def PHS_1D_MG_plot(window):
"""
MG mapping. Returns 1D cumulative PHS plot for raw events.
"""
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')
plt.hist(clusters[clusters[typeCh] >= 0].adc,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
ec='black',
facecolor='lightgrey',
zorder=5)
# Declare parameters
number_bins = int(window.phsBins.text())
typeChs = ['gCh', 'wCh']
grids_or_wires = {'wCh': 'Wires', 'gCh': 'Grids'}
# Import data
df_20 = window.Events_20_layers
df_16 = window.Events_16_layers
# Initial filter
clusters_20 = filter_events(df_20, window)
clusters_16 = filter_events(df_16, window)
# Prepare figure
fig = plt.figure()
title = 'PHS (1D)\n(%s, ...)' % window.data_sets.splitlines()[0]
fig.suptitle(title, x=0.5, y=0.98)
fig.set_figheight(6)
fig.set_figwidth(9)
# Plot figure
# for 20 layers
for i, typeCh in enumerate(typeChs):
plt.subplot(2, 2, i + 1)
sub_title = "%s -- 20 layers" % grids_or_wires[typeCh]
PHS_1D_plot_bus(clusters_20, typeCh, sub_title, number_bins)
plt.tight_layout()
# for 16 layers
for i, typeCh in enumerate(typeChs):
plt.subplot(2, 2, i + 3)
sub_title = "%s -- 16 layers" % grids_or_wires[typeCh]
PHS_1D_plot_bus(clusters_16, typeCh, sub_title, number_bins)
#plt.tight_layout()
plt.subplots_adjust(left=0.07,
right=0.95,
top=0.88,
bottom=0.08,
wspace=0.25,
hspace=0.35)
return fig
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 PHS_1D_overlay_plot(window):
def PHS_1D_overlay_plot_bus(events, 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 typeCh == 'gCh':
if window.gM_filter.isChecked():
adcs_clusters = events[(window.gM_min.value() <= events.gM)
& (events.gM <= window.gM_max.value())
& (events.wM >= window.wM_min.value())
& (events.wM <= window.wM_max.value())
& (events.chip_id == 2)].adc
else:
adcs_clusters = events[events.chip_id == 2].adc
elif typeCh == 'wCh':
if window.wM_filter.isChecked():
adcs_clusters = events[(window.wM_min.value() <= events.wM)
& (events.wM <= window.wM_max.value())
& (events.gM >= window.gM_min.value())
& (events.gM <= window.gM_max.value())
& (events.chip_id != 2)].adc
else:
adcs_clusters = events[events.chip_id != 2].adc
plt.hist(events[events[typeCh] >= 0].adc,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
ec='black',
facecolor='lightgrey',
zorder=5,
label='raw')
plt.hist(adcs_clusters,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
ec='black',
facecolor='lightblue',
alpha=0.6,
zorder=5,
label='clustered')
plt.legend()
# Import data
raw_20 = window.Events_20_layers
raw_16 = window.Events_16_layers
# Apply filters
events_16 = filter_events(raw_16, window)
events_20 = filter_events(raw_20, window)
number_bins = int(window.phsBins.text())
typeChs = ['gCh', 'wCh']
grids_or_wires = {'wCh': 'Wires', 'gCh': 'Grids'}
# Prepare figure
fig = plt.figure()
title = 'PHS overlay (1D)\n(%s, ...)' % window.data_sets.splitlines()[0]
fig.suptitle(title, x=0.5, y=0.98)
fig.set_figheight(6)
fig.set_figwidth(8)
# Plot figure
# for 20 layers
for i, typeCh in enumerate(typeChs):
plt.subplot(2, 2, i + 1)
sub_title = "%s -- 20 layers" % (grids_or_wires[typeCh])
PHS_1D_overlay_plot_bus(events_20, typeCh, sub_title, number_bins)
# for 16 layers
for i, typeCh in enumerate(typeChs):
plt.subplot(2, 2, i + 3)
