| 1 | #!/usr/bin/python -tt
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| 2 | #
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| 3 | # Werner Lustermann, Dominik Neise
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| 4 | # ETH Zurich, TU Dortmund
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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 | import pprint # for SlowData
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| 9 | from scipy import signal
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| 10 |
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| 11 | # get the ROOT stuff + my shared libs
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| 12 | from ROOT import gSystem
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| 13 | # factfits_h.so is made from factfits.h and is used to access the data
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| 14 | # make sure the location of factfits_h.so is in LD_LIBRARY_PATH.
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| 15 | # having it in PYTHONPATH is *not* sufficient
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| 16 | gSystem.Load('factfits_h.so')
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| 17 | from ROOT import *
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| 18 |
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| 19 | class RawDataFeeder( object ):
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| 20 | """ Wrapper class for RawData class
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| 21 | capable of iterating over multiple RawData Files
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| 22 | """
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| 23 |
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| 24 | def __init__(self, filelist):
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| 25 | """ *filelist* list of files to iterate over
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| 26 | the list should contain tuples, or sublists of two filenames
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| 27 | the first should be a data file (\*.fits.gz)
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| 28 | the second should be an amplitude calibration file(\*.drs.fits.gz)
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| 29 | """
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| 30 | # sanity check for input
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| 31 | if type(filelist) != type(list()):
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| 32 | raise TypeError('filelist should be a list')
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| 33 | for entry in filelist:
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| 34 | if len(entry) != 2:
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| 35 | raise TypeError('the entries of filelist should have length == 2')
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| 36 | for path in entry:
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| 37 | if type(path) != type(str()):
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| 38 | raise TypeError('the entries of filelist should be path, i.e. of type str()')
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| 39 | #todo check if 'path' is a valid path
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| 40 | # else: throw an Exception, or Warning?
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| 41 |
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| 42 | self.filelist = filelist
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| 43 | self._current_RawData = RawData(filelist[0][0], filelist[0][1], return_dict=True)
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| 44 | del filelist[0]
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| 45 |
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| 46 | def __iter__(self):
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| 47 | return self
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| 48 |
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| 49 | def next():
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| 50 | """ Method being called by the iterator.
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| 51 | Since the RawData Objects are simply looped over, the event_id from the
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| 52 | RawData object will not be unique.
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| 53 | Each RawData obejct will start with event_id = 1 as usual.
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| 54 | """
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| 55 | try:
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| 56 | return self._current_RawData.next()
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| 57 | except StopIteration:
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| 58 | # current_RawData was completely processed
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| 59 | # delete it (I hope this calls the destructor of the fits file and/or closes it)
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| 60 | del self._current_RawData
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| 61 | # and remake it, if possible
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| 62 | if len(self.filelist) > 0:
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| 63 | self._current_RawData = RawData(filelist[0][0], filelist[0][1], return_dict=True)
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| 64 | del filelist[0]
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| 65 | else:
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| 66 | raise
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| 67 |
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| 68 |
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| 69 |
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| 70 | class RawData( object ):
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| 71 | """ raw data access and calibration
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| 72 |
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| 73 | - open raw data file and drs calibration file
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| 74 | - performs amplitude calibration
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| 75 | - performs baseline substraction if wanted
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| 76 | - provides all data in an array:
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| 77 | row = number of pixel
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| 78 | col = length of region of interest
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| 79 |
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| 80 | """
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| 81 |
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| 82 |
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| 83 | def __init__(self, data_file_name, calib_file_name,
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| 84 | user_action_calib=lambda acal_data, data, blm, tom, gm, scells, nroi: None,
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| 85 | baseline_file_name='',
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| 86 | return_dict = None,
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| 87 | do_calibration = True):
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| 88 | """ initialize object
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| 89 |
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| 90 | open data file and calibration data file
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| 91 | get basic information about the data in data_file_name
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| 92 | allocate buffers for data access
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| 93 |
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| 94 | data_file_name : fits or fits.gz file of the data including the path
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| 95 | calib_file_name : fits or fits.gz file containing DRS calibration data
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| 96 | baseline_file_name : npy file containing the baseline values
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| 97 | """
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| 98 | self.__module__='pyfact'
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| 99 | # manual implementation of default value, but I need to find out
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| 100 | # if the user of this class is aware of the new option
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| 101 | if return_dict == None:
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| 102 | print 'Warning: Rawdata.__init__() has a new option "return_dict"'
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| 103 | print 'the default value of this option is False, so nothing changes for you at the moment.'
