def PHS_1D_plot(window):
def PHS_1D_plot_bus(clusters, typeCh, sub_title, number_bins):
# Plot
plt.title(sub_title)
plt.xlabel('Collected charge [ADC channels]')
plt.ylabel('Counts')
plt.grid(True, which='major', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.yscale('log')
if wg == 'g':
adcs = clusters['gADC_m1'].append(clusters['gADC_m2'])
else:
adcs = clusters['wADC_m1']
plt.hist(adcs,
bins=number_bins,
range=[0, 4095],
histtype='step',
color='black',
zorder=5)
# Import data
df_20 = window.Clusters_20_layers
df_16 = window.Clusters_16_layers
# Intial filter
clusters_20 = filter_clusters(df_20, window)
clusters_16 = filter_clusters(df_16, window)
# Declare parameters
number_bins = int(window.phsBins.text())
wg_list = ['w', 'g']
grids_or_wires = {'w': 'Wires', 'g': 'Grids'}
# Prepare figure
fig = plt.figure()
title = 'PHS (1D)\n(%s, ...)' % window.data_sets.splitlines()[0]
fig.suptitle(title, x=0.5, y=1.03)
fig.set_figheight(4)
fig.set_figwidth(10)
# Plot figure for 20 layers
for i, wg in enumerate(wg_list):
plt.subplot(2, 2, i + 1)
sub_title = grids_or_wires[wg] + " -- 20 layers"
PHS_1D_plot_bus(clusters_20, wg, sub_title, number_bins)
plt.tight_layout()
# Plot figure for 16 layers
for i, wg in enumerate(wg_list):
plt.subplot(2, 2, i + 3)
sub_title = grids_or_wires[wg] + " -- 16 layers"
PHS_1D_plot_bus(clusters_16, wg, sub_title, number_bins)
plt.tight_layout()
return fig
def rate_action(self):
if self.data_sets != '':
# Declare clusters
ce_20 = self.Clusters_20_layers
ce_16 = self.Clusters_16_layers
# Filter
ce_red_20 = filter_clusters(ce_20, self)
ce_red_16 = filter_clusters(ce_16, self)
# Get measurement time
measurement_time = self.get_measurement_time()
rate_20 = ce_red_20.shape[0]/measurement_time
rate_16 = ce_red_16.shape[0]/measurement_time
print('Rate 20 layers: %f Hz' % rate_20)
print('Rate 16 layers: %f Hz' % rate_16)
def Coincidences_2D_plot(window):
# Declare parameters (added with condition if empty array)
data_sets = window.data_sets.splitlines()[0]
df_20 = window.Clusters_20_layers
df_16 = window.Clusters_16_layers
# Intial filter
clusters_20 = filter_clusters(df_20, window)
clusters_16 = filter_clusters(df_16, window)
#print(clusters_20)
clusters_vec = [clusters_20, clusters_16]
clusters_dict = {
'ce_20': {
'w': None,
'g': None
},
'ce_16': {
'w': None,
'g': None
}
}
# Select grids with highest collected charge
for clusters, name in zip(clusters_vec, ['ce_20', 'ce_16']):
#print('gADC_m1')
#print(clusters['gADC_m1'])
#print('gADC_m2')
#print(clusters['gADC_m2'])
#print('gCh_m1')
#print(clusters['gCh_m1'])
#print('gCh_2')
#print(clusters['gCh_m2'])
#print('wADC_m1')
#print(clusters['wADC_m1'])
#print('wCh_m1')
#print(clusters['gCh_m1'])
channels_g1 = clusters[
clusters['gADC_m1'] > clusters['gADC_m2']]['gCh_m1']
channels_w1 = clusters[
clusters['gADC_m1'] > clusters['gADC_m2']]['wCh_m1']
channels_g2 = clusters[
clusters['gADC_m1'] <= clusters['gADC_m2']]['gCh_m2']
channels_w2 = clusters[