sub_title = "%s -- 16 layers" % (grids_or_wires[typeCh])
PHS_1D_overlay_plot_bus(events_16, typeCh, sub_title, number_bins)
plt.subplots_adjust(left=0.1,
right=0.93,
top=0.88,
bottom=0.12,
wspace=0.3,
hspace=0.4)
return fig
def PHS_Individual_Channel_plot(window, channel):
# Import data
df_events_20 = window.Events_20_layers
df_events_16 = window.Events_16_layers
# Intial filter
events_20 = filter_events(df_events_20, window)
events_16 = filter_events(df_events_16, window)
if window.ind_ch_20.isChecked():
events = events_20
layers = '20 layers'
else:
events = events_16
layers = '16 layers'
number_bins = int(window.phsBins.text())
# Plot
fig = plt.figure()
# Get ADC values
if window.PHS_raw.isChecked():
if window.ind_gCh.isChecked():
adcs = events[events.gCh == channel]['adc']
w_or_g = 'grid'
elif window.ind_wCh.isChecked():
adcs = events[events.wCh == channel]['adc']
w_or_g = 'wire'
plt.hist(adcs,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightgrey',
ec='black',
zorder=5)
elif window.PHS_clustered.isChecked():
if window.ind_gCh.isChecked():
w_or_g = 'grid'
if window.gM_filter.isChecked():
adcs = events[(events.gCh == channel)
& (window.gM_min.value() <= events.gM)
& (events.gM <= window.gM_max.value())
& (events.wM >= window.wM_min.value())
& (events.wM <= window.wM_max.value())
& (events.chip_id == 2)].adc
else:
adcs = events[(events.gCh == channel)
& (events.chip_id == 2)].adc
elif window.ind_wCh.isChecked():
w_or_g = 'wire'
if window.wM_filter.isChecked():
adcs = events[(events.wCh == channel)
& (window.wM_min.value() <= events.wM)
& (events.wM <= window.wM_max.value())
& (events.gM >= window.gM_min.value())
& (events.gM <= window.gM_max.value())
& (events.chip_id != 2)].adc
else:
adcs = events[(events.wCh == channel)
& (events.chip_id != 2)].adc
plt.hist(adcs,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightblue',
ec='black',
zorder=5)
elif window.PHS_overlay.isChecked():
if window.ind_gCh.isChecked():
w_or_g = 'grid'
adcs_events = events[events.gCh == channel]['adc']
if window.gM_filter.isChecked():
adcs_clusters = events[(events.gCh == channel)
& (window.gM_min.value() <= events.gM)
& (events.gM <= window.gM_max.value())
& (events.wM >= window.wM_min.value())
& (events.wM <= window.wM_max.value())
& (events.chip_id == 2)].adc
else:
adcs_clusters = events[(events.gCh == channel)
& (events.chip_id == 2)].adc
elif window.ind_wCh.isChecked():
w_or_g = 'wire'
adcs_events = events[events.wCh == channel]['adc']
if window.wM_filter.isChecked():
adcs_clusters = events[(events.wCh == channel)
& (window.wM_min.value() <= events.wM)
& (events.wM <= window.wM_max.value())
& (events.gM >= window.gM_min.value())
& (events.gM <= window.gM_max.value())
& (events.chip_id != 2)].adc
else:
adcs_clusters = events[(events.wCh == channel)
& (events.chip_id != 2)].adc
plt.hist(adcs_events,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightgrey',
ec='black',
zorder=5,
label='raw')
plt.hist(adcs_clusters,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightblue',
ec='black',
alpha=0.6,
zorder=5,
label='clustered')
plt.legend()
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 %s channel %d -- %s\nData set: %s' %
(w_or_g, channel, layers, window.data_sets))
