def setUp(self):
_file_path = os.path.dirname(__file__)
self.folder = os.path.abspath(
os.path.join(_file_path, '../../ResoFit/data/_mock_data_for_test'))
self.data_file = os.path.join(self.folder,
'_data_calibration_fitting_test.txt')
self.spectra_file = os.path.join(
self.folder, '_spectra_calibration_fitting_test.txt')
energy_min = 7
energy_max = 150
energy_step = 0.01
layer_1 = 'U'
thickness_1 = 0.075
density_1 = None
layer = Layer()
layer.add_layer(layer=layer_1,
thickness_mm=thickness_1,
density_gcm3=density_1)
self.calibration = Calibration(data_file=self.data_file,
spectra_file=self.spectra_file,
layer=layer,
energy_min=energy_min,
energy_max=energy_max,
energy_step=energy_step,
folder=self.folder,
database=self.database)
from ResoFit.calibration import Calibration from ResoFit.fitresonance import FitResonance import matplotlib.pyplot as plt import numpy as np from ResoFit._utilities import get_foil_density_gcm3 from ResoFit._utilities import Layer import pprint # Global parameters energy_min = 4.09 energy_max = 1000 energy_step = 0.01 # Input sample name or names as str, case sensitive layers = Layer() layers.add_layer(layer='Ag', thickness_mm=0.025) # layers.add_layer(layer='Co', thickness_mm=0.025) # layers.add_layer(layer='Hf', thickness_mm=0.025) # layers.add_layer(layer='W', thickness_mm=0.05) # layers.add_layer(layer='In', thickness_mm=0.05) # layers.add_layer(layer='Cd', thickness_mm=0.5) # layers.add_layer(layer='Au', thickness_mm=0.01) # simu = Simulation(energy_min=energy_min, energy_max=energy_max, energy_step=energy_step) # simu.add_Layer(layer=layers) # peak_dict = simu.peak_map(thres=0.015, min_dist=20) # pprint.pprint(peak_dict) folder = 'data/IPTS_13639/reso_data_13639' data_file = 'Ag.csv' spectra_file = 'spectra.csv' image_start = 300 # Can be omitted or =None image_end = 2700 # Can be omitted or =None
# Input sample name or names as str, case sensitive
# layer = 'UGd'
# thickness = 0.018 # mm
# density = get_foil_density_gcm3(length_mm=25, width_mm=25, thickness_mm=0.025, mass_g=0.14)
# density = None
# density = 8.86
layer_1 = 'U'
thickness_1 = 0.018
density_1 = None
# layer_2 = 'Gd'
# thickness_2 = 0.015
# density_2 = None
# layer_3 = 'Cd'
# thickness_3 = 0.015
# density_3 = None
layer = Layer()
layer.add_layer(layer=layer_1,
thickness_mm=thickness_1,
density_gcm3=density_1)
# layer.add_Layer(layer=layer_2, thickness_mm=thickness_2, density_gcm3=density_2)
# layer.add_Layer(layer=layer_3, thickness_mm=thickness_3, density_gcm3=density_3)
folder = 'data/IPTS_18521/reso_data_18521'
data_file = 'run_33_resonance.txt'
spectra_file = 'Image033_Spectra.txt'
image_start = None # Can be omitted or =None
image_end = None # Can be omitted or =None
norm_to_file = 'run_33_resonance_ob.txt' #None # 'sphere_background_1.csv'
baseline = True
each_step = False
from ResoFit.fitresonance import FitResonance from ResoFit.experiment import Experiment import matplotlib.pyplot as plt import numpy as np import pprint from ResoFit._utilities import get_foil_density_gcm3 from ResoFit._utilities import Layer import lmfit # Global parameters energy_min = 4.1 energy_max = 600 energy_step = 0.01 database = 'ENDF_VIII' # Input sample name or names as str, case sensitive layers = Layer() layers.add_layer(layer='Ta', thickness_mm=0.127) folder = 'data/IPTS_20440' spectra_file = 'spectra.txt' data_file = 'Ta_80C_12pC.csv' norm_to_file = 'OB_80C_12pC.csv' image_start = None # Can be omitted or =None image_end = None # Can be omitted or =None baseline = False baseline_deg = 3 each_step = False norm_factor = 1 source_to_detector_m = 16.45 # 16#16.445359069030175#16.447496101100739
from ResoFit.calibration import Calibration from ResoFit.fitresonance import FitResonance import matplotlib.pyplot as plt import numpy as np from ResoFit._utilities import get_foil_density_gcm3 from ResoFit._utilities import Layer import pprint # Global parameters energy_min = 1 energy_max = 1000 energy_step = 0.01 # Input sample name or names as str, case sensitive layers = Layer() # layers.add_layer(layer='Ag', thickness_mm=0.025) # layers.add_layer(layer='Co', thickness_mm=0.025) # layers.add_layer(layer='Hf', thickness_mm=0.025) # layers.add_layer(layer='W', thickness_mm=0.05) layers.add_layer(layer='In', thickness_mm=0.05) # layers.add_layer(layer='Cd', thickness_mm=0.5) # layers.add_layer(layer='Au', thickness_mm=0.01) # simu = Simulation(energy_min=energy_min, energy_max=energy_max, energy_step=energy_step) # simu.add_Layer(layer=layers) # peak_dict = simu.peak_map(thres=0.015, min_dist=20) # pprint.pprint(peak_dict) folder = 'data/IPTS_13639/reso_data_13639' data_file = 'In.csv' spectra_file = 'spectra.csv' image_start = 300 # Can be omitted or =None image_end = 2730 # Can be omitted or =None
