Source code for pybop.plot.parameters

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




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

    Parameters
    ----------
    optim : object
        Optimisation object 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 = optim.cost.parameters
    x = list(range(len(optim.log.x)))
    y = [list(item) for item in zip(*optim.log.x)]

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

    if isinstance(optim.cost, GaussianLogLikelihood):
        axis_titles.append(("Function Call", "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