Add option to use dispersion fitter without rich.progress

tests/test_plugins/test_dispersion_fitter.py

@@ -4,6 +4,7 @@
 import pytest
 import responses
 import tidy3d as td
+from tidy3d.components.dispersion_fitter import fit
 from tidy3d.exceptions import SetupError, ValidationError
 from tidy3d.plugins.dispersion import (
     AdvancedFastFitterParam,
@@ -285,3 +286,18 @@ def test_dispersion_loss_samples():
     ep = nAlGaN_mat.eps_model(freq_list)
     for e in ep:
         assert e.imag >= 0
+
+
+@responses.activate
+def test_fit_no_progress(random_data):
+    wvl_um, n_data, k_data = random_data
+    eps_complex = (n_data + 1j * k_data) ** 2
+    omega = 2 * np.pi * td.C_0 / wvl_um
+
+    medium, rms = fit(
+        omega_data=omega,
+        resp_data=eps_complex,
+        scale_factor=td.HBAR,
+        advanced_param=advanced_param,
+        show_progress=False,
+    )

tidy3d/components/dispersion_fitter.py

@@ -7,7 +7,6 @@
 import numpy as np
 import scipy
 from pydantic.v1 import Field, NonNegativeFloat, PositiveFloat, PositiveInt, validator
-from rich.progress import Progress
 
 from ..constants import fp_eps
 from ..exceptions import ValidationError
@@ -759,6 +758,7 @@ def fit(
     tolerance_rms: NonNegativeFloat = DEFAULT_TOLERANCE_RMS,
     advanced_param: AdvancedFastFitterParam = None,
     scale_factor: PositiveFloat = 1,
+    show_progress: bool = True,
 ) -> Tuple[Tuple[float, ArrayComplex1D, ArrayComplex1D], float]:
     """Fit data using a fast fitting algorithm.
 
@@ -815,6 +815,8 @@ def fit(
         Advanced parameters for fitting.
     scale_factor : PositiveFloat, optional
         Factor to rescale frequency by before fitting.
+    show_progress : bool = True
+        Whether to show a progress bar for the fitting.
 
     Returns
     -------
@@ -823,6 +825,8 @@ def fit(
         The dispersive medium parameters have the form (resp_inf, poles, residues)
         and are in the original unscaled units.
     """
+    if show_progress:
+        from rich.progress import Progress
 
     if max_num_poles < min_num_poles:
         raise ValidationError(
@@ -862,6 +866,82 @@ def make_configs():
 
     configs = make_configs()
 
+    if not show_progress:
+        for num_poles, relaxed, smooth, logspacing, optimize_eps_inf in configs:
+            model = init_model.updated_copy(
+                num_poles=num_poles,
+                relaxed=relaxed,
+                smooth=smooth,
+                logspacing=logspacing,
+                optimize_eps_inf=optimize_eps_inf,
+            )
+            model = _fit_fixed_parameters((min_num_poles, max_num_poles), model)
+
+            if model.rms_error < best_model.rms_error:
+                log.debug(
+                    f"Fitter: possible improved fit with "
+                    f"rms_error={model.rms_error:.3g} found using "
+                    f"relaxed={model.relaxed}, "
+                    f"smooth={model.smooth}, "
+                    f"logspacing={model.logspacing}, "
+                    f"optimize_eps_inf={model.optimize_eps_inf}, "
+                    f"loss_in_bounds={model.loss_in_bounds}, "
+                    f"passivity_optimized={model.passivity_optimized}, "
+                    f"sellmeier_passivity={model.sellmeier_passivity}."
+                )
+                if model.loss_in_bounds and model.sellmeier_passivity:
+                    best_model = model
+                else:
+                    if not warned_about_passivity_num_iters and model.passivity_num_iters_too_small:
+                        warned_about_passivity_num_iters = True
+                        log.warning(
+                            "Did not finish enforcing passivity in dispersion fitter. "
+                            "If the fit is not good enough, consider increasing "
+                            "'AdvancedFastFitterParam.passivity_num_iters'."
+                        )
+                    if (
+                        not warned_about_slsqp_constraint_scale
+                        and model.slsqp_constraint_scale_too_small
+                    ):
+                        warned_about_slsqp_constraint_scale = True
+                        log.warning(
+                            "SLSQP constraint scale may be too small. "
+                            "If the fit is not good enough, consider increasing "
+                            "'AdvancedFastFitterParam.slsqp_constraint_scale'."
+                        )
+
+            # if below tolerance, return
+            if best_model.rms_error < tolerance_rms:
+                log.info(
+                    "Found optimal fit with weighted RMS error %.3g",
+                    best_model.rms_error,
+                )
+                if best_model.show_unweighted_rms:
+                    log.info(
+                        "Unweighted RMS error %.3g",
+                        best_model.unweighted_rms_error,
+                    )
+                return (
+                    best_model.pole_residue,
+                    best_model.rms_error,
+                )
+
+        # if exited loop, did not reach tolerance (warn)
+        log.warning(
+            "Unable to fit with weighted RMS error under 'tolerance_rms' of %.3g", tolerance_rms
+        )
+        log.info("Returning best fit with weighted RMS error %.3g", best_model.rms_error)
+        if best_model.show_unweighted_rms:
+            log.info(
+                "Unweighted RMS error %.3g",
+                best_model.unweighted_rms_error,
+            )
+
+        return (
+            best_model.pole_residue,
+            best_model.rms_error,
+        )
+
     with Progress(console=get_logging_console()) as progress:
         task = progress.add_task(
             f"Fitting to weighted RMS of {tolerance_rms}...",