| 1 | #!/usr/bin/python -itt
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| 2 | #
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| 3 | # Dominik Neise
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| 4 | #
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| 5 | # cleaning a small step towards the truth
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| 6 | from pyfact import RawData
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| 7 | import os.path
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| 8 | import matplotlib.pyplot as plt
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| 9 | import numpy as np
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| 10 | from fir_filter import *
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| 11 | from extractor import *
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| 12 | from drs_spikes import *
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| 13 | from plotters import *
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| 14 | import time as t
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| 15 | from coor import Coordinator
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| 16 | confirm_next_step = False# this is for user interaction
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| 17 |
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| 18 | data_file_name = '/media/daten_platte/FACT/data/20120229_144.fits.gz'
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| 19 | calib_file_name = '/media/daten_platte/FACT/data/20120229_132.drs.fits.gz'
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| 20 | if not os.path.isfile(data_file_name):
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| 21 | print 'not able to find file:', data_file_name
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| 22 | sys.exit(-1)
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| 23 | if not os.path.isfile(calib_file_name ):
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| 24 | print 'not able to find file:', calib_file_name
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| 25 | sys.exit(-1)
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| 26 |
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| 27 | run = RawData(data_file_name, calib_file_name)
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| 28 | despike = DRSSpikes()
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| 29 | smooth = SlidingAverage(8)
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| 30 | extract = GlobalMaxFinder(40,200)
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| 31 |
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| 32 | #plotA = CamPlotter('amplitudes')
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| 33 | #plotT = CamPlotter('times')
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| 34 | #plotCA = CamPlotter('cleaned amplitudes')
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| 35 |
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| 36 | #plotArea = HistPlotter('area', 1440, (0,1440) )
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| 37 | #plotSize = HistPlotter('size', 1000, (0,10000) )
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| 38 |
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| 39 | #funnyPlot = Plotter('area vs log(size')
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| 40 |
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| 41 |
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| 42 | coreTHR = 45 # copied from F. Temme
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| 43 | edgeTHR = 18 # copied from F. Temme
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| 44 |
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| 45 | # get dictionary of next neighbors
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| 46 | nn = Coordinator().nn
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| 47 |
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| 48 | print 'Core threshold=', coreTHR
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| 49 | print 'Edge threshold=', edgeTHR
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| 50 | print '*'*70
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| 51 |
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| 52 | areas = []
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| 53 | sizes = []
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| 54 |
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| 55 | for data,startcell,tt in run:
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| 56 | if tt==4:
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| 57 | data = despike(data)
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| 58 | data = smooth(data)
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| 59 | amplitude, time_of_max = extract(data)
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| 60 |
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| 61 | #plotA.name='amplitudes EvtID:' + str(run.event_id.value) + ' TT:' + str(tt)
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| 62 | #plotA(amplitude)
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| 63 | #plotT(time_of_max)
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| 64 |
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| 65 | # Here we start the cleaning ... just like that...
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| 66 | #print 'cleaning away all pixel <' , coreTHR
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| 67 | core_chid_candidates = np.where( amplitude > coreTHR)[0]
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| 68 | core_chids = [] # coor chids, which survive Gauks step 1
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| 69 | survivors = [] # coor & edge pixel
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| 70 | #print 'number of core candidates:', len(core_chid_candidates)
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| 71 |
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| 72 | #print 'throwing away all pixel w/o any neighbor'
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| 73 | # get rid of single coor pics
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| 74 | for cand in core_chid_candidates:
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| 75 | neighbor_found = False
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| 76 | # loop over all neigbors of cand'idate
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| 77 | for n in nn[cand]:
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| 78 | if n in core_chid_candidates:
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| 79 | neighbor_found = True
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| 80 | if neighbor_found:
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| 81 | core_chids.append(cand)
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| 82 |
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| 83 | #print 'after deletion of single coor pixels'
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| 84 |
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| 85 |
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| 86 | #add edge pixel to the edge of the coors
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| 87 | #print 'resurrecting edge pixels ... i.e. all pixel >', edgeTHR
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| 88 | survivors = core_chids[:]
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| 89 | for coor in core_chids:
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| 90 | for n in nn[coor]:
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| 91 | # if neighbor is a core pixel, then do nothing
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| 92 | if n in core_chids:
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| 93 | pass
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| 94 | elif amplitude[n] > edgeTHR:
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| 95 | survivors.append(n)
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| 96 | survivors = np.array(survivors, dtype=int)
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| 97 | print '#survivors', len(survivors), 'evtID', run.event_id.value
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| 98 |
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| 99 | #plotCA(data=amplitude, mask=survivors)
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| 100 |
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| 101 | size = 0
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| 102 | for pixel in survivors:
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| 103 | size += amplitude[pixel]
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| 104 |
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| 105 | if len(survivors) > 0:
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| 106 | areas.append( len(survivors) )
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| 107 | sizes.append( size )
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| 108 |
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| 109 |
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| 110 | #plotArea(areas, 'areas of ' + str(run.event_id.value) + 'events')
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| 111 | #plotSize(sizes, 'sizes of ' + str(run.event_id.value) + 'events')
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| 112 |
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| 113 | if confirm_next_step:
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| 114 | user_input = raw_input("'q'-quit, 'r'-run, anything else goes one step")
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| 115 | number=None
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| 116 | try:
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| 117 | number=int(user_input)
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| 118 | except:
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| 119 | number=None
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| 120 | if user_input.find('q') != -1:
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| 121 | sys.exit(0)
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| 122 | elif user_input.find('r') != -1:
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| 123 | confirm_next_step = False
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| 124 | elif number!=None:
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| 125 | run += number
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| 126 |
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| 127 | total_event_number = run.event_id.value
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| 128 |
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| 129 | plt.ion()
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| 130 | myfig = plt.figure()
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| 131 | myn = myfig.number
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| 132 | logsize = np.log10(np.array(sizes))
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| 133 | areas = np.array(areas)
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| 134 |
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| 135 | plt.figure(myn)
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| 136 | plt.title('area vs. log10(size) of '+ str(total_event_number) + 'events')
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| 137 | plt.xlabel('log10(size/1mV)')
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| 138 | plt.ylabel('area [#pixel]')
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| 139 | plt.plot( logsize,areas, '.')
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