Source code for pybop

#
# Root of the pybop module.
# Provides access to all shared functionality (models, solvers, etc.).
#
# This file is adapted from Pints
# (see https://github.com/pints-team/pints)
#
import sys
from os import path

#
# Multiprocessing
#
try:
    import multiprocessing as mp
    if sys.platform == "win32":
        mp.set_start_method("spawn")
    else:
        mp.set_start_method("fork")
except Exception as e: # pragma: no cover


    error_message = (
        "Multiprocessing context could not be set. "
        "Continuing import without setting context.\n"
        f"Error: {e}"
    ) # pragma: no cover

    print(error_message) # pragma: no cover
    pass # pragma: no cover

#
# Version info
#
from pybop._version import __version__

#
# Constants
#
# Float format: a float can be converted to a 17 digit decimal and back without
# loss of information


FLOAT_FORMAT = "{: .17e}"

# Absolute path to the pybop repo


script_path = path.dirname(__file__)


#
# Utilities
#
from ._utils import is_numeric

#
# Experiment class
#
from ._experiment import Experiment

#
# Dataset class
#
from ._dataset import Dataset

#
# Parameter classes
#
from .parameters.parameter import Parameter, Parameters
from .parameters.parameter_set import ParameterSet
from .parameters.priors import BasePrior, Gaussian, Uniform, Exponential

#
# Model classes
#
from .models.base_model import BaseModel
from .models import lithium_ion
from .models import empirical
from .models.base_model import TimeSeriesState
from .models.base_model import Inputs

#
# Problem class
#
from .problems.base_problem import BaseProblem
from .problems.fitting_problem import FittingProblem
from .problems.design_problem import DesignProblem

#
# Cost function class
#
from .costs.base_cost import BaseCost
from .costs.fitting_costs import (
    RootMeanSquaredError,
    SumSquaredError,
    ObserverCost,
)
from .costs.design_costs import (
    DesignCost,
    GravimetricEnergyDensity,
    VolumetricEnergyDensity,
)
from .costs._likelihoods import (
    BaseLikelihood,
    GaussianLogLikelihood,
    GaussianLogLikelihoodKnownSigma,
    MAP,
)

#
# Optimiser class
#

from .optimisers._cuckoo import CuckooSearchImpl
from .optimisers._adamw import AdamWImpl
from .optimisers.base_optimiser import BaseOptimiser, Result
from .optimisers.base_pints_optimiser import BasePintsOptimiser
from .optimisers.scipy_optimisers import (
    BaseSciPyOptimiser,
    SciPyMinimize,
    SciPyDifferentialEvolution
)
from .optimisers.pints_optimisers import (
    GradientDescent,
    Adam,
    CMAES,
    IRPropMin,
    NelderMead,
    PSO,
    SNES,
    XNES,
    CuckooSearch,
    AdamW,
)
from .optimisers.optimisation import Optimisation

#
# Observer classes
#
from .observers.unscented_kalman import UnscentedKalmanFilterObserver
from .observers.observer import Observer

#
# Plotting class
#
from .plotting.plotly_manager import PlotlyManager
from .plotting.quick_plot import StandardPlot, StandardSubplot, plot_trajectories
from .plotting.plot2d import plot2d
from .plotting.plot_dataset import plot_dataset
from .plotting.plot_convergence import plot_convergence
from .plotting.plot_parameters import plot_parameters
from .plotting.plot_problem import quick_plot

#
# Remove any imported modules, so we don't expose them as part of pybop
#
del sys