def test_elastic_model():
area = 11
energy = 10
offset = 0.02
fanoprime = 0.03
e_offset = 0
e_linear = 0.01
e_quadratic = 0
true_param = [fanoprime, area, energy]
x = np.arange(800, 1200, 1)
out = elastic(x, area, energy, offset, fanoprime,
e_offset, e_linear, e_quadratic)
elastic_model = ElasticModel()
# fwhm_offset is not a sensitive parameter, used as a fixed value
elastic_model.set_param_hint(name='e_offset', value=0, vary=False)
elastic_model.set_param_hint(name='e_linear', value=0.01, vary=False)
elastic_model.set_param_hint(name='e_quadratic', value=0, vary=False)
elastic_model.set_param_hint(name='coherent_sct_energy', value=10,
vary=False)
elastic_model.set_param_hint(name='fwhm_offset', value=0.02, vary=False)
elastic_model.set_param_hint(name='fwhm_fanoprime', value=0.03, vary=False)
result = elastic_model.fit(out, x=x, coherent_sct_amplitude=10)
fitted_val = [result.values['fwhm_fanoprime'],
result.values['coherent_sct_amplitude'],
result.values['coherent_sct_energy']]
assert_array_almost_equal(true_param, fitted_val, decimal=2)
def test_elastic_peak():
"""
test of elastic peak from xrf fit
"""
y_true = [
0.00085311, 0.00164853, 0.00307974, 0.00556237, 0.00971259, 0.01639604,
0.02675911, 0.04222145, 0.06440556, 0.09498223, 0.13542228, 0.18666663,
0.24875512, 0.32048386, 0.39918028, 0.48068522, 0.55960456, 0.62984039,
0.68534389, 0.72096698, 0.73324816, 0.72096698, 0.68534389, 0.62984039,
0.55960456, 0.48068522, 0.39918028, 0.32048386, 0.24875512, 0.18666663,
0.13542228, 0.09498223, 0.06440556, 0.04222145, 0.02675911, 0.01639604,
0.00971259, 0.00556237, 0.00307974, 0.00164853
]
area = 1
energy = 10
offset = 0.01
fanoprime = 0.01
e_offset = 0
e_linear = 1
e_quadratic = 0
ev = np.arange(8, 12, 0.1)
out = elastic(ev, area, energy, offset, fanoprime, e_offset, e_linear,
e_quadratic)
assert_array_almost_equal(y_true, out)
def test_elastic_peak():
"""
test of elastic peak from xrf fit
"""
y_true = [0.00085311, 0.00164853, 0.00307974, 0.00556237, 0.00971259,
0.01639604, 0.02675911, 0.04222145, 0.06440556, 0.09498223,
0.13542228, 0.18666663, 0.24875512, 0.32048386, 0.39918028,
0.48068522, 0.55960456, 0.62984039, 0.68534389, 0.72096698,
0.73324816, 0.72096698, 0.68534389, 0.62984039, 0.55960456,
0.48068522, 0.39918028, 0.32048386, 0.24875512, 0.18666663,
0.13542228, 0.09498223, 0.06440556, 0.04222145, 0.02675911,
0.01639604, 0.00971259, 0.00556237, 0.00307974, 0.00164853]
area = 1
energy = 10
offset = 0.01
fanoprime = 0.01
e_offset = 0
e_linear = 1
e_quadratic = 0
ev = np.arange(8, 12, 0.1)
out = elastic(ev, area, energy,
offset, fanoprime,
e_offset, e_linear, e_quadratic)
assert_array_almost_equal(y_true, out)
def test_elastic_model():
area = 11
energy = 10
offset = 0.02
fanoprime = 0.03
e_offset = 0
e_linear = 0.01
e_quadratic = 0
true_param = [fanoprime, area, energy]
x = np.arange(800, 1200, 1)
out = elastic(x, area, energy, offset, fanoprime, e_offset, e_linear,
e_quadratic)
elastic_model = ElasticModel()
# fwhm_offset is not a sensitive parameter, used as a fixed value
elastic_model.set_param_hint(name='e_offset', value=0, vary=False)
elastic_model.set_param_hint(name='e_linear', value=0.01, vary=False)
elastic_model.set_param_hint(name='e_quadratic', value=0, vary=False)
elastic_model.set_param_hint(name='coherent_sct_energy',
value=10,
vary=False)
elastic_model.set_param_hint(name='fwhm_offset', value=0.02, vary=False)
elastic_model.set_param_hint(name='fwhm_fanoprime', value=0.03, vary=False)
result = elastic_model.fit(out, x=x, coherent_sct_amplitude=10)
fitted_val = [
result.values['fwhm_fanoprime'],
result.values['coherent_sct_amplitude'],
result.values['coherent_sct_energy']
]
assert_array_almost_equal(true_param, fitted_val, decimal=2)
