def ToF_sweep_action(self):
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
number_bins = int(self.tofBins.text())
bus_start = self.module_min.value()
bus_stop = self.module_max.value()
ToF_Sweep_Animation(ce_filtered, number_bins, bus_start, bus_stop)
def ToF_Overlay_action(self):
paths = QFileDialog.getOpenFileNames(self, "", "../data")[0]
if len(paths) > 0:
# Declare parameters
filter_parameters = get_filter_parameters(self)
number_bins = int(self.tofBins.text())
labels = ['Beam', 'Background']
# Plot
fig = plt.figure()
hists = []
bin_centers_vec = []
range = [0, 71e3]
for path, label in zip(paths, labels):
# Get data
data_set = path.rsplit('/', 1)[-1][:-3] + '.zip'
ce = pd.read_hdf(path, 'ce')
ce_filtered = filter_clusters(ce, filter_parameters)
# Get normalization
norm = 1 / self.BM_counts_dict[data_set]
# Plot
hist, bins = ToF_histogram(ce_filtered, number_bins, label,
norm, range)
hists.append(hist)
bin_centers_vec.append(bins)
plt.plot(bin_centers_vec[0],
hists[0] - hists[1],
label='Difference',
zorder=5)
plt.title('ToF')
plt.xlabel('ToF [$\mu$s]')
plt.ylabel('Counts (Normalized by beam monitor)')
plt.legend()
plt.grid(True, which='major', linestyle='--', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
fig.show()
def PHS_wires_vs_grids_action(self):
if (self.data_sets != ''):
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
bus_start = self.module_min.value()
bus_stop = self.module_max.value()
fig = PHS_wires_vs_grids_plot(ce_filtered, bus_start, bus_stop)
fig.show()
def Multiplicity_action(self):
if (self.data_sets != ''):
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
bus_start = self.module_min.value()
bus_stop = self.module_max.value()
fig = multiplicity_plot(ce_filtered, bus_start, bus_stop)
fig.show()
def CE_2D_comparison_action(self):
paths = QFileDialog.getOpenFileNames(self, "", "../data")[0]
if len(paths) > 0:
# Declare parameters
filter_parameters = get_filter_parameters(self)
front_hists = []
top_hists = []
side_hists = []
# Extract histogram data
for i, path in enumerate(paths):
# Import data
data_set = path.rsplit('/', 1)[-1][:-3] + '.zip'
ce = pd.read_hdf(path, 'ce')
ce_filtered = filter_clusters(ce, filter_parameters)
# Extract histograms
bus_start = self.module_min.value()
bus_stop = self.module_max.value()
norm = 1 / self.BM_counts_dict[data_set]
__, histograms = coincidences_projections_plot(
ce_filtered, bus_start, bus_stop, norm)
front_hists.append(np.transpose(histograms[0]))
top_hists.append(np.transpose(histograms[1]))
side_hists.append(np.transpose(histograms[2]))
# Take difference between histograms
projections_hists = [front_hists, top_hists, side_hists]
diffs = []
for hists in projections_hists:
diffs.append(hists[0] - hists[1])
# Define figure properties
labels_vec = [['Front view', 'Row', 'Grid'],
['Top view', 'Row', 'Layer'],
['Side view', 'Layer', 'Grid']]
limits_vec = [[3e-6, 2e-3], [3e-4, 4e-3], [3e-5, 2e-3]]
# Plot difference
fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(17)
for i, (diff, labels,
limits) in enumerate(zip(diffs, labels_vec, limits_vec)):
vmin, vmax = limits
plt.subplot(1, 3, 1 + i)
plt.imshow(diff,
cmap='jet',
norm=LogNorm(),
origin='lower',
interpolation='nearest',
aspect='auto',
vmin=vmin,
vmax=vmax)
# Stylize plot
title, xlabel, ylabel = labels
plt.title(title)
plt.xlabel(xlabel)
plt.ylabel(ylabel)
cbar = plt.colorbar()
cbar.set_label('Counts (Normalized to beam monitor)')
plt.tight_layout()
fig.show()
def Energy_Resolution_action(self):
if (self.data_sets != ''):
