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Plot event wf and job script datatypes #81

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merged 5 commits into from
Mar 20, 2024
Merged

Plot event wf and job script datatypes #81

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7631fc4
plot waveform
FaroutYLq Mar 18, 2024
c128c19
annotation
FaroutYLq Mar 18, 2024
ab6c9fd
rename
FaroutYLq Mar 18, 2024
720abde
manually process peaklet_classification
FaroutYLq Mar 19, 2024
1d9cb2c
added peaks middle step
FaroutYLq Mar 19, 2024
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5 changes: 3 additions & 2 deletions jobs/job.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,9 @@
storage_to_patch = config.get('job', 'storage_to_patch').split(',')
delete_records = config.getboolean('job', 'delete_records')

to_process_dtypes = ['peaklets', 'merged_s2s', 'peak_basics',
'events', 'event_basics', 'event_info', 'event_pattern_fit',
to_process_dtypes = ['peaklets', 'peaklet_classification', 'merged_s2s', 'peak_basics',
'events', 'peak_positions_mlp', 'peak_positions_gcn', 'peak_positions_cnn',
'event_basics', 'event_info', 'event_pattern_fit',
'event_shadow', 'event_ambience', 'event_n_channel','veto_intervals',
'cuts_basic']
if not skip_records:
Expand Down
235 changes: 200 additions & 35 deletions saltax/match/visual.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,13 @@
import matplotlib.pyplot as plt
import numpy as np

def plot_wf(ind, st_salt, st_simu, st_data, runid, matched_simu,
event_ext_window_ns=2.4e6,
s1_ext_window_samples=25,
s2_ext_window_samples=100):
def plot_event_wf_w_data(ind, st_salt, st_simu, st_data, runid, matched_simu,
event_ext_window_ns=2.4e6,
s1_ext_window_samples=25,
s2_ext_window_samples=100):
"""
Plot waveforms for a single event, including full event waveform and zoomed-in S1 and S2 waveforms.
Plot waveforms for a single event, including full event waveform and zoomed-in S1 and S2 waveforms.
This plot function will show info from salt, simu and data mode.
:param ind: index of the event in the matched_simu dataframe
:param st_salt: saltax context for salt mode
:param st_simu: saltax context for simu mode
Expand Down Expand Up @@ -119,22 +120,22 @@ def plot_wf(ind, st_salt, st_simu, st_data, runid, matched_simu,
print("Plotting waveforms for the whole event...")
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 8), dpi=150)
ax1.plot(wf_data, label='Data', color='k', alpha=0.2)
ax1.plot(wf_simu_s1, label='Simu S1', color='tab:blue')
ax1.plot(wf_simu_s2, label='Simu S2', color='tab:orange')
ax1.plot(wf_simu_others, label='Simu Others', color='tab:green')
ax1.axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simu S1 Range')
ax1.axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simu S2 Range')
ax1.plot(wf_simu_s1, label='Simulated S1', color='tab:blue')
ax1.plot(wf_simu_s2, label='Simulated S2', color='tab:orange')
ax1.plot(wf_simu_others, label='Simulated Others', color='tab:green')
ax1.axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
ax1.axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
ax1.legend()
ax1.set_title("Run %s Event %s: Simu CS1=%sPE, Simu CS2=%sPE"%(runid, ind,
int(10*matched_simu['cs1'][ind])/10,
int(10*matched_simu['cs2'][ind])/10))

ax2.plot(wf_data, label='Data', color='k', alpha=0.2)
ax2.plot(wf_salt_s1, label='Salt S1', color='b')
ax2.plot(wf_salt_s2, label='Salt S2', color='r')
ax2.plot(wf_salt_others, label='Salt Others', color='g')
ax2.axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simu S1 Range')
ax2.axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simu S2 Range')
ax2.plot(wf_salt_s1, label='Sprinkled S1', color='b')
ax2.plot(wf_salt_s2, label='Sprinkled S2', color='r')
ax2.plot(wf_salt_others, label='Sprinkled Others', color='g')
ax2.axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
ax2.axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
ax2.legend()

