Merge pull request #502 from pybop-team/release-v24.9.1

Release v24.9.1

.all-contributorsrc

@@ -130,6 +130,15 @@
       "contributions": [
         "example"
       ]
+    },
+    {
+      "login": "Dibyendu-IITKGP",
+      "name": "Dibyendu-IITKGP",
+      "avatar_url": "https://avatars.githubusercontent.com/u/32595915?v=4",
+      "profile": "https://github.com/Dibyendu-IITKGP",
+      "contributions": [
+        "example"
+      ]
     }
   ],
   "contributorsPerLine": 7,

.github/workflows/scheduled_tests.yaml

@@ -6,9 +6,9 @@ on:
     branches:
       - main
 
-  # runs every day at 09:00 UTC
+  # runs every day at 03:00 UTC
   schedule:
-    - cron: '0 9 * * *'
+    - cron: '0 3 * * *'
 
 # Check noxfile.py for associated environment variables
 env:

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.6.4"
+    rev: "v0.6.5"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

CHANGELOG.md

@@ -4,6 +4,19 @@
 
 ## Bug Fixes
 
+- [#505](https://github.com/pybop-team/PyBOP/pull/505) - Bug fixes for `LogPosterior` with transformed `GaussianLogLikelihood` likelihood.
+
+## Breaking Changes
+
+# [v24.9.1](https://github.com/pybop-team/PyBOP/tree/v24.9.0) - 2024-09-16
+
+
+## Features
+
+## Bug Fixes
+
+- [#495](https://github.com/pybop-team/PyBOP/pull/495) - Bugfixes for Transformation class, adds `apply_transform` optional arg to `BaseCost` for transformation functionality.
+
 ## Breaking Changes
 
 # [v24.9.0](https://github.com/pybop-team/PyBOP/tree/v24.9.0) - 2024-09-10

CITATION.cff

@@ -11,5 +11,5 @@ authors:
     family-names: Courtier
   - given-names: David
     family-names: Howey
-version: "24.9.0" # Update this when you release a new version
+version: "24.9.1" # Update this when you release a new version
 repository-code: 'https://www.github.com/pybop-team/pybop'

README.md

@@ -15,12 +15,12 @@
   [![Open Issues](https://img.shields.io/github/issues/pybop-team/PyBOP)](https://github.com/pybop-team/PyBOP/issues/)
   [![License](https://img.shields.io/github/license/pybop-team/PyBOP?color=blue)](https://github.com/pybop-team/PyBOP/blob/develop/LICENSE)
   [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pybop-team/PyBOP/blob/develop/)
+  [![nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/pybop-team/PyBOP/tree/develop/examples/notebooks/)
   [![Static Badge](https://img.shields.io/badge/https%3A%2F%2Fpybop-team.github.io%2Fpybop-bench%2F?label=Benchmarks)](https://pybop-team.github.io/pybop-bench/)
   [![Releases](https://img.shields.io/github/v/release/pybop-team/PyBOP?color=gold)](https://github.com/pybop-team/PyBOP/releases)
 
 </div>
 
-## PyBOP
 PyBOP provides a complete set of tools for parameterisation and optimisation of battery models, using both Bayesian and frequentist approaches, with [example workflows](https://github.com/pybop-team/PyBOP/tree/develop/examples/notebooks) to assist the user. PyBOP can be used to parameterise various battery models, including electrochemical and equivalent circuit models available in [PyBaMM](https://pybamm.org/). PyBOP prioritises clear and informative diagnostics for the user, while also allowing for advanced probabilistic methods.
 
 The diagram below shows the conceptual framework of PyBOP. This package is currently under development, so users can expect the API to evolve with future releases.
@@ -54,44 +54,50 @@ To check that PyBOP is installed correctly, run one of the examples in the follo
 ## Using PyBOP
 PyBOP has two intended uses:
 
-1. Parameter estimation from battery test data.
+1. Parameter inference from battery test data.
 