clusters['gADC_m1'] <= clusters['gADC_m2']]['wCh_m1']
clusters_dict[name]['g'] = channels_g1.append(channels_g2)
clusters_dict[name]['w'] = channels_w1.append(channels_w2)
fig = plt.figure()
plt.subplot(1, 2, 1)
plt.title('16 layers')
hist_all = plt.hist2d(clusters_dict['ce_16']['w'],
clusters_dict['ce_16']['g'],
bins=[64, 12],
range=[[-0.5, 63.5], [-0.5, 11.5]],
norm=LogNorm(),
cmap='jet')
hist = hist_all[0]
els = []
for row in hist:
for i in row:
els.append(i)
max_16 = max(els)
min_16 = min(els)
if min_16 == 0:
min_16 = 1
plt.xlabel('Wires')
plt.ylabel('Grids')
plt.colorbar()
plt.subplot(1, 2, 2)
plt.title('20 layers')
plt.hist2d(clusters_dict['ce_20']['w'],
clusters_dict['ce_20']['g'],
bins=[80, 12],
range=[[-0.5, 79.5], [-0.5, 11.5]],
norm=LogNorm(),
cmap='jet',
vmin=min_16,
vmax=max_16)
print("Using color axis from 16-layers plot also for 20-layers plot")
plt.xlabel('Wires')
plt.ylabel('Grids')
fig.suptitle('Coincident events (2D) -- Data set(s): %s' % data_sets)
plt.colorbar()
plt.tight_layout()
return fig
def Coincidences_3D_plot(window):
# Intial filter
#df = filter_clusters(df, window)
df_20 = window.Clusters_20_layers
df_16 = window.Clusters_16_layers
#print(df_20[['wCh_m1', 'gCh_m1']])
# Intial filter
clusters_20 = filter_clusters(df_20, window)
clusters_16 = filter_clusters(df_16, window)
clusters_vec = [clusters_20, clusters_16]
clusters_dict = {
'ce_20': {
'w': None,
'g': None
},
'ce_16': {
'w': None,
'g': None
}
}
# Select grids with highest collected charge
for clusters, name in zip(clusters_vec, ['ce_20', 'ce_16']):
channels_g1 = clusters[
clusters['gADC_m1'] > clusters['gADC_m2']]['gCh_m1']
channels_w1 = clusters[
clusters['gADC_m1'] > clusters['gADC_m2']]['wCh_m1']
channels_g2 = clusters[
clusters['gADC_m1'] <= clusters['gADC_m2']]['gCh_m2']
channels_w2 = clusters[
clusters['gADC_m1'] <= clusters['gADC_m2']]['wCh_m1']
clusters_dict[name]['g'] = channels_g1.append(channels_g2)
clusters_dict[name]['w'] = channels_w1.append(channels_w2)
# Declare max and min count
min_count = 0
max_count = np.inf
# Initiate 'voxel_id -> (x, y, z)'-mapping
MG24_ch_to_coord_20, MG24_ch_to_coord_16 = get_MG24_to_XYZ_mapping(window)
#print(MG24_ch_to_coord_16)
#print("20 wires", clusters_dict['ce_20']['w'].values)
#print("20 grids", clusters_dict['ce_20']['g'].values)
##wires = clusters_dict['ce_20']['w']
#print(np.array([clusters_dict['ce_20']['w'].values,
# clusters_dict['ce_20']['g'].values]))
# Calculate 3D histogram
H_20, edges_20 = np.histogramdd((clusters_dict['ce_20']['w'].values,
clusters_dict['ce_20']['g'].values),
bins=(80, 13),
range=((0, 80), (0, 13)))
# Insert results into an array
hist_20 = [[], [], [], []]
loc_20 = 0
labels_20 = []
for wCh in range(0, 80):
for gCh in range(0, 13):
over_min = H_20[wCh, gCh] > min_count
under_max = H_20[wCh, gCh] <= max_count
if over_min and under_max:
coord = MG24_ch_to_coord_20[gCh, wCh]
hist_20[0].append(coord['x'])
hist_20[1].append(coord['y'])
hist_20[2].append(coord['z'])
hist_20[3].append(H_20[wCh, gCh])
