 | 7.1E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 15| +2.50E+02 | +8.3E-09 | -1.2E-03 | 2.0E-03 | 3.6E-02 | 5.1E-03 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 40| +1.48E+02 | -2.2E-02 | +9.3E-01 | 3.5E-02 | 2.2E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 26| +1.52E+02 | +7.5E-01 | -4.8E+00 | 5.0E-01 | 8.2E+00 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 0| +1.34E+03 | NaN | NaN | 1.0E+00 | 5.1E+03 | NaN | NaN |1\n", - "2023-02-17 16:05:38 fides(INFO) 28| +1.46E+02 | +1.8E+01 | -1.1E+01 | 3.0E-01 | 1.1E+01 | 4.6E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 25| +2.12E+02 | -8.2E-04 | +3.2E-01 | 3.7E-03 | 5.9E-01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 84| +1.48E+02 | +6.4E-07 | -2.2E+00 | 4.5E-04 | 4.7E-02 | 4.7E-05 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 16| +2.50E+02 | +8.3E-09 | -1.2E-03 | 4.9E-04 | 3.6E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 41| +1.48E+02 | -3.6E-02 | +9.6E-01 | 7.1E-02 | 4.3E+00 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 1| +3.07E+02 | -1.0E+03 | +1.2E-01 | 1.0E+00 | 5.1E+03 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 29| +1.46E+02 | +6.2E-02 | -1.5E-01 | 7.6E-02 | 1.1E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 26| +2.12E+02 | -4.3E-04 | +2.0E-01 | 3.7E-03 | 5.5E-01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 85| +1.48E+02 | +9.7E-07 | -3.5E+00 | 1.1E-04 | 4.7E-02 | 1.6E-05 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 17| +2.50E+02 | -3.0E-06 | +4.3E-01 | 1.2E-04 | 3.6E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 42| +1.48E+02 | -3.5E-02 | +7.2E-01 | 1.4E-01 | 1.8E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 30| +1.46E+02 | -1.2E-01 | +7.6E-01 | 1.9E-02 | 1.1E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 27| +2.12E+02 | -8.1E-04 | +8.5E-01 | 9.3E-04 | 5.8E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 2| +2.71E+02 | -3.6E+01 | +9.2E-01 | 2.5E-01 | 6.0E+01 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 27| +1.52E+02 | -9.8E-02 | +1.1E+00 | 1.2E-01 | 8.2E+00 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 86| +1.48E+02 | +2.9E-06 | -1.0E+01 | 2.8E-05 | 4.7E-02 | 1.2E-05 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 31| +1.46E+02 | -5.6E-02 | +3.6E-01 | 3.8E-02 | 1.1E+01 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 18| +2.50E+02 | -1.0E-09 | +6.2E-04 | 1.2E-04 | 1.6E-02 | 2.7E-04 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 43| +1.48E+02 | +1.3E-01 | -1.6E+00 | 1.4E-01 | 3.0E+00 | 7.4E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 28| +2.12E+02 | -2.7E-04 | +4.4E-01 | 1.9E-03 | 2.7E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 3| +2.71E+02 | +5.8E+01 | -2.0E+00 | 5.0E-01 | 4.4E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 32| +1.46E+02 | -1.2E-01 | +9.1E-01 | 3.8E-02 | 6.3E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 87| +1.48E+02 | +1.3E-06 | -4.8E+00 | 7.1E-06 | 4.7E-02 | 1.2E-05 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 44| +1.48E+02 | -1.2E-02 | +4.6E-01 | 3.5E-02 | 3.0E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 29| +2.12E+02 | -1.9E-04 | +3.6E-01 | 1.9E-03 | 2.5E-01 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 19| +2.50E+02 | -7.5E-07 | +7.5E-01 | 3.1E-05 | 1.6E-02 | 7.7E-05 | 2d |1\n", - "2023-02-17 16:05:38 fides(WARNING) Stopping as function difference 7.52E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:38 fides(INFO) 28| +1.52E+02 | -1.7E-01 | +7.3E-01 | 2.5E-01 | 1.4E+01 | 5.4E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 4| +2.59E+02 | -1.2E+01 | +9.0E-01 | 1.2E-01 | 4.4E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 33| +1.46E+02 | -5.1E-02 | +5.2E-01 | 7.6E-02 | 1.3E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 88| +1.48E+02 | +5.7E-07 | -4.4E+00 | 1.8E-06 | 4.7E-02 | 3.2E-06 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 30| +2.12E+02 | -1.9E-04 | +4.0E-01 | 1.9E-03 | 2.5E-01 | 4.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 45| +1.48E+02 | -6.6E-03 | +4.9E-01 | 3.5E-02 | 2.9E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 34| +1.46E+02 | -2.7E-02 | +2.1E-01 | 7.6E-02 | 3.9E+00 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 31| +2.12E+02 | -1.6E-04 | +4.1E-01 | 1.9E-03 | 2.1E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 35| +1.46E+02 | -3.7E-02 | +5.5E-01 | 1.9E-02 | 4.7E+00 | 2.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 5| +2.56E+02 | -3.2E+00 | +7.9E-02 | 2.5E-01 | 3.2E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 89| +1.48E+02 | -1.2E-06 | +3.2E+01 | 4.4E-07 | 4.7E-02 | 8.1E-07 | 2d |1\n", - "2023-02-17 16:05:38 fides(WARNING) Stopping as function difference 1.15E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:38 fides(INFO) 29| +1.52E+02 | -1.9E-01 | +9.5E-01 | 2.5E-01 | 1.7E+01 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 46| +1.48E+02 | -4.5E-03 | +3.6E-01 | 3.5E-02 | 1.4E+00 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 32| +2.12E+02 | -1.6E-04 | +4.5E-01 | 1.9E-03 | 2.0E-01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 36| +1.46E+02 | -2.1E-02 | +9.8E-01 | 1.9E-02 | 1.1E+01 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 6| +2.51E+02 | -4.8E+00 | +8.2E-01 | 6.2E-02 | 6.2E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 37| +1.46E+02 | -1.9E-02 | +1.0E+00 | 3.8E-02 | 3.3E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 33| +2.12E+02 | -1.5E-04 | +4.8E-01 | 1.9E-03 | 1.8E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 47| +1.48E+02 | -6.2E-03 | +4.0E-01 | 3.5E-02 | 4.7E+00 | 3.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 38| +1.46E+02 | -3.3E-02 | +9.9E-01 | 7.6E-02 | 4.0E+00 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 7| +2.50E+02 | -6.8E-01 | +4.1E-01 | 1.2E-01 | 2.0E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 34| +2.12E+02 | -1.5E-04 | +5.2E-01 | 1.9E-03 | 1.7E-01 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 30| +1.52E+02 | -1.1E-01 | +5.6E-01 | 5.0E-01 | 1.5E+01 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 48| +1.48E+02 | +2.8E-03 | -2.5E-01 | 3.5E-02 | 1.2E+00 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 39| +1.46E+02 | -4.2E-02 | +9.3E-01 | 1.5E-01 | 3.9E+00 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 35| +2.12E+02 | -1.5E-04 | +5.4E-01 | 1.9E-03 | 1.5E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 8| +2.50E+02 | +2.2E-01 | -4.2E-01 | 1.2E-01 | 8.6E+00 | 3.1E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 49| +1.48E+02 | -1.2E-03 | +2.1E-01 | 8.8E-03 | 1.2E+00 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 40| +1.46E+02 | +2.2E+00 | -2.2E+01 | 3.0E-01 | 2.3E+00 | 2.6E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 36| +2.12E+02 | -1.5E-04 | +5.7E-01 | 1.9E-03 | 1.5E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 9| +2.50E+02 | -2.2E-01 | +4.6E-01 | 3.1E-02 | 8.6E+00 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 41| +1.46E+02 | +5.6E-02 | -1.3E+00 | 7.6E-02 | 2.3E+00 | 6.4E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 31| +1.51E+02 | -1.5E-01 | +1.1E+00 | 5.0E-01 | 1.3E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 50| +1.48E+02 | -2.8E-03 | +9.5E-01 | 2.2E-03 | 4.8E+00 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 37| +2.12E+02 | -1.5E-04 | +5.9E-01 | 1.9E-03 | 1.4E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 42| +1.46E+02 | -6.3E-03 | +5.0E-01 | 1.9E-02 | 2.3E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 10| +2.50E+02 | -1.0E-02 | +1.3E-01 | 3.1E-02 | 3.8E+00 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 38| +2.12E+02 | -1.5E-04 | +6.1E-01 | 1.9E-03 | 1.4E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 51| +1.48E+02 | -6.1E-04 | +9.1E-01 | 4.4E-03 | 4.3E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 43| +1.46E+02 | -4.6E-03 | +7.4E-01 | 1.9E-02 | 2.4E+00 | 8.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 32| +1.51E+02 | -2.2E-01 | +1.5E+00 | 1.0E+00 | 1.2E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 39| +2.12E+02 | -1.5E-04 | +6.1E-01 | 1.9E-03 | 1.3E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 11| +2.50E+02 | -2.9E-02 | +6.5E-01 | 7.8E-03 | 3.1E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 44| +1.46E+02 | -4.1E-03 | +8.6E-01 | 1.9E-02 | 2.6E+00 | 8.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 52| +1.48E+02 | -8.0E-04 | +9.0E-01 | 8.8E-03 | 7.9E-01 | 6.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 40| +2.12E+02 | -1.5E-04 | +6.3E-01 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 45| +1.46E+02 | -6.0E-03 | +7.8E-01 | 3.8E-02 | 1.7E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 33| +1.51E+02 | -4.8E-01 | +1.7E+00 | 2.0E+00 | 7.4E+00 | 8.4E-01 | trr |1\n", - "2023-02-17 16:05:38 fides(INFO) 53| +1.48E+02 | -1.2E-03 | +9.6E-01 | 1.8E-02 | 3.0E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 12| +2.50E+02 | -1.2E-04 | +4.4E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 46| +1.46E+02 | +2.2E-02 | -1.2E+00 | 7.6E-02 | 3.3E+00 | 4.7E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 41| +2.12E+02 | -1.5E-04 | +6.2E-01 | 1.9E-03 | 1.3E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 54| +1.48E+02 | -1.8E-03 | +9.4E-01 | 3.5E-02 | 7.2E-01 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 34| +1.51E+02 | +3.7E+00 | -3.8E+00 | 2.0E+00 | 2.9E+01 | 6.4E-01 | trr |0\n", - "2023-02-17 16:05:38 fides(INFO) 47| +1.46E+02 | -3.5E-03 | +5.8E-01 | 1.9E-02 | 3.3E+00 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 42| +2.12E+02 | -1.5E-04 | +6.4E-01 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 13| +2.50E+02 | -1.2E-04 | +4.5E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 48| +1.46E+02 | -3.5E-03 | +5.0E-01 | 1.9E-02 | 1.2E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 43| +2.12E+02 | -1.5E-04 | +6.3E-01 | 1.9E-03 | 1.3E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 55| +1.48E+02 | -2.2E-03 | +8.2E-01 | 7.1E-02 | 3.4E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 35| +1.50E+02 | -4.4E-01 | +1.0E+00 | 1.6E-01 | 2.9E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 14| +2.50E+02 | -1.3E-04 | +4.7E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 49| +1.46E+02 | -3.8E-03 | +4.4E-01 | 1.9E-02 | 4.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 44| +2.12E+02 | -1.6E-04 | +6.4E-01 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 56| +1.48E+02 | +2.8E-02 | -4.9E+00 | 1.4E-01 | 5.2E-01 | 1.1E-01 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 50| +1.46E+02 | -4.1E-04 | +9.3E-02 | 1.9E-02 | 1.2E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 36| +1.50E+02 | -5.2E-01 | +8.3E-01 | 3.2E-01 | 9.2E+00 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 15| +2.50E+02 | -1.3E-04 | +4.8E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 45| +2.12E+02 | -1.5E-04 | +6.3E-01 | 1.9E-03 | 1.3E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 57| +1.48E+02 | +1.2E-04 | -6.6E-02 | 3.5E-02 | 5.2E-01 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 51| +1.46E+02 | -1.5E-03 | +7.5E-01 | 4.7E-03 | 9.2E-01 | 4.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 46| +2.12E+02 | -1.6E-04 | +6.4E-01 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 16| +2.50E+02 | -1.4E-04 | +5.0E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 37| +1.50E+02 | -1.0E-02 | +1.2E-02 | 6.3E-01 | 1.0E+01 | 2.8E-01 | trr |1\n", - "2023-02-17 16:05:38 fides(INFO) 52| +1.46E+02 | -1.0E-03 | +7.5E-01 | 9.4E-03 | 1.5E+00 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 58| +1.48E+02 | -4.1E-04 | +7.3E-01 | 8.8E-03 | 5.2E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 47| +2.12E+02 | -1.6E-04 | +6.3E-01 | 1.9E-03 | 1.3E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 17| +2.50E+02 | -1.4E-04 | +5.0E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 53| +1.46E+02 | -2.0E-03 | +9.4E-01 | 9.4E-03 | 6.3E-01 | 6.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 38| +1.50E+02 | -5.3E-02 | +2.7E-01 | 1.1E-01 | 7.8E+00 | 1.6E-01 | trr |1\n", - "2023-02-17 16:05:38 fides(INFO) 48| +2.12E+02 | -1.6E-04 | +6.4E-01 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 59| +1.48E+02 | -1.9E-04 | +9.8E-01 | 8.8E-03 | 1.8E-01 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 54| +1.46E+02 | -1.3E-03 | +7.1E-01 | 1.9E-02 | 2.8E+00 | 7.0E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 18| +2.50E+02 | -1.5E-04 | +5.2E-01 | 7.8E-03 | 1.6E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 49| +2.12E+02 | -2.3E-04 | +1.0E+00 | 1.9E-03 | 1.3E-01 | 5.5E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 39| +1.50E+02 | -3.6E-02 | +6.0E-01 | 1.1E-01 | 4.2E+00 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 60| +1.48E+02 | -2.9E-04 | +9.6E-01 | 1.8E-02 | 1.4E-01 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 55| +1.46E+02 | +2.2E-05 | -1.7E-02 | 1.9E-02 | 4.3E-01 | 7.8E-03 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 19| +2.50E+02 | -1.5E-04 | +5.3E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 50| +2.12E+02 | -2.9E-04 | +6.7E-01 | 3.7E-03 | 4.4E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 56| +1.46E+02 | -3.5E-04 | +7.1E-01 | 4.7E-03 | 4.3E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 61| +1.48E+02 | -4.4E-04 | +9.7E-01 | 3.5E-02 | 2.2E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 40| +1.50E+02 | -8.0E-03 | +4.8E-01 | 1.1E-01 | 7.4E+00 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 20| +2.50E+02 | -1.6E-04 | +5.5E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 51| +2.12E+02 | -3.5E-04 | +1.0E+00 | 3.7E-03 | 2.5E-01 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 57| +1.46E+02 | -2.4E-04 | +9.8E-01 | 4.7E-03 | 9.6E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 62| +1.48E+02 | -2.8E-04 | +5.6E-01 | 7.1E-02 | 9.4E-02 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 41| +1.50E+02 | +1.7E-03 | -4.2E-01 | 1.1E-01 | 1.9E+00 | 4.2E-02 | trr |0\n", - "2023-02-17 16:05:38 fides(INFO) 58| +1.46E+02 | -3.1E-04 | +1.0E+00 | 9.4E-03 | 3.4E-01 | 2.2E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 52| +2.12E+02 | -4.9E-04 | +9.4E-01 | 7.4E-03 | 9.2E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 21| +2.50E+02 | -1.7E-04 | +5.6E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 63| +1.48E+02 | +1.4E-02 | -6.8E+00 | 7.1E-02 | 2.9E-01 | 3.9E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 59| +1.46E+02 | -5.5E-04 | +9.8E-01 | 1.9E-02 | 4.5E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 53| +2.12E+02 | -6.4E-04 | +8.5E-01 | 1.5E-02 | 1.0E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 42| +1.50E+02 | -1.5E-03 | +7.5E-01 | 2.0E-02 | 1.9E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 22| +2.50E+02 | -1.7E-04 | +5.7E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 54| +2.12E+02 | -2.3E-03 | +5.8E-01 | 3.0E-02 | 1.8E-01 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:38 fides(INFO) 60| +1.46E+02 | -8.3E-04 | +9.0E-01 | 3.8E-02 | 3.7E-01 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 64| +1.48E+02 | +3.5E-04 | -4.5E-01 | 1.8E-02 | 2.9E-01 | 9.8E-03 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 43| +1.50E+02 | -1.8E-04 | +9.0E-01 | 2.0E-02 | 5.2E-01 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 61| +1.46E+02 | +1.1E-02 | -4.2E+00 | 7.6E-02 | 2.4E-01 | 2.8E-02 | 2d |0\n", - "2023-02-17 16:05:38 fides(INFO) 23| +2.50E+02 | -1.8E-04 | +5.8E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 55| +2.12E+02 | -5.7E-03 | +1.2E+00 | 3.0E-02 | 7.0E-01 | 8.5E-02 | 2d |1\n", - "2023-02-17 16:05:38 fides(INFO) 65| +1.48E+02 | -1.4E-04 | +5.8E-01 | 4.4E-03 | 2.9E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 62| +1.46E+02 | +6.0E-05 | -8.3E-02 | 1.9E-02 | 2.4E-01 | 7.1E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 44| +1.50E+02 | -4.4E-05 | +6.5E-01 | 4.0E-02 | 1.8E-01 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 56| +2.12E+02 | -6.3E-03 | +5.1E-01 | 5.9E-02 | 4.3E-01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 24| +2.50E+02 | -1.9E-04 | +6.0E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 66| +1.48E+02 | -3.8E-05 | +9.7E-01 | 4.4E-03 | 4.8E-01 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 63| +1.46E+02 | -1.7E-04 | +7.3E-01 | 4.7E-03 | 2.4E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 57| +2.12E+02 | -2.1E-02 | +1.4E+00 | 5.9E-02 | 1.5E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 45| +1.50E+02 | -1.5E-06 | +1.0E-01 | 4.0E-02 | 1.9E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 64| +1.46E+02 | -1.0E-04 | +1.0E+00 | 4.7E-03 | 4.9E-01 | 9.3E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 25| +2.50E+02 | -1.9E-04 | +6.0E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 67| +1.48E+02 | -1.5E-05 | +7.8E-01 | 8.8E-03 | 5.7E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 58| +2.12E+02 | -5.7E-02 | +1.5E+00 | 1.2E-01 | 1.2E+00 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 65| +1.46E+02 | -2.8E-04 | +9.3E-01 | 9.4E-03 | 2.0E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 46| +1.50E+02 | +2.1E-06 | -1.1E-01 | 1.0E-02 | 3.4E-02 | 2.7E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 26| +2.50E+02 | -2.0E-04 | +6.2E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 59| +2.11E+02 | -3.5E-01 | +2.2E+00 | 2.4E-01 | 1.9E+00 | 6.8E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 68| +1.48E+02 | -1.8E-05 | +6.3E-01 | 1.8E-02 | 1.4E-01 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 66| +1.46E+02 | -2.4E-04 | +5.5E-01 | 1.9E-02 | 1.0E+00 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 47| +1.50E+02 | -6.1E-06 | +8.0E-01 | 2.5E-03 | 3.4E-02 | 6.8E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 67| +1.46E+02 | +9.1E-04 | -8.5E-01 | 1.9E-02 | 2.3E-01 | 8.1E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 60| +2.11E+02 | +2.7E+00 | -4.0E+00 | 4.7E-01 | 4.4E+00 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 27| +2.50E+02 | -2.1E-04 | +6.3E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 69| +1.48E+02 | +3.4E-05 | -7.1E-01 | 1.8E-02 | 6.2E-02 | 1.8E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 68| +1.46E+02 | -1.5E-04 | +5.2E-01 | 4.7E-03 | 2.3E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 61| +2.11E+02 | -4.6E-01 | +1.4E+00 | 1.2E-01 | 4.4E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 48| +1.50E+02 | -1.9E-06 | +6.3E-01 | 5.0E-03 | 3.1E-02 | 6.8E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 70| +1.48E+02 | -8.7E-06 | +5.0E-01 | 4.4E-03 | 6.2E-02 | 5.5E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 28| +2.50E+02 | -2.2E-04 | +6.4E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 69| +1.46E+02 | -7.2E-05 | +9.6E-01 | 4.7E-03 | 4.5E-01 | 9.2E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 62| +2.10E+02 | -3.7E-01 | +2.5E-01 | 2.4E-01 | 5.1E+00 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 49| +1.50E+02 | -2.3E-07 | +8.0E-01 | 5.0E-03 | 2.7E-02 | 1.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(WARNING) Stopping as function difference 2.33E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:39 fides(INFO) 71| +1.48E+02 | -8.1E-06 | +7.6E-01 | 4.4E-03 | 2.0E-01 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 70| +1.46E+02 | -1.0E-04 | +9.9E-01 | 9.4E-03 | 1.3E-01 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 29| +2.50E+02 | -2.2E-04 | +6.5E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 63| +2.10E+02 | +3.0E-01 | -4.3E-01 | 5.9E-02 | 7.4E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 71| +1.46E+02 | -1.8E-04 | +9.6E-01 | 1.9E-02 | 2.0E-01 | 3.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 72| +1.48E+02 | -3.2E-06 | +6.3E-01 | 8.8E-03 | 2.7E-02 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 30| +2.50E+02 | -2.4E-04 | +6.6E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 64| +2.10E+02 | -1.6E-01 | +7.2E-01 | 1.5E-02 | 7.4E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 72| +1.46E+02 | -2.7E-05 | +2.0E-02 | 3.8E-02 | 1.3E-01 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 73| +1.48E+02 | -2.1E-06 | +5.3E-01 | 8.8E-03 | 2.9E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 31| +2.50E+02 | -2.4E-04 | +6.7E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 73| +1.46E+02 | +1.2E-03 | -7.7E-01 | 9.4E-03 | 2.3E-01 | 9.2E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 65| +2.10E+02 | -1.1E-01 | +9.5E-01 | 1.5E-02 | 4.7E+00 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 74| +1.48E+02 | -5.4E-06 | +1.8E+00 | 8.8E-03 | 2.0E-02 | 8.6E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 74| +1.46E+02 | -1.2E-04 | +2.3E-01 | 2.4E-03 | 2.3E-01 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 32| +2.50E+02 | -3.0E-04 | +9.6E-01 | 7.8E-03 | 1.5E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 66| +2.10E+02 | -1.7E-01 | +9.6E-01 | 3.0E-02 | 3.9E+00 | 6.2E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 75| +1.46E+02 | -9.9E-05 | +9.7E-01 | 5.9E-04 | 1.0E+00 | 2.3E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 75| +1.48E+02 | +2.5E-06 | -4.7E-01 | 1.8E-02 | 3.1E-02 | 2.6E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 67| +2.10E+02 | -1.4E-01 | +7.0E-01 | 5.9E-02 | 3.0E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 33| +2.50E+02 | +1.8E-04 | -2.4E-01 | 1.6E-02 | 9.8E-02 | 3.8E-02 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 76| +1.46E+02 | -3.6E-05 | +9.3E-01 | 1.2E-03 | 2.3E-01 | 3.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 68| +2.10E+02 | -1.8E-01 | +9.7E-01 | 5.9E-02 | 4.4E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 76| +1.48E+02 | +1.1E-07 | -6.7E-02 | 4.4E-03 | 3.1E-02 | 6.5E-04 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 77| +1.46E+02 | -2.4E-05 | +8.3E-01 | 2.4E-03 | 1.1E-01 | 4.7E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 34| +2.50E+02 | +8.1E-05 | -3.2E-01 | 3.9E-03 | 9.8E-02 | 9.6E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 69| +2.09E+02 | -2.6E-01 | +1.0E+00 | 1.2E-01 | 2.6E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 78| +1.46E+02 | -3.8E-05 | +9.7E-01 | 4.7E-03 | 3.6E-01 | 6.5E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 77| +1.48E+02 | +2.3E-07 | -4.7E-01 | 1.1E-03 | 3.1E-02 | 1.6E-04 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 70| +2.09E+02 | +7.3E-01 | -2.3E+00 | 2.4E-01 | 2.2E+00 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 79| +1.46E+02 | -5.3E-05 | +9.6E-01 | 9.4E-03 | 8.1E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 35| +2.50E+02 | +5.2E-05 | -4.6E-01 | 9.8E-04 | 9.8E-02 | 2.4E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 78| +1.48E+02 | +2.2E-08 | -1.0E-01 | 2.8E-04 | 3.1E-02 | 4.2E-05 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 80| +1.46E+02 | -6.1E-05 | +5.4E-01 | 1.9E-02 | 1.3E-01 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 71| +2.09E+02 | -1.3E-01 | +1.1E+00 | 5.9E-02 | 2.2E+00 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 36| +2.50E+02 | -2.7E-05 | +5.8E-01 | 2.4E-04 | 9.8E-02 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 79| +1.48E+02 | +2.0E-06 | -1.4E+01 | 6.9E-05 | 3.1E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 81| +1.46E+02 | +1.2E-03 | -2.3E+00 | 1.9E-02 | 1.2E-01 | 7.4E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 72| +2.09E+02 | -4.1E-01 | +1.2E+00 | 1.2E-01 | 2.7E+00 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 37| +2.50E+02 | -9.9E-06 | +6.0E-01 | 2.4E-04 | 4.0E-02 | 5.6E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 82| +1.46E+02 | -3.1E-05 | +2.2E-01 | 4.7E-03 | 1.2E-01 | 1.9E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 80| +1.48E+02 | +4.8E-07 | -3.9E+00 | 1.7E-05 | 3.1E-02 | 8.0E-06 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 73| +2.08E+02 | -3.8E-01 | +3.3E-01 | 2.4E-01 | 3.5E+00 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 83| +1.46E+02 | -2.2E-05 | +7.7E-01 | 1.2E-03 | 1.9E-01 | 4.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 38| +2.50E+02 | -1.0E-05 | +1.0E+00 | 2.4E-04 | 3.1E-02 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 81| +1.48E+02 | +2.0E-06 | -1.7E+01 | 4.3E-06 | 3.1E-02 | 7.5E-06 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 74| +2.07E+02 | -1.3E+00 | +6.8E-01 | 2.4E-01 | 1.3E+01 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 84| +1.46E+02 | -1.6E-05 | +7.2E-01 | 2.4E-03 | 6.8E-02 | 5.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 39| +2.50E+02 | -2.2E-05 | +1.0E+00 | 4.9E-04 | 2.3E-02 | 1.3E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 82| +1.48E+02 | +2.0E-06 | -2.8E+01 | 1.1E-06 | 3.1E-02 | 2.8E-06 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 85| +1.46E+02 | -9.7E-06 | +9.4E-01 | 2.4E-03 | 6.8E-02 | 2.6E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 75| +2.03E+02 | -3.9E+00 | +1.3E+00 | 2.4E-01 | 1.7E+01 | 6.5E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 86| +1.46E+02 | -2.0E-05 | +1.1E+00 | 4.7E-03 | 9.1E-02 | 4.7E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 40| +2.50E+02 | -3.8E-05 | +1.0E+00 | 9.8E-04 | 2.9E-02 | 2.4E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 76| +2.03E+02 | +2.0E+01 | -3.6E+00 | 4.7E-01 | 1.8E+01 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 87| +1.46E+02 | -3.6E-05 | +9.9E-01 | 9.4E-03 | 5.7E-02 | 9.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 83| +1.48E+02 | +9.3E-08 | -4.5E+00 | 2.7E-07 | 3.1E-02 | 7.1E-07 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 77| +2.01E+02 | -2.5E+00 | +1.1E+00 | 1.2E-01 | 1.8E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 41| +2.50E+02 | -8.7E-05 | +1.0E+00 | 2.0E-03 | 2.3E-02 | 5.2E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 88| +1.46E+02 | -5.8E-05 | +9.4E-01 | 1.9E-02 | 1.4E-01 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 84| +1.48E+02 | +2.8E-07 | -5.2E+01 | 6.7E-08 | 3.1E-02 | 1.8E-07 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 78| +2.01E+02 | +7.4E+00 | -9.2E-01 | 2.4E-01 | 1.9E+01 | 5.8E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 89| +1.46E+02 | +7.9E-04 | -5.4E+00 | 3.8E-02 | 5.1E-02 | 5.9E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 42| +2.50E+02 | -1.7E-04 | +1.0E+00 | 3.9E-03 | 3.9E-02 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 79| +1.99E+02 | -1.7E+00 | +7.2E-01 | 5.9E-02 | 1.9E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 90| +1.46E+02 | +1.4E-05 | -3.2E-01 | 9.4E-03 | 5.1E-02 | 1.5E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 85| +1.48E+02 | +1.5E-07 | -1.1E+02 | 1.7E-08 | 3.1E-02 | 4.5E-08 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 43| +2.50E+02 | -3.4E-04 | +1.0E+00 | 7.8E-03 | 3.1E-02 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 91| +1.46E+02 | -8.3E-06 | +6.2E-01 | 2.4E-03 | 5.1E-02 | 3.9E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 80| +1.98E+02 | -1.1E+00 | +8.2E-01 | 5.9E-02 | 1.4E+01 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 86| +1.48E+02 | +4.8E-07 | -1.4E+03 | 4.2E-09 | 3.1E-02 | 1.1E-08 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 92| +1.46E+02 | -8.6E-06 | +1.1E+00 | 2.4E-03 | 1.1E-01 | 2.4E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 44| +2.50E+02 | -7.2E-04 | +1.1E+00 | 1.6E-02 | 3.8E-02 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 81| +1.96E+02 | -1.8E+00 | +9.3E-01 | 1.2E-01 | 1.2E+01 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 93| +1.46E+02 | -1.1E-05 | +9.3E-01 | 4.7E-03 | 4.1E-02 | 3.5E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 87| +1.48E+02 | +6.8E-07 | -7.9E+03 | 1.1E-09 | 3.1E-02 | 2.8E-09 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 45| +2.50E+02 | -1.6E-03 | +1.1E+00 | 3.1E-02 | 6.3E-02 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 82| +1.93E+02 | -3.0E+00 | +7.0E-01 | 2.4E-01 | 1.6E+01 | 5.8E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 94| +1.46E+02 | -2.0E-05 | +8.7E-01 | 9.4E-03 | 5.5E-02 | 7.7E-04 | 2d |1\n", - "2023-02-17 16:05:39.482 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0965 and h = 4.6875e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:39.482 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0965: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:39 fides(INFO) 88| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.6E-10 | 3.1E-02 | 7.0E-10 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 46| +2.50E+02 | -4.3E-03 | +1.2E+00 | 6.2E-02 | 1.2E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 95| +1.46E+02 | +7.0E-05 | -1.4E+00 | 1.9E-02 | 4.4E-02 | 2.3E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 83| +1.84E+02 | -9.3E+00 | +1.9E+00 | 2.4E-01 | 2.0E+01 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 89| +1.48E+02 | +2.6E-07 | -4.8E+04 | 6.6E-11 | 3.1E-02 | 1.7E-10 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 96| +1.46E+02 | -8.8E-06 | +5.4E-01 | 4.7E-03 | 4.4E-02 | 5.7E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 47| +2.50E+02 | -1.5E-02 | +1.5E+00 | 1.2E-01 | 2.4E-01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 84| +1.84E+02 | +8.0E+01 | -4.0E+00 | 4.7E-01 | 3.5E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 97| +1.46E+02 | -4.7E-07 | +5.4E-03 | 4.7E-03 | 5.9E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 90| +1.48E+02 | +3.6E-07 | -2.7E+05 | 1.6E-11 | 3.1E-02 | 4.3E-11 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 48| +2.50E+02 | -9.1E-02 | +2.2E+00 | 2.5E-01 | 4.8E-01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 98| +1.46E+02 | -1.4E-05 | +6.8E-01 | 1.2E-03 | 6.9E-02 | 4.8E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 85| +1.76E+02 | -8.3E+00 | +1.1E+00 | 1.2E-01 | 3.5E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 91| +1.48E+02 | +1.2E-07 | -3.7E+05 | 4.1E-12 | 3.1E-02 | 1.1E-11 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 99| +1.46E+02 | -4.0E-06 | +1.1E+00 | 1.2E-03 | 8.8E-02 | 1.2E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 49| +2.48E+02 | -1.5E+00 | +4.2E+00 | 5.0E-01 | 1.1E+00 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 86| +1.76E+02 | +8.7E+00 | -8.3E-01 | 2.4E-01 | 5.1E+01 | 5.6E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 100| +1.46E+02 | -4.3E-06 | +9.3E-01 | 2.4E-03 | 3.5E-02 | 1.5E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 92| +1.48E+02 | +4.8E-07 | -5.7E+06 | 1.0E-12 | 3.1E-02 | 2.7E-12 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 50| +2.26E+02 | -2.2E+01 | +3.4E+00 | 1.0E+00 | 7.4E+00 | 2.2E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 101| +1.46E+02 | -8.4E-06 | +9.5E-01 | 4.7E-03 | 4.6E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 93| +1.48E+02 | +4.5E-08 | -2.2E+06 | 2.6E-13 | 3.1E-02 | 6.8E-13 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 87| +1.71E+02 | -4.7E+00 | +1.1E+00 | 5.9E-02 | 5.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 51| +2.26E+02 | +1.6E+02 | -5.5E+00 | 2.0E+00 | 5.0E+01 | 3.9E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 102| +1.46E+02 | -1.2E-05 | +6.8E-01 | 9.4E-03 | 3.4E-02 | 6.1E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 94| +1.48E+02 | +1.9E-08 | -3.6E+06 | 6.4E-14 | 3.1E-02 | 1.7E-13 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 52| +2.12E+02 | -1.4E+01 | +3.7E-01 | 5.0E-01 | 5.0E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 103| +1.46E+02 | +1.1E-04 | -1.9E+00 | 9.4E-03 | 4.5E-02 | 2.5E-03 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 88| +1.62E+02 | -9.0E+00 | +9.6E-01 | 1.2E-01 | 4.4E+01 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 104| +1.46E+02 | -5.5E-06 | +3.6E-01 | 2.4E-03 | 4.5E-02 | 6.2E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 53| +2.12E+02 | +3.3E+02 | -5.8E+00 | 5.0E-01 | 1.3E+02 | 1.3E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 95| +1.48E+02 | -4.2E-08 | +3.2E+07 | 1.6E-14 | 3.1E-02 | 4.2E-14 | 2d |1\n", - "2023-02-17 16:05:39 fides(WARNING) Stopping as function difference 4.16E-08 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:39 fides(INFO) 89| +1.62E+02 | +1.3E+01 | -1.2E+00 | 2.4E-01 | 4.3E+01 | 4.5E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 105| +1.46E+02 | -3.5E-06 | +3.0E-01 | 2.4E-03 | 5.0E-02 | 4.8E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 54| +1.96E+02 | -1.6E+01 | +5.0E-01 | 1.2E-01 | 1.3E+02 | 3.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 90| +1.59E+02 | -3.0E+00 | +7.8E-01 | 5.9E-02 | 4.3E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 55| +1.90E+02 | -5.4E+00 | +4.8E-01 | 1.2E-01 | 5.5E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 106| +1.46E+02 | -4.1E-06 | +4.4E-01 | 2.4E-03 | 4.3E-02 | 4.6E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 107| +1.46E+02 | -2.7E-06 | +3.3E-01 | 2.4E-03 | 3.9E-02 | 3.9E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 56| +1.82E+02 | -8.4E+00 | +9.9E-01 | 1.2E-01 | 3.0E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 91| +1.57E+02 | -1.8E+00 | +3.5E-01 | 1.2E-01 | 4.6E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 108| +1.46E+02 | -3.9E-06 | +5.0E-01 | 2.4E-03 | 4.3E-02 | 4.2E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 57| +1.71E+02 | -1.1E+01 | +7.8E-01 | 2.5E-01 | 2.8E+01 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 92| +1.56E+02 | -1.4E+00 | +2.7E-01 | 1.2E-01 | 1.0E+02 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 109| +1.46E+02 | -2.0E-06 | +2.5E-01 | 2.4E-03 | 3.7E-02 | 4.0E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 0| +7.05E+09 | NaN | NaN | 1.0E+00 | 3.2E+10 | NaN | NaN |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 110| +1.46E+02 | -2.5E-06 | +2.8E-01 | 2.4E-03 | 4.4E-02 | 4.7E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 58| +1.71E+02 | +7.6E+02 | -4.2E+01 | 5.0E-01 | 8.6E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 93| +1.54E+02 | -2.0E+00 | +3.0E-01 | 1.2E-01 | 1.1E+02 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 1| +1.37E+04 | -7.0E+09 | +9.2E-02 | 1.0E+00 | 3.2E+10 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 111| +1.46E+02 | -4.3E-06 | +4.3E-01 | 2.4E-03 | 3.9E-02 | 5.1E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 59| +1.70E+02 | -1.3E+00 | +1.0E-01 | 1.2E-01 | 8.6E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:39.776 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 38.3134 and h = 1.45372e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:39.776 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 38.3134: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:39 fides(INFO) 94| +1.54E+02 | +0.0E+00 | +0.0E+00 | 1.2E-01 | 2.4E+01 | 2.1E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 2| +2.35E+03 | -1.1E+04 | +9.0E-01 | 2.5E-01 | 6.2E+04 | 6.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 112| +1.46E+02 | +8.4E-07 | -7.0E-02 | 2.4E-03 | 4.8E-02 | 6.5E-04 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 3| +5.66E+02 | -1.8E+03 | +9.0E-01 | 5.0E-01 | 9.8E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 60| +1.64E+02 | -5.7E+00 | +8.9E-01 | 3.1E-02 | 1.2E+02 | 8.4E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 113| +1.46E+02 | -1.6E-06 | +4.4E-01 | 5.9E-04 | 4.8E-02 | 1.7E-04 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 95| +1.53E+02 | -8.4E-01 | +9.9E-01 | 3.0E-02 | 2.4E+01 | 5.6E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 114| +1.46E+02 | -1.3E-06 | +1.1E+00 | 5.9E-04 | 3.3E-02 | 7.7E-05 | 2d |1\n", - "2023-02-17 16:05:39 fides(WARNING) Stopping as function difference 1.28E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:39 fides(INFO) 4| +2.86E+02 | -2.8E+02 | +8.9E-01 | 1.0E+00 | 1.5E+03 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 61| +1.60E+02 | -3.8E+00 | +7.7E-01 | 6.2E-02 | 4.6E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 96| +1.52E+02 | -6.3E-01 | +5.6E-01 | 5.9E-02 | 1.5E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 5| +2.41E+02 | -4.5E+01 | +9.6E-01 | 2.0E+00 | 2.3E+02 | 4.4E+00 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 62| +1.58E+02 | -2.2E+00 | +2.6E-01 | 1.2E-01 | 4.2E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 97| +1.51E+02 | -9.1E-01 | +8.4E-01 | 5.9E-02 | 5.1E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 6| +2.41E+02 | +7.7E+02 | -3.0E+01 | 4.0E+00 | 3.5E+01 | 1.1E+01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 98| +1.50E+02 | -1.3E+00 | +1.1E+00 | 1.2E-01 | 2.0E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 63| +1.53E+02 | -5.3E+00 | +6.5E-01 | 1.2E-01 | 5.5E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 7| +2.41E+02 | +6.3E+01 | -5.5E+00 | 1.0E+00 | 3.5E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 99| +1.50E+02 | +8.1E-01 | -4.9E-01 | 2.4E-01 | 1.4E+01 | 4.2E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 64| +1.53E+02 | +3.7E+00 | -5.1E-01 | 1.2E-01 | 9.3E+01 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 8| +2.41E+02 | +5.7E+00 | -5.6E-01 | 2.5E-01 | 3.5E+01 | 6.4E-01 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 100| +1.49E+02 | -7.2E-01 | +8.7E-01 | 5.9E-02 | 1.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 65| +1.51E+02 | -1.9E+00 | +5.6E-01 | 3.1E-02 | 9.3E+01 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 9| +2.36E+02 | -4.3E+00 | +8.8E-01 | 6.2E-02 | 3.5E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 101| +1.48E+02 | -6.8E-01 | +6.4E-01 | 1.2E-01 | 7.6E+00 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 66| +1.50E+02 | -1.4E+00 | +8.9E-01 | 3.1E-02 | 4.7E+01 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 10| +2.36E+02 | -3.2E-01 | +5.3E-02 | 1.2E-01 | 2.0E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 102| +1.48E+02 | -3.2E-01 | +4.4E-01 | 1.2E-01 | 1.7E+01 | 1.9E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 67| +1.49E+02 | -1.6E-01 | +4.7E-02 | 6.2E-02 | 2.6E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 11| +2.35E+02 | -1.4E+00 | +8.9E-01 | 3.1E-02 | 3.3E+01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 103| +1.48E+02 | -3.9E-01 | +5.5E-01 | 1.2E-01 | 4.8E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 68| +1.48E+02 | -1.2E+00 | +6.0E-01 | 1.6E-02 | 1.1E+02 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 12| +2.34E+02 | -1.2E+00 | +7.6E-01 | 6.2E-02 | 2.0E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 104| +1.48E+02 | -4.4E-02 | +1.7E-01 | 1.2E-01 | 2.2E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 69| +1.48E+02 | -2.4E-01 | +8.0E-01 | 1.6E-02 | 3.8E+01 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 13| +2.32E+02 | -1.4E+00 | +8.9E-01 | 1.2E-01 | 1.1E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) 105| +1.48E+02 | -1.1E-01 | +8.6E-01 | 2.2E-02 | 7.1E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:39 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:39 fides(INFO) 70| +1.48E+02 | +1.1E-01 | -6.8E-01 | 3.1E-02 | 8.6E+00 | 5.5E-02 | 2d |0\n", - "2023-02-17 16:05:39 fides(INFO) 14| +2.29E+02 | -3.4E+00 | +9.5E-01 | 2.5E-01 | 1.9E+01 | 6.0E-01 | 2d |1\n", - "2023-02-17 16:05:39.998 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.601 and h = 3.69658e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:39.998 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.601: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:39 fides(INFO) 106| +1.48E+02 | +0.0E+00 | +0.0E+00 | 4.4E-02 | 6.3E+00 | 3.7E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 71| +1.48E+02 | -4.8E-02 | +7.4E-01 | 7.8E-03 | 8.6E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 15| +2.18E+02 | -1.1E+01 | +1.1E+00 | 5.0E-01 | 1.8E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 107| +1.48E+02 | -1.5E-02 | +7.3E-01 | 3.8E-03 | 6.3E+00 | 9.2E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 72| +1.48E+02 | -6.0E-02 | +7.5E-01 | 7.8E-03 | 1.6E+01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 16| +2.02E+02 | -1.6E+01 | +4.2E-01 | 1.0E+00 | 4.2E+01 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 108| +1.48E+02 | -5.3E-03 | +1.1E+00 | 3.8E-03 | 3.7E+00 | 5.3E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 17| +2.02E+02 | +7.0E+02 | -2.2E+01 | 1.0E+00 | 9.6E+01 | 1.7E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 73| +1.48E+02 | +1.9E-02 | -2.4E-01 | 1.6E-02 | 9.7E+00 | 2.3E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 109| +1.48E+02 | -7.6E-03 | +1.1E+00 | 7.5E-03 | 1.4E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 18| +1.84E+02 | -1.8E+01 | +8.6E-01 | 2.5E-01 | 9.6E+01 | 4.3E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 74| +1.48E+02 | -2.1E-02 | +6.2E-01 | 3.9E-03 | 9.7E+00 | 6.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 110| +1.48E+02 | -1.0E-02 | +1.0E+00 | 1.5E-02 | 1.8E+00 | 2.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 19| +1.78E+02 | -6.2E+00 | +6.2E-01 | 5.0E-01 | 4.1E+01 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 75| +1.48E+02 | -2.8E-02 | +7.6E-01 | 3.9E-03 | 1.0E+01 | 7.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 111| +1.48E+02 | -1.2E-02 | +1.0E+00 | 3.0E-02 | 8.1E-01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 20| +1.67E+02 | -1.1E+01 | +1.4E+00 | 5.0E-01 | 7.6E+01 | 7.7E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 112| +1.48E+02 | -1.4E-02 | +1.2E+00 | 6.0E-02 | 5.6E-01 | 8.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 76| +1.48E+02 | +6.3E-03 | -1.5E-01 | 7.8E-03 | 6.7E+00 | 2.0E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 21| +1.67E+02 | +8.1E+02 | -3.3E+01 | 1.0E+00 | 9.4E+01 | 1.9E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 113| +1.48E+02 | -4.5E-03 | +1.1E+00 | 1.2E-01 | 3.0E-01 | 7.5E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 77| +1.48E+02 | -1.5E-02 | +8.1E-01 | 2.0E-03 | 6.7E+00 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 22| +1.53E+02 | -1.4E+01 | +1.0E+00 | 2.5E-01 | 9.4E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 114| +1.48E+02 | -1.2E-03 | +7.5E-01 | 1.2E-01 | 5.1E-01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 78| +1.48E+02 | -2.5E-02 | +8.2E-01 | 3.9E-03 | 7.3E+00 | 9.0E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 23| +1.53E+02 | +1.1E+02 | -1.2E+01 | 5.0E-01 | 5.0E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 115| +1.48E+02 | -1.6E-04 | +1.0E+00 | 1.2E-01 | 4.1E-01 | 3.7E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 24| +1.49E+02 | -3.9E+00 | +8.2E-01 | 1.2E-01 | 5.0E+01 | 3.0E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 79| +1.48E+02 | -8.8E-03 | +3.4E-01 | 7.8E-03 | 6.3E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 116| +1.48E+02 | -7.5E-06 | +9.8E-01 | 1.2E-01 | 4.2E-02 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 25| +1.48E+02 | -7.0E-01 | +5.6E-01 | 2.5E-01 | 2.7E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 80| +1.48E+02 | -3.2E-02 | +9.0E-01 | 7.8E-03 | 7.2E+00 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 117| +1.48E+02 | +4.5E-06 | -3.1E+00 | 1.2E-01 | 3.5E-02 | 8.3E-04 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 26| +1.48E+02 | -4.2E-01 | +9.4E-01 | 2.5E-01 | 3.2E+01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 81| +1.48E+02 | +3.6E-01 | -5.1E+00 | 1.6E-02 | 4.2E+00 | 4.1E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 118| +1.48E+02 | -3.8E-07 | +4.2E-01 | 1.6E-04 | 3.5E-02 | 2.1E-04 | 2d |1\n", - "2023-02-17 16:05:40 fides(WARNING) Stopping as function difference 3.76E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:40.211 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.5253 and h = 1.64262e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:40.211 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.5253: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:40 fides(INFO) 27| +1.48E+02 | +0.0E+00 | +0.0E+00 | 5.0E-01 | 2.0E+01 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 82| +1.48E+02 | +1.8E-02 | -7.8E-01 | 3.9E-03 | 4.2E+00 | 1.0E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 28| +1.48E+02 | -1.4E-01 | +9.6E-01 | 1.2E-01 | 2.0E+01 | 9.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 29| +1.48E+02 | -1.0E-01 | +7.2E-01 | 2.5E-01 | 1.5E+01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 83| +1.48E+02 | -4.3E-03 | +5.1E-01 | 9.8E-04 | 4.2E+00 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 30| +1.48E+02 | +3.2E-01 | -1.5E+00 | 2.5E-01 | 1.3E+01 | 2.8E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 84| +1.48E+02 | -5.8E-03 | +9.7E-01 | 9.8E-04 | 5.3E+00 | 2.0E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 0| +1.71E+03 | NaN | NaN | 1.0E+00 | 6.0E+03 | NaN | NaN |1\n", - "2023-02-17 16:05:40 fides(INFO) 31| +1.48E+02 | -4.1E-02 | +5.4E-01 | 6.2E-02 | 1.3E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 85| +1.48E+02 | -7.2E-03 | +9.3E-01 | 2.0E-03 | 3.0E+00 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 1| +4.70E+02 | -1.2E+03 | +1.3E-01 | 1.0E+00 | 6.0E+03 | 2.1E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 32| +1.48E+02 | -2.5E-02 | +7.6E-01 | 6.2E-02 | 8.2E+00 | 5.3E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 2| +4.23E+02 | -4.7E+01 | +1.0E+00 | 2.5E-01 | 6.4E+01 | 7.5E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 86| +1.48E+02 | -1.1E-02 | +7.6E-01 | 3.9E-03 | 5.2E+00 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 33| +1.48E+02 | -1.2E-02 | +3.5E-01 | 1.2E-01 | 9.1E+00 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 3| +3.42E+02 | -8.1E+01 | +9.0E-01 | 5.0E-01 | 6.3E+01 | 1.5E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 87| +1.48E+02 | -2.8E-03 | +1.2E-01 | 7.8E-03 | 2.9E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 34| +1.48E+02 | +4.8E-02 | -6.0E-01 | 1.2E-01 | 4.6E+00 | 1.4E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 4| +3.42E+02 | +3.9E+02 | -7.8E+00 | 1.0E+00 | 5.6E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 88| +1.48E+02 | -3.8E-03 | +6.2E-01 | 2.0E-03 | 3.2E+00 | 4.1E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 5| +3.06E+02 | -3.6E+01 | +9.8E-01 | 2.5E-01 | 5.6E+01 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 35| +1.48E+02 | -3.9E-03 | +6.5E-02 | 3.1E-02 | 4.6E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 89| +1.48E+02 | -3.4E-03 | +6.5E-01 | 2.0E-03 | 4.4E+00 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 6| +2.83E+02 | -2.3E+01 | +1.7E-01 | 5.0E-01 | 5.0E+01 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 36| +1.48E+02 | -1.4E-02 | +8.8E-01 | 7.8E-03 | 7.7E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 90| +1.48E+02 | -3.3E-03 | +8.6E-01 | 2.0E-03 | 2.5E+00 | 3.