 2. Design optimisation under battery manufacturing/use constraints.
 
 These include a wide variety of optimisation problems that require careful consideration due to the choice of battery model, data availability and/or the choice of design parameters.
 
-### Notebooks
-PyBOP comes with a number of [example notebooks](https://github.com/pybop-team/PyBOP/blob/develop/examples), which can be found in the examples folder. A few noteworthy ones are listed below.
+### Jupyter Notebooks
 
-- [Gravimetric design optimisation of a single particle model](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/spm_electrode_design.ipynb)
-- [Experimental GITT fitting of an ECM for an LG M50](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb)
-- [Compare PyBOP optimisers for parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/multi_optimiser_identification.ipynb)
-- [Parameter identification for a spatial pouch cell model](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/pouch_cell_identification.ipynb)
+Explore our [example notebooks](https://github.com/pybop-team/PyBOP/blob/develop/examples) for hands-on demonstrations:
 
-### Scripts
-Additional script-based examples can be found in the [examples directory](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/). Some notable scripts are listed below.
+- [Gravimetric design optimisation (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/spm_electrode_design.ipynb)
+- [GITT fitting of an ECM for an LG M50](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/LG_M50_ECM/1-single-pulse-circuit-model.ipynb)
+- [Non-linear constrained ECM parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/ecm_trust-constr.ipynb)
+- [Optimiser comparison for parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/multi_optimiser_identification.ipynb)
+- [Parameter identification for spatial pouch cell model](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/pouch_cell_identification.ipynb)
+
+### Python Scripts
+
+Find additional script-based examples in the [examples directory](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/):
+
+- [UKF parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_UKF.py)
+- [BPX format parameter import/export](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/BPX_spm.py)
+- [Electrochemical Impendence Spectroscopy (EIS) parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/eis_fitting.py)
+- [Maximum a Posteriori parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/BPX_spm.py)
+- [Gradient-based parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_AdamW.py)
 
-- [Unscented Kalman filter parameter identification of a SPM](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_UKF.py)
-- [Import and export parameters using Faraday's BPX format](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/BPX_spm.py)
-- [Maximum a posteriori parameter identification of a SPM](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/BPX_spm.py)
-- [Gradient based parameter identification of a SPM](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_AdamW.py)
 
 ### Supported Methods
 The table below lists the currently supported [models](https://github.com/pybop-team/PyBOP/tree/develop/pybop/models), [optimisers](https://github.com/pybop-team/PyBOP/tree/develop/pybop/optimisers), and [cost functions](https://github.com/pybop-team/PyBOP/tree/develop/pybop/costs) in PyBOP.
 
