Merge pull request #654 from pybop-team/25.1

Release v25.1

Author: BradyPlanden

.all-contributorsrc

@@ -141,6 +141,24 @@
         "test",
         "code"
       ]
+    },
+    {
+      "login": "noelhallemans",
+      "name": "Noël Hallemans",
+      "avatar_url": "https://avatars.githubusercontent.com/u/90609505?v=4",
+      "profile": "https://eng.ox.ac.uk/people/noel-hallemans/",
+      "contributions": [
+        "example"
+      ]
+    },
+    {
+      "login": "pipliggins",
+      "name": "Pip Liggins",
+      "avatar_url": "https://avatars.githubusercontent.com/u/55396775?v=4",
+      "profile": "https://github.com/pipliggins",
+      "contributions": [
+        "code"
+      ]
     }
   ],
   "contributorsPerLine": 7,

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: "v0.8.3"
+    rev: "v0.9.4"
     hooks:
       - id: ruff
         args: [--fix, --show-fixes]

CHANGELOG.md

@@ -8,6 +8,34 @@
 
 ## Breaking Changes
 
+
+# [v25.1](https://github.com/pybop-team/PyBOP/tree/v25.1) - 2025-02-03
+
+## Features
+
+- [#636](https://github.com/pybop-team/PyBOP/pull/636) - Adds `pybop.IRPropPlus` optimiser with corresponding tests.
+- [#635](https://github.com/pybop-team/PyBOP/pull/635) - Adds support for multi-proposal evaluation of list-like objects to `BaseCost` classes.
+- [#635](https://github.com/pybop-team/PyBOP/pull/635) - Adds global parameter sensitivity analysis with method `BaseCost.sensitivity_analysis`. This is computation is added to `OptimisationResult` if optimiser arg `compute_sensitivities` is `True`. An additional arg is added to select the number of samples for analysis: `n_sensitivity_samples`.
+- [#630] (https://github.com/pybop-team/PyBOP/pull/632) - Fisher Information Matrix added to `BaseLikelihood` class.
+- [#619](https://github.com/pybop-team/PyBOP/pull/619) - Adds `pybop.SimulatingAnnealing` optimiser with corresponding tests.
+- [#565](https://github.com/pybop-team/PyBOP/pull/627) - DigiBatt added as funding partner.
+
+## Optimisations
+
+- [#638](https://github.com/pybop-team/PyBOP/pull/638) - Allows the problem class to accept any domain name.
+- [#618](https://github.com/pybop-team/PyBOP/pull/618) - Adds Mean Absolute Error (MAE) and Mean Squared Error (MSE) costs.
+- [#601](https://github.com/pybop-team/PyBOP/pull/601) - Deprecates `MultiOptimisationResult` by merging with `OptimisationResult`.
+- [#600](https://github.com/pybop-team/PyBOP/pull/600) - Removes repetitious functionality within the cost classes.
+
+## Bug Fixes
+
+- [#602](https://github.com/pybop-team/PyBOP/pull/602) - Aligns the standard quick plot of `MultiFittingProblem` outputs.
+
+## Breaking Changes
+
+- [#656](https://github.com/pybop-team/PyBOP/pull/656) - Completes `ParameterSet` changes from #593 and aligns the simulation options in `model.predict` with the model properties such as the solver.
+- [#593](https://github.com/pybop-team/PyBOP/pull/593) - Enables `ParameterSet` to systematically return a `pybamm.ParameterValues` object within the model class.
+
 # [v24.12](https://github.com/pybop-team/PyBOP/tree/v24.12) - 2024-12-21
 
 ## Features

CITATION.cff

@@ -1,15 +1,35 @@
 cff-version: 1.2.0
-title: 'Python Battery Optimisation and Parameterisation (PyBOP)'
+title: 'PyBOP: A Python package for battery model optimisation and parameterisation'
 message: >-
-  If you use this software, please cite it using the
-  metadata from this file.
-type: software
+  If you use this software, please cite the article below.
 authors:
   - given-names: Brady
     family-names: Planden
+    orcid: "https://orcid.org/0000-0002-1082-9125"
   - given-names: Nicola
     family-names: Courtier
+    orcid: "https://orcid.org/0000-0002-5714-1096"
+  - given-names: Martin
+    family-names: Robinson
+    orcid: "https://orcid.org/0000-0002-1572-6782"
+  - given-names: Agriya
+    family-names: Khetarpal
+    orcid: "https://orcid.org/0000-0002-1112-1786"
+  - given-names: Ferran
+    family-names: Brosa Planella
+    orcid: "https://orcid.org/0000-0001-6363-2812"
   - given-names: David
     family-names: Howey
-version: "24.12" # Update this when you release a new version
+    orcid: "https://orcid.org/0000-0002-0620-3955"
+
+keywords:
+  - "python"
+  - "battery models"
+  - "parameter inference"
+  - "optimization"
+
+journal: "arXiv"
+date-released: 2024-12-20
+doi: 10.48550/arXiv.2412.15859
+version: "25.1" # Update this alongside new releases
 repository-code: 'https://www.github.com/pybop-team/pybop'

README.md

@@ -74,30 +74,32 @@ Explore our [example notebooks](https://github.com/pybop-team/PyBOP/blob/develop
 