return fig
def PHS_Individual_plot(window):
# Import data
df_events_20 = window.Events_20_layers
df_clusters_20 = window.Events_20_layers #window.Clusters_20_layers
df_events_16 = window.Events_16_layers
df_clusters_16 = window.Events_16_layers #window.Clusters_16_layers
# Intial filter
events_20 = filter_events(df_events_20, window)
clusters_20 = filter_events(df_clusters_20, window)
events_16 = filter_events(df_events_16, window)
clusters_16 = filter_events(df_clusters_16, window)
# Declare parameters
events_vec = [events_16, events_20]
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())
if window.PHS_raw.isChecked():
# Save all PHS
for events, detector, layers in zip(events_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 = events[events.wCh == wCh]['adc']
# Plot
fig = plt.figure()
plt.hist(adcs,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightgrey',
ec='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_%s_raw/Channel_%d.pdf' % (
folder_path, detector, layers, 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 = events[events.gCh == gCh]['adc']
# Plot
fig = plt.figure()
plt.hist(adcs,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightgrey',
ec='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_%s_raw/Channel_%d.pdf' % (
folder_path, detector, layers, gCh)
fig.savefig(output_path, bbox_inches='tight')
plt.close()
elif window.PHS_clustered.isChecked():
# 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
if window.wM_filter.isChecked():
adcs = clusters[(clusters.wCh == wCh)
& (window.wM_min.value() <= clusters.wM)
& (clusters.wM <= window.wM_max.value())
& (clusters.gM >= window.gM_min.value())
& (clusters.gM <= window.gM_max.value())
& (clusters.chip_id != 2)].adc
else:
adcs = clusters[(clusters.wCh == wCh)
& (clusters.chip_id != 2)].adc
# Plot
fig = plt.figure()
plt.hist(adcs,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightblue',
ec='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_%s_clustered/Channel_%d.pdf' % (
folder_path, detector, layers, 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
if window.gM_filter.isChecked():
adcs = clusters[(clusters.gCh == gCh)
& (window.gM_min.value() <= clusters.gM)
& (clusters.gM <= window.gM_max.value())
& (clusters.wM >= window.wM_min.value())
& (clusters.wM <= window.wM_max.value())
& (clusters.chip_id == 2)].adc
else:
adcs = clusters[(clusters.gCh == gCh)
& (clusters.chip_id == 2)].adc
# Plot
fig = plt.figure()
plt.hist(adcs,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightblue',
ec='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_%s_clustered/Channel_%d.pdf' % (
folder_path, detector, layers, gCh)
fig.savefig(output_path, bbox_inches='tight')
plt.close()
elif window.PHS_overlay.isChecked():
# Save all PHS
for clusters, events, detector, layers in zip(clusters_vec, events_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_events = events[events.wCh == wCh]['adc']
if window.wM_filter.isChecked():
adcs_clusters = clusters[
(clusters.wCh == wCh)
& (window.wM_min.value() <= clusters.wM)
& (clusters.wM <= window.wM_max.value())
& (clusters.gM >= window.gM_min.value())
& (clusters.gM <= window.gM_max.value())
& (clusters.chip_id != 2)].adc
