def He3_PHS_action(self):
number_bins = int(self.phsBins.text())
parameters = get_He3_filter_parameters(self)
df_red = filter_He3(self.He3_df, parameters)
fig = plt.figure()
He3_PHS_plot(df_red, number_bins)
fig.show()
def He3_Energy_action(self):
parameters = get_He3_filter_parameters(self)
df_red = filter_He3(self.He3_df, parameters)
number_bins = int(self.dE_bins.text())
plot_energy = True
fig = plt.figure()
plt.subplot(1, 2, 1)
hist_full, bins_full = energy_plot_He3(df_red, number_bins,
plot_energy, 'Full Data')
hist_pileup, bins_pileup = energy_plot_He3(df_red[df_red.PileUp == 1],
number_bins, plot_energy,
'Pile Up Events')
plt.legend()
plt.subplot(1, 2, 2)
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.xlabel('Energy (meV)')
plt.ylabel('Fraction of Counts (PileUp/Full)')
plt.title('Investigation of Pileup')
plt.plot(bins_full,
hist_pileup / hist_full,
color='black',
zorder=5,
label='PileUpEvents/AllEvents')
plt.xscale('log')
plt.legend()
fig.show()
def Lineshape_action(self):
origin_voxel = [
int(self.bus_origin.text()),
int(self.gCh_origin.text()),
int(self.wCh_origin.text())
]
MG_filter_parameters = get_filter_parameters(self)
He3_filter_parameters = get_He3_filter_parameters(self)
analyze_all_lineshapes(origin_voxel, MG_filter_parameters,
He3_filter_parameters)
def full_analysis_action(self):
# Prepare filter parameters
origin_voxel = [
int(self.bus_origin.text()),
int(self.gCh_origin.text()),
int(self.wCh_origin.text())
]
MG_filter_parameters = get_filter_parameters(self)
He3_filter_parameters = get_He3_filter_parameters(self)
# Define colors and normalization
colors = {'MG_Coated': 'blue', 'MG_Non_Coated': 'green', 'He3': 'red'}
monitor_norm_coated = 1 / 11411036
monitor_norm_non_coated = 1 / 9020907
monitor_norm_He3 = 1 / 10723199
# Prepare data
full_data = prepare_data(origin_voxel, MG_filter_parameters,
He3_filter_parameters)
MG_coated_data, MG_non_coated_data, He3_data = full_data[0], full_data[
1], full_data[4]
MG_coated_background, MG_non_coated_background, He3_background = full_data[
2], full_data[3], full_data[5]
# Plot all peaks
Coated_values = plot_all_peaks(MG_coated_data, 'MG_Coated',
colors['MG_Coated'], 28.413)
NonCoated_values = plot_all_peaks(MG_non_coated_data, 'MG_Non_Coated',
colors['MG_Non_Coated'],
28.413 + 1.5e-3)
He3_values = plot_all_peaks(He3_data, 'He3', colors['He3'],
28.239 + 3e-3)
# Extract key values
energies_Coated, FoM_Coated, FoM_err_Coated, peak_areas_Coated, peak_err_Coated = Coated_values
energies_NonCoated, FoM_NonCoated, FoM_err_NonCoated, peak_areas_NonCoated, peak_err_NonCoated = NonCoated_values
energies_He3, FoM_He3, FoM_err_He3, peak_areas_He3, peak_err_He3 = He3_values
# Store all values in vectors
energies = [energies_Coated, energies_NonCoated, energies_He3]
labels = ['MG_Coated', 'MG_Non_Coated', 'He3']
FoMs = [FoM_Coated, FoM_NonCoated, FoM_He3]
FoM_errors = [FoM_err_Coated, FoM_err_NonCoated, FoM_err_He3]
# Plot efficiency
fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(15)
plot_efficiency(np.array(energies_He3), np.array(energies_NonCoated),
np.array(peak_areas_He3),
np.array(peak_areas_NonCoated), np.array(peak_err_He3),
np.array(peak_err_NonCoated), monitor_norm_He3,
monitor_norm_non_coated)
fig.show()
# Plot FoM
fig = plt.figure()
for energy, FoM, error, label in zip(energies, FoMs, FoM_errors,
labels):
plot_FoM(energy, FoM, error, label, colors[label])
plt.legend()
fig.show()
def He3_pileup_action(self):
