def Coincidences_2D_plot(window):
# Declare parameters (added with condition if empty array)
data_sets = window.data_sets.splitlines()[0]
# Import data
df_20 = window.Clusters_20_layers
df_16 = window.Clusters_16_layers
# Initial filter, keep only coincident events
clusters_20 = filter_coincident_events(df_20, window)
clusters_16 = filter_coincident_events(df_16, window)
# Plot data
fig = plt.figure()
fig.set_figheight(4.5)
fig.set_figwidth(9)
plt.suptitle('Coincident events (2D)\nData set(s): %s' % data_sets)
# for 20 layers
plt.subplot(1, 2, 1)
plt.title('20 layers')
plt.hist2d(clusters_20.wCh,
clusters_20.gCh,
bins=[80, 12],
range=[[-0.5, 79.5], [-0.5, 11.5]],
norm=LogNorm(),
cmap='jet')
plt.xlabel('Wire [Channel number]')
plt.ylabel('Grid [Channel number]')
plt.colorbar()
# for 16 layers
plt.subplot(1, 2, 2)
plt.title('16 layers')
plt.hist2d(clusters_16.wCh,
clusters_16.gCh,
bins=[64, 12],
range=[[-0.5, 63.5], [-0.5, 11.5]],
norm=LogNorm(),
cmap='jet')
plt.xlabel('Wire [Channel number]')
plt.ylabel('Grid [Channel number]')
plt.colorbar()
plt.subplots_adjust(left=0.08,
right=0.96,
top=0.82,
bottom=0.12,
wspace=0.32)
return fig
def rate_action(self):
if self.data_sets != '':
ce_16 = self.Clusters_16_layers
ce_20 = self.Clusters_20_layers
layers_vec = [16, 20]
events_vec = [ce_16, ce_20]
for layer, events in zip(layers_vec, events_vec):
ce_red = filter_coincident_events(events, self)
start_time = ce_red.head(1)['Time'].values[0]
end_time = ce_red.tail(1)['Time'].values[0]
rate = ce_red.shape[0] / ((end_time - start_time) * 1e-9)
print('Total neutron rate (%s layers): %f Hz' % (layer, rate))
def channel_rates(window):
"""plots neutron event rate for each channel
raw: for all events
clustered: only neutron (coincidence) events
"""
def channel_rates_plot_bus(events, subtitle, typeCh, 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 = len(events[events.gCh == gCh])
rate = counts / ((end_time - start_time) * 1e-9)
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 = len(events[events.wCh == wCh])
rate = counts / ((end_time - start_time) * 1e-9)
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)
if window.raw_rates.isChecked():
events_16 = window.Events_16_layers
events_16 = filter_events(events_16, window)
events_20 = window.Events_20_layers
events_20 = filter_events(events_20, window)
start_time = events_16.head(1)['srs_timestamp'].values[0]
end_time = events_16.tail(1)['srs_timestamp'].values[0]
tag = "raw data"
else:
events_16 = window.Clusters_16_layers
events_16 = filter_coincident_events(events_16, window)
events_20 = window.Clusters_20_layers
events_20 = filter_coincident_events(events_20, window)
start_time = events_16.head(1)['Time'].values[0]
end_time = events_16.tail(1)['Time'].values[0]
tag = "neutrons"
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 -- %s\n%s)' %
(tag, 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
plt.subplot(2, 2, i + 1)
channel_rates_plot_bus(events_20, sub_title, typeCh, wires)
# for 16 layers
for i, typeCh in enumerate(typeChs):
sub_title = "%s -- 16 layers" % grids_or_wires[typeCh]
plt.subplot(2, 2, i + 3)
wires = 64
channel_rates_plot_bus(events_16, sub_title, typeCh, 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 Coincidences_3D_plot(window):
# Import data
df_20 = window.Clusters_20_layers
df_16 = window.Clusters_16_layers
data_sets = window.data_sets.splitlines()[0]
# Perform initial filters
clusters_20 = filter_coincident_events(df_20, window)
clusters_16 = filter_coincident_events(df_16, window)
# 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()
# Calculate 3D histogram
# 20 layers
H_20, edges_20 = np.histogramdd(clusters_20[['wCh', 'gCh']].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]))
# 16 layers
H_16, edges_16 = np.histogramdd(clusters_16[['wCh', 'gCh']].values,
bins=(80, 12),
range=((0, 80), (0, 12)))
# Insert results into an array
hist_16 = [[], [], [], []]
loc_16 = 0
labels_16 = []
for wCh in range(0, 80):
for gCh in range(0, 12):
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]))
# Produce 3D histogram plot
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[2].extend(hist_16[2])
hist[3].extend(hist_20[3])
hist[3].extend(hist_16[3])
MG_3D_trace = go.Scatter3d(x=hist[0],
y=hist[1],
z=hist[2],
mode='markers',
marker=dict(
size=20,
color=np.log10(hist[3]),
colorscale='Jet',
opacity=1,
colorbar=dict(thickness=20,
title='log10(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)
# making the axis lengths according to data (important when only three grid rows)
fig.update_layout(scene_aspectmode='data')
# 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) \n%s' % 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)