Unreleased

Features

#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.

#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.

#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