from ResoFit.calibration import Calibration from ResoFit.fitresonance import FitResonance from ResoFit.experiment import Experiment import matplotlib.pyplot as plt import numpy as np import pprint from ResoFit._utilities import get_foil_density_gcm3 from ResoFit._utilities import Layer # Global parameters energy_min = 7 energy_max = 300 energy_step = 0.01 layers = Layer() layers.add_layer(layer='U', thickness_mm=0.018, density_gcm3=None) layers.add_layer(layer='Gd', thickness_mm=0.015, density_gcm3=None) folder = 'data/IPTS_19558/reso_data_19558' data_file = 'spheres.csv' spectra_file = 'Image002_Spectra.txt' database = 'ENDF_VIII' image_start = 300 # Can be omitted or =None image_end = None # Can be omitted or =None norm_to_file = None # 'sphere_background_1.csv' # norm_to_file = 'sphere_background_1.csv' baseline = True each_step = False baseline_deg = 3 norm_factor = 1
from ResoFit.calibration import Calibration from ResoFit.fitresonance import FitResonance from ResoFit.experiment import Experiment import matplotlib.pyplot as plt import numpy as np import pprint from ResoFit._utilities import get_foil_density_gcm3 from ResoFit._utilities import Layer import lmfit # Global parameters energy_min = 7 energy_max = 1000 energy_step = 0.01 # Input sample name or names as str, case sensitive layers = Layer() layers.add_layer(layer='Gd', thickness_mm=0.075) folder = 'data/IPTS_19558/reso_data_19558' data_file = 'Gd_thick.csv' spectra_file = 'Image002_Spectra.txt' image_start = None # Can be omitted or =None image_end = None # Can be omitted or =None norm_to_file = None # 'sphere_background_1.csv' # norm_to_file = 'Gd_thin.csv' # norm_to_file = 'sphere_background_1.csv' baseline = False each_step = False norm_factor = 1.03 source_to_detector_m = 16.44 # 16#16.445359069030175#16.447496101100739
from ResoFit.calibration import Calibration from ResoFit.fitresonance import FitResonance from ResoFit.experiment import Experiment import matplotlib.pyplot as plt import numpy as np import pprint from ResoFit._utilities import get_foil_density_gcm3 from ResoFit._utilities import Layer import lmfit # Global parameters energy_min = 4.1 energy_max = 600 energy_step = 0.01 # Input sample name or names as str, case sensitive layers = Layer() layers.add_layer(layer='Cu', thickness_mm=0.5) layers.add_layer(layer='Co', thickness_mm=0.3) folder = 'data/IPTS_22631/reso_data_22631' spectra_file = 'Ta_lead_10mm__0__040_Spectra.txt' data_file = 'HEA_long.csv' # data_file = 'HEA_long_18C.csv' image_start = None # Can be omitted or =None image_end = None # Can be omitted or =None norm_to_file = 'HEA_blank.csv' # norm_to_file = 'HEA_blank_18C.csv' baseline = True # baseline = False each_step = False
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)
from ResoFit.calibration import Calibration from ResoFit.fitresonance import FitResonance import matplotlib.pyplot as plt import numpy as np from ResoFit._utilities import get_foil_density_gcm3 from ResoFit._utilities import Layer import pprint # Global parameters energy_min = 4.1 energy_max = 1000 energy_step = 0.01 # Input sample name or names as str, case sensitive layers = Layer() # layers.add_layer(layer='Ag', thickness_mm=0.025) # layers.add_layer(layer='Co', thickness_mm=0.025) # layers.add_layer(layer='Hf', thickness_mm=0.025) # layers.add_layer(layer='W', thickness_mm=0.05) # layers.add_layer(layer='In', thickness_mm=0.05) # layers.add_layer(layer='Cd', thickness_mm=0.5) layers.add_layer(layer='Au', thickness_mm=0.01) # simu = Simulation(energy_min=energy_min, energy_max=energy_max, energy_step=energy_step) # simu.add_Layer(layer=layers) # peak_dict = simu.peak_map(thres=0.015, min_dist=20) # pprint.pprint(peak_dict) folder = 'data/IPTS_13639/reso_data_13639' data_file = 'Au.csv' spectra_file = 'spectra.csv' image_start = 300 # Can be omitted or =None image_end = 2700 # Can be omitted or =None