def trim_escape_peak(data, param_dict, y_size):
"""
Calculate escape peak within required range.
Parameters
----------
data : array
raw spectrum
param_dict : dict
parameters for fitting
y_size : int
the size of trimmed spectrum
Returns
-------
array :
trimmed escape peak spectrum
"""
ratio = param_dict['non_fitting_values']['escape_ratio']
xe, ye = compute_escape_peak(data, ratio, param_dict)
lowv = param_dict['non_fitting_values']['energy_bound_low']['value']
highv = param_dict['non_fitting_values']['energy_bound_high']['value']
xe, es_peak = trim(xe, ye, lowv, highv)
logger.info('Escape peak is considered with ratio {}'.format(ratio))
# align to the same length
if y_size > es_peak.size:
temp = es_peak
es_peak = np.zeros(y_size)
es_peak[:temp.size] = temp
else:
es_peak = es_peak[:y_size]
return es_peak
def define_range(data, low, high, a0, a1):
"""
Cut x range according to values define in param_dict.
Parameters
----------
data : array
raw spectrum
low : float
low bound in KeV
high : float
high bound in KeV
a0 : float
offset term of energy calibration
a1 : float
linear term of energy calibration
Returns
-------
x : array
trimmed channel number
y : array
trimmed spectrum according to x
"""
x = np.arange(data.size)
# ratio to transfer energy value back to channel value
#approx_ratio = 100
low_new = int(np.around((low - a0)/a1))
high_new = int(np.around((high - a0)/a1))
x0, y0 = trim(x, data, low_new, high_new)
return x0, y0
def trim_escape_peak(data, param_dict, y_size):
"""
Calculate escape peak within required range.
Parameters
----------
data : array
raw spectrum
param_dict : dict
parameters for fitting
y_size : int
the size of trimmed spectrum
Returns
-------
array :
trimmed escape peak spectrum
"""
ratio = param_dict['non_fitting_values']['escape_ratio']
xe, ye = compute_escape_peak(data, ratio, param_dict)
lowv = param_dict['non_fitting_values']['energy_bound_low']['value']
highv = param_dict['non_fitting_values']['energy_bound_high']['value']
xe, es_peak = trim(xe, ye, lowv, highv)
logger.info('Escape peak is considered with ratio {}'.format(ratio))
# align to the same length
if y_size > es_peak.size:
temp = es_peak
es_peak = np.zeros(y_size)
es_peak[:temp.size] = temp
else:
es_peak = es_peak[:y_size]
return es_peak
def define_range(data, low, high, a0, a1):
"""
Cut x range according to values define in param_dict.
Parameters
----------
data : array
raw spectrum
low : float
low bound in KeV
high : float
high bound in KeV
a0 : float
offset term of energy calibration
a1 : float
linear term of energy calibration
Returns
-------
x : array
trimmed channel number
y : array
trimmed spectrum according to x
"""
x = np.arange(data.size)
# ratio to transfer energy value back to channel value
#approx_ratio = 100
low_new = int(np.around((low - a0)/a1))
high_new = int(np.around((high - a0)/a1))
x0, y0 = trim(x, data, low_new, high_new)
return x0, y0
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])