| 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 | gSystem.Load('calfactfits_h.so')
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| 18 | from ROOT import *
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| 19 |
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| 20 | class RawDataFeeder( object ):
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| 21 | """ Wrapper class for RawData class
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| 22 | capable of iterating over multiple RawData Files
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| 23 | """
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| 24 |
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| 25 | def __init__(self, filelist):
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| 26 | """ *filelist* list of files to iterate over
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| 27 | the list should contain tuples, or sublists of two filenames
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| 28 | the first should be a data file (\*.fits.gz)
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| 29 | the second should be an amplitude calibration file(\*.drs.fits.gz)
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| 30 | """
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| 31 | # sanity check for input
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| 32 | if type(filelist) != type(list()):
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| 33 | raise TypeError('filelist should be a list')
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| 34 | for entry in filelist:
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| 35 | if len(entry) != 2:
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| 36 | raise TypeError('the entries of filelist should have length == 2')
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| 37 | for path in entry:
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| 38 | if type(path) != type(str()):
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| 39 | raise TypeError('the entries of filelist should be path, i.e. of type str()')
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| 40 | #todo check if 'path' is a valid path
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| 41 | # else: throw an Exception, or Warning?
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| 42 |
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| 43 | self.filelist = filelist
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| 44 | self._current_RawData = RawData(filelist[0][0], filelist[0][1], return_dict=True)
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| 45 | del filelist[0]
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| 46 |
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| 47 | def __iter__(self):
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| 48 | return self
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| 49 |
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| 50 | def next():
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| 51 | """ Method being called by the iterator.
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| 52 | Since the RawData Objects are simply looped over, the event_id from the
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| 53 | RawData object will not be unique.
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| 54 | Each RawData obejct will start with event_id = 1 as usual.
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| 55 | """
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| 56 | try:
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| 57 | return self._current_RawData.next()
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| 58 | except StopIteration:
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| 59 | # current_RawData was completely processed
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| 60 | # delete it (I hope this calls the destructor of the fits file and/or closes it)
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| 61 | del self._current_RawData
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| 62 | # and remake it, if possible
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| 63 | if len(self.filelist) > 0:
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| 64 | self._current_RawData = RawData(filelist[0][0], filelist[0][1], return_dict=True)
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| 65 | del filelist[0]
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| 66 | else:
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| 67 | raise
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| 68 |
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| 69 |
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| 70 |
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| 71 | class RawData( object ):
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| 72 | """ raw data access and calibration
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| 73 |
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| 74 | - open raw data file and drs calibration file
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| 75 | - performs amplitude calibration
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| 76 | - performs baseline substraction if wanted
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| 77 | - provides all data in an array:
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| 78 | row = number of pixel
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| 79 | col = length of region of interest
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| 80 |
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| 81 | """
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| 82 |
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| 83 |
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| 84 | def __init__(self, data_file_name, calib_file_name,
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| 85 | user_action_calib=lambda acal_data, data, blm, tom, gm, scells, nroi: None,
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| 86 | baseline_file_name='',
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| 87 | return_dict = True,
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| 88 | do_calibration = True,
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| 89 | use_CalFactFits = True):
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| 90 | """ initialize object
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| 91 |
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| 92 | open data file and calibration data file
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| 93 | get basic information about the data in data_file_name
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| 94 | allocate buffers for data access
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| 95 |
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| 96 | data_file_name : fits or fits.gz file of the data including the path
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| 97 | calib_file_name : fits or fits.gz file containing DRS calibration data
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| 98 | baseline_file_name : npy file containing the baseline values
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| 99 | """
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| 100 | self.__module__='pyfact'
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| 101 | # manual implementation of default value, but I need to find out
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| 102 | # if the user of this class is aware of the new option
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| 103 | if return_dict == False:
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| 104 | print 'DEPRECATION WARNING:'
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| 105 | print 'you are using RawData in a way, which is nor supported anymore.'
