def test_set_param():
param = get_para()
elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M']
MS = ModelSpectrum(param, elemental_lines)
MS.assemble_models()
# get compton model
compton = MS.mod.components[0]
input_param = {'bound_type': 'other', 'max': 13.0, 'min': 9.0, 'value': 11.0}
_set_parameter_hint('coherent_sct_energy', input_param, compton)
def test_set_param():
param = get_para()
elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M']
MS = ModelSpectrum(param, elemental_lines)
MS.assemble_models()
# get compton model
compton = MS.mod.components[0]
input_param = {'bound_type': 'other', 'max': 13.0, 'min': 9.0,
'value': 11.0}
_set_parameter_hint('coherent_sct_energy', input_param, compton)
def test_set_param_hint():
param = get_para()
elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M']
bound_options = ['none', 'lohi', 'fixed', 'lo', 'hi']
MS = ModelSpectrum(param, elemental_lines)
MS.assemble_models()
# get compton model
compton = MS.mod.components[0]
for v in bound_options:
input_param = {'bound_type': v, 'max': 13.0, 'min': 9.0, 'value': 11.0}
_set_parameter_hint('coherent_sct_energy', input_param, compton)
p = compton.make_params()
if v == 'fixed':
assert_equal(p['coherent_sct_energy'].vary, False)
else:
assert_equal(p['coherent_sct_energy'].vary, True)
def test_set_param_hint():
param = get_para()
elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M']
bound_options = ['none', 'lohi', 'fixed', 'lo', 'hi']
MS = ModelSpectrum(param, elemental_lines)
MS.assemble_models()
# get compton model
compton = MS.mod.components[0]
for v in bound_options:
input_param = {'bound_type': v, 'max': 13.0, 'min': 9.0, 'value': 11.0}
_set_parameter_hint('coherent_sct_energy', input_param, compton)
p = compton.make_params()
if v == 'fixed':
assert_equal(p['coherent_sct_energy'].vary, False)
else:
assert_equal(p['coherent_sct_energy'].vary, True)
def test_fit():
param = get_para()
pileup_peak = ['Si_Ka1-Si_Ka1', 'Si_Ka1-Ce_La1']
user_peak = ['user_peak1']
elemental_lines = (['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M'] + pileup_peak +
user_peak)
x0 = np.arange(2000)
y0 = synthetic_spectrum()
default_area = 1e5
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 + 1), maxfev=200)
# check area of each element
for k, v in six.iteritems(result.values):
if 'area' in k:
# error smaller than 1e-6
assert abs(v - default_area) / default_area < 1e-6
# multiple peak sumed, so value should be larger than one peak area 1e5
sum_Fe = sum_area('Fe_K', result)
assert sum_Fe > default_area
sum_Ce = sum_area('Ce_L', result)
assert sum_Ce > default_area
sum_Pt = sum_area('Pt_M', result)
assert sum_Pt > default_area
# 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])
def test_fit():
param = get_para()
pileup_peak = ['Si_Ka1-Si_Ka1', 'Si_Ka1-Ce_La1']
user_peak = ['user_peak1']
elemental_lines = (['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M'] + pileup_peak +
user_peak)
x0 = np.arange(2000)
y0 = synthetic_spectrum()
default_area = 1e5
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+1), maxfev=200)
# check area of each element
for k, v in six.iteritems(result.values):
if 'area' in k:
# error smaller than 1e-6
assert_true(abs(v - default_area)/default_area < 1e-6)
# multiple peak sumed, so value should be larger than one peak area 1e5
sum_Fe = sum_area('Fe_K', result)
assert_true(sum_Fe > default_area)
sum_Ce = sum_area('Ce_L', result)
assert_true(sum_Ce > default_area)
sum_Pt = sum_area('Pt_M', result)
assert_true(sum_Pt > default_area)
# 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])