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 7| +2.58E+02 | -2.5E+01 | +8.3E-01 | 1.2E-01 | 1.9E+02 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 37| +1.48E+02 | -8.0E-03 | +7.2E-01 | 1.6E-02 | 2.9E+00 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 8| +2.53E+02 | -5.1E+00 | +3.2E-01 | 2.5E-01 | 5.0E+01 | 5.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 91| +1.48E+02 | -5.2E-03 | +8.1E-01 | 3.9E-03 | 1.5E+00 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 38| +1.48E+02 | -1.8E-03 | +8.9E-01 | 1.6E-02 | 3.0E+00 | 6.3E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 9| +2.52E+02 | -1.1E+00 | +1.5E-01 | 2.5E-01 | 3.5E+01 | 5.9E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 39| +1.48E+02 | -1.3E-03 | +8.1E-01 | 3.1E-02 | 7.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 92| +1.48E+02 | +5.4E-04 | -1.7E-01 | 7.8E-03 | 1.6E+00 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 10| +2.50E+02 | -2.2E+00 | +7.5E-01 | 6.2E-02 | 3.0E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 40| +1.48E+02 | -1.5E-03 | +9.1E-01 | 6.2E-02 | 1.7E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 93| +1.48E+02 | -3.9E-04 | +2.7E-01 | 2.0E-03 | 1.6E+00 | 4.5E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 11| +2.50E+02 | -1.9E-02 | +1.1E-01 | 6.2E-02 | 5.5E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 41| +1.48E+02 | -1.7E-04 | +8.4E-02 | 1.2E-01 | 3.6E-01 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 94| +1.48E+02 | -1.3E-03 | +7.0E-01 | 2.0E-03 | 2.6E+00 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 12| +2.50E+02 | -5.5E-02 | +4.8E-01 | 1.6E-02 | 4.8E+00 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 42| +1.48E+02 | -6.0E-04 | +1.4E-01 | 3.1E-02 | 7.5E-01 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 95| +1.48E+02 | -3.7E-04 | +8.8E-01 | 2.0E-03 | 6.0E-01 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 13| +2.50E+02 | -3.2E-03 | +1.2E-01 | 1.6E-02 | 2.1E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 43| +1.48E+02 | -7.6E-04 | +7.7E-01 | 7.8E-03 | 1.4E+00 | 4.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 14| +2.50E+02 | -9.8E-03 | +6.9E-01 | 3.9E-03 | 1.8E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 96| +1.48E+02 | -4.1E-04 | +9.8E-01 | 3.9E-03 | 4.8E-01 | 9.7E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 44| +1.48E+02 | -1.9E-04 | +5.7E-01 | 1.6E-02 | 4.1E-01 | 4.4E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 15| +2.50E+02 | -2.3E-04 | +7.7E-01 | 3.9E-03 | 1.1E-01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 97| +1.48E+02 | -5.5E-04 | +9.8E-01 | 7.8E-03 | 2.6E-01 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 45| +1.48E+02 | -6.1E-05 | +1.0E+00 | 1.6E-02 | 2.7E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 16| +2.50E+02 | -4.6E-04 | +8.3E-01 | 7.8E-03 | 1.3E-01 | 1.9E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 98| +1.48E+02 | -9.2E-04 | +1.0E+00 | 1.6E-02 | 1.6E-01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 46| +1.48E+02 | -9.5E-05 | +9.5E-01 | 3.1E-02 | 1.7E-01 | 8.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 17| +2.50E+02 | -9.6E-04 | +9.0E-01 | 1.6E-02 | 1.5E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 99| +1.48E+02 | -1.7E-03 | +1.1E+00 | 3.1E-02 | 2.2E-01 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 47| +1.48E+02 | -1.0E-04 | +9.0E-01 | 6.2E-02 | 2.9E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 18| +2.50E+02 | -2.1E-03 | +9.7E-01 | 3.1E-02 | 1.7E-01 | 7.9E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 100| +1.48E+02 | -3.8E-03 | +1.1E+00 | 6.2E-02 | 4.1E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 48| +1.48E+02 | +4.3E-04 | -3.0E+00 | 1.2E-01 | 8.1E-02 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 19| +2.50E+02 | -4.9E-03 | +1.1E+00 | 6.2E-02 | 2.0E-01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 49| +1.48E+02 | -1.6E-05 | +3.0E-01 | 3.1E-02 | 8.1E-02 | 5.4E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 20| +2.50E+02 | -1.4E-02 | +1.2E+00 | 1.2E-01 | 2.2E-01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 101| +1.48E+02 | -1.0E-02 | +1.4E+00 | 1.2E-01 | 1.0E+00 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 21| +2.50E+02 | -5.3E-02 | +1.5E+00 | 2.5E-01 | 2.8E-01 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 50| +1.48E+02 | +4.5E-05 | -3.8E-01 | 3.1E-02 | 1.3E-01 | 9.3E-03 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 102| +1.48E+02 | -1.9E-02 | +7.9E-01 | 2.5E-01 | 1.3E+00 | 6.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 22| +2.49E+02 | -3.6E-01 | +2.3E+00 | 5.0E-01 | 3.6E-01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 51| +1.48E+02 | -2.0E-05 | +5.8E-01 | 7.8E-03 | 1.3E-01 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 23| +2.44E+02 | -4.8E+00 | +3.3E+00 | 1.0E+00 | 1.8E+00 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 103| +1.48E+02 | -1.3E-02 | +6.3E-01 | 5.0E-01 | 7.7E+00 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 52| +1.48E+02 | -8.4E-06 | +1.5E+00 | 7.8E-03 | 6.6E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 24| +2.20E+02 | -2.4E+01 | +1.9E+00 | 2.0E+00 | 1.3E+01 | 4.1E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 104| +1.48E+02 | -3.7E-03 | +3.5E-01 | 5.0E-01 | 2.2E+00 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 53| +1.48E+02 | -7.8E-06 | +7.2E-01 | 1.6E-02 | 4.5E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 54| +1.48E+02 | -8.2E-06 | +1.1E+00 | 1.6E-02 | 6.5E-02 | 2.7E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 25| +2.20E+02 | +4.5E+03 | -1.4E+02 | 4.0E+00 | 2.7E+01 | 7.7E+00 | trr |0\n", - "2023-02-17 16:05:40 fides(INFO) 105| +1.48E+02 | -4.6E-04 | +1.3E-02 | 5.0E-01 | 6.5E-01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 55| +1.48E+02 | -5.8E-06 | +5.9E-01 | 3.1E-02 | 2.3E-02 | 4.3E-03 | 2d |1\n", - "2023-02-17 16:05:40.669 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 118.415 and h = 1.50222e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:40.669 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 118.415: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:40 fides(INFO) 26| +2.20E+02 | +0.0E+00 | +0.0E+00 | 8.0E-01 | 2.7E+01 | 1.6E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 106| +1.48E+02 | -4.5E-03 | +8.4E-01 | 3.1E-02 | 1.5E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 56| +1.48E+02 | +1.5E-05 | -7.1E-01 | 3.1E-02 | 4.0E-02 | 5.5E-03 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 27| +2.17E+02 | -3.1E+00 | +3.3E-01 | 2.0E-01 | 2.7E+01 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 107| +1.48E+02 | -8.4E-04 | +1.1E+00 | 3.1E-02 | 8.0E-01 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 57| +1.48E+02 | -5.1E-06 | +8.4E-01 | 7.8E-03 | 4.0E-02 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 28| +2.07E+02 | -1.0E+01 | +9.2E-01 | 2.0E-01 | 5.8E+01 | 4.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 108| +1.48E+02 | -7.2E-05 | +1.5E+00 | 3.1E-02 | 1.7E-01 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 58| +1.48E+02 | +2.7E-06 | -1.2E+00 | 1.6E-02 | 2.1E-02 | 1.8E-03 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 29| +1.96E+02 | -1.1E+01 | +4.1E-01 | 4.0E-01 | 2.9E+01 | 8.7E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 59| +1.48E+02 | +7.2E-07 | -1.1E+00 | 3.9E-03 | 2.1E-02 | 4.5E-04 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 109| +1.48E+02 | -7.8E-05 | +1.2E+00 | 3.1E-02 | 8.2E-02 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 30| +1.66E+02 | -3.0E+01 | +9.6E-01 | 4.0E-01 | 1.4E+02 | 6.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 60| +1.48E+02 | +2.4E-06 | -1.2E+01 | 9.8E-04 | 2.1E-02 | 1.1E-04 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 110| +1.48E+02 | -1.7E-05 | +1.6E+00 | 3.1E-02 | 4.5E-02 | 8.0E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 31| +1.56E+02 | -9.8E+00 | +4.4E-01 | 8.0E-01 | 6.1E+01 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 61| +1.48E+02 | +8.6E-07 | -8.0E+00 | 2.4E-04 | 2.1E-02 | 3.0E-05 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 111| +1.48E+02 | -8.0E-05 | +1.6E+00 | 3.1E-02 | 4.4E-02 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 32| +1.51E+02 | -5.1E+00 | +5.1E-01 | 8.0E-01 | 2.4E+02 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 62| +1.48E+02 | +6.9E-08 | -8.7E-01 | 6.1E-05 | 2.1E-02 | 1.0E-05 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 112| +1.48E+02 | -8.8E-05 | +1.3E+00 | 3.1E-02 | 7.5E-02 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 33| +1.49E+02 | -2.2E+00 | +9.5E-01 | 8.0E-01 | 1.3E+02 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 63| +1.48E+02 | -1.0E-06 | +1.4E+01 | 1.5E-05 | 2.1E-02 | 7.1E-06 | 2d |1\n", - "2023-02-17 16:05:40 fides(WARNING) Stopping as function difference 9.98E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:40 fides(INFO) 113| +1.48E+02 | -1.7E-04 | +1.1E+00 | 6.2E-02 | 8.5E-02 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 34| +1.49E+02 | +2.6E+01 | -1.2E+01 | 1.6E+00 | 3.1E+01 | 4.8E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 35| +1.48E+02 | -5.8E-01 | +2.8E-01 | 4.0E-01 | 3.1E+01 | 1.2E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 114| +1.48E+02 | -2.7E-04 | +1.1E+00 | 1.2E-01 | 8.2E-02 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 36| +1.48E+02 | +1.9E+01 | -8.7E+00 | 4.0E-01 | 3.9E+01 | 6.3E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 115| +1.48E+02 | -2.4E-04 | +1.3E+00 | 2.5E-01 | 9.1E-02 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 0| +1.46E+07 | NaN | NaN | 1.0E+00 | 6.7E+07 | NaN | NaN |1\n", - "2023-02-17 16:05:40.850 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.3823 and h = 1.24949e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:40.850 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.3823: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:40 fides(INFO) 37| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.0E-01 | 3.9E+01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 116| +1.48E+02 | -9.9E-05 | +1.5E+00 | 2.5E-01 | 1.9E-02 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 1| +3.72E+02 | -1.5E+07 | +1.1E-01 | 1.0E+00 | 6.7E+07 | 2.5E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 38| +1.48E+02 | -2.9E-01 | +8.5E-01 | 2.5E-02 | 3.9E+01 | 4.1E-02 | 2d |1\n", - "2023-02-17 16:05:40.874 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.9091 and h = 1.33625e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:40.874 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.9091: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:40 fides(INFO) 117| +1.48E+02 | +0.0E+00 | +0.0E+00 | 2.5E-01 | 1.5E-02 | 2.4E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 2| +2.65E+02 | -1.1E+02 | +8.9E-01 | 2.5E-01 | 6.3E+02 | 5.3E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 39| +1.48E+02 | -9.9E-03 | +2.7E-02 | 5.0E-02 | 5.7E+00 | 7.6E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 3| +2.50E+02 | -1.5E+01 | +7.5E-01 | 5.0E-01 | 9.3E+01 | 1.1E+00 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 118| +1.48E+02 | -1.7E-05 | +1.1E+00 | 4.6E-02 | 1.5E-02 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 40| +1.48E+02 | -6.2E-02 | +9.3E-01 | 1.3E-02 | 1.7E+01 | 9.8E-03 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 4| +2.50E+02 | +8.8E-01 | -5.9E-01 | 1.0E+00 | 1.4E+01 | 2.8E+00 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 119| +1.48E+02 | -2.8E-05 | +1.1E+00 | 9.2E-02 | 1.2E-02 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 41| +1.48E+02 | -2.7E-02 | +2.8E-01 | 2.5E-02 | 6.2E+00 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 5| +2.50E+02 | +8.8E-01 | -5.9E-01 | 2.5E-01 | 1.4E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 120| +1.48E+02 | -3.2E-05 | +1.1E+00 | 1.8E-01 | 1.5E-02 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 42| +1.48E+02 | -3.7E-02 | +8.1E-01 | 2.5E-02 | 1.6E+01 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 6| +2.50E+02 | -3.7E-01 | +2.4E-01 | 6.2E-02 | 1.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 121| +1.48E+02 | -2.0E-05 | +1.3E+00 | 3.7E-01 | 4.5E-03 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 43| +1.48E+02 | -9.4E-03 | +9.4E-01 | 5.0E-02 | 1.6E+00 | 3.5E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 7| +2.50E+02 | -2.6E-01 | +8.7E-01 | 1.6E-02 | 1.2E+01 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 122| +1.48E+02 | -1.5E-05 | +1.9E+00 | 3.7E-01 | 7.8E-04 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 44| +1.48E+02 | -1.2E-02 | +8.9E-01 | 1.0E-01 | 2.0E+00 | 5.9E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 8| +2.50E+02 | -6.9E-02 | +3.3E-01 | 3.1E-02 | 6.4E+00 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 123| +1.48E+02 | -4.1E-06 | +8.8E-01 | 3.7E-01 | 3.2E-03 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 45| +1.48E+02 | +3.5E-02 | -2.2E+00 | 2.0E-01 | 8.2E-01 | 1.6E-01 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 9| +2.50E+02 | +7.7E-03 | -9.9E-02 | 3.1E-02 | 3.6E+00 | 8.4E-02 | 2d |0\n", - "2023-02-17 16:05:40 fides(INFO) 124| +1.48E+02 | -7.2E-06 | +3.1E+00 | 3.7E-01 | 2.4E-03 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:40 fides(INFO) 10| +2.50E+02 | -4.0E-02 | +7.1E-01 | 7.8E-03 | 3.6E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:40 fides(INFO) 46| +1.48E+02 | -2.5E-03 | +4.2E-01 | 5.0E-02 | 8.2E-01 | 3.9E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 125| +1.48E+02 | +2.8E-07 | -5.5E-01 | 3.7E-01 | 1.1E-03 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 11| +2.50E+02 | +6.8E-06 | -9.8E-03 | 7.8E-03 | 3.5E-01 | 2.1E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 47| +1.48E+02 | +1.0E-03 | -1.0E-01 | 5.0E-02 | 1.0E+00 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 126| +1.48E+02 | +4.5E-06 | -9.3E+00 | 7.2E-02 | 1.1E-03 | 4.5E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 12| +2.50E+02 | +6.8E-06 | -9.8E-03 | 2.0E-03 | 3.5E-01 | 5.1E-03 | 2d |0\n", - "2023-02-17 16:05:41.025 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.328 and h = 3.65848e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:41.025 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.328: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:41 fides(INFO) 48| +1.48E+02 | +0.0E+00 | +0.0E+00 | 1.3E-02 | 1.0E+00 | 4.9E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 13| +2.50E+02 | -3.0E-04 | +8.3E-01 | 4.9E-04 | 3.5E-01 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 127| +1.48E+02 | +3.0E-06 | -2.2E+01 | 1.8E-02 | 1.1E-03 | 1.1E-02 | 2d |0\n", - "2023-02-17 16:05:41.037 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.4913 and h = 1.14652e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:41.037 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.4913: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:41 fides(INFO) 49| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.1E-03 | 1.0E+00 | 1.7E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 128| +1.48E+02 | +5.1E-06 | -1.5E+02 | 4.5E-03 | 1.1E-03 | 2.8E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 14| +2.50E+02 | +4.7E-07 | -4.1E-03 | 9.8E-04 | 1.4E-01 | 2.6E-03 | 2d |0\n", - "2023-02-17 16:05:41.053 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.1737 and h = 1.14938e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:41.053 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.1737: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 50| +1.48E+02 | +0.0E+00 | +0.0E+00 | 7.8E-04 | 1.0E+00 | 9.4E-04 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 15| +2.50E+02 | -5.6E-05 | +7.8E-01 | 2.4E-04 | 1.4E-01 | 6.2E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 129| +1.48E+02 | +3.9E-06 | -4.3E+02 | 1.1E-03 | 1.1E-03 | 7.1E-04 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 16| +2.50E+02 | +9.6E-09 | -1.2E-03 | 4.9E-04 | 3.8E-02 | 1.3E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 51| +1.48E+02 | -3.5E-04 | +9.6E-01 | 2.0E-04 | 1.0E+00 | 5.0E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 130| +1.48E+02 | +2.7E-06 | -1.1E+03 | 2.8E-04 | 1.1E-03 | 1.8E-04 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 17| +2.50E+02 | -3.8E-06 | +4.9E-01 | 1.2E-04 | 3.8E-02 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 131| +1.48E+02 | +3.4E-06 | -4.1E+03 | 7.0E-05 | 1.1E-03 | 4.4E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 52| +1.48E+02 | -5.3E-04 | +9.3E-01 | 3.9E-04 | 1.2E+00 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 18| +2.50E+02 | -5.7E-10 | +4.8E-04 | 1.2E-04 | 1.4E-02 | 2.9E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 132| +1.48E+02 | +5.2E-06 | -1.3E+04 | 1.8E-05 | 1.1E-03 | 1.1E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 53| +1.48E+02 | -3.7E-04 | +9.5E-01 | 7.8E-04 | 1.1E+00 | 6.4E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 19| +2.50E+02 | -5.6E-07 | +6.9E-01 | 3.1E-05 | 1.4E-02 | 7.7E-05 | 2d |1\n", - "2023-02-17 16:05:41 fides(WARNING) Stopping as function difference 5.57E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:41 fides(INFO) 133| +1.48E+02 | +2.8E-06 | -9.2E+03 | 4.4E-06 | 1.1E-03 | 2.8E-06 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 54| +1.48E+02 | -4.3E-04 | +9.1E-01 | 1.6E-03 | 1.1E+00 | 8.6E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 134| +1.48E+02 | +4.9E-06 | -1.8E+04 | 1.1E-06 | 1.1E-03 | 8.5E-07 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 55| +1.48E+02 | -4.7E-04 | +9.5E-01 | 3.1E-03 | 1.0E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 135| +1.48E+02 | +1.7E-06 | -6.4E+03 | 2.8E-07 | 1.1E-03 | 5.2E-07 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 56| +1.48E+02 | -6.2E-04 | +8.7E-01 | 6.3E-03 | 7.6E-01 | 2.5E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 136| +1.48E+02 | +2.8E-06 | -1.8E+04 | 6.9E-08 | 1.1E-03 | 1.8E-07 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 57| +1.48E+02 | -7.6E-04 | +9.5E-01 | 1.3E-02 | 1.0E+00 | 5.9E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 137| +1.48E+02 | +2.6E-06 | -5.8E+04 | 1.7E-08 | 1.1E-03 | 4.5E-08 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 58| +1.48E+02 | -1.2E-03 | +9.5E-01 | 2.5E-02 | 4.2E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 138| +1.48E+02 | +4.1E-06 | -3.5E+05 | 4.3E-09 | 1.1E-03 | 1.1E-08 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 59| +1.48E+02 | -1.3E-03 | +6.8E-01 | 5.0E-02 | 1.1E+00 | 3.0E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 139| +1.48E+02 | +4.5E-06 | -1.5E+06 | 1.1E-09 | 1.1E-03 | 2.8E-09 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 60| +1.48E+02 | +2.5E-03 | -8.0E-01 | 5.0E-02 | 4.9E-01 | 1.3E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 140| +1.48E+02 | +3.6E-06 | -4.9E+06 | 2.7E-10 | 1.1E-03 | 7.0E-10 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 61| +1.48E+02 | -2.4E-04 | +1.9E-01 | 1.3E-02 | 4.9E-01 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:41.250 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.431 and h = 4.24931e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:41.250 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.431: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:41 fides(INFO) 141| +1.48E+02 | +0.0E+00 | +0.0E+00 | 6.7E-11 | 1.1E-03 | 1.7E-10 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 62| +1.48E+02 | -5.3E-04 | +9.3E-01 | 3.1E-03 | 1.9E+00 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 142| +1.48E+02 | +4.7E-06 | -1.0E+08 | 1.7E-11 | 1.1E-03 | 4.4E-11 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 63| +1.48E+02 | -2.0E-04 | +8.5E-01 | 6.3E-03 | 1.8E-01 | 4.0E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 143| +1.48E+02 | +3.6E-06 | -3.1E+08 | 4.2E-12 | 1.1E-03 | 1.1E-11 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 64| +1.48E+02 | -2.2E-04 | +8.7E-01 | 1.3E-02 | 2.4E-01 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 144| +1.48E+02 | +5.3E-06 | -1.8E+09 | 1.1E-12 | 1.1E-03 | 2.7E-12 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 65| +1.48E+02 | -3.5E-04 | +9.5E-01 | 2.5E-02 | 1.6E-01 | 9.5E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 145| +1.48E+02 | +4.6E-06 | -6.4E+09 | 2.6E-13 | 1.1E-03 | 6.8E-13 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 66| +1.48E+02 | -2.7E-04 | +4.8E-01 | 5.0E-02 | 4.2E-01 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 146| +1.48E+02 | +3.9E-06 | -2.2E+10 | 6.6E-14 | 1.1E-03 | 1.7E-13 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 67| +1.48E+02 | +4.3E-03 | -2.3E+00 | 5.0E-02 | 3.1E-01 | 1.8E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 147| +1.48E+02 | +4.5E-06 | -1.0E+11 | 1.6E-14 | 1.1E-03 | 4.3E-14 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 68| +1.48E+02 | +9.9E-05 | -1.4E-01 | 1.3E-02 | 3.1E-01 | 5.1E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 148| +1.48E+02 | +3.0E-06 | -2.7E+11 | 4.1E-15 | 1.1E-03 | 1.1E-14 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 69| +1.48E+02 | -8.9E-05 | +2.7E-01 | 3.1E-03 | 3.1E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 70| +1.48E+02 | -1.8E-04 | +9.4E-01 | 3.1E-03 | 1.2E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 149| +1.48E+02 | +3.2E-06 | -1.1E+12 | 1.0E-15 | 1.1E-03 | 2.7E-15 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 71| +1.48E+02 | -3.2E-05 | +1.2E+00 | 6.3E-03 | 8.9E-02 | 1.8E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 150| +1.48E+02 | +5.3E-06 | -7.6E+12 | 2.6E-16 | 1.1E-03 | 6.6E-16 | 2d |0\n", - "2023-02-17 16:05:41 fides(WARNING) Stopping as trust region radius 6.41E-17 is smaller than machine precision.\n", - "2023-02-17 16:05:41 fides(INFO) 72| +1.48E+02 | -3.7E-05 | +8.6E-01 | 1.3E-02 | 8.6E-02 | 3.3E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 73| +1.48E+02 | -6.3E-05 | +9.8E-01 | 2.5E-02 | 7.2E-02 | 5.6E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 74| +1.48E+02 | -4.4E-05 | +6.1E-01 | 5.0E-02 | 5.9E-02 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 75| +1.48E+02 | +1.5E-03 | -5.8E+00 | 5.0E-02 | 9.9E-02 | 1.2E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 0| +9.34E+11 | NaN | NaN | 1.0E+00 | 4.2E+12 | NaN | NaN |1\n", - "2023-02-17 16:05:41 fides(INFO) 76| +1.48E+02 | +3.1E-05 | -3.4E-01 | 1.3E-02 | 9.9E-02 | 3.1E-03 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 1| +1.55E+10 | -9.2E+11 | +9.0E-02 | 1.0E+00 | 4.2E+12 | 2.9E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 77| +1.48E+02 | -1.9E-05 | +6.6E-01 | 3.1E-03 | 9.9E-02 | 8.3E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 2| +2.31E+09 | -1.3E+10 | +8.9E-01 | 2.5E-01 | 7.1E+10 | 6.2E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 78| +1.48E+02 | -3.1E-06 | +5.1E-01 | 3.1E-03 | 2.0E-01 | 3.1E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 3| +3.45E+08 | -2.0E+09 | +8.9E-01 | 5.0E-01 | 1.1E+10 | 8.8E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 79| +1.48E+02 | -7.2E-06 | +4.2E+00 | 3.1E-03 | 2.3E-02 | 3.3E-04 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 4| +5.14E+07 | -2.9E+08 | +8.9E-01 | 5.0E-01 | 1.6E+09 | 8.3E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 5| +7.57E+06 | -4.4E+07 | +9.0E-01 | 5.0E-01 | 2.4E+08 | 8.1E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 80| +1.48E+02 | +1.0E-06 | -4.2E-01 | 6.3E-03 | 5.3E-02 | 7.8E-04 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 6| +1.09E+06 | -6.5E+06 | +9.0E-01 | 5.0E-01 | 3.5E+07 | 8.6E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 81| +1.48E+02 | +4.3E-06 | -5.0E+00 | 1.6E-03 | 5.3E-02 | 2.0E-04 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 7| +1.53E+05 | -9.3E+05 | +9.0E-01 | 5.0E-01 | 5.1E+06 | 7.8E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 82| +1.48E+02 | +8.8E-07 | -1.8E+00 | 3.9E-04 | 5.3E-02 | 5.1E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 83| +1.48E+02 | +4.2E-06 | -1.1E+01 | 9.8E-05 | 5.3E-02 | 1.8E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 8| +2.17E+04 | -1.3E+05 | +9.0E-01 | 5.0E-01 | 7.4E+05 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 84| +1.48E+02 | +2.8E-06 | -7.9E+00 | 2.4E-05 | 5.3E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 9| +3.33E+03 | -1.8E+04 | +9.0E-01 | 5.0E-01 | 1.1E+05 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 85| +1.48E+02 | +5.6E-06 | -1.6E+01 | 6.1E-06 | 5.3E-02 | 1.3E-05 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 10| +6.49E+02 | -2.7E+03 | +9.0E-01 | 5.0E-01 | 1.7E+04 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 86| +1.48E+02 | +4.9E-06 | -2.7E+01 | 1.5E-06 | 5.3E-02 | 4.0E-06 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 11| +2.51E+02 | -4.0E+02 | +9.0E-01 | 5.0E-01 | 2.7E+03 | 4.6E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 87| +1.48E+02 | -4.1E-07 | +8.0E+00 | 3.8E-07 | 5.3E-02 | 1.0E-06 | 2d |1\n", - "2023-02-17 16:05:41 fides(WARNING) Stopping as function difference 4.13E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:41 fides(INFO) 12| +1.78E+02 | -7.3E+01 | +9.8E-01 | 5.0E-01 | 4.4E+02 | 1.0E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 13| +1.67E+02 | -1.1E+01 | +5.5E-02 | 1.0E+00 | 8.3E+01 | 2.0E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 14| +1.57E+02 | -1.0E+01 | +9.3E-01 | 2.5E-01 | 1.4E+02 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 0| +8.42E+08 | NaN | NaN | 1.0E+00 | 3.9E+09 | NaN | NaN |1\n", - "2023-02-17 16:05:41 fides(INFO) 15| +1.57E+02 | +1.3E+00 | -3.3E-01 | 5.0E-01 | 5.6E+01 | 7.4E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 1| +1.10E+04 | -8.4E+08 | +9.9E-02 | 1.0E+00 | 3.9E+09 | 2.7E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 16| +1.53E+02 | -4.2E+00 | +7.0E-01 | 1.2E-01 | 5.6E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 2| +2.54E+03 | -8.5E+03 | +9.1E-01 | 2.5E-01 | 3.8E+04 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 17| +1.51E+02 | -1.8E+00 | +9.4E-01 | 1.2E-01 | 9.4E+01 | 1.6E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 3| +6.03E+02 | -1.9E+03 | +8.9E-01 | 5.0E-01 | 7.7E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 18| +1.50E+02 | -1.3E+00 | +9.4E-01 | 2.5E-01 | 1.8E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 4| +2.82E+02 | -3.2E+02 | +8.9E-01 | 1.0E+00 | 1.3E+03 | 2.3E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 5| +2.50E+02 | -3.2E+01 | +8.0E-01 | 2.0E+00 | 1.8E+02 | 4.0E+00 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 19| +1.50E+02 | +3.5E-01 | -3.1E-01 | 5.0E-01 | 2.1E+01 | 5.6E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 6| +2.33E+02 | -1.7E+01 | +2.8E+01 | 4.0E+00 | 5.7E+00 | 7.6E+00 | trr |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 20| +1.49E+02 | -5.1E-01 | +7.8E-01 | 1.2E-01 | 2.1E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 7| +2.33E+02 | +9.0E+03 | -4.0E+02 | 8.0E+00 | 4.7E+01 | 7.1E+00 | trr |0\n", - "2023-02-17 16:05:41 fides(INFO) 21| +1.49E+02 | -6.7E-02 | +6.0E-02 | 2.5E-01 | 1.8E+01 | 2.9E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 8| +2.33E+02 | +1.5E+02 | -4.6E+00 | 5.8E-01 | 4.7E+01 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 22| +1.49E+02 | -5.2E-01 | +8.7E-01 | 6.2E-02 | 2.0E+01 | 6.9E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 9| +2.18E+02 | -1.4E+01 | +8.8E-01 | 1.4E-01 | 4.7E+01 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 23| +1.48E+02 | -3.8E-01 | +6.5E-01 | 1.2E-01 | 2.4E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 10| +2.18E+02 | +1.7E+01 | -1.6E+00 | 2.9E-01 | 3.1E+01 | 7.2E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 11| +2.14E+02 | -4.4E+00 | +8.7E-01 | 7.2E-02 | 3.1E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 24| +1.48E+02 | -1.3E-01 | +5.2E-01 | 1.2E-01 | 1.5E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 12| +2.14E+02 | +2.9E+00 | -9.6E-01 | 1.4E-01 | 1.8E+01 | 3.5E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 25| +1.48E+02 | -1.4E-01 | +3.7E-01 | 1.2E-01 | 8.7E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 13| +2.13E+02 | -1.2E+00 | +8.6E-01 | 3.6E-02 | 1.8E+01 | 8.8E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 26| +1.48E+02 | +4.6E-01 | -8.0E-01 | 1.2E-01 | 1.0E+01 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 14| +2.13E+02 | +2.7E-01 | -4.0E-01 | 7.2E-02 | 9.2E+00 | 1.9E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 27| +1.48E+02 | -1.2E-01 | +6.6E-01 | 3.1E-02 | 1.0E+01 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 15| +2.12E+02 | -3.0E-01 | +8.4E-01 | 1.8E-02 | 9.2E+00 | 4.5E-02 | 2d |1\n", - "2023-02-17 16:05:41.877 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 141.82 and h = 3.32306e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:41.877 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 141.82: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:41 fides(INFO) 28| +1.48E+02 | +0.0E+00 | +0.0E+00 | 3.1E-02 | 2.6E+00 | 3.0E-02 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 16| +2.12E+02 | -1.0E-01 | +4.0E-01 | 3.6E-02 | 4.1E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 29| +1.48E+02 | -8.7E-03 | +9.5E-01 | 7.8E-03 | 2.6E+00 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 17| +2.12E+02 | -2.0E-01 | +5.2E-01 | 3.6E-02 | 7.7E+00 | 8.3E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 30| +1.48E+02 | -1.5E-02 | +9.5E-01 | 1.6E-02 | 2.5E+00 | 1.5E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 18| +2.12E+02 | -2.3E-02 | +7.4E-02 | 3.6E-02 | 5.0E+00 | 9.6E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 19| +2.12E+02 | -1.3E-01 | +9.0E-01 | 9.0E-03 | 8.6E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 31| +1.48E+02 | -2.4E-02 | +9.4E-01 | 3.1E-02 | 2.8E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:41 fides(INFO) 20| +2.12E+02 | -1.1E-01 | +7.3E-01 | 1.8E-02 | 5.2E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 32| +1.48E+02 | -4.2E-02 | +9.7E-01 | 6.2E-02 | 2.4E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 21| +2.12E+02 | -7.5E-02 | +8.7E-01 | 1.8E-02 | 2.0E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 33| +1.48E+02 | -6.1E-02 | +9.1E-01 | 1.2E-01 | 3.7E+00 | 9.9E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 22| +2.12E+02 | -2.6E-02 | +3.2E-01 | 3.6E-02 | 2.5E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 34| +1.48E+02 | -3.0E-02 | +3.1E-01 | 2.5E-01 | 1.9E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 23| +2.12E+02 | +9.0E-03 | -8.2E-02 | 3.6E-02 | 4.2E+00 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 35| +1.48E+02 | +2.3E+00 | -4.2E+00 | 2.5E-01 | 6.9E+00 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:41 fides(INFO) 24| +2.12E+02 | -5.8E-02 | +7.7E-01 | 9.0E-03 | 4.2E+00 | 2.2E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 36| +1.48E+02 | -2.0E-02 | +1.1E-01 | 6.2E-02 | 6.9E+00 | 9.0E-02 | 2d |1\n", - "2023-02-17 16:05:41 fides(INFO) 25| +2.12E+02 | -7.7E-03 | +3.5E-01 | 1.8E-02 | 1.2E+00 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 26| +2.12E+02 | -2.0E-03 | +5.5E-02 | 1.8E-02 | 2.4E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 37| +1.48E+02 | -2.4E-02 | +7.1E-01 | 1.6E-02 | 3.6E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 27| +2.12E+02 | -1.6E-02 | +8.1E-01 | 4.5E-03 | 2.5E+00 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 38| +1.48E+02 | -1.1E-02 | +8.5E-01 | 1.6E-02 | 7.5E+00 | 7.5E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 28| +2.12E+02 | -2.1E-03 | +2.6E-01 | 9.0E-03 | 1.0E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 39| +1.48E+02 | -4.4E-03 | +8.4E-01 | 3.1E-02 | 1.3E+00 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 29| +2.12E+02 | -4.7E-04 | +5.5E-02 | 9.0E-03 | 1.2E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 40| +1.48E+02 | -5.5E-03 | +8.9E-01 | 6.2E-02 | 9.8E-01 | 4.0E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 30| +2.12E+02 | -3.8E-03 | +8.3E-01 | 2.3E-03 | 1.2E+00 | 4.7E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 41| +1.48E+02 | -2.2E-03 | +2.4E-01 | 1.2E-01 | 5.8E-01 | 5.7E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 31| +2.12E+02 | -6.4E-04 | +3.1E-01 | 4.5E-03 | 5.1E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 42| +1.48E+02 | -4.0E-03 | +2.7E-01 | 3.1E-02 | 1.2E+00 | 3.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 32| +2.12E+02 | -3.0E-04 | +1.6E-01 | 4.5E-03 | 5.1E-01 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 43| +1.48E+02 | -9.5E-04 | +1.8E-01 | 3.1E-02 | 1.5E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 33| +2.12E+02 | -7.6E-04 | +8.1E-01 | 1.1E-03 | 5.2E-01 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 44| +1.48E+02 | -9.1E-04 | +6.6E-01 | 7.8E-03 | 4.3E-01 | 7.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 34| +2.12E+02 | -2.0E-04 | +5.3E-01 | 2.3E-03 | 1.8E-01 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 35| +2.12E+02 | -1.5E-04 | +4.4E-01 | 2.3E-03 | 1.9E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 45| +1.48E+02 | -4.2E-04 | +9.1E-01 | 7.8E-03 | 1.2E+00 | 5.0E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 36| +2.12E+02 | -1.8E-04 | +5.0E-01 | 2.3E-03 | 1.9E-01 | 6.5E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 46| +1.48E+02 | -3.1E-04 | +8.6E-01 | 1.6E-02 | 1.1E-01 | 8.3E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 37| +2.12E+02 | -1.6E-04 | +4.8E-01 | 2.3E-03 | 1.7E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 47| +1.48E+02 | -4.5E-04 | +9.6E-01 | 3.1E-02 | 3.1E-01 | 1.2E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 38| +2.12E+02 | -1.7E-04 | +5.3E-01 | 2.3E-03 | 1.7E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 48| +1.48E+02 | -4.7E-04 | +7.8E-01 | 6.2E-02 | 1.0E-01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 39| +2.12E+02 | -1.6E-04 | +5.3E-01 | 2.3E-03 | 1.6E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 49| +1.48E+02 | +1.2E-02 | -9.4E+00 | 1.2E-01 | 2.7E-01 | 2.4E-02 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 40| +2.12E+02 | -1.7E-04 | +5.6E-01 | 2.3E-03 | 1.6E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 41| +2.12E+02 | -1.6E-04 | +5.6E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 50| +1.48E+02 | +2.7E-04 | -4.9E-01 | 3.1E-02 | 2.7E-01 | 6.0E-03 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 42| +2.12E+02 | -1.7E-04 | +5.8E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 51| +1.48E+02 | -1.1E-04 | +6.3E-01 | 7.7E-03 | 2.7E-01 | 1.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 43| +2.12E+02 | -1.6E-04 | +5.7E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 52| +1.48E+02 | -4.0E-05 | +9.6E-01 | 7.7E-03 | 2.9E-01 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 44| +2.12E+02 | -1.7E-04 | +5.9E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 53| +1.48E+02 | -5.5E-05 | +9.2E-01 | 1.5E-02 | 6.6E-02 | 3.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 45| +2.12E+02 | -1.6E-04 | +5.7E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 54| +1.48E+02 | -5.8E-05 | +6.3E-01 | 3.1E-02 | 1.9E-01 | 9.0E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 46| +2.12E+02 | -1.8E-04 | +5.9E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 55| +1.48E+02 | +2.9E-04 | -1.5E+00 | 3.1E-02 | 1.1E-01 | 4.8E-03 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 47| +2.12E+02 | -1.7E-04 | +5.8E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 56| +1.48E+02 | -1.5E-05 | +2.1E-01 | 7.7E-03 | 1.1E-01 | 1.4E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 48| +2.12E+02 | -1.8E-04 | +5.9E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 57| +1.48E+02 | -1.8E-05 | +6.5E-01 | 1.9E-03 | 3.8E-01 | 7.5E-04 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 49| +2.12E+02 | -1.7E-04 | +5.7E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 50| +2.12E+02 | -1.8E-04 | +5.9E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 58| +1.48E+02 | -7.5E-06 | +1.3E+00 | 1.9E-03 | 3.9E-02 | 5.8E-04 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 51| +2.12E+02 | -1.7E-04 | +5.7E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 59| +1.48E+02 | -6.3E-06 | +8.7E-01 | 3.8E-03 | 3.4E-02 | 9.8E-04 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 52| +2.12E+02 | -1.9E-04 | +5.9E-01 | 2.3E-03 | 1.5E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 60| +1.48E+02 | -8.8E-06 | +1.1E+00 | 7.7E-03 | 2.4E-02 | 1.5E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 53| +2.12E+02 | -1.7E-04 | +5.7E-01 | 2.3E-03 | 1.5E-01 | 6.9E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 61| +1.48E+02 | -1.1E-05 | +9.2E-01 | 1.5E-02 | 3.1E-02 | 2.3E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 54| +2.12E+02 | -1.9E-04 | +5.9E-01 | 2.3E-03 | 1.6E-01 | 6.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 62| +1.48E+02 | -1.2E-05 | +8.7E-01 | 3.1E-02 | 2.7E-02 | 4.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 55| +2.12E+02 | -1.8E-04 | +9.9E-01 | 2.3E-03 | 1.6E-01 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 63| +1.48E+02 | +2.0E-04 | -9.4E+00 | 6.2E-02 | 2.6E-02 | 4.2E-03 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) 56| +2.12E+02 | -2.6E-04 | +8.5E-01 | 4.5E-03 | 5.1E-02 | 9.6E-03 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 64| +1.48E+02 | +8.0E-05 | -4.7E+00 | 1.5E-02 | 2.6E-02 | 1.6E-03 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 57| +2.12E+02 | -5.6E-04 | +9.6E-01 | 9.0E-03 | 2.1E-01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 58| +2.12E+02 | -8.4E-04 | +9.1E-01 | 1.8E-02 | 8.4E-02 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 65| +1.48E+02 | +6.1E-05 | -4.2E+00 | 3.7E-03 | 2.6E-02 | 1.2E-03 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 59| +2.12E+02 | -1.3E-03 | +6.2E-01 | 3.6E-02 | 2.1E-01 | 6.1E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 66| +1.48E+02 | +5.9E-05 | -4.2E+00 | 9.2E-04 | 2.6E-02 | 1.1E-03 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 60| +2.12E+02 | -4.4E-03 | +9.8E-01 | 3.6E-02 | 4.4E-01 | 9.1E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 67| +1.48E+02 | +1.5E-05 | -1.5E+00 | 2.3E-04 | 2.6E-02 | 5.1E-04 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 61| +2.12E+02 | -1.1E-02 | +1.3E+00 | 7.2E-02 | 5.4E-01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 68| +1.48E+02 | -1.4E-06 | +4.7E-01 | 5.8E-05 | 2.6E-02 | 1.3E-04 | 2d |1\n", - "2023-02-17 16:05:42 fides(WARNING) Stopping as function difference 1.38E-06 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:42 fides(INFO) 62| +2.12E+02 | -3.6E-02 | +1.5E+00 | 1.4E-01 | 7.1E-01 | 3.6E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 63| +2.11E+02 | -2.5E-01 | +2.4E+00 | 2.9E-01 | 1.5E+00 | 7.4E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 64| +2.11E+02 | +2.4E+00 | -5.0E+00 | 5.8E-01 | 3.3E+00 | 1.5E+00 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 65| +2.11E+02 | -3.8E-01 | +1.5E+00 | 1.4E-01 | 3.3E+00 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 66| +2.10E+02 | -5.4E-01 | +4.7E-01 | 2.9E-01 | 3.4E+00 | 7.5E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 67| +2.10E+02 | +6.8E-01 | -7.8E-01 | 2.9E-01 | 4.7E+00 | 4.5E-01 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 68| +2.10E+02 | -4.3E-02 | +8.2E-02 | 7.2E-02 | 4.7E+00 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 69| +2.10E+02 | -2.4E-01 | +9.2E-01 | 1.8E-02 | 9.5E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 70| +2.10E+02 | -6.3E-02 | +9.4E-01 | 3.6E-02 | 3.3E+00 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 71| +2.10E+02 | -6.0E-02 | +8.9E-01 | 7.2E-02 | 1.8E+00 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 72| +2.10E+02 | -1.1E-01 | +9.6E-01 | 1.4E-01 | 2.1E+00 | 3.3E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 73| +2.10E+02 | -2.5E-01 | +1.3E+00 | 2.9E-01 | 1.2E+00 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 74| +2.10E+02 | +4.9E+00 | -1.4E+01 | 5.8E-01 | 2.4E+00 | 1.5E+00 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) 75| +2.10E+02 | -1.7E-01 | +1.1E+00 | 1.4E-01 | 2.4E+00 | 3.5E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 76| +2.09E+02 | -5.6E-01 | +9.5E-01 | 2.9E-01 | 3.0E+00 | 7.2E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 77| +2.01E+02 | -7.5E+00 | +4.2E+00 | 5.8E-01 | 9.0E+00 | 1.2E+00 | trr |1\n", - "2023-02-17 16:05:42 fides(INFO) 78| +2.01E+02 | +8.4E+00 | -6.4E-01 | 1.2E+00 | 2.8E+01 | 1.4E+00 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) 79| +1.89E+02 | -1.3E+01 | +1.5E+00 | 2.9E-01 | 2.8E+01 | 5.4E-01 | trr |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 80| +1.89E+02 | +2.6E+02 | -1.0E+01 | 5.8E-01 | 4.4E+01 | 1.3E+00 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) 81| +1.83E+02 | -5.8E+00 | +7.1E-01 | 1.4E-01 | 4.4E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 82| +1.70E+02 | -1.3E+01 | +8.3E-01 | 1.4E-01 | 7.0E+01 | 3.1E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 83| +1.70E+02 | +7.7E+00 | -5.5E-01 | 2.9E-01 | 4.8E+01 | 6.2E-01 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 84| +1.65E+02 | -5.3E+00 | +8.0E-01 | 7.2E-02 | 4.8E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 85| +1.58E+02 | -6.6E+00 | +8.3E-01 | 1.4E-01 | 7.3E+01 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:42.744 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 124.452 and h = 1.67549e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2023-02-17 16:05:42.744 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 124.452: AMICI failed to integrate the forward problem\n", - "2023-02-17 16:05:42 fides(INFO) 86| +1.58E+02 | +0.0E+00 | +0.0E+00 | 2.9E-01 | 7.6E+01 | 5.5E-01 | trr |0\n", - "2023-02-17 16:05:42 fides(INFO) 87| +1.54E+02 | -4.1E+00 | +7.1E-01 | 7.2E-02 | 7.6E+01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 88| +1.51E+02 | -2.7E+00 | +8.3E-01 | 7.2E-02 | 8.1E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 89| +1.49E+02 | -2.5E+00 | +5.7E-01 | 1.4E-01 | 4.4E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 90| +1.49E+02 | +9.5E-01 | -4.4E-01 | 1.4E-01 | 5.9E+01 | 1.8E-01 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 91| +1.49E+02 | +1.7E-01 | -1.5E-01 | 3.6E-02 | 5.9E+01 | 5.1E-02 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 92| +1.48E+02 | -3.6E-01 | +5.3E-01 | 9.0E-03 | 5.9E+01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 93| +1.48E+02 | -8.1E-02 | +9.6E-01 | 9.0E-03 | 1.7E+01 | 1.0E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 94| +1.48E+02 | -1.1E-01 | +7.4E-01 | 1.8E-02 | 1.2E+01 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 95| +1.48E+02 | -1.1E-01 | +8.2E-01 | 1.8E-02 | 2.1E+01 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 96| +1.48E+02 | -1.3E-01 | +8.8E-01 | 3.6E-02 | 1.2E+01 | 5.2E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 97| +1.48E+02 | -1.6E-01 | +7.5E-01 | 7.2E-02 | 8.8E+00 | 9.5E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 98| +1.48E+02 | +3.0E+00 | -5.2E+00 | 1.4E-01 | 1.5E+01 | 2.3E-01 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) 99| +1.48E+02 | +6.0E-02 | -2.5E-01 | 3.6E-02 | 1.5E+01 | 5.6E-02 | 2d |0\n", - "2023-02-17 16:05:42 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:42 fides(INFO) 100| +1.48E+02 | -5.5E-02 | +6.6E-01 | 9.0E-03 | 1.5E+01 | 1.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 101| +1.48E+02 | -2.2E-02 | +8.1E-01 | 9.0E-03 | 5.3E+00 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 102| +1.48E+02 | -2.6E-02 | +5.8E-01 | 1.8E-02 | 5.6E+00 | 2.7E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 103| +1.48E+02 | -3.4E-02 | +9.3E-01 | 1.8E-02 | 8.4E+00 | 2.4E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 104| +1.48E+02 | -5.2E-02 | +8.2E-01 | 3.6E-02 | 4.3E+00 | 4.6E-02 | 2d |1\n", - "2023-02-17 16:05:42 fides(INFO) 105| +1.48E+02 | +5.5E-02 | -6.4E-01 | 7.2E-02 | 5.2E+00 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 106| +1.48E+02 | -1.9E-02 | +7.2E-01 | 1.8E-02 | 5.2E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 107| +1.48E+02 | +4.0E-02 | -1.0E+00 | 1.8E-02 | 2.5E+00 | 2.7E-02 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 108| +1.48E+02 | +2.1E-02 | -1.4E+00 | 4.5E-03 | 2.5E+00 | 8.4E-03 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 109| +1.48E+02 | +6.9E-04 | -1.2E-01 | 1.1E-03 | 2.5E+00 | 2.7E-03 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 110| +1.48E+02 | -1.2E-03 | +7.3E-01 | 2.8E-04 | 2.5E+00 | 6.9E-04 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 111| +1.48E+02 | -1.1E-03 | +9.9E-01 | 2.8E-04 | 2.2E+00 | 5.6E-04 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 112| +1.48E+02 | -2.0E-03 | +1.0E+00 | 5.6E-04 | 2.0E+00 | 1.2E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 113| +1.48E+02 | -3.3E-03 | +9.9E-01 | 1.1E-03 | 2.0E+00 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 114| +1.48E+02 | -3.2E-03 | +9.9E-01 | 2.3E-03 | 1.9E+00 | 3.2E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 115| +1.48E+02 | -5.6E-03 | +9.7E-01 | 4.5E-03 | 2.1E+00 | 6.4E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 116| +1.48E+02 | -1.0E-02 | +9.8E-01 | 9.0E-03 | 2.1E+00 | 1.3E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 117| +1.48E+02 | -2.0E-02 | +1.0E+00 | 1.8E-02 | 1.9E+00 | 2.