def test_pre_fit():
# No pre-defined elements. Use all possible elements activated at
# given energy
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 v in y_total
sum1 = np.sum([v for v in y_total.values()], axis=0)
# r squares as a measurement
r1 = 1 - np.sum((sum1 - y0)**2) / np.sum((y0 - np.mean(y0))**2)
assert r1 > 0.85
# fit with weights
w = 1 / np.sqrt(y0 + 1)
x, y_total, area_v = linear_spectrum_fitting(x0, y0, param, weights=w)
for v in item_list:
assert v in y_total
sum2 = np.sum([v for v in y_total.values()], axis=0)
# r squares as a measurement
r2 = 1 - np.sum((sum2 - y0)**2) / np.sum((y0 - np.mean(y0))**2)
assert r2 > 0.85
def test_pre_fit():
# No pre-defined elements. Use all possible elements activated at
# given energy
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([v for v in y_total.values()], axis=0)
# 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+1)
x, y_total, area_v = linear_spectrum_fitting(x0, y0, param, weights=w)
for v in item_list:
assert_true(v in y_total)
sum2 = np.sum([v for v in y_total.values()], axis=0)
# 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 fit_each_pixel_with_nnls(data, params, elemental_lines=None,
weights=None):
"""
Fit a spectrum with a linear model.
Parameters
----------
data : array
spectrum intensity
param : dict
fitting parameters
elemental_lines : list, optional
e.g., ['Na_K', Mg_K', 'Pt_M'] refers to the
K lines of Sodium, the K lines of Magnesium, and the M
lines of Platinum. If elemental_lines is set as None,
all the possible lines activated at given energy will be used.
"""
param = copy.deepcopy(parameter)
if incident_energy is not None:
param['coherent_sct_amplitude']['value'] = incident_energy
# cut data into proper range
low = param['non_fitting_values']['energy_bound_low']['value']
high = param['non_fitting_values']['energy_bound_high']['value']
a0 = param['e_offset']['value']
a1 = param['e_linear']['value']
x, y = define_range(data, low, high, a0, a1)
# pixel fitting
_, result_dict, area_dict = linear_spectrum_fitting(x, y,
elemental_lines=elemental_lines,
weighs=weighs)
return result_dict
def fit_pixel_per_file_no_multi(dir_path,
file_prefix,
fileID,
param,
interpath,
save_spectrum=True):
"""
Single pixel fit of experiment data. No multiprocess is applied.
.. warning :: This function is not optimized as it calls
linear_spectrum_fitting function, where lots of repeated
calculation are processed.
Parameters
----------
data : array
3D data of experiment spectrum
param : dict
fitting parameters
Returns
-------
dict :
fitting values for all the elements
"""
num_str = "{:03d}".format(fileID)
filename = file_prefix + num_str
file_path = os.path.join(dir_path, filename)
with h5py.File(file_path, "r") as f:
data = f[interpath][:]
datas = data.shape
elist = param["non_fitting_values"]["element_list"].split(", ")
elist = [e.strip(" ") for e in elist]
non_element = ["compton", "elastic", "background"]
total_list = elist + non_element
result_map = dict()
for v in total_list:
if save_spectrum:
result_map.update({v: np.zeros([datas[0], datas[1], datas[2]])})
else:
result_map.update({v: np.zeros([datas[0], datas[1]])})
for i in range(datas[0]):
for j in range(datas[1]):
x, result, area_v = linear_spectrum_fitting(data[i, j, :],
param,
elemental_lines=elist,
constant_weight=1.0)
for v in total_list:
if v in result:
if save_spectrum:
result_map[v][i, j, :len(result[v])] = result[v]
else:
result_map[v][i, j] = np.sum(result[v])
return result_map
def find_peak(self, threshv=0.1):
"""
Run automatic peak finding, and save results as dict of object.
Parameters
----------
threshv : float
The value will not be shown on GUI if it is smaller than the threshold.
"""
self.define_range() # in case the energy calibraiton changes
self.prefit_x, out_dict, area_dict = linear_spectrum_fitting(self.x0,
self.y0,
self.param_new)
logger.info('Energy range: {}, {}'.format(
self.param_new['non_fitting_values']['energy_bound_low']['value'],
self.param_new['non_fitting_values']['energy_bound_high']['value']))
prefit_dict = OrderedDict()
for k, v in six.iteritems(out_dict):
ps = PreFitStatus(z=get_Z(k),
energy=get_energy(k),
area=area_dict[k],
spectrum=v,
maxv=np.around(np.max(v), self.max_area_dig),
norm=-1,
lbd_stat=False)
prefit_dict.update({k: ps})
logger.info('Automatic Peak Finding found elements as : {}'.format(
list(prefit_dict.keys())))
self.EC.delete_all()
self.EC.add_to_dict(prefit_dict)
def find_peak(self, threshv=0.1):
"""
Run automatic peak finding, and save results as dict of object.
Parameters
----------
threshv : float
The value will not be shown on GUI if it is smaller than the threshold.
"""
self.define_range() # in case the energy calibraiton changes
self.prefit_x, out_dict, area_dict = linear_spectrum_fitting(self.x0,
self.y0,
self.param_new)
logger.info('Energy range: {}, {}'.format(
self.param_new['non_fitting_values']['energy_bound_low']['value'],
self.param_new['non_fitting_values']['energy_bound_high']['value']))
prefit_dict = OrderedDict()
for k, v in six.iteritems(out_dict):
ps = PreFitStatus(z=get_Z(k),
energy=get_energy(k),
area=area_dict[k],
spectrum=v,
maxv=np.around(np.max(v), self.max_area_dig),
norm=-1,
lbd_stat=False)
prefit_dict.update({k: ps})
logger.info('Automatic Peak Finding found elements as : {}'.format(
list(prefit_dict.keys())))
self.EC.delete_all()
self.EC.add_to_dict(prefit_dict)
def fit_pixel_per_file_no_multi(dir_path, file_prefix,
fileID, param, interpath,
save_spectrum=True):
"""
Single pixel fit of experiment data. No multiprocess is applied.
.. warning :: This function is not optimized as it calls
linear_spectrum_fitting function, where lots of repeated
calculation are processed.
Parameters
----------
data : array
3D data of experiment spectrum
param : dict
fitting parameters
Returns
-------
dict :
fitting values for all the elements
"""
num_str = '{:03d}'.format(fileID)
filename = file_prefix + num_str
file_path = os.path.join(dir_path, filename)
with h5py.File(file_path, 'r') as f:
data = f[interpath][:]
datas = data.shape
elist = param['non_fitting_values']['element_list'].split(', ')
elist = [e.strip(' ') for e in elist]
non_element = ['compton', 'elastic', 'background']
total_list = elist + non_element
result_map = dict()
for v in total_list:
if save_spectrum:
result_map.update({v: np.zeros([datas[0], datas[1], datas[2]])})
else:
result_map.update({v: np.zeros([datas[0], datas[1]])})
for i in xrange(datas[0]):
for j in xrange(datas[1]):
x, result, area_v = linear_spectrum_fitting(data[i, j, :], param,
elemental_lines=elist,
constant_weight=1.0)
for v in total_list:
if v in result:
if save_spectrum:
result_map[v][i, j, :len(result[v])] = result[v]
else:
result_map[v][i, j] = np.sum(result[v])
return result_map