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
dE_values = calculate_energy_transfer(ce_filtered, Ei)
# NEED A WAY TO GET HE3 VALUES
FWHM = calculate_energy_resolution(dE_values, self.Ei,
filter_parameters)
print('FWHM: %.2f' % FWHM)
def Animation_3D_action(self):
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
origin_voxel = [
int(self.bus_origin.text()),
int(self.gCh_origin.text()),
int(self.wCh_origin.text())
]
Animation_3D_plot(ce_filtered, origin_voxel)
def Coincidences_3D_action(self):
if self.data_sets != '':
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
origin_voxel = [
int(self.bus_origin.text()),
int(self.gCh_origin.text()),
int(self.wCh_origin.text())
]
coincidences_3D_plot(ce_filtered, origin_voxel)
def PHS_1D_action(self):
if self.data_sets != '':
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
number_bins = int(self.phsBins.text())
fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(10)
PHS_1D_plot(ce_filtered, number_bins)
fig.show()
def time_sweep_action(self):
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
origin_voxel = [
int(self.bus_origin.text()),
int(self.gCh_origin.text()),
int(self.wCh_origin.text())
]
number_bins = int(self.tofBins.text())
bus_start = self.module_min.value()
bus_stop = self.module_max.value()
Time_Sweep_Animation(ce_filtered, number_bins, origin_voxel, bus_start,
bus_stop)
def Count_Rate_action(self):
if (self.data_sets != ''):
# Declare parameters
number_bins = int(self.tofBins.text())
time_offset = (0.6e-3)
period_time = (1 / 14)
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
ToF_values = (ce_filtered.ToF * 62.5e-9 +
time_offset) % period_time
fig = plt.figure()
count_rate = calculate_count_rate(ToF_values,
self.measurement_time,
number_bins)
fig.show()
print('Count rate: %.1f [Hz]' % count_rate)
def Wavelength_action(self):
if (self.data_sets != ''):
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
number_bins = int(self.dE_bins.text())
origin_voxel = [
int(self.bus_origin.text()),
int(self.gCh_origin.text()),
int(self.wCh_origin.text())
]
fig = plt.figure()
start = 0
stop = 10
plot_energy = False
energy_plot(ce_filtered, origin_voxel, number_bins, start, stop,
plot_energy)
fig.show()
def Coincidences_2D_action(self):
if self.data_sets != '':
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
bus_start = self.module_min.value()
bus_stop = self.module_max.value()
fig, histograms = coincidences_2D_plot(ce_filtered,
self.measurement_time,
bus_start, bus_stop)
# Export histograms to text
dir_name = os.path.dirname(__file__)
output_path = os.path.join(dir_name, '../output/')
for bus, histogram in enumerate(histograms):
path = output_path + '2D_Coincidences_Bus_%d.txt' % bus
np.savetxt(path, histogram, fmt="%d", delimiter=",")
# Plot figure
fig.show()
def Timestamp_action(self):
if (self.data_sets != ''):
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
number_bins = int(self.tofBins.text())
unit = 'hours'
fig = plt.figure()
bus_start = self.module_min.value()
bus_stop = self.module_max.value()
for Bus in np.arange(bus_start, bus_stop + 1, 1):
df_temp = ce_filtered[ce_filtered.Bus == Bus]
if df_temp.shape[0] > 0:
Time = (df_temp.Time * 62.5e-9) / (60**2)
label = 'Bus %d' % Bus
timestamp_plot(Time, number_bins, unit, label)
plt.legend(loc=2)
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 Layers_action(self):
# Extract origin voxel
origin_voxel = [
int(self.bus_origin.text()),
int(self.gCh_origin.text()),
int(self.wCh_origin.text())
]
# Get MG data
MG_parameters = get_filter_parameters(self)
