| 1 | #!/usr/bin/python
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| 2 | #
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| 3 | # Werner Lustermann
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| 4 | # ETH Zurich
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| 5 | #
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| 6 | from ctypes import *
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| 7 | import numpy as np
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| 8 | from scipy import signal
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| 9 |
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| 10 | # get the ROOT stuff + my shared libs
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| 11 | from ROOT import gSystem
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| 12 | # fitslib.so is made from fits.h and is used to access the data
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| 13 | gSystem.Load('~/py/fitslib.so')
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| 14 | from ROOT import *
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| 15 |
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| 16 | class rawdata( object ):
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| 17 | """
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| 18 | raw data access and calibration
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| 19 | - open raw data file and drs calibration file
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| 20 | - performs amplitude calibration
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| 21 | - performs baseline substraction if wanted
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| 22 | - provides all data in an array:
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| 23 | row = number of pixel
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| 24 | col = length of region of interest
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| 25 | """
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| 26 | # constructor of the classe
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| 27 | def __init__( self, dfname, calfname, bslfname='' ):
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| 28 | """
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| 29 | open data file and calibration data file
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| 30 | get basic information about the data in dfname
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| 31 | allocate buffers for data access
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| 32 |
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| 33 | dfname : fits or fits.gz file containing the data including the path
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| 34 | calfname : fits or fits.gz file containing DRS calibration data
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| 35 | bslfname : npy file containing the baseline values
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| 36 | """
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| 37 | self.dfname = dfname
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| 38 | self.calfname = calfname
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| 39 | self.bslfname = bslfname
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| 40 |
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| 41 | # baseline correction: True / False
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| 42 | if len( bslfname ) == 0:
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| 43 | self.correct_baseline = False
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| 44 | else:
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| 45 | self.correct_baseline = True
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| 46 |
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| 47 | # access data file
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| 48 | try:
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| 49 | df = fits( self.dfname )
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| 50 | except IOError:
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| 51 | print 'problem accessing data file: ', dfname
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| 52 | raise # stop ! no data
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| 53 | self.df = df
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| 54 |
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| 55 | # get basic information about the data file
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| 56 | self.NROI = df.GetUInt( 'NROI' ) # region of interest (length of DRS pipeline read out)
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| 57 | self.NPIX = df.GetUInt( 'NPIX' ) # number of pixels (should be 1440)
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| 58 | self.NEvents = df.GetNumRows() # find number of events
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| 59 | # allocate the data memories
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| 60 | self.evNum = c_ulong()
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| 61 | self.trigType = c_ushort()
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| 62 | self.Data = np.zeros( self.NPIX * self.NROI, np.int16 )
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| 63 | self.startCells = np.zeros( self.NPIX, np.int16 )
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| 64 | # set the pointers to the data++
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| 65 | df.SetPtrAddress( 'EventNum', self.evNum )
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| 66 | df.SetPtrAddress( 'TriggerType', self.trigType )
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| 67 | df.SetPtrAddress( 'StartCellData', self.startCells ) # DRS readout start cell
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| 68 | df.SetPtrAddress( 'Data', self.Data ) # this is what you would expect
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| 69 | # df.GetNextRow() # access the first event
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| 70 |
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| 71 | # access calibration file
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| 72 | try:
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| 73 | calf = fits( self.calfname )
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| 74 | except IOError:
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| 75 | print 'problem accessing calibration file: ', calfname
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| 76 | raise
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| 77 | self.calf = calf
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| 78 | #
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| 79 | BaselineMean = calf.GetN('BaselineMean')
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| 80 | GainMean = calf.GetN('GainMean')
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| 81 | TriggerOffsetMean = calf.GetN('TriggerOffsetMean')
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| 82 |
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| 83 | self.blm = np.zeros( BaselineMean, np.float32 )
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| 84 | self.gm = np.zeros( GainMean, np.float32 )
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| 85 | self.tom = np.zeros( TriggerOffsetMean, np.float32 )
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| 86 |
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| 87 | self.Nblm = BaselineMean / self.NPIX
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| 88 | self.Ngm = GainMean / self.NPIX
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| 89 | self.Ntom = TriggerOffsetMean / self.NPIX
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| 90 |
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| 91 | calf.SetPtrAddress( 'BaselineMean', self.blm )
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| 92 | calf.SetPtrAddress( 'GainMean', self.gm )
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| 93 | calf.SetPtrAddress( 'TriggerOffsetMean', self.tom )
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| 94 | calf.GetRow(0)
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| 95 |
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| 96 | self.v_bsl = np.zeros( self.NPIX ) # array with baseline values (all ZERO)
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| 97 | self.smoothData = None
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| 98 | self.maxPos = None
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| 99 | self.maxAmp = None
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| 100 |
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| 101 |
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| 102 | def next( self ):
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| 103 | """
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| 104 | load the next event from disk and calibrate it
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| 105 | """
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| 106 | self.df.GetNextRow()
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| 107 | self.calibrate_drsAmplitude()
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| 108 |
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| 109 |
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| 110 | def calibrate_drsAmplitude( self ):
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| 111 | """
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| 112 | perform amplitude calibration for the event
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| 113 | """
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| 114 | tomV = 2000./4096.
