def y_gap_for_fitting(params,
exp_x_interp,
exp_y_interp,
layer_list,
energy_min,
energy_max,
energy_step,
database,
each_step=False):
parvals = params.valuesdict()
simulation = Simulation(energy_min=energy_min,
energy_max=energy_max,
energy_step=energy_step,
database=database)
for each_layer in layer_list:
simulation.add_layer(
layer=each_layer,
thickness_mm=parvals['thickness_mm_' + each_layer],
density_gcm3=parvals['density_gcm3_' + each_layer])
simu_x = simulation.get_x(x_type='energy')
simu_y = simulation.get_y(y_type='attenuation')
gap = (exp_y_interp - simu_y) # ** 2
if each_step is True:
for each_layer in layer_list:
print(
"Trying: density_gcm3_{}: {} thickness_mm_{}: {} chi^2: {}"
.format(each_layer, parvals['density_gcm3_' + each_layer],
each_layer, parvals['thickness_mm_' + each_layer],
sum((exp_y_interp - simu_y)**2)))
return gap
def y_gap_for_iso_fitting(params,
exp_x_interp,
exp_y_interp,
layer,
formatted_isotope_list,
fitted_simulation: Simulation,
each_step=False):
parvals = params.valuesdict()
isotope_ratio_list = []
for _isotope_index in range(len(formatted_isotope_list)):
isotope_ratio_list.append(
parvals[formatted_isotope_list[_isotope_index]])
fitted_simulation.set_isotopic_ratio(
layer=layer, element=layer, new_isotopic_ratio_list=isotope_ratio_list)
simu_x = fitted_simulation.get_x(x_type='energy')
simu_y = fitted_simulation.get_y(y_type='attenuation')
gap = (exp_y_interp - simu_y) # ** 2
if each_step is True:
for each_iso in formatted_isotope_list:
print("Trying: {}: {} chi^2: {}".format(
each_iso, parvals[each_iso], sum((exp_y_interp - simu_y)**2)))
return gap
def __init__(
self,
# Initialize ResoFit.experiment
spectra_file: str,
data_file: str,
folder: str,
exp_source_to_detector_m,
exp_offset_us,
baseline: bool,
baseline_deg: int,
# Initialize ResoFit.simulation
layer: fit_util.Layer,
energy_min,
energy_max,
energy_step,
database: str,
x_type: str,
y_type: str):
"""
Initialization with passed file location and sample info
:param spectra_file:
:type spectra_file:
:param data_file:
:type data_file:
:param layer: Layer()
:type layer:
:param energy_min:
:type energy_min:
:param energy_max:
:type energy_max:
:param energy_step:
:type energy_step:
:param folder:
:type folder:
:param baseline: True -> to remove baseline/background by detrend
:type baseline: boolean
"""
self.x_type = x_type
self.y_type = y_type
self.energy_min = energy_min
self.energy_max = energy_max
self.energy_step = energy_step
self.simulation = Simulation(energy_min=energy_min,
energy_max=energy_max,
energy_step=energy_step,
database=database)
self.simulation.add_Layer(layer=layer)
self.experiment = Experiment(
spectra_file=spectra_file,
data_file=data_file,
folder=folder,
source_to_detector_m=exp_source_to_detector_m,
offset_us=exp_offset_us,
baseline=baseline,
baseline_deg=baseline_deg)
self.experiment.t_start_us = self.experiment.t_start_us + _exp_time_offset_us
self.init_source_to_detector_m = exp_source_to_detector_m
self.init_offset_us = exp_offset_us
self.calibrated_offset_us = None
self.calibrated_source_to_detector_m = None
self.calibrate_result = None
self.params_to_calibrate = None
class Calibration(object):
def __init__(
self,
# Initialize ResoFit.experiment
spectra_file: str,
data_file: str,
folder: str,
exp_source_to_detector_m,
exp_offset_us,
baseline: bool,
baseline_deg: int,
# Initialize ResoFit.simulation
layer: fit_util.Layer,
energy_min,
energy_max,
energy_step,
database: str,
x_type: str,
y_type: str):
"""
Initialization with passed file location and sample info
:param spectra_file:
:type spectra_file:
:param data_file:
:type data_file:
:param layer: Layer()
:type layer:
:param energy_min:
:type energy_min:
:param energy_max:
:type energy_max:
:param energy_step:
:type energy_step:
:param folder:
:type folder:
:param baseline: True -> to remove baseline/background by detrend
:type baseline: boolean
"""
self.x_type = x_type
self.y_type = y_type
self.energy_min = energy_min
self.energy_max = energy_max
self.energy_step = energy_step
self.simulation = Simulation(energy_min=energy_min,
energy_max=energy_max,
energy_step=energy_step,
database=database)
self.simulation.add_Layer(layer=layer)
self.experiment = Experiment(
spectra_file=spectra_file,
data_file=data_file,
folder=folder,
source_to_detector_m=exp_source_to_detector_m,
offset_us=exp_offset_us,
baseline=baseline,
baseline_deg=baseline_deg)
self.experiment.t_start_us = self.experiment.t_start_us + _exp_time_offset_us
self.init_source_to_detector_m = exp_source_to_detector_m
self.init_offset_us = exp_offset_us
self.calibrated_offset_us = None
self.calibrated_source_to_detector_m = None
self.calibrate_result = None
self.params_to_calibrate = None
def calibrate(self,
source_to_detector_m=None,
offset_us=None,
vary='all',
each_step=False):
"""
calibrate the instrumental parameters: source-to-detector-distance & detector delay
:param each_step: boolean. True -> show values and chi^2 of each step
:param source_to_detector_m: estimated distance in m
:param offset_us: estimated time offset in us
:param vary: vary one of or both of 'source_to_detector' and 'offset' to calibrate (default: 'all')
:return: lmfit MinimizerResult
"""
# Overwrite init values if input detected
if source_to_detector_m is None:
source_to_detector_m = self.init_source_to_detector_m
if offset_us is None:
offset_us = self.init_offset_us
vary_type_list = ['source_to_detector', 'offset', 'all', 'none']
if vary not in vary_type_list:
raise ValueError(
"'vary=' can only be one of '{}'".format(vary_type_list))
simu_x = self.simulation.get_x(
x_type='energy',
offset_us=offset_us,
source_to_detector_m=source_to_detector_m)
simu_y = self.simulation.get_y(y_type='attenuation')
_run = True
if vary == 'all':
source_to_detector_vary_tag = True
offset_vary_tag = True
elif vary == 'source_to_detector':
source_to_detector_vary_tag = True
offset_vary_tag = False
elif vary == 'offset':
source_to_detector_vary_tag = False
offset_vary_tag = True
else: # vary == 'none':
source_to_detector_vary_tag = False
offset_vary_tag = False
_run = False
self.params_to_calibrate = Parameters()
self.params_to_calibrate.add('source_to_detector_m',
value=source_to_detector_m,
vary=source_to_detector_vary_tag)
self.params_to_calibrate.add('offset_us',
value=offset_us,
vary=offset_vary_tag)
# Print before
print(
"+----------------- Calibration -----------------+\nParams before:"
)
self.params_to_calibrate.pretty_print()
# Use lmfit to obtain 'source_to_detector_m' & 'offset_us' to minimize 'y_gap_for_calibration'
if _run:
self.calibrate_result = minimize(
y_gap_for_calibration,
self.params_to_calibrate,
method='leastsq',
args=(simu_x, simu_y, self.energy_min, self.energy_max,
self.energy_step, self.experiment, 'energy',
'attenuation', each_step))
# Print after
print("\nParams after:")
self.calibrate_result.__dict__['params'].pretty_print()
# Print chi^2
# self.calibrated_residual = self.calibrate_result.__dict__['residual']
print("Calibration chi^2 : {}\n".format(
self.calibrate_result.__dict__['chisqr']))
self.calibrated_offset_us = self.calibrate_result.__dict__[
'params'].valuesdict()['offset_us']
self.calibrated_source_to_detector_m = \
self.calibrate_result.__dict__['params'].valuesdict()['source_to_detector_m']
return self.calibrate_result
else:
self.calibrated_offset_us = offset_us
self.calibrated_source_to_detector_m = source_to_detector_m
print(
"\ncalibrate() was not run as requested, input values used:\n"
"calibrated_offset_us = {}\ncalibrated_source_to_detector_m = {}"
.format(offset_us, source_to_detector_m))
# self.experiment.xy_scaled(energy_min=self.energy_min,
# energy_max=self.energy_max,
# energy_step=self.energy_step,
# x_type='energy',
# y_type='attenuation',
# offset_us=offset_us,
# source_to_detector_m=source_to_detector_m,
# )
def __find_peak(self, thres, min_dist):
# load detected peak with x in image number
# if self.calibrate_result is None:
if self.calibrated_source_to_detector_m is None or self.calibrated_offset_us is None:
raise ValueError("Instrument params have not been calibrated.")
self.experiment.find_peak(x_type=self.x_type,
y_type=self.y_type,
thres=thres,
min_dist=min_dist)
# self.experiment.o_peak._scale_peak_df(energy_min=self.energy_min, energy_max=self.energy_max,
# )
return self.experiment.o_peak.peak_dict
def index_peak(self,
thres_exp,
min_dist_exp,
thres_map,
min_dist_map,
rel_tol,
impr_reso=True):
if self.experiment.o_peak is None:
self.__find_peak(thres=thres_exp, min_dist=min_dist_exp)
# find peak map using Simulation.peak_map()
_peak_map_dict = self.simulation.peak_map(
thres=thres_map,
min_dist=min_dist_map,
impr_reso=impr_reso,
x_type=self.x_type,
y_type=self.y_type,
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m,
t_unit=self.experiment.t_unit,
t_start_us=self.experiment.t_start_us,
time_resolution_us=self.experiment.time_resolution_us,
num_offset=self.experiment.img_start)
# pass peak map to Peak()
assert _peak_map_dict['x_type'] == self.experiment.o_peak.peak_dict[
'x_type']
assert _peak_map_dict['y_type'] == self.experiment.o_peak.peak_dict[
'y_type']
self.experiment.o_peak.peak_map_full = _peak_map_dict['peak_map']
# index using Peak()
self.experiment.o_peak.index_peak(_peak_map_dict, rel_tol=rel_tol)
# return self.experiment.o_peak.peak_map_indexed
def analyze_peak(self, fit_model, report=False, show_fit=False):
if self.experiment.o_peak is None:
raise AttributeError(
"Please run 'Calibration.index_peak()' before peak analysis.")
