Skip to content

Latest commit

 

History

History
207 lines (159 loc) · 20.4 KB

CHANGELOG.md

File metadata and controls

207 lines (159 loc) · 20.4 KB
Raw

Unreleased

Features

  • #452 - Extends cell_mass and approximate_capacity for half-cell models.
  • #544 - Allows iterative plotting using StandardPlot.
  • #541 - Adds ScaledLogLikelihood and BaseMetaLikelihood classes.
  • #409 - Adds plotting and convergence methods for Monte Carlo sampling. Includes open-access Tesla 4680 dataset for Bayesian inference example. Fixes transformations for sampling.
  • #531 - Adds Voronoi optimiser surface plot (pybop.plot.surface) for fast optimiser aligned cost visualisation.
  • #532 - Adds linked_parameters example script which shows how to update linked parameters during design optimisation.
  • #529 - Adds GravimetricPowerDensity and VolumetricPowerDensity costs, along with the mathjax extension for Sphinx.

Optimisations

  • #512 - Refactors LogPosterior with attributes pointing to composed likelihood object.

Bug Fixes

  • #505 - Bug fixes for LogPosterior with transformed GaussianLogLikelihood likelihood.

Breaking Changes

  • #531 - Plot methods moved to pybop.plot with mostly minimal renaming. For example, pybop.plot_parameters is now pybop.plot.parameters. Other breaking changes include: pybop.plot2d to pybop.plot.contour.
  • #526 - Refactor OptimisationResults classes, with optim.run() now return the full object. Adds finite cost value check for optimised parameters.

v24.9.1 - 2024-09-16

Features

Bug Fixes

  • #495 - Bugfixes for Transformation class, adds apply_transform optional arg to BaseCost for transformation functionality.

Breaking Changes

v24.9.0 - 2024-09-10

Features

  • #462 - Enables multidimensional learning rate for pybop.AdamW with updated (more robust) integration testing. Fixes bug in Minkowski and SumofPower cost functions for gradient-based optimisers.
  • #411 - Updates notebooks with README in examples/ directory, removes kaleido dependency and moves to nbviewer rendering, displays notebook figures with notebook_connected plotly renderer
  • #6 - Adds Monte Carlo functionality, with methods based on Pints' algorithms. A base class is added BaseSampler, in addition to PintsBaseSampler.
  • #353 - Allow user-defined check_params functions to enforce nonlinear constraints, and enable SciPy constrained optimisation methods
  • #222 - Adds an example for performing and electrode balancing.
  • #441 - Adds an example for estimating constants within a pybamm.FunctionalParameter.
  • #405 - Adds frequency-domain based EIS prediction methods via model.simulateEIS and updates to problem.evaluate with examples and tests.
  • #460 - Notebook example files added for ECM and folder structure updated.
  • #450 - Adds support for IDAKLU with output variables, and corresponding examples, tests.
  • #364 - Adds the MultiFittingProblem class and the multi_fitting example script.
  • #444 - Merge BaseModel build() and rebuild() functionality.
  • #435 - Adds SLF001 linting for private members.
  • #418 - Wraps the get_parameter_info method from PyBaMM to get a dictionary of parameter names and types.
  • #413 - Adds DesignCost functionality to WeightedCost class with additional tests.
  • #357 - Adds Transformation() class with LogTransformation(), IdentityTransformation(), and ScaledTransformation(), ComposedTransformation() implementations with corresponding examples and tests.
  • #427 - Adds the nbstripout pre-commit hook to remove unnecessary metadata from notebooks.
  • #327 - Adds the WeightedCost subclass, defines when to evaluate a problem and adds the spm_weighted_cost example script.
  • #393 - Adds Minkowski and SumofPower cost classes, with an example and corresponding tests.
  • #403 - Adds lychee link checking action.

Bug Fixes

  • #473 - Bugfixes for transformation class, adds optional apply_transform arg to BaseCost.__call__(), adds log_update() method to BaseOptimiser
  • #464 - Fix order of design parameter_set updates and refactor update_capacity.
  • #468 - Renames quick_plot.py to standard_plots.py.
  • #454 - Fixes benchmarking suite.
  • #421 - Adds a default value for the initial SOC for design problems.

