| 1 | #!/usr/bin/python -tt
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| 2 | #
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| 3 | #
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| 4 |
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| 5 | from pyfact import RawData
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| 6 | from drs_spikes import DRSSpikes
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| 7 | from fir_filter import CFD
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| 8 | from fir_filter import SlidingAverage
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| 9 |
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| 10 | import os.path
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| 11 | import sys
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| 12 | import numpy as np
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| 13 | try:
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| 14 | import h5py
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| 15 | except:
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| 16 | print 'module h5py needed for this process ... aborting'
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| 17 | sys.exit(1)
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| 18 |
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| 19 | data_filename = 'data/20111017_010.fits.gz'
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| 20 | calib_filename = 'data/20111017_006.drs.fits.gz'
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| 21 |
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| 22 | run = RawData(data_filename, calib_filename, return_dict = True, do_calibration=True)
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| 23 | despike = DRSSpikes()
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| 24 | smooth = SlidingAverage(7)
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| 25 | cfd = CFD()
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| 26 |
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| 27 | thr = 3
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| 28 | filter_delay = 3
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| 29 | search_window_size = 12
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| 30 | # shortcut
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| 31 | sws = search_window_size
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| 32 |
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| 33 |
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| 34 | # we try to determine the:
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| 35 | # * Position
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| 36 | # * and Height (in two versions: filtered, and unfiltered data)
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| 37 | # of dark count peaks in:
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| 38 | # a) all events
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| 39 | # b) all pixel
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| 40 | #
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| 41 | # we limit ourselfs to 10 peaks per pixel max!
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| 42 | # and 1000 events max!
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| 43 | #
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| 44 | # So in order to store this stuff, we prepare an n-dim-array
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| 45 |
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| 46 | PEAKS_MAX = 30
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| 47 |
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| 48 | # In order not to allocate so much mem, we use hdf5 files for storing this information
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| 49 | f = h5py.File('20111017_010-006.h5', 'w')
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| 50 |
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| 51 | # we write some meta data into the file, so we know afterwards, where this stuff
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| 52 | # came from
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| 53 | f.attrs['data_file'] = os.path.realpath(data_filename)
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| 54 | f.attrs['calib_file'] = os.path.realpath(calib_filename)
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| 55 | # this script
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| 56 | f.attrs['source_python_script'] = open(sys.argv[0], 'r').read()
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| 57 | # SVN revision, should go here ... but of what? this file??
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| 58 | f.attrs['settings'] = [
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| 59 | 'threshold = 3',
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| 60 | 'SlidingAverage = 7 --> Filter delay = 3'
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| 61 | 'Search Window Size = 12'
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| 62 | 'Maximum Number of Peaks, in data sets = 30'
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| 63 | 'Default value of peak_positions = -1'
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| 64 | 'Default value of peak_height_unfiltered = -np.inf'
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| 65 | 'Default value of peak_height_smoothed = -np.inf'
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| 66 | ]
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| 67 |
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| 68 | result_peak_positions = f.create_dataset('peak_positions',
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| 69 | (run.nevents, run.npix, PEAKS_MAX),
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| 70 | dtype=np.int16,
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| 71 | fillvalue=-1,
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| 72 | compression='gzip')
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| 73 | result_peak_unfiltered_height = f.create_dataset('peak_height_unfiltered',
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| 74 | (run.nevents, run.npix, PEAKS_MAX),
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| 75 | dtype=np.float32,
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| 76 | fillvalue=-np.inf,
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| 77 | compression='gzip')
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| 78 | result_peak_smoothed_height = f.create_dataset('peak_height_smoothed',
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| 79 | (run.nevents, run.npix, PEAKS_MAX),
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| 80 | dtype=np.float32,
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| 81 | fillvalue=-np.inf,
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| 82 | compression='gzip')
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| 83 |
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| 84 | result_baseline = f.create_dataset('baseline',
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| 85 | (run.nevents, run.npix),
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| 86 | dtype=np.float32,
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| 87 |
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| 88 |
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| 89 |
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| 90 | try:
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| 91 |
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| 92 | for event in run:
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| 93 | event_id = event['event_id'].value - 1
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| 94 |
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| 95 | data = event['acal_data']
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| 96 | data = despike(data)
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| 97 | data_orig = data.copy()
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| 98 | data = smooth(data)
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| 99 | filtered = cfd(data)
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| 100 | filtered = smooth(filtered)
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| 101 |
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| 102 | # this is a loop over all pixel of this event
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| 103 | pixel_id = -1
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| 104 | for dat, fil, orig in zip(data, filtered, data_orig):
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| 105 | pixel_id += 1
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| 106 | print event_id, pixel_id
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| 107 |
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| 108 | prod = fil[:-1] * fil[1:]
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| 109 | cand = np.where( prod <= 0)[0]
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| 110 | if len(cand) == 0:
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| 111 | continue
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| 112 | # zero crossing with rising edge
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| 113 | cross = cand[np.where(fil[cand] < 0)[0]]
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| 114 | if len(cross) == 0:
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| 115 | continue
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| 116 | over_thr = cross[np.where(dat[cross-4] > thr)[0]]
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| 117 |
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| 118 | # Now since we have these values, we will throw away all those,
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| 119 | # which are probably on a falling edge of its predecessor
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| 120 | dover = np.diff(over_thr)
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| 121 | if len(dover) == 0:
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| 122 | good = over_thr
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| 123 | else:
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| 124 | good = []
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| 125 | good.append(over_thr[0])
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| 126 | for i in range(len(dover)):
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| 127 | if dover[-i-1] > 100:
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| 128 | good.append(over_thr[-i-1])
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| 129 |
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| 130 | good = np.array(sorted(good))
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| 131 | # these positions, we just found, do not exactly point to the maximum
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| 132 | # of a peak, but the peak will be on the left side of it.
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| 133 | # we use the smoothed data to find the position of the local maximum
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| 134 | # and then stopre this position and the value of both
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| 135 | # the smoothed data and the original data.
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| 136 |
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| 137 | max_pos = np.zeros( good.shape )
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| 138 | max_smoothed = np.zeros( good.shape )
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| 139 | max_orig = np.zeros( good.shape )
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| 140 |
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| 141 | for i in range(len(good)):
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| 142 | # We search for a local maximum in a window of size 12
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| 143 | if len(dat[good[i]-sws:good[i]]) > 0:
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| 144 |
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| 145 | max_pos[i] = good[i]-sws + np.argmax(dat[good[i]-sws:good[i]])
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| 146 | max_smoothed[i] = dat[max_pos[i]]
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| 147 | max_orig[i] = orig[max_pos[i]-filter_delay]
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| 148 | if len(max_pos) > 0:
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| 149 | result_peak_positions[event_id,pixel_id, :len(max_pos)] = max_pos
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| 150 | result_peak_unfiltered_height[event_id,pixel_id, :len(max_pos)] =max_orig
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| 151 | result_peak_smoothed_height[event_id,pixel_id, :len(max_pos)] = max_smoothed
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| 152 |
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| 153 |
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| 154 | f.close()
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| 155 |
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| 156 | except:
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| 157 | print 'An Exception occured .. closing file'
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| 158 | f.close()
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| 159 | raise
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| 160 |
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| 161 |
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| 162 |
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