self.experiment.o_peak.analyze(report=report, fit_model=fit_model)
if show_fit:
self.experiment.o_peak.plot_fit()
# def calibrate_peak_pos(self, thres=0.15, min_dist=2, vary='all', each_step=False):
# """
# calibrate the instrumental parameters: source-to-detector-distance & detector delay
# based on peak positions obtained from the instrument parameters after Calibration.calibrate().
#
# :param thres:
# :type thres:
# :param min_dist:
# :type min_dist:
# :param vary: vary one of or both of 'source_to_detector' and 'offset' to calibrate (default: 'all')
# :type vary:
# :param each_step: True -> show values and chi^2 of each step
# :type each_step: boolean.
# :return: calibration result
# :rtype: lmfit MinimizerResult
# """
# if self.peak_map_indexed is None:
# raise ValueError('Calibrate must be run before running advanced calibration.')
# # self.init_source_to_detector_m = source_to_detector_m
# # self.init_offset_us = offset_us
# if vary not in ['source_to_detector', 'offset', 'all', 'none']:
# raise ValueError("'vary=' can only be one of ['source_to_detector', 'offset', 'all' 'none']")
# ideal_x = []
# for _ele in self.peak_map_indexed.keys():
# ideal_x = ideal_x + list(self.peak_map_indexed[_ele]['ideal']['x'])
# sorted(ideal_x)
# print(ideal_x)
#
# source_to_detector_vary_tag = True
# offset_vary_tag = True
# if vary == 'source_to_detector':
# offset_vary_tag = False
# if vary == 'offset':
# source_to_detector_vary_tag = False
# if vary == 'none':
# source_to_detector_vary_tag = False
# offset_vary_tag = False
# self.params_to_calibrate = Parameters()
# self.params_to_calibrate.add('source_to_detector_m',
# value=self.calibrated_source_to_detector_m,
# vary=source_to_detector_vary_tag)
# self.params_to_calibrate.add('offset_us',
# value=self.calibrated_offset_us,
# vary=offset_vary_tag)
# # Print before
# print("-------Calibration(advanced)-------\nParams before:")
# self.params_to_calibrate.pretty_print()
# # Use lmfit to obtain 'source_to_detector_m' & 'offset_us' to minimize 'y_gap_for_calibration'
# self.calibrate_result = minimize(y_gap_for_adv_calibration,
# self.params_to_calibrate,
# method='leastsq',
# args=(ideal_x, thres, min_dist,
# self.experiment, each_step))
# # Print after
# print("Params after:")
# self.calibrate_result.__dict__['params'].pretty_print()
# # Print chi^2
# self.calibrated_residual = self.calibrate_result.__dict__['residual']
# print("Calibration chi^2 : {}\n".format(sum(self.calibrated_residual ** 2)))
# self.calibrated_offset_us = self.calibrate_result.__dict__['params'].valuesdict()['offset_us']
# self.calibrated_source_to_detector_m = \
# self.calibrate_result.__dict__['params'].valuesdict()['source_to_detector_m']
#
# # Save the calibrated experimental x & y in Calibration class
# self.exp_x_raw_calibrated = self.experiment.x_raw(angstrom=False,
# offset_us=self.calibrated_offset_us,
# source_to_detector_m=self.calibrated_source_to_detector_m)
# self.exp_y_raw_calibrated = self.experiment.y_raw(transmission=False, baseline=self.baseline)
#
# self.exp_x_interp_calibrated, self.exp_y_interp_calibrated = self.experiment.xy_scaled(
# energy_min=self.energy_min,
# energy_max=self.energy_max,
# energy_step=self.energy_step,
# offset_us=self.calibrated_offset_us,
# source_to_detector_m=self.calibrated_source_to_detector_m,
# baseline=self.baseline)
#
# return self.calibrate_result
def plot(self,
x_type=None,
y_type=None,
t_unit='us',
index_level='iso',
peak_id='indexed',
peak_exp='indexed',
peak_height=True,
before=False,
interp=False,
mixed=False,
logx=True,
logy=False,
table=True,
grid=True,
save_fig=False):
""""""
fit_util.check_if_in_list(peak_id, fit_util.peak_type_list)
fit_util.check_if_in_list(peak_exp, fit_util.peak_type_list)
fit_util.check_if_in_list(index_level, fit_util.index_level_list)
if x_type is None:
x_type = self.x_type
if y_type is None:
y_type = self.y_type
old_colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
new_colors = [
'#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
'#e377c2', '#7f7f7f', '#bcbd22', '#17becf'
]
marker_styles = [
'o', 'v', '^', '<', '>', '8', 's', 'p', '*', 'h', 'H', 'D', 'd',
'P', 'X'
]
color_cycle = cycle(new_colors)
# color_cycle_2 = cycle(new_colors)
# color_cycle_3 = cycle(new_colors)
# color_cycle_4 = cycle(new_colors)
style_cycle = cycle(marker_styles)
simu_label = 'Ideal'
exp_label = 'Exp'
exp_before_label = 'Exp_init'
exp_interp_label = 'Exp_interp'
sample_name = ' & '.join(self.simulation.layer_list)
fig_title = "Calibration result of sample ('{}')".format(sample_name)
fig = plt.Figure()
# plot table + graph
if table:
ax1 = plt.subplot2grid(shape=(10, 10),
loc=(0, 1),
rowspan=8,
colspan=8)
# plot graph only
else:
ax1 = plt.subplot(111)
# Plot simulated total signal
if mixed:
_x = self.simulation.get_x(
x_type=x_type,
t_unit=t_unit,
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m,
t_start_us=self.experiment.t_start_us,
time_resolution_us=self.experiment.time_resolution_us,
num_offset=self.experiment.slice_start)
_y = self.simulation.get_y(y_type=y_type)
ax1.plot(_x, _y, 'b-', label=simu_label, linewidth=1)
"""Plot options"""
# 1.
if before:
# Plot the raw data before fitting
_x_init = self.experiment.get_x(
x_type=x_type,
t_unit=t_unit,
offset_us=self.init_offset_us,
source_to_detector_m=self.init_source_to_detector_m,
)
_y_init = self.experiment.get_y(y_type=y_type)
ax1.plot(_x_init,
_y_init,
linestyle='-',
linewidth=1,
marker='o',
markersize=2,
color='c',
label=exp_before_label)
# 2.
if interp:
_exp_x_interp_calibrated, _exp_y_interp_calibrated = self.experiment.xy_scaled(
x_type=x_type,
y_type=y_type,
energy_min=self.energy_min,
energy_max=self.energy_max,
energy_step=self.energy_step,
t_unit=t_unit,
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m,
)
# plot the interpolated raw data
ax1.plot(_exp_x_interp_calibrated,
_exp_y_interp_calibrated,
'r:',
label=exp_interp_label,
linewidth=1)
else:
# plot the calibrated raw data
_x_cali = self.experiment.get_x(
x_type=x_type,
t_unit=t_unit,
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m)
_y_cali = self.experiment.get_y(y_type=y_type)
ax1.plot(_x_cali,
_y_cali,
linestyle='-',
linewidth=1,
marker='o',
markersize=2,
color='r',
label=exp_label)
if peak_exp == 'all':
# _peak_x_exp = fit_util.convert_exp_peak_df(x_type=x_type, peak_df=_peak_df_scaled, t_unit=t_unit)
_peak_df_scaled = self.experiment.o_peak.peak_dict['df']
_peak_x_exp = _peak_df_scaled['x']
# if x_type == 'time':
# _peak_x_exp = fit_util.convert_s(x=_peak_x_exp, t_unit=t_unit)
# _peak_y_exp = fit_util.convert_attenuation_to(y_type=y_type, y=_peak_df_scaled['y'])
_peak_y_exp = _peak_df_scaled['y']
ax1.scatter(
_peak_x_exp,
_peak_y_exp,
c='k',
marker='x',
# s=30,
# marker='o',
# facecolors='none',
# edgecolors='k',
label='_nolegend_')
# plot peaks detected and indexed
if self.experiment.o_peak is not None:
if self.experiment.o_peak.peak_map_indexed_dict is not None:
if y_type == 'transmission':
_start_point = 1
ax1.set_ylim(top=1.1, bottom=-0.01)
_pos = 1.05
else:
_start_point = 0
ax1.set_ylim(top=1.01, bottom=-0.1)
_pos = -0.05
_peak_map_indexed = self.experiment.o_peak.peak_map_indexed_dict[
'peak_map_indexed']
_peak_map_full = self.experiment.o_peak.peak_map_full
if index_level == 'iso':
_peak_name_list = [
_name for _name in _peak_map_indexed.keys()
if '-' in _name
]
else:
_peak_name_list = [
_name for _name in _peak_map_indexed.keys()
if '-' not in _name
]
if peak_id == 'all':
_current_peak_map = _peak_map_full
# _tag = 'ideal'
else: # peak_id == 'indexed'
_current_peak_map = _peak_map_indexed
_tag = 'ideal'
for _peak_name in _peak_name_list:
if len(_current_peak_map[_peak_name][_tag]) > 0:
_peak_x = _current_peak_map[_peak_name][_tag]['x']
_peak_y = _current_peak_map[_peak_name][_tag]['y']
if peak_exp == 'indexed':
_legend_name = '_nolegend_'
else:
_legend_name = _peak_name
_current_color = next(color_cycle)
_current_style = next(style_cycle)
ax1.plot(_peak_x, [_pos] * len(_peak_x),
'|',
ms=10,
color=_current_color,
label=_legend_name)
if peak_height:
ax1.plot(
_peak_x,
_peak_y,
'_',
# marker=next(style_cycle_1),
# ms=4,
color=_current_color,
label='_nolegend_')
ax1.vlines(_peak_x,
_start_point,
_peak_y,
color=_current_color,
alpha=1,
label='_nolegend_')
if peak_exp == 'indexed':
_peak_x_exp = _peak_map_indexed[_peak_name]['exp'][
'x']
_peak_y_exp = _peak_map_indexed[_peak_name]['exp'][
'y']
ax1.scatter(
_peak_x_exp,
_peak_y_exp,
marker=_current_style,
# ms=4,
color=_current_color,
label=_peak_name)
if 'peak_span' in _peak_map_indexed[_peak_name].keys():
if len(_peak_map_indexed[_peak_name]['exp']) > 0:
_data_point_x = _peak_map_indexed[_peak_name][
'peak_span']['x']
_data_point_y = _peak_map_indexed[_peak_name][
'peak_span']['y']
ax1.scatter(_data_point_x,
_data_point_y,
label='_nolegend_')
# Set plot limit and captions
ax1 = fit_util.set_plt(ax1,
fig_title=fig_title,
grid=grid,
x_type=x_type,
y_type=y_type,
t_unit=t_unit,
logx=logx,
logy=logy)
# Plot table
if table:
# ax2 = plt.subplot2grid(shape=(10, 7), loc=(0, 1), rowspan=4, colspan=5)
# ax2.axis('off')
# columns = list(self.calibrate_result.__dict__['params'].valuesdict().keys())
columns_to_show = [r'$L$ (m)', r'$\Delta t$ ($\rm{\mu}$s)']
rows = ['Before', 'After']
_row_before = [self.init_source_to_detector_m, self.init_offset_us]
_row_after = [
self.calibrated_source_to_detector_m, self.calibrated_offset_us
]
# for _each in columns:
# _row_after.append(self.calibrate_result.__dict__['params'].valuesdict()[_each])
# _row_before.append(self.params_to_calibrate.valuesdict()[_each])
table = ax1.table(
rowLabels=rows,
colLabels=columns_to_show, # colWidths=
cellText=[_row_before, _row_after], # rows of data values
bbox=[0, -0.33, 1.0, 0.18] # [left,bottom,width,height]
)
# table.scale(0.5, 1)
table.auto_set_font_size(False)
table.set_fontsize(10)
plt.tight_layout()
if save_fig:
_sample_name = '_'.join(self.simulation.layer_list)
_filename = 'calibration_' + _sample_name + '.png'
plt.savefig(_filename, dpi=600, transparent=True)
plt.close()
return ax1
def export(self,
x_type='energy',
y_type='attenuation',
t_unit='us',
index_level='iso',
peak_id='indexed',
before=False,
interp=False,
mixed=True):
simu_label = 'ideal'
exp_label = 'exp_raw'
exp_before_label = 'exp_init'
exp_interp_label = 'exp_interp'
_df = pd.DataFrame()
_col_suffix = fit_util.get_df_col_name(x_type=x_type)
# Simulated total signal
if mixed:
_x = self.simulation.get_x(
x_type=x_type,
t_unit=t_unit,
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m,
t_start_us=self.experiment.t_start_us,
time_resolution_us=self.experiment.time_resolution_us)
_y = self.simulation.get_y(y_type=y_type)
_df['x_' + simu_label] = _x
_df['y_' + simu_label] = _y
"""Plot options"""
# Raw data before fitting
if before:
_x_init = self.experiment.get_x(
x_type=x_type,
t_unit=t_unit,
offset_us=self.init_offset_us,
source_to_detector_m=self.init_source_to_detector_m)
_y_init = self.experiment.get_y(y_type=y_type,
baseline=self.baseline)
_df['x_' + exp_before_label] = _x_init
_df['y_' + exp_before_label] = _y_init
# 2.
if interp:
_exp_x_interp_calibrated, _exp_y_interp_calibrated = self.experiment.xy_scaled(
x_type=x_type,
y_type=y_type,
energy_min=self.energy_min,
energy_max=self.energy_max,
energy_step=self.energy_step,
t_unit=t_unit,
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m,
baseline=self.baseline)
# Interpolated raw data
_df['x_' + exp_interp_label +
_col_suffix] = _exp_x_interp_calibrated
_df['y_' + exp_interp_label] = _exp_y_interp_calibrated
else:
# plot the calibrated raw data
_x_cali = self.experiment.get_x(
x_type=x_type,
t_unit=t_unit,
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m)
_y_cali = self.experiment.get_y(y_type=y_type,
baseline=self.baseline)
_df['x_' + exp_label + _col_suffix] = pd.Series(_x_cali)
_df['y_' + exp_label] = pd.Series(_y_cali)
# plot peaks detected and indexed
if self.experiment.o_peak and self.experiment.o_peak.peak_map_indexed is not None:
_peak_df_scaled = self.experiment.o_peak.peak_df_scaled
_peak_map_indexed = self.experiment.o_peak.peak_map_indexed
_peak_map_full = self.experiment.o_peak.peak_map_full
_x_peak_exp_all = fit_util.convert_exp_peak_df(
x_type=x_type, peak_df=_peak_df_scaled, t_unit=t_unit)
_y_peak_exp_all = fit_util.convert_attenuation_to(
y_type=y_type, y=_peak_df_scaled['y'])
# _df = pd.concat([_df, _peak_df_scaled], axis=1)
_df['x_peak_exp_all'] = pd.Series(_x_peak_exp_all)
_df['y_peak_exp_all'] = pd.Series(_y_peak_exp_all)
x_tag = fit_util.get_peak_tag(x_type=x_type)
for _peak_name in _peak_map_indexed.keys():
if len(_peak_map_full[_peak_name]['ideal']) > 0:
_x_peak_ideal_all = _peak_map_full[_peak_name]['ideal'][
x_tag]
_y_peak_ideal_all = _peak_map_full[_peak_name]['ideal'][
'y']
_df['x_peak_ideal_all(' + _peak_name +
')'] = _x_peak_ideal_all
_df['y_peak_ideal_all(' + _peak_name +
')'] = _y_peak_ideal_all
if len(_peak_map_indexed[_peak_name]['ideal']) > 0:
_x_peak_ideal_indexed = _peak_map_indexed[_peak_name][
'ideal'][x_tag]
_y_peak_ideal_indexed = _peak_map_indexed[_peak_name][
'ideal']['y']
_x_peak_exp_indexed = _peak_map_indexed[_peak_name]['exp'][
x_tag]
_y_peak_exp_indexed = _peak_map_indexed[_peak_name]['exp'][
'y']
_df['x_peak_exp(' + _peak_name + ')'] = _x_peak_exp_indexed
_df['y_peak_exp(' + _peak_name + ')'] = _y_peak_exp_indexed
_df['x_peak_ideal(' + _peak_name +
')'] = _x_peak_ideal_indexed
_df['y_peak_ideal(' + _peak_name +
')'] = _y_peak_ideal_indexed
_df.to_clipboard(index=False)
return _df
class TestItems(unittest.TestCase):
layer_1 = 'U'
thickness_1 = 0.05
layer_2 = 'Ag'
thickness_2 = 0.05
layers = fit_util.Layer()
layers.add_layer(layer=layer_1, thickness_mm=thickness_1)
layers.add_layer(layer=layer_2, thickness_mm=thickness_2)
database = '_data_for_unittest'
simulation = Simulation(database=database)
simulation.add_Layer(layers)
items = fit_util.Items(simulation.o_reso)
def test_raises(self):
name = 'AG'
pytest.raises(ValueError, fit_util._shape_items, name=name)
name = 'aG'
pytest.raises(ValueError, fit_util._shape_items, name=name)
name = 'AgO'
pytest.raises(ValueError, fit_util._shape_items, name=name)
name = 'ag'
pytest.raises(ValueError, fit_util._shape_items, name=name)
name = ''
pytest.raises(ValueError, fit_util._shape_items, name=name)
name = []
pytest.raises(ValueError, fit_util._shape_items, name=name)
def test_isotope_format(self):
name = '238-U'
expected_path = ['U', 'U', '238-U']
assert fit_util._shape_items(name) == expected_path
name = '238U'
assert fit_util._shape_items(name) == expected_path
name = 'U-238'
assert fit_util._shape_items(name) == expected_path
name = 'U238'
assert fit_util._shape_items(name) == expected_path
def test_fill_iso_to_items(self):
name = 'U*'
expected_path_list = [['U', 'U', '233-U'], ['U', 'U', '234-U'],
['U', 'U', '235-U'], ['U', 'U', '238-U']]
assert fit_util._fill_iso_to_items(
name, database=self.database) == expected_path_list
name = 'U'
pytest.raises(ValueError,
fit_util._fill_iso_to_items,
name=name,
database=self.database)
def test_shape_items(self):
name = 'U'
expected_path = ['U', 'U']
assert fit_util._shape_items(name) == expected_path
name = 'u'
expected_path = ['U', 'U']
assert fit_util._shape_items(name) == expected_path
name = 'Gd'
expected_path = ['Gd', 'Gd']
assert fit_util._shape_items(name) == expected_path
def test_items_shaped(self):
_input = ['Gd', ['U'], 'U-238', 'U*']