loc_20 += 1
labels_20.append('Wire Channel: ' + str(wCh) + '<br>' +
'Grid Channel: ' + str(gCh) + '<br>' +
'Counts: ' + str(H_20[wCh, gCh]))
# for 16 layers
H_16, edges_16 = np.histogramdd((clusters_dict['ce_16']['w'].values,
clusters_dict['ce_16']['g'].values),
bins=(64, 13),
range=((0, 64), (0, 13)))
# Insert results into an array
hist_16 = [[], [], [], []]
loc_16 = 0
labels_16 = []
for wCh in range(0, 64):
for gCh in range(0, 13):
over_min = H_16[wCh, gCh] > min_count
under_max = H_16[wCh, gCh] <= max_count
if over_min and under_max:
coord = MG24_ch_to_coord_16[gCh, wCh]
hist_16[0].append(coord['x'])
hist_16[1].append(coord['y'])
hist_16[2].append(coord['z'])
hist_16[3].append(H_16[wCh, gCh])
loc_16 += 1
labels_16.append('Wire Channel: ' + str(wCh) + '<br>' +
'Grid Channel: ' + str(gCh) + '<br>' +
'Counts: ' + str(H_16[wCh, gCh]))
#print("hist20", hist_20[3])
#print("hist16", hist_16[3])
labels = []
labels.extend(labels_20)
labels.extend(labels_16)
# Produce 3D histogram plot
hist = [[], [], [], []]
hist_x_offset = [i + 100 for i in hist_20[0]]
hist_z_offset = [i + 40 for i in hist_16[2]]
hist[0].extend(hist_x_offset)
hist[0].extend(hist_16[0])
hist[1].extend(hist_20[1])
hist[1].extend(hist_16[1])
hist[2].extend(hist_20[2])
hist[2].extend(hist_z_offset)
hist[3].extend(hist_20[3])
hist[3].extend(hist_16[3])
#print(hist_20[0])
#print("hist0", hist[0])
#print("hist1", hist[1])
#print("hist2", hist[2])
#print("hist3", hist[3])
MG_3D_trace = go.Scatter3d(
x=hist[0],
y=hist[1],
z=hist[2],
mode='markers',
marker=dict(
size=20,
color=hist[3], #(np.log10(hist[3])),
colorscale='Jet',
opacity=1,
colorbar=dict(thickness=20, title='Counts'),
),
text=labels,
name='Multi-Grid',
scene='scene1')
# Introduce figure and put everything together
fig = py.tools.make_subplots(rows=1, cols=1, specs=[[{'is_3d': True}]])
# Insert histogram
fig.append_trace(MG_3D_trace, 1, 1)
# Assign layout with axis labels, title and camera angle
fig['layout']['scene1']['xaxis'].update(title='x [mm]')
fig['layout']['scene1']['yaxis'].update(title='y [mm]')
fig['layout']['scene1']['zaxis'].update(title='z [mm]')
fig['layout'].update(title='Coincidences (3D) - ' + window.data_sets)
fig.layout.showlegend = False
# If in plot He3-tubes histogram, return traces, else save HTML and plot
py.offline.plot(fig,
filename='../Results/Coincidences_3D/Ce3Dhistogram.html',
auto_open=True)
def Channels_rates_plot(window, measurement_time):
"""plots neutron event rate for each channel"""
def channel_rates_plot_bus(events, subtitle, typeCh, name, wires):
plt.xlabel('grid channel')
plt.ylabel('Rate of total counts')
plt.grid(True, which='major', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.title("Grid rates")
#print("Grid channel \t rate")
gChs = []
g_rates = []
if typeCh == 'gCh':
for gCh in np.arange(0, 12, 1):
counts = 0
vals = clusters_dict[name]['g'].values
for val in vals:
if val == gCh:
counts += 1
rate = counts / ((measurement_time))
gChs.append(gCh)