ax1.set_ylabel("Amplitude [PE/10ns]")
Expand All @@ -146,44 +147,208 @@ def plot_wf(ind, st_salt, st_simu, st_data, runid, matched_simu,
print("Zooming into S1 and S2 respectively...")
fig, axs = plt.subplots(2, 2, figsize=(15, 8), dpi=150)
axs[0,0].plot(wf_data, label='Data', color='k', alpha=0.2)
axs[0,0].plot(wf_simu_s1, label='Simu S1', color='tab:blue')
axs[0,0].plot(wf_simu_s2, label='Simu S2', color='tab:orange')
axs[0,0].plot(wf_simu_others, label='Simu Others', color='tab:green')
axs[0,0].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simu S1 Range')
axs[0,0].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simu S2 Range')
axs[0,0].plot(wf_simu_s1, label='Simulated S1', color='tab:blue')
axs[0,0].plot(wf_simu_s2, label='Simulated S2', color='tab:orange')
axs[0,0].plot(wf_simu_others, label='Simulated Others', color='tab:green')
axs[0,0].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
axs[0,0].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
axs[0,0].set_xlim(matched_simu_s1_timerange_i[0]-s1_ext_window_samples,
matched_simu_s1_timerange_i[1]+s1_ext_window_samples)
axs[0,0].set_ylabel("Amplitude [PE/10ns]")
axs[0,0].legend()

axs[0,1].plot(wf_data, label='Data', color='k', alpha=0.2)
axs[0,1].plot(wf_simu_s1, label='Simu S1', color='tab:blue')
axs[0,1].plot(wf_simu_s2, label='Simu S2', color='tab:orange')
axs[0,1].plot(wf_simu_others, label='Simu Others', color='tab:green')
axs[0,1].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simu S1 Range')
axs[0,1].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simu S2 Range')
axs[0,1].plot(wf_simu_s1, label='Simulated S1', color='tab:blue')
axs[0,1].plot(wf_simu_s2, label='Simulated S2', color='tab:orange')
axs[0,1].plot(wf_simu_others, label='Simulated Others', color='tab:green')
axs[0,1].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
axs[0,1].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
axs[0,1].set_xlim(matched_simu_s2_timerange_i[0]-s2_ext_window_samples,
matched_simu_s2_timerange_i[1]+s2_ext_window_samples)
axs[0,1].legend()

axs[1,0].plot(wf_data, label='Data', color='k', alpha=0.2)
axs[1,0].plot(wf_salt_s1, label='Salt S1', color='b')
axs[1,0].plot(wf_salt_s2, label='Salt S2', color='r')
axs[1,0].plot(wf_salt_others, label='Salt Others', color='g')
axs[1,0].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simu S1 Range')
axs[1,0].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simu S2 Range')
axs[1,0].plot(wf_salt_s1, label='Sprinkled S1', color='b')
axs[1,0].plot(wf_salt_s2, label='Sprinkled S2', color='r')
axs[1,0].plot(wf_salt_others, label='Sprinkled Others', color='g')
axs[1,0].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
axs[1,0].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
axs[1,0].set_xlim(matched_simu_s1_timerange_i[0]-s1_ext_window_samples,
matched_simu_s1_timerange_i[1]+s1_ext_window_samples)
axs[1,0].set_xlabel("Time [10ns]")
axs[1,0].set_ylabel("Amplitude [PE/10ns]")
axs[1,0].legend()

axs[1,1].plot(wf_data, label='Data', color='k', alpha=0.2)
axs[1,1].plot(wf_salt_s1, label='Salt S1', color='b')
axs[1,1].plot(wf_salt_s2, label='Salt S2', color='r')
axs[1,1].plot(wf_salt_others, label='Salt Others', color='g')
axs[1,1].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simu S1 Range')
axs[1,1].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simu S2 Range')
axs[1,1].plot(wf_salt_s1, label='Sprinkled S1', color='b')
axs[1,1].plot(wf_salt_s2, label='Sprinkled S2', color='r')
axs[1,1].plot(wf_salt_others, label='Sprinkled Others', color='g')
axs[1,1].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
axs[1,1].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
axs[1,1].set_xlim(matched_simu_s2_timerange_i[0]-s2_ext_window_samples,
matched_simu_s2_timerange_i[1]+s2_ext_window_samples)
axs[1,1].set_xlabel("Time [10ns]")
axs[1,1].legend()
fig.show()