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 118| +1.48E+02 | -2.3E-02 | +8.0E-01 | 3.6E-02 | 1.5E+00 | 5.0E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 119| +1.48E+02 | -2.5E-02 | +4.5E-01 | 7.2E-02 | 2.4E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 120| +1.48E+02 | +3.0E-02 | -6.7E-01 | 7.2E-02 | 3.0E+00 | 1.0E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 121| +1.48E+02 | -1.6E-02 | +8.0E-01 | 1.8E-02 | 3.0E+00 | 2.5E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 122| +1.48E+02 | -1.4E-02 | +8.7E-01 | 3.6E-02 | 2.4E+00 | 4.9E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 123| +1.48E+02 | -4.8E-03 | +4.7E-01 | 7.2E-02 | 2.4E+00 | 8.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 124| +1.48E+02 | -5.4E-03 | +1.1E+00 | 7.2E-02 | 5.7E+00 | 3.3E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 125| +1.48E+02 | -2.8E-04 | +3.9E-01 | 7.2E-02 | 7.7E-01 | 1.8E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 126| +1.48E+02 | -3.6E-04 | +1.0E+00 | 7.2E-02 | 9.5E-01 | 8.8E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 127| +1.48E+02 | -1.4E-05 | +7.5E-01 | 7.2E-02 | 1.4E-01 | 5.1E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 128| +1.48E+02 | -3.9E-06 | +1.3E+00 | 7.2E-02 | 5.9E-02 | 1.1E-03 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 129| +1.48E+02 | +3.8E-06 | -1.5E+02 | 7.2E-02 | 1.1E-02 | 5.7E-05 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 130| +1.48E+02 | +1.3E-06 | -7.0E+01 | 1.2E-05 | 1.1E-02 | 1.4E-05 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 131| +1.48E+02 | +2.4E-06 | -1.7E+02 | 3.0E-06 | 1.1E-02 | 4.1E-06 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 132| +1.48E+02 | +1.2E-06 | -1.1E+02 | 7.4E-07 | 1.1E-02 | 1.4E-06 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 133| +1.48E+02 | -7.8E-07 | +2.3E+02 | 1.9E-07 | 1.1E-02 | 3.4E-07 | 2d |1\n", - "2023-02-17 16:05:43 fides(WARNING) Stopping as function difference 7.78E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 0| +2.42E+10 | NaN | NaN | 1.0E+00 | 1.1E+11 | NaN | NaN |1\n", - "2023-02-17 16:05:43 fides(INFO) 1| +5.45E+04 | -2.4E+10 | +9.3E-02 | 1.0E+00 | 1.1E+11 | 2.8E+00 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 2| +8.86E+03 | -4.6E+04 | +9.0E-01 | 2.5E-01 | 2.5E+05 | 6.6E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 3| +1.61E+03 | -7.3E+03 | +9.0E-01 | 5.0E-01 | 3.9E+04 | 1.4E+00 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 4| +1.61E+03 | +4.5E+02 | -2.3E+00 | 1.0E+00 | 6.3E+03 | 2.4E+00 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 5| +4.66E+02 | -1.1E+03 | +9.0E-01 | 2.5E-01 | 6.3E+03 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 6| +2.88E+02 | -1.8E+02 | +9.0E-01 | 5.0E-01 | 1.0E+03 | 1.3E+00 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 7| +2.57E+02 | -3.2E+01 | +8.7E-01 | 1.0E+00 | 1.5E+02 | 2.4E+00 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 8| +2.57E+02 | +1.7E+02 | -1.3E+01 | 2.0E+00 | 3.2E+01 | 4.1E+00 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 9| +2.57E+02 | +1.4E+02 | -1.0E+01 | 5.0E-01 | 3.2E+01 | 1.2E+00 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 10| +2.50E+02 | -6.5E+00 | +7.3E-01 | 1.2E-01 | 3.2E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 11| +2.50E+02 | +9.7E-02 | -1.6E-01 | 1.2E-01 | 1.1E+01 | 2.7E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 12| +2.50E+02 | -3.3E-01 | +6.1E-01 | 3.1E-02 | 1.1E+01 | 7.1E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 13| +2.50E+02 | -1.0E-02 | +2.0E-01 | 3.1E-02 | 2.9E+00 | 6.5E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 14| +2.50E+02 | -1.7E-02 | +5.1E-01 | 7.8E-03 | 2.5E+00 | 2.0E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 15| +2.50E+02 | -1.9E-03 | +3.6E-01 | 7.8E-03 | 8.3E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 16| +2.50E+02 | -1.6E-03 | +3.5E-01 | 7.8E-03 | 7.2E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 17| +2.50E+02 | -1.8E-03 | +4.2E-01 | 7.8E-03 | 7.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 18| +2.50E+02 | -1.6E-03 | +4.2E-01 | 7.8E-03 | 6.4E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 19| +2.50E+02 | -1.8E-03 | +4.7E-01 | 7.8E-03 | 6.4E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 20| +2.50E+02 | -1.7E-03 | +4.6E-01 | 7.8E-03 | 6.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 21| +2.50E+02 | -1.9E-03 | +5.0E-01 | 7.8E-03 | 6.1E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 22| +2.50E+02 | -1.7E-03 | +4.8E-01 | 7.8E-03 | 5.8E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 23| +2.50E+02 | -2.0E-03 | +5.2E-01 | 7.8E-03 | 6.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 24| +2.50E+02 | -1.8E-03 | +5.0E-01 | 7.8E-03 | 5.8E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 25| +2.50E+02 | -2.0E-03 | +5.3E-01 | 7.8E-03 | 6.0E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 26| +2.50E+02 | -1.9E-03 | +5.0E-01 | 7.8E-03 | 5.8E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 27| +2.50E+02 | -2.1E-03 | +5.3E-01 | 7.8E-03 | 6.1E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 28| +2.50E+02 | -2.0E-03 | +5.1E-01 | 7.8E-03 | 5.9E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 29| +2.50E+02 | -2.2E-03 | +5.4E-01 | 7.8E-03 | 6.2E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 30| +2.50E+02 | -2.1E-03 | +5.1E-01 | 7.8E-03 | 6.1E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 31| +2.50E+02 | -2.3E-03 | +5.4E-01 | 7.8E-03 | 6.4E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 32| +2.50E+02 | -2.1E-03 | +5.1E-01 | 7.8E-03 | 6.2E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 33| +2.50E+02 | -2.5E-03 | +5.4E-01 | 7.8E-03 | 6.5E-01 | 1.6E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 34| +2.50E+02 | -2.2E-03 | +5.1E-01 | 7.8E-03 | 6.3E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 35| +2.50E+02 | -4.6E-03 | +1.0E+00 | 7.8E-03 | 6.7E-01 | 1.7E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 36| +2.50E+02 | -7.5E-03 | +9.8E-01 | 1.6E-02 | 2.6E-01 | 3.4E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 37| +2.50E+02 | -1.7E-02 | +1.1E+00 | 3.1E-02 | 6.0E-01 | 6.8E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 38| +2.50E+02 | -4.1E-02 | +1.2E+00 | 6.2E-02 | 7.2E-01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 39| +2.49E+02 | -1.2E-01 | +1.4E+00 | 1.2E-01 | 1.2E+00 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 40| +2.49E+02 | -5.2E-01 | +1.8E+00 | 2.5E-01 | 2.3E+00 | 5.2E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 41| +2.45E+02 | -3.6E+00 | +2.2E+00 | 5.0E-01 | 4.7E+00 | 9.9E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 42| +2.33E+02 | -1.2E+01 | +1.5E+00 | 1.0E+00 | 1.5E+01 | 1.8E+00 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 43| +2.27E+02 | -6.1E+00 | +3.3E-01 | 2.0E+00 | 4.0E+01 | 1.9E+00 | trr |1\n", - "2023-02-17 16:05:43 fides(INFO) 44| +2.18E+02 | -9.2E+00 | +1.4E+00 | 2.0E+00 | 4.2E+01 | 5.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 45| +2.18E+02 | +8.7E+02 | -6.4E+01 | 2.0E+00 | 4.2E+01 | 3.6E+00 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 46| +2.13E+02 | -4.5E+00 | +3.2E-01 | 5.0E-01 | 4.2E+01 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 47| +2.13E+02 | +3.7E+00 | -7.4E-01 | 5.0E-01 | 3.7E+01 | 1.0E+00 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 48| +2.11E+02 | -2.5E+00 | +4.9E-01 | 1.2E-01 | 3.7E+01 | 2.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 49| +2.10E+02 | -3.4E-01 | +3.7E-01 | 1.2E-01 | 1.1E+01 | 3.2E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 50| +2.10E+02 | +7.4E-02 | -1.4E-01 | 1.2E-01 | 5.6E+00 | 2.2E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 51| +2.10E+02 | -1.6E-01 | +6.6E-01 | 3.1E-02 | 5.6E+00 | 6.0E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 52| +2.10E+02 | -7.0E-02 | +8.0E-01 | 3.1E-02 | 2.8E+00 | 5.1E-02 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 53| +2.10E+02 | -2.7E-02 | +8.5E-01 | 6.2E-02 | 1.2E+00 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 54| +2.10E+02 | -2.2E-02 | +1.3E+00 | 1.2E-01 | 8.3E-01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 55| +2.10E+02 | -5.4E-02 | +1.8E+00 | 1.2E-01 | 6.3E-01 | 3.8E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 56| +2.10E+02 | -1.4E-01 | +1.9E+00 | 2.5E-01 | 1.7E+00 | 7.0E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 57| +2.10E+02 | +3.2E+00 | -3.0E+01 | 5.0E-01 | 3.1E+00 | 9.9E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 58| +2.10E+02 | -9.1E-02 | +1.1E+00 | 8.6E-02 | 3.1E+00 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 59| +2.09E+02 | -3.9E-01 | +5.6E-01 | 1.7E-01 | 3.4E+00 | 3.7E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:43 fides(INFO) 60| +2.08E+02 | -1.0E+00 | +1.5E+00 | 1.7E-01 | 1.1E+01 | 4.8E-01 | 2d |1\n", - "2023-02-17 16:05:43 fides(INFO) 61| +2.08E+02 | +1.2E+01 | -1.1E+01 | 3.4E-01 | 8.2E+00 | 9.3E-01 | 2d |0\n", - "2023-02-17 16:05:43 fides(INFO) 62| +2.08E+02 | -5.2E-01 | +1.0E+00 | 8.6E-02 | 8.2E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 63| +2.08E+02 | +3.6E+00 | -1.8E+00 | 1.7E-01 | 8.9E+00 | 4.4E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 64| +2.07E+02 | -3.3E-01 | +5.1E-01 | 4.3E-02 | 8.9E+00 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 65| +2.07E+02 | -6.0E-01 | +1.2E+00 | 4.3E-02 | 8.4E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 66| +2.06E+02 | -1.1E+00 | +1.0E+00 | 8.6E-02 | 5.5E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 67| +2.05E+02 | -7.5E-01 | +3.2E-01 | 1.7E-01 | 8.6E+00 | 4.5E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 68| +2.02E+02 | -2.9E+00 | +1.4E+00 | 1.7E-01 | 2.4E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 69| +2.00E+02 | -2.6E+00 | +5.0E-01 | 3.4E-01 | 1.4E+01 | 9.4E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 70| +2.00E+02 | +3.8E+00 | -8.3E-01 | 3.4E-01 | 2.9E+01 | 6.8E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 71| +1.97E+02 | -2.7E+00 | +1.1E+00 | 8.6E-02 | 2.9E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 72| +1.96E+02 | -5.2E-01 | +1.4E-01 | 1.7E-01 | 1.8E+01 | 4.0E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 73| +1.95E+02 | -1.4E+00 | +9.5E-01 | 4.3E-02 | 2.5E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 74| +1.94E+02 | -8.7E-01 | +1.1E+00 | 8.6E-02 | 8.7E+00 | 2.3E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 75| +1.92E+02 | -2.3E+00 | +1.3E+00 | 1.7E-01 | 4.5E+00 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 76| +1.85E+02 | -6.7E+00 | +9.0E-01 | 3.4E-01 | 1.5E+01 | 8.9E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 77| +1.78E+02 | -7.2E+00 | +4.8E-01 | 6.9E-01 | 6.7E+01 | 4.7E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 78| +1.78E+02 | +1.2E+02 | -7.6E+00 | 6.9E-01 | 9.0E+01 | 1.1E+00 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 79| +1.67E+02 | -1.1E+01 | +9.0E-01 | 1.1E-01 | 9.0E+01 | 2.8E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 80| +1.64E+02 | -2.7E+00 | +3.1E-01 | 2.2E-01 | 6.3E+01 | 4.9E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 81| +1.64E+02 | +2.7E+01 | -1.8E+00 | 2.2E-01 | 1.2E+02 | 3.6E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 82| +1.62E+02 | -2.3E+00 | +2.4E-01 | 5.4E-02 | 1.2E+02 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 83| +1.60E+02 | -2.5E+00 | +9.9E-01 | 1.3E-02 | 1.1E+02 | 3.7E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 84| +1.58E+02 | -1.1E+00 | +9.2E-01 | 2.7E-02 | 3.6E+01 | 7.2E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 85| +1.57E+02 | -1.1E+00 | +6.7E-01 | 5.4E-02 | 2.4E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 86| +1.56E+02 | -8.8E-01 | +6.7E-01 | 5.4E-02 | 4.5E+01 | 1.3E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 87| +1.56E+02 | +1.1E-01 | -1.1E-01 | 5.4E-02 | 1.4E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 88| +1.56E+02 | -3.1E-01 | +7.8E-01 | 1.3E-02 | 1.4E+01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 89| +1.56E+02 | -4.0E-01 | +9.3E-01 | 2.7E-02 | 1.8E+01 | 5.4E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 90| +1.55E+02 | -6.1E-01 | +1.2E+00 | 5.4E-02 | 1.2E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 91| +1.54E+02 | -1.1E+00 | +1.2E+00 | 1.1E-01 | 1.1E+01 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 92| +1.52E+02 | -1.7E+00 | +7.5E-01 | 2.2E-01 | 2.0E+01 | 4.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 93| +1.52E+02 | +9.3E-01 | -6.7E-01 | 4.3E-01 | 4.5E+01 | 4.5E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 94| +1.51E+02 | -9.4E-01 | +9.2E-01 | 5.2E-02 | 4.5E+01 | 1.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 95| +1.50E+02 | -9.8E-01 | +9.8E-01 | 1.0E-01 | 1.5E+01 | 2.0E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 96| +1.50E+02 | +8.3E-01 | -5.9E-01 | 2.1E-01 | 3.5E+01 | 4.0E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 97| +1.50E+02 | -7.9E-01 | +9.1E-01 | 5.2E-02 | 3.5E+01 | 1.0E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 98| +1.49E+02 | -4.0E-01 | +3.9E-01 | 1.0E-01 | 1.2E+01 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 99| +1.49E+02 | -2.9E-01 | +3.3E-01 | 1.0E-01 | 4.3E+01 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 100| +1.49E+02 | +5.0E-02 | -6.4E-02 | 1.0E-01 | 4.6E+01 | 1.2E-01 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 101| +1.49E+02 | -4.0E-01 | +8.0E-01 | 1.2E-02 | 4.6E+01 | 3.1E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 102| +1.48E+02 | -2.5E-01 | +1.3E+00 | 2.3E-02 | 9.7E+00 | 4.2E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 103| +1.48E+02 | -3.6E-01 | +1.0E+00 | 4.7E-02 | 6.9E+00 | 8.0E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 104| +1.48E+02 | -2.6E-01 | +8.5E-01 | 9.3E-02 | 5.4E+00 | 1.5E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 105| +1.48E+02 | -8.8E-02 | +1.1E+00 | 1.9E-01 | 5.6E+00 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 106| +1.48E+02 | -5.0E-02 | +8.4E-01 | 1.9E-01 | 1.3E+01 | 1.2E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 107| +1.48E+02 | -3.8E-02 | +1.2E+00 | 1.9E-01 | 5.0E+00 | 8.1E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 108| +1.48E+02 | -7.9E-03 | +1.2E+00 | 1.9E-01 | 1.1E+00 | 5.8E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 109| +1.48E+02 | -3.1E-03 | +1.2E+00 | 1.9E-01 | 4.2E-01 | 5.5E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 110| +1.48E+02 | -7.6E-04 | +1.1E+00 | 1.9E-01 | 5.3E-01 | 3.2E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 111| +1.48E+02 | -4.2E-05 | +1.2E+00 | 1.9E-01 | 7.0E-02 | 9.0E-03 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 112| +1.48E+02 | -1.6E-06 | +8.5E-01 | 1.9E-01 | 2.7E-02 | 2.1E-03 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 113| +1.48E+02 | -2.4E-06 | +1.5E+00 | 1.9E-01 | 1.8E-02 | 3.4E-03 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 114| +1.48E+02 | -6.7E-06 | +1.6E+00 | 1.9E-01 | 1.2E-02 | 1.1E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 115| +1.48E+02 | -1.4E-05 | +1.5E+00 | 1.9E-01 | 5.9E-02 | 2.9E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 116| +1.48E+02 | -3.4E-05 | +1.5E+00 | 1.9E-01 | 1.3E-01 | 7.0E-02 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 117| +1.48E+02 | -7.2E-05 | +1.6E+00 | 1.9E-01 | 2.1E-01 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 118| +1.48E+02 | -1.1E-04 | +1.5E+00 | 1.9E-01 | 2.7E-01 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 119| +1.48E+02 | -8.0E-05 | +1.2E+00 | 1.9E-01 | 2.4E-01 | 2.5E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:44 fides(INFO) 120| +1.48E+02 | -4.5E-05 | +1.3E+00 | 3.7E-01 | 9.9E-02 | 2.6E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 121| +1.48E+02 | -1.8E-05 | +2.1E+00 | 3.7E-01 | 7.7E-03 | 1.8E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 122| +1.48E+02 | -8.9E-06 | +1.3E+00 | 3.7E-01 | 4.4E-02 | 2.1E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 123| +1.48E+02 | -8.8E-06 | +1.5E+00 | 3.7E-01 | 4.7E-02 | 2.4E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 124| +1.48E+02 | -4.1E-06 | +1.3E+00 | 3.7E-01 | 2.5E-02 | 2.2E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 125| +1.48E+02 | -1.9E-06 | +1.3E+00 | 3.7E-01 | 1.7E-03 | 1.7E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 126| +1.48E+02 | -3.0E-06 | +4.5E+00 | 3.7E-01 | 7.0E-03 | 1.4E-01 | 2d |1\n", - "2023-02-17 16:05:44 fides(INFO) 127| +1.48E+02 | +1.9E-06 | -6.9E+00 | 3.7E-01 | 6.7E-03 | 8.7E-02 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 128| +1.48E+02 | +2.9E-06 | -2.2E+01 | 6.4E-02 | 6.7E-03 | 2.2E-02 | 2d |0\n", - "2023-02-17 16:05:44 fides(INFO) 129| +1.48E+02 | +2.2E-06 | -5.4E+01 | 1.6E-02 | 6.7E-03 | 5.4E-03 | 2d |0\n", - "2023-02-17 16:05:45 fides(INFO) iter| fval | fdiff | tr ratio |tr radius| ||g|| | ||step||| step|acc\n", - "2023-02-17 16:05:45 fides(INFO) 130| +1.48E+02 | +3.1E-06 | -2.3E+02 | 4.0E-03 | 6.7E-03 | 1.4E-03 | 2d |0\n", - "2023-02-17 16:05:45 fides(INFO) 131| +1.48E+02 | +2.5E-06 | -3.6E+02 | 1.0E-03 | 6.7E-03 | 3.4E-04 | 2d |0\n", - "2023-02-17 16:05:45 fides(INFO) 132| +1.48E+02 | -9.0E-07 | +1.7E+02 | 2.5E-04 | 6.7E-03 | 8.5E-05 | 2d |1\n", - "2023-02-17 16:05:45 fides(WARNING) Stopping as function difference 8.99E-07 was smaller than specified tolerances (atol=1.00E-08, rtol=1.00E-08)\n", - "Performing parallel task execution on 8 processes.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 5.44 s, sys: 2.02 s, total: 7.45 s\n", - "Wall time: 10min 31s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "%%capture --no-display\n", - "n_starts = 100\n", + "n_starts = 10\n", "\n", "# Due to run time we already use parallelization. \n", "# This will be introduced in more detail later.\n", @@ -8839,26 +436,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "optimizer_results = [\n", " result_lbfgsb,\n", @@ -8886,26 +470,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average Run time per start:\n", - "-------------------\n", - "Scipy: L-BFGS-B: 3.741806 s\n", - "Scipy: Powell: 9.719779 s\n", - "Fides: 2.701986 s\n", - "pyswarm: 25.507524 s\n" - ] - } - ], + "outputs": [], "source": [ "print('Average Run time per start:')\n", "print('-------------------')\n", @@ -8942,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -8971,30 +542,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", - "Performing parallel task execution on 8 processes.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 280 ms, sys: 148 ms, total: 427 ms\n", - "Wall time: 2min 1s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "%%capture\n", @@ -9033,22 +587,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1min 26s, sys: 5.81 s, total: 1min 32s\n", - "Wall time: 1min 32s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "%%capture\n", @@ -9073,26 +618,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# adapt x_labels..\n", "x_labels = [f'Log10({name})' for name in problem.x_names]\n", @@ -9115,26 +647,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ax = pypesto.visualize.profile_cis(\n", " result, confidence_level=0.95, show_bounds=True\n", @@ -9158,22 +677,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████| 10000/10000 [00:27<00:00, 368.56it/s]\n", - "Elapsed time: 30.99180300000002\n" - ] - } - ], + "outputs": [], "source": [ "import pypesto.sample as sample\n", "\n", @@ -9199,36 +709,13 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[-1.56903514, -5. , -2.20988782, ..., 0.58572263,\n", - " 0.81880994, 0.49856843],\n", - " [-1.56903514, -5. , -2.20988782, ..., 0.58572263,\n", - " 0.81880994, 0.49856843],\n", - " [-1.56903514, -5. , -2.20988782, ..., 0.58572263,\n", - " 0.81880994, 0.49856843],\n", - " ...,\n", - " [-1.42590748, -4.42706952, -2.00183415, ..., 0.73049356,\n", - " 0.85349596, 0.54844474],\n", - " [-1.48877016, -4.17264394, -2.22043926, ..., 0.61516694,\n", - " 0.93106418, 0.6043891 ],\n", - " [-1.48877016, -4.17264394, -2.22043926, ..., 0.61516694,\n", - " 0.93106418, 0.6043891 ]]])" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "result.sample_result['trace_x']" ] @@ -9248,31 +735,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 3000\n" - ] - }, - { - "data": { - "text/plain": [ - "3000" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sample.geweke_test(result=result)\n", "result.sample_result['burn_in']" @@ -9280,32 +749,13 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Estimated chain autocorrelation: 454.31751144999856\n", - "Estimated effective sample size: 15.376083335131788\n" - ] - }, - { - "data": { - "text/plain": [ - "15.376083335131788" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sample.effective_sample_size(result=result)\n", "result.sample_result['effective_sample_size']" @@ -9324,34 +774,13 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# scatter plots\n", "visualize.sampling_scatter(result)\n", @@ -9394,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" @@ -9428,42 +857,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: You are loading a problem.\n", - "This problem is not to be used without a separately created objective.\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# read result\n", "result_loaded = store.read_result(result_file_name)\n", @@ -9495,6 +895,12 @@ }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Further topics\n", "\n", @@ -9503,13 +909,7 @@ "* Model Selection\n", "* Hierarchical Optimization of scaling/noise parameters\n", "* Categorical Data" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] } ], "metadata": { @@ -9528,7 +928,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/doc/example/hierarchical.ipynb b/doc/example/hierarchical.ipynb index 639ab9dd2..d4ae34f0a 100644 --- a/doc/example/hierarchical.ipynb +++ b/doc/example/hierarchical.ipynb @@ -53,7 +53,9 @@ " NumericalInnerSolver,\n", ")\n", "from pypesto.optimize.options import OptimizeOptions\n", - "from pypesto.petab import PetabImporter" + "from pypesto.petab import PetabImporter\n", + "\n", + "np.random.seed(0)" ] }, { @@ -129,9 +131,7 @@ "source": [ "# Create a pypesto problem with hierarchical optimization (`problem`)\n", "importer = PetabImporter(petab_problem_hierarchical, hierarchical=True)\n", - "model = importer.create_model(verbose=False)\n", - "objective = importer.create_objective(model=model)\n", - "problem = importer.create_problem(objective)\n", + "problem = importer.create_problem(verbose=False)\n", "# set option to compute objective function gradients using adjoint sensitivity analysis\n", "problem.objective.amici_solver.setSensitivityMethod(\n", " amici.SensitivityMethod.adjoint\n", @@ -139,9 +139,7 @@ "\n", "# ... and create another pypesto problem without hierarchical optimization (`problem2`)\n", "importer2 = PetabImporter(petab_problem, hierarchical=False)\n", - "model2 = importer2.create_model(verbose=False)\n", - "objective2 = importer2.create_objective(model=model2)\n", - "problem2 = importer2.create_problem(objective2)\n", + "problem2 = importer2.create_problem(verbose=False)\n", "problem2.objective.amici_solver.setSensitivityMethod(\n", " amici.SensitivityMethod.adjoint\n", ")\n", diff --git a/pypesto/petab/importer.py b/pypesto/petab/importer.py index 6f57922bb..65aef3c05 100644 --- a/pypesto/petab/importer.py +++ b/pypesto/petab/importer.py @@ -428,7 +428,10 @@ def create_objective( # create model if model is None: - model = self.create_model(force_compile=force_compile) + verbose = kwargs.pop('verbose', True) + model = self.create_model( + force_compile=force_compile, verbose=verbose + ) # create solver if solver is None: solver = self.create_solver(model) From e2cf7df8d030880059888d02131aaa2e2ffee83a Mon Sep 17 00:00:00 2001 From: Doresic Date: Wed, 3 Jan 2024 14:50:06 +0100 Subject: [PATCH 14/31] Continue reduce notebook run time I made the following changes in the example notebooks: - `amici.py` -- verbose=False on model compilation, seed for reproducability, clear outputs - `juliy.py` -- set number of starts 100 --> 20, set seed for reproducability, samples 10000 -> 2000, clear outputs - `nonlinear_monotone` -- verbose=False on model compilation - `ordinal` -- verbose=False on model compilation - `petab_import` -- verbose=False on model compilation - `sampler_study` -- verbose=False on model compilation - `store` -- verbose=False on model compilation, fides verbose ERROR - `synthetic_data` -- n_starts 100 -> 20, verbose=False on model compilation - `workflow_comparison` -- verbose=False on model compilation, n_starts 25 - 10 --- doc/example/amici.ipynb | 1450 +++++++++---------------- doc/example/julia.ipynb | 372 +------ doc/example/nonlinear_monotone.ipynb | 2 +- doc/example/ordinal.ipynb | 3 +- doc/example/petab_import.ipynb | 217 +--- doc/example/sampler_study.ipynb | 4 +- doc/example/store.ipynb | 555 +++++++++- doc/example/synthetic_data.ipynb | 10 +- doc/example/workflow_comparison.ipynb | 6 +- 9 files changed, 1125 insertions(+), 1494 deletions(-) diff --git a/doc/example/amici.ipynb b/doc/example/amici.ipynb index 67bdc5551..67de7a80a 100644 --- a/doc/example/amici.ipynb +++ b/doc/example/amici.ipynb @@ -2,6 +2,12 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "# AMICI in pyPESTO\n", "\n", @@ -20,17 +26,17 @@ "In order to run optimizations and/or uncertainty analysis, we turn to pyPESTO (**P**arameter **ES**timation **TO**olbox for python).\n", "\n", "pyPESTO is a Python tool for parameter estimation. It provides an interface to the model simulation tool [AMICI](https://github.com/AMICI-dev/AMICI) for the simulation of Ordinary Differential Equation (ODE) models specified in the SBML format. With it, we can optimize our model parameters given measurement data, we can do uncertainty analysis via profile likelihoods and/or through sampling methods. pyPESTO provides an interface to many optimizers, global and local, such as e.g. SciPy optimizers, Fides and Pyswarm. Additionally, it interfaces samplers such as pymc, emcee and some of its own samplers." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 1, + }, "outputs": [], "source": [ "# import\n", @@ -64,138 +70,116 @@ "\n", "# output directory\n", "model_output_dir = \"tmp/\" + model_name" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } + }, + "source": [ + "## 1. Create a pyPESTO problem" + ] }, { "cell_type": "markdown", - "source": [ - "## 1. Create a pyPESTO problem" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Create a pyPESTO objective from AMICI\n", "\n", "Before we can use AMICI to simulate our model, the SBML model needs to be translated to C++ code. This is done by `amici.SbmlImporter`." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 2, + }, "outputs": [], "source": [ "sbml_file = f\"./{model_name}/{model_name}.xml\"\n", "# Create an SbmlImporter instance for our SBML model\n", "sbml_importer = amici.SbmlImporter(sbml_file)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "In this example, we want to specify fixed parameters, observables and a $\\sigma$ parameter. Unfortunately, the latter two are not part of the [SBML standard](https://sbml.org/). However, they can be provided to `amici.SbmlImporter.sbml2amici` as demonstrated in the following." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "In this example, we want to specify fixed parameters, observables and a $\\sigma$ parameter. Unfortunately, the latter two are not part of the [SBML standard](https://sbml.org/). However, they can be provided to `amici.SbmlImporter.sbml2amici` as demonstrated in the following." + ] }, { "cell_type": "markdown", - "source": [ - "#### Constant parameters\n", - "\n", - "Constant parameters, i.e., parameters with respect to which no sensitivities are to be computed (these are often parameters specifying a certain experimental condition) are provided as a list of parameter names." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Constant parameters\n", + "\n", + "Constant parameters, i.e., parameters with respect to which no sensitivities are to be computed (these are often parameters specifying a certain experimental condition) are provided as a list of parameter names." + ] }, { "cell_type": "code", - "execution_count": 3, - "outputs": [], - "source": [ - "constant_parameters = [\"ratio\", \"specC17\"]" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "constant_parameters = [\"ratio\", \"specC17\"]" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "#### Observables\n", "\n", "We used SBML's [`AssignmentRule`](https://sbml.org/software/libsbml/5.18.0/docs/formatted/python-api/classlibsbml_1_1_assignment_rule.html) as a non-standard way to specify *Model outputs* within the SBML file. These rules need to be removed prior to the model import (AMICI does at this time not support these rules). This can be easily done using `amici.assignmentRules2observables()`.\n", "\n", "In this example, we introduced parameters named `observable_*` as targets of the observable AssignmentRules. Where applicable we have `observable_*_sigma` parameters for $\\sigma$ parameters (see below)." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Observables:\n", - "{'observable_pSTAT5A_rel': {'formula': '(100 * pApB + 200 * pApA * specC17) / '\n", - " '(pApB + STAT5A * specC17 + 2 * pApA * '\n", - " 'specC17)',\n", - " 'name': 'observable_pSTAT5A_rel'},\n", - " 'observable_pSTAT5B_rel': {'formula': '-(100 * pApB - 200 * pBpB * (specC17 - '\n", - " '1)) / (STAT5B * (specC17 - 1) - pApB + '\n", - " '2 * pBpB * (specC17 - 1))',\n", - " 'name': 'observable_pSTAT5B_rel'},\n", - " 'observable_rSTAT5A_rel': {'formula': '(100 * pApB + 100 * STAT5A * specC17 + '\n", - " '200 * pApA * specC17) / (2 * pApB + '\n", - " 'STAT5A * specC17 + 2 * pApA * specC17 '\n", - " '- STAT5B * (specC17 - 1) - 2 * pBpB * '\n", - " '(specC17 - 1))',\n", - " 'name': 'observable_rSTAT5A_rel'}}\n" - ] - } - ], + }, + "outputs": [], "source": [ "# Retrieve model output names and formulae from AssignmentRules and remove the respective rules\n", "observables = amici.assignmentRules2observables(\n", @@ -205,82 +189,63 @@ ")\n", "print(\"Observables:\")\n", "pprint(observables)" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "#### $\\sigma$ parameters\n", "\n", "To specify measurement noise as a parameter, we simply provide a dictionary with observable names as keys and a list of (preexisting) parameter names as values to indicate which sigma parameter is to be used for which observable." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'observable_pSTAT5A_rel': 'sd_pSTAT5A_rel',\n", - " 'observable_pSTAT5B_rel': 'sd_pSTAT5B_rel',\n", - " 'observable_rSTAT5A_rel': 'sd_rSTAT5A_rel'}\n" - ] - } - ], + }, + "outputs": [], "source": [ "sigma_vals = [\"sd_pSTAT5A_rel\", \"sd_pSTAT5B_rel\", \"sd_rSTAT5A_rel\"]\n", "observable_names = observables.keys()\n", "sigmas = dict(zip(list(observable_names), sigma_vals))\n", "pprint(sigmas)" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "#### Generating the module\n", "\n", "Now we can generate the python module for our model. `amici.SbmlImporter.sbml2amici` will symbolically derive the sensitivity equations, generate C++ code for model simulation, and assemble the python module." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.72 s, sys: 212 ms, total: 1.93 s\n", - "Wall time: 53.7 s\n" - ] - } - ], + }, + "outputs": [], "source": [ "%%time\n", "\n", @@ -292,99 +257,89 @@ " constant_parameters=constant_parameters,\n", " sigmas=sigmas,\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "#### Importing the module and loading the model\n", - "\n", - "If everything went well, we are ready to load the newly generated model:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Importing the module and loading the model\n", + "\n", + "If everything went well, we are ready to load the newly generated model:" + ] }, { "cell_type": "code", - "execution_count": 7, - "outputs": [], - "source": [ - "model_module = amici.import_model_module(model_name, model_output_dir)" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "model_module = amici.import_model_module(model_name, model_output_dir)" + ] }, { "cell_type": "markdown", - "source": [ - "Afterwards, we can get an instance of our model from which we can retrieve information such as parameter names:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Afterwards, we can get an instance of our model from which we can retrieve information such as parameter names:" + ] }, { "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model parameters: ['Epo_degradation_BaF3', 'k_exp_hetero', 'k_exp_homo', 'k_imp_hetero', 'k_imp_homo', 'k_phos', 'sd_pSTAT5A_rel', 'sd_pSTAT5B_rel', 'sd_rSTAT5A_rel']\n", - "Model outputs: ['observable_pSTAT5A_rel', 'observable_pSTAT5B_rel', 'observable_rSTAT5A_rel']\n", - "Model states: ['STAT5A', 'STAT5B', 'pApB', 'pApA', 'pBpB', 'nucpApA', 'nucpApB', 'nucpBpB']\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], "source": [ "model = model_module.getModel()\n", "\n", "print(\"Model parameters:\", list(model.getParameterIds()))\n", "print(\"Model outputs: \", list(model.getObservableIds()))\n", "print(\"Model states: \", list(model.getStateIds()))" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "#### Running simulations and analyzing results\n", "\n", "After importing the model, we can run simulations using `amici.runAmiciSimulation`. This requires a `Model` instance and a `Solver` instance. But, in order go gain a value of fit, we also need to provide some data." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 9, + }, "outputs": [], "source": [ "# we prepare our data as it is reported in the benchmark collection\n", @@ -472,17 +427,17 @@ " 32.211077,\n", " ]\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 10, + }, "outputs": [], "source": [ "benchmark_parameters = np.array(\n", @@ -513,17 +468,17 @@ "\n", "# Run simulation using model parameters from the benchmark collection and default solver options\n", "rdata = amici.runAmiciSimulation(model, solver)" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 11, + }, "outputs": [], "source": [ "# Create edata instance with dimensions and timepoints\n", @@ -542,59 +497,50 @@ "edata.setObservedDataStdDev(np.array(16 * [10**0.585755271]), 0)\n", "edata.setObservedDataStdDev(np.array(16 * [10**0.818982819]), 1)\n", "edata.setObservedDataStdDev(np.array(16 * [10**0.498684404]), 2)" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Chi2 value reported in benchmark collection: 47.9765479\n", - "chi2 value using AMICI: 47.97654319310132\n" - ] - } - ], + }, + "outputs": [], "source": [ "rdata = amici.runAmiciSimulation(model, solver, edata)\n", "\n", "print(\"Chi2 value reported in benchmark collection: 47.9765479\")\n", "print(f\"chi2 value using AMICI: {rdata['chi2']}\")" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "#### Creating pyPESTO objective\n", "\n", "We are now set up to create our pyPESTO objective. This objective is a vital part of the pyPESTO infrastructure as it provides a blackbox interface to call any predefined objective function with some parameters and evaluate it. We can easily create an AmiciObjective by supplying the model, an amici solver and the data.\n", "\n", "Keep in mind, however, that you can use ANY function you would like for this." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 13, + }, "outputs": [], "source": [ "# we make some more adjustments to our model and the solver\n", @@ -607,39 +553,30 @@ "objective = pypesto.AmiciObjective(\n", " amici_model=model, amici_solver=solver, edatas=[edata], max_sensi_order=1\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "We can now call the objective function directly for any parameter. The value that is put out is the likelihood function. If we want to interact more with the AMICI returns, we can also return this by call and e.g., retrieve the chi2 value." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "We can now call the objective function directly for any parameter. The value that is put out is the likelihood function. If we want to interact more with the AMICI returns, we can also return this by call and e.g., retrieve the chi2 value." + ] }, { "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Objective value: 138.22199735603812\n", - "Chi^2 value of the same parameters: 47.97654319310132\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], "source": [ "# the generic objective call\n", "print(f\"Objective value: {objective(benchmark_parameters)}\")\n", @@ -648,40 +585,40 @@ "print(\n", " f'Chi^2 value of the same parameters: {obj_call_with_dict[\"rdatas\"][0][\"chi2\"]}'\n", ")" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } + }, + "source": [ + "Now this makes the whole process already somewhat easier, but still, getting here took us quite some coding and effort. This will only get more complicated, the more complex the model is. Therefore, in the next part, we will show you how to bypass the tedious lines of code by using PEtab." + ] }, { "cell_type": "markdown", - "source": [ - "Now this makes the whole process already somewhat easier, but still, getting here took us quite some coding and effort. This will only get more complicated, the more complex the model is. Therefore, in the next part, we will show you how to bypass the tedious lines of code by using PEtab." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Create a pyPESTO problem + objective from Petab" + ] }, { "cell_type": "markdown", - "source": [ - "### Create a pyPESTO problem + objective from Petab" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "#### Background on PEtab\n", "\n", @@ -692,17 +629,17 @@ "A PEtab problem consist of an [SBML](https://sbml.org) file, defining the model topology and a set of `.tsv` files, defining experimental conditions, observables, measurements and parameters (and their optimization bounds, scale, priors...). All files that make up a PEtab problem can be structured in a `.yaml` file. The `pypesto.Objective` coming from a PEtab problem corresponds to the negative-log-likelihood/negative-log-posterior distribution of the parameters.\n", "\n", "For more details on PEtab, the interested reader is referred to [PEtab's format definition](https://petab.readthedocs.io/en/latest/documentation_data_format.html), for examples the reader is referred to the [PEtab benchmark collection](https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab). The Model from _[Böhm et al. JProteomRes 2014](https://pubs.acs.org/doi/abs/10.1021/pr5006923)_ is part of the benchmark collection and will be used as the running example throughout this notebook.\n" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 15, + }, "outputs": [], "source": [ "%%capture\n", @@ -710,141 +647,91 @@ "\n", "petab_problem = petab.Problem.from_yaml(petab_yaml)\n", "importer = pypesto.petab.PetabImporter(petab_problem)\n", - "problem = importer.create_problem()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + "problem = importer.create_problem(verbose=False)" + ] }, { "cell_type": "code", - "execution_count": 16, - "outputs": [ - { - "data": { - "text/plain": " parameterName parameterScale lowerBound \\\nparameterId \nEpo_degradation_BaF3 EPO_{degradation,BaF3} log10 0.00001 \nk_exp_hetero k_{exp,hetero} log10 0.00001 \nk_exp_homo k_{exp,homo} log10 0.00001 \nk_imp_hetero k_{imp,hetero} log10 0.00001 \nk_imp_homo k_{imp,homo} log10 0.00001 \n\n upperBound nominalValue estimate \nparameterId \nEpo_degradation_BaF3 100000 0.026983 1 \nk_exp_hetero 100000 0.000010 1 \nk_exp_homo 100000 0.006170 1 \nk_imp_hetero 100000 0.016368 1 \nk_imp_homo 100000 97749.379402 1 ", - "text/html": "
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" - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check the dataframes. First the parameter dataframe\n", - "petab_problem.parameter_df.head()" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# Check the dataframes. First the parameter dataframe\n", + "petab_problem.parameter_df.head()" + ] }, { "cell_type": "code", - "execution_count": 17, - "outputs": [ - { - "data": { - "text/plain": " observableName \\\nobservableId \npSTAT5A_rel NaN \npSTAT5B_rel NaN \nrSTAT5A_rel NaN \n\n observableFormula \\\nobservableId \npSTAT5A_rel (100 * pApB + 200 * pApA * specC17) / (pApB + ... \npSTAT5B_rel -(100 * pApB - 200 * pBpB * (specC17 - 1)) / (... \nrSTAT5A_rel (100 * pApB + 100 * STAT5A * specC17 + 200 * p... \n\n noiseFormula observableTransformation \\\nobservableId \npSTAT5A_rel noiseParameter1_pSTAT5A_rel lin \npSTAT5B_rel noiseParameter1_pSTAT5B_rel lin \nrSTAT5A_rel noiseParameter1_rSTAT5A_rel lin \n\n noiseDistribution \nobservableId \npSTAT5A_rel normal \npSTAT5B_rel normal \nrSTAT5A_rel normal ", - "text/html": "
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observableNameobservableFormulanoiseFormulaobservableTransformationnoiseDistribution
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pSTAT5A_relNaN(100 * pApB + 200 * pApA * specC17) / (pApB + ...noiseParameter1_pSTAT5A_rellinnormal
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" - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check the observable dataframe\n", - "petab_problem.observable_df.head()" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# Check the observable dataframe\n", + "petab_problem.observable_df.head()" + ] }, { "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "data": { - "text/plain": " observableId preequilibrationConditionId simulationConditionId \\\n0 pSTAT5A_rel NaN model1_data1 \n1 pSTAT5A_rel NaN model1_data1 \n2 pSTAT5A_rel NaN model1_data1 \n3 pSTAT5A_rel NaN model1_data1 \n4 pSTAT5A_rel NaN model1_data1 \n\n measurement time observableParameters noiseParameters \\\n0 7.901073 0.0 NaN sd_pSTAT5A_rel \n1 66.363494 2.5 NaN sd_pSTAT5A_rel \n2 81.171324 5.0 NaN sd_pSTAT5A_rel \n3 94.730308 10.0 NaN sd_pSTAT5A_rel \n4 95.116483 15.0 NaN sd_pSTAT5A_rel \n\n datasetId \n0 model1_data1_pSTAT5A_rel \n1 model1_data1_pSTAT5A_rel \n2 model1_data1_pSTAT5A_rel \n3 model1_data1_pSTAT5A_rel \n4 model1_data1_pSTAT5A_rel ", - "text/html": "
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observableIdpreequilibrationConditionIdsimulationConditionIdmeasurementtimeobservableParametersnoiseParametersdatasetId
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" - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Check the measurement dataframe\n", - "petab_problem.measurement_df.head()" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# Check the measurement dataframe\n", + "petab_problem.measurement_df.head()" + ] }, { "cell_type": "code", - "execution_count": 19, - "outputs": [ - { - "data": { - "text/plain": " conditionName\nconditionId \nmodel1_data1 condition1", - "text/html": "