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| 115 | acalData = self.Data * tomV # convert into mV
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| 116 |
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| 117 | # reshape arrays: row = pixel, col = drs_slice
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| 118 | acalData = np.reshape( acalData, (self.NPIX, self.NROI) )
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| 119 | blm = np.reshape( self.blm, (self.NPIX, 1024) )
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| 120 | tom = np.reshape( self.tom, (self.NPIX, 1024) )
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| 121 | gm = np.reshape( self.gm, (self.NPIX, 1024) )
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| 122 |
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| 123 | # print 'acal Data ', acalData.shape
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| 124 | # print 'blm shape ', blm.shape
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| 125 | # print 'gm shape ', gm.shape
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| 126 |
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| 127 | for pixel in range( self.NPIX ):
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| 128 | # rotate the pixel baseline mean to the Data startCell
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| 129 | blm_pixel = np.roll( blm[pixel,:], -self.startCells[pixel] )
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| 130 | acalData[pixel,:] -= blm_pixel[0:self.NROI]
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| 131 | acalData[pixel,:] -= tom[pixel, 0:self.NROI]
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| 132 | acalData[pixel,:] /= gm[pixel, 0:self.NROI]
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| 133 |
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| 134 | self.acalData = acalData * 1907.35
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| 135 |
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| 136 | # print 'acalData ', self.acalData[0:2,0:20]
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| 137 |
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| 138 | def filterSlidingAverage( self , windowSize = 4):
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| 139 | """ sliding average filter
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| 140 | using:
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| 141 | self.acalData
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| 142 | filling array:
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| 143 | self.smoothData
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| 144 | """
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| 145 | #scipy.signal.lfilter(b, a, x, axis=-1, zi=None)
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| 146 | smoothData = self.acalData.copy()
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| 147 | b = np.ones( windowSize )
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| 148 | a = np.zeros( windowSize )
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| 149 | a[0] = len(b)
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| 150 | smoothData[:,:] = signal.lfilter(b, a, smoothData[:,:])
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| 151 |
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| 152 | self.smoothData = smoothData
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| 153 |
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| 154 | def filterCFD( self, length=10, ratio=0.75):
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| 155 | """ constant fraction filter
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| 156 | using:
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| 157 | self.smoothData
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| 158 | filling array:
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| 159 | self.cfdData
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| 160 | """
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| 161 | if self.smoothData == None:
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| 162 | print 'error pyfact.filterCFD was called without prior call to filterSlidingAverage'
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| 163 | print ' variable self.smoothData is needed '
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| 164 | pass
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| 165 | cfdData = self.smoothData.copy()
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| 166 | b = np.zeros( length )
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| 167 | a = np.zeros( length )
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| 168 | b[0] = -1. * ratio
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| 169 | b[length-1] = 1.
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| 170 | a[0] = 1.
