Rust-backed diagnostics quickstart¶

Use the lightweight basic recorder to capture convergence history and Fourier coverage, then render the two overview plots inline. Before running the code, install the released package in this notebook's environment with %pip install "fpm-rs[notebook]".

In [ ]:

import matplotlib.pyplot as plt
import numpy as np
from IPython.display import display

import fpm_rs as fpm

import matplotlib.pyplot as plt import numpy as np from IPython.display import display import fpm_rs as fpm

In [ ]:

optics = fpm.Optics(532e-9, 0.10, 4.0, 6.5e-6)
illumination = fpm.LEDArray((3, 3), 4e-3, 90e-3, (1.0, 1.0))
model = fpm.compile_model(optics, illumination, (16, 16), (32, 32))

row, column = np.indices(model.reconstruction_shape)
amplitude = 0.7 + 0.3 * ((row // 4 + column // 4) % 2)
phase = 0.3 * np.sin(row / 5.0) * np.cos(column / 7.0)
object_field = np.asarray(amplitude * np.exp(1j * phase), dtype=np.complex128)

simulation = fpm.simulate(model, object_field, seed=17)
problem = fpm.ReconstructionProblem(
    simulation.measurements,
    simulation.reconstruction_model,
    name="diagnostics-quickstart",
)
recorder = fpm.DiagnosticRecorder("basic")
result = fpm.AlternatingProjection(iterations=6).run(
    problem,
    callbacks=[recorder],
)
diagnostics = recorder.diagnostics()
result.runtime.completed_iterations, result.final_loss

optics = fpm.Optics(532e-9, 0.10, 4.0, 6.5e-6) illumination = fpm.LEDArray((3, 3), 4e-3, 90e-3, (1.0, 1.0)) model = fpm.compile_model(optics, illumination, (16, 16), (32, 32)) row, column = np.indices(model.reconstruction_shape) amplitude = 0.7 + 0.3 * ((row // 4 + column // 4) % 2) phase = 0.3 * np.sin(row / 5.0) * np.cos(column / 7.0) object_field = np.asarray(amplitude * np.exp(1j * phase), dtype=np.complex128) simulation = fpm.simulate(model, object_field, seed=17) problem = fpm.ReconstructionProblem( simulation.measurements, simulation.reconstruction_model, name="diagnostics-quickstart", ) recorder = fpm.DiagnosticRecorder("basic") result = fpm.AlternatingProjection(iterations=6).run( problem, callbacks=[recorder], ) diagnostics = recorder.diagnostics() result.runtime.completed_iterations, result.final_loss

In [ ]:

diagnostics["iteration_history"][-1]

diagnostics["iteration_history"][-1]

In [ ]:

plot_results = {
    "convergence": fpm.plot.plot_convergence(diagnostics),
    "fourier_coverage": fpm.plot.plot_fourier_coverage(diagnostics),
}
for plot_name, plot in plot_results.items():
    if plot is None:
        continue
    figure, axes = plot
    display(figure)
    # figure.savefig(f"{plot_name}.png", dpi=180, bbox_inches="tight")
    plt.close(figure)

plot_results = { "convergence": fpm.plot.plot_convergence(diagnostics), "fourier_coverage": fpm.plot.plot_fourier_coverage(diagnostics), } for plot_name, plot in plot_results.items(): if plot is None: continue figure, axes = plot display(figure) # figure.savefig(f"{plot_name}.png", dpi=180, bbox_inches="tight") plt.close(figure)