def test_parameter_controller():
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
PC = ParamController(param, elemental_lines)
set_opt = dict(pos='hi', width='lohi', area='hi', ratio='lo')
PC.update_element_prop(['Fe_K', 'Ce_L', pileup_peak[0]], **set_opt)
PC.set_strategy('linear')
# check boundary value
for k, v in six.iteritems(PC.params):
if 'Fe' in k:
if 'ratio' in k:
assert_equal(str(v['bound_type']), set_opt['ratio'])
if 'center' in k:
assert_equal(str(v['bound_type']), set_opt['pos'])
elif 'area' in k:
assert_equal(str(v['bound_type']), set_opt['area'])
elif 'sigma' in k:
assert_equal(str(v['bound_type']), set_opt['width'])
elif ('pileup_'+pileup_peak[0].replace('-', '_')) in k:
if 'ratio' in k:
assert_equal(str(v['bound_type']), set_opt['ratio'])
if 'center' in k:
assert_equal(str(v['bound_type']), set_opt['pos'])
elif 'area' in k:
assert_equal(str(v['bound_type']), set_opt['area'])
elif 'sigma' in k:
assert_equal(str(v['bound_type']), set_opt['width'])
def synthetic_spectrum():
param = get_para()
x = np.arange(2000)
pileup_peak = ['Si_Ka1-Si_Ka1', 'Si_Ka1-Ce_La1']
elemental_lines = ['Ar_K', 'Fe_K', 'Ce_L', 'Pt_M'] + pileup_peak
elist, matv, area_v = construct_linear_model(x, param, elemental_lines, default_area=1e5)
return np.sum(matv, 1) + 100 # avoid zero values
def test_escape_peak():
y0 = synthetic_spectrum()
ratio = 0.01
param = get_para()
xnew, ynew = compute_escape_peak(y0, ratio, param)
# ratio should be the same
assert_array_almost_equal(np.sum(ynew)/np.sum(y0), ratio, decimal=3)
def test_pre_fit():
y0 = synthetic_spectrum()
x0 = np.arange(len(y0))
# the following items should appear
item_list = ['Ar_K', 'Fe_K', 'compton', 'elastic']
param = get_para()
# fit without weights
x, y_total, area_v = linear_spectrum_fitting(x0, y0, param, weights=None)
for v in item_list:
assert_true(v in y_total)
sum1 = np.sum(six.itervalues(y_total))
# r squares as a measurement
r1 = 1- np.sum((sum1-y0)**2)/np.sum((y0-np.mean(y0))**2)
assert_true(r1 > 0.85)
# fit with weights
w = 1/np.sqrt(y0)
x, y_total, area_v = linear_spectrum_fitting(x0, y0, param, weights=1/np.sqrt(y0))
for v in item_list:
assert_true(v in y_total)
sum2 = np.sum(six.itervalues(y_total))
# r squares as a measurement
r2 = 1- np.sum((sum2-y0)**2)/np.sum((y0-np.mean(y0))**2)
assert_true(r2 > 0.85)
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_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)
def test_pre_fit():
y0 = synthetic_spectrum()
x0 = np.arange(len(y0))
# the following items should appear
item_list = ['Ar_K', 'Fe_K', 'compton', 'elastic']
param = get_para()
# with weight pre fit
x, y_total, area_v = linear_spectrum_fitting(x0, y0, param)
for v in item_list:
assert_true(v in y_total)
# no weight pre fit
x, y_total, area_v = linear_spectrum_fitting(x0, y0, param, constant_weight=None)
for v in item_list:
assert_true(v in y_total)
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_param_controller_fail():
param = get_para()
PC = ParamController(param, [])
assert_raises(ValueError, PC._add_area_param, 'Ar')