expected = [['Gd', 'Gd'], ['U', 'U'], ['U', 'U', '233-U'],
['U', 'U', '234-U'], ['U', 'U', '235-U'],
['U', 'U', '238-U']]
obtained = self.items.shaped(_input)
assert obtained == expected
def test_items_original(self):
_input = [['Gd', 'Gd'], ['U', 'U'], ['U', 'U', '233-U'],
['U', 'U', '234-U'], ['U', 'U', '235-U'],
['U', 'U', '238-U']]
expected = [['Gd', 'Gd'], ['U', 'U'], ['U', 'U', '233-U'],
['U', 'U', '234-U'], ['U', 'U', '235-U'],
['U', 'U', '238-U']]
obtained = self.items.shaped(_input)
assert obtained == expected
class TestPeaks(unittest.TestCase):
energy_min = 7
energy_max = 150
energy_step = 0.01
simulation = Simulation(energy_min=energy_min,
energy_max=energy_max,
energy_step=energy_step,
database='_data_for_unittest')
simulation.add_layer(layer='U', thickness_mm=0.05)
x = simulation.o_reso.stack_sigma['U']['U']['energy_eV']
y = simulation.o_reso.stack_sigma['U']['U']['sigma_b']
def test_findpeak1(self):
peak_found = fit_util._find_peak(y=self.y,
x=self.x,
thres=0.015,
min_dist=1,
imprv_reso=False)
peak_expected = {
'x': [20.87, 36.68, 66.03, 80.75, 102.57, 116.91],
'y': [
9801.184720322835, 13337.61249582717, 4356.4307835150385,
276.22478464487637, 6022.958717161858, 2003.9245670422251
],
}
self.assertDictEqual(peak_found, peak_expected)
def test_findpeak2(self):
peak_found = fit_util._find_peak(y=self.y,
x=self.x,
thres=0.015,
min_dist=1,
imprv_reso=True)
peak_expected = {
'x': [
20.87274280816388, 36.68474982964769, 66.03358430830164,
80.75548785869171, 102.56856740218703, 116.91012795048718
],
'y': [
9801.184720322835, 13337.61249582717, 4356.4307835150385,
276.22478464487637, 6022.958717161858, 2003.9245670422251
]
}
self.assertDictEqual(peak_found, peak_expected)
def test_findpeak3(self):
peak_found = fit_util._find_peak(y=self.y,
thres=0.015,
min_dist=1,
imprv_reso=False)
peak_expected = {
'x': [1387, 2968, 5903, 7375, 9557, 10991],
'y': [
9801.184720322835, 13337.61249582717, 4356.4307835150385,
276.22478464487637, 6022.958717161858, 2003.9245670422251
]
}
self.assertDictEqual(peak_found, peak_expected)
def test_findpeak4(self):
peak_found = fit_util._find_peak(y=self.y,
thres=0.015,
min_dist=1,
imprv_reso=True)
peak_expected = {
'x': [
1387.2742808163878, 2968.47498296478, 5903.358430830144,
7375.5487858692095, 9556.85674021868, 10991.012795048715
],
'y': [
9801.184720322835, 13337.61249582717, 4356.4307835150385,
276.22478464487637, 6022.958717161858, 2003.9245670422251
]
}
print(peak_found)
self.assertDictEqual(peak_found, peak_expected)
def test_indexes(self):
x = self.simulation.o_reso.stack_sigma['U']['U']['energy_eV']
y = self.simulation.o_reso.stack_sigma['U']['U']['sigma_b']
peak_df = fit_util.find_peak(y=y,
x=x,
x_name='x',
thres=0.015,
min_dist=1)
peak_df_expected = {
'x': [20.87, 36.68, 66.03, 80.75, 102.57, 116.91],
'y': [
9801.18472032, 13337.61249583, 4356.43078352, 276.22478464,
6022.95871716, 2003.92456704
],
}
assert peak_df['x'].tolist() == pytest.approx(peak_df_expected['x'])
assert peak_df['y'].tolist() == pytest.approx(peak_df_expected['y'])
peak_df = fit_util.find_peak(y=y, x_name='x', thres=0.015, min_dist=1)
peak_df_expected = {
'x': [1387, 2968, 5903, 7375, 9557, 10991],
'y': [
9801.18472032, 13337.61249583, 4356.43078352, 276.22478464,
6022.95871716, 2003.92456704
],
}
assert peak_df['x'].tolist() == pytest.approx(peak_df_expected['x'])
assert peak_df['y'].tolist() == pytest.approx(peak_df_expected['y'])
def plot(self,
error=True,
table=True,
grid=True,
before=False,
interp=False,
total=True,
all_elements=False,
all_isotopes=False,
items_to_plot=None,
peak_mark=True,
peak_id='indexed',
y_type='transmission',
x_type='energy',
t_unit='us',
logx=False,
logy=False,
save_fig=False):
"""
:param error:
:type error:
:param table:
:type table:
:param grid:
:type grid:
:param before:
:type before:
:param interp:
:type interp:
:param total:
:type total:
:param all_elements:
:type all_elements:
:param all_isotopes:
:type all_isotopes:
:param items_to_plot:
:type items_to_plot:
:param peak_mark:
:type peak_mark:
:param peak_id:
:type peak_id:
:param y_type:
:type y_type:
:param x_type:
:type x_type:
:param t_unit:
:type t_unit:
:param logx:
:type logx:
:param logy:
:type logy:
:param save_fig:
:type save_fig:
:return:
:rtype:
"""
# Form signals from fitted_layer
if self.fitted_simulation is None:
self.fitted_simulation = Simulation(energy_min=self.energy_min,
energy_max=self.energy_max,
energy_step=self.energy_step)
for each_layer in self.layer_list:
self.fitted_simulation.add_layer(
layer=each_layer,
thickness_mm=self.fitted_layer.info[each_layer]
['thickness']['value'],
density_gcm3=self.fitted_layer.info[each_layer]['density']
['value'])
if peak_id not in ['indexed', 'all']:
raise ValueError("'peak=' must be one of ['indexed', 'full'].")
simu_x = self.fitted_simulation.get_x(x_type='energy')
simu_y = self.fitted_simulation.get_y(y_type='attenuation')
# Get plot labels
simu_label = 'Fit'
simu_before_label = 'Fit_init'
exp_label = 'Exp'
exp_interp_label = 'Exp_interp'
sample_name = ' & '.join(self.layer_list)
if self.sample_vary is None:
raise ValueError("Vary type ['density'|'thickness'] is not set.")
fig_title = 'Fitting result of sample (' + sample_name + ')'
# Create pd.DataFrame
self.df = pd.DataFrame()
# Clear any left plt
plt.close()
# plot table + graph
if table is True:
ax1 = plt.subplot2grid(shape=(10, 10),
loc=(0, 1),
rowspan=8,
colspan=8)
# plot graph only
else:
ax1 = plt.subplot(111)
# Plot after fitting
if total is True:
ax1.plot(simu_x, simu_y, 'b-', label=simu_label, linewidth=1)
# Save to df
_live_df_x_label = simu_label + '_eV'
_live_df_y_label = simu_label + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = simu_y
"""Plot options"""
# 1.
if before is True:
# Plot before fitting
# Form signals from raw_layer
simulation = Simulation(energy_min=self.energy_min,
energy_max=self.energy_max,
energy_step=self.energy_step)
for each_layer in self.layer_list:
simulation.add_layer(
layer=each_layer,
thickness_mm=self.raw_layer.info[each_layer]['thickness']
['value'],
density_gcm3=self.raw_layer.info[each_layer]['density']
['value'])
simu_x = simulation.get_x(x_type='energy')
simu_y_before = simulation.get_y(y_type='attenuation')
ax1.plot(simu_x,
simu_y_before,
'c-.',
label=simu_before_label,
linewidth=1)
# Save to df
_live_df_x_label = simu_before_label + '_eV'
_live_df_y_label = simu_before_label + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = simu_y_before
# 2.
if interp is True:
# Plot exp. data (interpolated)
x_interp, y_interp = self.experiment.xy_scaled(
energy_max=self.energy_max,
energy_min=self.energy_min,
energy_step=self.energy_step,
x_type='energy',
y_type='attenuation',
baseline=self.baseline,
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m)
ax1.plot(x_interp,
y_interp,
'r:',
label=exp_interp_label,
linewidth=1)
# Save to df
_live_df_x_label = exp_interp_label + '_eV'
_live_df_y_label = exp_interp_label + '_attenuation'
self.df[_live_df_x_label] = x_interp
self.df[_live_df_y_label] = y_interp
else:
# Plot exp. data (raw)
exp_x = self.experiment.get_x(
x_type='energy',
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m)
exp_y = self.experiment.get_y(y_type='attenuation',
baseline=self.baseline)
ax1.plot(exp_x,
exp_y,
linestyle='-',
linewidth=1,
marker='o',
markersize=2,
color='r',
label=exp_label)
# Save to df
_df = pd.DataFrame()
_live_df_x_label = exp_label + '_eV'
_live_df_y_label = exp_label + '_attenuation'
_df[_live_df_x_label] = exp_x
_df[_live_df_y_label] = exp_y
# Concatenate since the length of raw and simu are not the same
self.df = pd.concat([self.df, _df], axis=1)
# 3.
if error is True:
# Plot fitting differences
error_label = 'Diff.'