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" - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# check the condition dataframe\n", - "petab_problem.condition_df.head()" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# check the condition dataframe\n", + "petab_problem.condition_df.head()" + ] }, { "cell_type": "markdown", - "source": [ - "This was really straightforward. With this, we are still able to do all the same things we did before and also adjust solver setting, change the model, etc." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "This was really straightforward. With this, we are still able to do all the same things we did before and also adjust solver setting, change the model, etc." + ] }, { "cell_type": "code", - "execution_count": 20, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Objective value: 138.22199736863757\n", - "Absolute tolerance before change: 1e-16\n", - "Absolute tolerance after change: 1e-15\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], "source": [ "# call the objective function\n", "print(f\"Objective value: {problem.objective(benchmark_parameters)}\")\n", @@ -858,59 +745,59 @@ "print(\n", " f\"Absolute tolerance after change: {problem.objective.amici_solver.getAbsoluteTolerance()}\"\n", ")" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } + }, + "source": [ + "Now we are good to go and start the first optimization." + ] }, { "cell_type": "markdown", - "source": [ - "Now we are good to go and start the first optimization." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "## 2. Optimization\n", "\n", "Once setup, the optimization can be done very quickly with default settings. If needed, these settings can be highly individualized and change according to the needs of our model. In this section, we shall go over some of these settings." - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Optimizer\n", "\n", "The optimizer determines the algorithm with which we optimize our model. The main disjunction is between global and local optimizers.\n", "\n", "pyPESTO provides an interface to many optimizers, such as Fides, ScipyOptimizers, Pyswarm and many more. For a whole list of supported optimizers with settings for each optimizer you can [have a look here](https://pypesto.readthedocs.io/en/latest/api.html#optimize)." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 21, + }, "outputs": [], "source": [ "optimizer_options = {'maxiter': 1e4, 'fatol': 1e-12, 'frtol': 1e-12}\n", @@ -918,228 +805,137 @@ "optimizer = optimize.FidesOptimizer(\n", " options=optimizer_options, verbose=logging.WARN\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "### Startpoint method\n", - "\n", - "The startpoint method describes how you want to choose your startpoints, in case you do a multistart optimization. The default here is `uniform` meaning that each startpoint is a uniform sample from the allowed parameter space. The other two notable options are either `latin_hypercube` or a self defined function." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Startpoint method\n", + "\n", + "The startpoint method describes how you want to choose your startpoints, in case you do a multistart optimization. The default here is `uniform` meaning that each startpoint is a uniform sample from the allowed parameter space. The other two notable options are either `latin_hypercube` or a self defined function." + ] }, { "cell_type": "code", - "execution_count": 22, - "outputs": [], - "source": [ - "startpoint_method = pypesto.startpoint.uniform" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "startpoint_method = pypesto.startpoint.uniform" + ] }, { "cell_type": "markdown", - "source": [ - "### History options\n", - "\n", - "In some cases, it is good to trace what the optimizer did in each step, i.e., the history. There is a multitude of options on what to report here, but the most important one is `trace_record` which turns the history function on and off." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### History options\n", + "\n", + "In some cases, it is good to trace what the optimizer did in each step, i.e., the history. There is a multitude of options on what to report here, but the most important one is `trace_record` which turns the history function on and off." + ] }, { "cell_type": "code", - "execution_count": 23, - "outputs": [], - "source": [ - "# save optimizer trace\n", - "history_options = pypesto.HistoryOptions(trace_record=True)" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# save optimizer trace\n", + "history_options = pypesto.HistoryOptions(trace_record=True)" + ] }, { "cell_type": "markdown", - "source": [ - "### Optimization options\n", - "\n", - "Some further possible options for the optimization. Notably `allow_failed_starts`, which in case of a very complicated objective function, can help get to the desired number of optimizations when turned off. As we do not need this here, we create the default options." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Optimization options\n", + "\n", + "Some further possible options for the optimization. Notably `allow_failed_starts`, which in case of a very complicated objective function, can help get to the desired number of optimizations when turned off. As we do not need this here, we create the default options." + ] }, { "cell_type": "code", - "execution_count": 24, - "outputs": [ - { - "data": { - "text/plain": "{'allow_failed_starts': True,\n 'report_sres': True,\n 'report_hess': True,\n 'history_beats_optimizer': True}" - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "opt_options = optimize.OptimizeOptions()\n", - "opt_options" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "opt_options = optimize.OptimizeOptions()\n", + "opt_options" + ] }, { "cell_type": "markdown", - "source": [ - "### Running the optimization\n", - "\n", - "We now only need to decide on the number of starts as well as the engine we want to use for the optimization." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Running the optimization\n", + "\n", + "We now only need to decide on the number of starts as well as the engine we want to use for the optimization." + ] }, { "cell_type": "code", - "execution_count": 25, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine will use up to 8 processes (= CPU count).\n" - ] + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" } - ], + }, + "outputs": [], "source": [ "n_starts = 20 # usually a value >= 100 should be used\n", "engine = pypesto.engine.MultiProcessEngine()" - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 43, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/20 [00:00", - "text/markdown": "## Optimization Result \n\n* number of starts: 20 \n* best value: 145.7594473944086, id=10\n* worst value: 249.74599744335123, id=0\n* number of non-finite values: 0\n\n* execution time summary:\n\t* Mean execution time: 3.773s\n\t* Maximum execution time: 8.952s,\tid=4\n\t* Minimum execution time: 0.690s,\tid=1\n* summary of optimizer messages:\n\n | Count | Message |\n |--------:|:-----------------------------------------|\n | 18 | Trust Region Radius too small to proceed |\n | 2 | Converged according to fval difference |\n\n* best value found (approximately) 2 time(s)\n* number of plateaus found: 6\n\nA summary of the best run:\n\n### Optimizer Result\n\n* optimizer used: \n* message: Trust Region Radius too small to proceed \n* number of evaluations: 128\n* time taken to optimize: 3.092s\n* startpoint: [-0.15890311 -2.669684 -3.07785865 -2.33924103 2.20218441 2.69202827\n -4.60815317 4.61526113 4.65892721]\n* endpoint: [-1.52341922 -4.99872444 -2.03436045 -1.83082223 -1.67391186 3.94118859\n 0.50779203 0.80380654 0.79609736]\n* final objective value: 145.7594473944086\n* final gradient value: [ 1.25643019e-02 2.81170156e-02 4.13210117e-03 1.72722532e-02\n 4.46876982e-03 -9.07197649e-03 7.34889144e-04 -7.21740127e-04\n -6.05508217e-05]\n* final hessian value: [[ 2.37372423e+03 3.60035009e-01 2.64671284e+02 2.61587473e+03\n 8.75419211e+02 -8.64972412e+02 6.90582554e+01 -4.51763973e+01\n -2.39107885e+01]\n [ 3.60035009e-01 2.96735578e-04 2.94533113e-02 3.65701398e-01\n 1.60931779e-01 -1.70965758e-01 -1.76438574e-04 -2.71229932e-02\n -3.74423892e-02]\n [ 2.64671284e+02 2.94533113e-02 7.03217478e+01 2.78164570e+02\n 1.49629446e+02 -1.35646123e+02 -3.40041260e+00 -3.44539004e-01\n 3.73543709e+00]\n [ 2.61587473e+03 3.65701398e-01 2.78164570e+02 2.91408845e+03\n 9.22097568e+02 -8.64604114e+02 8.15865701e+01 -4.77283835e+01\n -3.38979574e+01]\n [ 8.75419211e+02 1.60931779e-01 1.49629446e+02 9.22097568e+02\n 4.25560637e+02 -4.03115866e+02 9.18790468e+00 -1.99750037e+01\n 1.07768093e+01]\n [-8.64972412e+02 -1.70965758e-01 -1.35646123e+02 -8.64604114e+02\n -4.03115866e+02 1.14623637e+03 -2.12431899e+01 -1.23869791e+01\n 3.36510580e+01]\n [ 6.90582554e+01 -1.76438574e-04 -3.40041260e+00 8.15865701e+01\n 9.18790468e+00 -2.12431899e+01 8.48286776e+01 0.00000000e+00\n 0.00000000e+00]\n [-4.51763973e+01 -2.71229932e-02 -3.44539004e-01 -4.77283835e+01\n -1.99750037e+01 -1.23869791e+01 0.00000000e+00 8.48320316e+01\n 0.00000000e+00]\n [-2.39107885e+01 -3.74423892e-02 3.73543709e+00 -3.38979574e+01\n 1.07768093e+01 3.36510580e+01 0.00000000e+00 0.00000000e+00\n 8.48305092e+01]]\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "display(Markdown(result.summary()))" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "display(Markdown(result.summary()))" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "We can see some informative statistics, such as the mean execution time, best and worst values, a small table on the exit messages of the optimizer as well as detailed info on the best optimizer.\n", "\n", "As our best start is just as good as the reported benchmark value, we shall now further inspect the result thorough some useful visualisations." - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "## 3. Optimization visualization" + ] }, { "cell_type": "markdown", - "source": [ - "## 3. Optimization visualization" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Model fit\n", "\n", "Probably the most useful visualization there is, is one, where we visualize the found parameter dynamics against the measurements. This way we can see whether the fit is qualitatively and/or quantitatively good." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 45, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], + }, + "outputs": [], "source": [ "ax = model_fit.visualize_optimized_model_fit(\n", " petab_problem=petab_problem, result=result, pypesto_problem=problem\n", ")" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Waterfall plot\n", "\n", "The waterfall plot is a visualization of the final objective function values of each start. They are sorted from small to high and then plotted. Similar values will get clustered and get the same color.\n", "\n", "This helps to determine whether the result is reproducible and whether we reliably found a local minimum that we hope to be the global one." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", - "execution_count": 46, - "outputs": [ - { - "data": { - "text/plain": "
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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.waterfall(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.waterfall(result);" + ] }, { "cell_type": "markdown", - "source": [ - "### Parameter plots\n", - "\n", - "To visualize the parameters, there is a multitude of options:" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Parameter plots\n", + "\n", + "To visualize the parameters, there is a multitude of options:" + ] }, { "cell_type": "markdown", - "source": [ - "#### Parameter overview\n", - "\n", - "Here we plot the parameters of all starts within their bounds. This can tell us whether some bounds are always hit and might need to be questioned and if the best starts are similar or differ amongst themselves, hinting already for some non-identifiabilities." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Parameter overview\n", + "\n", + "Here we plot the parameters of all starts within their bounds. This can tell us whether some bounds are always hit and might need to be questioned and if the best starts are similar or differ amongst themselves, hinting already for some non-identifiabilities." + ] }, { "cell_type": "code", - "execution_count": 47, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.parameters(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.parameters(result);" + ] }, { "cell_type": "markdown", - "source": [ - "#### Parameter correlation plot\n", - "\n", - "To further look into possible uncertainties, we can plot the correlation of the final points. Sometimes, pairs of parameters are dependent on each other and fixing one might solve some non-identifiability." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Parameter correlation plot\n", + "\n", + "To further look into possible uncertainties, we can plot the correlation of the final points. Sometimes, pairs of parameters are dependent on each other and fixing one might solve some non-identifiability." + ] }, { "cell_type": "code", - "execution_count": 48, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.parameters_correlation_matrix(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.parameters_correlation_matrix(result);" + ] }, { "cell_type": "markdown", - "source": [ - "#### Parameter histogram + scatter\n", - "\n", - "In case we found some dependencies and for further investigation, we can also specifically look at the histograms of certain parameters and the pairwise parameter scatter plot." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Parameter histogram + scatter\n", + "\n", + "In case we found some dependencies and for further investigation, we can also specifically look at the histograms of certain parameters and the pairwise parameter scatter plot." + ] }, { "cell_type": "code", - "execution_count": 49, - "outputs": [ - { - "data": { - "text/plain": "
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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.parameter_hist(result=result, parameter_name=\"k_exp_hetero\")\n", - "visualize.parameter_hist(result=result, parameter_name=\"k_imp_homo\");" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.parameter_hist(result=result, parameter_name=\"k_exp_hetero\")\n", + "visualize.parameter_hist(result=result, parameter_name=\"k_imp_homo\");" + ] }, { "cell_type": "code", - "execution_count": 50, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.optimization_scatter(result, parameter_indices=[1, 4]);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.optimization_scatter(result, parameter_indices=[1, 4]);" + ] }, { "cell_type": "markdown", - "source": [ - "We definitely need to look further into it, and thus we turn to uncertainty quantification in the next section." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "We definitely need to look further into it, and thus we turn to uncertainty quantification in the next section." + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## 4. Uncertainty quantification\n", "\n", "This mainly consists of two parts:\n", "* Profile Likelihoods\n", "* MCMC sampling" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "### Profile likelihood\n", "\n", "The profile likelihood uses an optimization scheme to calculate the confidence intervals for each parameter. We start with the best found parameter set of the optimization. Then in each step, we increase/decrease the parameter of interest, fix it and then run one local optimization. We do this until we either hit the bounds or reach a sufficiently bad fit.\n", "\n", "To run the profiling, we do not need a lot of setup, as we did this already for the optimization." - ], + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { - "name": "#%% md\n" + "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 51, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/2 [00:00", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot profiles\n", - "pypesto.visualize.profiles(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# plot profiles\n", + "pypesto.visualize.profiles(result);" + ] }, { "cell_type": "markdown", - "source": [ - "### Sampling\n", - "\n", - "We can use MCMC sampling to get a distribution on the posterior of the parameters. Here again, we do not need a lot of setup. We only need to define a sampler, of which pyPESTO offers a multitude." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Sampling\n", + "\n", + "We can use MCMC sampling to get a distribution on the posterior of the parameters. Here again, we do not need a lot of setup. We only need to define a sampler, of which pyPESTO offers a multitude." + ] }, { "cell_type": "code", - "execution_count": 53, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 5000/5000 [00:18<00:00, 267.95it/s]\n", - "Elapsed time: 22.839196\n" - ] - } - ], + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], "source": [ "# Sampling\n", "sampler = sample.AdaptiveMetropolisSampler()\n", @@ -1633,102 +1297,84 @@ " n_samples=5000,\n", " result=result,\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "For visualization purposes, we can visualize the trace of the objective function value, as well as a scatter plot of the parameters, just like in the optimization. We do omit the scatter plot here, as it has a very large size." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "For visualization purposes, we can visualize the trace of the objective function value, as well as a scatter plot of the parameters, just like in the optimization. We do omit the scatter plot here, as it has a very large size." + ] }, { "cell_type": "code", - "execution_count": 54, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot objective function trace\n", - "visualize.sampling_fval_traces(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# plot objective function trace\n", + "visualize.sampling_fval_traces(result);" + ] }, { "cell_type": "code", - "execution_count": 55, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "visualize.sampling_1d_marginals(result);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "visualize.sampling_1d_marginals(result);" + ] }, { "cell_type": "markdown", - "source": [ - "## 5. Saving results\n", - "\n", - "Lastly, the whole process took quite some time, but is not necessarily finished. It is therefore very useful, to be able to save the result as is. pyPESTO uses the HDF5 format, and with two very short commands we are able to read and write a result from and to an HDF5 file." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "## 5. Saving results\n", + "\n", + "Lastly, the whole process took quite some time, but is not necessarily finished. It is therefore very useful, to be able to save the result as is. pyPESTO uses the HDF5 format, and with two very short commands we are able to read and write a result from and to an HDF5 file." + ] }, { "cell_type": "markdown", - "source": [ - "### Save result object in HDF5 File" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Save result object in HDF5 File" + ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# create temporary file\n", @@ -1745,123 +1391,99 @@ " sample=True,\n", " profile=True,\n", ")" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "### Reload results" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Reload results" + ] }, { "cell_type": "code", - "execution_count": 57, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: You are loading a problem.\n", - "This problem is not to be used without a separately created objective.\n" - ] - } - ], - "source": [ - "# Read result\n", - "result2 = store.read_result(fn, problem=True)" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# Read result\n", + "result2 = store.read_result(fn, problem=True)" + ] }, { "cell_type": "markdown", - "source": [ - "As the warning already suggests, we need to assign the problem again correctly." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "As the warning already suggests, we need to assign the problem again correctly." + ] }, { "cell_type": "code", - "execution_count": 58, - "outputs": [], - "source": [ - "result2.problem = problem" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "result2.problem = problem" + ] }, { "cell_type": "markdown", - "source": [ - "Now we are able to quickly load the results and visualize them." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Now we are able to quickly load the results and visualize them." + ] }, { "cell_type": "markdown", - "source": [ - "### Plot (reloaded) results" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Plot (reloaded) results" + ] }, { "cell_type": "code", - "execution_count": 59, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# plot profiles\n", - "pypesto.visualize.profiles(result2);" - ], + "execution_count": null, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# plot profiles\n", + "pypesto.visualize.profiles(result2);" + ] } ], "metadata": { @@ -1880,7 +1502,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/doc/example/julia.ipynb b/doc/example/julia.ipynb index 29ecc5afe..692518ffc 100644 --- a/doc/example/julia.ipynb +++ b/doc/example/julia.ipynb @@ -33,157 +33,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ce619530-f9c0-4c7f-ba8f-e1c5a55fda4f", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
# ODE model of SIR disease dynamics\n",
-       "\n",
-       "module SIR\n",
-       "\n",
-       "# Install dependencies\n",
-       "import Pkg\n",
-       "Pkg.add(["Catalyst", "OrdinaryDiffEq", "Zygote", "SciMLSensitivity"])\n",
-       "\n",
-       "# Define reaction network\n",
-       "using Catalyst: @reaction_network\n",
-       "sir_model = @reaction_network begin\n",
-       "    r1, S + I --> 2I\n",
-       "    r2, I --> R\n",
-       "end r1 r2\n",
-       "\n",
-       "# Ground truth parameter\n",
-       "p_true = [0.0001, 0.01]\n",
-       "# Initial state\n",
-       "u0 = [999; 1; 0]\n",
-       "# Time span\n",
-       "tspan = (0.0, 250.0)\n",
-       "\n",
-       "# Formulate as ODE problem\n",
-       "using OrdinaryDiffEq: ODEProblem, solve, Tsit5\n",
-       "prob = ODEProblem(sir_model, u0, tspan, p_true)\n",
-       "\n",
-       "# True trajectory\n",
-       "sol_true = solve(prob, Tsit5(), saveat=25)\n",
-       "\n",
-       "# Observed data\n",
-       "using Random: randn, MersenneTwister\n",
-       "sigma = 40.0\n",
-       "rng = MersenneTwister(1234)\n",
-       "data = sol_true + sigma * randn(rng, size(sol_true))\n",
-       "\n",
-       "using SciMLSensitivity: remake\n",
-       "\n",
-       "# Define log-likelihood\n",
-       "function fun(p)\n",
-       "    # untransform parameters\n",
-       "    p = 10.0 .^ p\n",
-       "    # simulate\n",
-       "    _prob = remake(prob, p=p)\n",
-       "    sol_sim = solve(_prob, Tsit5(), saveat=25)\n",
-       "    # calculate log-likelihood\n",
-       "    0.5 * (log(2 * pi * sigma^2) + sum((sol_sim .- data).^2) / sigma^2)\n",
-       "end\n",
-       "\n",
-       "# Define gradient and Hessian\n",
-       "using Zygote: gradient, hessian\n",
-       "\n",
-       "function grad(p)\n",
-       "    gradient(fun, p)[1]\n",
-       "end\n",
-       "\n",
-       "function hess(p)\n",
-       "    hessian(fun, p)[1]\n",
-       "end\n",
-       "\n",
-       "end  # module\n",
-       "
\n" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from pypesto.objective.julia import display_source_ipython\n", "\n", @@ -200,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "96df9672-94ad-4671-9ff4-d6e426e321ce", "metadata": {}, "outputs": [], @@ -214,16 +67,7 @@ "execution_count": 3, "id": "724abb5f-3ce2-4ac5-89fe-2b48f89d8733", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1min 42s, sys: 3.78 s, total: 1min 46s\n", - "Wall time: 1min 48s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "\n", @@ -263,29 +107,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "8579d6f5-193e-444f-a8ff-44ae90293470", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "23.12228487889986\n", - "CPU times: user 1.36 s, sys: 12.5 ms, total: 1.38 s\n", - "Wall time: 1.37 s\n", - "23.12228487889986\n", - "CPU times: user 208 µs, sys: 11 µs, total: 219 µs\n", - "Wall time: 215 µs\n", - "[-38.6806348 19.9557434]\n", - "CPU times: user 1min 42s, sys: 2.21 s, total: 1min 44s\n", - "Wall time: 1min 43s\n", - "[-38.6806348 19.9557434]\n", - "CPU times: user 1.17 ms, sys: 0 ns, total: 1.17 ms\n", - "Wall time: 1.13 ms\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -307,21 +132,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "38c8c4aa-3faf-4f96-bf9d-271fb8a8d8b9", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "numpy.ndarray" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "type(obj.get_grad(x))" ] @@ -336,21 +150,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "9a43d18f-ccf8-4c3f-a30a-2ad824e1e87c", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-38.75164238, 19.9661208 ])" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from pypesto import FD\n", "\n", @@ -369,23 +172,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "3b826499-e4c7-443e-9a59-d8d90e10d3b4", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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GriD17HnafLiEB5oH8Z/7I+0djhDA5S3ugu13Wr/D8czjzEuYx9a0rZiUiTY129AzvCcdanXA09XT3qHb3bVu5ErSFwCM+nkbP2xIYuVrHaleUf7TCMdQkOh739Kb73Z9R/0q9dmaupVccy71KtejV3gv7q5zt8PXu7E1Gb0jruupmDp8F5fIV6sP8lr3BvYORwgA6lSqQ+2KtZm8bTJgdOc8WP9Beob3tFs9+rJOkr4AILSqD3dG1GDW2kM82yGcCp7OMeVclE1JZ5OYvn06P+75kTydR2jFUNKy0vhPzH9oXbO1vcMr02RGrrjg6fbhnM3O49t1ifYORZRT+07u4/UVr3PPz/cwd89cTCYTo9uO5rf7fmNcx3GMXD7SqouEl0fS0hcXRAZXonW4P1NXHmBAm1A8XB1r8QfhvLambmXKtiksPbwUL1cvHmn4CB4uHrQKbOW0o2jsRZK+KOLp9uE8Pi2OXzcfoe9ttewdjnBiWmvWpqxl6raprDu6joruFXmmyTP0a9DvqssKWnuR8PJIkr4oIqZeVRoFVmTC8gR6twjGZHLgG2VaQ9ZJ8K5i70jETTBrM0sTlzJl2xTi0+Op5lWNEVHGCJ0bmS0rSkaSvihCKcWQ9nV4YfYWFu48Rrdba9g7pKtb+H+w+nOo1Qoa94ZG94JvNXtHJa4i15zLHwf+YOq2qew/vZ9aFWrx1u1v0TO8J+4usl6zrcg4fXGZvHwzHT9eRlVfD356prVjDovbvwxm9jJm4GakQupOUC5Qp4PxAdDgHvCsaO8oBZCdl83P+35mevx0jpw7wi2Vb7lQB6csLTNYlsg4fXFTXF1MPBlTh3/9up31B08SHeZg3SdZJ+HnZ8C/Hjz8Pbh7w7HtsO0HiP8BfnkGXF6EW7oZHwD1uoGbTDiztbM5Z/l+9/d8veNrTmSfoGm1przR6g1igmIcsyFRTkjSF1fUp0UtPl20lwmxCY6X9H9/Gc4dh4cWGgkfIOBW46vzvyBpvfEBsP0n2DkPPCoaLf/GD0BYB3CRX/vSlJ6Vzqyds5i9azYZuRm0qdmGwY0H0yKghSR7ByC//eKKvNxd6H97KJ8s2sPuo2epX6OCvUMybJ0L8T9CxzevvFKWUlAr2vjqNhoOLodtP8LO3+Cfb8GnmtH337g3BEeDSaaqWMuRjCNM3z6dn/b+RE5+Dl1rd2VQ40E08m9k79BEIdKnL67q5LkcWn+whDsb12Bs36b2DgdOJ8H/WkO1+jDwj5trsedmw76Fxl8Ae/6EvGzwC4GI+40PgIAI4wND3LT9p/YzNX4qC/YvAKBHeA8GRgwkzC/MzpGVX9KnL4qlso87D95Wi1lrD/HyHfUJquRlv2DMZvj5adD5cP/Em++icfOEhj2Mr+wzsHuB8QGw+nNY9SlUawARvY0uoCp1rns6AdvTtjNl2xQWJy7Gw8WDhxo8RP9b+1PDx4FHfAlp6YtrSzqZSfv/LqP/7aH8q4cd/0xfPR7+fgN6fg7NH7feec+lwY5fjC6gxNXGvqAWxgdAxP1QQRJYYVpr1h9dz5RtU1iTsoYKbhV4uOHDPNLwEap4Oti9n3JMSiuLEhn+/Rb+2n6U1SM7UcnbDuOpj22HSR2g3h3w4KzS64Y5ddi4+bttLhzdBihjVa6I3tCoJ3hVLp3rlgFaa5YnLWfStklsTd2Kv6c/j9/6OH1v6YuvexlYeKecKbWkr5SqBEwBIgANPAHsBr4HQoGDQF+t9Ull3LYfB9wFZAIDtNabrnV+SfqOYdfRM3T/dAUvd72F5zvXs+3F887DpI7GaJ1n14KPjeqmp+4xhn9umwsn9oPJDep1hYgHoP6d4F5+Zo4eOH2A0etGszZlLUG+QQy8dSD31rsXDxcPe4cmruJaSb+kQxfGAX9qrRsATYCdwEhgsda6HrDYsg1wJ1DP8vUU8GUJry1spEGNinSoX43pqw+SnZtv24sv+Tcc3w69vrBdwgeodgt0HAXPb4Inl0LLIXBkM/w4CD6sAz8OhqPxxrEHlsPKT20Xm41k5WXx2abPuH/e/WxP286olqOYf998HmzwoCT8MqzYSV8p5Qe0A6YCaK1ztNangF7ADMthM4B7Lc97ATO1YS1QSSkVWNzrC9t6un046edymLvhsO0uemCF0ZffYqAx0coelDKGhnZ7H4ZvhztGg9Kwa4HR5fTzM8ai7FcaPlpGaa1ZkriEe3+5l8nbJnNX2F3Mu28eDzd4WGbQOoGStPTDgFTgK6XUZqXUFKWUDxCgtU6xHHMUCLA8DwIKZ4wky74ilFJPKaU2KKU2pKamliA8YU0tw6rQtFYlJq84QF6+ufQvmHXKGK1TpY6RcB2ByQVaPweP/ACuHlC1rjH236MCePrZOzqrOHz2MEOXDOWFpS/g7ebN9O7Teb/t+7IcoRMpyce2K9AceF5rvU4pNY6LXTkAaK21UuqmbhporScBk8Do0y9BfMKKlFI83T6cp2dt5I/4o/RoUrN0L7jgFTibAoMWOl7/eVg7uG0wLP8Ibr0fDq0y7ju0HQ7tXzU+EMqY8/nnmRY/janbpuKiXBgRNYJ+DfvhZiqfK6jl5uaSlJREdna2vUO5Jk9PT4KDg3Fzu/F/p5Ik/SQgSWu9zrL9A0bSP6aUCtRap1i6b45bXk8GChdoD7bsE2VE10YB1Knqw4TYBO6JDCy9KfXxP8K2OdDhdQhuUTrXKIkDy2HDVGj3qvHY83PYOR9WjIFdv8O9XxjDPq1oWvw0IvwjitSSj0uJIz49nicinijRuVcmr2T0utEcPnuY7qHdGRE1ggCfgAuvl+a1HVVSUhIVKlQgNDTUYUtHaK1JT08nKSmJsLAbnwhX7O4drfVR4LBSqr5lV2dgBzAP6G/Z1x/41fJ8HvC4MrQCThfqBhJlgItJ8VS7Omw/coaV+9JK5yJnjsD84RAUBTEjSucaJXFgudGH32c6dHrDeJz3PDR9GPrNhezTMKULLHzLmAVsJRH+EYyIHXFhqcC4lDhGxI4gwj+i2OdMyUhh+NLhPLPoGVyUC5O6TuK/7f9bJOGX1rUdXXZ2Nv7+/g6b8MH469vf3//m/xrRWhf7C2gKbAC2Ar8AlQF/jFE7e4FFQBXLsQr4AkgAtgFR1zt/ixYttHAs2bl5+rb3FupHJq+1/snz87We0VPr92ponbbP+ue3hhWfaL0/tui+/bHGfq21zjql9S/Paf1WRa0/j9I6cZ3VLr3uyDod812M/nzT5zrmuxi97kjxzp2Tl6Mnb52sb5t1m476OkpP3jpZn887b5NrlxU7duywdwg37EqxAhv01fL21V5whC9J+o7py2X7dO3X5uuth09Z98Rr/mcky/XTrHtee9i7SOuxt2r9lp/Wf47S+vw5q5z2802f64jpEfrzTZ8X6/1rj6zVPX7uoSOmR+hhi4fp5LPJNrt2WeLMSV9KDIqb1q9lCBU8XJmwPMF6Jz2+0+gSuaU7tBhgvfPaS93O8MxqiBoIa8bDhDZwaHWJThmXEsec3XMYEjmEObvnXOhuuRGpmam8uvxVBv89mNz8XL7o/AXjOo2jpu+N3ZAvybVF8b3//vvceuutREZG0rRpU9atW3f9N13P1T4NHOFLWvqOa/SCHTps5Hx9MC2j5CfLPa/1l220/rCO1mePlfx8jiZhmdafRBit/gWvan3+5n9mBd0rBd0ql25fTW5+rp65faZu+U1L3Xxmc/3F5i90Vm6WTa5dljlCS3/16tW6VatWOjs7W2utdWpqqk5OvvwvM2npC5sY1CYMV5OJySv2l/xkS983at30/Bx8q5f8fI6mTnt4Zg1EPwnrJsCXrY2JZzchPj2eMe3HXBhBEx0YzZj2Y4hPj7/qezYf38yD8x/ko/Uf0ax6M37u9TPPNn0WT9ebW0WsONcWJZeSkkLVqlXx8DCGAFetWpWaNUs+VFoKroliG/njVn7enMyqkZ2o6lvMsekHV8H0u6H5Y0bSd3YHV8Gvz8HJAxA1CLq+Y0zusqL0rHTGbhzLvIR51PCpwcjbRtIppJNDj0RxNDt37qRhw4YAvPPbdnYcOWPV8zeqWZG3etx6zWMyMjJo27YtmZmZdOnShQcffJD27dtfM9YCpVl7R5RjT7arQ06+memrDhbvBNmnjVm3lUOh23+sGZrjCm1j9PW3eg42TDMWhUlYapVT55vzmb1rNj1+6cGCAwsYFDGIX3v9SufanSXhl0G+vr5s3LiRSZMmUa1aNR588EGmT59e4vNKIQ1RbOHVfLmjUQAz1xzkmQ7h+Hjc5K/TH6/BmSR44i/wKEfled29oftoaNTLaPV/fa+xRsAd7xW7nMO21G28t+49dqTvoGWNloxqNYo6frIYjDVcr0VemlxcXOjQoQMdOnSgcePGzJgxgwEDBpTonNLSFyXydPtwzmTn8V1c4s29cfsv8M93xgSsWtHXPdwphbSEp1dAmxdg8yz43+2wd+FNneJU9ineWfMOjyx4hNTMVD5q9xGT75gsCd8J7N69m717917Y3rJlC7Vr1y7xeaWlL0qkWUhlokOr8NWqgwxoHYqryw20I86kwPwXoWZzo1ZNeebmBV3fhYY9jVb/N72h6SNGkblrLNpi1mZ+2fcLn2z8hLM5Z3m00aM82+RZWdDEiWRkZPD8889z6tQpXF1dqVu3LpMmTSrxeSXpi2KbEJtAZLAfg2PCeOproxCbv687W5NO83T78Cu/SWsjueVmw/2TwKV8FvS6THAUDFkOsR8atfn3LYYenxoLtlxi14ldvLf2Pf5J/Yfm1ZszquUo6lepf9lxomxr0aIFq1eXbG7HlUj3jii2yGA/hn67GR93V0L9vfl04R6GfruZyOBr9EvHTYaExdDtPahq41W4HJ2rB3T+Fzy5GLz94buH4McnIfMEAHnmPMZtGseD8x/k8NnDvNfmPaZ3ny4JX9wUaemLYmsdXpXx/ZoZiT7Ij2V7UnmrRyNah1+l9nrqblj4f8Zat1GDbBtsWVKzGTy1zKjaueJj2L+M1K5v8crRRWw8tpH7693PSy1ews/DOWr4C9uSlr4okdbhVXm0ZQjL9qTi4Wpi7f70Kx+YlwM/PWnUxu85vvQWN3cWru7Gco1PLiXOz58+G0ez49hmRt82indavyMJXxSbJH1RIqsT0pi1LpFhneqiFPy1/RgH085dfmDsB5DyD/QYBxUCLn9dXMaszUxOXceTnllU9KrCt0eO0WP+G7DtB+PeiBDFIElfFNvqhDSGfruZ8f2a8dId9RnbpwkAoxfsLHpg4lpY+Qk0fRQa9rBDpGXPqexTDF08lM82f0a30G7M7vM3dZ9YApVCjMXZp99zcWF2IW6CJH1RbFuTTjO+X7MLffh3RdYkpl5Vluw6zqnMHOOg7DPw01PgVwvu/MCO0ZYdW1O30nd+X9amrOXNlm/yYcyHeLt5Q0AjGLQI7v4Yjm+HiTEw/6ULN3qFuBGS9EWxPd0+/LKbtqPuakieWfPNOstkrT9fh9OHjeGZVq4x42y01nyz8xv6/9kfkzLx9Z1f82CDB4uWUHBxNdbnfX6T8bhxOnzWDNZNgvw8u8UurM/Xt3TmXEjSF1bVMLAiMfWqMmP1QXLjf4Uts4wFw0Na2Ts0h5aRk8GI2BF8EPcBbWu25ft7vufWqteY/u9dBe76Lzy9EgIj4Y9XjJb//ljbBS3KJEn6wuoGtQ1Dnz2K+ddhENgE2o+0d0gObc/JPTz8+8MsTlzM8BbDGddp3I2PzgloBI/Pg75fQ04GzOwJ3z8KJw+VbtCizJJx+sLq2teryhe+0yA3E33fJJSru71Dcli/7vuV99a+RwX3Cky5YwpRNa5YDffalIJGPaFeV1g9HlaOhT1/Q5thxl9Z7j7WD7w8+WOksd6DNdVobLd7XNLSF1anNk4jOm8j7+c+zOozV5moVc5l52Xz1uq3eHPVm0RWi2ROjznFS/iFuXlB+1dg6AZjlNTy/8L422SIpyhCWvrCutL2wl9vkl+nEwsO3cPhFftpU1cSf2GHzhzi5WUvs/vkbp5s/CTPNX0OF5OL9S7gFwS9pxo3ev941RjiuX6q0bIMbGK965QXTjbqTFr6wnryc41Zt26euNz7Px67PYylu1PZd/ysvSNzGAsPLeTB+Q9yNPMo/+v8P4Y1H3ZjCX/lp3BgedF9B5Yb+6+m9u1GOYce4yBtN0xsD7+9AOfSbi7o4lxbOCxJ+sJ6Yj+CI5uNJFMxkEdbheDhamLqygP2jszucvNz+TDuQ15a9hLhfuHMvWcuMcExN36CoOYwd8DF5HtgubEd1Pza7zO5QIsBxhDPVs/Apq/h8+aw9kvjQ7o0ry0ckiR9YR2H44wCYU36GStCAf6+HtzfPJgfNyWTlnHezgHaz9FzRxn410Bm7ZzFIw0fYXr36QT6Bt7cScLaQZ/pRrJd8r7x2Ge6sf9GeFWC7v8xlmqs2Rz+HAkT2kLCktK/tiiWjIyMUjmvJH1RcuczjFm3FYMv6/8c1DaMnDwzs9aWzyGEK5NX0ue3Puw7tY8x7ccwMnokbsVdQyCsnVGddPlHxmNxkm71BvDYz/DQt5CXDV/fB9/1gxPX+WvMGtcWDqHESV8p5aKU2qyUmm/ZDlNKrVNK7VNKfa+Ucrfs97Bs77O8HlrSawsH8dcoOHkQ7p942Rqvdav70qlBdb5ec4js3Hz7xGcH+eZ8xm8ez7OLnqWadzVm3z2bbqHdSnbSA8thw1Ro96rxeGk/+41SChrcDc+uM+r3718GX0TDoneMD/DSvLawO2u09F8AClfY+hD4RGtdFzgJFBROHwSctOz/xHKcKOuObIZNM+D256B26yseMjgmjPRzOfyyOdnGwdlHelY6QxYNYeLWifSq24tv7vqGUL/Qkp20oB+9z3To9MbF7paSJF83T4h5GZ7fALfeZ4zvHx8F/3xfdIhnaVxb2E2Jkr5SKhi4G5hi2VZAJ+AHyyEzgHstz3tZtrG83lkpKapepq381BgN4lXl4lq3VxjVcXsdfxoFVmTKygOYzc49XnzTsU30/a0vW45v4d3W7/LvNv/Gy9Wr5CdO3lS0H72gnz15U8nPXbGmURtp0EKoUAN+fgqm3nHx3KV5bWFzJR2n/ynwKlBQScsfOKW1Lqj8lAQEWZ4HAYcBtNZ5SqnTluOLjB9TSj0FPAUQEhJSwvBEqUv5B6IGG906hVuEhSileLJdGMO//4fYval0rF/dLqFaw7T4aUT4RxAdGH1hX1xKHNvStmFSJsZtGkeQbxD/6/I/6y5j2PbFy/eFtbNu33qtaBi8BLZ8A4vfgcmdoNmj0Pkt8K1WutcWNlPslr5S6h7guNZ6oxXjQWs9SWsdpbWOqlat2vXfIOzDbIb4H8CnOuz4+bqjOu5uXJOAih5MWbHf5qFaU4R/BCNiRxCXEgcYCf/l2JdZdngZYzeOpVNIJ2bfM7vsrltrMkHzx+D5jUaX3T/fGUM8V483Vj8TZV5JunfaAD2VUgeB2RjdOuOASkqpgr8ggoGCjtxkoBaA5XU/4Cpr6wmHF/+DUY+k2/s3NKrD3dXEgNZhrNqXzo4jZ2wcrPVEB0Yzpv0YRsSOYPzm8by47EVclSvxafG8dttrfNz+Yyq4O0EJaU8/49/22bVQqyX8/QZ82doo6SAlnG3CxcWFpk2bEhERQY8ePTh16pRVzlvspK+1fl1rHay1DgUeApZorR8BlgK9LYf1B361PJ9n2cby+hKtpSBImZR3Hpb82yga5RNww6M6+kWH4O3uwpSVZbu1Hx0YTd/6fZm4dSLncs7hYnLhq+5f8WijR3G621RV68GjP0C/OaBMRkmH8S2Msg65WfaOzql5eXmxZcsW4uPjqVKlCl988YVVzlsa4/RfA15SSu3D6LOfatk/FfC37H8JkHq7ZdX6qXAqESJ6w48Db3hUh5+3G32javHbP0c4dibblhFb1arkVXwV/xUAJpOJ16Nfp2n1pvYNqrTd0s1o9T/4DXhXhd9fgk8bw4qPIeuUvaNzerfffjvJydYZ/WaVgmta62XAMsvz/UD0FY7JBvpY43rCjrJPG9Ub63Qwtq82quMq3TwD24QyY81BZqw+yKvdG9giYqtadngZw5cOJ0/n8WzTZ2lWvRmvxr5KBfcKRW7uOiWTCRreY4zxP7TKWPd48buw4hOIGgitnoWKNznTuAz4MO5Ddp3YZdVzNqjSgNeiX7uhY/Pz81m8eDGDBg26/sE3QKpsipuzahxknYAub0PNZpe/fp1RHbX9fejWqAbfrEtkaKe6eLuXnV/BtKw03l3zLmZtZnTb0fQINxZ5H9N+DPHp8c6f9AsoBaFtja+UrcbvxJrxsG4CNHkIWr8AVevaO8oyLysri6ZNm5KcnEzDhg3p2rWrVc6rHLlbPSoqSm/YsMHeYYgCZ1KM9Vgb3G2U7i2mDQdP0HvCGt7tdSuP3x5qvfhKUeKZRIYsHEJ6djpjO4ylbVBbe4fkWE4cgNWfG8M9884bi7q0ebHMFmXbuXMnDRs2tGsMvr6+ZGRkkJmZSbdu3ejTpw/Dhg277LgrxaqU2qi1vuICDVJ7R9y4Zf8Bcx50erNEp2lRuzJNa1Vi2soD5JeByVo70nfw2B+PkZGbwdQ7pkrCv5IqYXDPWHhxG8S8BAnLYHJHmNETEpbKIi4l4O3tzWeffcbHH39MXl7JR05J0hc3JnU3bP4abhtk/AcvAaUUg2PCOJieyaKdx6wUYOlYc2QNA/8ciKeLJzPvnEnjao3tHZJj861u1PMZHg9d/2383nx9L0zqANt/BnP5qb9kTc2aNSMyMpLvvvuuxOeSpC9uzOJ3wc0H2r1ildN1v7UGQZW8mLrCcWvt/3nwT55d/CxBFYL4+q6vCfMr2YddueJZ0Vij98Wt0OMzY9H2uQOM2j4bpxtdQOKaLi2t/Ntvv/HYY4+V+LyS9MX1Ja6FXfOhzQvgY52lD11dTDzRNoy4gyf45/Apq5zTmr7Z+Q2vxr5KZNVIpnefTnXvsls6wq5cPaBFf3guDvrONCZ9/faCMdxz5aeQXXYn6pVVkvTFtWkNC98C3wC4/VmrnrpvVDAVPFyZ4kAra2mt+WzTZ3wQ9wEda3VkYteJVHSvaO+wyj6Ti7G4zpNL4fFfoXojWPQWfBJhlHTOOG7vCMsNSfri2nYvgMNrocNIcPex6qkreLrxcMsQFmxLIfmU/Wd35pnzeHvN20zeNpkH6j3Axx0+xtPV095hOReljDkej/9irN9btxOs+tRI/vOHwwnHma3tyCMbCxQnRkn64ury84xWmH9daPZ4qVyif+tQAKavsm9rPzsvm+HLhvPT3p8YEjmEt25/C1dT2ZlDUCbVbGZM5hu6AZo+DJtnwectYO5Ao3qrHXl6epKenu7QiV9rTXp6Op6eN9cwkd9qcXVbvoG03dD3a3ApnV+VoEpe3N04kNlxhxnWuR4VPIu5lGAJnD5/mmFLhrH5+GZGtRzFww0etnkM5Zp/OPQYBx1eNxZs3zANtv8EVcIh6gmj2mdBTaMDy40Z31cqNW1FwcHBJCUlkZqaWqrXKSlPT0+Cg4Nv6j0yOUtcWU6mUVLXL9hYXKMUC4ltTTpFz/GrePPuhgyOqVNq17mSY+eO8fSipzl05hD/iflPyZc0FCWXfdpI/Cs/hexTUPUWaP8aeFYyFniRRdmvSyZniZu37ks4mwJd3y3VhA8QGVyJ6LAqfLXqIHn55lK9VmH7T+/nsT8eI+VcCl92+VISvqPw9IO2w+Hl3UY9n/R9RnXPb3pDUAtwcZfJXiUgSV9cLvOE0cq65c6rrntrbYPbhpF8Kos/tx+1yfW2pm6l/x/9OZ9/nmndptEysKVNritugpsndP8PtH3J2A5oBAdXwbRu8FlTWPaBUf5B3BRJ+uJyy8cYk2m6vGWzS3ZpGECovzeTVxwo9ZtnK5JWMPjvwfi6+TLrzlk08m9UqtcTJXBgOWz8yliv4exR6PMV3DcRKtU2kv5nTWFad9g4w+gWEtclSV8UdfIQrJ8MTftBddsVnDKZFIPahvHP4VNsPHSy1K7zW8JvDFsyjNCKoXx919fUqlir1K4lSqjwmssF6zX88oyxkHv/eUaph85vQWY6/DYMxtxijPzZ87es7nUNkvRFUUvfN1ZI6jDK5pd+oEUwlbzdmFxK6+hOj5/OqJWjaBHQgmndplHVyzqzi0UpSd509fUawBhkEPOSMdv3yaXQvD/sXwbf9oGxDeGvN4wlPUURMnpHXJSyFSa2M8otdH3HLiH8969d/G9ZAstGdKC2v3Umg5m1mbEbxjJjxwzuqH0H/4n5D+4u7lY5t3AweTmwb6GxoPvuP8GcCwERRp3/xn2gQg17R2gTMnpH3JhFb18cOWEn/W8PxdWkmGal0gy55lzeXPkmM3bM4KH6D/FRu48k4TszV3djvYcHZ8GIPXDXGHD1hL/fNFr/sx4wFncvx+v7StIXhv3LIGExtBsBXpXsFkb1ip70bBLEnA1JnM7MLdG5MnMzGbZkGL/t/42hTYcyquUoXEwuVopUODzvKhD9JDy52Jj12/Ylo9Tzj4OM/v9fhxqjgcy2GybsCCTpC+OXfuFb4FcLbnvS3tEwOCaMrNx8vok7VOxznMw+yeC/B7P6yGreuv0thjQZgirl+QbCgVWtB53/D17YCv3nQ8OeRn3/6XfBZ01gyfuQnmDvKG1Ckr4wprynbIGObxhjo+2sYWBF2tatyozVB8nJu/lW2JGMIzz+x+PsPrGbsR3G0vuW3qUQpSiTTCYIi4F7vzC6f+6fbNSWWjHGmIE+9Q5jNnBW6Y0gszdJ+uVdXg4s+bdxsyuyr72juWBwTBjHzpxn/tYjN/W+vSf38tiCx0jPSmdi14l0DulcShGKMs/dx/idf+xnGL4durxjjPWfP9zo/pnzOOxaYNv+/5WfGkNVCzuw3NhvJZL0y7uNX8HJg9DlbaPmuYNof0s16lX3ZcpNTNbadGwT/f/sj0Yz/c7pRNW44uAFIS5XsaZRxO3ZtRA9BOp1N/r7Zz8MH4bC5M7wXb/SnwEc1NyYm1CQ+AvmKlhxgXmpslmeZZ+B2A8hNAbqdrF3NEUUrKP72o/bWJOQTuu61x5TvzRxKa8sf4VAn0AmdJ1AkG+QjSIVTkUpaHiPkWgfmALaDBumw+7fjee7fwf/elDvDqjXBWq3MVYHs5aCuQhzB0DUINgw1eoF5qSlX56t/tyYzdj1nVIvqlYcvZoGUdXX/bora/2450deXPYit1S+hZl3zpSEL0qmIPH+OBgS10HiamO1r+c3QfcPoXJtWD8Fvr4PPgyDbx+C9VPhVKL1rh81CJZ/ZDxauaJosVv6SqlawEwgANDAJK31OKVUFeB7IBQ4CPTVWp9UxtCJccBdQCYwQGu9qWThi2I7ewzWjIdb7zMqFzogTzcXHmsVyieL9rDv+FnqVq9Q5HWtNVO2TeGzzZ/RpmYbxnYYi7ebt52iFU6lcOJt9+rFxOsfDq2eNkqPH1wBexfC3r9gzx/G69UaQL2uxl8CtVoZ8wZu1oHlRgu/3avGY1iMw7T084CXtdaNgFbAc0qpRsBIYLHWuh6w2LINcCdQz/L1FPBlCa4tSir2A8jPgU7/Z+9IrunRViF4uJqYuvJgkf05+Tm8s+YdPtv8GffUuYfPO30uCV9Yz6WJ99Kbq+7ecEs3uHuMMQz0ufXQbbQx43ftBJjRAz4Kg9mPGMXgztzggIQr1Rsq3MdvBcVu6WutU4AUy/OzSqmdQBDQC+hgOWwGsAx4zbJ/pjbuyq1VSlVSSgVaziNsKW2f8YsY9YTRcnFg/r4e3N88mJ82JTHijlvw9/UgNTOV4cuG80/qPzzZ+EmGNhuKSUlPpbCSwok3rJ3R0i68fSmloNotxtftz8H5DOMce/82/hLYNd84LiDC+CugbleoFQ0uV1gl7lr1hqzU2rdK7R2lVCiwHIgAErXWlSz7FXBSa11JKTUf+EBrvdLy2mLgNa31hkvO9RTGXwKEhIS0OHSo+BN0xFV8/xgkLIFhW8C3mr2jua59xzPoMjaW4V1uoUOTTIYvHU5Gbgb/bvNvWfhEWN/KT43RMoWTbHGXadQaUndd/ABIXAPmPPDwg/COlg+BLlavCXSt2jslHr2jlPIFfgRe1FqfKTzrUWutlVI39amitZ4ETAKj4FpJ4xOXOLweds4z1iMtAwkfoG51Xzo1qM70bd8zI+UnvFy9eO2214ok/LiUOOLT43ki4gk7RiqcwpUSe1i74rW0lTJKlFdvaBQyzD5jlDzZ+zfsWwQ7fjGOC2xi/AVQ7w4IjirV4dMlSvpKKTeMhP+N1vony+5jBd02SqlA4LhlfzJQuHh5sGWfsBWtYdFb4FMdbh9q72huWK45F5+a88hTvxDm1ZQXbxvI22veplaFWkQHRhOXEseI2BGMaT/G3qEKcW2eFaFRT+NLazgWb/krYBGs/MSYGexZCep2NgZZNOxh9RBKMnpHAVOBnVrrsYVemgf0Bz6wPP5aaP9QpdRsoCVwWvrzbWzPX3BoFdz9MXj42juaG5Kelc7LsS+z8dhGKmR3ITO9Bx0f6Iivuy8jYkfQt35f5uyew5j2Y4gOjLZ3uELcOKWgRmPjK+Zlo/RDwlLjL4C9C8HNy7GSPtAGeAzYppTaYtk3CiPZz1FKDQIOAQVz+xdgDNfchzFkc2AJri1uljnfKJ1cJdxYbKIM2J6+nReWvMCp86f4IOYDck43Yfj3/xC7J5UO9aPpW78vE7dOZEjkEEn4ouzzqgwR9xtfZrOxZGkpKMnonZXA1Wb0XFbwxDJq57niXk+U0D/fQepO6DPjyqMGHMxvCb/xzpp3qOJZhZl3zqSRfyNy8sx88Mcupqw4gHfFg8zZPYchkUOYs3sO0TWiJfEL52EyGV1BpUDKMJQHuVmwdLQxCatRL3tHc0155jzGbhzL1zu+5rYatzGm/RiqeFYBwN3VxIDWYYxZ/jv7l87l044fEx0YTXSN6At9+pL4hbg2GdxcHqybCGeSoeu7DlluocDJ7JM8vfBpvt7xNY80fISJXSdeSPgF+kWH4OmbTH317IUEHx0YzZj2Y4hPj7dH2EKUKdLSd3aZJ2DlWGMoWGhbe0dzVbtP7OaFpS+QmpnKv9v8m3vr3nvF4/y83ehT93G+WXeI42eyqV7RqP8fHSjdO0LcCGnpO7uVY42xwV3etnckV/XngT95dMGj5Jpzmd59+lUTfgEPVxO5+ZoZaw5e2Lc6IY0JseVj5SMhSkKSvjM7dRjWTYImD0PArfaO5jL55nzGbhzLK8tfoaF/Q76/53saV2t83fe1r18NNxfF9FUHyczJY3VCGkO/3UxksJ8NohaibJPuHWe2dLTx2HGUfeO4gtPnT/Pa8tdYdWQVfW/py8jokbjd4Kii1uFVeeOuhrz92w6enbWJrcmnGd+vGa3Dr11zXwghLX3ndWy7MUyz5RCoVOv6x9vQvpP7ePj3h1l3dB3/uv1f/N/t/3fDCb9A/9ahBFT0YNmeVO5qXEMSvhA3SJK+s1r0tjHOt+1we0dSxKJDi+i3oB9ZeVl81e0r+tzSp1jnWbM/ncycfNxcFLPWJvLUzA2cOJdj5WiFcD6S9J3RgRVGPY+Yl8G7yvWPtwGzNjN+83iGLxtO3Up1mX33bJpWb1qscxX04U98rAVrXu9Ml4bV+XvHMdp8sJiJsQlk5+ZbN3ghnIhVSiuXlqioKL1hw4brHygu0hqmdIazR+H5jUb9Djs7m3OW11e8TmxSLPfWvZc3W72Jh0vx1xWdEJtAZLBfkS6duRsOMyE2gYTUcwRX9uLV7g3oERmIcuB5CUKUlmuVVpakbyXT4qcR4R9RZKy4Xcr9bv8F5vaHXv+DZo/Y7rpXsf/0fl5Y8gJJZ5N4NfpVHqr/UKkm4lX70njv953sTDlDk1qV+L+7GxIV6hh/7QhhK9dK+tK9YyUR/hGMiB1BXEocwIVyvxH+EbYLIj8XFr8L1RtBk4dsd92rWHZ4Gf1+78eZnDNMumMSDzd4uNRb3m3qVmX+8235b+9Ijp7OoveENTwzayOH0s+V6nWFKCukpW9FBYnebuV+4ybDghHQb46xfqedmLWZSVsn8cWWL2hYpSHjOo4j0DfQ5nFk5uQxefkBJi5PIDffzGOtQhnWuS6VvIuxWLUQZYi09G0kOvBiud++9fvaJuGv/NRYyi0jFWI/hNptwNXT2G8H53LP8fKyl/liyxfcU+ceZt450y4JH8Db3ZUXutRj2YgOPNA8mOmrD9D+v8uYsmI/OXlmu8QkhL1J0reiuJS4IuV+C7p6So3WRpnkWQ/A2AZGnZ1b74MfBhprfJayafHTinyPiWcSuf/X+1mcuJhXol5hdNvReLp6lnoc11O9oicfPBDJghdiiAz2473fd9L1k1gWbEvBkf/SFaI0SPeOlRResu/SJfys3uLPPAFbvoWN0yF9L7j5gM6Hpv1gx6/QZ3rx1vO8SYW/xxxzDi8te4nsvGyGtxjOwAjHXSMndk8qo3/fye5jZ4mqXZk37m5Is5DK9g5LCKuR0Ts2UOqjd7SGxDWw4Ssjseefh+BoaDHAaN2v/ASWfwTtXoVOb5T8ejfo132/8t7a98jOz8ZFufB+2/e5u87dNrt+ceXlm5m7MYmP/95DWsZ57okM5LXuDahVxdveoQlRYpL0y7LME/DPbKNVn7YbPCpC5INGsq9hGRl0YDnMHQBRg2DD1FJt6eeb89mWto0lh5ewNHEpB88cvPDaExFPMLyFY80Avp6M83lMik1g0or9mM0wsE0oz3asi5+X468uJsTVSNK3hZWfGv3ohZPtgeWQvAnavnhz59IaEtfCxq+Mcff55yEoCqIGGq16d5+i15g74GKiv3TbCrLzslmbspalh5ey7PAyTmSfwFW5ElUjijp+dfh9/+881OChMr1AecrpLMb8tYefNidRycuNFzrX45FWtXFzkdteouyRpG8L1ki+WScvtupTd1la9X0trfqrlBy25odNISezTxKbFMvSxKWsSVlDVl4Wvm6+tA1qS8daHWkb3JZd6btsdx/DRuKTT/P+7ztZsz+dOlV9GHlnA7o2CpCZvaJMkaRvK8XpZtEaDq8zEv32nyEv21jLtsVAiLi/aKv+Cqx5LyHxTCJLDy9lSeIStqRuwazNBHgH0KFWBzrV6sRtNW4rUg3TYWYhW5nWmiW7jjN6wU4SUs/RMqwKb9zdkMjgSlcsAbE6IY2tSad5un24HaMW1uIM/8aS9G1pyfs3dkM16xRs/d5I9sd3gHuFi636wMgbvlxJRg2ZtZltadtYmmh02yScNlaeql+5Ph1qdaBjSEcaVWlUblu5uflmZscl8smivZw4l8N9zYLo1KA6b83bfqF+f0HxN6nn7zwu/Tcti//GkvRt5Xotfa0hab0xAmf7z5CXBTWbG4k+4gHw8C3WZW9mJvD5/POsS1nHksQlxCbFkpaVhotyISogio4hHelQqwNBvkHFisNZncnO5ctlCUxdeQAFdL+1Bsv3pvJYq9rMWpdYppKBuDEFif7RliFl8t/4WklfVs6ylkv78MNiLm7XiIStcyyt+u3g7mvUxmkxAGo2LfGlC88EHhI55LKEfyr7FMuTl7M0cSmrjqwiKy8Lb1dvo38+pCMxQTH4echSg1dT0dON17o34JGWIfz3r938uuUIXm4mPluyj2a1KrE9+QypZ88TUNGTGhU9CajoiZe7i73DFiXQOrwqj7YM4bMl+xjWqW6ZSvjXI0nfWpI3FW3Zh8ZA+9fh738ZN2XzsiCwKfQYBxG9i92qv5JLZwJH14gm0DeQpYlLWXp4KZuPbyZf51Pdqzo96vSgY0hHomtE4+4iNWhuRnBlb8Y91Iyo2pV5d/4O/Lxc2Xz4FJsPn7rs2AqergRU9CSgoofl0ZOACsbz6pb91St44u56Y6ODnKGfuSxZnZDGrHWJDOtUl1nrEmkV7u80id/mSV8p1R0YB7gAU7TWH9g6BqvTGlo+DTnn4MQB2LfIaNUfi7e06i3j6ms2s/ql1x5ZyyvLX+HNVm8S6BNI0tkknlz4JGZt1JapW6kuT0Q8QaeQTjTyb4RJyRDEklidkMYni/Yy44loo793XxrPfbuJUXc1JNDPi2Nnsjl2NpvjZ85z9LTxfN3+Exw/m01u/uVdqf4+7hc+BAIqWB79PC3PjW1/Xw8ig/2u2s8srOvSPvxW4f5lrk//Wmzap6+UcgH2AF2BJGA98LDWeseVji+VPn2zGXIzjQSdk2F5PHeF7as9v8q2vmS1psAmxgicxr3BowIAeeY8MvMyyczNJDMvk6zcrCLb53LPXXhesD8rL+vivkLHFezPzs8uclmTMlG3Ul1qeNdgZMuR1KrgWOvjlnXFbXGbzZqTmTkcO3Pe+GA4k208P5vN8TPZHLVsp2Wc59L/ki4mRTVfD3zcXUg8mUm96r4kpJ6jY/1q1Pb3wc3FZHy5KtwLnruYcHNRuLtesu1iwtXy3M3FVOj1Qu91tbxuMmEylb+b+M7wV5XD3MhVSt0OvK217mbZfh1Aa/2fKx1f3KSfkriKb5a9js7PRZtzjMf8XMzmXLQ5Dw1Fv5S68NxseQQwK4U2uaFNrmgXV7TJBW1yxWxyhYLnysWy3wVteZ7j5kmmi8uFpF2QzHPMN76Gq4eLB96u3ni7eePl6oW3m7exbdnn7eqNj5sPXm5eF/b5ufsRXSOaSp6VbvpnJhxDXr6ZtIwcjlk+CI4XfDicyebY2fNsTz5N+rkcPN1MuJlM5OSbyck3X/ZBYS2uJnXhQ8HNxfgQMClQWB6VwmS6uG1SClWw/8K2QgEm0yXbVz3+4nHGtbjwnosDydSF5wW7lCWuC88vvH7xwIvHqiLvKzhPwUg1VejEF48sfP2i177ya9d4nyp65JVeiwzy46HoEIrDkW7kBgGHC20nAS0LH6CUegp4CiAkpHjf8ImcM8wxnwSTwmRSKFcTSnmi8DJ+kTAZ/7jKhEmZLL8AJpTJhFImlHIxukGUyfLLXHCMuvhY8NyybbIcC+Du4oqvmw/VvapfNWn7uPkUSeCFk7eXqxduJikDUB65upio4edJDT9PmlzyWkG3Q0E/c+HuhnyzJjffbPkynufkXbKdbyY375Ltgq88TU6+mTzL8UVey9cXzmXWxjwGs9ZoTdFtjG3jtYLX9YVjCm+bLZ9SZq0xmy/uzzcXvkahR/SFDzatLzbMCjdaL7xe+NhCx1048grv11d7/zU+TItc+7LXCj2/5NWir13jfRoeKoU5jg53I1drPQmYBEZL/2bfXzBhKG7Atgv7nGHCkCjfrtfP7GJSuJhc8HSTUUPi2mx9Vy8ZKNzJHGzZZzUOsWyhEFa2Nel0kZZ96/CqjO/XjK1Jp+0cmShrbN2n74pxI7czRrJfD/TTWm+/0vHF6tNf+SlxPr6M2D3z4mSl+o8TfS6jRLVohBCirHCY5RK11nnAUOAvYCcw52oJv9iCmhO9cDR9A1oZyxYGtCJ64WibrCQlhBCOzuZ9+lrrBcCCUrtAWDviuo5izpZPGVIpgjkHFxDddRTRNlhJSgghHJ3TzdSJS4ljxO6ZjAnoxNB//mBMQCdG7J5Z+uvVCuGkJsQmsDohrci+1QlpTIhNsFNEzssWP2unS/rx6fFGH378fGj3KtHx8xlT/3Hi0+PtHZoQZVLBbOCCZFQwkigyWOo1WZstftbOV2XTBitJCVHelPWqk2WJNX7WDnMj1yYuLXwW1s7YTt5kz6iEKNMKV518tGWIJPxSVNo/a+dL+m1fvLxFH9ZOhmsKUQKXVp28tN9ZWE9p/6ydL+kLIayq8Gzgl+6oz/h+zYr0OwvrscXPWpK+EOKaZDaw7djiZ+18N3KFEKKcK183coUQQlyVJH0hhChHJOkLIUQ5IklfCCHKEUn6QghRjkjSF0I4JCn0Vjok6QshHJIUeisdDrdGrhBCwMWJSVLozbqkpS+EcFhS6M36JOkLIRyWFHqzPkn6QgiHJIXeSockfSGEQ5JCb6VDCq4JIYSTkYJrQgghAEn6QghRrkjSF0KIckSSvhBClCOS9IUQohxx6NE7SqlU4FAJTlEVKE+Desvb9wvyPZcX8j3fnNpa62pXesGhk35JKaU2XG3YkjMqb98vyPdcXsj3bD3SvSOEEOWIJH0hhChHnD3pT7J3ADZW3r5fkO+5vJDv2Uqcuk9fCCFEUc7e0hdCCFGIJH0hhChHnDLpK6W6K6V2K6X2KaVG2jue0qaUqqWUWqqU2qGU2q6UesHeMdmKUspFKbVZKTXf3rHYglKqklLqB6XULqXUTqXU7faOqbQppYZbfq/jlVLfKaU87R2TtSmlpimljiul4gvtq6KUWqiU2mt5rGyNazld0ldKuQBfAHcCjYCHlVKN7BtVqcsDXtZaNwJaAc+Vg++5wAvATnsHYUPjgD+11g2AJjj5966UCgKGAVFa6wjABXjIvlGViulA90v2jQQWa63rAYst2yXmdEkfiAb2aa33a61zgNlALzvHVKq01ila602W52cxEkGQfaMqfUqpYOBuYIq9Y7EFpZQf0A6YCqC1ztFan7JrULbhCngppVwBb+CIneOxOq31cuDEJbt7ATMsz2cA91rjWs6Y9IOAw4W2kygHCbCAUioUaAass3MotvAp8CpgtnMcthIGpAJfWbq0piilfOwdVGnSWicDY4BEIAU4rbX+275R2UyA1jrF8vwoEGCNkzpj0i+3lFK+wI/Ai1rrM/aOpzQppe4BjmutN9o7FhtyBZoDX2qtmwHnsNKf/I7K0o/dC+MDrybgo5R61L5R2Z42xtZbZXy9Myb9ZKBWoe1gyz6nppRyw0j432itf7J3PDbQBuiplDqI0YXXSSk1y74hlbokIElrXfBX3A8YHwLOrAtwQGudqrXOBX4CWts5Jls5ppQKBLA8HrfGSZ0x6a8H6imlwpRS7hg3febZOaZSpZRSGP28O7XWY+0djy1orV/XWgdrrUMx/o2XaK2dugWotT4KHFZK1bfs6gzssGNItpAItFJKeVt+zzvj5DevC5kH9Lc87w/8ao2TulrjJI5Ea52nlBoK/IVxp3+a1nq7ncMqbW2Ax4BtSqktln2jtNYL7BeSKCXPA99YGjT7gYF2jqdUaa3XKaV+ADZhjFLbjBOWZFBKfQd0AKoqpZKAt4APgDlKqUEYJeb7WuVaUoZBCCHKD2fs3hFCCHEVkvSFEKIckaQvhBDliCR9IYQoRyTpCyFEOSJJXwghyhFJ+kIIUY78P4t5TGJF/uD+AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "sol_true = np.asarray(obj.get(\"sol_true\"))\n", "data = obj.get(\"data\")\n", @@ -410,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "d38e3540-9693-4b24-a734-cabdb5159d36", "metadata": {}, "outputs": [], @@ -436,26 +226,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "c2267d70-85fc-46cd-bb81-5bf7742c8d84", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 77.83it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.38 s, sys: 88.2 ms, total: 1.47 s\n", - "Wall time: 1.47 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "\n", @@ -477,58 +251,26 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "2b929224-6fa6-4f63-8892-37b2475b3738", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", - "2022-09-07 00:49:47,222 - pypesto.engine.multi_process - WARNING - Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", - "Performing parallel task execution on 8 processes.\n", - "2022-09-07 00:49:47,233 - pypesto.engine.multi_process - INFO - Performing parallel task execution on 8 processes.\n", - "100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 130.42it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 285 ms, sys: 177 ms, total: 462 ms\n", - "Wall time: 1.73 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "\n", "from pypesto.engine import MultiProcessEngine\n", "\n", + "np.random.seed(1)\n", "engine = MultiProcessEngine()\n", - "result = optimize.minimize(problem, engine=engine, n_starts=100)" + "result = optimize.minimize(problem, engine=engine, n_starts=20)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "05888c10-1027-4580-aa0a-1076410579c6", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "from pypesto import visualize\n", "\n", @@ -547,28 +289,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "b88b414b-598a-40f1-876e-ef1255a50c93", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|████████████████████████████████████████████████████████| 10000/10000 [00:08<00:00, 1234.51it/s]\n", - "Elapsed time: 8.135626907000017\n", - "2022-09-07 00:49:57,692 - pypesto.sample.sample - INFO - Elapsed time: 8.135626907000017\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.87 s, sys: 320 ms, total: 8.18 s\n", - "Wall time: 8.15 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "\n", @@ -578,38 +302,15 @@ " internal_sampler=sample.AdaptiveMetropolisSampler(), n_chains=3\n", ")\n", "\n", - "result = sample.sample(\n", - " problem, n_samples=10000, sampler=sampler, result=result\n", - ")" + "result = sample.sample(problem, n_samples=2000, sampler=sampler, result=result)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "1fa22993-8100-4ec4-9b75-64ea745e77f1", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Geweke burn-in index: 0\n", - "2022-09-07 00:49:57,807 - pypesto.sample.diagnostics - INFO - Geweke burn-in index: 0\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "visualize.sampling_scatter(result, size=[13, 6]);" ] @@ -641,9 +331,9 @@ ], "metadata": { "kernelspec": { - "display_name": "python-jl", + "display_name": "dev_venv", "language": "python", - "name": "python-jl" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -655,7 +345,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/doc/example/nonlinear_monotone.ipynb b/doc/example/nonlinear_monotone.ipynb index 85b2d0e45..3b39721c5 100644 --- a/doc/example/nonlinear_monotone.ipynb +++ b/doc/example/nonlinear_monotone.ipynb @@ -194,7 +194,7 @@ "metadata": {}, "outputs": [], "source": [ - "objective = importer.create_objective()" + "objective = importer.create_objective(verbose=False)" ] }, { diff --git a/doc/example/ordinal.ipynb b/doc/example/ordinal.ipynb index 220140929..e038d1571 100644 --- a/doc/example/ordinal.ipynb +++ b/doc/example/ordinal.ipynb @@ -152,7 +152,7 @@ "metadata": {}, "outputs": [], "source": [ - "objective = importer.create_objective()" + "objective = importer.create_objective(verbose=False)" ] }, { @@ -176,6 +176,7 @@ " \"interval_constraints\": \"max\",\n", " \"min_gap\": 0.1,\n", " },\n", + " verbose=False,\n", ")" ] }, diff --git a/doc/example/petab_import.ipynb b/doc/example/petab_import.ipynb index 5642e53c5..fc40baba4 100644 --- a/doc/example/petab_import.ipynb +++ b/doc/example/petab_import.ipynb @@ -17,24 +17,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-10-19T09:12:27.344220Z", "start_time": "2023-10-19T09:12:11.884260Z" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\r\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip available: \u001B[0m\u001B[31;49m22.3.