def test_fit_fermi_linear(self, fig=None, verbose=0):
expected_parameters = [0.01000295, 0.51806569, -4.88800525, 0.12838861, 0.25382811]
x_data = np.arange(-20, 10, 0.1)
y_data = FermiLinear(x_data, *expected_parameters)
y_data += 0.005 * np.random.rand(y_data.size)
actual_parameters, _ = fitFermiLinear(x_data, y_data, verbose=verbose, fig=fig, use_lmfit=False)
absolute_difference_parameters = np.abs(actual_parameters - expected_parameters)
y_data_fitted = FermiLinear(x_data, *actual_parameters)
max_difference = np.max(np.abs(y_data_fitted - y_data))
if verbose:
print('expected: %s' % expected_parameters)
print('fitted: %s' % actual_parameters)
print('temperature: %.2f' % (actual_parameters[-1]))
print('max diff parameters: %.2f' % (absolute_difference_parameters.max()))
print('max diff values: %.4f' % (max_difference.max()))
self.assertTrue(np.all(max_difference < 1.0e-2))
self.assertTrue(np.all(absolute_difference_parameters < 0.6))
try:
import lmfit
have_lmfit = True
except ImportError:
have_lmfit = False
if have_lmfit:
self.assertIsNotNone(lmfit.__file__)
actual_parameters, _ = fitFermiLinear(x_data, y_data, verbose=1, fig=fig, use_lmfit=True)
absolute_difference_parameters = np.abs(actual_parameters - expected_parameters)
self.assertTrue(np.all(absolute_difference_parameters < 0.1))
# test with data from F006
x_data = np.array([-20., -19.9, -19.8, -19.7, -19.6, -19.5, -19.4, -19.3, -19.2,
-19.1, -19., -18.9, -18.8, -18.7, -18.6, -18.5, -18.4, -18.3,
-18.2, -18.1, -18., -17.9, -17.8, -17.7, -17.6, -17.5, -17.4,
-17.3, -17.2, -17.1, -17., -16.9, -16.8, -16.7, -16.6, -16.5,
-16.4, -16.3, -16.2, -16.1, -16., -15.9, -15.8, -15.7, -15.6,
-15.5, -15.4, -15.3, -15.2, -15.1, -15., -14.9, -14.8, -14.7,
-14.6, -14.5, -14.4, -14.3, -14.2, -14.1])
y_data = np.array([0.03055045, 0.0311075, 0.03098561, 0.03033496, 0.03006341,
0.03072266, 0.03183486, 0.03170599, 0.03199145, 0.03224666,
0.03164276, 0.03156053, 0.03133487, 0.03184649, 0.03224385,
0.03207413, 0.03196082, 0.03229934, 0.03158735, 0.03120681,
0.03119833, 0.03220412, 0.03185901, 0.03124884, 0.03129008,
0.0314923, 0.0315841, 0.0313667, 0.03115382, 0.03069049,
0.03058055, 0.02923863, 0.02789339, 0.02437544, 0.01896179,
0.01776424, 0.01175409, 0.01074043, 0.00950811, 0.0074723,
0.0060949, 0.00575982, 0.00501728, 0.00490061, 0.00465821,
0.00440039, 0.00434098, 0.00429608, 0.00421024, 0.0042945,
0.0042552, 0.00433429, 0.00440945, 0.00446915, 0.00446351,
0.00439317, 0.00447768, 0.0044295, 0.00450926, 0.0045605])
figx = fig if fig is None else fig + 100
linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=figx)
np.testing.assert_almost_equal(linear_part, [0, 0], decimal=1)
np.testing.assert_almost_equal(fermi_part, [-16.7, 0.02755, 0.01731], decimal=2)
plt.close('all')
def test_initial_estimate_fermi_linear(self, fig=None):
expected_parameters = [0.01000295, 0.51806569, -4.88800525, 0.12838861, 0.25382811]
x_data = np.arange(-20, 10, 0.1)
y_data = FermiLinear(x_data, *expected_parameters)
y_data += 0.005 * np.random.rand(y_data.size)
linear_part, fermi_part = initFermiLinear(x_data, y_data, fig=fig)
ylin = linear_function(x_data, *linear_part)
yr = y_data - ylin
cc, A = _estimate_fermi_model_center_amplitude(x_data, yr, fig=fig)
np.testing.assert_almost_equal(cc, expected_parameters[2], decimal=1)
np.testing.assert_almost_equal(A, expected_parameters[3], decimal=1)
self.assertTrue(fermi_part is not None)
plt.close('all')