def plot_event_wf_wo_data(ind, st_salt, st_simu, runid, matched_simu,
event_ext_window_ns=2.4e6,
s1_ext_window_samples=25,
s2_ext_window_samples=100):
"""
Plot waveforms for a single event, including full event waveform and zoomed-in S1 and S2 waveforms.
This plot function will show info from salt, simu but no data mode.
:param ind: index of the event in the matched_simu dataframe
:param st_salt: saltax context for salt mode
:param st_simu: saltax context for simu mode
:param runid: runid of the event, example: '066666'
:param matched_simu: simu event_info already matched to truth. (Not necessarily matched to data!)
:param event_ext_window_ns: time window in ns to plot around the event, default 2.4e6 ns = 2.4 ms
:param s1_ext_window_samples: time window in samples to plot around S1, default 25 samples
:param s2_ext_window_samples: time window in samples to plot around S2, default 100 samples
"""
print("Loading peaks and lone_hits for run %s event %s"%(runid, ind))

# Get time ranges in indices for events, S1 and S2
extended_simu_event_timerange_ns = (matched_simu['s1_time'][ind]-event_ext_window_ns,
matched_simu['s2_endtime'][ind]+event_ext_window_ns)
matched_simu_s1_timerange_i = (int((matched_simu['s1_time'][ind] - extended_simu_event_timerange_ns[0])/10),
int((matched_simu['s1_endtime'][ind]-extended_simu_event_timerange_ns[0])/10))
matched_simu_s2_timerange_i = (int((matched_simu['s2_time'][ind] - extended_simu_event_timerange_ns[0])/10),
int((matched_simu['s2_endtime'][ind]-extended_simu_event_timerange_ns[0])/10))


# Get peaks and lone hits for the event
peaks_salt_selected = st_salt.get_array(runid, "peaks",
time_range=extended_simu_event_timerange_ns,
progress_bar=False)
peaks_simu_selected = st_simu.get_array(runid, "peaks",
time_range=extended_simu_event_timerange_ns,
progress_bar=False)
lhs_salt_selected = st_salt.get_array(runid, "lone_hits",
time_range=extended_simu_event_timerange_ns,
progress_bar=False)
lhs_simu_selected = st_simu.get_array(runid, "lone_hits",
time_range=extended_simu_event_timerange_ns,
progress_bar=False)


# Get waveforms for the event
print("Building waveforms...")
total_length = int((extended_simu_event_timerange_ns[1] - extended_simu_event_timerange_ns[0])/10)
to_pes = st_salt.get_single_plugin(runid, 'peaklets').to_pe

wf_salt_s1 = np.zeros(total_length)
wf_simu_s1 = np.zeros(total_length)
wf_salt_s2 = np.zeros(total_length)
wf_simu_s2 = np.zeros(total_length)
wf_salt_others = np.zeros(total_length)
wf_simu_others = np.zeros(total_length)

if len(peaks_salt_selected):
for p in peaks_salt_selected:
start_i = int((p['time'] - int(extended_simu_event_timerange_ns[0]))/10)
length = p['length']
dt = p['dt']
if p['type'] == 1:
for i in range(length):
wf_salt_s1[start_i+i*int(dt/10):start_i+(i+1)*int(dt/10)] = p['data'][i] / dt * 10
elif p['type'] == 2:
for i in range(length):
wf_salt_s2[start_i+i*int(dt/10):start_i+(i+1)*int(dt/10)] = p['data'][i] / dt * 10
else:
for i in range(length):
wf_salt_others[start_i+i*int(dt/10):start_i+(i+1)*int(dt/10)] = p['data'][i] / dt * 10
if len(lhs_salt_selected):
for l in lhs_salt_selected:
time_i = int((l['time'] - int(extended_simu_event_timerange_ns[0]))/10)
amp = l['area'] * to_pes[l['channel']]
wf_salt_others[time_i] += amp/10

if len(peaks_simu_selected):
for p in peaks_simu_selected:
start_i = int((p['time'] - int(extended_simu_event_timerange_ns[0]))/10)
length = p['length']
dt = p['dt']
if p['type'] == 1:
for i in range(length):
wf_simu_s1[start_i+i*int(dt/10):start_i+(i+1)*int(dt/10)] = p['data'][i] / dt * 10
elif p['type'] == 2:
for i in range(length):
wf_simu_s2[start_i+i*int(dt/10):start_i+(i+1)*int(dt/10)] = p['data'][i] / dt * 10
else:
for i in range(length):
wf_simu_others[start_i+i*int(dt/10):start_i+(i+1)*int(dt/10)] = p['data'][i] / dt * 10
if len(lhs_simu_selected):
for l in lhs_simu_selected:
time_i = int((l['time'] - int(extended_simu_event_timerange_ns[0]))/10)
amp = l['area'] * to_pes[l['channel']]
wf_simu_others[time_i] += amp/10