Breaking Changes

  • #499 - BPX is added as an optional dependency.
  • #483 - Replaces pybop.MAP with pybop.LogPosterior with an updated call args and bugfixes.
  • #436 - API Change: The functionality from BaseCost.evaluate/S1 & BaseCost._evaluate/S1 is represented in BaseCost.__call__ & BaseCost.compute. BaseCost.compute directly acts on the predictions, while BaseCost.__call__ calls BaseProblem.evaluate/S1 before BaseCost.compute. compute has optional args for gradient cost calculations.
  • #424 - Replaces the init_soc input to FittingProblem with the option to pass an initial OCV value, updates BaseModel and fixes multi_model_identification.ipynb and spm_electrode_design.ipynb.

v24.6.1 - 2024-07-31

Features

  • #313 - Fixes for PyBaMM v24.5, drops support for PyBaMM v23.9, v24.1

Bug Fixes

Breaking Changes

v24.6 - 2024-07-08

Features

  • #319 - Adds CuckooSearch optimiser with corresponding tests.
  • #359 - Aligning Inputs between problem, observer and model.
  • #379 - Adds model.simulateS1 to weekly benchmarks.
  • #174 - Adds new logo and updates Readme for accessibility.
  • #316 - Adds Adam with weight decay (AdamW) optimiser, adds depreciation warning for pints.Adam implementation.
  • #271 - Aligns the output of the optimisers via a generalisation of Result class.
  • #315 - Updates init structure to remove circular import issues and minimises dependancy imports across codebase for faster PyBOP module import. Adds type-hints to BaseModel and refactors rebuild parameter variables.
  • #236 - Restructures the optimiser classes, adds a new optimisation API through direct construction and keyword arguments, and fixes the setting of max_iterations, and _minimising. Introduces pybop.BaseOptimiser, pybop.BasePintsOptimiser, and pybop.BaseSciPyOptimiser classes.
  • #322 - Add Parameters class to store and access multiple parameters in one object.
  • #321 - Updates Prior classes with BaseClass, adds a problem.sample_initial_conditions method to improve stability of SciPy.Minimize optimiser.
  • #249 - Add WeppnerHuggins model and GITT example.
  • #304 - Decreases the testing suite completion time.
  • #301 - Updates default echem solver to "fast with events" mode.
  • #251 - Increment PyBaMM > v23.5, remove redundant tests within integration tests, increment citation version, fix examples with incorrect model definitions.
  • #285 - Drop support for Python 3.8.
  • #275 - Adds Maximum a Posteriori (MAP) cost function with corresponding tests.
  • #273 - Adds notebooks to nox examples session and updates CI workflows for change.
  • #250 - Adds DFN, MPM, MSMR models and moves multiple construction variables to BaseEChem. Adds exception catch on simulate & simulateS1.
  • #241 - Adds experimental circuit model fitting notebook with LG M50 data.
  • #268 - Fixes the GitHub Release artifact uploads, allowing verification of codesigned binaries and source distributions via sigstore-python.
  • #79 - Adds BPX as a dependency and imports BPX support from PyBaMM.
  • #267 - Add classifiers to pyproject.toml, update project.urls.
  • #195 - Adds the Nelder-Mead optimiser from PINTS as another option.

Bug Fixes

  • #393 - General integration test fixes. Adds UserWarning when using Plot2d with prior generated bounds.
  • #338 - Fixes GaussianLogLikelihood class, adds integration tests, updates non-bounded parameter implementation by applying bounds from priors and boundary_multiplier argument. Bugfixes to CMAES construction.
  • #339 - Updates the calculation of the cyclable lithium capacity in the spme_max_energy example.
  • #387 - Adds keys to ParameterSet and updates ECM OCV check.
  • #380 - Restore self._boundaries construction for pybop.PSO
  • #372 - Converts np.array to np.asarray for Numpy v2.0 support.
  • #165 - Stores the attempted and best parameter values and the best cost for each iteration in the log attribute of the optimiser and updates the associated plots.
  • #354 - Fixes the calculation of the gradient in the RootMeanSquaredError cost.
  • #347 - Resets options between MSMR tests to cope with a bug in PyBaMM v23.9 which is fixed in PyBaMM v24.1.
  • #337 - Restores benchmarks, relaxes CI schedule for benchmarks and scheduled tests.
  • #231 - Allows passing of keyword arguments to PyBaMM models and disables build on initialisation.
  • #321 - Improves integration/test_spm_parameterisation.py stability, adds flakly pytest plugin, and test_thevenin_parameterisation.py integration test.
  • #330 - Fixes implementation of default plotting options.
  • #317 - Installs seed packages into nox sessions, ensuring that scheduled tests can pass.
  • #308 - Enables testing on both macOS Intel and macOS ARM (Silicon) runners and fixes the scheduled tests.
  • #299 - Bugfix multiprocessing support for Linux, MacOS, Windows (WSL) and improves coverage.
  • #270 - Updates PR template.
  • #91 - Adds a check on the number of parameters for CMAES and makes XNES the default optimiser.