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| 104 | print
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| 105 | print 'you probably want, to get a dictionary out of the next() method anyway'
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| 106 | print ' so please change your scripts and set this option to True, for the moment'
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| 107 | print 'e.g. like this: run = RawData(data_filename, calib_filename, return_dict = True)'
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| 108 | print "after a while, the default value, will turn to True .. so you don't have to give the option anymore"
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| 109 | print 'and some time later, the option will not be supported anymore'
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| 110 | return_dict = False
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| 111 | self.return_dict = return_dict
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| 112 |
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| 113 | self.do_calibration = do_calibration
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| 114 |
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| 115 | self.data_file_name = data_file_name
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| 116 | self.calib_file_name = calib_file_name
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| 117 | self.baseline_file_name = baseline_file_name
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| 118 |
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| 119 | self.user_action_calib = user_action_calib
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| 120 |
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| 121 | # baseline correction: True / False
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| 122 | if len(baseline_file_name) == 0:
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| 123 | self.correct_baseline = False
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| 124 | else:
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| 125 | self.correct_baseline = True
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| 126 |
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| 127 | # access data file
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| 128 | try:
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| 129 | data_file = FactFits(self.data_file_name)
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| 130 | except IOError:
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| 131 | print 'problem accessing data file: ', data_file_name
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| 132 | raise # stop ! no data
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| 133 | #: data file (fits object)
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| 134 | self.data_file = data_file
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| 135 |
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| 136 | # get basic information about the data file
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| 137 | #: region of interest (number of DRS slices read)
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| 138 | self.nroi = data_file.GetUInt('NROI')
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| 139 | #: number of pixels (should be 1440)
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| 140 | self.npix = data_file.GetUInt('NPIX')
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| 141 | #: number of events in the data run
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| 142 | self.nevents = data_file.GetNumRows()
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| 143 |
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| 144 | # allocate the data memories
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| 145 | self.event_id = c_ulong()
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| 146 | self.trigger_type = c_ushort()
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| 147 | #: 1D array with raw data
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| 148 | self.data = np.zeros( self.npix * self.nroi, np.int16 ).reshape(self.npix ,self.nroi)
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| 149 | #: slice where drs readout started
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| 150 | self.start_cells = np.zeros( self.npix, np.int16 )
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| 151 | #: time when the FAD was triggered, in some strange units...
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| 152 | self.board_times = np.zeros( 40, np.int32 )
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| 153 |
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| 154 | # set the pointers to the data++
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| 155 | data_file.SetPtrAddress('EventNum', self.event_id)
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| 156 | data_file.SetPtrAddress('TriggerType', self.trigger_type)
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| 157 | data_file.SetPtrAddress('StartCellData', self.start_cells)
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| 158 | data_file.SetPtrAddress('Data', self.data)
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| 159 | data_file.SetPtrAddress('BoardTime', self.board_times)
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| 160 |
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| 161 | # open the calibration file
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| 162 | try:
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| 163 | calib_file = FactFits(self.calib_file_name)
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| 164 | except IOError:
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| 165 | print 'problem accessing calibration file: ', calib_file_name
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| 166 | raise
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| 167 | #: drs calibration file
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| 168 | self.calib_file = calib_file
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| 169 |
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| 170 | baseline_mean = calib_file.GetN('BaselineMean')
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| 171 | gain_mean = calib_file.GetN('GainMean')
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| 172 | trigger_offset_mean = calib_file.GetN('TriggerOffsetMean')
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| 173 |
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| 174 | self.Nblm = baseline_mean / self.npix
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| 175 | self.Ngm = gain_mean / self.npix
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| 176 | self.Ntom = trigger_offset_mean / self.npix
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| 177 |
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| 178 | self.blm = np.zeros(baseline_mean, np.float32).reshape(self.npix , self.Nblm)
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| 179 | self.gm = np.zeros(gain_mean, np.float32).reshape(self.npix , self.Ngm)
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| 180 | self.tom = np.zeros(trigger_offset_mean, np.float32).reshape(self.npix , self.Ntom)
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| 181 |
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| 182 | calib_file.SetPtrAddress('BaselineMean', self.blm)
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| 183 | calib_file.SetPtrAddress('GainMean', self.gm)
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| 184 | calib_file.SetPtrAddress('TriggerOffsetMean', self.tom)
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| 185 | calib_file.GetRow(0)
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| 186 |
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| 187 | # make calibration constants double, so we never need to roll
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| 188 | self.blm = np.hstack((self.blm, self.blm))
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| 189 | self.gm = np.hstack((self.gm, self.gm))
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| 190 | self.tom = np.hstack((self.tom, self.tom))
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| 191 |
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| 192 | self.v_bsl = np.zeros(self.npix) # array of baseline values (all ZERO)
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| 193 |
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| 194 | def __iter__(self):
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| 195 | """ iterator """
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| 196 | return self
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| 197 |
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| 198 | def __add__(self, jump_over):
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| 199 | self.data_file.GetRow(jump_over)
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| 200 | return self
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| 201 |
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| 202 | def next(self):
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| 203 | """ used by __iter__ """
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| 204 | if self.data_file.GetNextRow() == False:
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| 205 | raise StopIteration
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| 206 | else:
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| 207 | if self.do_calibration == True:
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| 208 | self.calibrate_drs_amplitude()
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| 209 |
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| 210 | #print 'nevents = ', self.nevents, 'event_id = ', self.event_id.value
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| 211 | if self.return_dict:
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| 212 | return self.__dict__
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| 213 | else:
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| 214 | return self.acal_data, self.start_cells, self.trigger_type.value
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| 215 |
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| 216 | def next_event(self):
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| 217 | """ load the next event from disk and calibrate it
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| 218 | """
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| 219 | self.data_file.GetNextRow()
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| 220 | self.calibrate_drs_amplitude()
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| 221 |
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| 222 | def calibrate_drs_amplitude(self):
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| 223 | """ perform the drs amplitude calibration of the event data
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| 224 |
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| 225 | """
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| 226 | # shortcuts
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| 227 | blm = self.blm
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| 228 | gm = self.gm
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| 229 | tom = self.tom
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| 230 |
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| 231 | to_mV = 2000./4096.