# Plot full event waveform
print("Plotting waveforms for the whole event...")
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 8), dpi=150)
ax1.plot(wf_simu_s1, label='Simulated S1', color='tab:blue')
ax1.plot(wf_simu_s2, label='Simulated S2', color='tab:orange')
ax1.plot(wf_simu_others, label='Simulated Others', color='tab:green')
ax1.axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
ax1.axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
ax1.legend()
ax1.set_title("Run %s Event %s: Simu CS1=%sPE, Simu CS2=%sPE"%(runid, ind,
int(10*matched_simu['cs1'][ind])/10,
int(10*matched_simu['cs2'][ind])/10))

ax2.plot(wf_salt_s1, label='Sprinkled S1', color='b')
ax2.plot(wf_salt_s2, label='Sprinkled S2', color='r')
ax2.plot(wf_salt_others, label='Sprinkled Others', color='g')
ax2.axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
ax2.axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
ax2.legend()

ax1.set_ylabel("Amplitude [PE/10ns]")
ax2.set_xlabel("Time [10ns]")
ax2.set_ylabel("Amplitude [PE/10ns]")
fig.show()

# Zoom into S1 and S2 waveforms
print("Zooming into S1 and S2 respectively...")
fig, axs = plt.subplots(2, 2, figsize=(15, 8), dpi=150)
axs[0,0].plot(wf_simu_s1, label='Simulated S1', color='tab:blue')
axs[0,0].plot(wf_simu_s2, label='Simulated S2', color='tab:orange')
axs[0,0].plot(wf_simu_others, label='Simulated Others', color='tab:green')
axs[0,0].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
axs[0,0].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
axs[0,0].set_xlim(matched_simu_s1_timerange_i[0]-s1_ext_window_samples,
matched_simu_s1_timerange_i[1]+s1_ext_window_samples)
axs[0,0].set_ylabel("Amplitude [PE/10ns]")
axs[0,0].legend()

axs[0,1].plot(wf_simu_s1, label='Simulated S1', color='tab:blue')
axs[0,1].plot(wf_simu_s2, label='Simulated S2', color='tab:orange')
axs[0,1].plot(wf_simu_others, label='Simulated Others', color='tab:green')
axs[0,1].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
axs[0,1].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
axs[0,1].set_xlim(matched_simu_s2_timerange_i[0]-s2_ext_window_samples,
matched_simu_s2_timerange_i[1]+s2_ext_window_samples)
axs[0,1].legend()

axs[1,0].plot(wf_salt_s1, label='Sprinkled S1', color='b')
axs[1,0].plot(wf_salt_s2, label='Sprinkled S2', color='r')
axs[1,0].plot(wf_salt_others, label='Sprinkled Others', color='g')
axs[1,0].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
axs[1,0].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
axs[1,0].set_xlim(matched_simu_s1_timerange_i[0]-s1_ext_window_samples,
matched_simu_s1_timerange_i[1]+s1_ext_window_samples)
axs[1,0].set_xlabel("Time [10ns]")
axs[1,0].set_ylabel("Amplitude [PE/10ns]")
axs[1,0].legend()

axs[1,1].plot(wf_salt_s1, label='Sprinkled S1', color='b')
axs[1,1].plot(wf_salt_s2, label='Sprinkled S2', color='r')
axs[1,1].plot(wf_salt_others, label='Sprinkled Others', color='g')
axs[1,1].axvspan(matched_simu_s1_timerange_i[0],matched_simu_s1_timerange_i[1], color='tab:blue', alpha=0.1, label='Simulated S1 Range')
axs[1,1].axvspan(matched_simu_s2_timerange_i[0],matched_simu_s2_timerange_i[1], color='tab:orange', alpha=0.1, label='Simulated S2 Range')
axs[1,1].set_xlim(matched_simu_s2_timerange_i[0]-s2_ext_window_samples,
matched_simu_s2_timerange_i[1]+s2_ext_window_samples)
axs[1,1].set_xlabel("Time [10ns]")
Expand Down
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