 <p align="center">
 
-| Battery Models | Optimization Algorithms | Cost Functions |
-|-----------------------------------------------|-------------------------------------------------------------|------------------------------------------|
-| Single Particle Model (SPM) | Covariance Matrix Adaptation Evolution Strategy (CMA-ES) | Sum of Squared Errors (SSE) <tr></tr> |
-| Single Particle Model with Electrolyte (SPMe) | Particle Swarm Optimization (PSO) | Root Mean Squared Error (RMSE) <tr></tr> |
-| Doyle-Fuller-Newman (DFN) | Exponential Natural Evolution Strategy (xNES)  | Gaussian Log Likelihood <tr></tr> |
-| Many Particle Model (MPM) | Separable Natural Evolution Strategy (sNES) | Gaussian Log Likelihood w/ known variance <tr></tr> |
-| Multi-Species Multi-Reactants (MSMR) | Adaptive Moment Estimation with Weight Decay (AdamW) | Maximum a Posteriori (MAP) <tr></tr> |
-| Equivalent Circuit Models (ECM) | Improved Resilient Backpropagation (iRProp-) | Unscented Kalman Filter (UKF) <tr></tr> |
-| | SciPy Minimize & Differential Evolution | Gravimetric Energy Density <tr></tr> |
-| | Gradient Descent| Volumetric Energy Density<tr></tr> |
-| | Nelder-Mead | <tr></tr> |
+| Battery Models                                | Optimization Algorithms                                  | Cost Functions                           |
+|-----------------------------------------------|----------------------------------------------------------|------------------------------------------|
+| Single Particle Model (SPM)                   | Covariance Matrix Adaptation Evolution Strategy (CMA-ES) | Sum of Squared Errors (SSE) <tr></tr>    |
+| Single Particle Model with Electrolyte (SPMe) | Particle Swarm Optimization (PSO)                        | Root Mean Squared Error (RMSE) <tr></tr> |
+| Doyle-Fuller-Newman (DFN)                     | Exponential Natural Evolution Strategy (xNES)            | Minkowski <tr></tr>                      |
+| Many Particle Model (MPM)                     | Separable Natural Evolution Strategy (sNES)              | Sum of Power <tr></tr>                   |
+| Multi-Species Multi-Reactants (MSMR)          | Adaptive Moment Estimation with Weight Decay (AdamW)     | Gaussian Log Likelihood <tr></tr>        |
+| Weppner-Huggins                               | Improved Resilient Backpropagation (iRProp-)             | Log Posterior <tr></tr>                  |
+| Equivalent Circuit Models (ECM)               | SciPy Minimize & Differential Evolution                  | Unscented Kalman Filter (UKF) <tr></tr>  |
+|                                               | Cuckoo Search                                            | Gravimetric Energy Density <tr></tr>     |
+|                                               | Gradient Descent                                         | Volumetric Energy Density<tr></tr>       |
+|                                               | Nelder-Mead                                              | <tr></tr>                                |
 
 </p>
 
@@ -106,6 +112,10 @@ PyBOP aims to foster a broad consortium of developers and users, building on and
 
 -   User-friendliness (putting user requirements first via user-assistance & workflows)
 
+## License
+
+PyBOP is released under the [BSD 3-Clause License](https://github.com/pybop-team/PyBOP/blob/develop/LICENSE).
+
 ## Contributors ✨
 
 Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):
@@ -130,6 +140,7 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <td align="center" valign="top" width="14.28%"><a href="https://www.linkedin.com/in/muhammedsogut/"><img src="https://avatars.githubusercontent.com/u/34511375?v=4?s=100" width="100px;" alt="Muhammed Nedim Sogut"/><br /><sub><b>Muhammed Nedim Sogut</b></sub></a><br /><a href="https://github.com/pybop-team/PyBOP/commits?author=muhammedsogut" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/MarkBlyth"><img src="https://avatars.githubusercontent.com/u/20501619?v=4?s=100" width="100px;" alt="MarkBlyth"/><br /><sub><b>MarkBlyth</b></sub></a><br /><a href="https://github.com/pybop-team/PyBOP/commits?author=MarkBlyth" title="Code">💻</a></td>
       <td align="center" valign="top" width="14.28%"><a href="https://github.com/f-g-r-i-m-m"><img src="https://avatars.githubusercontent.com/u/137511310?v=4?s=100" width="100px;" alt="f-g-r-i-m-m"/><br /><sub><b>f-g-r-i-m-m</b></sub></a><br /><a href="#example-f-g-r-i-m-m" title="Examples">💡</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/Dibyendu-IITKGP"><img src="https://avatars.githubusercontent.com/u/32595915?v=4?s=100" width="100px;" alt="Dibyendu-IITKGP"/><br /><sub><b>Dibyendu-IITKGP</b></sub></a><br /><a href="#example-Dibyendu-IITKGP" title="Examples">💡</a></td>
     </tr>
   </tbody>
 </table>

docs/_static/switcher.json

@@ -4,9 +4,9 @@
       "url": "https://pybop-docs.readthedocs.io/en/latest/"
     },
     {
-      "name": "v24.9.0 (stable)",
-      "version": "v24.9.0",
-      "url": "https://pybop-docs.readthedocs.io/en/v24.9.0/",
+      "name": "v24.9 (stable)",
+      "version": "v24.9",
+      "url": "https://pybop-docs.readthedocs.io/en/v24.9.1/",
       "preferred": true
     },
     {