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| 232 | #: 2D array with amplitude calibrated dat in mV
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| 233 | acal_data = self.data * to_mV # convert ADC counts to mV
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| 234 |
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| 235 |
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| 236 | for pixel in range( self.npix ):
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| 237 | #shortcuts
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| 238 | sc = self.start_cells[pixel]
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| 239 | roi = self.nroi
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| 240 | # rotate the pixel baseline mean to the Data startCell
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| 241 | acal_data[pixel,:] -= blm[pixel,sc:sc+roi]
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| 242 | # the 'trigger offset mean' does not need to be rolled
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| 243 | # on the contrary, it seems there is an offset in the DRS data,
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| 244 | # which is related to its distance to the startCell, not to its
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| 245 | # distance to the beginning of the physical pipeline in the DRS chip
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| 246 | acal_data[pixel,:] -= tom[pixel,0:roi]
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| 247 | # rotate the pixel gain mean to the Data startCell
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| 248 | acal_data[pixel,:] /= gm[pixel,sc:sc+roi]
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| 249 |
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| 250 |
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| 251 | self.acal_data = acal_data * 1907.35
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| 252 |
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| 253 | self.user_action_calib( self.acal_data,
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| 254 | np.reshape(self.data, (self.npix, self.nroi) ), blm, tom, gm, self.start_cells, self.nroi)
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| 255 |
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| 256 |
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| 257 | def baseline_read_values(self, file, bsl_hist='bsl_sum/hplt_mean'):
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| 258 | """
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| 259 |
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| 260 | open ROOT file with baseline histogram and read baseline values
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| 261 | file name of the root file
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| 262 | bsl_hist path to the histogram containing the basline values
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| 263 |
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| 264 | """
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| 265 |
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| 266 | try:
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| 267 | f = TFile(file)
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| 268 | except:
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| 269 | print 'Baseline data file could not be read: ', file
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| 270 | return
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| 271 |
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| 272 | h = f.Get(bsl_hist)
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| 273 |
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| 274 | for i in range(self.npix):
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| 275 | self.v_bsl[i] = h.GetBinContent(i+1)
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| 276 |
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| 277 | f.Close()
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| 278 |
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| 279 | def baseline_correct(self):
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| 280 | """ subtract baseline from the data
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| 281 |
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| 282 | """
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| 283 |
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| 284 | for pixel in range(self.npix):
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| 285 | self.acal_data[pixel,:] -= self.v_bsl[pixel]
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| 286 |
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| 287 | def info(self):
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| 288 | """ print run information
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| 289 |
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| 290 | """
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| 291 |
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| 292 | print 'data file: ', data_file_name
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| 293 | print 'calib file: ', calib_file_name
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| 294 | print 'calibration file'
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| 295 | print 'N baseline_mean: ', self.Nblm
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| 296 | print 'N gain mean: ', self.Ngm
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| 297 | print 'N TriggeroffsetMean: ', self.Ntom
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| 298 |
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| 299 | # -----------------------------------------------------------------------------
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| 300 | class RawDataFake( object ):
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| 301 | """ raw data FAKE access similar to real RawData access
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| 302 | """
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| 303 |
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| 304 |
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| 305 | def __init__(self, data_file_name, calib_file_name,
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| 306 | user_action_calib=lambda acal_data, data, blm, tom, gm, scells, nroi: None,
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| 307 | baseline_file_name=''):
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| 308 | self.__module__='pyfact'
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| 309 |
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| 310 | self.nroi = 300
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| 311 | self.npix = 9
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| 312 | self.nevents = 1000
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| 313 |
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| 314 | self.simulator = None
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| 315 |
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| 316 | self.time = np.ones(1024) * 0.5
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| 317 |
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| 318 |
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| 319 | self.event_id = c_ulong(0)
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| 320 | self.trigger_type = c_ushort(4)
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| 321 | self.data = np.zeros( self.npix * self.nroi, np.int16 ).reshape(self.npix ,self.nroi)
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| 322 | self.start_cells = np.zeros( self.npix, np.int16 )
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| 323 | self.board_times = np.zeros( 40, np.int32 )
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| 324 | def __iter__(self):
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| 325 | """ iterator """
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| 326 | return self
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| 327 |
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| 328 | def next(self):
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| 329 | """ used by __iter__ """
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| 330 | self.event_id = c_ulong(self.event_id.value + 1)
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| 331 | self.board_times = self.board_times + 42
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| 332 |
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| 333 | if self.event_id.value >= self.nevents:
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| 334 | raise StopIteration
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| 335 | else:
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| 336 | self._make_event_data()
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| 337 |
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| 338 | return self.__dict__
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| 339 |
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| 340 | def _make_event_data(self):
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| 341 | sample_times = self.time.cumsum() - time[0]
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| 342 |
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| 343 | # random start cell
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| 344 | self.start_cells = np.ones( self.npix, np.int16 ) * np.random.randint(0,1024)
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| 345 |
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| 346 | starttime = self.start_cells[0]
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| 347 |
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| 348 | signal = self._std_sinus_simu(sample_times, starttime)
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| 349 |
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| 350 | data = np.vstack( (signal,signal) )
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| 351 | for i in range(8):
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| 352 | data = np.vstack( (data,signal) )
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| 353 |
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| 354 | self.data = data
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| 355 |
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| 356 | def _std_sinus_simu(self, times, starttime):
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| 357 | period = 10 # in ns
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| 358 |
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| 359 | # give a jitter on starttime
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| 360 | starttime = np.random.normal(startime, 0.05)
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| 361 |
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| 362 | phase = 0.0
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| 363 | signal = 10 * np.sin(times * 2*np.pi/period + starttime + phase)
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| 364 |
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| 365 | # add some noise
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| 366 | noise = np.random.normal(0.0, 0.5, signal.shape)
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| 367 | signal += noise
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| 368 | return signal
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| 369 |
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| 370 | def info(self):
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| 371 | """ print run information
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| 372 |
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| 373 | """
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| 374 |
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| 375 | print 'data file: ', data_file_name
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| 376 | print 'calib file: ', calib_file_name
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| 377 | print 'calibration file'
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| 378 | print 'N baseline_mean: ', self.Nblm
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| 379 | print 'N gain mean: ', self.Ngm
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| 380 | print 'N TriggeroffsetMean: ', self.Ntom
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| 381 |
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| 382 | # -----------------------------------------------------------------------------
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| 383 |
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| 384 | class SlowData( FactFits ):
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| 385 | """ -Fact SlowData File-
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| 386 | A Python wrapper for the fits-class implemented in pyfits.h
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| 387 | provides easy access to the fits file meta data.