# Filter data
parameters_He3 = get_He3_filter_parameters(self)
He3_red = filter_He3(self.He3_df, parameters_He3)
# Plot data
fig = plt.figure()
fig.set_figheight(15)
fig.set_figwidth(15)
he3_pileup_plot(He3_red)
plt.tight_layout()
fig.show()
def He3_ToF_action(self):
number_bins = int(self.tofBins.text())
parameters = get_He3_filter_parameters(self)
df_red = filter_He3(self.He3_df, parameters)
fig = plt.figure()
hist, bins = He3_ToF_plot(df_red, number_bins, range=[0, 71429])
# Save histogram in ASCII-format
dir_name = os.path.dirname(__file__)
output_path = os.path.join(
dir_name, '../output/ToF_%s.txt' % self.He3_data_sets[5:-5])
np.savetxt(output_path,
np.transpose(np.array([bins, hist])),
delimiter=",",
header='ToF (µs), Counts')
plt.legend()
fig.show()
def ToF_MG_vs_ToF_He3_action(self):
# Get filter parameters for MG and He-3
parameters_MG = get_filter_parameters(self)
parameters_He3 = get_He3_filter_parameters(self)
# Filter data
MG_red = filter_clusters(self.ce, parameters_MG)
He3_red = filter_He3(self.He3_df, parameters_He3)
# Declare paremeters
number_bins = int(self.tofBins.text())
MG_label, He3_label = self.data_sets, self.He3_data_sets
useMaxNorm = True
# Plot data
fig = plt.figure()
He3_ToF_plot(He3_red, number_bins, He3_label)
ToF_histogram(MG_red, number_bins, MG_label)
plt.legend()
fig.show()
def Wavelength_overlay_action(self):
paths = QFileDialog.getOpenFileNames(self, "", "../data")[0]
number_bins = int(self.dE_bins.text())
origin_voxel = [
int(self.bus_origin.text()),
int(self.gCh_origin.text()),
int(self.wCh_origin.text())
]
MG_filter_parameters = get_filter_parameters(self)
He3_filter_parameters = get_He3_filter_parameters(self)
fig = plt.figure()
# Multi-Grid
for i, path in enumerate(paths):
label = path.rsplit('/', 1)[-1]
ce = pd.read_hdf(path, 'ce')
ce_filtered = filter_clusters(ce, MG_filter_parameters)
duration = get_duration(ce)
norm = 1 / duration
energy = calculate_energy(ce_filtered, origin_voxel)
plt.hist(meV_to_A(energy),
bins=number_bins,
range=[0, 10],
zorder=5,
histtype='step',
label=label,
weights=norm * np.ones(len(energy)))
print('Progress: %d/%d' % (i + 1, len(paths)))
# He-3
He3_df_red = filter_He3(self.He3_df, He3_filter_parameters)
energy_He3 = calculate_He3_energy(He3_df_red)
norm_He3 = 1 / 54304
plt.hist(meV_to_A(energy_He3),
bins=number_bins,
range=[0, 10],
zorder=5,
histtype='step',
label='2019_09_HZB_He3InBeam54304s_overnight.lst',
weights=norm * np.ones(len(energy_He3)))
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.ylabel('Counts (Normalized to duration)')
plt.xlabel('Wavelength [Å]')
plt.title('Wavelength Distribution')
plt.legend(loc=1)
fig.show()
def plot_efficiency(He3_energies, MG_energies,
He3_areas, MG_areas,
He3_err, MG_err,
monitor_norm_He3, monitor_norm_MG,
window):
"""
Calculates the efficiency of the Multi-Grid detector at energy 'Ei'. Does
through analysis in energy transfer spectra in three steps:
1. Calculate number of counts in elastic peak, removing the background
2. Get normalization on solid angle and, in the case of He-3, efficiency
3. Normalize peak data and take fraction between Multi-Grid and He-3
Args:
MG_dE_values (numpy array): Energy transfer values from Multi-Grid
He3_dE_values (numpy array): Energy transfer values from He-3 tubes
Ei (float): Incident energy in meV
parameters (dict): Dictionary containing the parameters on how the data
is reduced. Here we are only interested in the
filters which affects the total surface area.