_move_below_by = 0.2
moved_fitted_residual = self.fitted_residual - _move_below_by
ax1.plot(simu_x,
moved_fitted_residual,
'g-',
label=error_label,
linewidth=1,
alpha=1)
# Save to df
_live_df_x_label = error_label + '_eV'
_live_df_y_label = error_label + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = moved_fitted_residual
# 4.
if all_elements is True:
# show signal from each elements
_stack_signal = self.fitted_simulation.o_reso.stack_signal
_stack = self.fitted_simulation.o_reso.stack
y_axis_tag = 'attenuation'
for _layer in _stack.keys():
for _element in _stack[_layer]['elements']:
_y_axis = _stack_signal[_layer][_element][y_axis_tag]
ax1.plot(simu_x,
_y_axis,
label="{}".format(_element),
linewidth=1,
alpha=0.85)
# Save to df
_live_df_x_label = _element + '_eV'
_live_df_y_label = _element + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = _y_axis
# 4.
if all_isotopes is True:
# show signal from each isotopes
_stack_signal = self.fitted_simulation.o_reso.stack_signal
_stack = self.fitted_simulation.o_reso.stack
y_axis_tag = 'attenuation'
for _layer in _stack.keys():
for _element in _stack[_layer]['elements']:
for _isotope in _stack[_layer][_element]['isotopes'][
'list']:
_y_axis = _stack_signal[_layer][_element][_isotope][
y_axis_tag]
ax1.plot(simu_x,
_y_axis,
label="{}".format(_isotope),
linewidth=1,
alpha=1)
# Save to df
_live_df_x_label = _isotope + '_eV'
_live_df_y_label = _isotope + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = _y_axis
# 5.
if items_to_plot is not None:
# plot specified from 'items_to_plot'
y_axis_tag = 'attenuation'
items = fit_util.Items(o_reso=self.fitted_simulation.o_reso,
database=self.database)
shaped_items = items.shaped(items_list=items_to_plot)
_signal_dict = items.values(y_axis_type=y_axis_tag)
for _each_label in list(_signal_dict.keys()):
ax1.plot(simu_x,
_signal_dict[_each_label],
'--',
label=_each_label,
linewidth=1,
alpha=1)
# Save to df
_live_df_x_label = _each_label + '_eV'
_live_df_y_label = _each_label + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = _signal_dict[_each_label]
# plot peaks detected and indexed
if self.experiment.o_peak and self.experiment.o_peak.peak_map_indexed is not None:
_peak_df_scaled = self.experiment.o_peak.peak_df_scaled
_peak_map_indexed = self.experiment.o_peak.peak_map_indexed
_peak_map_full = self.experiment.o_peak.peak_map_full
if peak_mark is True:
ax1.plot(_peak_df_scaled['x'],
_peak_df_scaled['y'],
'kx',
label='_nolegend_')
if error is False:
ax1.set_ylim(ymin=-0.1)
for _ele_name in _peak_map_indexed.keys():
if peak_id is 'all':
ax1.plot(_peak_map_full[_ele_name]['ideal']['x'], [-0.05] *
len(_peak_map_full[_ele_name]['ideal']['x']),
'|',
ms=10,
label=_ele_name)
elif peak_id is 'indexed':
ax1.plot(_peak_map_indexed[_ele_name]['exp']['x'],
[-0.05] *
len(_peak_map_indexed[_ele_name]['exp']['x']),
'|',
ms=8,
label=_ele_name)
if 'peak_span' in _peak_map_indexed[_ele_name].keys():
_data_point_x = _peak_map_indexed[_ele_name]['peak_span'][
'energy_ev']
_data_point_y = _peak_map_indexed[_ele_name]['peak_span'][
'y']
ax1.scatter(_data_point_x,
_data_point_y,
label='_nolegend_')
# Set plot limit and captions
fit_util.set_plt(ax=ax1,
fig_title=fig_title,
grid=grid,
x_type=x_type,
y_type=y_type,
t_unit=t_unit,
logx=logx,
logy=logy)
# Plot table
if table is True:
if self.fitted_iso_result is None:
columns = list(
self.fit_result.__dict__['params'].valuesdict().keys())
else:
columns = self.fit_result.__dict__['var_names']
columns_to_show_dict = {}
for _each in columns:
_split = _each.split('_')
if _split[0] == 'thickness':
_name_to_show = r'$d_{\rm{' + _split[-1] + '}}$' + ' (mm)'
else:
_name_to_show = r'$\rho_{\rm{' + _split[
-1] + '}}$' + ' (g/cm$^3$)'
columns_to_show_dict[_each] = _name_to_show
columns_to_show = list(columns_to_show_dict.values())
rows = ['Before', 'After']
_row_before = []
_row_after = []
for _each in columns:
_row_after.append(
round(
self.fit_result.__dict__['params'].valuesdict()[_each],
3))
_row_before.append(
round(self.params_for_fit.valuesdict()[_each], 3))
if self.fitted_iso_result is not None:
_iso_columns = list(self.fitted_iso_result.__dict__['params'].
valuesdict().keys())
columns = columns + _iso_columns
_iso_columns_to_show_dict = {}
for _each_iso in _iso_columns:
_num_str = re.findall('\d+', _each_iso)[0]
_name_str = _each_iso[0]
_sup_name = r"$^{" + _num_str + "}$" + _name_str
_iso_columns_to_show_dict[_each_iso] = _sup_name
_iso_columns_to_show = list(_iso_columns_to_show_dict.values())
columns_to_show = columns_to_show + _iso_columns_to_show
for _each in _iso_columns:
_row_after.append(
round(
self.fitted_iso_result.__dict__['params'].
valuesdict()[_each], 3))
_row_before.append(
round(self.params_for_iso_fit.valuesdict()[_each], 3))
table = ax1.table(rowLabels=rows,
colLabels=columns_to_show,
cellText=[_row_before, _row_after],
loc='upper right',
bbox=[0, -0.33, 1.0, 0.18])
table.auto_set_font_size(False)
table.set_fontsize(10)
plt.tight_layout()
if save_fig:
_sample_name = '_'.join(self.layer_list)
_filename = 'fitting_' + _sample_name + '.png'
plt.savefig(_filename, dpi=600, transparent=True)
plt.close()
else:
plt.show()
def fit(self, raw_layer: fit_util.Layer, vary='density', each_step=False):
if vary not in ['density', 'thickness', 'none']:
raise ValueError(
"'vary=' can only be one of ['density', 'thickness', 'none']")
# Default vary is: 'density'
self.sample_vary = vary
thickness_vary_tag = False
density_vary_tag = True
if vary == 'thickness':
thickness_vary_tag = True
density_vary_tag = False
if vary == 'none':
density_vary_tag = False
self.raw_layer = raw_layer
'''Load params'''
print(raw_layer)
self.layer_list = list(raw_layer.info.keys())
self.params_for_fit = Parameters()
for _each_layer in self.layer_list:
if self.raw_layer.info[_each_layer]['density']['value'] is np.NaN:
self.raw_layer.info[_each_layer]['density'][
'value'] = pt.elements.isotope(_each_layer).density
self.params_for_fit.add(
'thickness_mm_' + _each_layer,
value=self.raw_layer.info[_each_layer]['thickness']['value'],
vary=thickness_vary_tag,
min=0)
self.params_for_fit.add(
'density_gcm3_' + _each_layer,
value=self.raw_layer.info[_each_layer]['density']['value'],
vary=density_vary_tag,
min=0)
# Print before
print(
"+----------------- Fitting ({}) -----------------+\nParams before:"
.format(vary))
self.params_for_fit.pretty_print()
# Fitting
self.fit_result = minimize(y_gap_for_fitting,
self.params_for_fit,
method='leastsq',
args=(self.exp_x_interp, self.exp_y_interp,
self.layer_list, self.energy_min,
self.energy_max, self.energy_step,
self.database, each_step))
# Print after
print("\nParams after:")
self.fit_result.__dict__['params'].pretty_print()
# Print chi^2
self.fitted_residual = self.fit_result.__dict__['residual']
print("Fitting chi^2 : {}\n".format(sum(self.fitted_residual**2)))
'''Export fitted params as Layer()'''
# Save the fitted 'density' or 'thickness' in Layer()
self.fitted_layer = Layer()
for _each_layer in self.layer_list:
self.fitted_layer.add_layer(
layer=_each_layer,
thickness_mm=self.fit_result.__dict__['params'].valuesdict()[
'thickness_mm_' + _each_layer],
density_gcm3=self.fit_result.__dict__['params'].valuesdict()[
'density_gcm3_' + _each_layer])
# self.fitted_fjac = self.fit_result.__dict__['fjac']
# print(self.fit_result.__dict__['fjac'][0])
'''Create fitted simulation'''
self.fitted_simulation = Simulation(energy_min=self.energy_min,
energy_max=self.energy_max,
energy_step=self.energy_step,
database=self.database)
for each_layer in self.layer_list:
self.fitted_simulation.add_layer(
layer=each_layer,
thickness_mm=self.fitted_layer.info[each_layer]['thickness']
['value'],
density_gcm3=self.fitted_layer.info[each_layer]['density']
['value'])
return self.fit_result
class FitResonance(object):
def __init__(self,
spectra_file,
data_file,
calibrated_offset_us,
calibrated_source_to_detector_m,
folder,
norm_factor=1,
baseline=False,
norm_to_file=None,
slice_start=None,
slice_end=None,
energy_min=1e-5,
energy_max=1000,
energy_step=0.01,
database='ENDF_VII'):
self.experiment = Experiment(spectra_file=spectra_file,
data_file=data_file,
folder=folder)
self.energy_min = energy_min
self.energy_max = energy_max
self.energy_step = energy_step
self.database = database
self.calibrated_offset_us = calibrated_offset_us
self.calibrated_source_to_detector_m = calibrated_source_to_detector_m
self.raw_layer = None
self.experiment.slice(start=slice_start, end=slice_end)
self.baseline = baseline
if norm_to_file is not None:
self.experiment.norm_to(norm_to_file, norm_factor=norm_factor)
self.exp_x_interp, self.exp_y_interp = self.experiment.xy_scaled(
energy_min=self.energy_min,
energy_max=self.energy_max,
energy_step=self.energy_step,
x_type='energy',
y_type='attenuation',
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m,
baseline=self.baseline)
self.fit_result = None
self.fitted_density_gcm3 = None
self.fitted_thickness_mm = None