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| 106 | print ' Please set: return_dict = True, in the __init__ call'
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| 107 | self.return_dict = return_dict
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| 108 | self.use_CalFactFits = use_CalFactFits
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| 109 |
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| 110 | self.do_calibration = do_calibration
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| 111 |
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| 112 | self.data_file_name = data_file_name
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| 113 | self.calib_file_name = calib_file_name
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| 114 | self.baseline_file_name = baseline_file_name
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| 115 |
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| 116 | self.user_action_calib = user_action_calib
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| 117 |
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| 118 | # baseline correction: True / False
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| 119 | if len(baseline_file_name) == 0:
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| 120 | self.correct_baseline = False
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| 121 | else:
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| 122 | self.correct_baseline = True
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| 123 |
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| 124 | # access data file
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| 125 | if use_CalFactFits:
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| 126 | try:
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| 127 | data_file = CalFactFits(data_file_name, calib_file_name)
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| 128 | except IOError:
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| 129 | print 'problem accessing data file: ', data_file_name
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| 130 | raise # stop ! no data
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| 131 |
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| 132 | self.data_file = data_file
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| 133 | self.data = np.empty( data_file.npix * data_file.nroi, np.float64)
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| 134 | data_file.SetNpcaldataPtr(self.data)
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| 135 | self.data = self.data.reshape( data_file.npix, data_file.nroi )
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| 136 | self.acal_data = self.data
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| 137 |
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| 138 | self.nroi = data_file.nroi
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| 139 | self.npix = data_file.npix
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| 140 | self.nevents = data_file.nevents
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| 141 |
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| 142 | # Data per event
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| 143 | self.event_id = None
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| 144 | self.trigger_type = None
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| 145 | self.start_cells = None
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| 146 | self.board_times = None
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| 147 |
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| 148 | else:
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| 149 | try:
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| 150 | data_file = FactFits(self.data_file_name)
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| 151 | except IOError:
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| 152 | print 'problem accessing data file: ', data_file_name
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| 153 | raise # stop ! no data
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| 154 |
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| 155 | self.data_file = data_file
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| 156 |
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| 157 | # get basic information about the data file
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| 158 | #: region of interest (number of DRS slices read)
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| 159 | self.nroi = data_file.GetUInt('NROI')
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| 160 | #: number of pixels (should be 1440)
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| 161 | self.npix = data_file.GetUInt('NPIX')
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| 162 | #: number of events in the data run
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| 163 | self.nevents = data_file.GetNumRows()
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| 164 |
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| 165 | # allocate the data memories
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| 166 | self.event_id = c_ulong()
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| 167 | self.trigger_type = c_ushort()
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| 168 | #: 1D array with raw data
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| 169 | self.data = np.zeros( self.npix * self.nroi, np.int16 ).reshape(self.npix ,self.nroi)
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| 170 | #: slice where drs readout started
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| 171 | self.start_cells = np.zeros( self.npix, np.int16 )
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| 172 | #: time when the FAD was triggered, in some strange units...