 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/comparison_examples/spm_UKF.py)
-- [BPX format parameter import/export](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/getting_started/BPX_spm.py)
-- [Electrochemical Impendence Spectroscopy (EIS) parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/battery_parameterisation/eis_fitting.py)
-- [Maximum a Posteriori parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/getting_started/BPX_spm.py)
-- [Gradient-based parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/comparison_examples/spm_AdamW.py)
+- [UKF parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/comparison_examples/unscented_kalman_filter.py)
+- [BPX format parameter import/export](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/getting_started/simple_BPX.py)
+- [Electrochemical Impendence Spectroscopy (EIS) parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/battery_parameterisation/simple_eis.py)
+- [Maximum a Posteriori parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/comparison_examples/maximum_a_posteriori.py)
+- [Gradient-based parameter identification (SPM)](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/comparison_examples/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)            | Minkowski <tr></tr>                      |
-| Many Particle Model (MPM)                     | Separable Natural Evolution Strategy (sNES)              | Sum of Power <tr></tr>                   |
-| Multi-Species Multi-Reaction (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>  |
-| Grouped-parameter SPMe (GroupedSPMe)          | Cuckoo Search                                            | Gravimetric Energy Density <tr></tr>     |
-|                                               | Gradient Descent                                         | Volumetric Energy Density<tr></tr>       |
-|                                               | Nelder-Mead                                              | <tr></tr>                                |
+| Battery Models                                | Cost Functions                     | Optimization Algorithms                                            |
+|-----------------------------------------------|------------------------------------|--------------------------------------------------------------------|
+| Single Particle Model (SPM)                   | Sum of Squared Error (SSE)         | Covariance Matrix Adaptation Evolution Strategy (CMA-ES) <tr></tr> |
+| Single Particle Model with Electrolyte (SPMe) | Root Mean Squared Error (RMSE)     | Particle Swarm Optimization (PSO) <tr></tr>                        |
+| Doyle-Fuller-Newman (DFN)                     | Mean Squared Error (MSE)           | Exponential Natural Evolution Strategy (xNES) <tr></tr>            |
+| Many Particle Model (MPM)                     | Mean Absolute Error (MAE)          | Separable Natural Evolution Strategy (sNES)  <tr></tr>             |
+| Multi-Species Multi-Reaction (MSMR)           | Minkowski                          | Weight Decayed Adaptive Moment Estimation (AdamW) <tr></tr>        |
+| Weppner-Huggins                               | Sum of Power                       | Improved Resilient Backpropagation (iRProp-) <tr></tr>             |
+| Equivalent Circuit Models (ECM)               | Gaussian Log Likelihood            | SciPy Minimize & Differential Evolution  <tr></tr>                 |
+| Grouped-parameter SPMe (GroupedSPMe)          | Log Posterior                      | Cuckoo Search  <tr></tr>                                           |
+|                                               | Gravimetric Energy / Power Density | Simulated Annealing  <tr></tr>                                     |
+|                                               | Volumetric Energy / Power Density  | Random Search <tr></tr>                                            |
+|                                               |                                    | Gradient Descent <tr></tr>                                         |
+|                                               |                                    | Nelder Mead <tr></tr>                                              |
 
 </p>
 
@@ -141,6 +143,10 @@ Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/d
       <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> <a href="https://github.com/pybop-team/PyBOP/commits?author=Dibyendu-IITKGP" title="Tests">⚠️</a> <a href="https://github.com/pybop-team/PyBOP/commits?author=Dibyendu-IITKGP" title="Code">💻</a></td>
+      <td align="center" valign="top" width="14.28%"><a href="https://eng.ox.ac.uk/people/noel-hallemans/"><img src="https://avatars.githubusercontent.com/u/90609505?v=4?s=100" width="100px;" alt="Noël Hallemans"/><br /><sub><b>Noël Hallemans</b></sub></a><br /><a href="#example-noelhallemans" title="Examples">💡</a></td>
+    </tr>
+    <tr>
+      <td align="center" valign="top" width="14.28%"><a href="https://github.com/pipliggins"><img src="https://avatars.githubusercontent.com/u/55396775?v=4?s=100" width="100px;" alt="Pip Liggins"/><br /><sub><b>Pip Liggins</b></sub></a><br /><a href="https://github.com/pybop-team/PyBOP/commits?author=pipliggins" title="Code">💻</a></td>
     </tr>
   </tbody>
 </table>

assets/PyBOP-high-level.svg

@@ -23,7 +23,7 @@ def setup(self, model, parameter_set):
         set_random_seed()
 
         # Create model instance
-        self.model = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set))
+        self.model = model(parameter_set=pybop.ParameterSet(parameter_set))
 
         # Define fitting parameters
         parameters = pybop.Parameters(

benchmarks/benchmark_model.py

@@ -25,7 +25,7 @@ def setup(self, model, parameter_set, optimiser):
         set_random_seed()
 
         # Create model instance
-        model_instance = model(parameter_set=pybop.ParameterSet.pybamm(parameter_set))
+        model_instance = model(parameter_set=pybop.ParameterSet(parameter_set))
 
         # Define fitting parameters
         parameters = pybop.Parameters(

benchmarks/benchmark_optim_construction.py

@@ -35,7 +35,7 @@ def setup(self, model, parameter_set, optimiser):
         set_random_seed()
 
         # Create model instance
-        params = pybop.ParameterSet.pybamm(parameter_set)
+        params = pybop.ParameterSet(parameter_set)
         params.update(
             {
                 "Negative electrode active material volume fraction": 0.63,

benchmarks/benchmark_parameterisation.py

@@ -35,7 +35,7 @@ def setup(self, model, parameter_set, optimiser):
         set_random_seed()
 
         # Create model instance
-        params = pybop.ParameterSet.pybamm(parameter_set)
+        params = pybop.ParameterSet(parameter_set)
         params.update(
             {
                 "Negative electrode active material volume fraction": 0.63,