self.fitted_residual = None
self.fitted_gap = None
self.fitted_fjac = None
self.fitted_layer = None
self.fitted_simulation = None
self.layer_list = None
self.raw_layer = None
self.fitted_iso_result = None
self.fitted_iso_residual = None
self.params_for_fit = None
self.params_for_iso_fit = None
self.isotope_stack = {}
self.sample_vary = None
self.df = None
# self.peak_map_full = None
# self.peak_map_indexed = None
def fit(self, raw_layer: fit_util.Layer, vary='density', each_step=False):
if vary not in ['density', 'thickness', 'none']:
raise ValueError(
"'vary=' can only be one of ['density', 'thickness', 'none']")
# Default vary is: 'density'
self.sample_vary = vary
thickness_vary_tag = False
density_vary_tag = True
if vary == 'thickness':
thickness_vary_tag = True
density_vary_tag = False
if vary == 'none':
density_vary_tag = False
self.raw_layer = raw_layer
'''Load params'''
print(raw_layer)
self.layer_list = list(raw_layer.info.keys())
self.params_for_fit = Parameters()
for _each_layer in self.layer_list:
if self.raw_layer.info[_each_layer]['density']['value'] is np.NaN:
self.raw_layer.info[_each_layer]['density'][
'value'] = pt.elements.isotope(_each_layer).density
self.params_for_fit.add(
'thickness_mm_' + _each_layer,
value=self.raw_layer.info[_each_layer]['thickness']['value'],
vary=thickness_vary_tag,
min=0)
self.params_for_fit.add(
'density_gcm3_' + _each_layer,
value=self.raw_layer.info[_each_layer]['density']['value'],
vary=density_vary_tag,
min=0)
# Print before
print(
"+----------------- Fitting ({}) -----------------+\nParams before:"
.format(vary))
self.params_for_fit.pretty_print()
# Fitting
self.fit_result = minimize(y_gap_for_fitting,
self.params_for_fit,
method='leastsq',
args=(self.exp_x_interp, self.exp_y_interp,
self.layer_list, self.energy_min,
self.energy_max, self.energy_step,
self.database, each_step))
# Print after
print("\nParams after:")
self.fit_result.__dict__['params'].pretty_print()
# Print chi^2
self.fitted_residual = self.fit_result.__dict__['residual']
print("Fitting chi^2 : {}\n".format(sum(self.fitted_residual**2)))
'''Export fitted params as Layer()'''
# Save the fitted 'density' or 'thickness' in Layer()
self.fitted_layer = Layer()
for _each_layer in self.layer_list:
self.fitted_layer.add_layer(
layer=_each_layer,
thickness_mm=self.fit_result.__dict__['params'].valuesdict()[
'thickness_mm_' + _each_layer],
density_gcm3=self.fit_result.__dict__['params'].valuesdict()[
'density_gcm3_' + _each_layer])
# self.fitted_fjac = self.fit_result.__dict__['fjac']
# print(self.fit_result.__dict__['fjac'][0])
'''Create fitted simulation'''
self.fitted_simulation = Simulation(energy_min=self.energy_min,
energy_max=self.energy_max,
energy_step=self.energy_step,
database=self.database)
for each_layer in self.layer_list:
self.fitted_simulation.add_layer(
layer=each_layer,
thickness_mm=self.fitted_layer.info[each_layer]['thickness']
['value'],
density_gcm3=self.fitted_layer.info[each_layer]['density']
['value'])
return self.fit_result
def fit_iso(self, layer, each_step=False):
"""
:param layer:
:type layer:
:param each_step:
:type each_step:
:return:
:rtype:
"""
self.params_for_iso_fit = Parameters()
self.isotope_stack[layer] = {
'list':
self.fitted_simulation.o_reso.stack[layer][layer]['isotopes']
['list'],
'ratios':
self.fitted_simulation.o_reso.stack[layer][layer]['isotopes']
['isotopic_ratio']
}
_formatted_isotope_list = []
_params_name_list = []
# Form list of param name
for _isotope_index in range(len(self.isotope_stack[layer]['list'])):
_split = self.isotope_stack[layer]['list'][_isotope_index].split(
'-')
_flip = _split[::-1]
_formatted_isotope_name = ''.join(_flip)
# _formatted_isotope_name = self.isotope_stack[layer]['list'][_isotope_index].replace('-', '_')
_formatted_isotope_list.append(_formatted_isotope_name)
_params_name_list = _formatted_isotope_list
# Form Parameters() for fitting
for _name_index in range(len(_params_name_list)):
self.params_for_iso_fit.add(
_params_name_list[_name_index],
value=self.isotope_stack[layer]['ratios'][_name_index],
min=0,
max=1)
# Constrain sum of isotope ratios to be 1
# _params_name_list_temp = _params_name_list[:]
# _constraint = '+'.join(_params_name_list_temp)
# self.params_for_iso_fit.add('sum', expr=_constraint)
_constraint_param = _params_name_list[-1]
_params_name_list_temp = _params_name_list[:]
_params_name_list_temp.remove(_constraint_param)
_constraint = '-'.join(_params_name_list_temp)
_constraint = '1-' + _constraint
self.params_for_iso_fit[_constraint_param].set(expr=_constraint)
# Print params before
print(
"+----------------- Fitting (isotopic at.%) -----------------+\nParams before:"
)
self.params_for_iso_fit.pretty_print()
# Fitting
self.fitted_iso_result = minimize(
y_gap_for_iso_fitting,
self.params_for_iso_fit,
method='leastsq',
args=(self.exp_x_interp, self.exp_y_interp, layer,
_formatted_isotope_list, self.fitted_simulation, each_step))
# Print params after
print("\nParams after:")
self.fitted_iso_result.__dict__['params'].pretty_print()
# Print chi^2
self.fitted_iso_residual = self.fitted_iso_result.__dict__['residual']
print("Fit iso chi^2 : {}\n".format(
self.fitted_iso_result.__dict__['chisqr']))
return
def molar_conc(self):
molar_conc_units = 'mol/cm3'
print(
"Molar-conc. ({})\tBefore_fit\tAfter_fit".format(molar_conc_units))
for _each_layer in self.layer_list:
molar_mass_value = self.fitted_simulation.o_reso.stack[
_each_layer][_each_layer]['molar_mass']['value']
molar_mass_units = self.fitted_simulation.o_reso.stack[
_each_layer][_each_layer]['molar_mass']['units']
# Adding molar_mass to fitted_layer info
self.fitted_layer.info[_each_layer]['molar_mass'][
'value'] = molar_mass_value
self.fitted_layer.info[_each_layer]['molar_mass'][
'units'] = molar_mass_units
# Adding molar_mass to raw_layer info
self.raw_layer.info[_each_layer]['molar_mass'][
'value'] = molar_mass_value
self.raw_layer.info[_each_layer]['molar_mass'][
'units'] = molar_mass_units
# Adding molar_concentration to fitted_layer info
molar_conc_value = self.fitted_layer.info[_each_layer]['density'][
'value'] / molar_mass_value
self.fitted_layer.info[_each_layer]['molar_conc'][
'value'] = molar_conc_value
self.fitted_layer.info[_each_layer]['molar_conc'][
'units'] = molar_conc_units
# Calculate starting molar_concentration and fitted_layer info
start_molar_conc_value = self.raw_layer.info[_each_layer][
'density']['value'] / molar_mass_value
self.raw_layer.info[_each_layer]['molar_conc'][
'value'] = start_molar_conc_value
self.raw_layer.info[_each_layer]['molar_conc'][
'units'] = molar_conc_units
# molar_conc_output[_each_layer] = {'Before_fit': start_molar_conc_value,
# 'After_fit': molar_conc_value}
print("{}\t{}\t{}".format(_each_layer, start_molar_conc_value,
molar_conc_value))
print('\n')
return self.fitted_layer.info
def index_peak(self,
thres,
min_dist,
map_thres=0.01,
map_min_dist=20,
rel_tol=5e-3,
isotope=False):
if self.experiment.o_peak is None:
self.experiment.find_peak(thres=thres, min_dist=min_dist)
self.experiment._scale_peak_with_ev(
energy_min=self.energy_min,
energy_max=self.energy_max,
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m)
assert self.experiment.o_peak.peak_df is not None
assert self.experiment.o_peak.peak_df_scaled is not None
_peak_map = self.fitted_simulation.peak_map(
thres=map_thres,
min_dist=map_min_dist,
impr_reso=True,
# isotope=isotope,
)
self.experiment.o_peak.peak_map_full = _peak_map
self.experiment.o_peak.index_peak(peak_map=_peak_map, rel_tol=rel_tol)
return self.experiment.o_peak.peak_map_indexed
# def analyze_peak(self):
# pass
def plot(self,
error=True,
table=True,
grid=True,
before=False,
interp=False,
total=True,
all_elements=False,
all_isotopes=False,
items_to_plot=None,
peak_mark=True,
peak_id='indexed',
y_type='transmission',
x_type='energy',
t_unit='us',
logx=False,
logy=False,
save_fig=False):
"""
:param error:
:type error:
:param table:
:type table:
:param grid:
:type grid:
:param before:
:type before:
:param interp:
:type interp:
:param total:
:type total:
:param all_elements:
:type all_elements:
:param all_isotopes:
:type all_isotopes:
:param items_to_plot:
:type items_to_plot:
:param peak_mark:
:type peak_mark:
:param peak_id:
:type peak_id:
:param y_type:
:type y_type:
:param x_type:
:type x_type:
:param t_unit:
:type t_unit:
:param logx:
:type logx:
:param logy:
:type logy:
:param save_fig:
:type save_fig:
:return:
:rtype:
"""
# Form signals from fitted_layer
if self.fitted_simulation is None:
self.fitted_simulation = Simulation(energy_min=self.energy_min,
energy_max=self.energy_max,
energy_step=self.energy_step)
for each_layer in self.layer_list:
self.fitted_simulation.add_layer(
layer=each_layer,
thickness_mm=self.fitted_layer.info[each_layer]
['thickness']['value'],
density_gcm3=self.fitted_layer.info[each_layer]['density']
['value'])
if peak_id not in ['indexed', 'all']:
raise ValueError("'peak=' must be one of ['indexed', 'full'].")