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| 388 | * dictionary of file metadata - self.meta
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| 389 | * dict of table metadata - self.columns
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| 390 | * variable table column access, thus possibly increased speed while looping
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| 391 | """
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| 392 | def __init__(self, path):
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| 393 | """ creates meta and columns dictionaries
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| 394 | """
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| 395 | self.path = path
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| 396 | try:
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| 397 | FactFits.__init__(self,path)
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| 398 | except IOError:
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| 399 | print 'problem accessing data file: ', data_file_name
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| 400 | raise # stop ! no data
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| 401 |
|
|---|
| 402 | self.meta = self._make_meta_dict()
|
|---|
| 403 | self.columns = self._make_columns_dict()
|
|---|
| 404 |
|
|---|
| 405 | self.treat_meta_dict()
|
|---|
| 406 |
|
|---|
| 407 |
|
|---|
| 408 | # list of columns, which are already registered
|
|---|
| 409 | # see method register()
|
|---|
| 410 | self._registered_cols = []
|
|---|
| 411 | # dict of column data, this is used, in order to be able to remove
|
|---|
| 412 | # the ctypes of
|
|---|
| 413 | self._table_cols = {}
|
|---|
| 414 |
|
|---|
| 415 | # I need to count the rows, since the normal loop mechanism seems not to work.
|
|---|
| 416 | self._current_row = 0
|
|---|
| 417 |
|
|---|
| 418 | self.stacked_cols = {}
|
|---|
| 419 |
|
|---|
| 420 | def _make_meta_dict(self):
|
|---|
| 421 | """ This method retrieves meta information about the fits file and
|
|---|
| 422 | stores this information in a dict
|
|---|
| 423 | return: dict
|
|---|
| 424 | key: string - all capital letters
|
|---|
| 425 | value: tuple( numerical value, string comment)
|
|---|
| 426 | """
|
|---|
| 427 | # intermediate variables for file metadata dict generation
|
|---|
| 428 | keys=self.GetPy_KeyKeys()
|
|---|
| 429 | values=self.GetPy_KeyValues()
|
|---|
| 430 | comments=self.GetPy_KeyComments()
|
|---|
| 431 | types=self.GetPy_KeyTypes()
|
|---|
| 432 |
|
|---|
| 433 | if len(keys) != len(values):
|
|---|
| 434 | raise TypeError('len(keys)',len(keys),' != len(values)', len(values))
|
|---|
| 435 | if len(keys) != len(types):
|
|---|
| 436 | raise TypeError('len(keys)',len(keys),' != len(types)', len(types))
|
|---|
| 437 | if len(keys) != len(comments):
|
|---|
| 438 | raise TypeError('len(keys)',len(keys),' != len(comments)', len(comments))
|
|---|
| 439 |
|
|---|
| 440 | meta_dict = {}
|
|---|
| 441 | for i in range(len(keys)):
|
|---|
| 442 | type = types[i]
|
|---|
| 443 | if type == 'I':
|
|---|
| 444 | value = int(values[i])
|
|---|
| 445 | elif type == 'F':
|
|---|
| 446 | value = float(values[i])
|
|---|
| 447 | elif type == 'B':
|
|---|
| 448 | if values[i] == 'T':
|
|---|
| 449 | value = True
|
|---|
| 450 | elif values[i] == 'F':
|
|---|
| 451 | value = False
|
|---|
| 452 | else:
|
|---|
| 453 | raise TypeError("meta-type is 'B', but meta-value is neither 'T' nor 'F'. meta-value:",values[i])
|
|---|
| 454 | elif type == 'T':
|
|---|
| 455 | value = values[i]
|
|---|
| 456 | else:
|
|---|
| 457 | raise TypeError("unknown meta-type: known meta types are: I,F,B and T. meta-type:",type)
|
|---|
| 458 | meta_dict[keys[i]]=(value, comments[i])
|
|---|
| 459 | return meta_dict
|
|---|
| 460 |
|
|---|
| 461 |
|
|---|
| 462 | def _make_columns_dict(self):
|
|---|
| 463 | """ This method retrieves information about the columns
|
|---|
| 464 | stored inside the fits files internal binary table.
