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soc_tools.py
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soc_tools.py
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import rat
import os, sys
import cPickle as pickle
import numpy as np
import collections
from copy import deepcopy
# Only extracting information, always run in airplane mode
# Airplane mode
db = rat.RAT.DB.Get()
db.SetAirplaneModeStatus(True)
db.SetDefaultPlaneLockStatus(False)
print('This is AIRPLANE MODE - be careful!')
def getTOAhistogram_detailed(infile_list = [],
outfile = '',
bin_width = 1, # Do not move this
time_window = 200.,
qhs_threshold = 0.,
start_time = -50.):
# Size of the array
time_edges = np.arange(start_time, start_time+time_window+bin_width, bin_width)
all_toa = np.zeros([9800, time_edges.size-1])
t0 = np.zeros(9800)
# Loop over all the files in infile_list
first_file = True
for one_file in infile_list:
print('\nReading ', one_file)
#try:
reader = rat.socreader(one_file)
soc, run = reader.next()
print('manip position', np.array(soc.GetCalib().GetPos()))
toa = np.zeros_like(all_toa)
for one_pmt in soc.GetSOCPMTIDs():
time_array = np.array(soc.GetSOCPMT(one_pmt).GetTimes())
qhs_array = np.array(soc.GetSOCPMT(one_pmt).GetQHSs())
qhs_bool = qhs_array > qhs_threshold
# Set the offset if the t0 is not set and it is the first file
if first_file and t0[one_pmt] == 0:
t0[one_pmt] = np.ceil(time_array[qhs_bool].mean())
counts, x = np.histogram(time_array[qhs_bool]-t0[one_pmt], time_edges)
toa[one_pmt,:] += counts
if one_pmt % 300 == 0:
print(one_pmt), time_array.mean(), t0[one_pmt]
all_toa += toa
reader.close()
first_file = False
#except:
# print('Could not open file! skipping it for now...')
# try: reader.close()
# except: print('No reader closed')
# The pickle dump is done inside the loop in case something goes wrong later on
myoutfile = open(outfile, 'wb')
pickle.dump({'time_edges':time_edges,
'toa':all_toa,
't0':t0},
myoutfile, protocol=2)
myoutfile.close()
print('Done with all the files!')
def getTOAhistogram(infile_list = [],
outfile = '',
bin_width = 1., # in ns
time_window = 400., # in ns
qhs_threshold = 0.,
start_time = 300.): # in ns - trigger is at ~340
# Size of the array
time_edges = np.arange(start_time, start_time+time_window+bin_width, bin_width)
all_toa = np.zeros([9800, time_edges.size-1])
# Loop over all the files in infile_list
for one_file in infile_list:
print('\nReading ', one_file)
try:
reader = rat.socreader(one_file)
soc, run = reader.next()
print('manip position', np.array(soc.calib.GetPos()))
toa = np.zeros_like(all_toa)
for one_pmt in soc.GetSOCPMTIDs():
if one_pmt % 300 == 0:
print(one_pmt)
time_array = np.array(soc.GetSOCPMT(one_pmt).GetTimes())
qhs_array = np.array(soc.GetSOCPMT(one_pmt).GetQHSs())
qhs_bool = qhs_array > qhs_threshold
counts, x = np.histogram(time_array[qhs_bool], time_edges)
toa[one_pmt,:] += counts
if one_pmt % 300 == 0:
print(one_pmt), time_array.mean()
all_toa += toa
reader.close()
except:
print('Could not open file! skipping it for now...')
try: reader.close()
except: print('No reader closed')
# The pickle dump is done inside the loop in case something goes wrong later on
pickle.dump({'time_edges':time_edges,
'toa':all_toa},
open(outfile, 'wb'), protocol=2)
print('Done with all the files!')
def loadTOAhistogram_detailed(infile_name):
infile = open(infile_name)
data = pickle.load(infile)
zero_bin = np.argwhere(data['time_edges']>=0)[0][0]
new_toa = np.zeros_like(data['toa'])
for ipmt in range(data['toa'].shape[0]):
if data['toa'][ipmt,:].sum() == 0 :
continue
# Find the peak
pmt_max = data['toa'][ipmt,:].argmax()
# Shift the peak until it reaches the position desired
peak_diff = zero_bin - pmt_max
# If positive, move forward.
if peak_diff == 0:
new_toa[ipmt, : ] = data['toa'][ipmt]
elif peak_diff > 0:
new_toa[ipmt, peak_diff:] = data['toa'][ipmt,:-peak_diff]
else:
new_toa[ipmt, :peak_diff] = data['toa'][ipmt,-peak_diff:]
data['toa'] = new_toa
infile.close()
return data