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| 173 | self.board_times = np.zeros( 40, np.int32 )
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| 174 |
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| 175 | # set the pointers to the data++
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| 176 | data_file.SetPtrAddress('EventNum', self.event_id)
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| 177 | data_file.SetPtrAddress('TriggerType', self.trigger_type)
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| 178 | data_file.SetPtrAddress('StartCellData', self.start_cells)
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| 179 | data_file.SetPtrAddress('Data', self.data)
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| 180 | data_file.SetPtrAddress('BoardTime', self.board_times)
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| 181 |
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| 182 | # open the calibration file
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| 183 | try:
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| 184 | calib_file = FactFits(self.calib_file_name)
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| 185 | except IOError:
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| 186 | print 'problem accessing calibration file: ', calib_file_name
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| 187 | raise
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| 188 | #: drs calibration file
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| 189 | self.calib_file = calib_file
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| 190 |
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| 191 | baseline_mean = calib_file.GetN('BaselineMean')
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| 192 | gain_mean = calib_file.GetN('GainMean')
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| 193 | trigger_offset_mean = calib_file.GetN('TriggerOffsetMean')
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| 194 |
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| 195 | self.Nblm = baseline_mean / self.npix
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| 196 | self.Ngm = gain_mean / self.npix
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| 197 | self.Ntom = trigger_offset_mean / self.npix
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| 198 |
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| 199 | self.blm = np.zeros(baseline_mean, np.float32).reshape(self.npix , self.Nblm)
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| 200 | self.gm = np.zeros(gain_mean, np.float32).reshape(self.npix , self.Ngm)
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| 201 | self.tom = np.zeros(trigger_offset_mean, np.float32).reshape(self.npix , self.Ntom)
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| 202 |
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| 203 | calib_file.SetPtrAddress('BaselineMean', self.blm)
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| 204 | calib_file.SetPtrAddress('GainMean', self.gm)
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| 205 | calib_file.SetPtrAddress('TriggerOffsetMean', self.tom)
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| 206 | calib_file.GetRow(0)
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| 207 |
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| 208 | # make calibration constants double, so we never need to roll
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| 209 | self.blm = np.hstack((self.blm, self.blm))
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| 210 | self.gm = np.hstack((self.gm, self.gm))
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| 211 | self.tom = np.hstack((self.tom, self.tom))
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| 212 |
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| 213 | self.v_bsl = np.zeros(self.npix) # array of baseline values (all ZERO)
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| 214 |
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| 215 | def __iter__(self):
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| 216 | """ iterator """
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| 217 | return self
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| 218 |
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| 219 | def next(self):
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| 220 | """ used by __iter__ """
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| 221 | if self.use_CalFactFits:
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| 222 | if self.data_file.GetCalEvent() == False:
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| 223 | raise StopIteration
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| 224 | else:
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| 225 | self.event_id = self.data_file.event_id
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| 226 | self.trigger_type = self.data_file.event_triggertype
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| 227 | self.start_cells = self.data_file.event_offset
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| 228 | self.board_times = self.data_file.event_boardtimes
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| 229 | #self.acal_data = self.data.copy().reshape(self.data_file.npix, self.data_file.nroi)
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| 230 | else:
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| 231 | if self.data_file.GetNextRow() == False:
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| 232 | raise StopIteration
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| 233 | else:
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| 234 | if self.do_calibration == True:
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| 235 | self.calibrate_drs_amplitude()
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| 236 |
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| 237 | #print 'nevents = ', self.nevents, 'event_id = ', self.event_id.value
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| 238 | if self.return_dict:
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| 239 | return self.__dict__
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| 240 | else:
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| 241 | return self.acal_data, self.start_cells, self.trigger_type.value
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| 242 |
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| 243 | def next_event(self):
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| 244 | """ load the next event from disk and calibrate it
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| 245 | """
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| 246 | if self.use_CalFactFits:
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| 247 | self.data_file.GetCalEvent()
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| 248 | else:
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| 249 | self.data_file.GetNextRow()
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| 250 | self.calibrate_drs_amplitude()
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| 251 |
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| 252 | def calibrate_drs_amplitude(self):
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| 253 | """ perform the drs amplitude calibration of the event data
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| 254 |
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| 255 | """
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| 256 | # shortcuts
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| 257 | blm = self.blm
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| 258 | gm = self.gm
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| 259 | tom = self.tom
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| 260 |
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| 261 | to_mV = 2000./4096.
