def test_fit():
param = get_para()
pileup_peak = ['Si_Ka1-Si_Ka1', 'Si_Ka1-Ce_La1']
elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M'] + pileup_peak
x0 = np.arange(2000)
y0 = synthetic_spectrum()
x, y = trim(x0, y0, 100, 1300)
MS = ModelSpectrum(param, elemental_lines)
MS.assemble_models()
result = MS.model_fit(x, y, weights=1/np.sqrt(y), maxfev=200)
# check area of each element
for k, v in six.iteritems(result.values):
if 'area' in k:
# error smaller than 1%
assert_true((v-1e5)/1e5 < 1e-2)
# multiple peak sumed, so value should be larger than one peak area 1e5
sum_Fe = sum_area('Fe_K', result)
assert_true(sum_Fe > 1e5)
sum_Ce = sum_area('Ce_L', result)
assert_true(sum_Ce > 1e5)
sum_Pt = sum_area('Pt_M', result)
assert_true(sum_Pt > 1e5)
# create full list of parameters
PC = ParamController(param, elemental_lines)
new_params = PC.params
# update values
update_parameter_dict(new_params, result)
for k, v in six.iteritems(new_params):
if 'area' in k:
assert_equal(v['value'], result.values[k])
MS = ModelSpectrum(new_params, elemental_lines)
MS.assemble_models()
result = MS.model_fit(x, y, weights=1/np.sqrt(y), maxfev=200)
# check area of each element
for k, v in six.iteritems(result.values):
if 'area' in k:
# error smaller than 0.1%
assert_true((v-1e5)/1e5 < 1e-3)