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m23.3\u001B[0m\r\n", - "\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n" - ] - } - ], + "outputs": [], "source": [ "# install if not done yet\n", "# !apt install libatlas-base-dev swig\n", @@ -44,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-10-19T09:12:33.778201Z", @@ -87,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-10-19T09:12:37.201025Z", @@ -133,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-10-19T10:40:50.415797Z", @@ -141,25 +131,11 @@ }, "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model parameters: ['Epo_degradation_BaF3', 'k_exp_hetero', 'k_exp_homo', 'k_imp_hetero', 'k_imp_homo', 'k_phos', 'noiseParameter1_pSTAT5A_rel', 'noiseParameter1_pSTAT5B_rel', 'noiseParameter1_rSTAT5A_rel'] \n", - "\n", - "Model const parameters: ['ratio', 'specC17'] \n", - "\n", - "Model outputs: ['pSTAT5A_rel', 'pSTAT5B_rel', 'rSTAT5A_rel'] \n", - "\n", - "Model states: ['STAT5A', 'STAT5B', 'pApB', 'pApA', 'pBpB', 'nucpApA', 'nucpApB', 'nucpBpB'] \n" - ] - } - ], + "outputs": [], "source": [ "importer = pypesto.petab.PetabImporter(petab_problem)\n", "\n", - "model = importer.create_model()\n", + "model = importer.create_model(verbose=False)\n", "\n", "# some model properties\n", "print(\"Model parameters:\", list(model.getParameterIds()), \"\\n\")\n", @@ -191,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2023-10-19T10:40:55.952163Z", @@ -217,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "scrolled": true }, @@ -245,19 +221,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'fval': 138.22199566457704, 'grad': array([ 2.20546436e-02, 5.53227499e-02, 5.78876640e-03, 5.42272184e-03,\n", - " -4.51595808e-05, 7.91009669e-03, 0.00000000e+00, 1.07876837e-02,\n", - " 2.40388572e-02, 1.91925085e-02, 0.00000000e+00]), 'rdatas': [ >)>]}\n" - ] - } - ], + "outputs": [], "source": [ "ret = obj(\n", " petab_problem.x_nominal_scaled,\n", @@ -277,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -293,17 +259,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[6, 10] [0, 1, 2, 3, 4, 5, 7, 8, 9]\n" - ] - } - ], + "outputs": [], "source": [ "print(problem.x_fixed_indices, problem.x_free_indices)" ] @@ -317,19 +275,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(138.22199566457704, array([ 2.20546436e-02, 5.53227499e-02, 5.78876640e-03, 5.42272184e-03,\n", - " -4.51595808e-05, 7.91009669e-03, 1.07876837e-02, 2.40388572e-02,\n", - " 1.91925085e-02]))\n" - ] - } - ], + "outputs": [], "source": [ "objective = problem.objective\n", "ret = objective(petab_problem.x_nominal_free_scaled, sensi_orders=(0, 1))\n", @@ -338,19 +286,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "fd: [ 0.02993985 0.05897443 -0.00149735 -0.00281785 -0.00925273 0.01197046\n", - " 0.01078638 0.02403756 0.01919121]\n", - "l2 difference: 0.017256061672716528\n" - ] - } - ], + "outputs": [], "source": [ "eps = 1e-4\n", "\n", @@ -389,22 +327,11 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Engine will use up to 8 processes (= CPU count).\n", - " 0%| | 0/10 [00:00" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "ref = visualize.create_references(\n", " x=petab_problem.x_nominal_scaled, fval=obj(petab_problem.x_nominal_scaled)\n", @@ -549,36 +425,13 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'plot1': ,\n", - " 'plot2': ,\n", - " 'plot3': }" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# we need to explicitly import the method\n", "from pypesto.visualize.model_fit import visualize_optimized_model_fit\n", @@ -605,7 +458,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.10" }, "toc": { "base_numbering": 1, diff --git a/doc/example/sampler_study.ipynb b/doc/example/sampler_study.ipynb index b974fa715..86000b567 100644 --- a/doc/example/sampler_study.ipynb +++ b/doc/example/sampler_study.ipynb @@ -60,7 +60,7 @@ "# import to pypesto\n", "importer = pypesto.petab.PetabImporter(petab_problem)\n", "# create problem\n", - "problem = importer.create_problem()" + "problem = importer.create_problem(verbose=False)" ] }, { @@ -768,7 +768,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/doc/example/store.ipynb b/doc/example/store.ipynb index 62696b653..26460d734 100644 --- a/doc/example/store.ipynb +++ b/doc/example/store.ipynb @@ -21,7 +21,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false, "jupyter": { @@ -50,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false, "jupyter": { @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { @@ -122,7 +122,7 @@ "petab_yaml = './boehm_JProteomeRes2014/boehm_JProteomeRes2014.yaml'\n", "\n", "importer = pypesto.petab.PetabImporter.from_yaml(petab_yaml)\n", - "problem = importer.create_problem()" + "problem = importer.create_problem(verbose=False)" ] }, { @@ -152,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { @@ -163,13 +163,79 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/15 [00:00\n", + "* message: Converged according to fval difference \n", + "* number of evaluations: 178\n", + "* time taken to optimize: 1.785s\n", + "* startpoint: [-3.82648946 4.99153731 -2.17952968 -1.39145733 -0.5747054 4.45740142\n", + " 2.35956422 0.77882698 -2.71223689]\n", + "* endpoint: [-1.56909619 -4.98901477 -2.20972883 -1.78588445 4.99964502 4.19771653\n", + " 0.58570452 0.81883751 0.49857757]\n", + "* final objective value: 138.2225919756037\n", + "* final gradient value: [ 6.99548540e-03 5.67684178e-02 4.15813519e-03 1.94420735e-02\n", + " -4.41997844e-05 -7.10489673e-03 9.89156691e-04 -6.98367891e-04\n", + " -4.07797538e-04]\n", + "* final hessian value: [[ 2.11240897e+03 6.03813646e-01 1.06890240e+02 2.81533877e+03\n", + " 8.82178847e-06 -7.86516037e+02 1.02527778e+02 -8.02295866e+01\n", + " -2.23142988e+01]\n", + " [ 6.03813646e-01 2.01317170e-03 -1.77299630e-01 7.30061900e-01\n", + " -3.70852695e-08 -3.28593144e-01 -1.52623294e-02 -5.82723434e-02\n", + " -5.71794398e-02]\n", + " [ 1.06890240e+02 -1.77299630e-01 6.99968515e+01 1.61225414e+02\n", + " 7.01455291e-06 -8.83050425e+01 -3.51659254e+00 -3.02430751e+00\n", + " 6.53132559e+00]\n", + " [ 2.81533877e+03 7.30061900e-01 1.61225414e+02 3.76198620e+03\n", + " 8.31596889e-06 -1.04206720e+03 1.32794432e+02 -1.05781631e+02\n", + " -2.70575675e+01]\n", + " [ 8.82178847e-06 -3.70852695e-08 7.01455291e-06 8.31596889e-06\n", + " 2.74313621e-10 -2.20192310e-04 4.21842641e-05 5.32054764e-05\n", + " 6.38402412e-06]\n", + " [-7.86516037e+02 -3.28593144e-01 -8.83050425e+01 -1.04206720e+03\n", + " -2.20192310e-04 9.30413190e+02 -8.20645071e+01 4.93179742e+01\n", + " 3.27628925e+01]\n", + " [ 1.02527778e+02 -1.52623294e-02 -3.51659254e+00 1.32794432e+02\n", + " 4.21842641e-05 -8.20645071e+01 8.48280922e+01 0.00000000e+00\n", + " 0.00000000e+00]\n", + " [-8.02295866e+01 -5.82723434e-02 -3.02430751e+00 -1.05781631e+02\n", + " 5.32054764e-05 4.93179742e+01 0.00000000e+00 8.48319778e+01\n", + " 0.00000000e+00]\n", + " [-2.23142988e+01 -5.71794398e-02 6.53132559e+00 -2.70575675e+01\n", + " 6.38402412e-06 3.27628925e+01 0.00000000e+00 0.00000000e+00\n", + " 8.48313088e+01]]\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display(Markdown(result.summary()))" ] @@ -212,7 +354,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false, "jupyter": { @@ -223,7 +365,30 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 9/9 [00:05<00:00, 1.53it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 5.66 s, sys: 325 ms, total: 5.98 s\n", + "Wall time: 5.88 s\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "%%time\n", "\n", @@ -249,7 +414,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { @@ -260,7 +425,24 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 5000/5000 [00:05<00:00, 853.90it/s]\n", + "Elapsed time: 6.2119409999999995\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 5.08 s, sys: 1.14 s, total: 6.22 s\n", + "Wall time: 5.86 s\n" + ] + } + ], "source": [ "%%time\n", "\n", @@ -290,7 +472,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { @@ -332,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { @@ -343,7 +525,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: You are loading a problem.\n", + "This problem is not to be used without a separately created objective.\n" + ] + } + ], "source": [ "# load result with read_result function\n", "result_loaded = pypesto.store.read_result(fn)" @@ -364,7 +555,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { @@ -384,7 +575,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "collapsed": false, "jupyter": { @@ -395,7 +586,16 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Objective function call: 138.22259071495967\n", + "Corresponding saved value: 138.2225919756037\n" + ] + } + ], "source": [ "result_loaded.problem = problem\n", "print(\n", @@ -439,7 +639,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "collapsed": false, "jupyter": { @@ -450,7 +650,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# waterfall plot original\n", "ax = visualize.waterfall(result)\n", @@ -459,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false, "jupyter": { @@ -470,7 +681,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# waterfall plot loaded\n", "ax = visualize.waterfall(result_loaded)\n", @@ -490,7 +712,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": { "collapsed": false, "jupyter": { @@ -501,7 +723,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# profile plot original\n", "ax = visualize.profiles(result)" @@ -509,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": { "collapsed": false, "jupyter": { @@ -520,7 +753,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# profile plot loaded\n", "ax = visualize.profiles(result_loaded)" @@ -539,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { "collapsed": false, "jupyter": { @@ -550,7 +794,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# sampling plot original\n", "ax = visualize.sampling_fval_traces(result)" @@ -558,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": { "collapsed": false, "jupyter": { @@ -569,7 +824,18 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# sampling plot loaded\n", "ax = visualize.sampling_fval_traces(result_loaded)" @@ -632,7 +898,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": { "collapsed": false, "jupyter": { @@ -643,7 +909,37 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 27%|██▋ | 4/15 [00:01<00:04, 2.21it/s]2024-01-03 14:38:17.013 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.8632 and h = 2.39949e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:17.013 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.8632: AMICI failed to integrate the forward problem\n", + " 33%|███▎ | 5/15 [00:02<00:05, 1.85it/s]2024-01-03 14:38:18.379 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 138.953 and h = 4.03132e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:18.380 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 138.953: AMICI failed to integrate the forward problem\n", + " 47%|████▋ | 7/15 [00:03<00:04, 1.74it/s]2024-01-03 14:38:19.107 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.221 and h = 5.19496e-06, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:19.108 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.221: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:19.131 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.3098 and h = 7.87193e-06, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:19.132 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.3098: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:19.180 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0964 and h = 1.00596e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:19.180 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0964: AMICI failed to integrate the forward problem\n", + " 53%|█████▎ | 8/15 [00:04<00:04, 1.72it/s]2024-01-03 14:38:19.489 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 141.902 and h = 3.68334e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:19.489 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 141.902: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:19.682 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.628 and h = 3.05681e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:19.682 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.628: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:19.690 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.751 and h = 2.68544e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:19.690 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.751: AMICI failed to integrate the forward problem\n", + " 67%|██████▋ | 10/15 [00:04<00:02, 2.11it/s]2024-01-03 14:38:20.224 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 173.778 and h = 1.59107e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:20.224 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 173.778: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:20.343 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.1925 and h = 9.62557e-06, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:20.343 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.1925: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:20.383 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2188 and h = 1.94091e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:20.384 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2188: AMICI failed to integrate the forward problem\n", + "100%|██████████| 15/15 [00:07<00:00, 2.03it/s]\n" + ] + } + ], "source": [ "# record the history\n", "history_options = pypesto.HistoryOptions(trace_record=True)\n", @@ -671,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": { "collapsed": false, "jupyter": { @@ -682,7 +978,25 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "History type: \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "print(\"History type: \", type(result.optimize_result.list[0].history))\n", "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", @@ -717,7 +1031,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "collapsed": false, "jupyter": { @@ -727,7 +1041,67 @@ "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 7%|▋ | 1/15 [00:01<00:16, 1.18s/it]2024-01-03 14:38:24.353 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 97.3155 and h = 1.86559e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:24.353 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 97.3155: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:24.635 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 143.725 and h = 3.65092e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:24.635 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 143.725: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:24.719 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 87.8335 and h = 3.32376e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:24.720 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 87.8335: AMICI failed to integrate the forward problem\n", + " 13%|█▎ | 2/15 [00:01<00:11, 1.11it/s]2024-01-03 14:38:25.363 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.382 and h = 3.83048e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:25.364 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.382: AMICI failed to integrate the forward problem\n", + " 20%|██ | 3/15 [00:02<00:09, 1.23it/s]2024-01-03 14:38:25.673 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 147.29 and h = 2.44013e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:25.673 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 147.29: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:25.896 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.507 and h = 3.82449e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:25.897 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.507: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:25.916 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.731 and h = 3.87887e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:25.916 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.731: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:25.948 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.707 and h = 3.75929e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:25.948 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.707: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:25.957 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1274 and h = 1.34801e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:25.958 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1274: AMICI failed to integrate the forward problem\n", + " 27%|██▋ | 4/15 [00:03<00:07, 1.43it/s]2024-01-03 14:38:26.576 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 85.3765 and h = 1.22847e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:26.576 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 85.3765: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:26.707 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.2611 and h = 1.05251e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:26.707 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.2611: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:26.815 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.6779 and h = 1.44723e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:26.815 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.6779: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:27.037 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.516 and h = 3.5887e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:27.037 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.516: AMICI failed to integrate the forward problem\n", + " 33%|███▎ | 5/15 [00:04<00:08, 1.21it/s]2024-01-03 14:38:27.490 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.661 and h = 3.9367e-06, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:27.490 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.661: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:27.531 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.016 and h = 4.47442e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:27.531 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.016: AMICI failed to integrate the forward problem\n", + " 40%|████ | 6/15 [00:04<00:06, 1.39it/s]2024-01-03 14:38:27.960 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1002 and h = 2.68972e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:27.960 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1002: AMICI failed to integrate the forward problem\n", + " 53%|█████▎ | 8/15 [00:05<00:04, 1.57it/s]2024-01-03 14:38:29.381 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.76 and h = 3.10334e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:29.381 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.76: AMICI failed to integrate the forward problem\n", + " 60%|██████ | 9/15 [00:06<00:04, 1.36it/s]2024-01-03 14:38:30.224 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.502 and h = 3.43856e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:30.224 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.502: AMICI failed to integrate the forward problem\n", + " 73%|███████▎ | 11/15 [00:07<00:02, 1.81it/s]2024-01-03 14:38:30.796 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 92.1328 and h = 1.73628e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:30.796 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 92.1328: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:30.886 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 146.791 and h = 3.76771e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:30.887 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 146.791: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:30.966 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.791 and h = 4.23167e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:30.966 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.791: AMICI failed to integrate the forward problem\n", + " 87%|████████▋ | 13/15 [00:08<00:00, 2.08it/s]2024-01-03 14:38:31.801 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 185.894 and h = 4.76848e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:31.801 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 185.894: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:32.260 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.366 and h = 4.40641e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:32.260 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.366: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:32.316 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.091 and h = 1.26056e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:32.316 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.091: AMICI failed to integrate the forward problem\n", + " 93%|█████████▎| 14/15 [00:09<00:00, 1.54it/s]2024-01-03 14:38:32.905 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1801 and h = 3.41591e-06, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:32.906 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1801: AMICI failed to integrate the forward problem\n", + "2024-01-03 14:38:32.936 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0972 and h = 2.20703e-05, the error test failed repeatedly or with |h| = hmin. \n", + "2024-01-03 14:38:32.936 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0972: AMICI failed to integrate the forward problem\n", + "100%|██████████| 15/15 [00:10<00:00, 1.49it/s]\n" + ] + } + ], "source": [ "# create temporary file\n", "fn_csv = tempfile.mktemp(\"_{id}.hdf5\")\n", @@ -759,7 +1133,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": { "collapsed": false, "jupyter": { @@ -769,7 +1143,25 @@ "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "History type: \n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "print(\"History type: \", type(result.optimize_result.list[0].history))\n", "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", @@ -810,7 +1202,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": { "collapsed": false, "jupyter": { @@ -820,7 +1212,39 @@ "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 0%| | 0/15 [00:00\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "print(\"History type: \", type(result.optimize_result.list[0].history))\n", "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", @@ -875,7 +1317,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false, "jupyter": { @@ -886,7 +1328,28 @@ }, "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: You are loading a problem.\n", + "This problem is not to be used without a separately created objective.\n", + "Loading the profiling result failed. It is highly likely that no profiling result exists within /var/folders/81/7b90kj9j2gz36205mdbf75y40000gn/T/tmpsf5ostbz.hdf5.\n", + "Loading the sampling result failed. It is highly likely that no sampling result exists within /var/folders/81/7b90kj9j2gz36205mdbf75y40000gn/T/tmpsf5ostbz.hdf5.\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# load the history\n", "result_loaded_w_history = pypesto.store.read_result(fn_hdf5)\n", @@ -914,7 +1377,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/doc/example/synthetic_data.ipynb b/doc/example/synthetic_data.ipynb index 7a7e3c927..cf2f5df20 100644 --- a/doc/example/synthetic_data.ipynb +++ b/doc/example/synthetic_data.ipynb @@ -124,7 +124,9 @@ "outputs": [], "source": [ "pypesto_importer_original = pypesto.petab.PetabImporter(petab_problem_original)\n", - "pypesto_problem_original = pypesto_importer_original.create_problem()" + "pypesto_problem_original = pypesto_importer_original.create_problem(\n", + " verbose=False\n", + ")" ] }, { @@ -149,7 +151,7 @@ "outputs": [], "source": [ "pypesto_result_original = pypesto.optimize.minimize(\n", - " pypesto_problem_original, n_starts=100\n", + " pypesto_problem_original, n_starts=20\n", ")" ] }, @@ -269,7 +271,7 @@ ")\n", "pypesto_problem_synthetic = pypesto_importer_synthetic.create_problem()\n", "pypesto_result_synthetic = pypesto.optimize.minimize(\n", - " pypesto_problem_synthetic, n_starts=100\n", + " pypesto_problem_synthetic, n_starts=20\n", ")" ] }, @@ -323,7 +325,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/doc/example/workflow_comparison.ipynb b/doc/example/workflow_comparison.ipynb index 4e1ef440d..c3ddec823 100644 --- a/doc/example/workflow_comparison.ipynb +++ b/doc/example/workflow_comparison.ipynb @@ -131,7 +131,7 @@ "\n", "# AMICI model complilation\n", "amici_model = amici.petab_import.import_petab_problem(\n", - " petab_problem, force_compile=True\n", + " petab_problem, force_compile=True, verbose=False\n", ")" ] }, @@ -314,7 +314,7 @@ "lb = -5 * np.ones(len(parameters))\n", "\n", "# number of starts\n", - "n_starts = 25\n", + "n_starts = 10\n", "\n", "# draw uniformly distributed parameters within these bounds\n", "x_guesses = np.random.random((n_starts, len(lb))) * (ub - lb) + lb\n", @@ -883,7 +883,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.10.10" } }, "nbformat": 4, From 8bdb1ad4a3147a9526215f02bc4ed22026b917ef Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 5 Jan 2024 14:39:21 +0100 Subject: [PATCH 15/31] quiet! --- doc/conf.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/doc/conf.py b/doc/conf.py index f211f10dd..8b6c3b569 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -22,6 +22,12 @@ sys.path.insert(0, os.path.abspath('../')) +# Silence: +# Debugger warning: It seems that frozen modules are being used, which may +# make the debugger miss breakpoints. Please pass -Xfrozen_modules=off +# to python to disable frozen modules. +# Note: Debugging will proceed. Set PYDEVD_DISABLE_FILE_VALIDATION=1 to disable this validation. +os.environ["PYDEVD_DISABLE_FILE_VALIDATION"] = "1" # -- General configuration ------------------------------------------------ From d1fd250e036ef23f89c63cabbfd293e6599b4536 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Fri, 5 Jan 2024 14:56:45 +0100 Subject: [PATCH 16/31] py310 --- .readthedocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index d82352514..e05223acf 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -26,4 +26,4 @@ build: - libhdf5-serial-dev - swig tools: - python: "3.11" + python: "3.10" From 49b3e5400e80d284703420a3e97e79a09ad836f1 Mon Sep 17 00:00:00 2001 From: Doresic Date: Fri, 5 Jan 2024 15:15:35 +0100 Subject: [PATCH 17/31] 3 profiles getting_started --- doc/example/getting_started.ipynb | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/doc/example/getting_started.ipynb b/doc/example/getting_started.ipynb index afa1610db..4f18762f1 100644 --- a/doc/example/getting_started.ipynb +++ b/doc/example/getting_started.ipynb @@ -602,7 +602,8 @@ "\n", "result = profile.parameter_profile(problem=problem, \n", " result=result, \n", - " optimizer=optimizer_scipy_lbfgsb)" + " optimizer=optimizer_scipy_lbfgsb,\n", + " profile_index=[0, 1, 2])" ] }, { From 4517d99e336e4a00f1633cc60bac6bec5f611eb1 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Tue, 9 Jan 2024 22:47:34 +0100 Subject: [PATCH 18/31] .. --- setup.cfg | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/setup.cfg b/setup.cfg index 326a85cdd..83bd8c409 100644 --- a/setup.cfg +++ b/setup.cfg @@ -141,7 +141,8 @@ doc = sphinxcontrib-bibtex >= 2.5.0 ipython >= 8.4.0 myst-parser >= 0.18.0 - ipykernel >= 6.15.1 + # pin to avoid `zmq.error.ZMQError: Too many open files` issues + ipykernel == 6.23.1 %(example)s %(select)s %(fides)s From ef4fdf9f6277e2e55b0647be43260a7eddaee55e Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Tue, 9 Jan 2024 23:02:27 +0100 Subject: [PATCH 19/31] .. --- doc/example/hierarchical.ipynb | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/doc/example/hierarchical.ipynb b/doc/example/hierarchical.ipynb index 61e47154a..6cf0fbcb5 100644 --- a/doc/example/hierarchical.ipynb +++ b/doc/example/hierarchical.ipynb @@ -165,7 +165,11 @@ "outputs": [], "source": [ "# Create a pypesto problem with hierarchical optimization (`problem`)\n", - "importer = PetabImporter(petab_problem_hierarchical, hierarchical=True)\n", + "importer = PetabImporter(\n", + " petab_problem_hierarchical,\n", + " hierarchical=True,\n", + " model_name=\"Boehm_JProteomeRes2014_hierarchical\",\n", + ")\n", "problem = importer.create_problem(verbose=False)\n", "# set option to compute objective function gradients using adjoint sensitivity analysis\n", "problem.objective.amici_solver.setSensitivityMethod(\n", @@ -173,7 +177,11 @@ ")\n", "\n", "# ... and create another pypesto problem without hierarchical optimization (`problem2`)\n", - "importer2 = PetabImporter(petab_problem, hierarchical=False)\n", + "importer2 = PetabImporter(\n", + " petab_problem,\n", + " hierarchical=False,\n", + " model_name=\"Boehm_JProteomeRes2014_hierarchical\",\n", + ")\n", "problem2 = importer2.create_problem(verbose=False)\n", "problem2.objective.amici_solver.setSensitivityMethod(\n", " amici.SensitivityMethod.adjoint\n", From be92e6dda2f91c297b300acc4e82ce38a2600837 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Tue, 9 Jan 2024 23:23:59 +0100 Subject: [PATCH 20/31] dep --- setup.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.cfg b/setup.cfg index 83bd8c409..5aa5289fc 100644 --- a/setup.cfg +++ b/setup.cfg @@ -152,6 +152,7 @@ doc = %(jax)s example = %(julia)s + %(pymc)s notebook >= 6.1.4 benchmark_models_petab @ git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python select = From 1c6a5fbe83ca7c9a68be3d737889d943edf37cf6 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Tue, 9 Jan 2024 23:41:45 +0100 Subject: [PATCH 21/31] deps --- setup.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.cfg b/setup.cfg index 5aa5289fc..799e37858 100644 --- a/setup.cfg +++ b/setup.cfg @@ -153,6 +153,7 @@ doc = example = %(julia)s %(pymc)s + %(emcee)s notebook >= 6.1.4 benchmark_models_petab @ git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python select = From c6d42ab1ba3e2837158030d9dc5f7cc30953ebd0 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Wed, 10 Jan 2024 07:08:29 +0100 Subject: [PATCH 22/31] deps --- setup.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.cfg b/setup.cfg index 799e37858..6d8fc28be 100644 --- a/setup.cfg +++ b/setup.cfg @@ -154,6 +154,7 @@ example = %(julia)s %(pymc)s %(emcee)s + %(dynesty)s notebook >= 6.1.4 benchmark_models_petab @ git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python select = From c30602f19ceb3cfe2ba030fcbe559ec31e7c3ed9 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Wed, 10 Jan 2024 07:41:08 +0100 Subject: [PATCH 23/31] .. --- setup.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/setup.cfg b/setup.cfg index 6d8fc28be..e6b2b6147 100644 --- a/setup.cfg +++ b/setup.cfg @@ -155,6 +155,7 @@ example = %(pymc)s %(emcee)s %(dynesty)s + %(nlopt)s notebook >= 6.1.4 benchmark_models_petab @ git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python select = From 47c8a737442ecc8f0fcd09dee7672e75e8d0ddaa Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Wed, 10 Jan 2024 09:20:23 +0100 Subject: [PATCH 24/31] Exclude doc/example/workflow_comparison.ipynb for now --- doc/example/workflow_comparison.ipynb | 1482 ++++++++++++++++++++++++- 1 file changed, 1433 insertions(+), 49 deletions(-) diff --git a/doc/example/workflow_comparison.ipynb b/doc/example/workflow_comparison.ipynb index c3ddec823..9461a59c6 100644 --- a/doc/example/workflow_comparison.ipynb +++ b/doc/example/workflow_comparison.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2023-07-13T09:24:44.842827Z", @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2023-07-13T09:24:47.602789Z", @@ -121,7 +121,228 @@ "outputs_hidden": false } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-08-25 15:26:01.472 - amici.petab_import - INFO - Importing model ...\n", + "2023-08-25 15:26:01.473 - amici.petab_import - INFO - Validating PEtab problem ...\n", + "2023-08-25 15:26:01.558 - amici.petab_import - INFO - Model name is 'FullModel'.\n", + "Writing model code to '/Users/pauljonasjost/Documents/GitHub_Folders/pyPESTO/doc/example/amici_models/FullModel'.\n", + "2023-08-25 15:26:01.560 - amici.petab_import - INFO - Species: 8\n", + "2023-08-25 15:26:01.562 - amici.petab_import - INFO - Global parameters: 15\n", + "2023-08-25 15:26:01.563 - amici.petab_import - INFO - Reactions: 9\n", + "2023-08-25 15:26:01.618 - amici.petab_import - INFO - Observables: 3\n", + "2023-08-25 15:26:01.620 - amici.petab_import - INFO - Sigmas: 3\n", + "2023-08-25 15:26:01.632 - amici.petab_import - DEBUG - Adding output parameters to model: ['noiseParameter1_pSTAT5A_rel', 'noiseParameter1_pSTAT5B_rel', 'noiseParameter1_rSTAT5A_rel']\n", + "2023-08-25 15:26:01.634 - amici.petab_import - DEBUG - Adding initial assignments for dict_keys([])\n", + "2023-08-25 15:26:01.657 - amici.petab_import - DEBUG - Condition table: (1, 1)\n", + "2023-08-25 15:26:01.658 - amici.petab_import - DEBUG - Fixed parameters are ['ratio', 'specC17']\n", + "2023-08-25 15:26:01.660 - amici.petab_import - INFO - Overall fixed parameters: 2\n", + "2023-08-25 15:26:01.661 - amici.petab_import - INFO - Variable parameters: 16\n", + "2023-08-25 15:26:01.684 - amici.sbml_import - DEBUG - Finished processing SBML annotations ++ (1.34E-04s)\n", + "2023-08-25 15:26:01.717 - amici.sbml_import - DEBUG - Finished gathering local SBML symbols ++ (2.14E-02s)\n", + "2023-08-25 15:26:01.752 - amici.sbml_import - DEBUG - Finished processing SBML parameters ++ (2.24E-02s)\n", + "2023-08-25 15:26:01.765 - amici.sbml_import - DEBUG - Finished processing SBML compartments ++ (4.04E-04s)\n", + "2023-08-25 15:26:01.788 - amici.sbml_import - DEBUG - Finished processing SBML species initials +++ (6.57E-03s)\n", + "2023-08-25 15:26:01.799 - amici.sbml_import - DEBUG - Finished processing SBML rate rules +++ (7.80E-05s)\n", + "2023-08-25 15:26:01.801 - amici.sbml_import - DEBUG - Finished processing SBML species ++ (2.70E-02s)\n", + "2023-08-25 15:26:01.818 - amici.sbml_import - DEBUG - Finished processing SBML reactions ++ (5.84E-03s)\n", + "2023-08-25 15:26:01.838 - amici.sbml_import - DEBUG - Finished processing SBML rules ++ (9.97E-03s)\n", + "2023-08-25 15:26:01.849 - amici.sbml_import - DEBUG - Finished processing SBML events ++ (8.28E-05s)\n", + "2023-08-25 15:26:01.859 - amici.sbml_import - DEBUG - Finished processing SBML initial assignments++ (1.03E-04s)\n", + "2023-08-25 15:26:01.870 - amici.sbml_import - DEBUG - Finished processing SBML species references ++ (5.32E-04s)\n", + "2023-08-25 15:26:01.871 - amici.sbml_import - DEBUG - Finished importing SBML + (1.95E-01s)\n", + "2023-08-25 15:26:01.926 - amici.sbml_import - DEBUG - Finished processing SBML observables + (4.47E-02s)\n", + "2023-08-25 15:26:01.938 - amici.sbml_import - DEBUG - Finished processing SBML event observables + (2.83E-06s)\n", + "2023-08-25 15:26:02.017 - amici.de_export - DEBUG - Finished running smart_multiply ++ (2.33E-03s)\n", + "2023-08-25 15:26:02.122 - amici.de_export - DEBUG - Finished simplifying xdot +++ (8.01E-03s)\n", + "2023-08-25 15:26:02.123 - amici.de_export - DEBUG - Finished computing xdot ++ (1.85E-02s)\n", + "2023-08-25 15:26:02.147 - amici.de_export - DEBUG - Finished simplifying x0 +++ (1.72E-03s)\n", + "2023-08-25 15:26:02.149 - amici.de_export - DEBUG - Finished computing x0 ++ (1.35E-02s)\n", + "2023-08-25 15:26:02.152 - amici.de_export - DEBUG - Finished importing SbmlImporter + (1.45E-01s)\n", + "2023-08-25 15:26:02.334 - amici.de_export - DEBUG - Finished simplifying Jy ++++ (1.42E-01s)\n", + "2023-08-25 15:26:02.335 - amici.de_export - DEBUG - Finished computing Jy +++ (1.51E-01s)\n", + "2023-08-25 15:26:02.404 - amici.de_export - DEBUG - Finished simplifying y ++++ (4.67E-02s)\n", + "2023-08-25 15:26:02.405 - amici.de_export - DEBUG - Finished computing y +++ (5.86E-02s)\n", + "2023-08-25 15:26:02.425 - amici.de_export - DEBUG - Finished simplifying sigmay ++++ (1.38E-04s)\n", + "2023-08-25 15:26:02.426 - amici.de_export - DEBUG - Finished computing sigmay +++ (8.85E-03s)\n", + "2023-08-25 15:26:02.458 - amici.de_export - DEBUG - Finished writing Jy.cpp ++ (2.84E-01s)\n", + "2023-08-25 15:26:02.523 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.62E-02s)\n", + "2023-08-25 15:26:02.547 - amici.de_export - DEBUG - Finished simplifying dJydsigma ++++ (1.39E-02s)\n", + "2023-08-25 15:26:02.548 - amici.de_export - DEBUG - Finished computing dJydsigma +++ (7.16E-02s)\n", + "2023-08-25 15:26:02.558 - amici.de_export - DEBUG - Finished writing dJydsigma.cpp ++ (8.93E-02s)\n", + "2023-08-25 15:26:02.601 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.81E-02s)\n", + "2023-08-25 15:26:02.629 - amici.de_export - DEBUG - Finished simplifying dJydy ++++ (1.72E-02s)\n", + "2023-08-25 15:26:02.630 - amici.de_export - DEBUG - Finished computing dJydy +++ (5.57E-02s)\n", + "2023-08-25 15:26:02.641 - amici.de_export - DEBUG - Finished writing dJydy.cpp ++ (7.41E-02s)\n", + "2023-08-25 15:26:02.669 - amici.de_export - DEBUG - Finished simplifying Jz ++++ (9.60E-05s)\n", + "2023-08-25 15:26:02.670 - amici.de_export - DEBUG - Finished computing Jz +++ (8.37E-03s)\n", + "2023-08-25 15:26:02.682 - amici.de_export - DEBUG - Finished computing z +++ (1.74E-04s)\n", + "2023-08-25 15:26:02.701 - amici.de_export - DEBUG - Finished simplifying sigmaz ++++ (1.33E-04s)\n", + "2023-08-25 15:26:02.701 - amici.de_export - DEBUG - Finished computing sigmaz +++ (7.93E-03s)\n", + "2023-08-25 15:26:02.702 - amici.de_export - DEBUG - Finished writing Jz.cpp ++ (4.87E-02s)\n", + "2023-08-25 15:26:02.729 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (7.59E-05s)\n", + "2023-08-25 15:26:02.739 - amici.de_export - DEBUG - Finished simplifying dJzdsigma ++++ (2.09E-04s)\n", + "2023-08-25 15:26:02.740 - amici.de_export - DEBUG - Finished computing dJzdsigma +++ (1.98E-02s)\n", + "2023-08-25 15:26:02.742 - amici.de_export - DEBUG - Finished writing dJzdsigma.cpp ++ (2.80E-02s)\n", + "2023-08-25 15:26:02.769 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (6.99E-05s)\n", + "2023-08-25 15:26:02.778 - amici.de_export - DEBUG - Finished simplifying dJzdz ++++ (9.28E-05s)\n", + "2023-08-25 15:26:02.779 - amici.de_export - DEBUG - Finished computing dJzdz +++ (1.78E-02s)\n", + "2023-08-25 15:26:02.780 - amici.de_export - DEBUG - Finished writing dJzdz.cpp ++ (2.73E-02s)\n", + "2023-08-25 15:26:02.807 - amici.de_export - DEBUG - Finished simplifying Jrz ++++ (1.01E-04s)\n", + "2023-08-25 15:26:02.808 - amici.de_export - DEBUG - Finished computing Jrz +++ (7.65E-03s)\n", + "2023-08-25 15:26:02.818 - amici.de_export - DEBUG - Finished computing rz +++ (2.55E-04s)\n", + "2023-08-25 15:26:02.819 - amici.de_export - DEBUG - Finished writing Jrz.cpp ++ (2.59E-02s)\n", + "2023-08-25 15:26:02.845 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (9.20E-05s)\n", + "2023-08-25 15:26:02.856 - amici.de_export - DEBUG - Finished simplifying dJrzdsigma ++++ (9.63E-05s)\n", + "2023-08-25 15:26:02.857 - amici.de_export - DEBUG - Finished computing dJrzdsigma +++ (2.05E-02s)\n", + "2023-08-25 15:26:02.858 - amici.de_export - DEBUG - Finished writing dJrzdsigma.cpp ++ (2.91E-02s)\n", + "2023-08-25 15:26:02.886 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (6.81E-05s)\n", + "2023-08-25 15:26:02.899 - amici.de_export - DEBUG - Finished simplifying dJrzdz ++++ (1.23E-04s)\n", + "2023-08-25 15:26:02.900 - amici.de_export - DEBUG - Finished computing dJrzdz +++ (2.28E-02s)\n", + "2023-08-25 15:26:02.901 - amici.de_export - DEBUG - Finished writing dJrzdz.cpp ++ (3.12E-02s)\n", + "2023-08-25 15:26:02.928 - amici.de_export - DEBUG - Finished simplifying root ++++ (1.70E-04s)\n", + "2023-08-25 15:26:02.929 - amici.de_export - DEBUG - Finished computing root +++ (9.40E-03s)\n", + "2023-08-25 15:26:02.931 - amici.de_export - DEBUG - Finished writing root.cpp ++ (1.89E-02s)\n", + "2023-08-25 15:26:02.999 - amici.de_export - DEBUG - Finished simplifying w +++++ (3.15E-02s)\n", + "2023-08-25 15:26:03.000 - amici.de_export - DEBUG - Finished computing w ++++ (4.06E-02s)\n", + "2023-08-25 15:26:03.035 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (2.51E-02s)\n", + "2023-08-25 15:26:03.055 - amici.de_export - DEBUG - Finished simplifying dwdp ++++ (1.14E-02s)\n", + "2023-08-25 15:26:03.056 - amici.de_export - DEBUG - Finished computing dwdp +++ (1.05E-01s)\n", + "2023-08-25 15:26:03.078 - amici.de_export - DEBUG - Finished simplifying spl ++++ (1.36E-04s)\n", + "2023-08-25 15:26:03.079 - amici.de_export - DEBUG - Finished computing spl +++ (8.40E-03s)\n", + "2023-08-25 15:26:03.101 - amici.de_export - DEBUG - Finished simplifying sspl ++++ (1.03E-04s)\n", + "2023-08-25 15:26:03.103 - amici.de_export - DEBUG - Finished computing sspl +++ (1.16E-02s)\n", + "2023-08-25 15:26:03.110 - amici.de_export - DEBUG - Finished writing dwdp.cpp ++ (1.66E-01s)\n", + "2023-08-25 15:26:03.219 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (8.22E-02s)\n", + "2023-08-25 15:26:03.318 - amici.de_export - DEBUG - Finished simplifying dwdx ++++ (8.91E-02s)\n", + "2023-08-25 15:26:03.319 - amici.de_export - DEBUG - Finished computing dwdx +++ (1.91E-01s)\n", + "2023-08-25 15:26:03.359 - amici.de_export - DEBUG - Finished writing dwdx.cpp ++ (2.39E-01s)\n", + "2023-08-25 15:26:03.369 - amici.de_export - DEBUG - Finished writing create_splines.cpp ++ (3.98E-04s)\n", + "2023-08-25 15:26:03.404 - amici.de_export - DEBUG - Finished simplifying spline_values +++++ (1.11E-04s)\n", + "2023-08-25 15:26:03.405 - amici.de_export - DEBUG - Finished computing spline_values ++++ (9.25E-03s)\n", + "2023-08-25 15:26:03.417 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.12E-04s)\n", + "2023-08-25 15:26:03.427 - amici.de_export - DEBUG - Finished simplifying dspline_valuesdp ++++ (9.45E-05s)\n", + "2023-08-25 15:26:03.428 - amici.de_export - DEBUG - Finished computing dspline_valuesdp +++ (3.99E-02s)\n", + "2023-08-25 15:26:03.429 - amici.de_export - DEBUG - Finished writing dspline_valuesdp.cpp ++ (4.87E-02s)\n", + "2023-08-25 15:26:03.464 - amici.de_export - DEBUG - Finished simplifying spline_slopes +++++ (1.12E-04s)\n", + "2023-08-25 15:26:03.465 - amici.de_export - DEBUG - Finished computing spline_slopes ++++ (9.20E-03s)\n", + "2023-08-25 15:26:03.476 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (7.68E-05s)\n", + "2023-08-25 15:26:03.485 - amici.de_export - DEBUG - Finished simplifying dspline_slopesdp ++++ (9.28E-05s)\n", + "2023-08-25 15:26:03.486 - amici.de_export - DEBUG - Finished computing dspline_slopesdp +++ (3.93E-02s)\n", + "2023-08-25 15:26:03.487 - amici.de_export - DEBUG - Finished writing dspline_slopesdp.cpp ++ (4.71E-02s)\n", + "2023-08-25 15:26:03.523 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (7.05E-03s)\n", + "2023-08-25 15:26:03.538 - amici.de_export - DEBUG - Finished simplifying dwdw ++++ (3.78E-03s)\n", + "2023-08-25 15:26:03.539 - amici.de_export - DEBUG - Finished computing dwdw +++ (3.13E-02s)\n", + "2023-08-25 15:26:03.543 - amici.de_export - DEBUG - Finished writing dwdw.cpp ++ (4.30E-02s)\n", + "2023-08-25 15:26:03.588 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.64E-02s)\n", + "2023-08-25 15:26:03.599 - amici.de_export - DEBUG - Finished simplifying dxdotdw ++++ (4.12E-04s)\n", + "2023-08-25 15:26:03.600 - amici.de_export - DEBUG - Finished computing dxdotdw +++ (3.57E-02s)\n", + "2023-08-25 15:26:03.610 - amici.de_export - DEBUG - Finished writing dxdotdw.cpp ++ (5.61E-02s)\n", + "2023-08-25 15:26:03.642 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.69E-03s)\n", + "2023-08-25 15:26:03.656 - amici.de_export - DEBUG - Finished simplifying dxdotdx_explicit ++++ (1.36E-04s)\n", + "2023-08-25 15:26:03.657 - amici.de_export - DEBUG - Finished computing dxdotdx_explicit +++ (2.76E-02s)\n", + "2023-08-25 15:26:03.660 - amici.de_export - DEBUG - Finished writing dxdotdx_explicit.cpp ++ (3.91E-02s)\n", + "2023-08-25 15:26:03.691 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.63E-03s)\n", + "2023-08-25 15:26:03.702 - amici.de_export - DEBUG - Finished simplifying dxdotdp_explicit ++++ (2.59E-04s)\n", + "2023-08-25 15:26:03.703 - amici.de_export - DEBUG - Finished computing dxdotdp_explicit +++ (2.19E-02s)\n", + "2023-08-25 15:26:03.705 - amici.de_export - DEBUG - Finished writing dxdotdp_explicit.cpp ++ (3.14E-02s)\n", + "2023-08-25 15:26:03.747 - amici.de_export - DEBUG - Finished running smart_jacobian +++++ (2.99E-03s)\n", + "2023-08-25 15:26:03.834 - amici.de_export - DEBUG - Finished simplifying dydx +++++ (7.46E-02s)\n", + "2023-08-25 15:26:03.834 - amici.de_export - DEBUG - Finished computing dydx ++++ (9.83E-02s)\n", + "2023-08-25 15:26:03.853 - amici.de_export - DEBUG - Finished running smart_jacobian +++++ (2.80E-04s)\n", + "2023-08-25 15:26:03.865 - amici.de_export - DEBUG - Finished simplifying dydw +++++ (1.05E-04s)\n", + "2023-08-25 15:26:03.866 - amici.de_export - DEBUG - Finished computing dydw ++++ (2.15E-02s)\n", + "2023-08-25 15:26:03.951 - amici.de_export - DEBUG - Finished simplifying dydx ++++ (7.01E-02s)\n", + "2023-08-25 15:26:03.952 - amici.de_export - DEBUG - Finished computing dydx +++ (2.25E-01s)\n", + "2023-08-25 15:26:03.981 - amici.de_export - DEBUG - Finished writing dydx.cpp ++ (2.62E-01s)\n", + "2023-08-25 15:26:04.018 - amici.de_export - DEBUG - Finished running smart_jacobian +++++ (2.60E-04s)\n", + "2023-08-25 15:26:04.028 - amici.de_export - DEBUG - Finished simplifying dydp +++++ (9.56E-05s)\n", + "2023-08-25 15:26:04.028 - amici.de_export - DEBUG - Finished computing dydp ++++ (1.88E-02s)\n", + "2023-08-25 15:26:04.039 - amici.de_export - DEBUG - Finished simplifying dydp ++++ (9.50E-05s)\n", + "2023-08-25 15:26:04.040 - amici.de_export - DEBUG - Finished computing dydp +++ (4.04E-02s)\n", + "2023-08-25 15:26:04.042 - amici.de_export - DEBUG - Finished writing dydp.cpp ++ (5.07E-02s)\n", + "2023-08-25 15:26:04.061 - amici.de_export - DEBUG - Finished computing dzdx +++ (1.50E-04s)\n", + "2023-08-25 15:26:04.061 - amici.de_export - DEBUG - Finished writing dzdx.cpp ++ (8.54E-03s)\n", + "2023-08-25 15:26:04.079 - amici.de_export - DEBUG - Finished computing dzdp +++ (1.53E-04s)\n", + "2023-08-25 15:26:04.081 - amici.de_export - DEBUG - Finished writing dzdp.cpp ++ (9.44E-03s)\n", + "2023-08-25 15:26:04.098 - amici.de_export - DEBUG - Finished computing drzdx +++ (1.45E-04s)\n", + "2023-08-25 15:26:04.099 - amici.de_export - DEBUG - Finished writing drzdx.cpp ++ (9.13E-03s)\n", + "2023-08-25 15:26:04.117 - amici.de_export - DEBUG - Finished computing drzdp +++ (1.53E-04s)\n", + "2023-08-25 15:26:04.118 - amici.de_export - DEBUG - Finished writing drzdp.cpp ++ (8.88E-03s)\n", + "2023-08-25 15:26:04.144 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.40E-04s)\n", + "2023-08-25 15:26:04.154 - amici.de_export - DEBUG - Finished simplifying dsigmaydy ++++ (8.96E-05s)\n", + "2023-08-25 15:26:04.154 - amici.de_export - DEBUG - Finished computing dsigmaydy +++ (2.02E-02s)\n", + "2023-08-25 15:26:04.155 - amici.de_export - DEBUG - Finished writing dsigmaydy.cpp ++ (2.86E-02s)\n", + "2023-08-25 15:26:04.183 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (1.20E-03s)\n", + "2023-08-25 15:26:04.193 - amici.de_export - DEBUG - Finished simplifying dsigmaydp ++++ (1.58E-04s)\n", + "2023-08-25 15:26:04.194 - amici.de_export - DEBUG - Finished computing dsigmaydp +++ (1.88E-02s)\n", + "2023-08-25 15:26:04.197 - amici.de_export - DEBUG - Finished writing dsigmaydp.cpp ++ (2.99E-02s)\n", + "2023-08-25 15:26:04.208 - amici.de_export - DEBUG - Finished writing sigmay.cpp ++ (1.52E-03s)\n", + "2023-08-25 15:26:04.232 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (7.05E-05s)\n", + "2023-08-25 15:26:04.243 - amici.de_export - DEBUG - Finished simplifying dsigmazdp ++++ (1.14E-04s)\n", + "2023-08-25 15:26:04.244 - amici.de_export - DEBUG - Finished computing dsigmazdp +++ (1.93E-02s)\n", + "2023-08-25 15:26:04.244 - amici.de_export - DEBUG - Finished writing dsigmazdp.cpp ++ (2.79E-02s)\n", + "2023-08-25 15:26:04.256 - amici.de_export - DEBUG - Finished writing sigmaz.cpp ++ (5.24E-05s)\n", + "2023-08-25 15:26:04.272 - amici.de_export - DEBUG - Finished computing stau +++ (1.40E-04s)\n", + "2023-08-25 15:26:04.273 - amici.de_export - DEBUG - Finished writing stau.cpp ++ (8.13E-03s)\n", + "2023-08-25 15:26:04.292 - amici.de_export - DEBUG - Finished computing deltax +++ (1.35E-04s)\n", + "2023-08-25 15:26:04.293 - amici.de_export - DEBUG - Finished writing deltax.cpp ++ (8.38E-03s)\n", + "2023-08-25 15:26:04.313 - amici.de_export - DEBUG - Finished computing deltasx +++ (2.33E-04s)\n", + "2023-08-25 15:26:04.314 - amici.de_export - DEBUG - Finished writing deltasx.cpp ++ (1.03E-02s)\n", + "2023-08-25 15:26:04.332 - amici.de_export - DEBUG - Finished writing w.cpp ++ (9.05E-03s)\n", + "2023-08-25 15:26:04.343 - amici.de_export - DEBUG - Finished writing x0.cpp ++ (1.96E-03s)\n", + "2023-08-25 15:26:04.375 - amici.de_export - DEBUG - Finished simplifying x0_fixedParameters ++++ (2.13E-03s)\n", + "2023-08-25 15:26:04.376 - amici.de_export - DEBUG - Finished computing x0_fixedParameters +++ (1.20E-02s)\n", + "2023-08-25 15:26:04.380 - amici.de_export - DEBUG - Finished writing x0_fixedParameters.cpp ++ (2.49E-02s)\n", + "2023-08-25 15:26:04.409 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (2.16E-03s)\n", + "2023-08-25 15:26:04.420 - amici.de_export - DEBUG - Finished simplifying sx0 ++++ (9.81E-05s)\n", + "2023-08-25 15:26:04.421 - amici.de_export - DEBUG - Finished computing sx0 +++ (2.15E-02s)\n", + "2023-08-25 15:26:04.422 - amici.de_export - DEBUG - Finished writing sx0.cpp ++ (3.13E-02s)\n", + "2023-08-25 15:26:04.450 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.50E-04s)\n", + "2023-08-25 15:26:04.460 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (3.11E-04s)\n", + "2023-08-25 15:26:04.471 - amici.de_export - DEBUG - Finished simplifying sx0_fixedParameters ++++ (1.77E-04s)\n", + "2023-08-25 15:26:04.472 - amici.de_export - DEBUG - Finished computing sx0_fixedParameters +++ (2.92E-02s)\n", + "2023-08-25 15:26:04.475 - amici.de_export - DEBUG - Finished writing sx0_fixedParameters.cpp ++ (3.96E-02s)\n", + "2023-08-25 15:26:04.499 - amici.de_export - DEBUG - Finished writing xdot.cpp ++ (1.48E-02s)\n", + "2023-08-25 15:26:04.513 - amici.de_export - DEBUG - Finished writing y.cpp ++ (4.81E-03s)\n", + "2023-08-25 15:26:04.537 - amici.de_export - DEBUG - Finished simplifying x_rdata ++++ (1.97E-04s)\n", + "2023-08-25 15:26:04.538 - amici.de_export - DEBUG - Finished computing x_rdata +++ (8.23E-03s)\n", + "2023-08-25 15:26:04.540 - amici.de_export - DEBUG - Finished writing x_rdata.cpp ++ (1.77E-02s)\n", + "2023-08-25 15:26:04.566 - amici.de_export - DEBUG - Finished simplifying total_cl ++++ (1.07E-04s)\n", + "2023-08-25 15:26:04.566 - amici.de_export - DEBUG - Finished computing total_cl +++ (8.30E-03s)\n", + "2023-08-25 15:26:04.567 - amici.de_export - DEBUG - Finished writing total_cl.cpp ++ (1.67E-02s)\n", + "2023-08-25 15:26:04.592 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (8.56E-05s)\n", + "2023-08-25 15:26:04.601 - amici.de_export - DEBUG - Finished simplifying dtotal_cldp ++++ (9.78E-05s)\n", + "2023-08-25 15:26:04.602 - amici.de_export - DEBUG - Finished computing dtotal_cldp +++ (1.75E-02s)\n", + "2023-08-25 15:26:04.603 - amici.de_export - DEBUG - Finished writing dtotal_cldp.cpp ++ (2.57E-02s)\n", + "2023-08-25 15:26:04.630 - amici.de_export - DEBUG - Finished simplifying dtotal_cldx_rdata ++++ (1.07E-04s)\n", + "2023-08-25 15:26:04.631 - amici.de_export - DEBUG - Finished computing dtotal_cldx_rdata +++ (8.89E-03s)\n", + "2023-08-25 15:26:04.632 - amici.de_export - DEBUG - Finished writing dtotal_cldx_rdata.cpp ++ (1.72E-02s)\n", + "2023-08-25 15:26:04.662 - amici.de_export - DEBUG - Finished simplifying x_solver ++++ (1.60E-04s)\n", + "2023-08-25 15:26:04.663 - amici.de_export - DEBUG - Finished computing x_solver +++ (9.63E-03s)\n", + "2023-08-25 15:26:04.666 - amici.de_export - DEBUG - Finished writing x_solver.cpp ++ (2.06E-02s)\n", + "2023-08-25 15:26:04.692 - amici.de_export - DEBUG - Finished simplifying dx_rdatadx_solver ++++ (6.79E-04s)\n", + "2023-08-25 15:26:04.693 - amici.de_export - DEBUG - Finished computing dx_rdatadx_solver +++ (9.45E-03s)\n", + "2023-08-25 15:26:04.694 - amici.de_export - DEBUG - Finished writing dx_rdatadx_solver.cpp ++ (1.80E-02s)\n", + "2023-08-25 15:26:04.722 - amici.de_export - DEBUG - Finished simplifying dx_rdatadp ++++ (8.74E-04s)\n", + "2023-08-25 15:26:04.723 - amici.de_export - DEBUG - Finished computing dx_rdatadp +++ (1.08E-02s)\n", + "2023-08-25 15:26:04.725 - amici.de_export - DEBUG - Finished writing dx_rdatadp.cpp ++ (2.03E-02s)\n", + "2023-08-25 15:26:04.749 - amici.de_export - DEBUG - Finished running smart_jacobian ++++ (6.97E-05s)\n", + "2023-08-25 15:26:04.758 - amici.de_export - DEBUG - Finished simplifying dx_rdatadtcl ++++ (8.82E-05s)\n", + "2023-08-25 15:26:04.759 - amici.de_export - DEBUG - Finished computing dx_rdatadtcl +++ (1.66E-02s)\n", + "2023-08-25 15:26:04.760 - amici.de_export - DEBUG - Finished writing dx_rdatadtcl.cpp ++ (2.47E-02s)\n", + "2023-08-25 15:26:04.770 - amici.de_export - DEBUG - Finished writing z.cpp ++ (4.95E-05s)\n", + "2023-08-25 15:26:04.779 - amici.de_export - DEBUG - Finished writing rz.cpp ++ (5.57E-05s)\n", + "2023-08-25 15:26:04.812 - amici.de_export - DEBUG - Finished generating cpp code + (2.65E+00s)\n", + "2023-08-25 15:26:59.498 - amici.de_export - DEBUG - Finished compiling cpp code + (5.47E+01s)\n", + "2023-08-25 15:26:59.945 - amici.petab_import - INFO - Finished Importing PEtab model (5.85E+01s)\n", + "2023-08-25 15:26:59.954 - amici.petab_import - INFO - Successfully loaded model FullModel from pyPESTO/doc/example/amici_models/FullModel.\n" + ] + } + ], "source": [ "%%capture\n", "# PEtab problem loading\n", @@ -131,7 +352,7 @@ "\n", "# AMICI model complilation\n", "amici_model = amici.petab_import.import_petab_problem(\n", - " petab_problem, force_compile=True, verbose=False\n", + " petab_problem, force_compile=True\n", ")" ] }, @@ -144,7 +365,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2023-07-13T09:24:50.218430Z", @@ -154,7 +375,36 @@ "outputs_hidden": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "PEtab benchmark parameters\n", + "{'edatas': [::value_type' at 0x12c917d80\n", + " condition 'model1_data1' starting at t=0.0 with custom parameter scales, constants, parameters\n", + " 16x3 time-resolved datapoints\n", + " (48/48 measurements & 0/48 sigmas set)\n", + " 10x0 event-resolved datapoints\n", + " (0/0 measurements & 0/0 sigmas set)\n", + ">],\n", + " 'llh': -138.22199656856435,\n", + " 'rdatas': [::pointer' at 0x12c917c60> >)>],\n", + " 'sllh': None}\n", + "Individualized parameters\n", + "{'edatas': [::value_type' at 0x12c7dadf0\n", + " condition 'model1_data1' starting at t=0.0 with custom parameter scales, constants, parameters\n", + " 16x3 time-resolved datapoints\n", + " (48/48 measurements & 0/48 sigmas set)\n", + " 10x0 event-resolved datapoints\n", + " (0/0 measurements & 0/0 sigmas set)\n", + ">],\n", + " 'llh': -185.54291970899519,\n", + " 'rdatas': [::pointer' at 0x12c917060> >)>],\n", + " 'sllh': None}\n" + ] + } + ], "source": [ "# Simulation with PEtab nominal parameter values\n", "print(\"PEtab benchmark parameters\")\n", @@ -185,13 +435,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "185.54291970899519\n" + ] + } + ], "source": [ "class Objective:\n", " \"\"\"\n", @@ -260,13 +518,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(185.5429188951038,\n", + " array([ 4.96886729e+02, 1.50715517e-01, 4.44258325e+01, 7.14778242e+02,\n", + " -5.19647592e-05, -1.66953531e+02, -1.50846999e+02, -6.86643591e+01,\n", + " -1.59022641e+01]))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "petab_yaml = f\"./{model_name}/{model_name}.yaml\"\n", "\n", @@ -301,20 +573,327 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "jupyter": { "outputs_hidden": false } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ message: STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT\n", + " success: False\n", + " status: 1\n", + " fun: 483.97739572289964\n", + " x: [-3.352e+00 -2.216e+00 -1.774e+00 -1.776e+00 6.234e-01\n", + " 3.661e+00 1.261e+00 6.150e-01 2.480e-01]\n", + " nit: 18\n", + " jac: [ 2.192e+02 3.002e+02 7.278e+02 -2.483e+02 3.634e+02\n", + " -3.849e+01 -3.175e+01 -1.073e+03 -4.504e+02]\n", + " nfev: 520\n", + " njev: 52\n", + " hess_inv: <9x9 LbfgsInvHessProduct with dtype=float64>,\n", + " message: STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT\n", + " success: False\n", + " status: 1\n", + " fun: 242.1633515170673\n", + " x: [-1.690e+00 1.599e+00 4.108e+00 -2.908e+00 4.344e+00\n", + " 2.980e+00 2.152e+00 1.482e+00 1.401e+00]\n", + " nit: 19\n", + " jac: [-1.918e+01 -8.125e+00 8.223e+00 -3.257e+00 -1.422e+01\n", + " -1.719e+01 3.437e+01 -1.304e+01 3.139e+01]\n", + " nfev: 520\n", + " njev: 52\n", + " hess_inv: <9x9 LbfgsInvHessProduct with dtype=float64>,\n", + " message: STOP: TOTAL NO. of f AND g EVALUATIONS EXCEEDS LIMIT\n", + " success: False\n", + " status: 1\n", + " fun: 214.20136458346468\n", + " x: [-6.979e-02 -3.372e+00 -1.727e+00 4.216e+00 -4.263e+00\n", + " 4.777e+00 1.342e+00 1.486e+00 1.120e+00]\n", + " nit: 24\n", + " jac: [ 1.194e+01 -6.911e-01 -5.608e-01 -4.737e-01 -1.027e+00\n", + " -1.151e+01 -5.561e+00 1.893e+01 -1.621e+01]\n", + " nfev: 530\n", + " njev: 53\n", + " hess_inv: <9x9 LbfgsInvHessProduct with dtype=float64>,\n", + " message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n", + " success: True\n", + " status: 0\n", + " fun: 249.74599720689707\n", + " x: [ 1.320e+00 -2.696e+00 -5.719e-02 2.128e+00 -9.272e-01\n", + " -1.710e+00 1.873e+00 1.759e+00 1.299e+00]\n", + " nit: 22\n", + " jac: [ 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00\n", + " -5.684e-06 5.684e-06 5.684e-06 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-0.13738387, -2.19222726, 2.40671846, -2.17865902]),\n", + " array([-4.85782185, 0.88722054, 3.46323867, -0.42272524, 2.97501061,\n", + " -0.44685985, -1.9617645 , 4.19526924, -0.68191772]),\n", + " array([ 2.75539723, -3.45625301, -1.74304845, 4.19486719, -4.36152045,\n", + " 3.20102079, 4.1666204 , 4.5071745 , 1.64830203]),\n", + " array([-0.13272644, -1.78655792, -2.05292081, 4.94102789, 0.68000657,\n", + " -0.41145952, 4.43118647, 2.86292729, -2.27641822]),\n", + " array([ 1.5071923 , 3.23408298, 4.40175342, -4.93504248, -3.68651524,\n", + " 4.89047865, -3.50203955, -3.98810331, -0.60343463]),\n", + " array([ 3.59031468, -0.64508741, 4.57795683, -4.55808472, 1.45169025,\n", + " 0.16191615, -0.9214029 , 1.8818166 , -2.04635126]),\n", + " array([-3.69691906, -1.11149925, -2.07266599, -1.6551983 , -1.05891694,\n", + " 0.25590375, 0.87136513, -1.83339326, -2.29220816]),\n", + " array([ 2.62294851, -3.86768587, 4.24057642, 0.67648706, 4.94028248,\n", + " -4.53014752, -4.41436998, 0.48069498, 2.08662195]),\n", + " array([ 1.11811741, -1.66877199, 4.78163474, 3.63123695, -4.84414353,\n", + " -4.69389636, -4.22521978, 1.05436896, 1.66464083]),\n", + " array([ 2.10705352, -4.17386158, -4.95145244, -0.4940422 , 2.44773506,\n", + " -0.72754709, -3.38821849, 4.3015123 , -4.03270095]),\n", + " array([-1.21935294, 4.99254589, -3.5227032 , -4.57026229, -4.27577682,\n", + " 1.65134668, -2.57941689, 3.3876373 , -3.08581727]),\n", + " array([-2.69338089, 1.3336723 , 4.00935726, 4.23436455, -4.97880599,\n", + " 0.66011236, -0.92734049, -0.72506365, -1.95148656]),\n", + " array([ 4.59360518, -1.45672536, 2.53472283, 1.59953602, 4.74752881,\n", + " 2.97708352, -1.75879731, 1.52861569, -4.47452224]),\n", + " array([ 2.78765335, -3.97396464, 3.98103304, 4.06162031, -4.66533684,\n", + " -3.28137522, 3.15208208, -2.66502967, -4.85197795]),\n", + " array([-2.01253459, -2.8480651 , 3.12268386, 1.19351138, -0.60901754,\n", + " 0.29935873, 3.43245553, 4.09645236, -0.05881582]),\n", + 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2.87775688, 2.25140401, -2.65489216,\n", + " 1.01047951, 3.69127283, 4.44893301, 2.65440911]),\n", + " array([0.66867122, 3.97870907, 2.13991535, 0.2612146 , 4.9597103 ,\n", + " 3.23568699, 4.22366861, 0.40266923, 3.15201217]),\n", + " array([ 4.03440768, 0.83760837, 3.55179682, 1.57898613, -4.87401594,\n", + " 4.33049155, 3.79628273, 1.90990052, 1.02667127])]\n" + ] + } + ], "source": [ "import nlopt\n", "\n", @@ -393,9 +1029,191 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'exitflag': 1,\n", + " 'fun': 1150653011.8393009,\n", + " 'message': 'Finished Successfully.',\n", + " 'x': array([-4.72537584, -3.8267521 , 0.17032373, 0.5309411 , 1.82691101,\n", + " -0.13738387, -2.19222726, 2.40671846, -2.17865902]),\n", + " 'x0': array([-4.72537584, -3.8267521 , 0.17032373, 0.5309411 , 1.82691101,\n", + " -0.13738387, -2.19222726, 2.40671846, 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4.22366861, 0.40266923, 3.15201217])},\n", + " {'exitflag': 1,\n", + " 'fun': 324.16551565091083,\n", + " 'message': 'Finished Successfully.',\n", + " 'x': array([ 4.03440768, 0.83760837, 3.55179682, 1.57898613, -4.87401594,\n", + " 4.33049155, 3.79628273, 1.90990052, 1.02667127]),\n", + " 'x0': array([ 4.03440768, 0.83760837, 3.55179682, 1.57898613, -4.87401594,\n", + " 4.33049155, 3.79628273, 1.90990052, 1.02667127])}]\n" + ] + } + ], "source": [ "results = []\n", "for x0 in x_guesses:\n", @@ -447,9 +1265,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Engine will use up to 8 processes (= CPU count).\n", + " 0%| | 0/25 [00:00\n", + "* message: ABNORMAL_TERMINATION_IN_LNSRCH \n", + "* number of evaluations: 141\n", + "* time taken to optimize: 6.583s\n", + "* startpoint: [-4.32816355 -4.74870318 -1.74009129 -1.26420992 3.53977881 0.54755365\n", + " 2.64804722 1.62058431 1.57747828]\n", + "* endpoint: [-1.56907929 -5. -2.2098163 -1.78589671 3.55917603 4.19771074\n", + " 0.58569077 0.81885971 0.49858833]\n", + "* final objective value: 138.2224842858494\n", + "* final gradient value: [-0.00783036 0.05534759 0.00129469 -0.00675505 -0.00121895 0.00394696\n", + " -0.00021472 0.00294705 0.00089969]\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "results_pypesto = optimize.minimize(\n", " problem=problem,\n", @@ -470,9 +1362,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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42320078.18806582,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([-0.13272644, -1.78655792, -2.05292081, 4.94102789, 0.68000657,\n", + " -0.41145952, 4.43118647, 2.86292729, -2.27641822]),\n", + " 'x0': array([-0.13272644, -1.78655792, -2.05292081, 4.94102789, 0.68000657,\n", + " -0.41145952, 4.43118647, 2.86292729, -2.27641822]),\n", + " 'fun': 113150729.31030881,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([-3.69691906, -1.11149925, -2.07266599, -1.6551983 , -1.05891694,\n", + " 0.25590375, 0.87136513, -1.83339326, -2.29220816]),\n", + " 'x0': array([-3.69691906, -1.11149925, -2.07266599, -1.6551983 , -1.05891694,\n", + " 0.25590375, 0.87136513, -1.83339326, -2.29220816]),\n", + " 'fun': 243163763.80728397,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([-4.85782185, 0.88722054, 3.46323867, -0.42272524, 2.97501061,\n", + " -0.44685985, -1.9617645 , 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'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([-2.59120601, 0.69656874, -3.88289712, -1.74846428, -1.58175173,\n", + " 3.39830011, 0.0917892 , -0.85030875, -3.77417568]),\n", + " 'x0': array([-2.59120601, 0.69656874, -3.88289712, -1.74846428, -1.58175173,\n", + " 3.39830011, 0.0917892 , -0.85030875, -3.77417568]),\n", + " 'fun': 36260050961.21613,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([ 2.10705352, -4.17386158, -4.95145244, -0.4940422 , 2.44773506,\n", + " -0.72754709, -3.38821849, 4.3015123 , -4.03270095]),\n", + " 'x0': array([ 2.10705352, -4.17386158, -4.95145244, -0.4940422 , 2.44773506,\n", + " -0.72754709, -3.38821849, 4.3015123 , -4.03270095]),\n", + " 'fun': 634294830118.3749,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([ 1.5071923 , 3.23408298, 4.40175342, -4.93504248, -3.68651524,\n", + " 4.89047865, -3.50203955, -3.98810331, -0.60343463]),\n", + " 'x0': array([ 1.5071923 , 3.23408298, 4.40175342, -4.93504248, -3.68651524,\n", + " 4.89047865, -3.50203955, -3.98810331, -0.60343463]),\n", + " 'fun': 2170773400755.13,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([ 4.59360518, -1.45672536, 2.53472283, 1.59953602, 4.74752881,\n", + " 2.97708352, -1.75879731, 1.52861569, -4.47452224]),\n", + " 'x0': array([ 4.59360518, -1.45672536, 2.53472283, 1.59953602, 4.74752881,\n", + " 2.97708352, -1.75879731, 1.52861569, -4.47452224]),\n", + " 'fun': 2817631865279.2437,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([ 2.45781935, 1.53870162, -0.24553228, 1.49870916, -3.42788561,\n", + " 4.98603203, 3.19947195, -4.22036418, 0.83316028]),\n", + " 'x0': array([ 2.45781935, 1.53870162, -0.24553228, 1.49870916, -3.42788561,\n", + " 4.98603203, 3.19947195, -4.22036418, 0.83316028]),\n", + " 'fun': 7147839056555.6,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([ 1.11811741, -1.66877199, 4.78163474, 3.63123695, -4.84414353,\n", + " -4.69389636, -4.22521978, 1.05436896, 1.66464083]),\n", + " 'x0': array([ 1.11811741, -1.66877199, 4.78163474, 3.63123695, -4.84414353,\n", + " -4.69389636, -4.22521978, 1.05436896, 1.66464083]),\n", + " 'fun': 12556009991670.39,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([ 2.78765335, -3.97396464, 3.98103304, 4.06162031, -4.66533684,\n", + " -3.28137522, 3.15208208, -2.66502967, -4.85197795]),\n", + " 'x0': array([ 2.78765335, -3.97396464, 3.98103304, 4.06162031, -4.66533684,\n", + " -3.28137522, 3.15208208, -2.66502967, -4.85197795]),\n", + " 'fun': 16029712077044.059,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1},\n", + " {'x': array([ 2.62294851, -3.86768587, 4.24057642, 0.67648706, 4.94028248,\n", + " -4.53014752, -4.41436998, 0.48069498, 2.08662195]),\n", + " 'x0': array([ 2.62294851, -3.86768587, 4.24057642, 0.67648706, 4.94028248,\n", + " -4.53014752, -4.41436998, 0.48069498, 2.08662195]),\n", + " 'fun': 30002126964787.39,\n", + " 'message': 'Finished Successfully.',\n", + " 'exitflag': 1}]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# sort the results\n", "results_sorted = sorted(results, key=lambda a: a[\"fun\"])\n", @@ -505,18 +1603,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([-5. , -2.2098163 , -1.78589671, 3.55917603, 4.19771074,\n", + " 0.58569077, 0.81885971, 0.49858833])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "results_pypesto.optimize_result[0][\"x\"][problem.x_free_indices][1:]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "direction: -1\n", + "direction: 1\n" + ] + } + ], "source": [ "# we optimimize the first parameter\n", "# x_start = results_sorted[0][\"x\"][1:]\n", @@ -579,9 +1698,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(\n", " [x[0] for x in x_profile], np.exp(np.min(fval_profile) - fval_profile)\n", @@ -609,9 +1739,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Engine will use up to 8 processes (= CPU count).\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:13<00:00, 13.82s/it]\n" + ] + }, + { + "data": { + "image/png": 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XLoxWPOkkqFAhsnjSTpOVFUrG11+HUaNg5cr8Y+XLhxG8xx8PPXpA/fqRxZQkSZJUPFgwFiMWjJIkSdJOEI/DlClhtOLw4bBhQ9iflgZnngl/+xs0bx5tRilKubkwY0YoG197Db78cvPjBx+cv25js2au2yhJkiSVQRaMxYgFoyRJkrQD/fILPPdcKBYXLszff9BBYbTiaadB5crR5ZOKo3g8/P/l13UbZ87c/HjjxvllY7t2kJwcTU5JkiRJO5UFYzFiwShJkiQVsXgcZs0KpeJLL+WvMVepEpxxRhit2KpVtBmlkmTFijCF6uuvw8SJkJ2df2yXXeDYY0PZ2KWLhb0kSZJUilkwFiMWjJIkSVIRWb0aXnwxFItz5+bvb948jFY84wxIT48snlQqrFkD48aFsnH06DBK+FcVKkDnzmHdxmOPhTp1IospSZIkqehZMBYjFoySJEnSdpo7N5SKL7wQyg+A1FQ49dQwWrFtW9eLk3aEnByYOjV/3cbFi/OPxWLh/3u/TqW6115RpZQkSZJURCwYixELRkmSJCkBWVnw8suhWPzt+nBNm4bRimedBTVqRJdPKmvicZg3L3/dxg8/3Pz4Xnvll41t2rhuoyRJklQCWTAWIxaMkiRJUiEsWACPPQZDhsCqVWFfSgr06hWKxY4dHa0oFQdLl8LIkaFsfOcd2Lgx/1jt2tCjRygbO3eGihWjyylJkiSpwCwYixELRkmSJGkbsrNhxIgwWnHy5Pz9DRuGKVDPOce13qTiLCMDxo4NZeOYMWH7V5UqQZcuYd3GY46BmjUjiylJkiTpz1kwFiMWjJIkSdJWfPklPP44PPMMrFwZ9iUnh5FPF14IRx0FSUnRZpRUOBs2wLvvhrJx5Ej49tv8Y0lJcNhh+VOpNm4cXU5JkiRJf2DBWIxYMEqSJEm/sXFjKB0eewwmTMjf36ABnH8+nHsu7LprdPkkFZ14HObMyV+38aOPNj++7775ZeNBB/lAgSRJkhQxC8ZixIJRkiRJApYsgSeegCefhO++C/tiMejaNYxW7N4dypWLNqOkHWvx4vx1G999F3Jz84/VqwfHHRfKxiOPhNTUyGJKkiRJZZUFYzFiwShJkqQyKzcX3nwzjFYcMwby8sL+2rXhvPPCiMWGDSONKCkiv/wS/r3w+uvh3xNr1uQfq1IlPHzQs2dYt7F69ehySpIkSWWIBWMxYsEoSZKkMmfFCnjqqTBiccmS/P1HHhlGK/bsCeXLR5dPUvGSnQ1vv52/buOKFfnHkpOhQwc4/vjw74499ogspiRJklTaWTAWIxaMkiRJKhPy8mDiRHj00VAS/Dr1YY0acM45cMEF0LRptBklFX95efDBB/nrNs6fv/nxAw7IX7exRYsw1bIkSZKkImHBWIxYMEqSJKlU+/FHePbZMA3qV1/l72/XLoxWPOkkqFAhsniSSrivvsovG6dOzZ9qGWC33fLXbezY0ZHRkiRJ0nayYCxGLBglSZJU6sTjMGVKGK04fDhs2BD2p6XBmWfC3/4GzZtHm1FS6bNyJbzxRigbx42DrKz8Y+np0K1bKBu7dQvbkiRJkgrFgrEYsWCUJElSqfHLL/Dcc6FYXLgwf/9BB4XRiqedBpUrR5dPUtmxbh289VYoG0eNgh9+yD+WkgKHHx7KxuOOCyMdJUmSJG2TBWMxYsEoSZKkEi0eh1mzQqk4dCisXx/2V6oEZ5wRRiu2ahVtRkllW24uzJyZP5XqZ59tfrxVq/x1G5s3d91GSZIkaSssGIsRC0ZJkiSVSKtXwwsvhLUV587N39+8eRiteMYZTkEoqXj67LP8snHGjPCgxK8aNswvG9u3h3LlIospSZIkFTcWjMWIBaMkSZJKlLlzw2jFF16ANWvCvtRUOPXUMFqxbVtH/0gqOb7/HkaPDmXjhAn5o7ABqleHY44JZePRR0PVqtHllCRJkooBC8ZixIJRkiRJxV5WFrz0UhitOHNm/v6mTcNoxbPOgho1ossnSUVh7VoYPz6UjaNHw08/5R8rXx46dcpft7FevehySpIkSRGxYCxGLBglSZJUbC1YEErFwYMhIyPsS0mBXr1Csdixo6MVJZVOOTkwfXr+VKpffbX58TZt8qdS3Wcf/10oSZKkMsGCsRixYJQkSVKxkp0Nw4eHYnHy5Pz9DRuGKVDPOQfq1IksniTtdPF4eODi17Jx1qzNjzdpkl82HnooJCdHk1OSJEnawSwYixELRkmSJBULX34Jjz8OzzwDK1eGfcnJ0KNHGK141FGQlBRtRkkqDpYvh1GjQtk4cSJs2JB/rGZNOPZY+OtfoUuX6DJKkiRJO4AFYzFiwShJkqTIbNwII0fCo4/CW2/l72/QAM4/H849F3bdNbp8klTcrV4NY8eGsvGNN2DVqvxjp58ODz8M1apFlU6SJEkqUhaMxYgFoyRJkna6b76BJ58Mr+++C/tiMejaNYxW7N4dypWLNqMklTQbN8KUKTBsGDzxBOTmwm67hXVsjzgi6nSSJEnSdrNgLEYsGCVJkrRT5ObCm2+G0YpjxoQ1xQBq14bzzgsjFhs2jDSiJJUa770HvXvDV1+FBziuugpuvx1SU6NOJkmSJCWsMJ2Wi6xIkiRJJdny5XDbbdCoUVhP8Y03Qrl45JHw8svw7bdwxx2Wi5JUlA45BObODQ9vxONw771w8MEwb17UySRJkqSdwoJRkiRJKok+/hhOPBF23x1uuikUiTVqhFE0n30GEyfCySdD+fJRJ5Wk0qlKFXj88bA+Y61a4d/LBx0E//oX5OVFnU6SJEnaoSwYJUmSpJIkHodBg8JImREjwtSo7drBc8/BsmVhFE3TplGnlKSy47jjwsjFY46BDRvCgx5HHQVLl0adTJIkSdphLBglSZKkkmLVKjjlFOjXD7Kzw5fZ8+bB1KlhLbAKFaJOKEllU506MGpUWAe3UiV4+21o3hyGDo06mSRJkrRDWDBKkiRJJcH770PLlvDKK1CuHNx3X/gye7/9ok4mSQKIxeBvf4M5c6B16/BQyF//Gh4AWbUq6nSSJElSkbJglCRJkoqzeBz+/e8wDeqiRdCwIUybBldeGb7MliQVL02bhn9P33QTJCfDCy/A/vvDpElRJ5MkSZKKjAWjJEmSVFz99FNY2+vKK2HjRjjxxDAy5uCDo04mSfozKSlwyy1hCuvGjeHbb+HII+Hqq8MU15IkSVIJZ8EoSZIkFUfTpsGBB8Lo0VC+PDz8MAwbBtWqRZ1MklRQhxwCc+fCeeeFEen33hseEvnkk6iTSZIkSdvFglGSJEkqTvLyYOBA6NgRli6Fv/wFZs6Eiy92SlRJKomqVIEnnoDXX4eaNeHjj+Ggg8L013l5UaeTJEmSEmLBKEmSJBUX338P3brBdddBbi6cfjp8+GEYyShJKtmOOy6MXDzmmDBN6pVXQpcu4WESSZIkqYSxYJQkSZKKg7ffDkXi+PFQsSI89RQ8/zxUrRp1MklSUalTB0aNgkcfhUqVYOJEaN4cXnop6mSSJElSoVgwSpIkSVHKzYWbb4bOneG776BZM3j/fejb1ylRJak0isXgb3+DOXOgdWtYtQpOOw169w7vJUmSpBLAglGSJEmKyvLloVi85RaIx+Hcc0O5uO++USeTJO1oTZvCtGlw002QlAQvvAD77w+TJkWdTJIkSdomC0ZJkiQpCmPHwgEHhC+SK1cO06E++WSYMk+SVDakpISHTKZOhcaN4dtv4cgj4ZprwjqNkiRJUjFlwShJkiTtTBs3Qv/+0K0brFwZSsbZs+GMM6JOJkmKStu2MHcunHdeGNF+zz3Qpg188knUySRJkqQtsmCUJEmSdpYlS6BjR7jrrrB98cXw3nthmjxJUtlWpQo88QS89hrUrAkffQQHHQT/+Q/k5UWdTpIkSdqMBaMkSZK0M7z+Ohx4IMyYAWlpMGwYPPwwVKgQdTJJUnHSsyfMmwfdu4dpUq+4Arp0gaVLo04mSZIkbWLBKEmSJO1I2dlw+eVw/PHwyy/QujXMmQMnnRR1MklScVW3LoweDY88AhUrwsSJsP/+8PLLUSeTJEmSAAtGSZIkacf56ito1w7uvz9sX3klTJ0Ke+4ZbS5JUvEXi8GFF4aHUg46KDykcuqpcOaZkJERdTpJkiSVcRaMkiRJ0o7w8svQogV8+CHUqAEjR8J990H58lEnkySVJHvtBdOnw403QlISPP98GM347rtRJ5MkSVIZZsEoSZIkFaV16+Cii8Iok9WrwwjGuXOhR4+ok0mSSqqUFLj11jAKvnFjWLIEjjgCrr02TMUtSZIk7WQWjJIkSVJR+fRTOOQQePTRMLXdddfBpEmw225RJ5MklQZt24aHVs47D+JxuPtuaNMG5s+POpkkSZLKGAtGSZIkqSg891xYI+vjj6FWLRg7Fu64A8qVizqZJKk0qVIFnngCXn0VataEjz6CVq3Cer95eVGnkyRJUhlhwShJkiRtj7Vr4ZxzoE+f8P6II8KXvV26RJ1MklSaHX88zJsH3buHaVIvvxyOPhqWLYs6mSRJksoAC0ZJkiQpUfPmQevW8OyzkJQEt9wCEyZAvXpRJ5MklQV168Lo0TBoEFSsCG+9Bc2bw8svR51MkiRJpZwFoyRJklRY8XiYnu7gg2HhwlAoTpwIN90EyclRp5MklSWxGFx0EcyZE6bq/uUXOPVUOPNMyMiIOp0kSZJKKQtGSZIkqTAyM+H00+GCC2D9eujaNUyJevjhUSeTJJVle+0F06fDjTeGUfXPPw/77w+TJ0edTJIkSaWQBaMkSZJUULNnQ6tWMHRoGKl4113wxhtQq1bUySRJgpQUuPVWmDIF9twTliwJD8Bce21Yp1GSJEkqIhaMkiRJ0rbE4/DQQ9C2LXz5Jey+exgRcs01YZSIJEnFyaGHwty5cO654c+wu++GNm1g/vyok0mSJKmU8NsQSZIk6c/88guceCJceils2AA9e4Z1rg49NOpkkiRtXdWq8OST8OqrULNmmM67VSu4/37Iy4s6nSRJkko4C0ZJkiRpa957D1q0CF/OpqSEL2VffRVq1Ig6mSRJBXP88TBvHnTrFqZJvfzysH7wsmVRJ5MkSVIJZsEoSZIk/V5eHtx7L7RvD998E9axmj4dLrsMYrGo00mSVDh164Y1gwcNgooVYcIEaN4chg2LOpkkSZJKKAtGSZIk6bdWroQePeDqqyEnB045BWbPhoMOijqZJEmJi8XgoovCNN8HHRSmAD/lFOjTBzIyok4nSZKkEsaCUZIkSfrV5Mlw4IEwZgykpsKjj8LQoZCeHnUySZKKxl57hVH5N9wASUnw3HNwwAHhz0BJkiSpgCwYJUmSpNxcuP12OOKIsCbVXnvBrFnwt785JaokqfRJSYHbboMpU8I04N98A4cfDv37w4YNUaeTJElSCWDBKEmSpLLtu+/g6KPhxhvD2ot9+sAHH8D++0edTJKkHevQQ2HuXOjbF+JxuOsuaNMG5s+POpkkSZKKOQtGSZIklV1vvRWmRJ04ESpVgmefhcGDoUqVqJNJkrRzVK0KTz0Fr74Ku+wSCsdWreCBB8KDN5IkSdIWWDBKkiSp7MnJCSMWu3SB77+H/fYLoxbPOivqZJIkReP442HePOjWDbKz4e9/h65dw9ThkiRJ0u9YMEqSJKlsWboUjjwyrLkYj8MFF4T1FvfZJ+pkkiRFq149eOMNePhhqFgRJkyA5s1h2LCok0mSJKmYsWCUJElS2fHGG2FK1ClTwpRw//0vPPZY+BJVkiRBLAYXXwyzZ4epUn/5BU45JYzyz8iIOp0kSZKKCQtGSZIklX4bN8LVV8Oxx8JPP0HLluGL09NOizqZJEnF0957w4wZcMMNkJQEQ4bAAQeEh3QkSZJU5lkwSpIkqXRbvBjat4d77w3bl14K06dDkyaRxpIkqdhLSYHbbgulYqNG8M030LEjDBgAGzZEnU6SJEkRsmCUJElS6TViBLRoATNnQrVqYfuBByA1NepkkiSVHIceCh99BH37hvWL77wTDjkEFiyIOpkkSZIiYsEoSZKk0mf9+jBS8cQTYdUqaNMG5syBE06IOpkkSSVT1arw1FPhYZ1ddgl/rrZqFR7cycuLOp0kSZJ2shJXMK5evZqbb76Z5s2bU6VKFdLT02ndujX33XcfG7Zzeo5XXnmFHj16UL9+fcqXL0/lypXZa6+9OP/885k7d27R/ACSJEnasb78Moy0eOihsH311WFqt4YNI40lSVKpcMIJMG8edO0aHuj5+9/D++XLo04mSZKknSgWj8fjUYcoqG+++YbDDz+cxYsXA1CpUiVyc3PJzs4GoEWLFkycOJHq1asX6rrZ2dmcfPLJjBo1atO+KlWqsGHDhk2lZVJSEvfeey9XXHFFoa6dmZlJeno6GRkZpKWlFeqzkiRJKqShQ+GCC2D16jC6YsgQ6N496lSSJJU+8Tg88gj84x+wbh3UqAGPPQYnnRR1MkmSJCWoMJ1WiRnBmJOTQ48ePVi8eDH16tVjwoQJrF27lqysLIYOHUrVqlWZM2cOvXv3LvS1//nPf24qFy+++GKWLl3K6tWrWbduHR988AGHHXYYeXl5XHXVVXz44YdF/aNJkiRpe2VlhWLxr38N5WL79jB3ruWiJEk7SiwGF18Ms2eHqVJ//hlOPhnOOgsyM6NOJ0mSpB2sxBSMgwcPZt68eQAMHz6czp07A2Fk4amnnspjjz0GwJgxY5g4cWKhrj1kyBAAOnbsyMMPP0yDBg02XbtVq1aMHj2aKlWqEI/HeeWVV4rqR5IkSVJRWLAgrLH4xBPhy84bb4S334Zdd406mSRJpd/ee8P06XD99ZCUFGYPOOCAMD25JEmSSq0SVTACHHHEEbRt2/YPx0877TQaNWoE5BeGBbVixQoADjrooC0eT09Pp2nTpgCsWbOmUNeWJEnSDvTss9C6NXzyCdSpAxMmwK23QrlyUSeTJKnsKF8ebr8dJk+GRo1g8WLo2BEGDID/LT0jSZKk0qVEFIxZWVlMmzYNgG7dum3xnFgsRteuXQEYP358oa6/5557Amx1+tOMjAw+//xzYOslpCRJknaiNWugTx8455wwPWrnzvDRR9CpU9TJJEkqu9q1C1OUn3NOWKPxzjvhkEPCbAOSJEkqVUpEwbhw4ULy8vIA2G+//bZ63q/HvvvuO37++ecCX/+iiy4CYNKkSfTr149ly5YBEI/HmT17Nsceeyxr1qyhbdu2Ca3xKEmSpCL00Udw0EHw3HNhKrbbb4dx48IIRkmSFK20NHj6aRg+HHbZBebMCWs0Pvgg/O+7HUmSJJV8JaJgXL58+ab3v66PuCW/Pfbbz2xLv379uOaaa0hKSmLQoEHsuuuuVK1alQoVKtCqVSu+/PJL+vfvz8SJE0lOTv7Ta2VnZ5OZmbnZS5IkSUUgHodHHw3rLX72GTRoAJMm5a/5JEmSio9evWDePOjaFdavh8sug27doBDf10iSJKn4KhHfxKxevXrT+0qVKm31vN8e++1ntiUpKYmBAwfy9NNPU6VKFSCstbjhf+sErF+/noyMDNauXbvNaw0cOJD09PRNr912263AOSRJkrQVGRlw2mlw0UWQnQ3HHBOmYGvfPupkkiRpa+rVgzFj4KGHoEIFGD8emjeHV16JOpkkSZK2U4koGHe0lStX0qlTJ84++2zatm3L1KlTWbVqFStWrGDEiBHUqlWLRx55hDZt2myaPnVrBgwYQEZGxqbXt99+u5N+CkmSpFLqgw+gZUt4+WUoVw7uvRdGjoSaNaNOJkmStiUWg379wlSpLVvCzz/DySfD2WeDsz5JkiSVWCWiYKxateqm91lZWVs977fHfvuZbTnrrLOYNGkSHTt2ZNy4cbRr14709HTq1q3LCSecwNSpU6lZsyZff/01/fv3/9NrpaamkpaWttlLkiRJCYjH4f774dBD4euvoWFDmDoVrrrKKVElSSpp9t4bZsyA664Lf44PHgwHHABTpkSdTJIkSQkoEd/M1K9ff9P7PxtB+Ntjv/3Mn1m4cCFjxowB4KqrriIWi/3hnNq1a9OnTx8ARowYQTweL9C1JUmSlKCff4bjj4fLL4eNG8M6TnPmhPUXJUlSyVS+PNxxB0yeDI0aweLF0LEjDBgA/1umRpIkSSVDiSgY99lnH5L+95T6J598stXzfj1Wt25datSoUaBrL1iwYNP7xo0bb/W8v/zlL0AYJfnDDz8U6NqSJElKwPTpcOCBYRrU8uXDuk2vvALVqkWdTJIkFYV27cJaymefHWYsuPNOOOQQWLgw6mSSJEkqoBJRMFaqVIl27doBMHbs2C2eE4/HGTduHABdunQp8LWTfjO91jfffLPV877//vtN76tUqVLg60uSJKmA8vLgrrugQwf49lto0gTeey+s27SFWSYkSVIJlpYGzzwDw4fDLrvkr9H44IOhdJQkSVKxViIKRgjrJAK88847zJw58w/Hhw0bxtdffw2waTrTgmjZsuWm94888sgWz1m7di1DhgwBYP/996dy5coFvr4kSZIK4IcfoHt36N8fcnPhr3+F2bOhRYuok0mSpB2pVy+YNw+OPhrWr4fLLoNu3WD58qiTSZIk6U+UqIKxefPmxONxTjzxRCZOnAhAXl4ew4YN4/zzzwegW7dudOrUabPP3nzzzcRiMWKxGIsXL97s2B577EGPHj0AGDVqFGeeeSZfffUV8XicjRs3Mn36dA4//PBN5eVVV121g39SSZKkMmbSpDAl6rhxUKECPPkkvPACVK0adTJJkrQz1KsHb74ZRi9WqBD+TtC8eRjdKEmSpGKpxBSM5cqVY+TIkTRs2JBly5bRuXNnKleuTOXKlTnllFPIzMykRYsWvPDCC4W+9tNPP02rVq0AeP7552nSpAlVqlTZNDXrBx98AMDVV19dqNGRkiRJ+hO5uXDLLdCpE6xYAfvsA++/D+ee65SokiSVNbEYXHJJmMGgZUv4+Wc46aSwTmNmZtTpJEmS9DslpmAEaNiwIR9//DE33XQT++23H7FYjJSUFFq1asW9997Le++9R/Xq1Qt93Zo1a/Lee+/x5JNPcvTRR1OnTh02btxIuXLl2HPPPenduzdTpkzh7rvv3gE/lSRJUhm0YgUcdRTcfHNYe/Gcc0K5uN9+USeTJElR2mcfmDEDBgyApCQYPBgOOACmTo06mSRJkn4jFo+7cvaOlJmZSXp6OhkZGaSlpUUdR5IkKXrjx0Pv3vDjj1C5Mjz6aNiWJEn6ralT4cwzYfHiUDZee214OKl8+aiTSZIklUqF6bRK1AhGSZIklWA5OXDddXD00aFcPOAA+PBDy0VJkrRlhx0GH30UpknNy4OBA6FtW1i4MOpkkiRJZZ4FoyRJkna8b7+Fww8PXwwCXHQRvPce7LVXpLEkSVIxl5YGzzwDr7wCNWrkr9H40EPgpFySJEmRsWCUJEnSjjVqFBx4IEybFr4kfPllGDQIKlSIOpkkSSopTjwR5s0LMyGsXw+XXgrdusHy5VEnkyRJKpMsGCVJkrRjbNgAV14Jxx0HP/8MBx0Ec+bAySdHnUySJJVE9evDm2/Cgw+GB5XGjYPmzWHEiKiTSZIklTkWjJIkSSp6X38d1k3697/D9hVXhBGMe+4ZbS5JklSyxWJwySVhqtQWLcJDTCeeCH37QlZW1OkkSZLKDAtGSZIkFa1XXglf+L3/PlSvDq+/Dv/6F5QvH3UySZJUWuyzT1jPecCAUDo+8wx07AgrVkSdTJIkqUywYJQkSVLRWL8eLr44TIGamQmHHgpz54YpUiVJkopa+fLwz3/CO+/ALrvABx/AwQeHKdklSZK0Q1kwSpIkaft9/jkccgg88kjY7t8fJk2C3XePNJYkSSoDOnaEmTPDqMalS8M07a+9FnUqSZKkUs2CUZIkSdvnhRegZUv46COoVQvGjoWBAyElJepkkiSprGjcGGbMgC5dwlqMvXrB3XdDPB51MkmSpFKpXFFdaPXq1cyePZsffvgBgNq1a9OyZUuqVq1aVLeQJElScbJ2LVx2GTz9dNg+4gh4/nmoXz/aXJIkqWxKT4c33oDLL4eHH4Zrr4VPP4VHH3UtaEmSpCK23QXjvHnzuP7663nzzTfJy8vb7FhSUhLHHHMMt912G82bN9/eW0mSJKm4+OqrsLbiggUQi8H//R/ccAMkJ0edTJIklWXlysFDD8Hee8Pf/w7PPBP+3jJ8ONSsGXU6SZKkUiMWjyc+V8SIESPo3bs32dnZbO0ysViM1NRUXnjhBU444YSEg5ZUmZmZpKenk5GRQVpaWtRxJEmStt8HH0D37vDjj1CvXpgi9Ygjok4lSZK0ubFj4dRTITMzTKE6enQoHiVJkrRFhem0El6DcdGiRZxxxhmsX7+ePfbYg0GDBvHFF1+wbt061q1bxxdffMGgQYNo2LAh69ev54wzzmDRokWJ3k6SJEnFwZtvwuGHh3LxwAPhww8tFyVJUvHUtWtYl7FRozCK8ZBD4K23ok4lSZJUKiRcMN5zzz1kZ2fTtm1bPv74Yy688EIaN25MamoqqampNG7cmAsvvJCPP/6Ytm3bkp2dzX333VeU2SVJkrQzPfss9OgR1l486ih4990wglGSJKm4atYMZs6Edu0gIyOUjo8+GnUqSZKkEi/hgvGtt94iFovx6KOPUqVKla2eV7lyZR599FHi8Tjjx49P9HaSJEmKSjwOd9wB55wDubnQu3eYYszp3yVJUklQqxZMnAh9+oS/y1x0UVifMScn6mSSJEklVsIF49KlS6latSrNmzff5rnNmzcnLS2NpUuXJno7SZIkRSE3F/r1gxtuCNv9+8OQIVC+fLS5JEmSCiM1NczG8M9/hu0HHoDjjgvrM0qSJKnQEi4YU1JS2LhxY4HOjcfjbNiwgZSUlERvJ0mSpJ1t3To46SR45BGIxeDBB2HgwPBekiSppInFYMAAeOUVqFgxrC196KGwaFHUySRJkkqchAvGJk2asH79esaNG7fNc8eNG8f69etp0qRJoreTJEnSzvTTT9C5M7z2Wnjif9gwuOSSqFNJkiRtvxNPhClToH59mD8f2rSBadOiTiVJklSiJFww9uzZk3g8zvnnn8/ChQu3et6CBQu44IILiMViHH/88YneTpIkSTvL4sXQrh1Mnw7VqsGECeGLOEmSpNKiVSuYNQtatoQff4Qjj4Tnn486lSRJUokRi8fj8UQ+mJmZyb777suyZcsoX748J598Mp06daJBgwZAWKNx4sSJvPLKK2zYsIFdd92V+fPnU7Vq1SL9AYq7zMxM0tPTycjIIC0tLeo4kiRJf27uXOjWDb77DnbdFcaOhX33jTqVJEnSjrF2LfTpAyNGhO3rr4dbb4WkhJ/JlyRJKrEK02klXDACzJ8/nx49erB48WJiW1mLJx6P06hRI0aOHMm+ZfDLKQtGSZJUYkycCCecAKtXQ/PmMGZMKBklSZJKs7w8uOGGsNY0hDWoBw+GSpWizSVJkrSTFabT2q7Hsfbdd18+/vhjBg4cyIEHHkhSUhLxeJx4PE5SUhIHHnggd911Fx999FGZLBclSZJKjBdeCCMXV6+Gww8P6xJZLkqSpLIgKQn++c9QKqakwCuvQMeOsHx51MkkSZKKre0awfh7Gzdu5OeffwagRo0apKSkFNWlSyxHMEqSpGItHod774Vrrgnbp54avlxLTY02lyRJUhSmTg0zOqxcCQ0awKhR0KJF1KkkSZJ2ip02gvH3UlJSqFOnDnXq1LFclCRJKu7y8uCKK/LLxSuugBdftFyUJEll12GHwcyZsM8+sGxZ2H7ttahTSZIkFTuuWC1JklQWrV8Pp50G998ftu+7D/71rzBFmCRJUlm2554wYwZ06QJZWdCrF9x1V5j5QZIkSQCUK8hJS5YsAcIIxXr16m22r7B23333hD4nSZKkIvLLL3D88TB5clhnaMiQUDZKkiQpSE+HN94IMzw89BD07w+ffgqPPQbly0edTpIkKXIFWoMxOTkZgL333pv58+dvtq9QN4vFyMnJKfTnSjLXYJQkScXKt99Ct24wfz6kpcGrr8KRR0adSpIkqfh6+GH4+98hNxc6dIDhw6FmzahTSZIkFbkiX4MxHo9vem1pX0FfeXl52/eTSZIkKXGffAJt24ZysX59mDLFclGSJGlb+vULoxnT0sIMEG3awMKFUaeSJEmKVIGmSF20aBEQpkj9/T5JkiSVAO++Cz17QkYG7LMPjB0LTl0vSZJUMEcfHdZlPPZY+Prr8NDWsGFw1FFRJ5MkSYpEgaZIVeKcIlWSJEVu2DDo3Rs2bIB27WDkSKhRI+pUkiRJJc+PP0KvXjB1KiQnw4MPwkUXRZ1KkiSpSBT5FKmSJEkqoR54AE49NZSLvXrBhAmWi5IkSYmqVQveegv69AlrMl58MVx2GeTkRJ1MkiRpp0q4YNxzzz055JBDCnx++/btady4caK3kyRJUmHk5cE118Df/w7xeFg76OWXoWLFqJNJkiSVbKmp8OyzMHBg2H7wQejRI0xFL0mSVEYkXDAuXryYJUuWFPj8pUuXsnjx4kRvJ0mSpILasAHOPBPuuSdsDxwYvvhKTo42lyRJUmkRi0H//jB8eHiAa+xYOPRQWLQo6mSSJEk7xU6bIjUnJ4ekJGdklSRJ2qEyM6F7d3jxRShXDgYPDl9+xWJRJ5MkSSp9evWCKVOgfn1YsAAOPjiszyhJklTK7ZTGb926dfzwww9UrVp1Z9xOkiSpbFq+HDp0gIkToUoVeOONsD6QJEmSdpxWrWDWLGjZElauhE6d4Lnnok4lSZK0Q5Ur6IlLliz5wxSnGzZsYMqUKcTj8S1+Jh6Ps2rVKl544QU2btxI8+bNtyusJEmStuLTT6FrV/jmG6hTB8aMCV9ySZIkacdr0AAmTw4Pd40YEf7z00/httvAGb0kSVIpVOCC8ZlnnuHWW2/dbN8vv/zC4Ycfvs3PxuNxYrEYf/vb3wodUJIkSdswfTr06AE//wx/+UtYA2jPPaNOJUmSVLZUrgzDhsGNN8I//xlen30GQ4ZApUpRp5MkSSpShXqEKh6Pb3rFYrHNtrf0AkhLS6Ndu3YMGTKE008/fYf8EJIkSWXWa6+Fabh+/hnatAllo+WiJElSNJKS4I47QqlYvjwMHx6msF++POpkkiRJRSoW39r8ptuQlJRE3bp1We5fkP5UZmYm6enpZGRkkJaWFnUcSZJUmjzyCFxyCeTlwbHHwksv+XS8JElScTF1KpxwQliXsUEDGDnSKewlSVKxVphOK+FJ4Pv06cMpp5yS6MclSZKUqHgcrr8eLr44lIvnnw+vvmq5KEmSVJwcdhjMnAnNmsGyZdC+ffg7myRJUimQ8AhGFYwjGCVJUpHauDEUioMHh+1bbgnr/MRi0eaSJEnSlmVkwKmnwrhxYfvOO+Gaa/z7myRJKnZ2yghGSZIk7WRr1kCPHqFcTE6GJ5+Em27yyylJkqTiLD0dRo+GSy8N2/37wznnQHZ2tLkkSZK2Q7miuMjSpUuZPn06S5cuZe3atfzZoMibbrqpKG4pSZJUtnz/PRxzDHz4YZgK9eWXw7YkSZKKv3Ll4IEHYO+94bLLwgNjX38NI0ZAzZpRp5MkSSq07ZoideXKlVx44YW89tprf1oqAsTjcWKxGLm5uYnerkRyilRJkrTdvvgCunYNX0LVrAlvvAEHHxx1KkmSJCVi/Hg4+WTIzIQ99wyjG/fZJ+pUkiRJO2eK1LVr13L44Yfz6quvkpKSQuvWrYnH46SkpNCuXTsaN25MPB4nHo9TvXp1OnbsSIcOHRK9nSRJUtk0axYcemgoF/fcE6ZPt1yUJEkqybp0gRkzwt/tvv4a2rYNpaMkSVIJknDB+PDDD7NgwQL22msvvv76a9577z0AatSoweTJk/n8889ZtGgRp5xyCqtWraJr16688847RRZckiSp1HvjDTjiCFi5Elq1CuXiX/4SdSpJkiRtr2bNYOZMaN8eMjKge3cYNCjqVJIkSQWWcMH46quvEovFGDhwIPXq1dviOXvssQdDhw7llFNO4brrrmPixIkJB5UkSSpTnnoKevaErCw4+miYNAnq1Ik6lSRJkopKzZowYQKcdRbk5kK/fmF9xpycqJNJkiRtU8IF46effgpA165dN9u/cePGP5x7xx13EI/HefDBBxO9nSRJUtkQj8Ott8J554Uvms46C0aNgipVok4mSZKkopaaCs88A3feGbYffBCOPTaMapQkSSrGEi4Y169fT/Xq1UlNTd20r0KFCqxZs+YP5zZq1Ij09HRmzZqV6O0kSZJKv5wc+Nvf4P/+L2xff334wiklJdpckiRJ2nFiMbj2WhgxAipVgnHj8tfgliRJKqYSLhjr1KlDdnb2Zvtq1arFhg0bWLp06Wb7c3NzWbt2LT/99FOit5MkSSrdsrKgVy944glISgpr8Nx+e/jCSZIkSaXfCSfAlClQvz4sWABt2sDUqVGnkiRJ2qKEC8bdd9+drKwsfvjhh037DjzwQCCsz/hbI0eOJCcnh9q1ayd6O0mSpNJr5Uo48sgwFWqFCjB8OFx0UdSpJEmStLO1bAmzZkGrVuHviJ06wZAhUaeSJEn6g4QLxrZt2wIwZcqUTftOPfVU4vE4AwYM4J577mHChAnce++9nHPOOcRiMbp167b9iSVJkkqTRYvCFFgzZ0L16vDWW3D88VGnkiRJUlQaNIDJk+HEE2HDhrAm93XXQV5e1MkkSZI2icXj8XgiH5w5cyZt27bluOOO47XXXgMgHo/TqVMnJk2aROw303nF43Hq1q3LBx98QP369YskeEmRmZlJeno6GRkZpKWlRR1HkiQVJ7NnQ/fu8P33sPvuMHYs7LNP1KkkSZJUHOTlwY03wj//GbZ79QqjGStXjjaXJEkqtQrTaSU8grFNmzbk5eVtKhcBYrEYb7zxBgMGDKBRo0aUK1eOXXbZhd69e/Pee++VuXJRkiRpq8aPh44dQ7l4wAEwY4bloiRJkvIlJcEdd4RSsXx5GDECOnSAZcuiTiZJkpT4CEYVjCMYJUnSHwwZAueeCzk5Ye3FESMgPT3qVJIkSSqupk0L0+ivXAn164e1u1u2jDqVJEkqZXbKCMYhQ4YwZMgQvv/++0QvIUmSVLbE43DnnWEdnZwc+Otf4c03LRclSZL059q1g1mzoFkzWL4c2rcPD6lJkiRFJOERjElJSZQrV45Vq1ZRqVKlos5VajiCUZIkAZCbC3//Ozz8cNi++upQNiYl/LyXJEmSypqMDDjttLB2N8DAgXDttRCLRZtLkiSVCjtlBGONGjVIS0uzXJQkSdqWdevglFNCuRiLwX/+A3ffbbkoSZKkwklPD9OjXnZZ2B4wAM4+G7KzI40lSZLKnoS/1dp7773JyMhgzZo1RZlHkiSpdPn5Z+jSJUxhVb48DB0aRjJKkiRJiShXDu6/Pzy8lpwc1vfu3DmszyhJkrSTJFwwnn322eTm5vLkk08WZR5JkqTSY8kSOOwwmDo1PG0+fnwYyShJkiRtr4svzl/Pe+pUOPhgWLAg6lSSJKmMSLhgPO+88zjxxBO59tprGTRoEDk5OUWZS5IkqWT7+GNo2xYWLoQGDcKXPh07Rp1KkiRJpclRR8GMGbDnnrBoUfj757hxUaeSJEllQCwej8cT+WDfvn2Jx+MMHz6ctWvXUr16dVq3bk3t2rVJTk7e8s1iMZ566qntClzSFGZBTEmSVEq8/TaccAJkZsK++4Yny3fbLepUkiRJKq1WroRevWDKlDBt6v33Q79+UaeSJEklTGE6rYQLxqSkJGKxGAX5+K/nxWIxcnNzE7ldiWXBKElSGTN0KPTpAxs3QocO8NprUL161KkkSZJU2mVnw4UXwrPPhu1+/eA//wlrNkqSJBVAYTqthP+G0adPH2KxWKIflyRJKn3+9S+46qrw/qST4LnnoEKFaDNJkiSpbEhNhaefhn32gf794eGH4Ysv4OWXwzqNkiRJRSjhEYwqGEcwSpJUBuTlwT/+Af/+d9i+7LLwPinh5a4lSZKkxL36KvTuDVlZoXAcPTqs0yhJkvQnCtNp+a2XJEnS9sjOhtNPzy8X7747TEVluShJkqSonHACTJ0KDRrAwoVw8MFhfUZJkqQi4jdfkiRJicrIgK5d4aWXICUFnn8err4anEZekiRJUWvRAmbNglat4KefoFMnGDw46lSSJKmUsGCUJElKxLJl0L49TJoEVavCmDFwxhlRp5IkSZLy1a8PkyeH9cE3boSzz4YBA8IU/5IkSdvBglGSJKmw5s+Htm1h3jyoWzd8adO5c9SpJEmSpD+qVCnMuHH99WH7zjvh5JNh7dpoc0mSpBLNglGSJKkwpkyBww6Db7+FvfaCGTPgwAOjTiVJkiRtXVIS3H47PPcclC8PI0ZAhw5hVg5JkqQEWDBKkiQV1PDhcNRRsGpVGME4bRo0bBh1KkmSJKlgeveGt9+GWrVg9mw4+GD48MOoU0mSpBLIglGSJKkgHnooTCWVnQ09e8LEibDLLlGnkiRJkgqnXTuYNQv23ReWLw/rio8YEXUqSZJUwlgwSpIk/Zl4HAYMgEsvDe8vvDCMZKxYMepkkiRJUmIaNoTp06FbN1i3Dk48EQYODH/flSRJKgALRkmSpK3ZsAHOOgvuvDNs3347DBoEycnR5pIkSZK2V1oajBwJl10Wtq+7Ds4+O8zYIUmStA0JF4xJSUmUK1eOL7/8sijzSJIkFQ+rV8Oxx8Jzz4VC8Zln4PrrIRaLOpkkSZJUNMqVg/vvz3+IbsgQ6NwZfvwx6mSSJKmYS7hgrFixIlWqVKFJkyZFmUeSJCl6330HHTvChAlQuTKMGhWe5pYkSZJKo4sugjffhPR0mDoV2rSBBQuiTiVJkoqxhAvGXXfdlY0bNxZlFkmSpOh99hm0bQtz5kCtWvDOO2FtGkmSJKk0O+ooeO89aNwYFi0KfyceNy7qVJIkqZhKuGA85phjWL9+Pe+++25R5pEkSYrOe+9Bu3aweDE0aQIzZkDr1lGnkiRJknaOvfeGmTOhQwfIzITu3eGhh6JOJUmSiqGEC8YBAwZQq1YtLrroIlasWFGUmSRJkna+kSPhyCPhp59CqThtWnh6W5IkSSpLdtklLBVwzjmQlweXXgr9+kFOTtTJJElSMRKLx+PxRD44efJkvvjiC6644gqSk5M588wzadeuHbVr1yY5OXmrn+vQoUPCYUuizMxM0tPTycjIIC0tLeo4kiRpSx5/PKw7k5cXntJ++eWw9qIkSZJUVsXjcM890L9/eN+lC7z0ElSrFnUySZK0gxSm00q4YExKSiIWixXqM7FYjJwy9rSTBaMkScVYPA433wy33hq2+/aFxx6DcuUijSVJkiQVG6+9BmecAVlZYQrV0aOd6UOSpFKqMJ1WwlOkAsTj8UK98vLytud2kiRJRWfjRjjvvPxy8aab4MknLRclSZKk3zr+eJg6FRo0gE8/hTZtYMqUqFNJkqSIJVww5uXlJfSSJEmK3Nq14YuSp5+GpKQwavGWW6CQszNIkiRJZUKLFjBrFhx0UFizvFMnePbZqFNJkqQIbdcIRkmSpBLnhx/giCNgzBioWBFefRUuuCDqVJIkSVLxVr8+vPsunHRSmA3knHNgwICwjrkkSSpzLBglSVLZ8dVX0K4dvP8+7LILTJwIxx0XdSpJkiSpZKhUCV56CW64IWzfeWcoHNeujTaXJEna6YpskaHVq1cze/ZsfvjhBwBq165Ny5YtqVq1alHdQpIkKXEffADdu8OPP0LDhjB2LOy1V9SpJEmSpJIlKQluuy38Xfrcc8OMIO3bw6hRYZ1GSZJUJmx3wThv3jyuv/563nzzzT+ssZiUlMQxxxzDbbfdRvPmzbf3VpIkSYl58004+eTwZHWLFmF61Lp1o04lSZIklVy9e8Oee4a1zefMgdatYeTIsE6jJEkq9bZritQRI0bQpk0b3njjDXJzc4nH45u9cnNzGTVqFG3atOHVV18tqsySJEkF9+yz0KNHKBePOiqsG2O5KEmSJG2/Qw+FWbNg331hxQro0AGGD486lSRJ2gkSLhgXLVrEGWecwfr169ljjz0YNGgQX3zxBevWrWPdunV88cUXDBo0iIYNG7J+/XrOOOMMFi1aVJTZJUmSti4ehzvugHPOgdzc8IT16NHg9O2SJElS0WnYEKZPh27dYN26sCbjP/8Z/j4uSZJKrYQLxnvuuYfs7Gzatm3Lxx9/zIUXXkjjxo1JTU0lNTWVxo0bc+GFF/Lxxx/Ttm1bsrOzue+++7Y78OrVq7n55ptp3rw5VapUIT09ndatW3PfffexYcOG7b7+d999x4033kirVq2oUaMGFStWZI899qBr167ceeedbNy4cbvvIUmSdrDcXLj4YrjhhrDdvz8MGQLly0ebS5IkSSqN0tLC9Kh//3vYvv56OOssyM6ONpckSdphYvF4Yo8TNW3alK+++oq5c+duc33FefPmccABB9CkSRM+//zzhIICfPPNNxx++OEsXrwYgEqVKpGbm0v2//6y0qJFCyZOnEj16tUTuv5LL73EBRdcQGZmJgAVKlSgfPnym7YBfvnlF6pVq1bga2ZmZpKenk5GRgZpaWkJ5ZIkSYWQlQWnnw6vvw6xGDzwAFxySdSpJEmSpLLh0UfD379zc6FdO3j1VahVK+pUkiSpAArTaSU8gnHp0qVUrVp1m+UiQPPmzUlLS2Pp0qWJ3o6cnBx69OjB4sWLqVevHhMmTGDt2rVkZWUxdOhQqlatypw5c+jdu3dC1x82bBinn346mZmZXHDBBcyfP59169aRkZFBZmYmkydP5oorriAlJSXhn0GSJO1gP/0EnTuHcjE1FYYNs1yUJEmSdqYLL4SxYyE9HaZNgzZtYP78qFNJkqQilvAIxvT0dHJycli7du02z43H41SuXJmUlBQyMjISuR1PPfUU5513HgDTp0+nbdu2mx3/73//y+mnnw7AW2+9RadOnQp87RUrVrDvvvvyyy+/cN9993HllVcmlHFLHMEoSdJOsngxdO0Kn30G1arBqFFw2GFRp5IkSZLKpk8/hWOPha++Cuugv/xy+Pu6JEkqtnbKCMYmTZqwfv16xo0bt81zx40bx/r162nSpEmit2Pw4MEAHHHEEX8oFwFOO+00GjVqBMCQIUMKde0HHniAX375hRYtWnDFFVcknFGSJEVk7lxo2zaUi7vtFp6UtlyUJEmSorP33jBzJnTsCKtXwzHHwEMPRZ1KkiQVkYQLxp49exKPxzn//PNZuHDhVs9bsGABF1xwAbFYjOOPPz6he2VlZTFt2jQAunXrtsVzYrEYXf/3FNT48eMLdf1fC8nevXsTi8USyihJkiIycSJ06ADffQfNm8OMGdCsWdSpJEmSJO2yC4wfD337Ql4eXHop9OsHOTlRJ5MkSdsp4YLx8ssvp0GDBixdupQWLVpw5pln8uyzzzJhwgQmTJjAM888Q+/evWnZsiVLly6lQYMGXH755Qnda+HCheTl5QGw3377bfW8X4999913/PzzzwW69qJFi1i+fDkArVq1Yt68eZx++unUq1eP1NRUdt11V0499dRNBackSSpGXngBunULT0QffjhMmQINGkSdSpIkSdKvypeHJ5+Eu++GWAwGDQqjGVetijqZJEnaDuUS/WBaWhpjx46lR48eLF68mBdffJEXX3zxD+fF43EaNWrEyJEjqVq1akL3+rUABGjwJ18a/vbY8uXLqVGjxjav/fnnn296P23aNG655RY2bNhAxYoVqVChAsuWLePll19m2LBh3HLLLdx4441/er3s7Gyys7M3bWdmZm4zgyRJKqR4HO65B669NmyfeioMHgypqdHmkiRJkvRHsRhcfTU0bQqnnx5GNbZtC6NHQ+PGUaeTJEkJSHgEI8C+++7Lxx9/zMCBAznwwANJSkoiHo8Tj8dJSkriwAMP5K677uKjjz5i3333Tfg+q1ev3vS+UqVKWz3vt8d++5k/88svv2x6f+ONN1K/fn0mTJjAmjVryMjIYP78+Rx++OHE43FuuukmRowY8afXGzhwIOnp6Zteu+22W4FySJKkAsrNhcsvzy8Xr7gCXnzRclGSJEkq7nr2hKlTYddd4dNPoU2bMAuJJEkqcbarYASoUqUK1157LR9++CFZWVmsWLGCFStWkJWVxYcffsjVV19NlSpViiLrDvHr1KsQRlsOHz6czp07k5QU/tE0a9aMUaNGUbduXQBuueWWP73egAEDyMjI2PT69ttvd1x4SZLKmvXr4bTT4IEHwvZ998G//gVJ2/1XGkmSJEk7Q4sWMGsWtG4NP/0EnTrBs89GnUqSJBVSkX4bl5KSQp06dahTpw4pKSlFdt3fTq2alZW11fN+e6yg07H+9rxOnTrRsmXLP5xTpUoV+vXrB8DHH3/M999/v9XrpaamkpaWttlLkiQVgV9+gaOPhldeCeu4/Pe/cOWVUaeSJEmSVFj16sGkSXDyybBxI5xzDgwYAL8ZCCBJkoq3EvG4f/369Te9X7Zs2VbP++2x337mz/x23cZ99tlnq+c1a9Zs0/tvvvmmQNeWJElF5NtvoX17mDwZ0tLgzTfDSEZJkiRJJVOlSjB0KNx4Y9i+80446SRYuzbaXJIkqUDKFeSkJUuWAGGEYr169TbbV1i77757oT+zzz77kJSURF5eHp988gndunXb4nmffPIJAHXr1qVGjRoFunazZs1ITk4mNzf3T8+Lx+Ob3sdisQImlyRJ2+2TT6BrV1i2LDzpPHYs7L9/1KkkSZIkba+kJLj1VthrL+jbF159NTxYOGoU/GZQgCRJKn4KVDA2atQIgL333pv58+dvtq8wYrEYOTk5hf5cpUqVaNeuHVOmTGHs2LFcffXVfzgnHo8zbtw4ALp06VLga1eoUIEOHTrwzjvvsHDhwq2et2DBAiD8DA0bNizcDyBJkhIzeTL07AmrVsE++4SRi3vsEXUqSZIkSUXpjDOgYUM44QSYMyeszzhyJBx0UNTJJEnSVhRoitR4PL7ptaV9BX3lbcc86meddRYA77zzDjNnzvzD8WHDhvH1118D0KdPn0Jd+5xzzgFg4sSJzJ49+w/H16xZw6BBgwBo06YNtWrVKtT1JUlSAl55BY46KpSL7drB1KmWi5IkSVJp1a4dzJoF++4LK1ZAhw4wfHjUqSRJ0lbE4r9tDbfi1zUHU1JSNq1tmOg6hHsk+MVgTk4OLVu2ZN68eTRo0IDBgwfTqVMn8vLyGD58OOeddx6ZmZl069aNMWPGbPbZm2++mVtuuQWARYsW/WEEYl5eHm3btmXWrFk0bNiQJ598kiOOOIKkpCQWLlxIv379eOedd0hKSmLChAkceeSRBc6dmZlJeno6GRkZpKWlJfSzS5JU5jz4IPz97xCPh6eYX3gBKlaMOpUkSZKkHS0zM6y3/uabYfuOO2DAAHDJIkmSdrjCdFoFmiJ1S6VgokVhosqVK8fIkSM54ogjWLx4MZ07d6ZSpUrk5eWxfv16AFq0aMELL7xQ6GsnJSXx+uuv06lTJxYsWLDp2ikpKWRkZAChXH344YcLVS5KkqRCyssLXx7cfXfYvvhieOABSE6ONpckSZKknSMtLUyPetVV4XeB66+HTz+FJ56A1NSo00mSpP8p0BSpWzJkyBCGDBnC999/X5R5/lTDhg35+OOPuemmm9hvv/2IxWKkpKTQqlUr7r33Xt577z2qV6+e0LXr1q3L7Nmzuffee2ndujUpKSmsW7eOhg0b0rdvX2bPns35559fxD+RJEnaZMMGOOus/HLxjjvgoYcsFyVJkqSyplw5uP9+GDQo/D7w3HPQqRP8+GPUySRJ0v8UaIrULUlKSqJcuXKsWrWKSpUqFXWuUsMpUiVJKoDVq+HEE2HChPAFwpNPwtlnR51KkiRJUtQmTICTT4aMDGjUCEaNCus0SpKkIleYTivhEYw1atQgLS3NclGSJG2f776Djh3DFweVK8Po0ZaLkiRJkoKjjoL33oPGjWHRIjj0UBg7NupUkiSVeQkXjHvvvTcZGRmsWbOmKPNIkqSy5LPPoG1bmDMHateGSZOga9eoU0mSJEkqTvbeG2bOhA4dIDMTjjkmLKcgSZIik3DBePbZZ5Obm8uTTz5ZlHkkSVJZ8d570K4dLF4cnkaePh0OOijqVJIkSZKKo112CbOenHMO5OXBpZdCv36QkxN1MkmSyqSEC8bzzjuPE088kWuvvZZBgwaR4x/mkiSpoEaNgiOPhJ9+CqXi9OmhZJQkSZKkrSlfHp56Cu66C2IxGDQojGZctSrqZJIklTmxeDweT+SDffv2JR6PM3z4cNauXUv16tVp3bo1tWvXJjk5ecs3i8V46qmntitwSVOYBTElSSoTnngCLrwwPHXcrRu8/DJUqRJ1KkmSJEklyWuvwRlnQFZWmEJ19GgfWpQkaTsVptNKuGBMSkoiFotRkI//el4sFiM3NzeR25VYFoySJP1PPA633BJeAH37wqOPQkpKtLkkSZIklUxz5kCPHrBsWZhC9dVXoX37qFNJklRiFabTKpfoTfr06UMsFkv045IkqSzJyYGLLoJf126+8cZQNPp3CUmSJEmJatECZs2Cnj3hgw+gUyd4/HE4++yok0mSVOolPIJRBeMIRklSmbd2LZx2WpiyKCkprJPyt79FnUqSJElSaZGVBWedBa+8Erb794c77gi/f0iSpAIrTKfln7KSJGnH+fHH8BTx6NFQoQKMGGG5KEmSJKloVaoEL70EN9wQtu+8E04+OTzsKEmSdggLRkmStGN8/TW0awczZ0KNGjBxYpi6SJIkSZKKWlIS3HYbPPcclC8fHm7s0CGszyhJkopcwmsw/tbIkSMZN24c33zzDevWrWPixImbjq1du5aPPvqIWCxG27Zti+J2kiSpuJs9G7p3h++/hz32gLFjYe+9o04lSZIkqbTr3RsaNYITTgi/lxx8MIwcCa1aRZ1MkqRSZbvWYPz222/p1asXs2fPBiAejxOLxcjNzd10zsaNG2nSpAlLly5l+vTptGnTZvtTlyCuwShJKnPGj4cTT4Q1a+CAA2DMGKhfP+pUkiRJksqSRYvg2GNhwQKoWBGefx569Yo6lSRJxdpOWYNx7dq1dOnShQ8//JAGDRrQr18/Kleu/IfzUlJSOPfcc4nH47z66quJ3k6SJJUEzz0HxxwTysVOnWDyZMtFSZIkSTtfo0YwfTp07Qrr1oWHIAcOhMTHWkiSpN9IuGB8+OGH+eyzz2jZsiULFy7kgQceoEqVKls8t+f/1luaNm1aoreTJEnFWTwOd90FffpATg789a9h5KKj9yVJkiRFJT0dRo2Cyy4L29ddB2efDdnZkcaSJKk0SLhgHD58OLFYjH/9619bHLn4W/vttx/Jycl8/vnnid5OkiQVV7m54Rf2/v3D9j/+EaYfKl8+2lySJEmSVK4c3H8/PPwwJCfDkCHQuTP8+GPUySRJKtESLhg/++wzkpOTadeu3TbPTU5Oplq1aqxatSrR20mSpOJo/Xo49VR46CGIxeDf/4Z77oGkhP+KIUmSJElF7+KL4c03w6jGqVOhTZuwPqMkSUpIwt/+ZWdnU7FiRZKTkwt0flZWFhUqVEj0dpIkqbj55Rfo0gWGDw+jFYcOhcsvjzqVJEmSJG3ZUUfBjBmw556waBG0bQvjxkWdSpKkEinhgrFOnTqsWbOmQKMS58+fz7p169htt90SvZ0kSSpOvv0WDjsMpkwJ6yyOHQunnBJ1KkmSJEn6c/vsAzNnQvv2kJkJ3buHGVkkSVKhJFwwHnbYYQC89NJL2zz37rvvJhaLccQRRyR6O0mSVFzMmxee9F2wAOrXD9ML+We8JEmSpJKiZk2YMAHOPhvy8uDSS+GSSyAnJ+pkkiSVGAkXjBdffDHxeJybb76ZTz75ZIvnbNiwgQEDBvDcc88Ri8W46KKLEg4qSZKKgUmTwpO+y5ZBs2ZheqHmzaNOJUmSJEmFk5oKTz8Nd90V1pN/+GE45hjIyIg6mSRJJULCBeOhhx7KpZdeyvfff88hhxzCSSedxJo1awC47rrrOOOMM9htt924++67Abjhhhto1qxZ0aSWJEk737BhcPTR4RfuX6dH3X33qFNJkiRJUmJiMbjmmrCufKVKMH58mK3l66+jTiZJUrEXi8fj8UQ/HI/Huemmm7jzzjvJzc0NF4zFNjterlw5brzxRm688cbtT1sCZWZmkp6eTkZGBmlpaVHHkSQpMQ88AJdfDvE49OoFL7wAFSpEnUqSJEmSisacOdCjR5itZZdd4NVXw+wtkiSVIYXptLarYPzVN998w7PPPsu0adNYvnw5ubm51K1bl3bt2tG3b1/23HPP7b1FiWXBKEkq0fLyoH9/uOeesN2vH9x/PyQnR5tLkiRJkora8uVw3HHw4YeQkgJPPAFnnRV1KkmSdpqdXjBq6ywYJUkl1oYN0LdvGK0IMHAgXHttmEZIkiRJkkqjrKxQKr7yStju3x/uuAOSEl5pSpKkEqMwnVbCfzIuWbKEZcuWFfj85cuXs2TJkkRvJ0mSdqbMTOjePZSL5crB4MHhF2vLRUmSJEmlWaVK8NJLcP31YfvOO+Hkk2Ht2mhzSZJUzCRcMDZs2JCDDz64wOe3a9euTE+VKklSibFiBXTsCBMnQuXKMHo09OkTdSpJkiRJ2jmSkuD222HIEChfHkaMgA4dwvqMkiQJ2I6CEaCws6s6G6skScXcZ5/BoYfC3LlQuza8+y4cfXTUqSRJkiRp5zvzTHj7bahZE2bPhoMPDuszSpKk7SsYC2P9+vWUK1duZ91OkiQV1owZoVxcvBiaNAnbrVpFnUqSJEmSotOuHcyaBc2awfLl0L59GNEoSVIZt1MKxuXLl/Pjjz+yyy677IzbSZKkwho5Eo48En7+OTyVO306OLW5JEmSJEGjRuF3pK5dYd06OPFEGDgQnK1NklSGFXhI4eTJk5k0adJm+9asWcOtt9661c/E43FWrVrFmDFjiMfjtGnTJuGgkiRpB3n8cbjoIsjLg2OOgZdeCmsvSpIkSZKC9HQYNQquugoeeACuuw4+/TT8PpWaGnU6SZJ2uli8gAsj3nLLLdxyyy3EYjEglIe/vt+WeDxOhQoVmDRpEgcffHDiaUugzMxM0tPTycjIIC0tLeo4kiTli8fh5pvh14eF+vaFxx4DpzSXJEmSpK0bNAguuwxyc+Gww8KUqbVqRZ1KkqTtVphOq8DfIDZs2JCOHTtu2n733XdJSUmhbdu2W/1MUlISaWlp7Lfffpx11lk0adKkoLeTJEk7Uk4OXHghPPVU2L7pplA2FvDhIUmSJEkqsy6+OKxbf/LJMHUqtGkDo0eHdRolSSojCjyC8feSkpKoW7cuy5cvL+pMpYojGCVJxc7atXDKKTBmDCQlwSOPwAUXRJ1KkiRJkkqWBQugRw/4+mtIS4OXX4ajj446lSRJCdshIxh/75lnnqFixYqJflySJEXhxx/DOovvvw8VK8LQoXDccVGnkiRJkqSSp1kzmDkTevWCKVOge/ewPmO/flEnkyRph0t4BKMKxhGMkqRi4+uvw9O0X34Ju+wCo0bBn0x1LkmSJEkqgOxs+NvfYPDgsH3JJfDvf7u+vSSpxClMp5W0IwLk5uby0EMP0bNnT0444QSe+nV9J0mSFI0PPwxl4pdfwh57wLRplouSJEmSVBRSU+GZZ+DOO8P2Qw/BscdCRka0uSRJ2oESLhiffvppkpOTOfXUU/9w7K9//St///vfGT16NK+//joXXHABp5122nYFlSRJCRo3Djp2hB9+gAMPhBkzYK+9ok4lSZIkSaVHLAbXXgsjRkClSuH3sEMPDTPJSJJUCiVcMI4fPx6A008/fbP9kyZN4pVXXiEej3PooYfSuXNnAIYNG8brr7++HVElSVKhDRkSnpxduxY6d4Z334V69aJOJUmSJEml0wknhPUY69eHBQugTRuYOjXqVJIkFbmEC8a5c+cC0K5du832DxkyBIDzzz+fKVOmMH78eG655Rbi8TjPPvtswkElSVIhxOMwcCCcdRbk5MAZZ8Abb4DrAUuSJEnSjtWyJcyaBa1awcqV0KlTePhTkqRSJOGCceXKlaSmplKzZs3N9r/11lvEYjEuu+yyTfv69esHwAcffJDo7SRJUkHl5sKll8J114Xtq68Ov8yWLx9tLkmSJEkqKxo0gMmT4cQTYcOG8PDndddBXl7UySRJKhIJF4yZmZlUqFBhs30rVqxg6dKl1K5dm3333XfT/urVq5OWlsaPP/6YeFJJkrRt69bBKafAww+HNUD+8x+4+25ISviPfEmSJElSIipVgpdfzn/4c+BAOPnksISFJEklXMLfNqanp5ORkUFWVtamfe+++y4Ahx566BY/8/tCUpIkFaGff4YuXWDEiDBacehQ+Pvfo04lSZIkSWVXUhLccUf+rDIjRkCHDrBsWdTJJEnaLgkXjPvttx8AL7/88qZ9Q4YMIRaL0bFjx83OzcjIIDMzk7p16yZ6O0mS9GeWLIHDDoOpUyE9HcaPDyMZJUmSJEnRO/NMmDgRataE2bPh4IPDf0qSVEIlXDD+9a9/JR6P069fPy666CJOOOEExo4dS/ny5Tnld19ozpgxA4C//OUv25dWkiT90bx50LYtLFwY1vmYOhV+97CPJEmSJClihx0GM2dCs2awfDm0bx9GNEqSVAIlXDCee+65dO7cmXXr1vH444/z+uuvE4vFuP322/8wUnHYsGFbHNkoSZK206RJ4ZfU5cvDL6kzZsD/ZhmQJEmSJBUze+4J06fD0UdDVhaceCLceSfE41EnkySpUMol+sHk5GTGjh3Lf//7X6ZPn061atXo3r077dq12+y8DRs2sGLFCjp06EC3bt22O7AkSfqfl16CPn1gw4bw5Ovrr0P16lGnkiRJkiT9mfR0GD0arrwSHnwQBgwIM9I8/jikpkadTpKkAonF4z4esyNlZmaSnp5ORkYGaWlpUceRJJUW//kPXHFFeH/iifD881ChQqSRJEmSJEmFNGgQXHYZ5OaG2WlefTWs0yhJUgQK02klPEWqJEmKQF4e/OMf+eXiJZeEkYyWi5IkSZJU8lx8MYwZA2lpMHUqHHwwLFgQdSpJkrYp4SlSf2/+/Pl88MEH/PDDDwDUrl2b1q1b06xZs6K6hSRJZduGDXD22fDf/4btO++Ea66BWCzSWJIkSZKk7dClC8yYAcceC4sWQdu28PLLYZ1GSZKKqe0uGMeNG8c111zDJ598ssXjzZs35+6776ZLly7beytJksquzEzo1QsmToRy5eDpp+HMM6NOJUmSJEkqCs2awcyZ4fe+qVPhmGPg/vuhX7+ok0mStEXbNUXqQw89xDHHHMMnn3xCPB4nKSmJ2rVrU7t2bZKTk4nH43z88cd069aNhx9+uKgyS5JUtixfDh06hHKxShV44w3LRUmSJEkqbWrVgrfegj59wpqMl1wCl14KOTlRJ5Mk6Q8SLhg/+ugjLr/8cvLy8jj44IMZM2YMa9asYcWKFaxYsYLVq1czZswY2rZtSzwe5/LLL+fjjz8uyuySJJV+n34Khx4KH30EderAu++G6XMkSZIkSaVPaio8+ywMHBi2H3ooTJ2akRFpLEmSfi/hgvFf//oXeXl59OjRg6lTp9K1a1dSU1M3HU9NTaVr165MnjyZHj16kJuby7///e8iCS1JUpkwfTq0awfffAN/+UvYbtky6lSSJEmSpB0pFoP+/WH4cKhYEcaNCw+efv111MkkSdok4YLx3XffJRaLcf/995OcnLzV85KTk/nPf/4DwDvvvJPo7SRJKltefx06dYKff4Y2bUK5uOeeUaeSJEmSJO0svXrBlClQvz4sWBB+N5w6NepUkiQB21Ewfv/996Snp9OwYcNtntuoUSOqVavG999/n+jtJEkqOx57LPwiuX59mApn4kSoWTPqVJIkSZKkna1VK5g1K8xms3JleBB1yJCoU0mSlHjBWLFiRbKyssgpwCLDOTk5ZGVlUbFixURvJ0lS6RePw403woUXQl4enHcevPoqVK4cdTJJkiRJUlQaNIDJk8ODqBs2wFlnwXXXhd8bJUmKSMIF4z777MPGjRt55ZVXtnnusGHD2LBhA/vss0+it5MkqXTbuBHOPRduvz1s33wzPP44lCsXaSxJkiRJUjFQuTIMGwYDBoTtgQPh5JNh7dpoc0mSyqyEC8aTTz6ZeDzOxRdfzMSJE7d63ltvvcXFF19MLBbjlFNOSfR2kiSVXmvXQs+e8MwzkJQUisX/+z+IxaJOJkmSJEkqLpKS4J//hMGDISUFRoyADh1g2bKok0mSyqBYPB6PJ/LB7OxsDjroIObPn08sFqNt27Z07tyZBg0aALB06VImTpzIjBkziMfj7LfffnzwwQeUL1++SH+A4i4zM5P09HQyMjJIS0uLOo4kqbj54YewzuL770PFivDSS9CjR9SpJEmSJEnF2dSpcMIJYV3G+vVh1KiwTqMkSduhMJ1WwgUjwPLly+nVqxezZs0KF/vdSItfL92mTRuGDx9O/fr1E71ViWXBKEnaqq++gq5d4csvYZddYPRoOOSQqFNJkiRJkkqCr78OD6wuXAiVKsHzz4fSUZKkBBWm00p4ilSA+vXrM336dIYOHcoJJ5zArrvuSvny5Slfvjy77rorJ5xwAi+99BLTpk0rk+WiJElb9cEH0LZtKBcbNoRp0ywXJUmSJEkFt+eeMGMGdOkCWVnQqxfcdRckPp5EkqQC264RjNo2RzBKkv5g7Fg46aSw9mKLFjBmDNStG3UqSZIkSVJJlJMDV1wBDz0Uts86Cx57DFJTo80lSSpxdtoIRkmSVEiDB4c1FteuhaOOgnfftVyUJEmSJCWuXDl48MFQMCYnh987jzoqrM8oSdIOUqQF4zfffMP777/P+++/zzfffFOUl5YkqWSLx+Gf/4Szzw5Pl/buHdZcrFo16mSSJEmSpNKgXz944w1IS4MpU6BNm7A+oyRJO8B2F4zLly/n0ksvpXbt2uy5554ccsghHHLIIey5557UqlWLSy+9lKVLlxZFVkmSSqbcXLjkErj++rB97bXhidLy5aPNJUmSJEkqXY4+OqzL2KgRfP01tG0L48dHnUqSVApt1xqM48eP59RTTyUzM5OtXSYWi1G1alWGDh1K165dEw5aUrkGoySVcevWwemnw2uvQSwG998Pl14adSpJkiRJUmn244/QqxdMnRqmTX3gAbj44qhTSZKKucJ0WgkXjJ999hktWrRg/fr11KhRgwsvvJAjjzySBg0aALBs2TLeeecdHnvsMVauXEmFChWYM2cOe+21VyK3K7EsGCWpDPv5ZzjuOJg2DVJT4fnn4aSTok4lSZIkSSoLsrPhggtgyJCwfeml8K9/hTUbJUnagp1SMPbu3ZsXX3yR/fffnwkTJlCrVq0tnrdy5Uo6d+7MvHnzOP3003nuuecSuV2JZcEoSWXUkiXQtWtY7yI9HUaOhA4dok4lSZIkSSpL4nG480647rqwffTR8NJL4fdUSZJ+pzCdVsJrME6cOJFYLMaTTz651XIRoGbNmjzxxBPE43HeeuutRG8nSVLJ8fHHYZ2LhQth113DlDSWi5IkSZKknS0WgwED4JVXoGJFGDcODj0UFi2KOpkkqYRLuGBctWoVVapU4aCDDtrmua1bt6ZKlSqsWrUq0dtJklQyvP02tG8Py5fDvvvC9Omw335Rp5IkSZIklWUnnghTpkD9+rBgARx8cHgYVpKkBCVcMNarV4/c3NwCn5+Xl0e9evUSvZ0kScXf0KFhWtTMzDBicepU2G23qFNJkiRJkgStWsGsWdCyJaxcCZ06QRlbzkqSVHQSLhi7d+/OunXrePvtt7d57sSJE8nKyuLYY49N9HaSJBVv//oX/PWvsHEjnHRSmHamWrWoU0mSJEmSlK9BA5g8GU44ATZsgD594PrrIS8v6mSSpBImFo/H44l88Pvvv+fAAw+kQoUKjBs3jqZNm27xvC+++IKjjz6a7Oxs5syZQ+3atbcrcElTmAUxJUklUF4eXH11KBgBLr0U/v1vSE6ONpckSZIkSVuTlwc33AADB4btE0+EIUOgUqVoc0mSIlWYTivhgnHy5Ml8/fXXXHHFFaxfv56TTz6ZI488kgYNGgCwbNky3nnnHYYNG0aFChX497//TaNGjbZ4rQ4dOiQSoUSwYJSkUiw7G84+O0yNCnD33fCPf0AsFmksSZIkSZIKZPBgOP/8MBtPq1YwcmRYp1GSVCbtlIIxKSmJWBF8gRqLxcjJydnu6xRXFoySVEplZIQpZd55B8qVg2eegd69o04lSZIkSVLhTJkSfr/96acwherIkWGdRklSmVOYTivhNRgB4vH4dr/ynN9bklTSLF8OHTqEcrFKFRgzxnJRkiRJklQytW8PM2fCPvvAsmVh+9VXo04lSSrmEi4Y8/LyiuwlSVKJsXAhtG0LH38MderA5Mlw1FFRp5IkSZIkKXGNG8OMGdClC2RlQa9ecNddkNjkd5KkMmC7RjBKklSmTJsG7drBkiXQtGn45atFi6hTSZIkSZK0/dLT4Y03oF+/sN2/P5xzDmRnR5tLklQsWTBKklQQr70GnTvDL7/AIYeEsrFRo6hTSZIkSZJUdMqVg4ceggcfhKQkGDw4zNqzcmXUySRJxUyRF4z/+te/uPXWW4v6spIkReeRR+DEE2H9eujRAyZOhJo1o04lSZIkSdKOccklYTRjWhpMmQJt2oQlQyRJ+p9YPF60E2nXq1ePH374gdzc3KK8bImVmZlJeno6GRkZpKWlRR1HklQY8TjccAP8859h+/zzYdCg8ESnJEmSJEml3YIFcOyxsGhRmEL15ZfDOo2SpFKpMJ2WU6RKkrQlGzdC37755eItt8Bjj1kuSpIkSZLKjmbNYOZMaNcOMjKge/fw4K0kqcyzYJQk6ffWrIHjjoNnn4XkZHjiCbjpJojFok4mSZIkSdLOVatWWCqkTx/IzYV+/eCvf4Vffok6mSQpQhaMkiT91g8/wBFHwNixULEivPYanHde1KkkSZIkSYpOamp4CHfgwPAg7tChsP/+8PbbUSeTJEXEglGSpF99+SUceih88AHUrAnvvBPWmpAkSZIkqayLxaB/f5g2DZo0gaVLoVMnuPJKWL8+6nSSpJ2syAvGeDxe1JeUJGnHe//9UC5+9RU0ahR+YWrTJupUkiRJkiQVL23awJw5cMEFYfvf/4bWreHjj6PNJUnaqYq8YPzggw/4+uuvi/qykiTtOG++CYcfDj/+CC1bwvTp0LRp1KkkSZIkSSqeqlSBxx6DkSPDGo2ffBJKxnvvhby8qNNJknaCIi8Yd911V/bYY4+ivqwkSTvGM89Ajx6QlQVdusCkSVC3btSpJEmSJEkq/nr0COVijx6wYQNcfXWYNnXJkqiTSZJ2MNdglCSVTfE43H479O0Lublw5pkwahRUrRp1MkmSJEmSSo7ateH11+Hxx6FSpfDg7v77wwsvhN+9JUmlUiye4KKJQ4YMKdT5FSpUoFq1auy77740aNAgkVsCsHr1au677z6GDx/OokWLSE5OpmnTppx22mlceumllC9fPuFr/96FF17IY489BsAee+zB4sWLC32NzMxM0tPTycjIIC0trciySZK2Q2Ym/O1vMHRo2B4wAO64IyxYL0mSJEmSEvPFF+EB3pkzw/app8Ijj0D16tHmkiQVSGE6rYQLxqSkJGIJfhG777770r9/f04//fRCfe6bb77h8MMP31T0VapUidzcXLKzswFo0aIFEydOpHoR/IH1zjvv0KlTJ379x2PBKEmlxAcfwGmnwVdfQXIy3H8/9OsXdSpJkiRJkkqHnJzwEO9tt4UZgxo0gMGDw9SpkqRirTCdVsJTpO6+++7svvvuVKxYkXg8TjweJzk5mTp16lCnTh2Sk5M37a9UqRK77bYbaWlpxONxPvnkE84880yuvPLKAt8vJyeHHj16sHjxYurVq8eECRNYu3YtWVlZDB06lKpVqzJnzhx69+6d6I+0SVZWFueffz7lypXjoIMO2u7rSZKKgXgc/vMfOPTQUC7uvjtMmWK5KEmSJElSUSpXDv7v/2DaNPjLX2DZMujcGa64AtavjzqdJKmIJFwwLl68mP79+5OTk8MRRxzBxIkTWbNmDcuXL2f58uWsWbOGiRMncuSRR5KTk8ONN97IL7/8wueff87ZZ59NPB7n/vvv55133inQ/QYPHsy8efMAGD58OJ07dw4/QFISp5566qapTMeMGcPEiRMT/bEAuP766/nqq6+45ppr2HfffbfrWpKkYmDlSjjuuPDLzMaNcMIJMHcutG0bdTJJkiRJkkqnNm1gzpywRAmEh34POgg++ijSWJKkopFwwfj222/Tr18/evXqxVtvvcURRxyx2fqH5cuX54gjjuCtt97ihBNO4MILL2Tq1Kk0adKEp59+mrPOOot4PM4TTzxRoPsNHjwYgCOOOIK2W/hC+LTTTqNRo0ZA4deH/K333nuPBx54gKZNm3LDDTckfB1JUjExeTIceCCMHg2pqfDwwzB8uOs/SJIkSZK0o1WuDI8+CqNGQe3aMH8+tG4Nd98dpk+VJJVYCReM9913H/F4nHvuuWebazHefffd5Obmcvfdd2/a179/fwCmT5++zXtlZWUxbdo0ALp167bFc2KxGF27dgVg/PjxBfoZfi87O5u+ffsSj8d5/PHHqVChQkLXkSQVA7m5cOutcMQRYTqWpk3hvffg4oshwTWEJUmSJElSAo49FubNC7MLbdwI114LRx4J33wTdTJJUoISLhg/+OADqlWrRoMGDbZ57q677kq1atWYOXPmpn177bUXlSpV4ocfftjm5xcuXEheXh4A++2331bP+/XYd999x88//7zN6/7erbfeysKFCzn33HPp2LFjoT8vSSomli8P6zv83/9BXh6cdRZ8+GEYyShJkiRJkna+2rXhtdfgiSfCyMbJk2H//eG55yAejzqdJKmQEi4YV69ezdq1a9m4ceM2z92wYQNr165l9erVm+1PSUmhXLly2/z88uXLN73/s0Lzt8d++5mCmDNnDnfffTd16tThnnvuKdRnJUnFyJtvwgEHwKRJ4ReWIUPg2WehSpWok0mSJEmSVLbFYnDeeTB3LhxyCGRmQp8+cOqpkMCAEUlSdBIuGBs2bMjGjRt58cUXt3nuf//7XzZu3EjDhg037VuzZg0ZGRnUrl17m5//bTFZqVKlrZ7322O/LzP/TE5ODn379iUnJ4cHHniAatWqFfizv5ednU1mZuZmL0nSTrBhA/zjH9C9O6xcGUrGDz+EM8+MOpkkSZIkSfqtJk1gypSwtElyMgwbBs2bw1tvRZ1MklRACReMJ598MvF4nH79+vHf//53q+cNHTqUfv36EYvFOOWUUzbtnzNnDhCmSo3anXfeydy5czn22GM3y5iIgQMHkp6evum12267FVFKSdJWff01tG8P990Xti+5JKy3WAz+jJEkSZIkSVtQrhzceCPMmAFNm4blTo46Ci6/HNatizqdJGkbEi4Yr732WvbZZx+ysrLo3bs3jRs35pxzzuH666/nhhtuoG/fvjRu3JgzzjiDrKws9t57b6655ppNnx8yZAgAnTt33ua9qlatuul9VlbWVs/77bHffubPLFiwgNtuu40qVaowaNCgAn3mzwwYMICMjIxNr2+//Xa7rylJ+hPDhkGLFjBrFlSrBiNGwIMPQoUKUSeTJEmSJEnb0ro1zJ4NF10Utu+/Hw46KEyjKkkqtra9AOJWVKpUiUmTJtGnTx/GjRvHokWLWLx48WbnxP+3OO9RRx3FkCFDNpvC9B//+AeXXHIJjRs33ua96tevv+n9smXL2H///bd43rJly7b4mT/Tr18/NmzYwC233EL16tVZs2bNZsdzcnI2/Sy/HktNTSUlJWWL10tNTSU1NbVA95YkbYd168JTjY8/HrYPPRRefBH22CPSWJIkSZIkqZAqV4ZBg+DYY6FvX1iwAA4+GG67LSyHkpwcdUJJ0u/E4r+2gNth2rRpDBs2jNmzZ/Pjjz8CUKtWLVq2bMlJJ53EYYcdtl3Xz8rKomrVquTl5XH3/7d353E61f3jx1/XjJkxYxmkSDS0kLVFmySkvbTvkjZR2ve6W1R3d4to1/JNpEVF6k4LRSSKlEQoKUvZhbGMZcyc3x/n57ppDLO6Znk9H4/r4brOOZ9z3tc1nxnnXO/zeX+eeILbb799u9tde+21vPjii9SuXZtFixblad/169dn3rx5+Yrnqaee4qabbsrTtqtXryY1NZX09HSqVq2ar+NIknIxY0Y4AfzPP4cTxN91Fzz4IORy84ckSZIkSSolli2Dq6+GDz8MX7dpAwMHQv36sYxKksqF/OS0CjyCcWutW7emdevWRbGr7UpJSaF169Z8/fXXDB8+fLsJxiAIGDFiBAAnnHBCscUiSYqhIID+/cM5Ftevh1q14I03wjkaJEmSJElS6bf77uH0J/37w403wtdfQ4sW8Pzz0LlzeKOxJCnmCjwH467WpUsXAEaPHs3EiRNzrB88eDB//PEHAJdeemme9zt37lyCIMj1seW4aWlp0WV5Hb0oSSpCq1dDp05w5ZVhcvH448P5GEwuSpIkSZJUtkQiYanUn34Kp0RZswa6dIHzz4e//451dJIkijDBuGbNGr766isGDx7M4MGD+eqrr1izZk1R7Z4uXbrQvHlzgiDgnHPOYdSoUQBkZ2czePBgunbtCsDJJ59Mhw4dtmnbs2dPIpEIkUgkxzyRkqRS4Icf4JBDYNCgcN6FRx+F4cOhdu1YRyZJkiRJkorLPvvAV1/Bv/8NFSrAkCHQvDl8/nmsI5Okcq/QJVKnTZvGv/71Lz777DOys7O3WRcXF8epp57Kww8/TPPmzQt1nAoVKvDRRx/Rvn175s6dy3HHHUdKSgrZ2dls2LABgIMPPpi33nqrUMeRJJUgQQDPPAN33AGZmbD33mGS8aijYh2ZJEmSJEnaFSpUgH/9C048ES65BH79NXx+/fXw+OOQnBzrCCWpXCrUCMahQ4dyxBFH8Mknn5CVlZWjvGhWVhbDhg3jiCOO4IMPPih0sPXr12fq1Kncf//9NGvWjEgkQkJCAi1btuTJJ59kwoQJVK9evdDHkSSVAH//DaefDjffHCYXzzwTfvzR5KIkSZIkSeXRoYfC5MnQo0f4+rnnoGXLcJkkaZeLBEEQFKThnDlzaNKkCRs3bqR+/frccccdHH/88dStWxeAv/76iy+++IJevXoxZ84cKlasyPTp02nQoEGRvoGSbvXq1aSmppKenk7VqlVjHY4klQ5ffw0XXQQLFkBiIvTpA9de60TukiRJkiQpnDbl8sth8eJwhONDD4XVj+LjYx2ZJJVq+clpFXgEY69evdi4cSOtWrVi6tSpdO/enX333ZekpCSSkpLYd9996d69O1OnTqVVq1Zs3LiR3r17F/RwkqTyICsLHn4Y2rULk4sNG8LEieHdiSYXJUmSJEkSwEknwbRpcPbZsHkz3HNP+F3CnDmxjkySyo0CJxhHjhxJJBLhpZdeonLlyrluV6lSJV566SWCIOBzJ9+VJOVm4UI4/ni4/37IzoZLL4UffoCDDop1ZJIkSZIkqaSpWROGDIH+/aFyZRg3Dg48EAYMgIIV7ZMk5UOBS6SmpKSQmJjIqlWr8rR9tWrV2LRpExkZGQU5XKlliVRJyoPhw8OE4rJlUKkS9O0bvpYkSZIkSdqZP/4Iv0cYPz58ffbZ8PLLYRJSkpRnu6REakJCApmZmXnaNggCNm3aREJCQkEPJ0kqizZtCudIOPnkMLl44IHhqEWTi5IkSZIkKa/22Qe++goeeSSck3HoUGjePLyhWZJULAqcYNxvv/3YsGEDI0aM2Om2I0aMYMOGDey3334FPZwkqayZMwfatIFevcLXPXrAhAnQqFFs45IkSZIkSaVPfHw4F+OECXDAAbB4cXhD83XXQTmrqidJu0KBE4xnnHEGQRDQtWtXZs6cmet2M2bM4OqrryYSiXDmmWcW9HCSpLJkyBA4+GD47juoVi28s/D556FixVhHJkmSJEmSSrOWLcPqSNddF75+4YX/LZMkFZkCz8G4evVqmjZtyoIFC0hMTOS8886jQ4cO7LXXXgD89ddfjBo1iiFDhrBp0ybq1q3L9OnTqVKlSpG+gZLOORglaSvr18Mtt8BLL4WvW7WCQYMgLS22cUmSJEmSpLJnxAi4/HJYtCgsndqzJ9x1VzjaUZKUQ35yWgVOMAJMnz6djh07MnfuXCKRyHa3CYKABg0a8NFHH9G0adOCHqrUMsEoSf/fzJlwwQUwbRpEIuEJ/YMPgvPzSpIkSZKk4vL339CtG7z/fvj6qKPgjTfCeRslSdvIT06rwCVSAZo2bcrUqVN59NFHOeigg4iLiyMIAoIgIC4ujoMOOojHH3+cn376qVwmFyVJQBDAa6/BoYeGycU99gjvIPzPf0wuSpIkSZKk4rXbbjB4MAwYAFWqwDffwIEHQv/+4XcWkqQCKdQIxn/KzMxkxYoVANSoUYMEvzh2BKOk8m3NGujeHd5+O3x93HHhXYK1a8c2LkmSJEmSVP7MmQOXXgrjxoWvzzoLXnkFataMbVySVELsshGM/5SQkECtWrWoVavWNsnF9PR0DjnkEFq2bFmUh5MklWQ//ACHHBImF+PjwxGLI0aYXJQkSZIkSbHRoAGMGQOPPhpWVfrgA2jeHD77LNaRSVKpU6QJxtxs3ryZKVOmMGXKlF1xOElSLAUBPPMMtGoFs2dDvXrw1Vdw990Qt0v+25EkSZIkSdq++Hi46y6YOBEaN4bFi+GUU6BHD8jIiHV0klRq+E2vJKno/P03nHEG3HQTZGbCmWfClCnQunWMA5MkSZIkSdrKwQeH1ZduuCF83bdvWInp++9jG5cklRImGCVJRePrr+Ggg2DYMEhMhOeeg6FDoUaNWEcmSZIkSZKUU3JyWIVpxAjYc0/49dewItO//w2bN8c6Okkq0UwwSpIKJysrPPFu1w7++gv23x8mTIDrroNIJNbRSZIkSZIk7dgJJ8C0aXDuuWFi8b774Jhj4PffYx2ZJJVYJhglSQW3aFF4En7ffZCdDZ07h+VFDj441pFJkiRJkiTl3W67wXvvweuvQ5Uq8O23YaWmfv0gCGIdnSSVOCYYJUkFM3w4HHggfPklpKTAgAEwcGB4Ei5JkiRJklTaRCJw6aUwdSq0aQNr18JVV8HZZ8OyZbGOTpJKFBOMkqT8ycyEO++Ek08OT65btIDJk6FLl1hHJkmSJEmSVHj168Po0fDYY5CQAB9+CM2bw6efxjoySSoxTDBKkvJuzpzwDr4nnghfX3stTJwIjRrFNi5JkiRJkqSiFB8f3mA9cSI0aQJLlsCpp8I118C6dbGOTpJiLs8Jxvj4+AI/9thjj+J8D5KkXWHIkHBuxYkToVo1eP99eOEFqFgx1pFJkiRJkiQVj4MPhu+/hxtvDF+/9BIccghMmhTbuCQpxvKcYAyCoFAPSVIptX59eHfeeedBejoceST8+GM4/4AkSZIkSVJZl5wMTz8Nn38OderArFnQqhU8/DBs3hzr6CQpJirkdcMHHnigOOOQJJVEM2fChReGk5sD3HUXPPRQOP+AJEmSJElSeXL88TBtWngj9nvvwf33h/Myvvkm7LtvrKOTpF0qEji8sFitXr2a1NRU0tPTqVq1aqzDkaS8CQIYMACuuw4yMmCPPeCNN+CEE2IdmSRJkiRJUmwFAbz1FvToAatXQ6VK4QjHK6+ESCTW0UlSgeUnp5XnEqmSpHJizRro3BmuuCJMLnboAD/9ZHJRkiRJkiQJwiTiJZeEFZ/atoV166BrVzjzTFi6NNbRSdIuYYJRkvQ/kyeHE5W/9RbEx8Mjj8CIEVC7dqwjkyRJkiRJKlnS0mDUKHjiiXA6mY8+gubN4ZNPYh2ZJBU7E4ySpLC0x7PPhhOUz54N9erBV1/BPfeEiUZJkiRJkiTlFB8Pt98O330HTZuGIxhPOw26dw9HNkpSGWWCUZLKuxUr4Kyz4MYbYdMmOOMMmDIFWreOdWSSJEmSJEmlw0EHwfffw003ha9ffhkOPjhMPEpSGWSCUZLKs3HjwhPg//4XEhPDUYwffAA1asQ6MkmSJEmSpNKlYkV46in44gvYay/47Tc46ih48EHYvDnW0UlSkTLBKEnlUVZWOL9iu3bw55+w//4wYQJcf304UbkkSZIkSZIK5rjjYOpUuOCC8DuYnj3h6KPDhKMklREmGCWpvFm0CE48Ee69NzzJveQS+OGHsGyHJEmSJEmSCq9GDRg0CN58E1JTYeLEsIrUK69AEMQ6OkkqNBOMklSejBgRnsyOGgUpKTBgAAwcCFWqxDoySZIkSZKksiUSgU6dwtGM7dpBRgZ06wZnnAFLl8Y6OkkqFBOMklQeZGbCnXfCSSeFJ7AtWoSjFrt0sSSqJEmSJElScdp77/Bm7yefhMREGDYMmjUL/5WkUsoEoySVdXPnwjHHwBNPhK+vuSacb/GAA2IaliRJkiRJUrkRFwe33gqTJoXJxWXL4PTTwxGNa9fGOjpJyjcTjJJUlr3/fji34oQJYb3/IUOgb19ITo51ZJIkSZIkSeVPixZhkvGWW8LXr7wSfnczcWJs45KkfDLBKEll0YYNcO21cO65sGoVHHkkTJkC55wT68gkSZIkSZLKt4oVoXfvsGxq3boweza0bg09e4bT3EhSKWCCUZLKml9+gSOOgBdfDF/feSeMHQv168c0LEmSJEmSJG3l2GNh6lS46CLIyoIHH4Sjj4bffot1ZJK0UyYYJamsCAIYMABatgxPTnffHYYPh8ceg4SEWEcnSZIkSZKkf6peHd5+O3ykpsJ338FBB8HLL4ff9UhSCWWCUZLKgjVr4NJL4fLLISMDOnSAn36CE0+MdWSSJEmSJEnamYsugmnToH378Lud7t3h9NNhyZJYRyZJ22WCUZJKux9/DEctvvkmxMXBv/8NI0bAnnvGOjJJkiRJkiTlVb16MHJkOD9jYiJ8/DE0bw4ffRTryCQpBxOMklRaBQE89xwceWRYm79uXfjqK/jXvyA+PtbRSZIkSZIkKb/i4uCWW+D778Pk4rJlcMYZ0LUrrF0b6+gkKcoEoySVRitWwFlnwQ03wKZNYcmMn34KJwKXJEmSJElS6da8OUyaBLfdBpEIvPpqODfjt9/GOjJJAkwwSlLpM358eEL53/+G5TKeeQY+/BBq1Ih1ZJIkSZIkSSoqSUnQqxd8+WVYPvX338Oby++/HzIzYx2dpHLOBKMklRZZWfDII9C2Lfz5J+y3X3jX2g03hHeySZIkSZIkqexp1w6mToVOnSA7Gx5+GFq3hlmzYh2ZpHLMBKMklQaLF8OJJ8K994aJxk6dYPJkOOSQWEcmSZIkSZKk4latGrz5JrzzTvh80qSwwtVLL0EQxDg4SeWRCUZJKuk+/xwOPBBGjYKUFOjfH954A6pUiXVkkiRJkiRJ2pUuuACmTYNjj4X16+Gaa6Bjx/DmdEnahUwwSlJJlZkJd90VjlxcujSc3Pv77+GyyyyJKkmSJEmSVF7VrQtffAF9+oTzNH7ySfi90X//G+vIJJUjJhglqSSaNy+ca/Hxx8PX11wDEydC48axjUuSJEmSJEmxFxcHN98c3ozeogUsXw5nnglXXQVr1sQ6OknlgAlGSSpphg4Na+h/+y2kpsLgwdC3LyQnxzoySZIkSZIklSTNmsF338Htt4cVr/r1+9/3SpJUjEwwSlJJsWED9OgB55wDq1bBEUfAlClw7rmxjkySJEmSJEklVVISPPEEjB4Ne+8Nf/wBRx8N990XTsEjScXABKMklQS//gpHHhmOVAS44w74+muoXz+mYUmSJEmSJKmUaNsWpk6FSy6B7Gz497/hqKPC750kqYiZYJSkWHv9dWjZEn76CXbfHT77LJx7MSEh1pFJkiRJkiSpNElNhTfegHffherVwzkaDz44vKk9CGIdnaQyxASjJMXKmjVw6aVw2WWwbh0ce2yYZDzppFhHJkmSJEmSpNLs/PNh2jQ47jhYvz6clufUU2Hx4lhHJqmMMMEoSbEwZUo4avGNNyAuDh5+GD7/HPbcM9aRSZIkSZIkqSzYay8YMQKefjqcp/Gzz6BZM/jgg1hHJqkMMMEoSbtSEMDzz8MRR8Bvv0HdujBmDNx7L8THxzo6SZIkSZIklSVxcXDjjfDDD3DQQfD333D22XDFFWF1LUkqIBOMkrSrrFgRnsBdfz1s2gSnnx6OZGzTJtaRSZIkSZIkqSxr2hQmTIA774RIBPr3hwMPhPHjYx2ZpFLKBKMk7QrffBNOqP3hh5CQEJam+PBD2G23GAcmSZIkSZKkciEpCR57LKymlZYGc+bAMceElbUyM2MdnaRSxgSjJBWn7Gx49NHwZG3+fNhvP/j227A0RSQS6+gkSZIkSZJU3hxzDPz0E3TuHH539cgj0KoV/PJLrCOTVIqYYJSk4rJ4MZx4ItxzD2RlwcUXw+TJ0LJlrCOTJEmSJElSeZaaCgMHwnvvQfXq4RyNhxwCL7wAQRDr6CSVAiYYJak4fPFFWMd+5EhISYHXXoM334QqVWIdmSRJkiRJkhQ67zyYNg2OPx7Wr4frroNTToFFi2IdmaQSzgSjJBWlzEy4++5w5OLSpdC8OXz/PVx+uSVRJUmSJEmSVPLstRcMHw7PPAMVK4bPmzeHoUNjHZmkEswEoyQVlXnzoG3bcLLsIIDu3WHiRGjcONaRSZIkSZIkSbmLi4MbbghLpR50EPz9N5xzTnjT/OrVsY5OUglkglGSisIHH4QnX99+C1WrhvXrX3wRkpNjHZkkSZIkSZKUN02ahDfM33VXWI1rwIBwGqBx42IdmaQSxgSjJBXGhg1hbfqzz4ZVq+Dww2HKlLB+vSRJkiRJklTaJCbCo4/CV19BWhrMnRtW7brnHti0KdbRSSohTDBKUkH9+isceSS88EL4+vbbw7u5GjSIbVySJEmSJElSYbVpA1OnQpcukJ0dJh1btYKZM2MdmaQSwASjJBXEwIHQsiX89BPsvjt89hk88QQkJMQ6MkmSJEmSJKloVK0alkkdMgRq1IDJk+GQQ+C55yAIYh2dpBgywShJ+bF2bXjXVpcusG4dtG8flkQ96aRYRyZJkiRJkiQVj3POgWnT4MQTwymDbrgh/D5s4cJYRyYpRkwwSlJeTZkSjlocOBDi4uChh+CLL6BOnVhHJkmSJEmSJBWvOnXCKl7PPQcVK8Lnn0Pz5uHoRknljglGSdqZIAjnWTzySJg1C+rWhTFj4L77ID4+1tFJkiRJkiRJu0YkAtdd979SqStWwHnnwWWXwerVsY5O0i5kglGSdmTlyrAExHXXwcaN0LFjOJKxTZtYRyZJkiRJkiTFRuPG8O23cM89YaWv11+HFi1g7NhYRyZpFzHBKEm5+eYbOOgg+OADSEiAp5+G//4Xdtst1pFJkiRJkiRJsZWYCI88EiYVGzSAefOgbVs44YRwWqEgiHWEkoqRCUZJ+qfsbHjsMTjmGJg/H/bdN7wj68YbwzIQkiRJkiRJkkKtW4cVv7p2DUczfvFFmGQ8+GB4803IzIx1hJKKgQlGSdrakiVw0klw992QlQUXXxzWlG/ZMtaRSZIkSZIkSSVT1arwyiswezbccAOkpMBPP0HnzrDPPtC7t3M0SmWMCUZJ2uKLL+DAA8N/k5OhX7/wLquqVWMdmSRJkiRJklTyNWgAzzwDf/4Zlk+tVQv++gtuuw3q1YM77oAFC2IdpaQiYIJRkjZvDiekPvHEcARjs2bw/fdwxRWWRJUkSZIkSZLyq0aN8Pu2uXPh1VfhgAPCEYy9ekH9+tClC0ybFusoJRWCCUZJ5dv8+eHk048+Gk483a0bfPcdNGkS68gkSZIkSZKk0q1iRbjySpg+HYYNg2OOCW/2HzgQWrSAk0+GUaPC7+UklSomGCWVT0uXwr/+Bc2bwzffhGVQ33sPXnopLI8qSZIkSZIkqWjExcFpp8FXX8HEiXDeeeGy4cPhuOOgZUt4+23IzIx1pJLyyASjpPJlzhzo0QPS0uA//wlLMxx+OPz4Y3hiI0mSJEmSJKn4HH54eKP/rFlw3XXhzf4//gidOsF++8FTT8GaNbGOUtJOmGCUVD5MnRqepOy/P/TtCxs2hCczH3wA334L++wT6wglSZIkSZKk8mPffeG55+DPP+Ghh2D33cPpjG65BerVg7vugoULYx2lpFyYYJRUto0bB6eeCgceGJZZyMqCE06A0aNhwgQ488ywHIMkSZIkSZKkXW+33eC++2DePHj5ZWjYENLT4fHHoX59uOKKcA5HSSWK36pLKnuys+Hjj+Hoo6FNG/j00zCJeP758MMPMGIEtGsHkUisI5UkSZIkSZIEYanUq6+GmTPhww/D7/YyM6F/f2jWLBxEMHo0BEGsI5WECUZJZUlmJrz5ZjhasWNHGD8eEhPDE5NffoF334VDDol1lJIkSZIkSZJyExcHZ5wBX38dTm10zjnhQIFPP4Vjj4XDDgu/59u8OdaRSuWaCUZJpV9GBjz/fDi/YufO8PPPUKUK3HEHzJ0bllbYf/9YRylJkiRJkiQpP448EoYMgVmz4JproGLFsELZhReG3/c9+yysXRvrKKVyKRIEjicuTqtXryY1NZX09HSqVq0a63CksmXlSnjhBXjmGVi+PFy2xx5w003hCUe1arGMTpIkSZIkSVJRWrYM+vYNBxts+T6wevXwu8Drr4fatWMbn1TK5SenZYKxmJlglIrBwoXw1FPw0kv/u0OpQQO4/Xa47LKwXrskSZIkSZKksmn9enj9dejdG2bPDpclJobVzW69FRo3jm18UimVn5yWJVIllR6zZkHXrmEy8cknw+Ri8+bw1lv/K5NgclGSJEmSJEkq25KToXt3+OUXGDoUWrWCTZugXz9o0gQ6doSxY8HxVVKxMcEoqeT7/ns47zw44AB49dXwZKFNG/jkE/jpJ7j4YqhQIdZRSpIkSZIkSdqV4uPhrLPgm29g/Hg480yIRODjj6FtWzjiCBg8GLKyYh2pVOaYYJRUMgUBjBoFxx8Phx0WTuYcBOHdR+PGhXcgnXJKeMIgSZIkSZIkqXw76ij44INwVGO3bpCUBJMmwfnnw/77h/M2rlsX6yilMsMEo6SSJSsL3n8fDj8cjjsORo4M70Tq3BmmTYOPPoLWrWMdpSRJkiRJkqSSqGFDeOklmD8f7r8fdtsN5syB66+HvfeG++6DJUtiHaVU6kWCwCLExSk/E2JK5drGjfDmm/DEE+F8igAVK8JVV4UTM9evH9PwJEmSJEmSJJVCGRkwYAD07g1//BEuS0qCSy8Nv3ds1Cim4UklSX5yWqVuBOOaNWvo2bMnzZs3p3LlyqSmpnLYYYfRu3dvNm3aVKB9LliwgL59+3Leeeex3377kZycTHJyMg0aNOCiiy7iyy+/LOJ3ISlqzRro0wf23TdMJs6aBdWqwb33wrx58NxzJhclSZIkSZIkFUxKClx7bfi945Ah4byMGzfC//0fHHAAnHFGOCWTY7GkfClVIxjnzZtHu3btmDt3LgApKSlkZWWxceNGAA4++GBGjRpF9erV87zPP//8k7S0NLb+GFJSUgiCgPXr10eXXXHFFbzyyivEx8fnK2ZHMEq5WLYsTB4+/zysXBkuq1MHbrkFrr4aqlSJbXySJEmSJEmSyp4ggPHjoVevcDqmLY48Em67Dc48M5yySSqHyuQIxs2bN9OxY0fmzp3LnnvuyRdffMG6devIyMjgnXfeoUqVKvz4449ccskl+dpvVlYWQRDQoUMHXn/9dRYsWMC6detYu3Yt06dP54wzzgDgtddeo2fPnsXwzqRyZt48uOEGSEuDhx8Ok4sNG8Krr4YlCm691eSiJEmSJEmSpOIRicDRR8N//wszZ0LXrmHJ1AkT4Nxzw5KpffuGpVUl5arUjGDs168fV111FQDffPMNrVq12mb9oEGDuPjiiwEYOXIkHTp0yNN+09PT+f333znkkEO2uz4IAk455RSGDx9O5cqVWbZsGRUrVsxz3I5glP6/n38O51d8+23IygqXtWwJd9/tXUGSJEmSJEmSYmfJkrDS2gsv/K/aWs2a0KNH+Nh999jGJ+0iZXIE4+uvvw5A+/btcyQXAS688EIaNGgAwMCBA/O839TU1FyTiwCRSIQrrrgCgLVr1zJz5sz8hC3p22/h9NOheXN4440wudihA3zxBUyaBOecY3JRkiRJkiRJUuzUqhVWW/vzT3j2WahfH5YvhwcfhL33hu7d4bffYh2lVKKUigRjRkYG48ePB+Dkk0/e7jaRSISTTjoJgM8//7xIj7/1iMWsLSOvJOUuCOCzz6BtWzjqKBg2LCw9cM458N13MHIkHHdcuEySJEmSJEmSSoJKleD668Nk4rvvwqGHwoYN8PLLYenUs8+Gb76JdZRSiVAqEowzZ84kOzsbgGbNmuW63ZZ1ixcvZsWKFUV2/DFjxgCQmJhIw4YNi2y/UpmzeTMMGgQHHQSnnAJjx0JCAlx5ZVjPfMgQOOywWEcpSZIkSZIkSbmrUAHOPz8cLDFmDJx2Wjio4oMPoHXr8PHBB/+bCkoqh0pFgnHhwoXR53vttVeu2229bus2hTFnzhxeeuklAC644ALnUZS2Z/16ePFFaNgQLr4Ypk4N7/a59VaYMwdefTW8w0eSJEmSJEmSSotIJKzSNmwYTJ8OV1wBiYnhKMazz4bGjcPRjevXxzpSaZcrFQnGNWvWRJ+npKTkut3W67ZuU1Dr16/nvPPOIyMjg5o1a/LYY4/ttM3GjRtZvXr1Ng+pzFq1Ch59NKxJfu21YTKxZs2wXvn8+fDkk7CDmwIkSZIkSZIkqVRo0gT69YO5c+Huu6FatbCUavfukJYGDz0UztsolROlIsEYC5s3b+biiy/mhx9+ICEhgbfeeos6derstN2jjz5Kampq9FGvXr1dEK20iy1aBHfdFf7Hec89sHRpONnxc8/BvHlw771Qo0aso5QkSZIkSZKkorXnnvCf/8Cff8LTT4ffkS5bBg88EH5H2qMHzJ4d6yilYlcqEoxVqlSJPs/IyMh1u63Xbd0mv7KysujUqRMffvghFSpU4O233+aEE07IU9u7776b9PT06OPPP/8scBxSiTN7dnhHToMG8PjjsHo1NG0KAweG6667DnYwyliSJEmSJEmSyoTKleHGG8PvRQcNgkMOCUul9u0bTiV17rkwcWKso5SKTalIMG49cnDBggW5brf1uryMNtyerKwsLrnkEt577z3i4+N58803Offcc/PcPikpiapVq27zkEq9H3+ECy8M51F8+WXYuBFatYKPPgrnW+zcGRISYh2lJEmSJEmSJO1aFSqE351+/z18+SWcfDIEAbz/Phx5JLRpE36Pmp0d60ilIlUqEoyNGzcmLi4M9eeff851uy3rateuTY0ClGfcMnLxnXfeiSYXL7jggoIFLZV2QQBjxsBJJ4V337z7bvif4CmnwNixMH48dOwIcaXiz4gkSZIkSZIkFZ9IBNq3h08/hWnT4LLLwkEZ48bBGWeEczj+3//Bhg2xjlQqEqUiM5CSkkLr1q0BGD58+Ha3CYKAESNGAOS5nOnWsrKyuPjii3n33XejycULL7yw4EFLpVV2Nnz4YThCsX17GDEiTCJefDFMmQKffBLedROJxDpSSZIkSZIkSSp5mjWD/v1h7ly4805ITYVff4Wrrw7nbPz3v+Hvv2MdpVQopSLBCNClSxcARo8ezcTt1C0ePHgwf/zxBwCXXnppvva9ZeTie++9R4UKFXjrrbdMLqr82bQJBgwI51Q866ywPnhSElx7Lfz2G7z1Fhx4YKyjlCRJkiRJkqTSoU4deOwx+PNP6NMH6tWDpUvhvvtg773h+uvh/+c1pNKmVCUYmzdvThAEnHPOOYwaNQqA7OxsBg8eTNeuXQE4+eST6dChwzZte/bsSSQSIRKJMHfu3G3WbZlz8d1336VChQq8/fbblkVV+bJuHTzzDOy3H1x+OfzyC1StCnffDfPmwQsvwD77xDpKSZIkSZIkSSqdqlSBm2+G338PB3IcdBBkZMDzz8P++8P558OkSbGOUsqXSBAEQayDyKu5c+fSvn37aJIwJSWF7OxsNvz/msUHH3wwo0aNonr16tu069mzJw8++CAAc+bMoX79+tF1Y8eOpW3btgAkJCTsdO7GZ555Jl8JyNWrV5Oamkp6ejpVq1bNczup2P39d/gf2LPPwooV4bLatcP/6Lp1C4ftS5IkSZIkSZKKVhDAqFHw5JPhFFVbHHMM3H47nHJKOG2VtIvlJ6dVYRfFVCTq16/P1KlTefLJJxk6dChz5swhISGBpk2bctFFF3H99deTmJiYr31mZ2dHn2dmZrJkyZIdbr9+/foCxS6VGFuG47/ySniXDMC++8Idd8Cll0LFirGNT5IkSZIkSZLKskgEjjsufEydCr17w9tvw9ix4aNxY7j1VrjkknAaK6kEKlUjGEsjRzCqxJg5E554At58EzZvDpcdfDDcdReccw7Ex8c2PkmSJEmSJEkqr/76K6w29/LLsHp1uKx27XCexmuugX9UbpSKQ35yWo6xlcq6iRPhrLOgSRMYMCBMLrZvHw69/+GHsL63yUVJkiRJkiRJip26dcMBIvPnQ69esNdesHgx/OtfUK8e3HQT/P/p46SSwASjVBYFAXz+ORx7LBx5JHz4Ybj8rLNgwgT48ks44YRwKL4kSZIkSZIkqWRITYXbboM//oCBA6FFC1i3Dp55Jpzq6sILw4EjUoyZYJTKkqwseO89aNkSTjwRRo+GChXgsstgxgwYOhSOOCLWUUqSJEmSJEmSdiQxETp3hilTwmp0xx0H2dnw7rtw6KHh4JJPPw0Hm0gxYIJRKgs2bIBXXoFGjeCCC+DHHyElJRw2/8cf0L9/ODGwJEmSJEmSJKn0iETCanRffBF+79upUzjl1ejRcOqp0Lx5ODXWxo2xjlTlTCQITG8Xp/xMiCnl2+rV8NJL8NRTYT1ugBo14IYb4LrrYLfdYhufJEmSJEmSJKlozZ8flkx95RVYuzZctueecOON0K0bVKsW0/BUeuUnp2WCsZiZYFSxWLIk/A+kb19ITw+X1a0b1ua+6iqoVCm28UmSJEmSJEmSiteqVWGS8ZlnYOHCcFnlytC1a5hsTEuLaXgqfUwwliAmGFWk/vgDnnwSXnvtf0PeGzeGO++Eiy4K63JLkiRJkiRJksqPTZtg0KDwu+Offw6XxceH02nddhscfHBs41OpkZ+clnMwSqXB1Klhbe3994cXXwyTi4cfDh98EP6H0aWLyUVJkiRJkiRJKo8SE8PviKdOhc8+g2OPhawsePttOOQQOO44GDECHG+mImSCUSqpggC+/jqcqPfAA8P/DLKz4cQTwwl8J0yAM8+EOH+NJUmSJEmSJKnci0TgpJNg1Cj44Yew6l18fPj6pJPC75kHDgxHPEqFZGZCKmmys2HYMDj6aDjmGPj00zCJeMEFMHkyDB8O7dqF/1lIkiRJkiRJkvRPhxwSDlqZPRtuugkqVYJp08KRjvvsA716QXp6rKNUKeYcjMXMORiVZ5mZ8M478PjjMH16uCwxES6/PKyTvd9+sY1PkiRJkiRJklQ6rVwJL78MzzwDixeHy6pUgauvhhtvhHr1YhufSoT85LRMMBYzE4zaqYwM6NcvnIB3/vxwWZUqcO214R/2PfeMbXySJEmSJEmSpLJh48ZwZOOTT8KMGeGyChXgwgvDgS4HHhjb+BRTJhhLEBOMytXKlfDCC+EdI8uXh8v22ANuvhm6d4dq1WIaniRJkiRJkiSpjMrOhs8+CxONY8b8b/nxx8Ptt8NxxzlNVzmUn5yWczBKu9qCBeGdIHvvDffdFyYXGzSAvn1h7ly46y6Ti5IkSZIkSZKk4hMXB6eeCqNHw6RJcMEF4bIvvoATToCDD4Y33wyn9pK2wwSjtKv8+itcdVWYTOzdG9auhRYtwuHos2bBNddAcnKso5QkSZIkSZIklSeHHgrvvAOzZ8P110NKCvz0E3TuDPvsE36fvXp1rKNUCWOCUSpu338P554LjRuHcy1mZsIxx8Cnn8KUKXDRRWGNa0mSJEmSJEmSYqVBA3j2WfjzT3jkEahVC/76K6zIV68e3HFHWKFPwgSjVDyCAEaODOtUH3YYvP9+uOz002H8ePjqKzj5ZGtYS5IkSZIkSZJKlho14J57wim9/u//4IADwhGMvXpB/frQpQtMmxbrKBVjJhilopSVBUOGhEnF44+HUaMgPj4cSv7zz/Df/8JRR8U6SkmSJEmSJEmSdqxixXDar+nT4aOPwsp8mzfDwIHh9F8nnxx+Bx4EsY5UMWCCUSoKGzeG5U+bNIHzzoMffgjnU7z+evj99/APbtOmsY5SkiRJkiRJkqT8iYuDjh3DynwTJoRTgsXFwfDhYRW/li3h7bfD6cFUbkSCwNRycVq9ejWpqamkp6dTtWrVWIejorZmDbzyCvTpAwsXhsuqV4frrguTi7vvHtv4JEmSJEmSJEkqar//Dk89Ba+9BuvXh8v23htuvhmuvBKqVIltfCqQ/OS0TDAWMxOMZdSyZeFkt88/D6tWhcvq1IFbb4WuXf3jKUmSJEmSJEkq+/7+G/r2heeeC783B6hWDbp3Dwfh1KkT0/CUPyYYSxATjGXM3LnQu3dYDnXLXRkNG8Kdd0KnTpCUFNPwJEmSJEmSJEna5davhzfeCL8/nzUrXJaQAJdcEg7McQqxUiE/OS3nYJTy4uefoXNn2G+/cNTi+vVw6KEwZAjMmAFXXGFyUZIkSZIkSZJUPiUnw9VXw8yZ8OGH0Lp1OCdj//7QrBmceiqMHg2OeSszTDBKO/LNN3D66dC8Obz5JmRlhZPWjhwJ330H55wD8fGxjlKSJEmSJEmSpNiLi4MzzoBx48Lv188+GyIR+PRTOPZYOOwwePdd2Lw51pGqkEwwSv8UBOEfu2OOCe+yGDYs/AN47rkwaRJ88QV06BAukyRJkiRJkiRJObVqBe+/D7/+CtdcAxUrwg8/wIUXwv77w7PPwtq1sY5SBeQcjMXMORhLkc2b4b334LHHYNq0cFlCAnTpArffHs61KEmSJEmSJEmS8m/ZMujbN5yGbPnycFn16mHy8frroXbt2ManfOW0TDAWMxOMpcD69WEd6F69YO7ccFnlytC9O9x0E+y1VyyjkyRJkiRJkiSp7MjIgIEDoXdvmD07XJaYCJ07w623QuPGsY2vHDPBWIKYYCzBVq2CF1+Ep5+GpUvDZTVrwo03Qo8e4Z0TkiRJkiRJkiSp6GVlwUcfhYN/vv32f8tPOy2sKtimjVOV7WL5yWk5B6PKn0WL4M47Ye+94Z57wuRiWho89xzMmwf33mtyUZIkSZIkSZKk4hQfD2edBd98A+PGwZlnhgnFjz+Gtm3hiCNg8OAwEakSxwSjyo/Zs6FbN6hfH554AtasgaZN4Y034Lff4LrrICUl1lFKkiRJkiRJklS+tG4NH3wAM2eG3+MnJcGkSXD++bD//uG8jevWxTpKbcUSqcXMEqklwOTJ8PjjMGQIZGeHy446Cu6+G045BeLMs0uSJEmSJEmSVGIsXRomFV94AVasCJfVqAHXXhsOFqpVK7bxlVGWSJWCAEaPhhNPhJYt4b33wuTiqafC11/D+PFhHWeTi5IkSZIkSZIklSx77AEPPQTz54eJxn32CRON//53OOVZt27w66+xjrJcM7uisiU7OxxGfeSRcOyx8PnnYRLx4ovhp5/C2s1HHx3rKCVJkiRJkiRJ0s5UqgQ9esCsWeF8jIcfDhs3wiuvwAEHwBlnhPM3WqxzlzPBqLJh0yYYMCCcU/Hss+G776BixXC49G+/wVtvQYsWsY5SkiRJkiRJkiTlV3w8nHsuTJgAY8fC6aeHyz/6CNq0CadFe/99yMqKbZzliAlGlW5r18LTT8O++8Lll8Mvv0BqKtxzD8ydG9Zn3mefWEcpSZIkSZIkSZIKKxIJE4r//S/MnAlXXQWJiWHi8dxzoVEj6NsXMjJiHWmZFwkCx40Wp/xMiKl8WL48rLv83HP/m+C1dm245Zaw9rKftSRJkiRJkiRJZd/ixWG+oG9fWLkyXFazZlha9cYboXr12MZXiuQnp2WCsZiZYCxi8+dDnz7wf//3vzsQ9tsP7rgDOncOy6JKkiRJkiRJkqTyZe1a6N8/zCHMnQtJSTBvHtSqFevISo385LQq7KKYpMKZMQOeeCKcS3Hz5nDZwQfD3XeHcy7Gx8c2PkmSJEmSJEmSFDuVK8P118M118DQoWGS0eRisTHBqJJtwgR4/HH48MP/LWvfHu66C44/Pqy3LEmSJEmSJEmSBFChApx/fqyjKPNMMKrkCQL4/HN47DEYM+Z/y886C+68E444ImahSZIkSZIkSZIklXcmGFXyrF0LF14Iq1aFdxp07gy33w6NG8c6MkmSJEmSJEmSpHLPBKNKnipVwoTi8uVw881Qr16sI5IkSZIkSZIkSdL/Z4JRJdM998Q6AkmSJEmSJEmSJG1HXKwDkCRJkiRJkiRJklR6mGCUJEmSJEmSJEmSlGcmGCVJkiRJkiRJkiTlmQlGSZIkSZIkSZIkSXlmglGSJEmSJEmSJElSnplglCRJkiRJkiRJkpRnJhglSZIkSZIkSZIk5ZkJRkmSJEmSJEmSJEl5ZoJRkiRJkiRJkiRJUp6ZYJQkSZIkSZIkSZKUZyYYJUmSJEmSJEmSJOWZCUZJkiRJkiRJkiRJeWaCUZIkSZIkSZIkSVKemWCUJEmSJEmSJEmSlGcmGCVJkiRJkiRJkiTlmQlGSZIkSZIkSZIkSXlmglGSJEmSJEmSJElSnplglCRJkiRJkiRJkpRnJhglSZIkSZIkSZIk5ZkJRkmSJEmSJEmSJEl5ZoJRkiRJkiRJkiRJUp5ViHUAZV0QBACsXr06xpFIkiRJkiRJkiRJ27cll7Ult7UjJhiL2Zo1awCoV69ejCORJEmSJEmSJEmSdmzNmjWkpqbucJtIkJc0pAosOzubhQsXUqVKFSKRSKzDkXJYvXo19erV488//6Rq1aqxDkcqMPuyygr7ssoK+7LKEvuzygr7ssoK+7LKCvuyygr7ctkRBAFr1qyhTp06xMXteJZFRzAWs7i4OOrWrRvrMKSdqlq1qn/8VSbYl1VW2JdVVtiXVZbYn1VW2JdVVtiXVVbYl1VW2JfLhp2NXNxix+lHSZIkSZIkSZIkSdqKCUZJkiRJkiRJkiRJeWaCUSrnkpKSeOCBB0hKSop1KFKh2JdVVtiXVVbYl1WW2J9VVtiXVVbYl1VW2JdVVtiXy6dIEARBrIOQJEmSJEmSJEmSVDo4glGSJEmSJEmSJElSnplglCRJkiRJkiRJkpRnJhglSZIkSZIkSZIk5ZkJRqkMyMjI4LPPPuPf//43Z599NmlpaUQiESKRCD179izUvufOnRvdV14el19+eY59zJ49m969e9OxY0fS0tJISkqiUqVKNGzYkCuvvJIffvihUDGq7CjOvvxPixcv5r777qNly5bUqFGD5ORk0tLSOOmkk3jsscfIzMzMte2QIUPo2LEjderUITExkUqVKtGoUSO6du3KlClTijROlU6lpS//U/fu3aNx1q9fv0jjVOlVWvrzkiVLuPXWW2nUqBHJycnUqFGDNm3a8Oqrr+K084Li78s9e/bM0/ny7Nmzd7qvH3/8kWuuuYZGjRpRuXJlqlatSsOGDbnwwgsZNGhQoWNV6VbS+/LUqVN55JFHOPHEE9lrr71ITEykSpUqNGvWjBtuuIFZs2YVOkaVDbvyHGNr+T3n9fpPO1Na+nJRt1fZVFr6s9d/JUQgqdQbPXp0AGz38cADDxRq3/Pnzw9q1aq1w0dqamr0eC+88MI27ceNG5cjpipVqgSJiYnR13FxccF9991XqDhVNhRnX97aO++8E1StWjW674oVK27zGghWrlyZo92GDRuCjh07brNd5cqVc/TnPn36FFmsKp1Kel/eni+//DKIRCLRdmlpaUUWp0q30tCfv//++2C33Xbb5m9zhQoVoq9PPPHEYOPGjUUWq0qn4u7LDzzwQAAECQkJOzx3njNnTq77yM7ODm6//fYgLi5um3Pn5OTk6OsDDzyw0LGqdCvJffnNN9/MEVNqamoQHx8ffZ2YmBi89NJLhY5Tpd+uOsfYWn7Oeb3+U16V9L5cHO1VdpWG/uz1X8nhCEapjKhevTodOnTg9ttvZ9CgQdSuXbtI9luvXj0WL168w0fnzp0BSE5O5uKLL96mfWZmJvHx8Zx55pkMHjyY5cuXs3r1ajIyMvjuu+84+uijyc7O5uGHH6Zfv35FErNKt+Lqy1sMHjyYiy++mNWrV3P11Vczffp01q9fT3p6OqtXr2bs2LHcfPPNJCQk5Gj7n//8h2HDhgFw7bXX8tdff7FmzRrWr1/P999/H+3Pt956qyNzVaL78j9lZGTQtWtXKlSowKGHHlqkcapsKMn9OT09ndNOO42///6bAw44gEmTJrFmzRrWrVvH888/T0JCAiNGjOCmm24q0phVOhV3XwY46qijdnjuvKM7sm+88UZ69epFSkoKTzzxBIsWLYqeOy9btoz333+fs88+u8hjVulTUvtyZmYmSUlJXHLJJXzyySekp6ezatUqMjIyGDlyJM2aNWPTpk1cc801jBw5sshjVumzK/ryFvk95/X6T/lRkvtyUbdX2VeS+7PXfyVMrDOckgpv8+bNOZalpaUV650lW6xfvz6oXr16AASXXHJJjvV//vlnMGvWrFzbb9y4MWjRokUABPvuu29xhqpSoLj78sKFC6P9tXfv3vluX79+/QAI2rZtu931q1atCipXrhwAwV133VXIaFWalfS+/E833XRTAAT/+te/gi5dungHq7ZR0vvzvffeGwBBcnJy8Mcff+RY/5///CcAgvj4+ODXX38tdLwqvYq7L28Z9ZXbecLOfPbZZ9FRY99++22h41HZVZL78i+//BIsWLAg1/UrV64MateuHQBBhw4dChGlyoJd/V1Gfs95vf5TXpX0vlzU7VW2lfT+7PVfyeIIRqkMiI+Pj9mxhw4dysqVKwG46qqrcqyvW7cu+++/f67tExMTueSSSwD4/fffo/tS+VTcffnZZ59l5cqVHHzwwdx88835br9o0SKAXO+oSk1NpWHDhgCsXbu24IGq1CvpfXlrEyZM4Nlnn6Vhw4bce++9RRShypKS3p8HDhwIwIUXXkiDBg1yrL/++uupXLkyWVlZvPXWW4WOV6VXLM+Z8+LBBx8EoEePHhx55JExjkYlWUnuy40aNaJOnTq5rq9WrVp0FO6kSZN2VVgqoXZlXy7IOa/Xf8qrkt6Xi7K9yr6S3p+9/itZTDBKKpQtZU33339/2rZtW6B9VKxYMfo8KyurSOKStmfLScgll1xCJBLJd/t99tkHINfyN+np6cyaNQvI/SJUKgqF7ctbbNy4kSuuuIIgCHjllVe2+Xss7SqF6c+//vor8+fPB+Dkk0/e7jaVK1emTZs2AHz++eeFiFQqPr/99hsTJkwAiE4/IJVVW843vPbTrlLQc16v/1TSFPb6zes/lSQF6Y9e/5U8JhglFdgff/zB6NGjAbjyyisLvJ8xY8YAsOeee7LbbrsVRWhSDnPmzGHhwoUAtGzZkmnTpnHxxRez5557kpSURN26dbngggsYP358rvu45pprgLDP9ujRgwULFgAQBAGTJ0/mtNNOY+3atbRq1So6MlcqakXRl7d46KGHmDlzJldeeWWBbxKRCqOw/fnnn3+OPm/WrFmux9mybsaMGUUYvbR906dPp1mzZqSkpFC5cmUaNWpE165d+fHHH3NtM27cOAASEhJo0aIFw4YN47jjjqN69eokJyfTsGFDrr/+eubOnbuL3oVUsL6cF1uu/5o3b14EUUo7V9BzXq//VNIU9vrN6z+VJAXpj17/lTwmGCUV2GuvvUYQBFSoUIEuXboUaB/ffvstH374IRCWWC3MSBxpR7bcWQowfvx4Dj30UAYNGkR6ejoVK1ZkwYIFvPfee7Rp04aHH354u/vo0aMHd9xxB3FxcfTt25e6detSpUoVKlasSMuWLZk9ezZ33XUXo0aNKtGlq1S6FUVfBvjxxx954oknqFWrFr169doVoUs5FLY/b0lOAuy11165HmfLutWrV1vCTMVu+fLlzJw5k+TkZDZu3MisWbN49dVXadmyZa6ln7b8LlSvXp177rmH008/nVGjRpGdnU0kEuG3337j+eefp3nz5gwfPnxXvh2VYwXpyzvz7rvvMnnyZAC6du1alOFK21WYc16v/1SSFPb6zes/lSQF7Y9e/5U8JhglFUhWVhYDBgwA4NRTT6V27dr53seyZcu46KKLyM7OZv/99+eOO+4o4iil/9l6fs/77ruPOnXq8MUXX7B27VrS09OZPn067dq1IwgC7r//foYOHZpjH3FxcTz66KO89tprVK5cGQjn2ti0aRMAGzZsID09nXXr1u2aN6VyqSj68ubNm7niiivYvHkzzz77LNWqVduF70D6n8L25zVr1kSfp6Sk5Hqcrddt3UYqSvvvvz9PPPEEv/76Kxs2bODvv/9m3bp1jBgxgpYtWxIEAY888gi9e/fO0XbL78Ly5cvp1asXxx57LDNmzCA9PZ21a9fy+eefs/fee7N27VrOP/985s2bt6vfnsqRwvTlHZk1axbdu3cH4Oijj+ayyy4rhuil/ynsOa/XfyopCtuXvf5TSVKY/uj1X8ljglGKgQEDBhCJRAr8KAl3LQ8fPjxaHuSqq67Kd/u1a9dy+umnM2/ePKpUqcLgwYOjJ+wqPUpTX87Ozo4+D4KA999/n+OOO464uPC/wiZNmjBs2LBosvzBBx/MsY/ly5fToUMHLrvsMlq1asW4ceNYtWoVixYtYujQoey+++68+OKLHHHEEdHfD5UO5a0vP/bYY0yZMoXTTjuN888/f9cErl2mvPVnlV2lqS8DdOrUidtvv52GDRuSkJAAQGJiIieccALjxo3jsMMOA6Bnz56kp6dv03bL70J2djZ16tRh2LBhNG7cGAi/4D7++OMZMmQIkUiENWvW0KdPn134zlRY5akv52bx4sWceuqprFq1ijp16jBo0KDo33qVHqWtLxf2nNfrv7KrvPVlr//KtvLWn1WyeDYnqUBeffVVIBxyntukurlZt24dp556KhMmTKBy5cp8+umnHHjggcURphRVpUqV6PMOHTpwyCGH5NimcuXK9OjRA4CpU6eyZMmSbdZ36dKFMWPG0LZtW0aMGEHr1q1JTU2ldu3anHXWWYwbN46aNWvyxx9/cNdddxXvG1K5Vdi+PGPGDB5++GEqV65M3759iz9gaQcK25+3bp+RkZHrcbZet3UbaVepWLEi//nPf4DwRrtRo0Zts37rfnnttddu947sww47jGOPPRaAzz//vBijlXK3s768PUuXLqVDhw7Mnj2bWrVqMWrUKOrWrVvcoaqcK4pzXq//VBIUti97/aeSpLD90eu/kqdCrAOQyqOLLrqI0047rcDtU1NTizCa/FuyZAkff/wxAJdddlm+5hrYklwcO3YslSpV4pNPPuHoo48urlBVzEpTX966NvuWEQHb06RJk+jzefPmUatWLQBmzpzJp59+CsCtt95KJJJzvtA99tiDSy+9lD59+jB06FAGDhy43e1U8pSnvtyjRw82bdrEgw8+SPXq1XPMR7B582YgHE22ZV1SUlJ0BINKvvLUn+vUqRNdvmDBAqpWrbrd9ltGFVStWtWKCaVIaerLedGqVavo8z/++GObdfn5XRg1apQlUkuZ8tSX/2np0qXRkr977LEHX375JQcccEBxh6hiUpr6cmHPeb3+K9vKU1/2+q/sK0/92eu/kscEoxQDSUlJJCUlxTqMAhs4cCCbN28mEolwxRVX5LndluTiV199RUpKCp988gnHHHNMMUaq4laa+nKTJk2Ij48nKytrh9sFQRB9vvXF4YwZM6LP991331zb77///kB4t9TSpUujX4KrZCtPfXnOnDkA3H333dx99925tp8/f370Tr+nnnqKm266qRBRa1cqT/25WbNm0ec///xzromZn3/+OXo8lR6lqS8XVosWLfK03ZbfBb/ALl3KU1/e2tKlS2nfvv02yUX/DpdupakvF/ac1+u/sq089WWv/8q+8tSfvf4reSyRKinf+vXrB0D79u3ZZ5998tRm3bp1nHLKKXz11VdUqlSJTz/9lLZt2xZnmNI2KlasGE1oz5w5M9fttlxIRiIR6tevH12+9RwxOxo1sHXpPu+SUnEobF+WSpLC9ueGDRuy9957A+Q6d8i6dev4+uuvATjhhBOKImypQCZMmBB93qBBg23WtW7dmkqVKgF5+134Z3tpV9pRX95iyZIlOZKLTZs23VUhSoXm9Z8klTxe/5U8Jhgl5cu4ceP49ddfAbjqqqvy1GZLcnFLWVSTi4qVyy+/HIBRo0YxefLkHOvXrl0brQF/xBFHsPvuu0fXbT0v2Isvvrjd/a9bt46BAwcC4UiELV8USkWtMH157ty5BEGQ66NLly4ApKWlRZd596qKU2H6cyQS4dJLLwXgnXfeYe7cuTnav/DCC6xdu5b4+Hg6depUDO9A2naU7fZs3LiRf/3rXwBUqlSJDh06bLM+OTmZCy64AIC+fftud06ZSZMmMXr0aAA6duxYFGFLORS2L8O2ZVFr1arF6NGjTS5qlyvsOa/XfyopCtuXvf5TSVLY/uj1X8ljglEqI1auXMny5cujj+zsbCAs07H18n/Wtgbo2bMnkUiESCSy3T/MW3v11VcBqFGjBmefffZO48rIyOC0005j7NixVK5cmc8++8yyqNqh4uzLnTp14vDDDycIAs455xxGjRoV3f/MmTM5/fTTWbx4MXFxcTzyyCPbtE1LS4t+mTds2DA6d+7M77//ThAEZGZm8s0339CuXbvoPDS33nprUX4sKoVKal+WCqIk9+fbbruN2rVrk5GRwamnnsoPP/wAwKZNm3jxxRe57777ALj66qtp2LBhUX0kKqWKqy+PHTuW4447jjfeeIO//vorujwzM5NRo0bRpk0bJk6cCMD9999PtWrVcuz/oYceIjU1lYULF3L66adHRzJmZ2czcuRIzj33XIIgYI899uCWW24pok9EpVVJ7cvLli2LJhdr167N6NGjLU+mHdpV32Xkl9d/yq+S2pelgijJ/dnrvxImkFQmpKWlBcBOH126dMnR9oEHHoiunzNnTq7HSE9PD1JSUgIguOGGG/IU1+uvvx7dd8WKFYNatWrt8DF+/PgCfgIqK4q7Ly9atCho0qRJdLuUlJQgNTU1+johISF45ZVXttt22bJlQcuWLbeJIyUlJahQocI2y26//fYi/ERUWpXkvrwjXbp0CYAgLS0t321VdpX0/vz9998Hu+22W3T7KlWqBAkJCdHXJ5xwQrBhw4Yi+jRUmhVXXx49evQ27ZOTk4OaNWtu0w/j4uKCe+65Z4fxjR07NqhWrVq0TWpqapCcnBx9vcceewQTJ04swk9EpVVJ7csPPvhgdLtKlSrt9Ppv/vz5xfDpqDTZFd9lbE9eznm9/lN+lOS+XJztVTaV9P7s9V/JUQFJyqN33nknWq4pr+VRt9zhArBhwwY2bNiww+03bdpU8AClPKhduzaTJ0/m+eef591332XWrFmsX7+e+vXrc+yxx3LzzTdvM2n01mrWrMmECRN4/fXXGTx4MFOmTGHFihVUqFCBvffem6OOOopu3bpx9NFH7+J3pfKoMH1ZKmkK259btmzJ9OnTefzxx/n444/5888/qVSpEs2aNaNLly5cccUV28ylJBW15s2b8+STT/Ltt98ybdo0li9fzqpVq0hJSaFJkya0adOGq6++mubNm+9wP23atGHGjBk8+eSTfPLJJ/z5559EIhGaN29Ox44duemmm7YpEywVtcL25a2v/9atW8e6det2eLysrKwijV8qSl7/SVLJ5PVfyREJgp0U2JckSZIkSZIkSZKk/880riRJkiRJkiRJkqQ8M8EoSZIkSZIkSZIkKc9MMEqSJEmSJEmSJEnKMxOMkiRJkiRJkiRJkvLMBKMkSZIkSZIkSZKkPDPBKEmSJEmSJEmSJCnPTDBKkiRJkiRJkiRJyjMTjJIkSZIkSZIkSZLyzASjJEmSJEmSJEmSpDwzwShJkiRJkiRJkiQpz0wwSpIkSVIJM3fuXCKRCJFIhLlz58Y6nBJpwIABRCIR6tevH5Pj9+zZk0gkQrt27WJyfEmSJEmKJROMkiRJkkqELQmbvD6k4jBlyhR69uzJ008/HetQdpl27dpt93csLi6OqlWrctBBB3Hbbbcxb968Yjn+mDFj8vx7P2bMmG3aDhs2jNtuu4327duz7777UrVqVRITE6lTpw4nn3wy/fv3Z/PmzcUStyRJklSeVYh1AJIkSZL0T7Vq1Yp1CCqnpkyZwoMPPkhaWho33XRTrtvVrFmTRo0asffee++64IpZQkICNWrUiL7OzMxkxYoV/PTTT/z000/07duX999/n5NPPrnYYqhevTqJiYm5rv/nurvvvpvp06dHX1epUoX4+HgWLVrEokWLGD58OM8//zyffvqpf1ckSZKkImSCUZIkSVKJs3jx4liHIO3Qddddx3XXXRfrMIrUUUcdlWOE4IYNG/jvf/9Ljx49+Pvvv7nkkkuYP38+lSpVKpYYhg4dmq+ys+eeey433ngjrVu3pkGDBiQnJwOwcOFCXn31VR588EEmT55Mly5dGD58eLHELEmSJJVHJhglSZIkSdJ2VaxYkQsuuIDMzEw6d+7MihUrGDt2bLGOYsyPnj17bnd5nTp1uP/++9mwYQOPPvooI0aM4K+//qJu3bq7NkBJkiSpjHIORkmSJEmlXv369YlEIgwYMIA1a9Zw991306hRI5KTk6lZsyZnnnkmEydO3OE+srKyeO211zj22GOpWbMmSUlJ7LXXXpx33nk5RnUVhQULFtCtWzfq1atHUlISdevW5fLLL2f27Nl5ar9p0yb69u1L+/btqVmzJomJidSuXZszzjiDzz77bIdt161bxwMPPEDjxo1JTk5mjz324JRTTmHUqFHAtp/n1ubOnRudC2/u3Ln8/vvvXH311TRo0ICkpCTq168f3XblypX069eP888/n+bNm1OjRg0qVqxIWloaF198MRMmTNjpe5wwYQJnnnkmNWvWJDk5mUaNGvGvf/2LtWvX7rBdRkYGgwYN4tJLL+Wggw5i9913JykpiTp16nDmmWfm+vlEIhEuv/xyAObNm5dj/r+tk1lb5gzd0Wi7H3/8kUsvvZS0tDQqVqxI9erVOeqoo3j66afZuHHjdtsMGDCASCQS/Sx/+OEHzj//fPbcc0+SkpLYZ599uOWWW1i5cuUOP4OidtBBB0Wfb+/zL4qfd3E48sgjo88XLFgQkxgkSZKkMimQJEmSpBLggQceCICgIJcpaWlpARD06dMnaNSoUQAEiYmJQdWqVaP7jIuLC/r167fd9qtWrQratWsX3TY+Pj6oVq1aEIlEostuu+22wr7FqB9++CGoXr16dN/JyclB5cqVAyCoWrVq8O6770bXzZkzJ0f7uXPnBk2bNo1uE4lEgtTU1OhrIOjevft2j71kyZKgSZMm0e0SEhKCatWqRffz4osvRj/P/v37b9N2zpw50XZvvfVWNOaUlJSgUqVKQVpaWnTbrX+e8fHxQfXq1YOkpKRtYn7mmWdy/Yz69esXxMXFRbdPTU0NEhMTAyA44IADgj59+gTANsfcon///jk+m5SUlG0+n1tvvTVHu1q1akX7TFxcXFCrVq1tHr169crx/tq2bbvd+Pv06bNN/0lNTQ0SEhKir1u0aBEsXLgw19jT0tKCt956K9omNTV1m8+jadOmwZo1a3L9/PKjbdu2O3wvQRAEb775ZvTYP/30U471hfl5jx49Orrd6NGji+Q9bXHbbbdFj7906dIi3bckSZJUnjmCUZIkSVKZ8eCDD7J06VLee+891q1bR3p6OjNmzKBt27ZkZ2fTrVs3Jk+enKPdlVdeyZgxY0hMTOTZZ59l9erVrFy5koULF3LFFVcA8OSTT/LSSy8VOsY1a9Zw1llnsXLlSvbee28+//xz1q1bx5o1a/jmm2+oV68e3bp1y7X9unXrOOmkk5g+fTrt2rVjzJgxrF+/nlWrVrFq1Sr69OlD5cqVeemll3jmmWdytO/SpQszZswgOTmZfv36sWbNGlauXMn8+fM5//zzufHGG1m2bNlO30e3bt1o2rQpkyZNYt26daxdu5bPP/88ur5OnTo88MADfP/992RkZLBixQrWr1/PH3/8wY033gjALbfcwo8//phj35MnT6Zbt25kZ2fTrl07Zs6cyapVq1i7di2DBg1i8eLFPPTQQ7nGVr16dW677TbGjRvH2rVrWbVqFevWrWPhwoU8+OCDJCQk0Lt3bz766KNt2i1evDj6mdWrV4/Fixdv87jtttt2+rkAfPzxx9xyyy0EQcAZZ5zBH3/8EY1/4MCBVKlShalTp3LuueeSlZW13X0sW7aMK664gi5dujB//nxWrVrFmjVreP7550lISGD69Ok88cQTeYqnMDZu3MiQIUO4+eabATjhhBNo0aJFju0K8/MuamvXruXnn3/mjjvuoHfv3gB07tyZ3XffvdiPLUmSJJUbsc5wSpIkSVIQbDsC6p8jx/75uOGGG7Zpu2XEHRCMHDkyx74zMjKC/fffPwCCU045ZZt1EyZMiLZ9+eWXtxvbOeecEwBBzZo1g/Xr1xfqfT7++OPREZYzZszIsX7RokXbjG785wjGhx56KDrabNOmTds9xtChQ6PxZmZmRpd//fXX0f2+8cYbOdplZWUF7du3j26zoxGMaWlphRpB16NHjwAIrrzyyhzrTj755AAIGjZsGGRkZORYP3z48G3iyK9evXoFQNChQ4cc67YeQbgjOxrB2Lhx4wAI2rRpE2zevDnH+o8++iga/+DBg7d7fCDo0qXLdo99yy23BECw33777TDGvNoygjEhIWGb37MaNWpEY6lXr15wxx13bPfnkRc7+nlvPYKxevXquf7ev/LKK7nu/9tvv91mhOqWR3x8fHDFFVcUOG5JkiRJ2+cIRkmSJEklzpIlS3b4SE9P32671q1b06FDhxzLk5OTuf322wEYPnz4Nu3fffddAOrWrctVV1213f0+/PDDACxfvpwvvviiUO/tnXfeAeC8886jcePGOdbXrl2b7t2759q+X79+QDgaLCEhYbvbnHnmmVStWpXly5fzww8/RJcPHjwYCOdY7NSpU452cXFx3HvvvXl6H9dddx2VK1fO07bbc+qppwIwbty4bZavWrWKESNGAHD77beTnJyco+2JJ55Iq1atCn3sb7/9NtcRhAU1depUZs6cCcC9995LfHx8jm06duzI4YcfDsCgQYNy3VduP4szzjgDgNmzZ5ORkVHYkKMyMzO3+T1bsWJFdF16ejorVqzI9XdvZ3L7ef/TypUrc/29X7duXa7tEhMTqVWrFrVq1SIxMTG6vFu3bjzwwAPb7UeSJEmSCs4EoyRJkqQSJwiCHT4GDBiw3XbHHntsrvvcsi47O3ubMqnff/89AO3btycubvuXSI0bN2avvfbaZvuC2LRpE9OmTctzrP+0YMEC5s2bB4RlXWvXrr3dx5577snatWsBotsD0fd9zDHHEIlEtnuM1q1bU6FChZ2+l9atW+90mz/++IPbbruNli1bUq1aNeLj44lEIkQiEU455RQA/vrrr23aTJ48mezsbKBgn9EWS5Ys4YEHHqBVq1bstttuVKhQIXrsJk2aAJCRkcHKlSt3+j7yY0v/qFChAm3bts11u+OPP36b7f+pRo0a7LfffttdV6dOnejzooy/bdu2OX7X0tPTGTlyJC1atODVV1/l8MMP57fffttu+4L8vP9p9OjRuf7e33TTTbm2O+SQQ6KlbNevX89vv/1Gjx49eOmll2jatGmOcriSJEmSCmfnV42SJEmSVEpsSQLubN3SpUtzPN9RWwhHOC5YsGCbtvm1YsUKNm/evNPj1a1bd7vLFy5cGH2+fPnyPB1z6xFuW+ZW3DpB9U9JSUnUrFmTxYsX73C/e+yxxw7Xf/DBB1x00UVs3Lgxuqxq1apUrFiRSCTCpk2bWLlyZY5RaVt/vgX5jCAcmXjKKaewatWq6LLKlSuTkpJCJBIhKysr+vmtW7eOmjVr7vC95MeW+GvWrElSUlKu222JP7f+VKVKlVzbbp0AzszMLEiYeVa1alU6dOjA4YcfTvPmzZk3bx7dunXjyy+/3Ga7gv68i0NcXBz77bcfzz//PPvssw+33nornTp1YtasWey5557FfnxJkiSpPHAEoyRJkiSVEluX85w5c+ZOR3oGQcBll12WYz+5jV7Mj+2V/tzi77//5rLLLmPjxo0ce+yxjBkzhoyMDNLT01myZAmLFy+Olmstaps3b+aiiy5i1apVHHTQQXz66aesXr2aNWvWRI89YcKE6PZBEBRLHGVNlSpVuOCCC4BwlOGiRYui62L5896Za6+9lqSkJNauXbvDcrSSJEmS8scEoyRJkqQyY8GCBXlat/Xouy3Pd1a6ccv6nY3c25EaNWpEE3N5jXVrtWvXjj7fuvRpXu2+++7AtiMh/2njxo15Hh2Zmy1JverVqzNs2DDatm2bYw683EZIbv35FuQz+vbbb5k3bx7x8fF8/PHHnHzyyTlGA+5sdGZhbIl/+fLl24zm+6ei6E+7WlpaWvT53Llzo88L8/MubhUrVqRGjRpAOGelJEmSpKJhglGSJElSmTF69OidrouLi+Pggw+OLj/00EOj67fM/fdPv/zySzShddhhhxU4vsTERFq0aLHTWP9ZfnKL+vXrR8uGDhs2LN/HP+SQQwD46quvct1m/Pjx0TKuBfXnn38C0KhRI1JSUra7zciRI3ONcctcmAX5jLYce/fdd8+1xGpuxwaixy7oyMYt/Wnz5s07/Jy3xFCY/rSrbZ2Er1SpUvR5YX7exW3NmjXR0sA7KjsrSZIkKX9MMEqSJEkqM8aNG8eYMWNyLN+wYQO9e/cG4MQTT6RatWrRdRdeeCEQjoh79dVXt7vf+++/Hwjn1TvuuOMKFeOWMpODBw/m119/zbF+6dKlvPTSS7m279q1KwD9+vXjxx9/3OGxVqxYsc3rc889FwhHn7399ts5tg+CgP/85z87fgN5kJqaCsCsWbPYsGFDjvVTpkzZ7vEBqlWrxgknnADAk08+ud32I0eO5JtvvtnhsZcsWcKSJUtyrP/rr7949tlnc429atWqANvM35gfLVq0oEmTJgD8+9//3qas7RaffvopEydOBOCiiy4q0HF2tQ0bNjB06FAgnM+yUaNG0XWF+XkXRl4S4b169Ypu165duyKPQZIkSSqvTDBKkiRJKjNSU1M555xzGDJkSDSp8Msvv3Dqqafyyy+/EB8fz0MPPbRNm8MPP5xzzjkHgOuvv57nn3+ejIwMICzr2LVr1+j8cQ8//DAVK1YsVIzXXHMNdevWZePGjZx00kmMGjUqOlpu4sSJHHfccbmOpAS49dZbad68ORs2bKB9+/Y8//zz/P3339H1q1at4rPPPuPSSy+lTZs227Rt06YNxx9/PBAmKgcMGBAt4/nXX3/RqVMnvv7661xHoeXVCSecQFxcHCtWrKBTp07R0Z+bNm3ivffe44QTTtjhaLKHH36Y+Pj46M9uSyJ28+bNvPfee5x//vnbJIm3dvTRR1OpUiWCIOD8889n1qxZQDh/5YgRI2jXrt0O56Bs1qwZAKtXr+a9994ryNvn8ccfB+Drr7/m3HPPZc6cOQBkZmby1ltvRZOKRx11FGeeeWaBjrErzZgxgwsvvDD6c+jRowdJSUnR9YX9eRfUW2+9xemnn87QoUNZunRpdHl2djZTp07l6quv5uGHHwagdevWnHTSSUUegyRJklRuBZIkSZJUAjzwwAMBEABBrVq1dvoYP358tG1aWloABH369AkaNWoUAEFSUlKQmpoa3WckEgleeeWV7R571apVQdu2baPbVqhQIahevXoQiUSiy2677bYie6+TJk0KqlWrFt13SkpKULly5QAIqlSpErz77rvRdXPmzMnRfsGCBcGRRx65zXurVq1aULVq1egyINhvv/1ytF20aFFwwAEHRLdJSEiIxhIXFxe88sorwd577x0AwaBBg7ZpO2fOnB3GtbU777xzm1hSU1ODhISEAAgaNGgQvPXWW9F12/Pyyy9v8/mnpqYGSUlJARAccMABQZ8+fQIgSEtLy9H2xRdf3ObYlStXDipWrBgAQc2aNYOPPvpoh++jQ4cO0fVVqlQJ0tLSgrS0tOCpp56KbrOlv7Zt23a78ffp02eb+KtVqxYkJiZGXzdv3jxYsGBBjnb9+/fP9X1tkZ+fQ15s6fsJCQk5fs9SUlK2+SwvuOCCYNOmTTn2UZif9+jRo6PrRo8enee4t3xWWx6VKlUKatasuc3nDATHHnts8PfffxfmI5IkSZL0D45glCRJklTibClvuaPHpk2bcrSrXr063333HXfddRd77703GzdupEaNGnTs2JHx48dHy4v+U2pqKqNGjaJfv360a9eOKlWqsHbtWmrXrs0555zD6NGj6dWrV5G9v0MPPZSpU6dy1VVXsddee7F582ZSU1Pp0qULkydP5vDDD99h+zp16jBu3DgGDRrE6aefzp577klGRgabNm2ifv36dOzYkaeffpqxY8fmaFu7dm0mTZrEfffdR6NGjYiLi6NChQqccsopfPnll3Tt2pX09HSAXEcJ5sVjjz3GwIEDOfzww0lOTiYzM5P99tuPe+65hx9//JE6derssP3VV1/N+PHj6dixIzVq1GDjxo2kpaVx9913891331G9evVc23bv3p1PPvmEdu3aUblyZTZv3sxee+3F9dd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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "result_pypesto = profile.parameter_profile(\n", " problem=problem,\n", @@ -639,9 +1788,182 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "State([[-1.24027462 1.78932702 -3.93716867 -1.83911125 -1.99677746 4.14486841\n", + " 1.47913514 2.0021859 1.35932731]\n", + " [-1.47022978 1.7611698 4.98482712 0.85855303 -4.1989386 -4.3008923\n", + " 1.8615283 1.95714177 1.35676185]\n", + " [-0.41107448 -0.14952555 -1.77644606 4.16117419 -4.81759753 4.76263479\n", + " 1.60195281 1.74819962 1.4403907 ]\n", + " [-1.43602926 2.50778511 -2.30100058 -2.81499186 -4.86002175 3.36625301\n", + " 1.68878632 1.88056349 1.14940453]\n", + " [-1.22391434 1.81872122 -4.926788 -1.83840725 -1.59900807 4.96243857\n", + " 1.52924675 2.06366179 1.22987441]\n", + " [ 3.17131352 2.57330524 -1.1846534 -1.70751361 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3787178673, 3949825982, 2088303512,\n", + " 3672572846, 2002937565, 1152259001, 2024262702, 3512380730,\n", + " 1978640799, 689801872, 1484426853, 2228701662], dtype=uint32), 379, 0, 0.0))" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import emcee\n", "\n", @@ -736,7 +2058,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -746,9 +2068,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(\n", " trace_neglogpost.reshape(\n", @@ -771,9 +2104,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:03<00:00, 311.04it/s]\n", + "Elapsed time: 3.9380469999998695\n" + ] + } + ], "source": [ "# Sampling\n", "sampler = sample.AdaptiveMetropolisSampler()\n", @@ -787,9 +2129,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# plot objective function trace\n", "visualize.sampling_fval_traces(result_pypesto);" @@ -797,9 +2150,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "visualize.sampling_1d_marginals(result_pypesto);" ] @@ -817,7 +2181,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -841,9 +2205,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: You are loading a problem.\n", + "This problem is not to be used without a separately created objective.\n" + ] + } + ], "source": [ "# Read result\n", "result2 = store.read_result(fn, problem=True)" @@ -851,9 +2224,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# plot profiles\n", "visualize.sampling_1d_marginals(result2);" @@ -883,7 +2267,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.10.2" } }, "nbformat": 4, From 14d9964d5c73c6a77398fb206fa078e227955462 Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Wed, 10 Jan 2024 10:07:10 +0100 Subject: [PATCH 25/31] py311 --- .readthedocs.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.readthedocs.yml b/.readthedocs.yml index 9fc16ea91..4035b7693 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -26,4 +26,4 @@ build: - libhdf5-serial-dev - swig tools: - python: "3.10" + python: "3.11" From 402d687bdab35307a73ef9123c0b60fe8847c93d Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Wed, 10 Jan 2024 12:50:06 +0100 Subject: [PATCH 26/31] .. --- doc/example/store.ipynb | 648 ++++------------------------------------ 1 file changed, 56 insertions(+), 592 deletions(-) diff --git a/doc/example/store.ipynb b/doc/example/store.ipynb index 26460d734..fc83fc3ed 100644 --- a/doc/example/store.ipynb +++ b/doc/example/store.ipynb @@ -11,9 +11,10 @@ "# Result Storage\n", "\n", "This notebook will show how to store pypesto result objects to be able to load them later on for visualization and further analysis.