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| 262 | #: 2D array with amplitude calibrated dat in mV
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| 263 | acal_data = self.data * to_mV # convert ADC counts to mV
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| 264 |
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| 265 |
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| 266 | for pixel in range( self.npix ):
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| 267 | #shortcuts
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| 268 | sc = self.start_cells[pixel]
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| 269 | roi = self.nroi
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| 270 | # rotate the pixel baseline mean to the Data startCell
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| 271 | acal_data[pixel,:] -= blm[pixel,sc:sc+roi]
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| 272 | # the 'trigger offset mean' does not need to be rolled
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| 273 | # on the contrary, it seems there is an offset in the DRS data,
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| 274 | # which is related to its distance to the startCell, not to its
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| 275 | # distance to the beginning of the physical pipeline in the DRS chip
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| 276 | acal_data[pixel,:] -= tom[pixel,0:roi]
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| 277 | # rotate the pixel gain mean to the Data startCell
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| 278 | acal_data[pixel,:] /= gm[pixel,sc:sc+roi]
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| 279 |
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| 280 |
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| 281 | self.acal_data = acal_data * 1907.35
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| 282 |
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| 283 | self.user_action_calib( self.acal_data,
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| 284 | np.reshape(self.data, (self.npix, self.nroi) ), blm, tom, gm, self.start_cells, self.nroi)
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| 285 |
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| 286 |
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| 287 | def baseline_read_values(self, file, bsl_hist='bsl_sum/hplt_mean'):
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| 288 | """
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| 289 |
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| 290 | open ROOT file with baseline histogram and read baseline values
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| 291 | file name of the root file
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| 292 | bsl_hist path to the histogram containing the basline values
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| 293 |
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| 294 | """
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| 295 |
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| 296 | try:
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| 297 | f = TFile(file)
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| 298 | except:
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| 299 | print 'Baseline data file could not be read: ', file
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| 300 | return
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| 301 |
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| 302 | h = f.Get(bsl_hist)
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| 303 |
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| 304 | for i in range(self.npix):
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| 305 | self.v_bsl[i] = h.GetBinContent(i+1)
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| 306 |
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| 307 | f.Close()
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| 308 |
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| 309 | def baseline_correct(self):
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| 310 | """ subtract baseline from the data
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| 311 |
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| 312 | """
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| 313 |
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| 314 | for pixel in range(self.npix):
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| 315 | self.acal_data[pixel,:] -= self.v_bsl[pixel]
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| 316 |
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| 317 | def info(self):
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| 318 | """ print run information
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| 319 |
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| 320 | """
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| 321 |
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| 322 | print 'data file: ', data_file_name
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| 323 | print 'calib file: ', calib_file_name
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| 324 | print 'calibration file'
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| 325 | print 'N baseline_mean: ', self.Nblm
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| 326 | print 'N gain mean: ', self.Ngm
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| 327 | print 'N TriggeroffsetMean: ', self.Ntom
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| 328 |
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| 329 | # -----------------------------------------------------------------------------
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| 330 | class RawDataFake( object ):
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| 331 | """ raw data FAKE access similar to real RawData access
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| 332 | """
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| 333 |
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| 334 |
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| 335 | def __init__(self, data_file_name, calib_file_name,
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| 336 | user_action_calib=lambda acal_data, data, blm, tom, gm, scells, nroi: None,
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| 337 | baseline_file_name=''):
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| 338 | self.__module__='pyfact'
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| 339 |
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| 340 | self.nroi = 300
