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import numpy as np
import pytest
import platform
from lmfit import Minimizer, Parameters
from lmfit.models import GaussianModel
# Define a custom marker to skip tests on 32-bit architecture
skip_on_32bit = pytest.mark.skipif(
platform.architecture()[0] == "32bit",
reason="Skipping test on 32-bit architecture."
)
def test_scalar_minimize_neg_value():
x0 = 3.14
fmin = -1.1
xtol = 0.001
ftol = 2.0 * xtol
def objective(pars):
return (pars['x'] - x0) ** 2.0 + fmin
params = Parameters()
params.add('x', value=2*x0)
minr = Minimizer(objective, params)
result = minr.scalar_minimize(method='Nelder-Mead',
options={'xatol': xtol, 'fatol': ftol})
assert abs(result.params['x'].value - x0) < xtol
assert abs(result.fun - fmin) < ftol
@skip_on_32bit
@pytest.mark.parametrize('method', ('leastsq', 'least_squares', 'nelder',
'lbfgsb', 'powell', 'cg', 'bfgs', 'brute',
'dual_annealing', 'differential_evolution',
'ampgo', 'shgo', 'cobyla', 'basinhopping'))
def test_aborted_solvers(method):
# github discussion #894
x = np.array([18.025, 18.075, 18.125, 18.175, 18.225, 18.275, 18.325, 18.375,
18.425, 18.475, 18.525, 18.575, 18.625, 18.675, 18.725, 18.775,
18.825, 18.875, 18.925, 18.975, 19.025, 19.075, 19.125, 19.175,
19.225, 19.275, 19.325, 19.375, 19.425, 19.475, 19.525, 19.575,
19.625, 19.675, 19.725, 19.775, 19.825, 19.875, 19.925, 19.975,
20.025, 20.075, 20.125, 20.175, 20.225, 20.275, 20.325, 20.375,
20.425, 20.475, 20.525, 20.575, 20.625, 20.675, 20.725, 20.775,
20.825, 20.875, 20.925, 20.975])
y = np.array([43, 27, 14, 16, 20, 10, 15, 8, 3, 9, 2, 4, 8, 3, 3, 1, 2, 4,
5, 0, 1, 3, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 23, 202, 824,
344, 279, 276, 23, 2, 7, 8, 17, 22, 3, 25, 24, 17, 99, 288,
172, 252, 103, 24, 20, 4, 1, 3, 1.0])
mod = GaussianModel(prefix='p1_') + GaussianModel(prefix='p2_')
max_nfev = 40
if method == 'cobyla':
max_nfev = 30
pars = mod.make_params(p1_amplitude={'value': 70, 'min': 0, 'max': 200},
p1_center={'value': 19.5, 'min': 8, 'max': 20.5},
p1_sigma={'value': 0.05, 'min': 0, 'max': 1},
p2_amplitude={'value': 40, 'min': 0, 'max': 100},
p2_center={'value': 20.5, 'min': 19.5, 'max': 21.5},
p2_sigma={'value': 0.05, 'min': 0, 'max': 1})
result = mod.fit(y, pars, x=x, max_nfev=max_nfev, method=method)
assert not result.success
assert not result.errorbars
assert result.redchi > 1000
assert result.redchi < 90000
assert result.nfev > max_nfev - 5
assert result.nfev < max_nfev + 5
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