|
|---|
| 465 | returns: dict
|
|---|
| 466 | key: string column name -- all capital letters
|
|---|
| 467 | values: tuple(
|
|---|
| 468 | number of elements in table field - integer
|
|---|
| 469 | size of element in bytes -- this is not really interesting for any user
|
|---|
| 470 | might be ommited in future versions
|
|---|
| 471 | type - a single character code -- should be translated into
|
|---|
| 472 | a comrehensible word
|
|---|
| 473 | unit - string like 'mV' or 'ADC count'
|
|---|
| 474 | """
|
|---|
| 475 | # intermediate variables for file table-metadata dict generation
|
|---|
| 476 | keys=self.GetPy_ColumnKeys()
|
|---|
| 477 | #offsets=self.GetPy_ColumnOffsets() #not needed on python level...
|
|---|
| 478 | nums=self.GetPy_ColumnNums()
|
|---|
| 479 | sizes=self.GetPy_ColumnSizes()
|
|---|
| 480 | types=self.GetPy_ColumnTypes()
|
|---|
| 481 | units=self.GetPy_ColumnUnits()
|
|---|
| 482 |
|
|---|
| 483 | # zip the values
|
|---|
| 484 | values = zip(nums,sizes,types,units)
|
|---|
| 485 | # create the columns dictionary
|
|---|
| 486 | columns = dict(zip(keys ,values))
|
|---|
| 487 | return columns
|
|---|
| 488 |
|
|---|
| 489 | def stack(self, on=True):
|
|---|
| 490 | self.next()
|
|---|
| 491 | for col in self._registered_cols:
|
|---|
| 492 | if isinstance( self.dict[col], type(np.array('')) ):
|
|---|
| 493 | self.stacked_cols[col] = self.dict[col]
|
|---|
| 494 | else:
|
|---|
| 495 | # elif isinstance(self.dict[col], ctypes._SimpleCData):
|
|---|
| 496 | self.stacked_cols[col] = np.array(self.dict[col])
|
|---|
| 497 | # else:
|
|---|
| 498 | # raise TypeError("I don't know how to stack "+col+". It is of type: "+str(type(self.dict[col])))
|
|---|
| 499 |
|
|---|
| 500 | def register(self, input_str):
|
|---|
| 501 | columns = self.columns
|
|---|
| 502 | if input_str.lower() == 'all':
|
|---|
| 503 | for col in columns:
|
|---|
| 504 | self._register(col)
|
|---|
| 505 | else:
|
|---|
| 506 | #check if colname is in columns:
|
|---|
| 507 | if input_str not in columns:
|
|---|
| 508 | error_msg = 'colname:'+ input_str +' is not a column in the binary table.\n'
|
|---|
| 509 | error_msg+= 'possible colnames are\n'
|
|---|
| 510 | for key in columns:
|
|---|
| 511 | error_msg += key+'\n'
|
|---|
| 512 | raise KeyError(error_msg)
|
|---|
| 513 | else:
|
|---|
| 514 | self._register(input_str)
|
|---|
| 515 |
|
|---|
| 516 | # 'private' method, do not use
|
|---|
| 517 | def _register( self, colname):
|
|---|
| 518 | columns = self.columns
|
|---|
| 519 | local = None
|
|---|
| 520 |
|
|---|
| 521 | number_of_elements = int(columns[colname][0])
|
|---|
| 522 | size_of_elements_in_bytes = int(columns[colname][1])
|
|---|
| 523 | ctypecode_of_elements = columns[colname][2]
|
|---|
| 524 | physical_unit_of_elements = columns[colname][3]
|
|---|
| 525 |
|
|---|
| 526 | # snippet from the C++ source code, or header file to be precise:
|
|---|
| 527 | #case 'L': gLog << "bool(8)"; break;
|
|---|
| 528 | #case 'B': gLog << "byte(8)"; break;
|
|---|
| 529 | #case 'I': gLog << "short(16)"; break;
|
|---|
| 530 | #case 'J': gLog << "int(32)"; break;
|
|---|
| 531 | #case 'K': gLog << "int(64)"; break;
|
|---|
| 532 | #case 'E': gLog << "float(32)"; break;
|
|---|
| 533 | #case 'D': gLog << "double(64)"; break;
|
|---|
| 534 |
|
|---|
| 535 |
|
|---|
| 536 |
|
|---|
| 537 | # the fields inside the columns can either contain single numbers,
|
|---|
| 538 | # or whole arrays of numbers as well.
|
|---|
| 539 | # we treat single elements differently...
