Fixing some warnings

pypesto/hierarchical/inner_calculator_collector.py

@@ -7,6 +7,7 @@
 from __future__ import annotations
 
 import copy
+import warnings
 from collections.abc import Sequence
 from typing import Union
 
@@ -404,14 +405,20 @@ def __call__(
 
         x_dct = copy.deepcopy(x_dct)
         x_dct.update(self.necessary_par_dummy_values)
-        # fill in parameters
-        fill_in_parameters(
-            edatas=edatas,
-            problem_parameters=x_dct,
-            scaled_parameters=True,
-            parameter_mapping=parameter_mapping,
-            amici_model=amici_model,
-        )
+        # fill in parameters, we expect here a RunTimeWarning to occur
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                message="The following problem parameters were not used:.*",
+                category=RuntimeWarning,
+            )
+            fill_in_parameters(
+                edatas=edatas,
+                problem_parameters=x_dct,
+                scaled_parameters=True,
+                parameter_mapping=parameter_mapping,
+                amici_model=amici_model,
+            )
 
         # run amici simulation
         rdatas = amici.runAmiciSimulations(

pypesto/objective/roadrunner/petab_importer_roadrunner.py

@@ -14,7 +14,7 @@
 from typing import Any
 
 import libsbml
-import petab.v1
+import petab.v1 as petab
 import roadrunner
 from petab.v1.C import (
     OBSERVABLE_FORMULA,

pypesto/sample/geweke_test.py

@@ -62,13 +62,16 @@ def spectrum(x: np.ndarray, nfft: int = None, nw: int = None) -> np.ndarray:
         Xx = np.absolute(np.fft.fft(xw, n=nfft, axis=0)) ** 2
         spectral_density += Xx
 
-    # Normalize
-    spectral_density = spectral_density * (1 / kmu)
-
     n2 = np.floor(nfft / 2).astype(int)
 
     spectral_density = spectral_density[0:n2]
 
+    # Normalize
+    if kmu != 0:
+        spectral_density = spectral_density * (1 / kmu)
+    else:
+        spectral_density = np.full(spectral_density.shape, np.nan)
+
     return spectral_density
 
 
@@ -149,16 +152,18 @@ def calculate_zscore(
         # Mean of Second fraction
         mean_b = np.mean(chain[index_b:, :], axis=0)
 
-    # Spectral estimates for variance
-    spectrum_a = spectrum0(chain[0:index_a, :])
-    spectrum_b = spectrum0(chain[index_b:, :])
+        # Spectral estimates for variance
+        spectrum_a = spectrum0(chain[0:index_a, :])
+        spectrum_b = spectrum0(chain[index_b:, :])
 
-    # Calculate z-score
-    z_score = (mean_a - mean_b) / (
-        np.sqrt(spectrum_a / index_a + spectrum_b / (nsamples - index_b + 1))
-    )
-    # Calculate significance (p value)
-    p = 2 * (1 - norm.cdf(np.absolute(z_score)))
+        # Calculate z-score
+        z_score = (mean_a - mean_b) / (
+            np.sqrt(
+                spectrum_a / index_a + spectrum_b / (nsamples - index_b + 1)
+            )
+        )
+        # Calculate significance (p value)
+        p = 2 * (1 - norm.cdf(np.absolute(z_score)))
 
     return z_score, p
 

pypesto/store/read_from_hdf5.py

@@ -2,7 +2,6 @@
 
 import ast
 import logging
-import warnings
 from pathlib import Path
 from typing import Union
 
@@ -121,10 +120,9 @@ def read(self, objective: ObjectiveBase = None) -> Problem:
         if objective is None:
             objective = Objective()
             # raise warning that objective is not loaded.
-            warnings.warn(
+            logger.debug(
                 "You are loading a problem. This problem is not to be used "
-                "without a separately created objective.",
-                stacklevel=2,
+                "without a separately created objective."
             )
         problem = Problem(objective, [], [])
 

test/base/test_store.py

@@ -4,7 +4,6 @@
 import tempfile
 
 import numpy as np
-import pytest
 import scipy.optimize as so
 
 import pypesto
@@ -95,8 +94,7 @@ def test_storage_problem(hdf5_file):
     problem_writer = ProblemHDF5Writer(hdf5_file)
     problem_writer.write(problem)
     problem_reader = ProblemHDF5Reader(hdf5_file)
-    with pytest.warns(UserWarning, match="loading a problem"):
-        read_problem = problem_reader.read()
+    read_problem = problem_reader.read()
     problem_attrs = [
         value
         for name, value in vars(ProblemHDF5Writer).items()
@@ -371,8 +369,7 @@ def test_storage_all():
     filename = "test_file.hdf5"
     try:
         write_result(result=result, filename=filename)
-        with pytest.warns(UserWarning, match="loading a problem"):
-            result_read = read_result(filename=filename)
+        result_read = read_result(filename=filename)
 
         # test optimize
         for i, opt_res in enumerate(result.optimize_result.list):

test/base/test_workflow.py

@@ -137,6 +137,6 @@ def test_sample():
         n_samples + 1,
         len(crproblem.p_true),
     )
-
+    sample.geweke_test(sample_result)
     # visualize the results
     visualize.sampling_1d_marginals(sample_result)

test/optimize/test_optimize.py

@@ -364,10 +364,7 @@ def test_mpipoolengine():
         )
 
         # read results
-        with pytest.warns(UserWarning, match="You are loading a problem."):
-            result1 = read_result(
-                "temp_result.h5", problem=True, optimize=True
-            )
+        result1 = read_result("temp_result.h5", problem=True, optimize=True)
         # set optimizer
         optimizer = optimize.FidesOptimizer(verbose=40)
         # initialize problem with x_guesses and objective

test/util.py

@@ -192,17 +192,22 @@ def get_fy(self):
 
         def fy(p):
             p0, p1 = p
-            e = anp.exp(-(p0 + p1) * self.ts)
-            x = (
-                1
-                / (-p0 - p1)
-                * anp.array(
-                    [
-                        [-p1 - p0 * e, -p1 + p1 * e],
-                        [-p0 + p0 * e, -p0 - p1 * e],
-                    ]
+            if p0 + p1 == 0:
+                # Return NaNs with the same shape as `x` would have
+                x_shape = (2, 2, len(self.ts))
+                x = anp.full(x_shape, anp.nan)
+            else:
+                e = anp.exp(-(p0 + p1) * self.ts)
+                x = (
+                    1
+                    / (-p0 - p1)
+                    * anp.array(
+                        [
+                            [-p1 - p0 * e, -p1 + p1 * e],
+                            [-p0 + p0 * e, -p0 - p1 * e],
+                        ]
+                    )
                 )
-            )
             y = anp.einsum("mnr,n->mr", x, self.x0)
             return y