else:
adcs_clusters = clusters[(clusters.wCh == wCh)
& (clusters.chip_id != 2)].adc
# Plot
fig = plt.figure()
plt.hist(adcs_events,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightgrey',
ec='black',
zorder=5,
label='raw')
plt.hist(adcs_clusters,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightblue',
ec='black',
alpha=0.6,
zorder=5,
label='clustered')
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.legend()
plt.title('PHS wires - Channel %d\nData set: %s' %
(wCh, window.data_sets))
# Save
output_path = '%s/%s/Wires_%s_overlay/Channel_%d.pdf' % (
folder_path, detector, layers, 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_events = events[events.gCh == gCh]['adc']
if window.gM_filter.isChecked():
adcs_clusters = clusters[
(clusters.gCh == gCh)
& (window.gM_min.value() <= clusters.gM)
& (clusters.gM <= window.gM_max.value())
& (clusters.wM >= window.wM_min.value())
& (clusters.wM <= window.wM_max.value())
& (clusters.chip_id == 2)].adc
else:
adcs_clusters = clusters[(clusters.gCh == gCh)
& (clusters.chip_id == 2)].adc
# Plot
fig = plt.figure()
plt.hist(adcs_events,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightgrey',
ec='black',
zorder=5,
label='raw')
plt.hist(adcs_clusters,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightblue',
ec='black',
alpha=0.6,
zorder=5,
label='clustered')
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.legend()
plt.title('PHS grids - Channel %d\nData set: %s' %
(gCh, window.data_sets))
# Save
output_path = '%s/%s/Grids_%s_overlay/Channel_%d.pdf' % (
folder_path, detector, layers, gCh)
fig.savefig(output_path, bbox_inches='tight')
plt.close()
def PHS_2D_MG_plot(window):
def PHS_2D_plot_bus(events, typeCh, limit, bins, sub_title, vmin, vmax):
plt.xlabel('Channel')
plt.ylabel('Charge [ADC channels]')
plt.title(sub_title)
plt.hist2d(events[typeCh],
events.adc,
bins=[bins, 120],
range=[limit, [0, 1050]],
norm=LogNorm(),
vmin=vmin,
vmax=vmax,
cmap='jet')
plt.colorbar()
def get_wire_events(events, window):
events_red = None
if window.wCh_filter.isChecked():
wCh_min = window.wCh_min.value()
wCh_max = window.wCh_max.value()
events_red = events[(events['wCh'] >= wCh_min)
& (events['wCh'] <= wCh_max)]
else:
events_red = events[(events['wCh'] >= 0) & (events['wCh'] <= 79)]
return events_red
def get_grid_events(events, window):
events_red = None
if window.gCh_filter.isChecked():
gCh_min = window.gCh_min.value()
gCh_max = window.gCh_max.value()
events_red = events[(events['gCh'] >= gCh_min)
& (events['gCh'] <= gCh_max)]
else:
events_red = events[(events['gCh'] >= 0) & (events['gCh'] <= 12)]
return events_red
# Import data
df_20 = window.Events_20_layers
df_16 = window.Events_16_layers
# Declare parameters
typeChs = ['gCh', 'wCh']
limits_20 = [[-0.5, 11.5], [-0.5, 78.5]]
bins_20 = [12, 79]
limits_16 = [[-0.5, 11.5], [-0.5, 62.5]]
bins_16 = [12, 63]
grids_or_wires = {'wCh': 'Wires', 'gCh': 'Grids'}
# Initial filter
clusters_20 = filter_events(df_20, window)
clusters_16 = filter_events(df_16, window)
# Prepare figure
fig = plt.figure()
title = 'PHS (2D) - MG\n(%s, ...)' % window.data_sets.splitlines()[0]
fig.suptitle(title, x=0.5, y=0.99)
vmin = None # 1
vmax_20 = None #clusters_20.shape[0] // 1000 + 100