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| 171 | cfdData[:,:] = signal.lfilter(b, a, cfdData[:,:])
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| 172 |
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| 173 | self.cfdData = cfdData
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| 174 |
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| 175 | def findPeak (self, min=30, max=250):
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| 176 | """ find maximum in search window
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| 177 | using:
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| 178 | self.smoothData
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| 179 | filling arrays:
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| 180 | self.maxPos
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| 181 | self.maxAmp
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| 182 | """
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| 183 | if self.smoothData == None:
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| 184 | print 'error pyfact.findPeakMax was called without prior call to filterSlidingAverage'
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| 185 | print ' variable self.smoothData is needed '
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| 186 | pass
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| 187 | maxPos = np.argmax( self.smoothData[:,min:max] , 1)
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| 188 | maxAmp = np.max( self.smoothData[:,min:max] , 1)
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| 189 | self.maxPos = maxPos
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| 190 | self.maxAmp = maxAmp
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| 191 |
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| 192 | def sumAroundPeak (self, left=13, right=23):
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| 193 | """ integrate signal in gate around Peak
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| 194 | using:
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| 195 | self.maxPos
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| 196 | self.acalData
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| 197 | filling array:
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| 198 | self.integral
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| 199 | """
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| 200 | if self.maxPos == None:
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| 201 | print 'error pyfact.sumAroundPeak was called without prior call of findPeak'
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| 202 | print ' variable self.maxPos is needed'
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| 203 | pass
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| 204 |
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| 205 | sums = np.empty( self.NPIX )
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| 206 | for pixel in range( self.NPIX ):
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| 207 | min = self.maxPos[pixel]-left
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| 208 | max = self.maxPos[pixel]+right
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| 209 | sums[pixel] = self.acalData[pixel,min:max].sum()
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| 210 |
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| 211 | self.integral = sums
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| 212 |
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| 213 | def ReadBaseline( self, file, bsl_hist = 'bsl_sum/hplt_mean' ):
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| 214 | """
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| 215 | open ROOT file with baseline histogram and read baseline values
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| 216 | file name of the root file
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| 217 | bsl_hist path to the histogram containing the basline values
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| 218 | """
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| 219 | try:
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| 220 | f = TFile( file )
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| 221 | except:
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| 222 | print 'Baseline data file could not be read: ', file
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| 223 | return
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| 224 |
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| 225 | h = f.Get( bsl_hist )
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| 226 |
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| 227 | for i in range( self.NPIX ):
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| 228 | self.v_bsl[i] = h.GetBinContent( i+1 )
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| 229 |
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| 230 | f.Close()
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| 231 |
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| 232 |
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| 233 | def CorrectBaseline( self ):
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| 234 | """
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| 235 | apply baseline correction
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| 236 | """
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| 237 | for pixel in range( self.NPIX ):
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| 238 | self.acalData[pixel,:] -= self.v_bsl[pixel]
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| 239 |
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| 240 |
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| 241 | def info( self ):
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| 242 | """
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| 243 | print information
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| 244 | """
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| 245 | print 'data file: ', dfname
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| 246 | print 'calib file: ', calfname
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| 247 | print 'calibration file'
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| 248 | print 'N BaselineMean: ', self.Nblm
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| 249 | print 'N GainMean: ', self.Ngm
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| 250 | print 'N TriggeroffsetMean: ', self.Ntom
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| 251 |
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| 252 | # --------------------------------------------------------------------------------
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| 253 | class fnames( object ):
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| 254 | """ organize file names of a FACT data run
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| 255 |
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| 256 | """
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| 257 |
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| 258 | def __init__( self, specifier = ['012', '023', '2011', '11', '24'],
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| 259 | rpath = '/scratch_nfs/bsl/',
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| 260 | zipped = True):
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| 261 | """
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| 262 | specifier : list of strings defined as:
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| 263 | [ 'DRS calibration file', 'Data file', 'YYYY', 'MM', 'DD']
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| 264 |
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| 265 | rpath : directory path for the results; YYYYMMDD will be appended to rpath
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| 266 | zipped : use zipped (True) or unzipped (Data)
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| 267 | """
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| 268 | self.specifier = specifier
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| 269 | self.rpath = rpath
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| 270 | self.zipped = zipped
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| 271 |
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| 272 | self.make( self.specifier, self.rpath, self.zipped )
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| 273 | # end of def __init__
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| 274 |
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| 275 | def make( self, specifier, rpath, zipped ):
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| 276 | """ create (make) the filenames
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| 277 |
