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"""Tests for the Iteration Callback Function."""
import numpy as np
import pytest
from lmfit.lineshapes import gaussian
from lmfit.minimizer import Minimizer
from lmfit.models import GaussianModel, LinearModel
try:
import numdifftools # noqa: F401
calc_covar_options = [False, True]
except ImportError:
calc_covar_options = [False]
np.random.seed(7)
x = np.linspace(0, 20, 401)
y = gaussian(x, amplitude=24.56, center=7.6543, sigma=1.23)
y -= 0.20*x + 3.333 + np.random.normal(scale=0.23, size=len(x))
mod = GaussianModel(prefix='peak_') + LinearModel(prefix='bkg_')
def residual(pars, x, data):
parvals = pars.valuesdict()
gauss = gaussian(x, parvals['peak_amplitude'], parvals['peak_center'],
parvals['peak_sigma'])
linear = parvals['bkg_slope']*x + parvals['bkg_intercept']
return data - gauss - linear
pars = mod.make_params(peak_amplitude=21.0, peak_center=7.0,
peak_sigma=2.0, bkg_intercept=2, bkg_slope=0.0)
# set bounds for use with 'differential_evolution' and 'brute'
pars['bkg_intercept'].set(min=0, max=10)
pars['bkg_slope'].set(min=-5, max=5)
pars['peak_amplitude'].set(min=20, max=25)
pars['peak_center'].set(min=5, max=10)
pars['peak_sigma'].set(min=0.5, max=2)
def per_iteration(pars, iteration, resid, *args, **kws):
"""Iteration callback, will abort at iteration 23."""
return iteration == 17
fitmethods = ['ampgo', 'brute', 'basinhopping', 'differential_evolution',
'leastsq', 'least_squares', 'nelder', 'shgo', 'dual_annealing']
@pytest.mark.parametrize("calc_covar", calc_covar_options)
@pytest.mark.parametrize("method", fitmethods)
def test_itercb_model_class(method, calc_covar):
"""Test the iteration callback for all solvers."""
out = mod.fit(y, pars, x=x, method=method, iter_cb=per_iteration,
calc_covar=calc_covar)
assert out.nfev == 17
assert out.aborted
assert not out.errorbars
assert not out.success
@pytest.mark.parametrize("calc_covar", calc_covar_options)
@pytest.mark.parametrize("method", fitmethods)
def test_itercb_minimizer_class(method, calc_covar):
"""Test the iteration callback for all solvers."""
mini = Minimizer(residual, pars, fcn_args=(x, y), iter_cb=per_iteration,
calc_covar=calc_covar)
out = mini.minimize(method=method)
assert out.nfev == 17
assert out.aborted
assert not out.errorbars
assert not out.success
if method not in ('nelder', 'differential_evolution'):
assert mini._abort
fitmethods = ['leastsq', 'least_squares']
@pytest.mark.parametrize("method", fitmethods)
def test_itercb_reset_abort(method):
"""Regression test for GH Issue #756.
Make sure that ``self._abort`` is reset to ``False`` at the start of each
fit.
"""
if method in ('nelder', 'differential_evolution'):
pytest.xfail("scalar_minimizers behave differently, but shouldn't!!")
must_stop = True
def callback(*args, **kwargs):
return must_stop
# perform minimization with ``iter_cb``
out_model = mod.fit(y, pars, x=x, method=method, iter_cb=callback)
mini = Minimizer(residual, pars, fcn_args=(x, y), iter_cb=callback)
out_minimizer = mini.minimize(method=method)
assert out_model.aborted is must_stop
assert out_model.errorbars is not must_stop
assert out_model.success is not must_stop
assert out_minimizer.aborted is must_stop
assert out_minimizer.errorbars is not must_stop
assert out_minimizer.success is not must_stop
assert mini._abort is must_stop
# perform another minimization now without ``iter_cb``
must_stop = False
out_minimizer_no_callback = mini.minimize(method=method)
assert out_minimizer_no_callback.aborted is must_stop
assert out_minimizer_no_callback.errorbars is not must_stop
assert out_minimizer_no_callback.success is not must_stop
assert mini._abort is must_stop
# reset to mini._abort to False and call the optimization method directly
func = getattr(mini, method)
out_no_callback = func()
assert out_no_callback.aborted is must_stop
assert out_no_callback.errorbars is not must_stop
assert out_no_callback.success is not must_stop
assert mini._abort is must_stop
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