Returns:
MG_efficiency (float): Efficiency of the Multi-Grid at energy Ei
"""
fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(15)
# Load calculated efficiencies, as a function of lambda
dirname = os.path.dirname(__file__)
He3_efficiency_path = os.path.join(dirname, '../../../../tables/He3_efficiency.txt')
MG_efficiency_path = os.path.join(dirname, '../../../../tables/MG_efficiency.txt')
He3_efficiency = np.loadtxt(He3_efficiency_path, delimiter=",", unpack=True)
MG_efficiency_calc = np.loadtxt(MG_efficiency_path, delimiter=",", unpack=True)[[0, 2]]
# Remove elements in MG data which are not recorded in He-3
MG_energies = np.delete(MG_energies, [0, 2, len(MG_energies)-5])
MG_areas = np.delete(MG_areas, [0, 2, len(MG_areas)-5])
MG_err = np.delete(MG_err, [0, 2, len(MG_err)-5])
# Iterate through energies to find matching efficiency from calculation to our measured data points
He3_efficiency_datapoints = []
for energy in He3_energies:
# Save He3 efficiencies for data points
idx = find_nearest(A_to_meV(He3_efficiency[0]), energy)
He3_efficiency_datapoints.append(He3_efficiency[1][idx])
He3_efficiency_datapoints = np.array(He3_efficiency_datapoints)
# Rescale our curve to fit calibration
idx = find_nearest(He3_efficiency[0], 2.5)
calculated_efficiency_at_2_5_A = He3_efficiency[1][idx]
He3_calculation_norm_upper = 0.964/calculated_efficiency_at_2_5_A
He3_calculation_norm_average = 0.957/calculated_efficiency_at_2_5_A
He3_calculation_norm_lower = 0.950/calculated_efficiency_at_2_5_A
# Calculate average, as well as upper and lower bound for uncertainity estimation
He3_efficiency_datapoints_upper = He3_efficiency_datapoints * He3_calculation_norm_upper
He3_efficiency_datapoints_average = He3_efficiency_datapoints * He3_calculation_norm_average
He3_efficiency_datapoints_lower = He3_efficiency_datapoints * He3_calculation_norm_lower
# Calculated measured efficiency
MG_efficiency = (MG_areas*monitor_norm_MG)/(He3_areas*(1/He3_efficiency_datapoints_average)*monitor_norm_He3)
MG_efficiency_stat_unc = np.sqrt((MG_err/MG_areas) ** 2 + (He3_err/He3_areas) ** 2) * MG_efficiency
# Calculate uncertainities
MG_efficiency_upper = (MG_areas*monitor_norm_MG)/(He3_areas*(1/He3_efficiency_datapoints_upper)*monitor_norm_He3)
MG_efficiency_lower = (MG_areas*monitor_norm_MG)/(He3_areas*(1/He3_efficiency_datapoints_lower)*monitor_norm_He3)
upper_errors = MG_efficiency_upper - MG_efficiency + MG_efficiency_stat_unc
lower_errors = MG_efficiency - MG_efficiency_lower + MG_efficiency_stat_unc
full_errors = np.array([lower_errors, upper_errors])
# Plot areas
plt.subplot(1, 3, 1)
plt.errorbar(He3_energies,
He3_areas*monitor_norm_He3,
He3_err*monitor_norm_He3,
fmt='.-', capsize=5, color='red', label='He-3', zorder=5)
plt.errorbar(MG_energies,
MG_areas*monitor_norm_MG,
MG_err*monitor_norm_MG,
fmt='.-', capsize=5, color='blue', label='Multi-Grid', zorder=5)
plt.xlabel('Energy (meV)')
plt.ylabel('Peak area (Counts normalized by beam monitor counts)')
plt.xlim(2, 120)
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.title('Comparison MG and He-3')
plt.legend()
plt.xscale('log')
plt.subplot(1, 3, 2)
plt.xlabel('Energy (meV)')
plt.ylabel('Efficiency')
plt.xlim(2, 120)
plt.errorbar(MG_energies, MG_efficiency, full_errors, fmt='.-',
capsize=5, color='blue', label='Measured MG efficiency', zorder=5)
plt.plot(A_to_meV(MG_efficiency_calc[0]), MG_efficiency_calc[1], color='black',
label='MG (90° incident angle)', zorder=5)
#plt.plot(He3_energies, He3_efficiency_datapoints, color='red',
# marker='o', linestyle='', label='He-3, Calculated', zorder=5)
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.title('Efficiency measurement')
plt.xscale('log')
plt.legend()
plt.subplot(1, 3, 3)