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| 278 | names : dictionary of filenames, tags { 'data', 'drscal', 'results' }
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| 279 | data : name of the data file
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| 280 | drscal : name of the drs calibration file
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| 281 | results : radikal of file name(s) for results (to be completed by suffixes)
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| 282 | """
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| 283 |
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| 284 | self.specifier = specifier
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| 285 |
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| 286 | if zipped:
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| 287 | dpath = '/data00/fact-construction/raw/'
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| 288 | ext = '.fits.gz'
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| 289 | else:
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| 290 | dpath = '/data03/fact-construction/raw/'
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| 291 | ext = '.fits'
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| 292 |
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| 293 | year = specifier[2]
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| 294 | month = specifier[3]
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| 295 | day = specifier[4]
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| 296 |
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| 297 | yyyymmdd = year + month + day
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| 298 | dfile = specifier[1]
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| 299 | cfile = specifier[0]
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| 300 |
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| 301 | rpath = rpath + yyyymmdd + '/'
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| 302 | self.rpath = rpath
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| 303 | self.names = {}
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| 304 |
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| 305 | tmp = dpath + year + '/' + month + '/' + day + '/' + yyyymmdd + '_'
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| 306 | self.names['data'] = tmp + dfile + ext
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| 307 | self.names['drscal'] = tmp + cfile + '.drs' + ext
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| 308 | self.names['results'] = rpath + yyyymmdd + '_' + dfile + '_' + cfile
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| 309 |
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| 310 | self.data = self.names['data']
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| 311 | self.drscal = self.names['drscal']
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| 312 | self.results = self.names['results']
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| 313 |
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| 314 | # end of make
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| 315 |
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| 316 | def info( self ):
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| 317 | """ print complete filenames
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| 318 |
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| 319 | """
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| 320 |
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| 321 | print 'file names:'
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| 322 | print 'data: ', self.names['data']
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| 323 | print 'drs-cal: ', self.names['drscal']
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| 324 | print 'results: ', self.names['results']
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| 325 | # end of def info
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| 326 |
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| 327 | # end of class definition: fnames( object )
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| 328 |
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| 329 |
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| 330 |
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| 331 | class histogramList( object ):
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| 332 |
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| 333 | def __init__( self, name ):
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| 334 | """ set the name and create empty lists """
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| 335 | self.name = name # name of the list
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| 336 | self.list = [] # list of the histograms
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| 337 | self.dict = {} # dictionary of histograms
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| 338 | self.hList = TObjArray() # list a la ROOT of the histograms
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| 339 |
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| 340 | def add( self, tag, h ):
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| 341 | self.list.append( h )
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| 342 | self.dict[tag] = h
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| 343 | self.hList.Add( h )
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| 344 |
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| 345 |
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| 346 | class pixelHisto1d ( object ):
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| 347 |
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| 348 | def __init__( self, name, title, Nbin, first, last, xtitle, ytitle, NPIX ):
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| 349 | """
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| 350 | book one dimensional histograms for each pixel
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| 351 | """
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| 352 | self.name = name
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| 353 |
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| 354 | self.list = [ x for x in range( NPIX ) ]
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| 355 | self.hList = TObjArray()
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| 356 |
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| 357 | for pixel in range( NPIX ):
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| 358 |
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| 359 | hname = name + ' ' + str( pixel )
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| 360 | htitle = title + ' ' + str( pixel )
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| 361 | self.list[pixel] = TH1F( hname, htitle, Nbin, first, last )
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| 362 |
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| 363 | self.list[pixel].GetXaxis().SetTitle( xtitle )
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| 364 | self.list[pixel].GetYaxis().SetTitle( ytitle )
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| 365 | self.hList.Add( self.list[pixel] )
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| 366 |
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| 367 | # simple test method
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| 368 | if __name__ == '__main__':
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| 369 | """
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| 370 | create an instance
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| 371 | """
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| 372 | dfname = '/data03/fact-construction/raw/2011/11/24/20111124_121.fits'
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| 373 | calfname = '/data03/fact-construction/raw/2011/11/24/20111124_111.drs.fits'
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| 374 | rd = rawdata( dfname, calfname )
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| 375 | rd.info()
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| 376 | rd.next()
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| 377 |
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| 378 | # for i in range(10):
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| 379 | # df.GetNextRow()
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| 380 |
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| 381 | # print 'evNum: ', evNum.value
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| 382 | # print 'startCells[0:9]: ', startCells[0:9]
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| 383 | # print 'evData[0:9]: ', evData[0:9]
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