g_rates.append(rate)
#print(gCh, "\t", rate, "Hz")
plt.scatter(gChs, g_rates, color="darkorange", zorder=2)
plt.title(sub_title)
#print("Wire channel \t rate")
wChs = []
w_rates = []
if typeCh == 'wCh':
for wCh in np.arange(0, wires, 1):
counts = 0
vals = clusters_dict[name]['w'].values
for val in vals:
if val == wCh:
counts += 1
rate = counts / ((measurement_time))
wChs.append(wCh)
w_rates.append(rate)
#print(wCh, "\t", rate, "Hz")
plt.scatter(wChs, w_rates, color="crimson", zorder=2)
plt.title(sub_title)
# Import data
ce_20 = window.Clusters_20_layers
ce_16 = window.Clusters_16_layers
# Filter
ce_red_20 = filter_clusters(ce_20, window)
ce_red_16 = filter_clusters(ce_16, window)
clusters_20 = ce_red_20 #.shape[0]
clusters_16 = ce_red_16 #.shape[0]
clusters_vec = [clusters_20, clusters_16]
clusters_dict = {
'ce_20': {
'w': None,
'g': None
},
'ce_16': {
'w': None,
'g': None
}
}
# Select grids with highest collected charge
for clusters, name in zip(clusters_vec, ['ce_20', 'ce_16']):
channels_g1 = clusters[
clusters['gADC_m1'] > clusters['gADC_m2']]['gCh_m1']
channels_w1 = clusters[
clusters['gADC_m1'] > clusters['gADC_m2']]['wCh_m1']
channels_g2 = clusters[
clusters['gADC_m1'] <= clusters['gADC_m2']]['gCh_m2']
channels_w2 = clusters[
clusters['gADC_m1'] <= clusters['gADC_m2']]['wCh_m1']
clusters_dict[name]['g'] = channels_g1.append(channels_g2)
clusters_dict[name]['w'] = channels_w1.append(channels_w2)
typeChs = ['gCh', 'wCh']
grids_or_wires = {'wCh': 'Wires', 'gCh': 'Grids'}
# plot
fig = plt.figure()
fig.set_figheight(5)
fig.set_figwidth(10)
plt.suptitle('Total rate per channel \n%s' %
window.data_sets.splitlines()[0])
# for 20 layers
for i, typeCh in enumerate(typeChs):
sub_title = "%s -- 20 layers" % grids_or_wires[typeCh]
wires = 80
name = 'ce_20'
plt.subplot(2, 2, i + 1)
channel_rates_plot_bus(clusters_20, sub_title, typeCh, name, wires)
# for 16 layers
for i, typeCh in enumerate(typeChs):
sub_title = "%s -- 16 layers" % grids_or_wires[typeCh]
name = 'ce_16'
plt.subplot(2, 2, i + 3)
wires = 64
channel_rates_plot_bus(clusters_16, sub_title, typeCh, name, wires)
plt.subplots_adjust(left=0.1,
right=0.98,
top=0.86,
bottom=0.09,
wspace=0.25,
hspace=0.45)
return fig
def PHS_2D_plot(window):
def PHS_2D_plot_bus(clusters, wg, limit, bins, sub_title, vmin, vmax):
plt.xlabel('Channel')
plt.ylabel('Charge [ADC channels]')
plt.title(sub_title)
if wg == 'g':
channels = clusters['gCh_m1'].append(clusters['gCh_m2'])
adcs = clusters['gADC_m1'].append(clusters['gADC_m2'])
else:
channels = clusters['wCh_m1']
adcs = clusters['wADC_m1']
plt.hist2d(
channels,
adcs,
bins=[bins, 120],
range=[limit, [0, 4095]],
norm=LogNorm(),
#vmin=vmin, vmax=vmax,
cmap='jet')
plt.colorbar()
# Import data
df_20 = window.Clusters_20_layers
df_16 = window.Clusters_16_layers
# Intial filter
clusters_20 = filter_clusters(df_20, window)
clusters_16 = filter_clusters(df_16, window)
# Declare parameters
wg_list = ['w', 'g']
limits_16 = [[-0.5, 63.5], [-0.5, 11.5]]
bins_16 = [64, 12]