vmax_16 = None #clusters_16.shape[0] // 1000 + 100
fig.set_figheight(6)
fig.set_figwidth(9)
# Plot figure
# for 20 layers
for i, (typeCh, limit, bins) in enumerate(zip(typeChs, limits_20,
bins_20)):
# Filter events based on wires or grids
if typeCh == 'wCh':
events_red_20 = get_wire_events(clusters_20, window)
else:
events_red_20 = get_grid_events(clusters_20, window)
plt.subplot(2, 2, i + 1)
sub_title = 'PHS: %s -- 20 layers' % grids_or_wires[typeCh]
PHS_2D_plot_bus(events_red_20, typeCh, limit, bins, sub_title, vmin,
vmax_20)
plt.tight_layout()
# for 16 layers
for i, (typeCh, limit, bins) in enumerate(zip(typeChs, limits_16,
bins_16)):
# Filter events based on wires or grids
if typeCh == 'wCh':
events_red_16 = get_wire_events(clusters_16, window)
else:
events_red_16 = get_grid_events(clusters_16, window)
plt.subplot(2, 2, i + 3)
sub_title = 'PHS: %s -- 16 layers' % grids_or_wires[typeCh]
PHS_2D_plot_bus(events_red_16, typeCh, limit, bins, sub_title, vmin,
vmax_16)
#plt.tight_layout()
plt.subplots_adjust(left=0.09,
right=0.98,
top=0.89,
bottom=0.1,
wspace=0.25,
hspace=0.35)
return fig
def PHS_2D_VMM_plot(window):
def PHS_2D_plot_bus(events, VMM, limit, bins, sub_title, vmin, vmax):
if VMM == 2:
sub_title += ' (Grids)'
else:
sub_title += ' (Wires)'
plt.xlabel('Channel')
plt.ylabel('Charge [ADC channels]')
plt.title(sub_title)
plt.hist2d(events.channel,
events.adc,
bins=[bins, 120],
range=[limit, [0, 1050]],
norm=LogNorm(),
vmin=vmin,
vmax=vmax,
cmap='jet')
plt.colorbar()
number_bins = int(window.phsBins.text())
# Import data
df_20 = window.Events_20_layers
df_16 = window.Events_16_layers
# Initial filter
clusters_20 = filter_events(df_20, window)
clusters_16 = filter_events(df_16, window)
# Declare parameters
VMM_order_20 = [2, 3, 4, 5]
VMM_order_16 = [2, 3, 4, 5]
VMM_limits_20 = [[15.5, 48.5], [17.5, 46.5], [16.5, 46.5], [16.5, 46.5]]
VMM_limits_16 = [[15.5, 48.5], [17.5, 46.5], [16.5, 46.5], [16.5, 46.5]]
VMM_bins_20 = [33, 29, 30, 30]
VMM_bins_16 = [33, 29, 30, 30]
# Prepare figure
fig = plt.figure()
title = 'PHS (2D) - VMM\n(%s, ...)' % window.data_sets.splitlines()[0]
fig.suptitle(title, x=0.5, y=0.99)
vmin = 1
vmax_20 = clusters_20.shape[0] // 1000 + 100
vmax_16 = clusters_16.shape[0] // 1000 + 100
fig.set_figheight(8)
fig.set_figwidth(13)
# Plot figure
# for 20 layers
for i, (VMM, limit,
bins) in enumerate(zip(VMM_order_20, VMM_limits_20, VMM_bins_20)):
events_VMM_20 = clusters_20[clusters_20.chip_id == VMM]
plt.subplot(2, 4, i + 1)
sub_title = 'VMM: %s -- 20 layers' % VMM
PHS_2D_plot_bus(events_VMM_20, VMM, limit, bins, sub_title, vmin,
vmax_20)
plt.tight_layout()
# for 16 layers
for i, (VMM, limit,
bins) in enumerate(zip(VMM_order_16, VMM_limits_16, VMM_bins_16)):
events_VMM_16 = clusters_16[clusters_16.chip_id == VMM]
plt.subplot(2, 4, i + 5)
sub_title = 'VMM: %s -- 16 layers' % VMM
PHS_2D_plot_bus(events_VMM_16, VMM, limit, bins, sub_title, vmin,
vmax_16)
#plt.tight_layout()
plt.subplots_adjust(left=0.06,
right=0.97,
top=0.89,
bottom=0.08,
wspace=0.38,
hspace=0.35)
return fig
def PHS_1D_VMM_plot(window):
def PHS_1D_plot_bus(events, sub_title, number_bins):
# Plot
if VMM == 2:
sub_title += ' (Grids)'
else:
sub_title += ' (Wires)'
