#!/usr/bin/python -tt # ******************************** # Test script for the CalFits class # # written by Thomas Kraehenbuehl, ETH Zurich # tpk@phys.ethz.ch, +41 44 633 3973 # April 2012 # ******************************** # # modified and adapted py Patrick Vogler # # ################################ from ROOT import gSystem gSystem.Load("calfactfits_h.so") # according to new naming scheme from ROOT import * #define filenames # 2012 04 17 #calibfilename = '/fact/raw/2012/04/17/20120417_003.drs.fits.gz' # NROI 300, pedestal calibfilename = '/fact/raw/2012/04/17/20120417_033.drs.fits.gz' # NROI 300, pedestal #datafilename = '/fact/raw/2012/04/17/20120417_015.fits.gz' # NROI 300, LP_ext #datafilename = '/fact/raw/2012/04/17/20120417_021.fits.gz' # NROI 300, LP_ext #datafilename = '/fact/raw/2012/04/17/20120417_036.fits.gz' # NROI 300, LP_ext datafilename = '/fact/raw/2012/04/17/20120417_042.fits.gz' # NROI 300, LP_ext # 2012 01 25 #datafilename = '/fact/raw/2012/01/25/20120125_042.fits.gz' # light-pulser-ext # = '/fact/raw/2012/01/25/20120125_095.fits.gz' # pedestal # = '/fact/raw/2012/01/25/20120125_094.fits.gz' # pedestal # = '/fact/raw/2012/01/25/20120125_093.fits.gz' # pedestal #datafilename = '/fact/raw/2012/01/25/20120125_075.fits.gz' #calibfilename = '/fact/raw/2012/01/25/20120125_088.drs.fits.gz' import numpy as np from scipy import weave from scipy.weave import converters from plotters import Plotter # ADC display from plotters import CamPlotter # event display #from drs_spikes import DRSSpikes print "Testing object creation: " caltest = CalFactFits(datafilename, calibfilename) npcalevent = np.empty( caltest.npix * caltest.nroi, np.float64) #.reshape(caltest.npix ,caltest.nroi) caltest.SetNpcaldataPtr(npcalevent) numroi = np.int64(caltest.nroi) numpix = np.int64(caltest.npix) numevents = np.int64(caltest.nevents) print "Common variables run information: " print "ROI: ", numroi print "#Pix: ", numpix print "Number of events: ", numevents print event = np.zeros((numpix, numroi)) # create an array to store an event in the format numpix * numroi (2-dim array) print "Array event created" run_average = np.zeros((numpix, numroi)) # create an array to store the "average event" of a run print "Array run_average created" extracted_average = np.zeros(numpix) print "Array extracted_average created " data_average_run = np.zeros(numroi) print "Arrays created " ##################################################################################################################### #print "creating plotters ..." #make a Plotter class ... this is an easy way for plotting ... but there are # many was to plot data ... without this class ... it was written for convenience, but there is no strong reason to use it... #myplotter = Plotter('titel of the plot', xlabel='time in slices', ylabel='amplitude calibrated data ... in mV') #myplotter2 = Plotter('titel of the plot', xlabel='time in slices', ylabel='amplitude calibrated data ... in mV') # make a CamPlotter class #mycamplotter = CamPlotter('titel of the plot', map_file_path = '../map_dn.txt', vmin=0, vmax=350) # for Lightpulser data #mycamplotter = CamPlotter('titel of the plot', map_file_path = '../map_dn.txt', vmin=-1.2, vmax=1.2) # for pedestal data #print "Plotters created " ################################################################################################################### print "... looping..." while caltest.GetCalEvent(): # Loop ueber alle events #print npcalevent ## Daten, Array 1 dim Laenge caltest.npix * caltest.nroi 1 Event event = np.reshape(npcalevent, (numpix, -1)) # bring the event the shape numpix * numroi (2-dim array) run_average += event # A first test of Adrians idea of primitive signal extractor # signal and background extracted_signal = np.zeros(numpix) for signalslice in range(90, 100): extracted_signal += event[0:(numpix), signalslice] # background subtraction for backgroundslice in range(15, 25): extracted_signal -= event[0:(numpix), backgroundslice] extracted_average += extracted_signal print "Looped ... " for i in range (0, (numpix - 1) ): data_average_run += run_average[i] print "Looped second loop " del caltest print "caltest deleted " print "Common variables run information: " print "ROI: ", numroi print "#Pix: ", numpix print "Number of events: ", numevents print ##################################################################################################################### print "creating plotters ..." #make a Plotter class ... this is an easy way for plotting ... but there are # many was to plot data ... without this class ... it was written for convenience, but there is no strong reason to use it... myplotter = Plotter('titel of the plot', xlabel='time in slices', ylabel='amplitude calibrated data ... in mV') myplotter2 = Plotter('titel of the plot', xlabel='time in slices', ylabel='amplitude calibrated data ... in mV') # make a CamPlotter class mycamplotter = CamPlotter('titel of the plot', map_file_path = 'map_dn.txt', vmin=0, vmax=350) # for Lightpulser data #mycamplotter = CamPlotter('titel of the plot', map_file_path = '../map_dn.txt', vmin=0, vmax=350) # for Lightpulser data #mycamplotter = CamPlotter('titel of the plot', map_file_path = '../map_dn.txt', vmin=-1.2, vmax=1.2) # for pedestal data #mycamplotter = CamPlotter('titel of the plot', map_file_path = 'map_dn.txt', vmin=-2, vmax=2) # for pedestal data print "Plotters created " ################################################################################################################### myplotter((run_average[22]) / numevents, 'pix 22, average' ) myplotter2( data_average_run / (numpix * numevents) , 'average signal (all pixel) over the whole run' ) #mycamplotter( data[0:1443, 100] ) # plot slice 100 of all pixel mycamplotter( extracted_average / (10 * numevents) ) # plot the extracted signal of all pixel answer = raw_input('type "quit" to quit ') while not 'quit' in answer: answer = raw_input('type "quit" to quit ') del caltest print "caltest deleted "