|
|---|
| 540 | if number_of_elements == 1:
|
|---|
| 541 | # allocate some memory for a single number according to its type
|
|---|
| 542 | if ctypecode_of_elements == 'J': # J is for a 4byte int, i.e. an unsigned long
|
|---|
| 543 | local = ctypes.c_ulong()
|
|---|
| 544 | un_c_type = long
|
|---|
| 545 | elif ctypecode_of_elements == 'I': # I is for a 2byte int, i.e. an unsinged int
|
|---|
| 546 | local = ctypes.c_ushort()
|
|---|
| 547 | un_c_type = int
|
|---|
| 548 | elif ctypecode_of_elements == 'B': # B is for a byte
|
|---|
| 549 | local = ctypes.c_ubyte()
|
|---|
| 550 | un_c_type = int
|
|---|
| 551 | elif ctypecode_of_elements == 'D':
|
|---|
| 552 | local = ctypes.c_double()
|
|---|
| 553 | un_c_type = float
|
|---|
| 554 | elif ctypecode_of_elements == 'E':
|
|---|
| 555 | local = ctypes.c_float()
|
|---|
| 556 | un_c_type = float
|
|---|
| 557 | elif ctypecode_of_elements == 'A':
|
|---|
| 558 | local = ctypes.c_uchar()
|
|---|
| 559 | un_c_type = chr
|
|---|
| 560 | elif ctypecode_of_elements == 'K':
|
|---|
| 561 | local = ctypes.c_ulonglong()
|
|---|
| 562 | un_c_type = long
|
|---|
| 563 | else:
|
|---|
| 564 | raise TypeError('unknown ctypecode_of_elements:',ctypecode_of_elements)
|
|---|
| 565 | else:
|
|---|
| 566 | if ctypecode_of_elements == 'B': # B is for a byte
|
|---|
| 567 | nptype = np.int8
|
|---|
| 568 | elif ctypecode_of_elements == 'A': # A is for a char .. but I don't know how to handle it
|
|---|
| 569 | nptype = np.int8
|
|---|
| 570 | elif ctypecode_of_elements == 'I': # I is for a 2byte int
|
|---|
| 571 | nptype = np.int16
|
|---|
| 572 | elif ctypecode_of_elements == 'J': # J is for a 4byte int
|
|---|
| 573 | nptype = np.int32
|
|---|
| 574 | elif ctypecode_of_elements == 'K': # B is for a byte
|
|---|
| 575 | nptype = np.int64
|
|---|
| 576 | elif ctypecode_of_elements == 'E': # B is for a byte
|
|---|
| 577 | nptype = np.float32
|
|---|
| 578 | elif ctypecode_of_elements == 'D': # B is for a byte
|
|---|
| 579 | nptype = np.float64
|
|---|
| 580 | else:
|
|---|
| 581 | raise TypeError('unknown ctypecode_of_elements:',ctypecode_of_elements)
|
|---|
| 582 | local = np.zeros( number_of_elements, nptype)
|
|---|
| 583 |
|
|---|
| 584 | # Set the Pointer Address
|
|---|
| 585 | self.SetPtrAddress(colname, local)
|
|---|
| 586 | self._table_cols[colname] = local
|
|---|
| 587 | if number_of_elements > 1:
|
|---|
| 588 | self.__dict__[colname] = local
|
|---|
| 589 | self.dict[colname] = local
|
|---|
| 590 | else:
|
|---|
| 591 | # remove any traces of ctypes:
|
|---|
| 592 | self.__dict__[colname] = local.value
|
|---|
| 593 | self.dict[colname] = local.value
|
|---|
| 594 | self._registered_cols.append(colname)
|
|---|
| 595 |
|
|---|
| 596 |
|
|---|
| 597 | def treat_meta_dict(self):
|
|---|
| 598 | """make 'interesting' meta information available like normal members.
|
|---|
| 599 | non interesting are:
|
|---|
| 600 | TFORM, TUNIT, and TTYPE
|
|---|
| 601 | since these are available via the columns dict.
|
|---|
| 602 | """
|
|---|
| 603 |
|
|---|
| 604 | self.number_of_rows = self.meta['NAXIS2'][0]
|
|---|
| 605 | self.number_of_columns = self.meta['TFIELDS'][0]
|
|---|
| 606 |
|
|---|
| 607 | # there are some information in the meta dict, which are alsways there:
|
|---|
| 608 | # there are regarded as not interesting:
|
|---|
| 609 | uninteresting_meta = {}
|
|---|
| 610 | uninteresting_meta['arraylike'] = {}
|
|---|
| 611 | uninteresting = ['NAXIS', 'NAXIS1', 'NAXIS2',
|
|---|
| 612 | 'TFIELDS',
|
|---|
| 613 | 'XTENSION','EXTNAME','EXTREL',
|
|---|
| 614 | 'BITPIX', 'PCOUNT', 'GCOUNT',
|
|---|
| 615 | 'ORIGIN',
|
|---|
| 616 | 'PACKAGE', 'COMPILED', 'CREATOR',
|
|---|
| 617 | 'TELESCOP','TIMESYS','TIMEUNIT','VERSION']
|
|---|
| 618 | for key in uninteresting:
|
|---|
| 619 | if key in self.meta:
|
|---|
| 620 | uninteresting_meta[key]=self.meta[key]
|
|---|
| 621 | del self.meta[key]
|
|---|
| 622 |
|
|---|
| 623 | # the table meta data contains
|
|---|
| 624 |
|
|---|
| 625 |
|
|---|
| 626 | # shortcut to access the meta dict. But this needs to
|
|---|
| 627 | # be cleaned up quickly!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
|---|
| 628 | meta = self.meta
|
|---|