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')
plt.hist(events.adc,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightgrey',
ec='black',
zorder=5)
# Declare parameters
VMM_order_20 = [2, 3, 4, 5]
VMM_order_16 = [2, 3, 4, 5]
number_bins = int(window.phsBins.text())
# Import data
df_20 = window.Events_20_layers
df_16 = window.Events_16_layers
#print(df_16)
# Initial filter
clusters_20 = filter_events(df_20, window)
clusters_16 = filter_events(df_16, window)
# Prepare figure
fig = plt.figure()
title = 'PHS (1D)\n(%s, ...)' % window.data_sets.splitlines()[0]
fig.suptitle(title, x=0.5, y=0.98)
fig.set_figheight(8)
fig.set_figwidth(13)
# Plot figure
# for 20 layers
for i, VMM in enumerate(VMM_order_20):
events_VMM_20 = clusters_20[clusters_20.chip_id == VMM]
plt.subplot(2, 4, i + 1)
sub_title = 'VMM: %s' % VMM + " -- 20 layers"
PHS_1D_plot_bus(events_VMM_20, sub_title, number_bins)
# for 16 layers
for i, VMM in enumerate(VMM_order_16):
events_VMM_16 = clusters_16[clusters_16.chip_id == VMM]
plt.subplot(2, 4, i + 5)
sub_title = 'VMM: %s' % VMM + " -- 16 layers"
PHS_1D_plot_bus(events_VMM_16, sub_title, number_bins)
#plt.tight_layout()
plt.subplots_adjust(left=0.07,
right=0.95,
top=0.87,
bottom=0.08,
wspace=0.35,
hspace=0.37)
return fig
def PHS_cluster_plot(window):
"""
MG mapping, makes 1D PHS plot for clustered (neutron)
events
"""
def PHS_cluster_plot_bus(clusters, typeCh, sub_title, number_bins):
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)
if typeCh == 'gCh':
if window.gM_filter.isChecked():
adcs = clusters[(window.gM_min.value() <= clusters.gM)
& (clusters.gM <= window.gM_max.value())
& (clusters.wM >= window.wM_min.value())
& (clusters.wM <= window.wM_max.value())
& (clusters.chip_id == 2)].adc
else:
adcs = clusters[clusters.chip_id == 2].adc
elif typeCh == 'wCh':
if window.wM_filter.isChecked():
adcs = clusters[(window.wM_min.value() <= clusters.wM)
& (clusters.wM <= window.wM_max.value())
& (clusters.gM >= window.gM_min.value())
& (clusters.gM <= window.gM_max.value())
& (clusters.chip_id != 2)].adc
else:
adcs = clusters[clusters.chip_id != 2].adc
#plt.yscale('log')
plt.hist(adcs,
bins=number_bins,
range=[0, 1050],
histtype='stepfilled',
facecolor='lightblue',
ec='black',
zorder=5)
# Import data
df_16 = window.Events_16_layers
df_20 = window.Events_20_layers
# Initial filter
clusters_16 = filter_events(df_16, window)
clusters_20 = filter_events(df_20, window)
number_bins = int(window.phsBins.text())
# Prepare figure
fig = plt.figure()
title = 'PHS clustered (1D)\n(%s, ...)' % window.data_sets.splitlines()[0]
fig.suptitle(title, x=0.5, y=0.98)
fig.set_figheight(6)
fig.set_figwidth(8)
typeChs = ['gCh', 'wCh']
grids_or_wires = {'wCh': 'Wires', 'gCh': 'Grids'}
# for 20 layers
for i, typeCh in enumerate(typeChs):
plt.subplot(2, 2, i + 1)
sub_title = "%s -- 20 layers" % (grids_or_wires[typeCh])
PHS_cluster_plot_bus(clusters_20, typeCh, sub_title, number_bins)
# for 16 layers
for i, typeCh in enumerate(typeChs):
plt.subplot(2, 2, i + 3)
sub_title = "%s -- 16 layers" % (grids_or_wires[typeCh])
PHS_cluster_plot_bus(clusters_16, typeCh, sub_title, number_bins)
plt.subplots_adjust(left=0.1,
right=0.93,
top=0.88,
bottom=0.1,
wspace=0.35,
hspace=0.35)
return fig