\n", - "This includes sampling, profiling and optimization. Additionally we will show how to use optimization history to look further into an optimization run and how to store the history.\n", + "This includes sampling, profiling and optimization. Additionally, we will show how to use optimization history to look further into an optimization run and how to store the history.\n", + "\n", + "After this notebook, you will...\n", "\n", - "After this notebook you will...\n", "* know how to store and load optimization, profiling and sampling results\n", "* know how to store and load optimization history\n", "* know basic plotting functions for optimization history to inspect optimization convergence" @@ -21,12 +22,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" } @@ -50,12 +48,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, @@ -70,10 +65,8 @@ "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import scipy as sp\n", "from IPython.display import Markdown, display\n", "\n", - "import pypesto\n", "import pypesto.optimize as optimize\n", "import pypesto.petab\n", "import pypesto.profile as profile\n", @@ -99,17 +92,14 @@ "## 0. Objective function and problem definition\n", "\n", "We will use the Boehm model from the [benchmark initiative](https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab) in this notebook as an example.\n", - "We load the model through [PEtab](https://petab.readthedocs.io/en/latest/), a data format for specifying parameter estimation problems in systems biology." + "We load the model through [PEtab](https://petab.readthedocs.io/en/latest/), a data format for specifying parameter estimation problems in systems biology." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, @@ -152,84 +142,15 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/15 [00:00\n", - "* message: Converged according to fval difference \n", - "* number of evaluations: 178\n", - "* time taken to optimize: 1.785s\n", - "* startpoint: [-3.82648946 4.99153731 -2.17952968 -1.39145733 -0.5747054 4.45740142\n", - " 2.35956422 0.77882698 -2.71223689]\n", - "* endpoint: [-1.56909619 -4.98901477 -2.20972883 -1.78588445 4.99964502 4.19771653\n", - " 0.58570452 0.81883751 0.49857757]\n", - "* final objective value: 138.2225919756037\n", - "* final gradient value: [ 6.99548540e-03 5.67684178e-02 4.15813519e-03 1.94420735e-02\n", - " -4.41997844e-05 -7.10489673e-03 9.89156691e-04 -6.98367891e-04\n", - " -4.07797538e-04]\n", - "* final hessian value: [[ 2.11240897e+03 6.03813646e-01 1.06890240e+02 2.81533877e+03\n", - " 8.82178847e-06 -7.86516037e+02 1.02527778e+02 -8.02295866e+01\n", - " -2.23142988e+01]\n", - " [ 6.03813646e-01 2.01317170e-03 -1.77299630e-01 7.30061900e-01\n", - " -3.70852695e-08 -3.28593144e-01 -1.52623294e-02 -5.82723434e-02\n", - " -5.71794398e-02]\n", - " [ 1.06890240e+02 -1.77299630e-01 6.99968515e+01 1.61225414e+02\n", - " 7.01455291e-06 -8.83050425e+01 -3.51659254e+00 -3.02430751e+00\n", - " 6.53132559e+00]\n", - " [ 2.81533877e+03 7.30061900e-01 1.61225414e+02 3.76198620e+03\n", - " 8.31596889e-06 -1.04206720e+03 1.32794432e+02 -1.05781631e+02\n", - " -2.70575675e+01]\n", - " [ 8.82178847e-06 -3.70852695e-08 7.01455291e-06 8.31596889e-06\n", - " 2.74313621e-10 -2.20192310e-04 4.21842641e-05 5.32054764e-05\n", - " 6.38402412e-06]\n", - " [-7.86516037e+02 -3.28593144e-01 -8.83050425e+01 -1.04206720e+03\n", - " -2.20192310e-04 9.30413190e+02 -8.20645071e+01 4.93179742e+01\n", - " 3.27628925e+01]\n", - " [ 1.02527778e+02 -1.52623294e-02 -3.51659254e+00 1.32794432e+02\n", - " 4.21842641e-05 -8.20645071e+01 8.48280922e+01 0.00000000e+00\n", - " 0.00000000e+00]\n", - " [-8.02295866e+01 -5.82723434e-02 -3.02430751e+00 -1.05781631e+02\n", - " 5.32054764e-05 4.93179742e+01 0.00000000e+00 8.48319778e+01\n", - " 0.00000000e+00]\n", - " [-2.23142988e+01 -5.71794398e-02 6.53132559e+00 -2.70575675e+01\n", - " 6.38402412e-06 3.27628925e+01 0.00000000e+00 0.00000000e+00\n", - " 8.48313088e+01]]\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "display(Markdown(result.summary()))" ] @@ -354,41 +196,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 9/9 [00:05<00:00, 1.53it/s]" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 5.66 s, sys: 325 ms, total: 5.98 s\n", - "Wall time: 5.88 s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "\n", @@ -414,35 +230,15 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 5000/5000 [00:05<00:00, 853.90it/s]\n", - "Elapsed time: 6.2119409999999995\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 5.08 s, sys: 1.14 s, total: 6.22 s\n", - "Wall time: 5.86 s\n" - ] - } - ], + "outputs": [], "source": [ "%%time\n", "\n", @@ -472,12 +268,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, @@ -488,7 +281,7 @@ "# create temporary file\n", "fn = tempfile.mktemp(\".hdf5\")\n", "\n", - "# write result with write_result function.\n", + "# write the result with the write_result function.\n", "# Choose which parts of the result object to save with\n", "# corresponding booleans.\n", "pypesto.store.write_result(\n", @@ -514,27 +307,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: You are loading a problem.\n", - "This problem is not to be used without a separately created objective.\n" - ] - } - ], + "outputs": [], "source": [ "# load result with read_result function\n", "result_loaded = pypesto.store.read_result(fn)" @@ -548,19 +329,16 @@ } }, "source": [ - "As you can see, when loading the result object, we get a warning regarding the problem. This is the case, as the problem is not fully saved into hdf5, as a big part of the problem is the objective function. Therefore after loading the result object we cannot evaluate the objective function anymore. We can however still use the result object for plotting and further analysis.\n", + "As you can see, when loading the result object, we get a warning regarding the problem. This is the case, as the problem is not fully saved into hdf5, as a big part of the problem is the objective function. Therefore, after loading the result object, we cannot evaluate the objective function anymore. We can, however, still use the result object for plotting and further analysis.\n", "\n", "The best practice would be to still create the problem through petab and insert it into the result object after loading it." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, @@ -575,27 +353,15 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Objective function call: 138.22259071495967\n", - "Corresponding saved value: 138.2225919756037\n" - ] - } - ], + "outputs": [], "source": [ "result_loaded.problem = problem\n", "print(\n", @@ -639,29 +405,15 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# waterfall plot original\n", "ax = visualize.waterfall(result)\n", @@ -670,29 +422,15 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# waterfall plot loaded\n", "ax = visualize.waterfall(result_loaded)\n", @@ -712,29 +450,15 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# profile plot original\n", "ax = visualize.profiles(result)" @@ -742,29 +466,15 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# profile plot loaded\n", "ax = visualize.profiles(result_loaded)" @@ -783,29 +493,15 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# sampling plot original\n", "ax = visualize.sampling_fval_traces(result)" @@ -813,29 +509,15 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# sampling plot loaded\n", "ax = visualize.sampling_fval_traces(result_loaded)" @@ -849,7 +531,7 @@ } }, "source": [ - "We can see that we are perfectly able to reproduce the plots from the loaded result object. With this we can reuse the result object for further analysis and visualization again and again without spending time and resources on rerunning the analyses." + "We can see that we are perfectly able to reproduce the plots from the loaded result object. With this, we can reuse the result object for further analysis and visualization again and again without spending time and resources on rerunning the analyses." ] }, { @@ -871,7 +553,7 @@ } }, "source": [ - "During optimization, it is possible to regularly write the objective function trace to file. This is useful e.g. when runs fail, or for various diagnostics. Currently, pyPESTO can save histories to 3 backends: in-memory, as CSV files, or to HDF5 files." + "During optimization, it is possible to regularly write the objective function trace to file. This is useful, e.g., when runs fail, or for various diagnostics. Currently, pyPESTO can save histories to 3 backends: in-memory, as CSV files, or to HDF5 files." ] }, { @@ -898,53 +580,20 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 27%|██▋ | 4/15 [00:01<00:04, 2.21it/s]2024-01-03 14:38:17.013 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 91.8632 and h = 2.39949e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:17.013 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 91.8632: AMICI failed to integrate the forward problem\n", - " 33%|███▎ | 5/15 [00:02<00:05, 1.85it/s]2024-01-03 14:38:18.379 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 138.953 and h = 4.03132e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:18.380 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 138.953: AMICI failed to integrate the forward problem\n", - " 47%|████▋ | 7/15 [00:03<00:04, 1.74it/s]2024-01-03 14:38:19.107 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.221 and h = 5.19496e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:19.108 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.221: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:19.131 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.3098 and h = 7.87193e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:19.132 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.3098: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:19.180 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0964 and h = 1.00596e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:19.180 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0964: AMICI failed to integrate the forward problem\n", - " 53%|█████▎ | 8/15 [00:04<00:04, 1.72it/s]2024-01-03 14:38:19.489 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 141.902 and h = 3.68334e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:19.489 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 141.902: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:19.682 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.628 and h = 3.05681e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:19.682 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.628: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:19.690 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.751 and h = 2.68544e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:19.690 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.751: AMICI failed to integrate the forward problem\n", - " 67%|██████▋ | 10/15 [00:04<00:02, 2.11it/s]2024-01-03 14:38:20.224 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 173.778 and h = 1.59107e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:20.224 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 173.778: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:20.343 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 90.1925 and h = 9.62557e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:20.343 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 90.1925: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:20.383 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.2188 and h = 1.94091e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:20.384 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.2188: AMICI failed to integrate the forward problem\n", - "100%|██████████| 15/15 [00:07<00:00, 2.03it/s]\n" - ] - } - ], + "outputs": [], "source": [ "# record the history\n", "history_options = pypesto.HistoryOptions(trace_record=True)\n", "\n", - "# Run optimizaitons\n", + "# Run optimizations\n", "result = optimize.minimize(\n", " problem=problem,\n", " optimizer=optimizer,\n", @@ -967,36 +616,15 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "History type: \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "print(\"History type: \", type(result.optimize_result.list[0].history))\n", "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", @@ -1026,82 +654,19 @@ } }, "source": [ - "The in-memory storage is however not stored anywhere. To do that, it is possible to store either to CSV or HDF5. This is specified via the `storage_file` option. If it ends in `.csv`, a `pypesto.objective.history.CsvHistory` will be employed; if it ends in `.hdf5` a `pypesto.objective.history.Hdf5History`. Occurrences of the substring `{id}` in the filename are replaced by the multistart id, allowing to maintain a separate file per run (this is necessary for CSV as otherwise runs are overwritten)." + "The in-memory storage is, however, not stored anywhere. To do that, it is possible to store either to CSV or HDF5. This is specified via the `storage_file` option. If it ends in `.csv`, a `pypesto.objective.history.CsvHistory` will be employed; if it ends in `.hdf5` a `pypesto.objective.history.Hdf5History`. Occurrences of the substring `{id}` in the filename are replaced by the multistart id, allowing to maintain a separate file per run (this is necessary for CSV as otherwise runs are overwritten)." ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 7%|▋ | 1/15 [00:01<00:16, 1.18s/it]2024-01-03 14:38:24.353 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 97.3155 and h = 1.86559e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:24.353 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 97.3155: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:24.635 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 143.725 and h = 3.65092e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:24.635 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 143.725: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:24.719 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 87.8335 and h = 3.32376e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:24.720 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 87.8335: AMICI failed to integrate the forward problem\n", - " 13%|█▎ | 2/15 [00:01<00:11, 1.11it/s]2024-01-03 14:38:25.363 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.382 and h = 3.83048e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:25.364 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.382: AMICI failed to integrate the forward problem\n", - " 20%|██ | 3/15 [00:02<00:09, 1.23it/s]2024-01-03 14:38:25.673 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 147.29 and h = 2.44013e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:25.673 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 147.29: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:25.896 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.507 and h = 3.82449e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:25.897 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.507: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:25.916 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.731 and h = 3.87887e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:25.916 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.731: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:25.948 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.707 and h = 3.75929e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:25.948 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.707: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:25.957 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1274 and h = 1.34801e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:25.958 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1274: AMICI failed to integrate the forward problem\n", - " 27%|██▋ | 4/15 [00:03<00:07, 1.43it/s]2024-01-03 14:38:26.576 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 85.3765 and h = 1.22847e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:26.576 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 85.3765: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:26.707 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.2611 and h = 1.05251e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:26.707 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.2611: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:26.815 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 88.6779 and h = 1.44723e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:26.815 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 88.6779: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:27.037 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.516 and h = 3.5887e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:27.037 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.516: AMICI failed to integrate the forward problem\n", - " 33%|███▎ | 5/15 [00:04<00:08, 1.21it/s]2024-01-03 14:38:27.490 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.661 and h = 3.9367e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:27.490 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.661: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:27.531 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.016 and h = 4.47442e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:27.531 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.016: AMICI failed to integrate the forward problem\n", - " 40%|████ | 6/15 [00:04<00:06, 1.39it/s]2024-01-03 14:38:27.960 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1002 and h = 2.68972e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:27.960 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1002: AMICI failed to integrate the forward problem\n", - " 53%|█████▎ | 8/15 [00:05<00:04, 1.57it/s]2024-01-03 14:38:29.381 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.76 and h = 3.10334e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:29.381 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.76: AMICI failed to integrate the forward problem\n", - " 60%|██████ | 9/15 [00:06<00:04, 1.36it/s]2024-01-03 14:38:30.224 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.502 and h = 3.43856e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:30.224 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.502: AMICI failed to integrate the forward problem\n", - " 73%|███████▎ | 11/15 [00:07<00:02, 1.81it/s]2024-01-03 14:38:30.796 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 92.1328 and h = 1.73628e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:30.796 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 92.1328: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:30.886 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 146.791 and h = 3.76771e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:30.887 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 146.791: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:30.966 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.791 and h = 4.23167e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:30.966 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.791: AMICI failed to integrate the forward problem\n", - " 87%|████████▋ | 13/15 [00:08<00:00, 2.08it/s]2024-01-03 14:38:31.801 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 185.894 and h = 4.76848e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:31.801 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 185.894: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:32.260 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 145.366 and h = 4.40641e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:32.260 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 145.366: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:32.316 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.091 and h = 1.26056e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:32.316 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.091: AMICI failed to integrate the forward problem\n", - " 93%|█████████▎| 14/15 [00:09<00:00, 1.54it/s]2024-01-03 14:38:32.905 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.1801 and h = 3.41591e-06, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:32.906 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.1801: AMICI failed to integrate the forward problem\n", - "2024-01-03 14:38:32.936 - amici.swig_wrappers - DEBUG - [model1_data1][CVODES:CVode:ERR_FAILURE] AMICI ERROR: in module CVODES in function CVode : At t = 89.0972 and h = 2.20703e-05, the error test failed repeatedly or with |h| = hmin. \n", - "2024-01-03 14:38:32.936 - amici.swig_wrappers - ERROR - [model1_data1][FORWARD_FAILURE] AMICI forward simulation failed at t = 89.0972: AMICI failed to integrate the forward problem\n", - "100%|██████████| 15/15 [00:10<00:00, 1.49it/s]\n" - ] - } - ], + "outputs": [], "source": [ "# create temporary file\n", "fn_csv = tempfile.mktemp(\"_{id}.hdf5\")\n", @@ -1110,7 +675,7 @@ " trace_record=True, storage_file=fn_csv\n", ")\n", "\n", - "# Run optimizaitons\n", + "# Run optimizations\n", "result = optimize.minimize(\n", " problem=problem,\n", " optimizer=optimizer,\n", @@ -1128,40 +693,19 @@ } }, "source": [ - "Note that for this simple cost function, saving to CSV takes a considerable amount of time. This overhead decreases for more costly simulators, e.g. using ODE simulations via AMICI." + "Note that for this simple cost function, saving to CSV takes a considerable amount of time. This overhead decreases for more costly simulators, e.g., using ODE simulations via AMICI." ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "History type: \n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "print(\"History type: \", type(result.optimize_result.list[0].history))\n", "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", @@ -1202,49 +746,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" } }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " 0%| | 0/15 [00:00\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "print(\"History type: \", type(result.optimize_result.list[0].history))\n", "# print(\"Function value trace of best run: \", result.optimize_result.list[0].history.get_fval_trace())\n", @@ -1317,39 +805,15 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": { "collapsed": false, - "jupyter": { - "outputs_hidden": false - }, "pycharm": { "name": "#%%\n" }, "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: You are loading a problem.\n", - "This problem is not to be used without a separately created objective.\n", - "Loading the profiling result failed. It is highly likely that no profiling result exists within /var/folders/81/7b90kj9j2gz36205mdbf75y40000gn/T/tmpsf5ostbz.hdf5.\n", - "Loading the sampling result failed. It is highly likely that no sampling result exists within /var/folders/81/7b90kj9j2gz36205mdbf75y40000gn/T/tmpsf5ostbz.hdf5.\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# load the history\n", "result_loaded_w_history = pypesto.store.read_result(fn_hdf5)\n", From 7693712007bebe69b43fb751061ec23126be2134 Mon Sep 17 00:00:00 2001 From: Doresic Date: Wed, 10 Jan 2024 16:30:18 +0100 Subject: [PATCH 27/31] .. --- doc/example/model_selection.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/example/model_selection.ipynb b/doc/example/model_selection.ipynb index 4365b8f0f..69b376461 100644 --- a/doc/example/model_selection.ipynb +++ b/doc/example/model_selection.ipynb @@ -569,7 +569,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.2" + "version": "3.11.2" }, "vscode": { "interpreter": { From f533b955f0e4a8a0bc22a318db935a7bc1fc2649 Mon Sep 17 00:00:00 2001 From: Doresic Date: Wed, 10 Jan 2024 17:53:33 +0100 Subject: [PATCH 28/31] Exclude julia.ipynb for now --- doc/example/julia.ipynb | 372 ++++++++++++++++++++++++++++++++++++---- 1 file changed, 341 insertions(+), 31 deletions(-) diff --git a/doc/example/julia.ipynb b/doc/example/julia.ipynb index 692518ffc..29ecc5afe 100644 --- a/doc/example/julia.ipynb +++ b/doc/example/julia.ipynb @@ -33,10 +33,157 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "ce619530-f9c0-4c7f-ba8f-e1c5a55fda4f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
# ODE model of SIR disease dynamics\n",
+       "\n",
+       "module SIR\n",
+       "\n",
+       "# Install dependencies\n",
+       "import Pkg\n",
+       "Pkg.add(["Catalyst", "OrdinaryDiffEq", "Zygote", "SciMLSensitivity"])\n",
+       "\n",
+       "# Define reaction network\n",
+       "using Catalyst: @reaction_network\n",
+       "sir_model = @reaction_network begin\n",
+       "    r1, S + I --> 2I\n",
+       "    r2, I --> R\n",
+       "end r1 r2\n",
+       "\n",
+       "# Ground truth parameter\n",
+       "p_true = [0.0001, 0.01]\n",
+       "# Initial state\n",
+       "u0 = [999; 1; 0]\n",
+       "# Time span\n",
+       "tspan = (0.0, 250.0)\n",
+       "\n",
+       "# Formulate as ODE problem\n",
+       "using OrdinaryDiffEq: ODEProblem, solve, Tsit5\n",
+       "prob = ODEProblem(sir_model, u0, tspan, p_true)\n",
+       "\n",
+       "# True trajectory\n",
+       "sol_true = solve(prob, Tsit5(), saveat=25)\n",
+       "\n",
+       "# Observed data\n",
+       "using Random: randn, MersenneTwister\n",
+       "sigma = 40.0\n",
+       "rng = MersenneTwister(1234)\n",
+       "data = sol_true + sigma * randn(rng, size(sol_true))\n",
+       "\n",
+       "using SciMLSensitivity: remake\n",
+       "\n",
+       "# Define log-likelihood\n",
+       "function fun(p)\n",
+       "    # untransform parameters\n",
+       "    p = 10.0 .^ p\n",
+       "    # simulate\n",
+       "    _prob = remake(prob, p=p)\n",
+       "    sol_sim = solve(_prob, Tsit5(), saveat=25)\n",
+       "    # calculate log-likelihood\n",
+       "    0.5 * (log(2 * pi * sigma^2) + sum((sol_sim .- data).^2) / sigma^2)\n",
+       "end\n",
+       "\n",
+       "# Define gradient and Hessian\n",
+       "using Zygote: gradient, hessian\n",
+       "\n",
+       "function grad(p)\n",
+       "    gradient(fun, p)[1]\n",
+       "end\n",
+       "\n",
+       "function hess(p)\n",
+       "    hessian(fun, p)[1]\n",
+       "end\n",
+       "\n",
+       "end  # module\n",
+       "
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from pypesto.objective.julia import display_source_ipython\n", "\n", @@ -53,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "96df9672-94ad-4671-9ff4-d6e426e321ce", "metadata": {}, "outputs": [], @@ -67,7 +214,16 @@ "execution_count": 3, "id": "724abb5f-3ce2-4ac5-89fe-2b48f89d8733", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 42s, sys: 3.78 s, total: 1min 46s\n", + "Wall time: 1min 48s\n" + ] + } + ], "source": [ "%%time\n", "\n", @@ -107,10 +263,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "8579d6f5-193e-444f-a8ff-44ae90293470", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "23.12228487889986\n", + "CPU times: user 1.36 s, sys: 12.5 ms, total: 1.38 s\n", + "Wall time: 1.37 s\n", + "23.12228487889986\n", + "CPU times: user 208 µs, sys: 11 µs, total: 219 µs\n", + "Wall time: 215 µs\n", + "[-38.6806348 19.9557434]\n", + "CPU times: user 1min 42s, sys: 2.21 s, total: 1min 44s\n", + "Wall time: 1min 43s\n", + "[-38.6806348 19.9557434]\n", + "CPU times: user 1.17 ms, sys: 0 ns, total: 1.17 ms\n", + "Wall time: 1.13 ms\n" + ] + } + ], "source": [ "import numpy as np\n", "\n", @@ -132,10 +307,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "38c8c4aa-3faf-4f96-bf9d-271fb8a8d8b9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "type(obj.get_grad(x))" ] @@ -150,10 +336,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "9a43d18f-ccf8-4c3f-a30a-2ad824e1e87c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([-38.75164238, 19.9661208 ])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from pypesto import FD\n", "\n", @@ -172,10 +369,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "3b826499-e4c7-443e-9a59-d8d90e10d3b4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sol_true = np.asarray(obj.get(\"sol_true\"))\n", "data = obj.get(\"data\")\n", @@ -200,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "d38e3540-9693-4b24-a734-cabdb5159d36", "metadata": {}, "outputs": [], @@ -226,10 +436,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "c2267d70-85fc-46cd-bb81-5bf7742c8d84", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 77.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1.38 s, sys: 88.2 ms, total: 1.47 s\n", + "Wall time: 1.47 s\n" + ] + } + ], "source": [ "%%time\n", "\n", @@ -251,26 +477,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "2b929224-6fa6-4f63-8892-37b2475b3738", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", + "2022-09-07 00:49:47,222 - pypesto.engine.multi_process - WARNING - Engine set up to use up to 8 processes in total. The number was automatically determined and might not be appropriate on some systems.\n", + "Performing parallel task execution on 8 processes.\n", + "2022-09-07 00:49:47,233 - pypesto.engine.multi_process - INFO - Performing parallel task execution on 8 processes.\n", + "100%|█████████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 130.42it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 285 ms, sys: 177 ms, total: 462 ms\n", + "Wall time: 1.73 s\n" + ] + } + ], "source": [ "%%time\n", "\n", "from pypesto.engine import MultiProcessEngine\n", "\n", - "np.random.seed(1)\n", "engine = MultiProcessEngine()\n", - "result = optimize.minimize(problem, engine=engine, n_starts=20)" + "result = optimize.minimize(problem, engine=engine, n_starts=100)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "05888c10-1027-4580-aa0a-1076410579c6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "from pypesto import visualize\n", "\n", @@ -289,10 +547,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "b88b414b-598a-40f1-876e-ef1255a50c93", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████| 10000/10000 [00:08<00:00, 1234.51it/s]\n", + "Elapsed time: 8.135626907000017\n", + "2022-09-07 00:49:57,692 - pypesto.sample.sample - INFO - Elapsed time: 8.135626907000017\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 7.87 s, sys: 320 ms, total: 8.18 s\n", + "Wall time: 8.15 s\n" + ] + } + ], "source": [ "%%time\n", "\n", @@ -302,15 +578,38 @@ " internal_sampler=sample.AdaptiveMetropolisSampler(), n_chains=3\n", ")\n", "\n", - "result = sample.sample(problem, n_samples=2000, sampler=sampler, result=result)" + "result = sample.sample(\n", + " problem, n_samples=10000, sampler=sampler, result=result\n", + ")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "1fa22993-8100-4ec4-9b75-64ea745e77f1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Geweke burn-in index: 0\n", + "2022-09-07 00:49:57,807 - pypesto.sample.diagnostics - INFO - Geweke burn-in index: 0\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "pypesto.sample.geweke_test(result)\n", "visualize.sampling_parameter_traces(\n", @@ -320,10 +619,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "e64acdf3-b4dd-4b9a-9bc3-aa5a219f937a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "visualize.sampling_scatter(result, size=[13, 6]);" ] @@ -331,9 +641,9 @@ ], "metadata": { "kernelspec": { - "display_name": "dev_venv", + "display_name": "python-jl", "language": "python", - "name": "python3" + "name": "python-jl" }, "language_info": { "codemirror_mode": { @@ -345,7 +655,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.10.4" } }, "nbformat": 4, From f50b75845e0c1f00c08fb7354b457f2245d055d1 Mon Sep 17 00:00:00 2001 From: Doresic Date: Thu, 11 Jan 2024 10:52:16 +0100 Subject: [PATCH 29/31] Sampling notebooks changes New changes: - `sampling_diagnostics` -- n_samples 10000 -> 1000 on both sampling - `sampler_study` -- n_samples 1e4 -> 1e3 where not necessary to have an extremely good sampling result, add seed for reproducibility (to have a consistently low burn-in to be able to have only 1e3 samples). --- doc/example/sampler_study.ipynb | 18 ++++++++++-------- doc/example/sampling_diagnostics.ipynb | 6 +++--- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/doc/example/sampler_study.ipynb b/doc/example/sampler_study.ipynb index 86000b567..d138010b9 100644 --- a/doc/example/sampler_study.ipynb +++ b/doc/example/sampler_study.ipynb @@ -45,6 +45,7 @@ "metadata": {}, "outputs": [], "source": [ + "import numpy as np\n", "import petab\n", "\n", "import pypesto\n", @@ -53,6 +54,8 @@ "import pypesto.sample as sample\n", "import pypesto.visualize as visualize\n", "\n", + "np.random.seed(0)\n", + "\n", "# import to petab\n", "petab_problem = petab.Problem.from_yaml(\n", " \"conversion_reaction/conversion_reaction.yaml\"\n", @@ -121,7 +124,7 @@ "source": [ "%%time\n", "result = sample.sample(\n", - " problem, n_samples=5000, sampler=sampler, result=result, filename=None\n", + " problem, n_samples=1000, sampler=sampler, result=result, filename=None\n", ")" ] }, @@ -235,7 +238,6 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", "import seaborn as sns\n", "from scipy.stats import multivariate_normal\n", "\n", @@ -376,7 +378,7 @@ " internal_sampler=sample.MetropolisSampler(), betas=[1, 1e-1, 1e-2]\n", ")\n", "result = sample.sample(\n", - " problem, 1e4, sampler, x0=np.array([0.5]), filename=None\n", + " problem, 1e3, sampler, x0=np.array([0.5]), filename=None\n", ")" ] }, @@ -422,7 +424,7 @@ "source": [ "sampler = sample.AdaptiveMetropolisSampler()\n", "result = sample.sample(\n", - " problem, 1e4, sampler, x0=np.array([0.5]), filename=None\n", + " problem, 1e3, sampler, x0=np.array([0.5]), filename=None\n", ")" ] }, @@ -460,7 +462,7 @@ " internal_sampler=sample.AdaptiveMetropolisSampler(), n_chains=3\n", ")\n", "result = sample.sample(\n", - " problem, 1e4, sampler, x0=np.array([0.5]), filename=None\n", + " problem, 1e3, sampler, x0=np.array([0.5]), filename=None\n", ")" ] }, @@ -503,7 +505,7 @@ "\n", "sampler = PymcSampler()\n", "result = sample.sample(\n", - " problem, 1e4, sampler, x0=np.array([0.5]), filename=None\n", + " problem, 1e3, sampler, x0=np.array([0.5]), filename=None\n", ")" ] }, @@ -542,7 +544,7 @@ "source": [ "sampler = sample.EmceeSampler(nwalkers=10, run_args={\"progress\": True})\n", "result = sample.sample(\n", - " problem, int(1e4), sampler, x0=np.array([0.5]), filename=None\n", + " problem, int(1e3), sampler, x0=np.array([0.5]), filename=None\n", ")" ] }, @@ -736,7 +738,7 @@ " internal_sampler=sample.AdaptiveMetropolisSampler(), n_chains=10\n", ")\n", "result = sample.sample(\n", - " problem, 1e4, sampler, x0=np.zeros(dim_full), filename=None\n", + " problem, 2e3, sampler, x0=np.zeros(dim_full), filename=None\n", ")" ] }, diff --git a/doc/example/sampling_diagnostics.ipynb b/doc/example/sampling_diagnostics.ipynb index 5bdad7458..98ffb70eb 100644 --- a/doc/example/sampling_diagnostics.ipynb +++ b/doc/example/sampling_diagnostics.ipynb @@ -126,7 +126,7 @@ "%%capture\n", "result = sample.sample(\n", " problem,\n", - " n_samples=10000,\n", + " n_samples=1000,\n", " sampler=sampler,\n", " x0=np.array([3, -4]),\n", " filename=None,\n", @@ -456,7 +456,7 @@ "source": [ "%%capture\n", "res = sample.sample(\n", - " problem, n_samples=10000, sampler=sampler, result=res, filename=None\n", + " problem, n_samples=1000, sampler=sampler, result=res, filename=None\n", ")\n", "elapsed_time = res.sample_result.time\n", "print(\"Elapsed time: \" + str(round(elapsed_time, 2)))" @@ -679,7 +679,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.11.2" } }, "nbformat": 4, From b3d8e6dc02c64b1c2729412d446145423af6f1a5 Mon Sep 17 00:00:00 2001 From: Doresic Date: Thu, 11 Jan 2024 11:41:05 +0100 Subject: [PATCH 30/31] Some further changes --- doc/example/censored.ipynb | 4 ++-- doc/example/custom_objective_function.ipynb | 9 ++++++--- doc/example/getting_started.ipynb | 10 +++++----- doc/example/hierarchical.ipynb | 2 +- doc/example/nonlinear_monotone.ipynb | 6 +++--- doc/example/ordinal.ipynb | 6 +++--- 6 files changed, 20 insertions(+), 17 deletions(-) diff --git a/doc/example/censored.ipynb b/doc/example/censored.ipynb index 5fce1728a..02cb474d4 100644 --- a/doc/example/censored.ipynb +++ b/doc/example/censored.ipynb @@ -168,7 +168,7 @@ "source": [ "problem = importer.create_problem(objective)\n", "\n", - "engine = pypesto.engine.SingleCoreEngine()\n", + "engine = pypesto.engine.MultiProcessEngine(n_procs=3)\n", "\n", "optimizer = optimize.ScipyOptimizer(\n", " method=\"L-BFGS-B\",\n", @@ -255,7 +255,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.11.2" }, "vscode": { "interpreter": { diff --git a/doc/example/custom_objective_function.ipynb b/doc/example/custom_objective_function.ipynb index 36f9938a3..4140cc0b0 100644 --- a/doc/example/custom_objective_function.ipynb +++ b/doc/example/custom_objective_function.ipynb @@ -241,7 +241,7 @@ "lb = -5 * np.ones((dim_full, 1))\n", "ub = 5 * np.ones((dim_full, 1))\n", "\n", - "# for the sake of comparison, we create 100 starts within these bounds\n", + "# for the sake of comparison, we create 20 starts within these bounds\n", "x_guesses = np.random.uniform(-5, 5, (20, dim_full))\n", "\n", "problem1 = pypesto.Problem(\n", @@ -373,7 +373,10 @@ "# optimizer\n", "optimizer = optimize.ScipyOptimizer()\n", "# engine\n", - "engine = pypesto.engine.MultiProcessEngine()\n", + "# In this notebook, it is faster to use a single core engine, due to the\n", + "# overhead of multiprocessing. But in general with more expensive problems\n", + "# it is recommended to use the MultiProcessEngine.\n", + "engine = pypesto.engine.SingleCoreEngine()\n", "# starts\n", "n_starts = 20" ] @@ -465,7 +468,7 @@ } }, "source": [ - "Here we can see already a big difference between the first three and the fourth. The one without gradients took approximately five times as long to finish the optimization as the other three. The best value found ist also not the same as for the others. But the biggest difference is probably the fact that while the first three all converged in all their starts, the one without gradient reach the maximum number of evaluations in most to all cases. Keep in mind: **All starts were the same for all problems**.\n", + "Here we can see already a big difference between the first three and the fourth. The one without gradients took approximately five times as long to finish the optimization as the other three. The best value found is also not the same as for the others. But the biggest difference is probably the fact that while the first three all converged in all their starts, the one without gradient reach the maximum number of evaluations in most to all cases. Keep in mind: **All starts were the same for all problems**.\n", "\n", "A small detail here:\n", "When comparing the first three results, one may notice two things. For one, leaving out the hessian seems not make any difference. And additionally, while they are rather close in speed compared to the fourth one, the second one sticks out to be somewhat slower.\n", diff --git a/doc/example/getting_started.ipynb b/doc/example/getting_started.ipynb index 96efb1bc9..a86e80f3d 100644 --- a/doc/example/getting_started.ipynb +++ b/doc/example/getting_started.ipynb @@ -267,7 +267,7 @@ "# do the optimization\n", "result = optimize.minimize(problem=problem, \n", " optimizer=optimizer,\n", - " n_starts=10)" + " n_starts=5)" ] }, { @@ -364,7 +364,7 @@ } }, "source": [ - "The following performs 100 multi-start runs with different optimizers in order to compare their performance. \n", + "The following performs 10 multi-start runs with different optimizers in order to compare their performance. For a faster execution of this notebook, we run only 10 starts. In application, one would use many more optimization starts: around 100-1000 in most cases.\n", "\n", "_Note_: `dlib` and `pyswarm` need to be installed for this section to run. Furthermore, the computation time is in the order of minutes, so you might want to skip the execution and jump to the section on large scale models. " ] @@ -560,7 +560,7 @@ "result = optimize.minimize(problem=problem, \n", " optimizer=optimizer_scipy_lbfgsb,\n", " engine=engine,\n", - " n_starts=100)" + " n_starts=25)" ] }, { @@ -688,7 +688,7 @@ "source": [ "import pypesto.sample as sample\n", "\n", - "n_samples = 10000\n", + "n_samples = 1000\n", "\n", "sampler = sample.AdaptiveMetropolisSampler()\n", "\n", @@ -929,7 +929,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.11.2" } }, "nbformat": 4, diff --git a/doc/example/hierarchical.ipynb b/doc/example/hierarchical.ipynb index 6cf0fbcb5..957acac7a 100644 --- a/doc/example/hierarchical.ipynb +++ b/doc/example/hierarchical.ipynb @@ -192,7 +192,7 @@ " # number of starts for multi-start optimization\n", " 'n_starts': 3,\n", " # number of processes for parallel multi-start optimization\n", - " 'engine': pypesto.engine.MultiProcessEngine(n_procs=6),\n", + " 'engine': pypesto.engine.MultiProcessEngine(n_procs=3),\n", " # raise in case of failures\n", " 'options': OptimizeOptions(allow_failed_starts=False),\n", " # use the Fides optimizer\n", diff --git a/doc/example/nonlinear_monotone.ipynb b/doc/example/nonlinear_monotone.ipynb index 3b39721c5..5d1922663 100644 --- a/doc/example/nonlinear_monotone.ipynb +++ b/doc/example/nonlinear_monotone.ipynb @@ -247,13 +247,13 @@ "source": [ "problem = importer.create_problem(objective)\n", "\n", - "engine = pypesto.engine.SingleCoreEngine()\n", + "engine = pypesto.engine.MultiProcessEngine(n_procs=3)\n", "\n", "optimizer = optimize.ScipyOptimizer(\n", " method=\"L-BFGS-B\",\n", " options={\"disp\": None, \"ftol\": 2.220446049250313e-09, \"gtol\": 1e-5},\n", ")\n", - "n_starts = 10" + "n_starts = 3" ] }, { @@ -467,7 +467,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.11.2" }, "vscode": { "interpreter": { diff --git a/doc/example/ordinal.ipynb b/doc/example/ordinal.ipynb index e038d1571..1baaa564e 100644 --- a/doc/example/ordinal.ipynb +++ b/doc/example/ordinal.ipynb @@ -204,13 +204,13 @@ "source": [ "problem = importer.create_problem(objective)\n", "\n", - "engine = pypesto.engine.SingleCoreEngine()\n", + "engine = pypesto.engine.MultiProcessEngine(n_procs=3)\n", "\n", "optimizer = optimize.ScipyOptimizer(\n", " method=\"L-BFGS-B\",\n", " options={\"disp\": None, \"ftol\": 2.220446049250313e-09, \"gtol\": 1e-5},\n", ")\n", - "n_starts = 10\n", + "n_starts = 3\n", "np.random.seed(n_starts)" ] }, @@ -457,7 +457,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.10" + "version": "3.11.2" }, "vscode": { "interpreter": { From 038d1b7f99d227015b71b9cbdf311156e1f787ba Mon Sep 17 00:00:00 2001 From: Daniel Weindl Date: Mon, 15 Jan 2024 08:58:44 +0100 Subject: [PATCH 31/31] Fix formatting --- doc/example/amici.ipynb | 9 ++------- doc/example/custom_objective_function.ipynb | 1 + doc/example/getting_started.ipynb | 2 +- 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/doc/example/amici.ipynb b/doc/example/amici.ipynb index 67de7a80a..71df4adcb 100644 --- a/doc/example/amici.ipynb +++ b/doc/example/amici.ipynb @@ -11,19 +11,14 @@ "source": [ "# AMICI in pyPESTO\n", "\n", - "---\n", - "---\n", "**After this notebook you can...**\n", + "\n", "* ...create a pyPESTO problem directly from an AMICI model or through PEtab.\n", "* ...perform parameter estimation of your amici model and adjust advanced settings for this.\n", "* ...evaluate an optimization through basic visualizations.\n", "* ...inspect parameter uncertainties through profile likelihoods and MCMC sampling.\n", "\n", - "---\n", - "---\n", - "\n", - "\n", - "In order to run optimizations and/or uncertainty analysis, we turn to pyPESTO (**P**arameter **ES**timation **TO**olbox for python).\n", + "To run optimizations and/or uncertainty analysis, we turn to pyPESTO (**P**arameter **ES**timation **TO**olbox for python).\n", "\n", "pyPESTO is a Python tool for parameter estimation. It provides an interface to the model simulation tool [AMICI](https://github.com/AMICI-dev/AMICI) for the simulation of Ordinary Differential Equation (ODE) models specified in the SBML format. With it, we can optimize our model parameters given measurement data, we can do uncertainty analysis via profile likelihoods and/or through sampling methods. pyPESTO provides an interface to many optimizers, global and local, such as e.g. SciPy optimizers, Fides and Pyswarm. Additionally, it interfaces samplers such as pymc, emcee and some of its own samplers." ] diff --git a/doc/example/custom_objective_function.ipynb b/doc/example/custom_objective_function.ipynb index 4140cc0b0..07d40819a 100644 --- a/doc/example/custom_objective_function.ipynb +++ b/doc/example/custom_objective_function.ipynb @@ -36,6 +36,7 @@ }, "source": [ "After this notebook, you should ...\n", + "\n", "* ... be able to create an objective from a given function.\n", "* ... be able to potentially add a gradient and hessian to the objective.\n", "* ... be able to run parameter estimation on the objective.\n", diff --git a/doc/example/getting_started.ipynb b/doc/example/getting_started.ipynb index a86e80f3d..4429ed7c0 100644 --- a/doc/example/getting_started.ipynb +++ b/doc/example/getting_started.ipynb @@ -870,7 +870,7 @@ "result_loaded = store.read_result(result_file_name)\n", "\n", "# e.g. do some visualisation\n", - "visualize.waterfall(result_loaded)" + "visualize.waterfall(result_loaded);" ] }, {