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| 341 | self.npix = 9
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| 342 | self.nevents = 1000
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| 343 |
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| 344 | self.simulator = None
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| 345 |
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| 346 | self.time = np.ones(1024) * 0.5
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| 347 |
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| 348 |
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| 349 | self.event_id = c_ulong(0)
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| 350 | self.trigger_type = c_ushort(4)
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| 351 | self.data = np.zeros( self.npix * self.nroi, np.int16 ).reshape(self.npix ,self.nroi)
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| 352 | self.start_cells = np.zeros( self.npix, np.int16 )
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| 353 | self.board_times = np.zeros( 40, np.int32 )
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| 354 | def __iter__(self):
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| 355 | """ iterator """
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| 356 | return self
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| 357 |
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| 358 | def next(self):
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| 359 | """ used by __iter__ """
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| 360 | self.event_id = c_ulong(self.event_id.value + 1)
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| 361 | self.board_times = self.board_times + 42
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| 362 |
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| 363 | if self.event_id.value >= self.nevents:
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| 364 | raise StopIteration
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| 365 | else:
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| 366 | self._make_event_data()
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| 367 |
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| 368 | return self.__dict__
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| 369 |
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| 370 | def _make_event_data(self):
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| 371 | sample_times = self.time.cumsum() - time[0]
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| 372 |
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| 373 | # random start cell
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| 374 | self.start_cells = np.ones( self.npix, np.int16 ) * np.random.randint(0,1024)
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| 375 |
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| 376 | starttime = self.start_cells[0]
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| 377 |
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| 378 | signal = self._std_sinus_simu(sample_times, starttime)
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| 379 |
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| 380 | data = np.vstack( (signal,signal) )
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| 381 | for i in range(8):
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| 382 | data = np.vstack( (data,signal) )
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| 383 |
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| 384 | self.data = data
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| 385 |
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| 386 | def _std_sinus_simu(self, times, starttime):
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| 387 | period = 10 # in ns
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| 388 |
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| 389 | # give a jitter on starttime
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| 390 | starttime = np.random.normal(startime, 0.05)
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| 391 |
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| 392 | phase = 0.0
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| 393 | signal = 10 * np.sin(times * 2*np.pi/period + starttime + phase)
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| 394 |
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| 395 | # add some noise
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| 396 | noise = np.random.normal(0.0, 0.5, signal.shape)
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| 397 | signal += noise
|
|---|
| 398 | return signal
|
|---|
| 399 |
|
|---|
| 400 | def info(self):
|
|---|
| 401 | """ print run information
|
|---|
| 402 |
|
|---|
| 403 | """
|
|---|
| 404 |
|
|---|
| 405 | print 'data file: ', data_file_name
|
|---|
| 406 | print 'calib file: ', calib_file_name
|
|---|
| 407 | print 'calibration file'
|
|---|
| 408 | print 'N baseline_mean: ', self.Nblm
|
|---|
| 409 | print 'N gain mean: ', self.Ngm
|
|---|
| 410 | print 'N TriggeroffsetMean: ', self.Ntom
|
|---|
| 411 |
|
|---|
| 412 | # -----------------------------------------------------------------------------
|
|---|
| 413 |
|
|---|
| 414 | class SlowData( FactFits ):
|
|---|
| 415 | """ -Fact SlowData File-
|
|---|
| 416 | A Python wrapper for the fits-class implemented in pyfits.h
|
|---|
| 417 | provides easy access to the fits file meta data.
|
|---|
| 418 | * dictionary of file metadata - self.meta
|
|---|
| 419 | * dict of table metadata - self.columns
|
|---|
| 420 | * variable table column access, thus possibly increased speed while looping
|
|---|
| 421 | """
|
|---|
| 422 | def __init__(self, path):
|
|---|
| 423 | """ creates meta and columns dictionaries
|
|---|
| 424 | """
|
|---|
| 425 | self.path = path
|
|---|
| 426 | try:
|
|---|
| 427 | FactFits.__init__(self,path)
|
|---|
| 428 | except IOError:
|
|---|
| 429 | print 'problem accessing data file: ', data_file_name
|
|---|
| 430 | raise # stop ! no data
|
|---|
| 431 |
|
|---|
| 432 | self.meta = self._make_meta_dict()
|
|---|
| 433 | self.columns = self._make_columns_dict()
|
|---|
| 434 |
|
|---|
| 435 | self.treat_meta_dict()
|
|---|
| 436 |
|
|---|
| 437 |
|
|---|
| 438 | # list of columns, which are already registered
|
|---|
| 439 | # see method register()
|
|---|
| 440 | self._registered_cols = []
|
|---|
| 441 | # dict of column data, this is used, in order to be able to remove
|
|---|
| 442 | # the ctypes of
|
|---|
| 443 | self._table_cols = {}
|
|---|
| 444 |
|
|---|
| 445 | # I need to count the rows, since the normal loop mechanism seems not to work.
