Source code for pybop.plot.parameters

from typing import TYPE_CHECKING

from pybop import GaussianLogLikelihood
from pybop.plot.standard_plots import StandardSubplot

if TYPE_CHECKING:
    from pybop._result import OptimisationResult




def parameters(result: "OptimisationResult", show=True, **layout_kwargs):
    """
    Plot the evolution of parameters during the optimisation process using Plotly.

    Parameters
    ----------
    result : pybop.OptimisationResult
        Optimisation result containing the history of parameter values and associated cost.
    show : bool, optional
        If True, the figure is shown upon creation (default: True).
    **layout_kwargs : optional
        Valid Plotly layout keys and their values,
        e.g. `xaxis_title="Time [s]"` or
        `xaxis={"title": "Time [s]", font={"size":14}}`

    Returns
    -------
    plotly.graph_objs.Figure
        A Plotly figure object showing the parameter evolution over iterations.
    """

    # Extract parameters and log from the optimisation object
    parameters = result.optim.problem.parameters
    x = list(range(len(result.x_model)))
    y = [list(item) for item in zip(*result.x_model, strict=False)]

    # Create lists of axis titles and trace names
    axis_titles = []
    trace_names = parameters.names
    for name in trace_names:
        axis_titles.append(("Evaluation", name))

    if isinstance(result.optim.problem, GaussianLogLikelihood):
        axis_titles.append(("Evaluation", "Sigma"))
        trace_names.append("Sigma")

    # Set subplot layout options
    layout_options = dict(
        title="Parameter Convergence",
        width=1024,
        height=576,
        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
    )

    # Create a plot dictionary
    plot_dict = StandardSubplot(
        x=x,
        y=y,
        axis_titles=axis_titles,
        layout_options=layout_options,
        trace_names=trace_names,
        trace_name_width=50,
    )

    # Generate the figure and update the layout
    fig = plot_dict(show=False)
    fig.update_layout(**layout_kwargs)
    if show:
        fig.show()

    return fig