limits_20 = [[-0.5, 79.5], [-0.5, 11.5]]
bins_20 = [80, 12]
grids_or_wires = {'w': 'Wires', 'g': 'Grids'}
# Prepare figure
fig = plt.figure()
title = 'PHS (2D) - MG\n(%s, ...)' % window.data_sets.splitlines()[0]
fig.suptitle(title, x=0.5, y=1.03)
vmin = 1
vmax_16 = clusters_16.shape[0] // 1000 + 100
vmax_20 = clusters_20.shape[0] // 1000 + 100
fig.set_figheight(4)
fig.set_figwidth(10)
# Plot figure
for i, (wg, limit_16,
bins_16) in enumerate(zip(wg_list, limits_16, bins_16)):
# Filter events based on wires or grids
plt.subplot(2, 2, i + 1)
sub_title = grids_or_wires[wg] + " -- 16 layers"
PHS_2D_plot_bus(clusters_16, wg, limit_16, bins_16, sub_title, vmin,
vmax_16)
plt.tight_layout()
for i, (wg, limit_20,
bins_20) in enumerate(zip(wg_list, limits_20, bins_20)):
# Filter events based on wires or grids
plt.subplot(2, 2, i + 3)
sub_title = grids_or_wires[wg] + " -- 20 layers"
PHS_2D_plot_bus(clusters_20, wg, limit_20, bins_20, sub_title, vmin,
vmax_20)
plt.tight_layout()
return fig
def PHS_Individual_plot(window):
# Import data
df_20 = window.Clusters_20_layers
df_16 = window.Clusters_16_layers
# Intial filter
clusters_16 = filter_clusters(df_16, window)
clusters_20 = filter_clusters(df_20, window)
# Declare parameters
clusters_vec = [clusters_16, clusters_20]
detectors = ['16_layers', '20_layers']
layers_vec = [16, 20]
dir_name = os.path.dirname(__file__)
folder_path = os.path.join(dir_name, '../../Results/PHS/')
number_bins = int(window.phsBins.text())
# Save all PHS
for clusters, detector, layers in zip(clusters_vec, detectors, layers_vec):
# Save wires PHS
for wCh in np.arange(0, layers * 4, 1):
print('%s, Wires: %d/%d' % (detector, wCh, layers * 4 - 1))
# Get ADC values
adcs = clusters[clusters.wCh_m1 == wCh]['wADC_m1']
# Plot
fig = plt.figure()
plt.hist(adcs,
bins=number_bins,
range=[0, 4095],
histtype='step',
color='black',
zorder=5)
plt.grid(True, which='major', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.xlabel('Collected charge [ADC channels]')
plt.ylabel('Counts')
plt.title('PHS wires - Channel %d\nData set: %s' %
(wCh, window.data_sets))
# Save
output_path = '%s/%s/Wires/Channel_%d.pdf' % (folder_path,
detector, wCh)
fig.savefig(output_path, bbox_inches='tight')
plt.close()
# Save grids PHS
for gCh in np.arange(0, 12, 1):
print('%s, Grids: %d/11' % (detector, gCh))
# Get ADC values
adcs_1 = clusters[clusters.gCh_m1 == gCh]['gADC_m1']
adcs_2 = clusters[clusters.gCh_m2 == gCh]['gADC_m2']
adcs = adcs_1.append(adcs_2)
# Plot
fig = plt.figure()
plt.hist(adcs,
bins=number_bins,
range=[0, 4095],
histtype='step',
color='black',
zorder=5)
plt.grid(True, which='major', zorder=0)
plt.grid(True, which='minor', linestyle='--', zorder=0)
plt.xlabel('Collected charge [ADC channels]')
plt.ylabel('Counts')
plt.title('PHS grids - Channel %d\nData set: %s' %
(gCh, window.data_sets))
# Save
output_path = '%s/%s/Grids/Channel_%d.pdf' % (folder_path,
detector, gCh)
fig.savefig(output_path, bbox_inches='tight')
plt.close()