simu_x = self.fitted_simulation.get_x(x_type='energy')
simu_y = self.fitted_simulation.get_y(y_type='attenuation')
# Get plot labels
simu_label = 'Fit'
simu_before_label = 'Fit_init'
exp_label = 'Exp'
exp_interp_label = 'Exp_interp'
sample_name = ' & '.join(self.layer_list)
if self.sample_vary is None:
raise ValueError("Vary type ['density'|'thickness'] is not set.")
fig_title = 'Fitting result of sample (' + sample_name + ')'
# Create pd.DataFrame
self.df = pd.DataFrame()
# Clear any left plt
plt.close()
# plot table + graph
if table is True:
ax1 = plt.subplot2grid(shape=(10, 10),
loc=(0, 1),
rowspan=8,
colspan=8)
# plot graph only
else:
ax1 = plt.subplot(111)
# Plot after fitting
if total is True:
ax1.plot(simu_x, simu_y, 'b-', label=simu_label, linewidth=1)
# Save to df
_live_df_x_label = simu_label + '_eV'
_live_df_y_label = simu_label + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = simu_y
"""Plot options"""
# 1.
if before is True:
# Plot before fitting
# Form signals from raw_layer
simulation = Simulation(energy_min=self.energy_min,
energy_max=self.energy_max,
energy_step=self.energy_step)
for each_layer in self.layer_list:
simulation.add_layer(
layer=each_layer,
thickness_mm=self.raw_layer.info[each_layer]['thickness']
['value'],
density_gcm3=self.raw_layer.info[each_layer]['density']
['value'])
simu_x = simulation.get_x(x_type='energy')
simu_y_before = simulation.get_y(y_type='attenuation')
ax1.plot(simu_x,
simu_y_before,
'c-.',
label=simu_before_label,
linewidth=1)
# Save to df
_live_df_x_label = simu_before_label + '_eV'
_live_df_y_label = simu_before_label + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = simu_y_before
# 2.
if interp is True:
# Plot exp. data (interpolated)
x_interp, y_interp = self.experiment.xy_scaled(
energy_max=self.energy_max,
energy_min=self.energy_min,
energy_step=self.energy_step,
x_type='energy',
y_type='attenuation',
baseline=self.baseline,
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m)
ax1.plot(x_interp,
y_interp,
'r:',
label=exp_interp_label,
linewidth=1)
# Save to df
_live_df_x_label = exp_interp_label + '_eV'
_live_df_y_label = exp_interp_label + '_attenuation'
self.df[_live_df_x_label] = x_interp
self.df[_live_df_y_label] = y_interp
else:
# Plot exp. data (raw)
exp_x = self.experiment.get_x(
x_type='energy',
offset_us=self.calibrated_offset_us,
source_to_detector_m=self.calibrated_source_to_detector_m)
exp_y = self.experiment.get_y(y_type='attenuation',
baseline=self.baseline)
ax1.plot(exp_x,
exp_y,
linestyle='-',
linewidth=1,
marker='o',
markersize=2,
color='r',
label=exp_label)
# Save to df
_df = pd.DataFrame()
_live_df_x_label = exp_label + '_eV'
_live_df_y_label = exp_label + '_attenuation'
_df[_live_df_x_label] = exp_x
_df[_live_df_y_label] = exp_y
# Concatenate since the length of raw and simu are not the same
self.df = pd.concat([self.df, _df], axis=1)
# 3.
if error is True:
# Plot fitting differences
error_label = 'Diff.'
_move_below_by = 0.2
moved_fitted_residual = self.fitted_residual - _move_below_by
ax1.plot(simu_x,
moved_fitted_residual,
'g-',
label=error_label,
linewidth=1,
alpha=1)
# Save to df
_live_df_x_label = error_label + '_eV'
_live_df_y_label = error_label + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = moved_fitted_residual
# 4.
if all_elements is True:
# show signal from each elements
_stack_signal = self.fitted_simulation.o_reso.stack_signal
_stack = self.fitted_simulation.o_reso.stack
y_axis_tag = 'attenuation'
for _layer in _stack.keys():
for _element in _stack[_layer]['elements']:
_y_axis = _stack_signal[_layer][_element][y_axis_tag]
ax1.plot(simu_x,
_y_axis,
label="{}".format(_element),
linewidth=1,
alpha=0.85)
# Save to df
_live_df_x_label = _element + '_eV'
_live_df_y_label = _element + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = _y_axis
# 4.
if all_isotopes is True:
# show signal from each isotopes
_stack_signal = self.fitted_simulation.o_reso.stack_signal
_stack = self.fitted_simulation.o_reso.stack
y_axis_tag = 'attenuation'
for _layer in _stack.keys():
for _element in _stack[_layer]['elements']:
for _isotope in _stack[_layer][_element]['isotopes'][
'list']:
_y_axis = _stack_signal[_layer][_element][_isotope][
y_axis_tag]
ax1.plot(simu_x,
_y_axis,
label="{}".format(_isotope),
linewidth=1,
alpha=1)
# Save to df
_live_df_x_label = _isotope + '_eV'
_live_df_y_label = _isotope + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = _y_axis
# 5.
if items_to_plot is not None:
# plot specified from 'items_to_plot'
y_axis_tag = 'attenuation'
items = fit_util.Items(o_reso=self.fitted_simulation.o_reso,
database=self.database)
shaped_items = items.shaped(items_list=items_to_plot)
_signal_dict = items.values(y_axis_type=y_axis_tag)
for _each_label in list(_signal_dict.keys()):
ax1.plot(simu_x,
_signal_dict[_each_label],
'--',
label=_each_label,
linewidth=1,
alpha=1)
# Save to df
_live_df_x_label = _each_label + '_eV'
_live_df_y_label = _each_label + '_attenuation'
self.df[_live_df_x_label] = simu_x
self.df[_live_df_y_label] = _signal_dict[_each_label]
# plot peaks detected and indexed
if self.experiment.o_peak and self.experiment.o_peak.peak_map_indexed is not None:
_peak_df_scaled = self.experiment.o_peak.peak_df_scaled
_peak_map_indexed = self.experiment.o_peak.peak_map_indexed
_peak_map_full = self.experiment.o_peak.peak_map_full
if peak_mark is True:
ax1.plot(_peak_df_scaled['x'],
_peak_df_scaled['y'],
'kx',
label='_nolegend_')
if error is False:
ax1.set_ylim(ymin=-0.1)
for _ele_name in _peak_map_indexed.keys():
if peak_id is 'all':
ax1.plot(_peak_map_full[_ele_name]['ideal']['x'], [-0.05] *
len(_peak_map_full[_ele_name]['ideal']['x']),
'|',
ms=10,
label=_ele_name)
elif peak_id is 'indexed':
ax1.plot(_peak_map_indexed[_ele_name]['exp']['x'],
[-0.05] *
len(_peak_map_indexed[_ele_name]['exp']['x']),
'|',
ms=8,
label=_ele_name)
if 'peak_span' in _peak_map_indexed[_ele_name].keys():
_data_point_x = _peak_map_indexed[_ele_name]['peak_span'][
'energy_ev']
_data_point_y = _peak_map_indexed[_ele_name]['peak_span'][
'y']
ax1.scatter(_data_point_x,
_data_point_y,
label='_nolegend_')
# Set plot limit and captions
fit_util.set_plt(ax=ax1,
fig_title=fig_title,
grid=grid,
x_type=x_type,
y_type=y_type,
t_unit=t_unit,
logx=logx,
logy=logy)
# Plot table
if table is True:
if self.fitted_iso_result is None:
columns = list(
self.fit_result.__dict__['params'].valuesdict().keys())
else:
columns = self.fit_result.__dict__['var_names']
columns_to_show_dict = {}
for _each in columns:
_split = _each.split('_')
if _split[0] == 'thickness':
_name_to_show = r'$d_{\rm{' + _split[-1] + '}}$' + ' (mm)'
else:
_name_to_show = r'$\rho_{\rm{' + _split[
-1] + '}}$' + ' (g/cm$^3$)'
columns_to_show_dict[_each] = _name_to_show
columns_to_show = list(columns_to_show_dict.values())
rows = ['Before', 'After']
_row_before = []
_row_after = []
for _each in columns:
_row_after.append(
round(
self.fit_result.__dict__['params'].valuesdict()[_each],
3))
_row_before.append(
round(self.params_for_fit.valuesdict()[_each], 3))
if self.fitted_iso_result is not None:
_iso_columns = list(self.fitted_iso_result.__dict__['params'].
valuesdict().keys())
columns = columns + _iso_columns
_iso_columns_to_show_dict = {}
for _each_iso in _iso_columns:
_num_str = re.findall('\d+', _each_iso)[0]
_name_str = _each_iso[0]
_sup_name = r"$^{" + _num_str + "}$" + _name_str
_iso_columns_to_show_dict[_each_iso] = _sup_name
_iso_columns_to_show = list(_iso_columns_to_show_dict.values())
columns_to_show = columns_to_show + _iso_columns_to_show
for _each in _iso_columns:
_row_after.append(
round(
self.fitted_iso_result.__dict__['params'].