Breaking Changes

  • #322 - Add Parameters class to store and access multiple parameters in one object (API change).
  • #285 - Drop support for Python 3.8.
  • #251 - Drop support for PyBaMM v23.5
  • #236 - Restructures the optimiser classes (API change).

v24.3.1 - 2024-06-17

Features

Bug Fixes

  • #369 - Upper pins Numpy < 2.0 due to breaking Pints' functionality.

v24.3 - 2024-03-25

Features

  • #245 - Updates ruff config for import linting.
  • #198 - Adds default subplot trace options, removes [] in axis plots as per SI standard, add varying signal length to quick_plot, restores design optimisation execption.
  • #224 - Updated prediction objects to dictionaries, cost class calculations, added additional_variables argument to problem class, updated scipy.minimize defualt method to Nelder-Mead, added gradient cost landscape plots with optional argument.
  • #179 - Adds asv configuration for benchmarking and initial benchmark suite.
  • #218 - Adds likelihood base class, GaussianLogLikelihoodKnownSigma, GaussianLogLikelihood, and ProbabilityBased cost function. As well as addition of a maximum likelihood estimation (MLE) example.
  • #185 - Adds a pull request template, additional nox sessions quick for standard tests + docs, pre-commit for pre-commit, test to run all standard tests, doctest for docs.
  • #215 - Adds release_workflow.md and updates release_action.yaml
  • #204 - Splits integration, unit, examples, plots tests, update workflows. Adds pytest --examples, --integration, --plots args. Adds tests for coverage after removal of examples. Adds examples and integrations nox sessions. Adds pybop.RMSE._evaluateS1() method
  • #206 - Adds Python 3.12 support with corresponding github actions changes.
  • #18 - Adds geometric parameter fitting capability, via model.rebuild() with model.rebuild_parameters.
  • #203 - Adds support for modern Python packaging via a pyproject.toml file and configures the pytest test runner and ruff linter to use their configurations stored as declarative metadata.
  • #123 - Configures scheduled tests to run against the last three PyPI releases of PyBaMM via dynamic GitHub Actions matrix generation.
  • #187 - Adds M1 Github runner to test_on_push workflow, updt. self-hosted supported python versions in scheduled tests.
  • #118 - Adds example jupyter notebooks.
  • #151 - Adds a standalone version of the Problem class.
  • #12 - Adds initial implementation of an Observer class and an unscented Kalman filter.
  • #190 - Adds a second example design cost, namely the VolumetricEnergyDensity.

Bug Fixes

  • #259 - Fix gradient calculation from model.simulateS1 to remove cross-polution and refactor cost._evaluateS1 for fitting costs.
  • #233 - Enforces model rebuild on initialisation of a Problem to allow a change of experiment, fixes if statement triggering current function update, updates predictions to simulation to keep distinction between predict and simulate and adds test_changes.
  • #123 - Reinstates check for availability of parameter sets via PyBaMM upon retrieval by pybop.ParameterSet.pybamm().
  • #196 - Fixes failing observer cost tests.
  • #63 - Removes NLOpt Optimiser from future releases. This is to support deployment to the Apple M-Series platform.
  • #164 - Fixes convergence issues with gradient-based optimisers, changes default model.check_params() to allow infeasible solutions during optimisation iterations. Adds a feasibility check on the optimal parameters.
  • #211 - Allows a subset of parameter bounds or bounds=None to be passed, returning warnings where needed.

v23.12 - 2023-12-19

Features

  • #141 - Adds documentation with Sphinx and PyData Sphinx Theme. Updates docstrings across package, relocates costs and dataset to top-level of package. Adds noxfile session and deployment workflow for docs.
  • #131 - Adds SciPyDifferentialEvolution optimiser, adds functionality for user-selectable maximum iteration limit to SciPyMinimize, NLoptOptimize, and BaseOptimiser classes.
  • #107 - Adds Equivalent Circuit Model (ECM) with examples, Import/Export parameter methods ParameterSet.import_parameter and ParameterSet.export_parameters, updates default FittingProblem.signal definition to "Voltage [V]", and testing infrastructure
  • #127 - Adds Windows and macOS runners to the test_on_push action
  • #114 - Adds standard plotting class pybop.StandardPlot() via plotly backend
  • #114 - Adds quick_plot(), plot_convergence(), and plot_cost2d() methods
  • #114 - Adds a SciPy minimize example and logging for non-Pints optimisers
  • #116 - Adds PSO, SNES, XNES, ADAM, and IPropMin optimisers to PintsOptimisers() class
  • #38 - Restructures the Problem classes ahead of adding a design optimisation example
  • #38 - Updates tests and adds a design optimisation example script spme_max_energy
  • #120 - Updates the parameterisation test settings including the number of iterations
  • #145 - Reformats Dataset to contain a dictionary and signal into a list of strings

Bug Fixes

  • #182 - Allow square-brackets indexing of Dataset

v23.11

  • Initial release
  • Adds Pints, NLOpt, and SciPy optimisers
  • Adds SumofSquareError and RootMeanSquareError cost functions
  • Adds Parameter and Dataset classes