df_MG = filter_clusters(self.ce, MG_parameters)
# Get He3 data
#He3_parameters = get_He3_filter_parameters(self)
#df_He3 = filter_He3(self.He3_df, He3_parameters)
# Investigate ToF spread
#investigate_layers_FWHM(df_MG, df_He3, origin_voxel)
#investigate_layers_delta_ToF(df_MG, df_He3, origin_voxel)
duration = get_duration(df_MG)
investigate_layers_counts(df_MG, duration)
def PHS_comparison_action(self):
paths = QFileDialog.getOpenFileNames(self, "", "../data")[0]
if len(paths) > 0:
# Declare parameters
filter_parameters = get_filter_parameters(self)
number_bins = int(self.phsBins.text())
ylabel = '(Normalized to beam monitor)'
intervals = [[0, 4095], [0, 10000]]
# Extract histogram data
fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(10)
labels = ['(Beam)', '(Background)']
hists = []
bins_vec = []
errors = []
norms = []
# Plot data
for path, label in zip(paths, labels):
# Import data
data_set = path.rsplit('/', 1)[-1][:-3] + '.zip'
ce = pd.read_hdf(path, 'ce')
ce_filtered = filter_clusters(ce, filter_parameters)
# Extract normalization
norm = 1 / self.BM_counts_dict[data_set]
bins, hist = PHS_1D_plot(ce_filtered, number_bins, label, norm,
ylabel, intervals)
hists.append(np.array(hist))
errors.append(np.array(hist) / norm)
norms.append(norm)
bins_vec.append(bins)
# Plot difference
for i in range(0, 2):
plt.subplot(1, 2, i + 1)
plt.plot(
bins_vec[0][i],
hists[0][i] - hists[1][i],
#np.sqrt((errors[0][i]*norms[0]) ** 2 + (errors[1][i]*norms[1]) ** 2),
#fmt='.-', capsize=5,
zorder=3,
label='Difference')
plt.legend(loc=1)
fig.show()
def PHS_comparison_region_action(self):
if (self.data_sets != ''):
# Declare parameters
number_bins = int(self.phsBins.text())
filter_parameters = get_filter_parameters(self)
df = filter_clusters(self.ce, filter_parameters)
# Extract data from different regions
df_cross = df[(((df.Bus * 4) + df.wCh // 20) >= 4)
& (((df.Bus * 4) + df.wCh // 20) <= 7) &
(df.gCh >= 98) & (df.gCh <= 101)]
df_neutron = df[(((df.Bus * 4) + df.wCh // 20) == 6)
& (df.gCh >= 87) & (df.gCh <= 89)]
norm = 1 / self.measurement_time
fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(10)
PHS_1D_plot(df_cross, number_bins, '(Cross talk)', norm)
PHS_1D_plot(df_neutron, number_bins, '(Neutrons)', norm)
plt.legend()
fig.show()
def ToF_action(self):
if self.data_sets != '':
# Filter data
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
number_bins = int(self.tofBins.text())
# Plot data
fig = plt.figure()
hist, bins = ToF_histogram(ce_filtered,
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.data_sets[5:-5])
np.savetxt(output_path,
np.transpose(np.array([bins, hist])),
delimiter=",",
header='ToF (µs), Counts')
fig.show()
def Coincidences_Projections_action(self):
if self.data_sets != '':
filter_parameters = get_filter_parameters(self)
ce_filtered = filter_clusters(self.ce, filter_parameters)
bus_start = self.module_min.value()
bus_stop = self.module_max.value()
# Get beam monitor data
file_name = self.data_sets[5:-5]
norm = 1 / get_duration(
ce_filtered) #self.BM_counts_dict[file_name]
fig, histograms = coincidences_projections_plot(
ce_filtered, bus_start, bus_stop, norm)
# Export histograms to text
dir_name = os.path.dirname(__file__)
output_path = os.path.join(dir_name, '../output/')
file_names = ['Front', 'Top', 'Side']
for file_name, histogram in zip(file_names, histograms):
path = output_path + '2D_Coincidences_Projections_%s.txt' % file_name
np.savetxt(path, histogram, fmt="%d", delimiter=",")
# Plot figure
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