| 629 |
|
|---|
| 630 | # loop over keys:
|
|---|
| 631 | # * try to find array-like keys
|
|---|
| 632 | arraylike = {}
|
|---|
| 633 | singlelike = []
|
|---|
| 634 | for key in self.meta:
|
|---|
| 635 | stripped = key.rstrip('1234567890')
|
|---|
| 636 | if stripped == key:
|
|---|
| 637 | singlelike.append(key)
|
|---|
| 638 | else:
|
|---|
| 639 | if stripped not in arraylike:
|
|---|
| 640 | arraylike[stripped] = 0
|
|---|
| 641 | else:
|
|---|
| 642 | arraylike[stripped] += 1
|
|---|
| 643 | newmeta = {}
|
|---|
| 644 | for key in singlelike:
|
|---|
| 645 | newmeta[key.lower()] = meta[key]
|
|---|
| 646 | for key in arraylike:
|
|---|
| 647 | uninteresting_meta['arraylike'][key.lower()] = []
|
|---|
| 648 | for i in range(arraylike[key]+1):
|
|---|
| 649 | if key+str(i) in meta:
|
|---|
| 650 | uninteresting_meta['arraylike'][key.lower()].append(meta[key+str(i)])
|
|---|
| 651 | self.ui_meta = uninteresting_meta
|
|---|
| 652 | # make newmeta self
|
|---|
| 653 | for key in newmeta:
|
|---|
| 654 | self.__dict__[key]=newmeta[key]
|
|---|
| 655 |
|
|---|
| 656 | dict = self.__dict__.copy()
|
|---|
| 657 | del dict['meta']
|
|---|
| 658 | del dict['ui_meta']
|
|---|
| 659 | self.dict = dict
|
|---|
| 660 |
|
|---|
| 661 | def __iter__(self):
|
|---|
| 662 | """ iterator """
|
|---|
| 663 | return self
|
|---|
| 664 |
|
|---|
| 665 | def next(self):
|
|---|
| 666 | """ used by __iter__ """
|
|---|
| 667 | # Here one might check, if looping makes any sense, and if not
|
|---|
| 668 | # one could stop looping or so...
|
|---|
| 669 | # like this:
|
|---|
| 670 | #
|
|---|
| 671 | # if len(self._registered_cols) == 0:
|
|---|
| 672 | # print 'warning: looping without any registered columns'
|
|---|
| 673 | if self._current_row < self.number_of_rows:
|
|---|
| 674 | if self.GetNextRow() == False:
|
|---|
| 675 | raise StopIteration
|
|---|
| 676 | for col in self._registered_cols:
|
|---|
| 677 | if isinstance(self._table_cols[col], ctypes._SimpleCData):
|
|---|
| 678 | self.__dict__[col] = self._table_cols[col].value
|
|---|
| 679 | self.dict[col] = self._table_cols[col].value
|
|---|
| 680 |
|
|---|
| 681 | for col in self.stacked_cols:
|
|---|
| 682 | if isinstance(self.dict[col], type(np.array(''))):
|
|---|
| 683 | self.stacked_cols[col] = np.vstack( (self.stacked_cols[col],self.dict[col]) )
|
|---|
| 684 | else:
|
|---|
| 685 | self.stacked_cols[col] = np.vstack( (self.stacked_cols[col],np.array(self.dict[col])) )
|
|---|
| 686 | self._current_row += 1
|
|---|
| 687 | else:
|
|---|
| 688 | raise StopIteration
|
|---|
| 689 | return self
|
|---|
| 690 |
|
|---|
| 691 | def show(self):
|
|---|
| 692 | pprint.pprint(self.dict)
|
|---|
| 693 |
|
|---|
| 694 |
|
|---|
| 695 |
|
|---|
| 696 |
|
|---|
| 697 | class fnames( object ):
|
|---|
| 698 | """ organize file names of a FACT data run
|
|---|
| 699 |
|
|---|
| 700 | """
|
|---|
| 701 |
|
|---|
| 702 | def __init__(self, specifier = ['012', '023', '2011', '11', '24'],
|
|---|
| 703 | rpath = '/scratch_nfs/res/bsl/',
|
|---|
| 704 | zipped = True):
|
|---|
| 705 | """
|
|---|
| 706 | specifier : list of strings defined as:
|
|---|
| 707 | [ 'DRS calibration file', 'Data file', 'YYYY', 'MM', 'DD']
|
|---|
| 708 |
|
|---|
| 709 | rpath : directory path for the results; YYYYMMDD will be appended to rpath
|
|---|
| 710 | zipped : use zipped (True) or unzipped (Data)
|
|---|
| 711 |
|
|---|
| 712 | """
|
|---|
| 713 |
|
|---|
| 714 | self.specifier = specifier
|
|---|
| 715 | self.rpath = rpath
|
|---|
| 716 | self.zipped = zipped
|
|---|
| 717 |
|
|---|
| 718 | self.make( self.specifier, self.rpath, self.zipped )
|
|---|
| 719 |
|
|---|
| 720 |
|
|---|
| 721 | def make( self, specifier, rpath, zipped ):
|
|---|
| 722 | """ create (make) the filenames
|
|---|
| 723 |
|
|---|
| 724 | names : dictionary of filenames, tags { 'data', 'drscal', 'results' }
|
|---|
| 725 | data : name of the data file
|
|---|
| 726 | drscal : name of the drs calibration file
|
|---|
| 727 | results : radikal of file name(s) for results (to be completed by suffixes)
|
|---|
| 728 | """
|
|---|
| 729 |
|
|---|
| 730 | self.specifier = specifier
|
|---|
| 731 |
|
|---|
| 732 | if zipped:
|
|---|
