InteractiveStreamingExample/python/streamDet_Uproot.py

0001 #!/usr/bin/python3.6
0002
0003 # Copyright 2020, Jefferson Science Associates, LLC.
0004 # Subject to the terms in the LICENSE file found in the top-level directory.
0005
0006 import numpy as np
0007 import uproot as ur
0008 import matplotlib.pyplot as plt
0009 import random, os, time
0010
0011 # end of stream flag, root input file
0012 eos = 0; rif = 'outFile.root'
0013 # initialize counters
0014 sizeDiffCntr = 0; fileEndCntr = 0
0015 # number of channels to analyze
0016 numChans = 80
0017
0018 # plot adc samples vs. tdc samples
0019 numRows = 6; numCols = 6
0020 fig, axs = plt.subplots(numRows, numCols)
0021 fig.set_size_inches(18.5, 10.5, forward = True)
0022
0023 # tfig = plt.figure()
0024 # tfig.set_size_inches(18.5, 10.5, forward = True)
0025 # gs = tfig.add_gridspec(numRows, numCols)
0026
0027 # lists for colors and markers
0028 cl = ['tab:blue',  'tab:orange', 'tab:green', 'tab:red',   'tab:purple',
0029       'tab:brown', 'tab:pink',   'tab:gray',  'tab:olive', 'tab:cyan']
0030 ml = ['o', '^', 's', 'p', 'P', '*', 'X', 'd']
0031
0032 # random channel list, ascending and non-repeating
0033 rcl = random.sample(range(1, numChans + 1), numRows*numCols)
0034 rcl.sort()
0035
0036 # event number list, hit threshold (adc channels), initialize occupancy array
0037 enl = []; hitThresh = 100
0038 detOcc = np.zeros(numChans)
0039
0040 # fit function
0041 def hitFunc(sample, peak, startTime, decayTime, baseLine):
0042     adcSample = np.piecewise(sample, [sample < startTime, sample >= startTime], [lambda sample: baseLine,
0043                              lambda sample: (peak * np.power(((sample - startTime) / decayTime), 4) *
0044                                              np.exp((-4) * ((sample - startTime) / decayTime)) + baseLine)])
0045     return adcSample
0046
0047 while eos == 0 :
0048     rifInitSize = os.stat(rif).st_size
0049     time.sleep(1)
0050     rifCurrSize = os.stat(rif).st_size
0051     print('rifInitSize = %d, rifCurrSize = %d, fileEndCntr = %d' % (rifInitSize, rifCurrSize, fileEndCntr))
0052     sizeDiff = rifCurrSize - rifInitSize
0053     if sizeDiff > 0 :
0054         sizeDiffCntr += 1
0055         print('sizeDiff = %d, sizeDiffCntr = %d' % (sizeDiff, sizeDiffCntr))
0056         # uproot the event tree
0057         et = ur.open(rif)['ET']
0058         # handle event number tracking
0059         enl.append(et[b'event'].numentries)
0060         currEvent = enl[len(enl) - 1]
0061         if len(enl) == 1 : prevEvent = 1
0062         else : prevEvent = enl[len(enl) - 2]
0063         numEventsBetween = currEvent - prevEvent
0064         print ('enl = ', enl)
0065         print ('currEvent = %d, prevEvent = %d, numEventsBetween = %d' % (currEvent, prevEvent, numEventsBetween))
0066         # uproot the sample tree
0067         st = ur.open(rif)['ST']
0068         # populate the data dictionaries
0069         adcDict = st.arrays(['adc*'])
0070         tdcDict = st.arrays(['tdc*'])
0071         print('eventNum = %d, size of adcDict[b\'adcSamplesChan_80\'] = %d' % (enl[len(enl) - 1], len(adcDict[b'adcSamplesChan_80'])))
0072         # update the detector occupancy array
0073         for chan in range(1, numChans + 1) :
0074             for ievent in range(1, numEventsBetween + 1) :
0075                 for sample in adcDict[b'adcSamplesChan_%d' % chan][1024*(prevEvent + ievent - 1):1024*(prevEvent + ievent)] :
0076                     if sample > hitThresh :
0077                         detOcc[chan-1] += 1
0078                         break
0079             if chan % 10 == 0 : print('decOcc for chan %d = ' % chan, detOcc[chan-1])
0080         ic = 0  # index counter
0081         for row in range(numRows) :
0082             for column in range(numCols) :
0083                 axs[row, column].cla()
0084                 axs[row, column].plot(tdcDict[b'tdcSamplesChan_%d' % rcl[ic]][-1024:],
0085                                       adcDict[b'adcSamplesChan_%d' % rcl[ic]][-1024:],
0086                                       color = cl[ic % len(cl)], marker = ml[ic % len(ml)],
0087                                       ls = '', label = 'Channel %d' % rcl[ic])
0088                 hitLoc = np.where(adcDict[b'adcSamplesChan_%d' % rcl[ic]][-1024:]>100)[0] + len(adcDict[b'adcSamplesChan_%d' % rcl[ic]]) - 1024
0089                 if len(hitLoc) != 0 : axs[row, column].set_xlim(np.min(hitLoc) - 10, np.max(hitLoc) + 20)
0090                 axs[row, column].set_ylim(0, 1024)
0091                 if column == 0 : axs[row, column].set_ylabel('ADC Value')
0092                 if row == numRows - 1 : axs[row, column].set_xlabel('TDC Sample Number')
0093                 axs[row, column].legend(loc = 'best', markerscale = 0, handletextpad = 0, handlelength = 0)
0094                 ic += 1
0095         plt.tight_layout()
0096         plt.pause(0.05)
0097     else :
0098         fileEndCntr += 1
0099     if fileEndCntr == 60 :
0100         print('\nData stream no longer present!  Exiting now...buh bye!\n')
0101         eos = 1