watch_progress#

nextnano_optimizers.plotting.watch_progress(json_path, interval=1.0, **plotter_kwargs)#

Display the progress of an optimization in a live-updating window.

Reads an optimization JSON log and updates the figures as new evaluations are recorded. The function waits if the log does not yet exist and blocks until the plotting window is closed.

Parameters:
json_pathstr or pathlib.Path

Path to the optimization JSON log. The file does not need to exist when the function is called.

intervalfloat, default=1.0

Time interval, in seconds, between checks for updated results.

**plotter_kwargs

Additional plotting configuration passed to OptimizationPlotter.

param_nameslist of str, optional

Labels for the optimization variables. Defaults to param1, param2, etc.

fitness_nameslist of str, optional

Labels for the objectives. Defaults to fitness1, fitness2, etc.

exclude_failedbool, optional

If True, exclude evaluations assigned the non_execution_penalty from the plots (i.e. failed evaluations).

fitness_limitslist or dict, optional

Plotting limits for each objective, specified as (min, max) pairs. If dictionary is used, the keys should match the fitness names.

progress_seriessequence of str or str, optional

Curves to display in the optimization progress plot. Use "all" to display all available curves. The series applicable to all optimizations: (“best”, “candidates”). The series also applicable to evolution: (“min-max”, “min”, “max”, “median”, “mean”).

series_labelsdict, optional

Custom legend labels for progress_series curves, keyed by curve tag from the progress_series.

series_specsdict, optional

Custom plotting properties for individual curves, keyed by curve name. Each entry can specify kind and/or style. See examples below.

iteration_labelstr, optional

Label used for optimization iterations.

progress_titlestr, optional

Title of the optimization progress plot.

population_labelstr, optional

Label used for candidates in the final iteration.

See also

OptimizationPlotter

Generate figures from optimization results.

Notes

The optimizers open the progress window automatically when run(live_progress=True) is used.

Calling watch_progress() directly is useful for monitoring an optimization running in another process or on a remote machine, provided its JSON log is accessible.

The function only reads the log and does not affect the optimization.

Examples

Monitor the progress of an optimization running in another process:

from nextnano_optimizers.plotting import watch_progress

watch_progress(
    "Simulation_00001.json",
    interval=2.0,
    param_names=["Well width (nm)", "Barrier width (nm)"],
    fitness_names=["Energy error (eV)"],
    progress_title="Quantum well optimization",
)
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