| 733 | dpath = '/data00/fact-construction/raw/'
|
|---|
| 734 | ext = '.fits.gz'
|
|---|
| 735 | else:
|
|---|
| 736 | dpath = '/data03/fact-construction/raw/'
|
|---|
| 737 | ext = '.fits'
|
|---|
| 738 |
|
|---|
| 739 | year = specifier[2]
|
|---|
| 740 | month = specifier[3]
|
|---|
| 741 | day = specifier[4]
|
|---|
| 742 |
|
|---|
| 743 | yyyymmdd = year + month + day
|
|---|
| 744 | dfile = specifier[1]
|
|---|
| 745 | cfile = specifier[0]
|
|---|
| 746 |
|
|---|
| 747 | rpath = rpath + yyyymmdd + '/'
|
|---|
| 748 | self.rpath = rpath
|
|---|
| 749 | self.names = {}
|
|---|
| 750 |
|
|---|
| 751 | tmp = dpath + year + '/' + month + '/' + day + '/' + yyyymmdd + '_'
|
|---|
| 752 | self.names['data'] = tmp + dfile + ext
|
|---|
| 753 | self.names['drscal'] = tmp + cfile + '.drs' + ext
|
|---|
| 754 | self.names['results'] = rpath + yyyymmdd + '_' + dfile + '_' + cfile
|
|---|
| 755 |
|
|---|
| 756 | self.data = self.names['data']
|
|---|
| 757 | self.drscal = self.names['drscal']
|
|---|
| 758 | self.results = self.names['results']
|
|---|
| 759 |
|
|---|
| 760 | def info( self ):
|
|---|
| 761 | """ print complete filenames
|
|---|
| 762 |
|
|---|
| 763 | """
|
|---|
| 764 |
|
|---|
| 765 | print 'file names:'
|
|---|
| 766 | print 'data: ', self.names['data']
|
|---|
| 767 | print 'drs-cal: ', self.names['drscal']
|
|---|
| 768 | print 'results: ', self.names['results']
|
|---|
| 769 |
|
|---|
| 770 | # end of class definition: fnames( object )
|
|---|
| 771 |
|
|---|
| 772 | def _test_SlowData( filename ):
|
|---|
| 773 | file = FactFits(filename)
|
|---|
| 774 | print '-'*70
|
|---|
| 775 | print "opened :", filename, " as 'file'"
|
|---|
| 776 | print
|
|---|
| 777 | print '-'*70
|
|---|
| 778 | print 'type file.show() to look at its contents'
|
|---|
| 779 | print "type file.register( columnname ) or file.register('all') in order to register columns"
|
|---|
| 780 | print
|
|---|
| 781 | print " due column-registration you declare, that you would like to retrieve the contents of one of the columns"
|
|---|
| 782 | print " after column-registration, the 'file' has new member variables, they are named like the columns"
|
|---|
| 783 | print " PLEASE NOTE: immediatly after registration, the members exist, but they are empty."
|
|---|
| 784 | print " the values are assigned only, when you call file.next() or when you loop over the 'file'"
|
|---|
| 785 | print
|
|---|
| 786 | print "in order to loop over it, just go like this:"
|
|---|
| 787 | print "for row in file:"
|
|---|
| 788 | print " print row.columnname_one, row.columnname_two"
|
|---|
| 789 | print
|
|---|
| 790 | print ""
|
|---|
| 791 | print '-'*70
|
|---|
| 792 |
|
|---|
| 793 |
|
|---|
| 794 |
|
|---|
| 795 | def _test_iter( nevents ):
|
|---|
| 796 | """ test for function __iter__ """
|
|---|
| 797 |
|
|---|
| 798 | data_file_name = '/data00/fact-construction/raw/2011/11/24/20111124_117.fits.gz'
|
|---|
| 799 | calib_file_name = '/data00/fact-construction/raw/2011/11/24/20111124_114.drs.fits.gz'
|
|---|
| 800 | # data_file_name = '/home/luster/win7/FACT/data/raw/20120114/20120114_028.fits.gz'
|
|---|
| 801 | # calib_file_name = '/home/luster/win7/FACT/data/raw/20120114/20120114_022.drs.fits.gz'
|
|---|
| 802 | run = RawData( data_file_name, calib_file_name , return_dict=True)
|
|---|
| 803 |
|
|---|
| 804 | for event in run:
|
|---|
| 805 | print 'ev ', event['event_id'].value, 'data[0,0] = ', event['acal_data'][0,0], 'start_cell[0] = ', event['start_cells'][0], 'trigger type = ', event['trigger_type']
|
|---|
| 806 | if run.event_id.value == nevents:
|
|---|
| 807 | break
|
|---|
| 808 |
|
|---|
| 809 | if __name__ == '__main__':
|
|---|
| 810 | """ tests """
|
|---|
| 811 | import sys
|
|---|
| 812 | if len(sys.argv) == 1:
|
|---|
| 813 | print 'showing test of iterator of RawData class'
|
|---|
| 814 | print 'in order to test the SlowData classe please use:', sys.argv[0], 'fits-file-name'
|
|---|
| 815 | _test_iter(10)
|
|---|
| 816 | else:
|
|---|
| 817 | print 'showing test of SlowData class'
|
|---|
| 818 | print 'in case you wanted to test the RawData class, please give no commandline arguments'
|
|---|
| 819 | _test_SlowData(sys.argv[1])
|
|---|