plt.xlabel('Wavelength (Å)')
plt.ylabel('Efficiency')
plt.errorbar(meV_to_A(MG_energies), MG_efficiency, full_errors, fmt='.-',
capsize=5, color='blue', label='Measured MG efficiency', zorder=5)
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.plot(MG_efficiency_calc[0], MG_efficiency_calc[1], color='black',
label='MG (90° incident angle)', zorder=5)
#plt.plot(meV_to_A(He3_energies), He3_efficiency_datapoints, color='red',
# marker='o', linestyle='', label='He-3, Calculated', zorder=5)
plt.title('Efficiency measurement')
plt.legend()
plt.tight_layout()
fig.show()
# Plot only efficiency vs lambda_sweep
# Get pile-up fraction
parameters = get_He3_filter_parameters(window)
df_red = filter_He3(window.He3_df, parameters)
number_bins = int(window.dE_bins.text())
plot_energy = False
hist_full, bins_full = energy_plot_He3(df_red, number_bins, plot_energy, 'All events')
hist_pileup, bins_pileup = energy_plot_He3(df_red[df_red.PileUp == 1], number_bins, plot_energy, 'Pile-up Events')
pileup_fraction = hist_pileup/hist_full
# Plot together with efficiency
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.plot(MG_efficiency_calc[0], MG_efficiency_calc[1], color='black',
label='Multi-Grid detector: calculated', zorder=5)
ax1.errorbar(meV_to_A(MG_energies), MG_efficiency, full_errors, fmt='.-',
capsize=5, color='blue', label='Multi-Grid detector: measured', zorder=5)
ax1.plot(bins_full, pileup_fraction, color='green', zorder=5, label='Helium-3 tube: pile-up fraction')
#ymin = 0
#ymax = max(MG_efficiency)
#y_ticks = np.linspace(ymin, ymax, 5)
#ax1.set_ylim(ymin, ymax)
ax1.set_xlim(0, 6.25)
other_y_axis_lim = ax1.get_ylim()
ax2.set_ylim(other_y_axis_lim)
ax2.spines['right'].set_color('green')
ax2.yaxis.label.set_color('green')
ax2.tick_params(axis='y', colors='green')
#ax2.set_ylim(ymin, ymax)
#ax1.set_yticks(y_ticks)
#ax2.set_yticks(y_ticks)
ax1.tick_params('y', color='black')
ax2.tick_params('y', color='green')
ax1.set_xlabel('Wavelength (Å)')
ax1.set_ylabel('Efficiency')
ax2.set_ylabel('Helium-3 pile-up fraction')
ax1.grid(True, which='major', linestyle='--', zorder=0)
ax1.grid(True, which='minor', linestyle='--', zorder=0)
#ax1.set_title('Figure-of-Merit')
ax1.legend()
fig.show()
# Plot together with saturation
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = ax1.twinx()
ax1.errorbar(meV_to_A(He3_energies),
He3_areas*monitor_norm_He3,
He3_err*monitor_norm_He3,
fmt='.-', capsize=5, color='red', label='Helium-3 tube', zorder=5)
ax1.errorbar(meV_to_A(MG_energies),
MG_areas*monitor_norm_MG,
MG_err*monitor_norm_MG,
fmt='.-', capsize=5, color='blue', label='Multi-Grid detector', zorder=5)
ax2.plot(bins_full, pileup_fraction, color='green', zorder=5, label='Helium-3 tube: pile-up fraction')
ax1.plot([], [], color='green', label='Helium-3 tube: pile-up fraction')
#ymin = 0
#ymax = max(MG_efficiency)
#y_ticks = np.linspace(ymin, ymax, 5)
#ax1.set_ylim(ymin, ymax)
ax1.set_xlim(0, 6.25)
#ax1.set_ylim(other_y_axis_lim)
ax2.set_ylim(other_y_axis_lim)
ax2.spines['right'].set_color('green')
ax2.yaxis.label.set_color('green')
ax2.tick_params(axis='y', colors='green')
#ax2.set_ylim(ymin, ymax)
#ax1.set_yticks(y_ticks)
#ax2.set_yticks(y_ticks)
ax1.tick_params('y', color='black')
ax2.tick_params('y', color='green')
ax1.set_xlabel('Wavelength (Å)')
ax1.set_ylabel('Peak area (Counts normalized by beam monitor counts)')
ax2.set_ylabel('Helium-3 pile-up fraction')
ax1.grid(True, which='major', linestyle='--', zorder=0)
ax1.grid(True, which='minor', linestyle='--', zorder=0)
#ax1.set_title('Figure-of-Merit')
ax1.legend()
fig.show()
def He3_Ch_action(self):
fig = plt.figure()
parameters = get_He3_filter_parameters(self)
df_red = filter_He3(self.He3_df, parameters)
He3_Ch_plot(df_red)
fig.show()