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 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_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 prepare_data(origin_voxel, MG_filter_parameters, He3_filter_parameters):
"""
Data is returned in following order:
1. Multi-Grid Coated Radial Blades, beam
2. Multi-Grid Non-Coated Radial Blades, beam
3. Multi-Grid Coated Radial Blades, background
4. Multi-Grid Non-Coated Radial Blades, background
5. He-3, beam
6. He-3, background
Within each data-list, data is returned in following order
(1. Energies - Beam Removed) <- Only for MG
2. Energies - Full
3. Histogram
4. Bin centers
(5. Peaks)
(6. Widths)
(7. PileUp) <- Only for He-3
(8. ADCs) <- Only for He-3
"""
# Declare parameters, such as distance offset and He3 duration
dirname = os.path.dirname(__file__)
number_bins = 5000
start = 0.8 # [meV]
end = 80 # [meV]
MG_distance_offsets = [1.5e-3, 0, 1.5e-3, 0]
He3_distance_offset = 3e-3
He3_durations = [54304, 58094]
# Declare heights used as threshold in peak finding algorithm
heights_MG_coated = [20000, 10000]
heights_MG_non_coated = [8000, 1000] # CHANGE TO [12000, 1000] when normal scattering analysis
heights_He3 = [20000, 1000]
heights_vec_MG = [heights_MG_coated, heights_MG_non_coated]
# Declare file names
MG_COATED = 'mvmelst_165_191002_111641_Det2_overnight3.h5'
MG_COATED_BACKGROUND = 'mvmelst_169_191003_075039_Det2_He3InBeam_overnight4.h5'
MG_NON_COATED = 'mvmelst_135_190930_141618_Det1_overnight2_30x80_14x60.h5'
MG_NON_COATED_BACKGROUND = 'mvmelst_141_191001_120405_He3InBeam_overnight3.h5'
HE_3 = '2019_09_HZB_He3InBeam54304s_overnight.h5'
HE_3_BACKGROUND = '2019_09_HZB_out_of_beam_overnight_58094s.h5'
MG_file_names = [MG_COATED, MG_NON_COATED, MG_COATED_BACKGROUND, MG_NON_COATED_BACKGROUND]
He3_file_names = [HE_3, HE_3_BACKGROUND]
# Declare list to store all data
full_data = []
# Store Multi-Grid data
print('Multi-Grid...')
for i, file_name in enumerate(MG_file_names):
path = os.path.join(dirname, '../../../data/Lineshape/%s' % file_name)
# Calculate energies and histograms
df = pd.read_hdf(path, 'ce')
df_red = filter_clusters(df, MG_filter_parameters)
duration = get_duration(df)
energies = calculate_energy(df_red, origin_voxel, MG_distance_offsets[i])
hist, bins = get_hist(energies, number_bins, start, end)
# Calculate energy for when row which neutron beam hits is removed
energies_no_beam = None
if i == 0:
df_no_beam = df_red[~((((df_red.Bus * 4) + df_red.wCh//20) == 6) &
(df_red.gCh >= 86) &
(df_red.gCh <= 89))]
energies_no_beam = calculate_energy(df_no_beam, origin_voxel, MG_distance_offsets[i])
elif i == 1:
df_no_beam = df_red[~((((df_red.Bus * 4) + df_red.wCh//20) == 6) &
(df_red.gCh >= 87) &
(df_red.gCh <= 89))]
energies_no_beam = calculate_energy(df_no_beam, origin_voxel, MG_distance_offsets[i])
# Store data
data = [energies_no_beam, energies, hist, bins]
if i < 2:
# If it is a beam measurement, extract peaks
peaks, __ = get_peaks(hist, heights_vec_MG[i], number_bins)
widths, *_ = peak_widths(hist, peaks)
data.extend([peaks, widths])
full_data.append(data)
# Store He-3 data
print('He-3...')
for i, (file_name, duration) in enumerate(zip(He3_file_names, He3_durations)):
path = os.path.join(dirname, '../../../data/Lineshape/%s' % file_name)
df = pd.read_hdf(path, 'df')
df_red = filter_He3(df, He3_filter_parameters)
energies = calculate_He3_energy(df_red, He3_distance_offset)
hist, bins = get_hist(energies, number_bins, start, end)
data = [None, energies, hist, bins]
if i < 1:
# If it is a beam measurement, extract peaks and pile up info
peaks, __ = get_peaks(hist, heights_He3, number_bins)
widths, *_ = peak_widths(hist, peaks)
data.extend([peaks, widths, df_red.PileUp, df_red.ADC])
full_data.append(data)
return full_data
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()