valuesdict()[_each], 3))
_row_before.append(
round(self.params_for_iso_fit.valuesdict()[_each], 3))
table = ax1.table(rowLabels=rows,
colLabels=columns_to_show,
cellText=[_row_before, _row_after],
loc='upper right',
bbox=[0, -0.33, 1.0, 0.18])
table.auto_set_font_size(False)
table.set_fontsize(10)
plt.tight_layout()
if save_fig:
_sample_name = '_'.join(self.layer_list)
_filename = 'fitting_' + _sample_name + '.png'
plt.savefig(_filename, dpi=600, transparent=True)
plt.close()
else:
plt.show()
def export(self, filename=None):
if self.df is None:
raise ValueError(
"pd.DataFrame is empty, please run required step: FitResonance.plot()"
)
elif filename is None:
self.df.to_clipboard(excel=True)
else:
self.df.to_csv(filename)
import matplotlib.pyplot as plt
from ResoFit.experiment import Experiment
from ResoFit.simulation import Simulation
from ResoFit._pulse_shape import NeutronPulse
overwrite_csv = False
source_to_detector_m = 16.45
simulation = Simulation(energy_min=78,
energy_max=82,
energy_step=0.01,
database='ENDF_VII')
simulation.add_layer(layer='Gd', thickness_mm=0.075)
simulation._convolve_beam_shapes(source_to_detector_m=source_to_detector_m,
model_index=1,
conv_proton=True,
proton_params={})
# model_index:
# 1: 'ikeda_carpenter',
# 2: 'cole_windsor',
# 3: 'pseudo_voigt',
# 4: 'ikeda_carpenter_jparc',
# 5: 'cole_windsor_jparc'
# folder = 'data/IPTS_19558/reso_data_19558'
# data_file1 = 'Gd_thin.csv'
# spectra_file = 'Image002_Spectra.txt'
# experiment1 = Experiment(data_file=data_file1,
# spectra_file=spectra_file,
# folder=folder,
class TestSimulation(unittest.TestCase):
energy_min = 7
energy_max = 10
energy_step = 1
database = '_data_for_unittest'
simulation = Simulation(energy_min=energy_min,
energy_max=energy_max,
energy_step=energy_step,
database='_data_for_unittest')
def test_add_layer(self):
simulation = self.simulation
simulation.add_layer(layer='U', thickness_mm=0.15)
_simu_x_returned = simulation.get_x(x_type='energy')
_simu_y_returned = simulation.get_y(y_type='attenuation')
_simu_x_expected = np.array([7., 8., 9., 10.])
_simu_y_expected = np.array(
[0.03699373, 0.00936537, 0.00854215, 0.00726004])
self.assertAlmostEqual(_simu_x_returned[0],
_simu_x_expected[0],
delta=0.000001)
self.assertAlmostEqual(_simu_x_returned[1],
_simu_x_expected[1],
delta=0.000001)
self.assertAlmostEqual(_simu_x_returned[2],
_simu_x_expected[2],
delta=0.000001)
self.assertAlmostEqual(_simu_x_returned[3],
_simu_x_expected[3],
delta=0.000001)
self.assertAlmostEqual(_simu_y_returned[0],
_simu_y_expected[0],
delta=0.000001)
self.assertAlmostEqual(_simu_y_returned[1],
_simu_y_expected[1],
delta=0.000001)
self.assertAlmostEqual(_simu_y_returned[2],
_simu_y_expected[2],
delta=0.000001)
self.assertAlmostEqual(_simu_y_returned[3],
_simu_y_expected[3],
delta=0.000001)
def test_set_isotopic_ratio(self):
simulation = self.simulation
simulation.add_layer(layer='U', thickness_mm=0.15)
simulation.set_isotopic_ratio('U', 'U', [0., 0., 0.99, 0.01])
_isotopic_ratio_list_wrong_len = [0., 0.99, 0.01]
self.assertRaises(
ValueError,
simulation.set_isotopic_ratio,
layer='U',
element='U',
new_isotopic_ratio_list=_isotopic_ratio_list_wrong_len)
_simu_x_returned = simulation.get_x(x_type='energy')
_simu_y_returned = simulation.get_y(y_type='attenuation')
_simu_x_expected = np.array([7., 8., 9., 10.])
_simu_y_expected = np.array(
[0.06464851, 0.01259978, 0.11890677, 0.02255858])
self.assertAlmostEqual(_simu_x_returned[0],
_simu_x_expected[0],
delta=0.000001)
self.assertAlmostEqual(_simu_x_returned[1],
_simu_x_expected[1],
delta=0.000001)
self.assertAlmostEqual(_simu_x_returned[2],
_simu_x_expected[2],
delta=0.000001)
self.assertAlmostEqual(_simu_x_returned[3],
_simu_x_expected[3],
delta=0.000001)
self.assertAlmostEqual(_simu_y_returned[0],
_simu_y_expected[0],
delta=0.000001)
self.assertAlmostEqual(_simu_y_returned[1],
_simu_y_expected[1],
delta=0.000001)
self.assertAlmostEqual(_simu_y_returned[2],
_simu_y_expected[2],
delta=0.000001)
self.assertAlmostEqual(_simu_y_returned[3],
_simu_y_expected[3],
delta=0.000001)
def test_x_angstrom(self):
simulation = self.simulation
simulation.add_layer(layer='U', thickness_mm=0.15)
_x_returned = simulation.get_x(x_type='lambda')
_x_expected = np.array(
[0.10809189, 0.10111071, 0.09532809, 0.09043617])
self.assertAlmostEqual(_x_returned[0], _x_expected[0], delta=0.000001)
self.assertAlmostEqual(_x_returned[1], _x_expected[1], delta=0.000001)
self.assertAlmostEqual(_x_returned[2], _x_expected[2], delta=0.000001)
self.assertAlmostEqual(_x_returned[3], _x_expected[3], delta=0.000001)
def test_y_transmission(self):
simulation = self.simulation
simulation.add_layer(layer='U', thickness_mm=0.15)
_y_returned = simulation.get_y(y_type='transmission')
_y_expected = np.array(
[0.96300627, 0.99063463, 0.99145785, 0.99273996])
self.assertAlmostEqual(_y_returned[0], _y_expected[0], delta=0.000001)
self.assertAlmostEqual(_y_returned[1], _y_expected[1], delta=0.000001)
self.assertAlmostEqual(_y_returned[2], _y_expected[2], delta=0.000001)
self.assertAlmostEqual(_y_returned[3], _y_expected[3], delta=0.000001)
def test_xy_simu(self):
simulation = self.simulation
simulation.add_layer(layer='U', thickness_mm=0.15)
_x_returned = simulation.get_x(x_type='lambda')
_y_returned = simulation.get_y(y_type='transmission')
_x_expected = np.array(
[0.10809189, 0.10111071, 0.09532809, 0.09043617])
self.assertAlmostEqual(_x_returned[0], _x_expected[0], delta=0.000001)
self.assertAlmostEqual(_x_returned[1], _x_expected[1], delta=0.000001)
self.assertAlmostEqual(_x_returned[2], _x_expected[2], delta=0.000001)
self.assertAlmostEqual(_x_returned[3], _x_expected[3], delta=0.000001)
_y_expected = np.array(
[0.96300627, 0.99063463, 0.99145785, 0.99273996])
self.assertAlmostEqual(_y_returned[0], _y_expected[0], delta=0.000001)
self.assertAlmostEqual(_y_returned[1], _y_expected[1], delta=0.000001)
self.assertAlmostEqual(_y_returned[2], _y_expected[2], delta=0.000001)
self.assertAlmostEqual(_y_returned[3], _y_expected[3], delta=0.000001)
_x_returned = simulation.get_x(x_type='energy')
_y_returned = simulation.get_y(y_type='attenuation')
_x_expected = np.array([7., 8., 9., 10.])
self.assertAlmostEqual(_x_returned[0], _x_expected[0], delta=0.000001)
self.assertAlmostEqual(_x_returned[1], _x_expected[1], delta=0.000001)
self.assertAlmostEqual(_x_returned[2], _x_expected[2], delta=0.000001)
self.assertAlmostEqual(_x_returned[3], _x_expected[3], delta=0.000001)
_y_expected = np.array(
[0.03699373, 0.00936537, 0.00854215, 0.00726004])
self.assertAlmostEqual(_y_returned[0], _y_expected[0], delta=0.000001)
self.assertAlmostEqual(_y_returned[1], _y_expected[1], delta=0.000001)
self.assertAlmostEqual(_y_returned[2], _y_expected[2], delta=0.000001)
self.assertAlmostEqual(_y_returned[3], _y_expected[3], delta=0.000001)
def test_peak_map(self):
pass
import numpy as np
import pprint
import matplotlib.pyplot as plt
from ResoFit.experiment import Experiment
import peakutils as pku
from ResoFit.simulation import Simulation
from scipy import signal
import scipy
folder = 'data/IPTS_19558/reso_data_19558'
data_file1 = 'spheres.csv'
# data_file2 = 'spheres_background_1.csv'
spectra_file = 'Image002_Spectra.txt'
#
# source_to_detector_m = 16.45 # 16#16.445359069030175#16.447496101100739
# offset_us = 2.752 # 0#2.7120797253959119#2.7355447625559037
# baseline = False
# energy_xmax = 150
# lambda_xmax = None
# x_axis = 'number'
#
# # # Calibrate the peak positions
experiment1 = Experiment(data_file=data_file1,
spectra_file=spectra_file,
folder=folder,
baseline=True)
experiment1.slice(start=300, reset_index=False)
peak_df = experiment1.find_peak()
experiment1.plot(x_axis='number', t_unit='s')
plt.plot(peak_df['x_num'], peak_df['y'], 'kx')
energy_min = 7
import numpy as np
import pprint
import matplotlib.pyplot as plt
from ResoFit.experiment import Experiment
import peakutils as pku
from ResoFit.simulation import Simulation
from scipy import signal
import scipy
folder = 'data/IPTS_20439/reso_data_20439'
sample_name = [
'Ta on MCP (6pC)',
'Ta on 10mm Pb on MCP (6pC)',
# 'Ta',
# 'Ta 9C',
# 'Ta Cd',
'Ta + Cd on MCP (9pC)',
'Ta inside ILL at 80C Cd on MCP (12pC)'
]
data_file = [
'Ta_no_lead_2C_total_6C.csv',
'Ta_10mm_lead_2C_total_6C.csv',
# 'Ta.csv',
# 'Ta_9C.csv',
# 'Ta_Cd.csv',
'Ta_Cd_9C.csv',
'Ta_80C_12pC.csv'
]
norm_to_file = [
'OB_no_lead_2C_total_6C.csv',
'OB_10mm_lead_2C_total_6C.csv',