|
|---|
| 446 | self._current_row = 0
|
|---|
| 447 |
|
|---|
| 448 | self.stacked_cols = {}
|
|---|
| 449 |
|
|---|
| 450 | def _make_meta_dict(self):
|
|---|
| 451 | """ This method retrieves meta information about the fits file and
|
|---|
| 452 | stores this information in a dict
|
|---|
| 453 | return: dict
|
|---|
| 454 | key: string - all capital letters
|
|---|
| 455 | value: tuple( numerical value, string comment)
|
|---|
| 456 | """
|
|---|
| 457 | # intermediate variables for file metadata dict generation
|
|---|
| 458 | keys=self.GetPy_KeyKeys()
|
|---|
| 459 | values=self.GetPy_KeyValues()
|
|---|
| 460 | comments=self.GetPy_KeyComments()
|
|---|
| 461 | types=self.GetPy_KeyTypes()
|
|---|
| 462 |
|
|---|
| 463 | if len(keys) != len(values):
|
|---|
| 464 | raise TypeError('len(keys)',len(keys),' != len(values)', len(values))
|
|---|
| 465 | if len(keys) != len(types):
|
|---|
| 466 | raise TypeError('len(keys)',len(keys),' != len(types)', len(types))
|
|---|
| 467 | if len(keys) != len(comments):
|
|---|
| 468 | raise TypeError('len(keys)',len(keys),' != len(comments)', len(comments))
|
|---|
| 469 |
|
|---|
| 470 | meta_dict = {}
|
|---|
| 471 | for i in range(len(keys)):
|
|---|
| 472 | type = types[i]
|
|---|
| 473 | if type == 'I':
|
|---|
| 474 | value = int(values[i])
|
|---|
| 475 | elif type == 'F':
|
|---|
| 476 | value = float(values[i])
|
|---|
| 477 | elif type == 'B':
|
|---|
| 478 | if values[i] == 'T':
|
|---|
| 479 | value = True
|
|---|
| 480 | elif values[i] == 'F':
|
|---|
| 481 | value = False
|
|---|
| 482 | else:
|
|---|
| 483 | raise TypeError("meta-type is 'B', but meta-value is neither 'T' nor 'F'. meta-value:",values[i])
|
|---|
| 484 | elif type == 'T':
|
|---|
| 485 | value = values[i]
|
|---|
| 486 | else:
|
|---|
| 487 | raise TypeError("unknown meta-type: known meta types are: I,F,B and T. meta-type:",type)
|
|---|
| 488 | meta_dict[keys[i]]=(value, comments[i])
|
|---|
| 489 | return meta_dict
|
|---|
| 490 |
|
|---|
| 491 |
|
|---|
| 492 | def _make_columns_dict(self):
|
|---|
| 493 | """ This method retrieves information about the columns
|
|---|
| 494 | stored inside the fits files internal binary table.
|
|---|
| 495 | returns: dict
|
|---|
| 496 | key: string column name -- all capital letters
|
|---|
| 497 | values: tuple(
|
|---|
| 498 | number of elements in table field - integer
|
|---|
| 499 | size of element in bytes -- this is not really interesting for any user
|
|---|
| 500 | might be ommited in future versions
|
|---|
| 501 | type - a single character code -- should be translated into
|
|---|
| 502 | a comrehensible word
|
|---|
| 503 | unit - string like 'mV' or 'ADC count'
|
|---|
| 504 | """
|
|---|
| 505 | # intermediate variables for file table-metadata dict generation
|
|---|
| 506 | keys=self.GetPy_ColumnKeys()
|
|---|
| 507 | #offsets=self.GetPy_ColumnOffsets() #not needed on python level...
|
|---|
| 508 | nums=self.GetPy_ColumnNums()
|
|---|
| 509 | sizes=self.GetPy_ColumnSizes()
|
|---|
| 510 | types=self.GetPy_ColumnTypes()
|
|---|
| 511 | units=self.GetPy_ColumnUnits()
|
|---|
| 512 |
|
|---|
| 513 | # zip the values
|
|---|
| 514 | values = zip(nums,sizes,types,units)
|
|---|
| 515 | # create the columns dictionary
|
|---|
| 516 | columns = dict(zip(keys ,values))
|
|---|
| 517 | return columns
|
|---|
| 518 |
|
|---|
| 519 | def stack(self, on=True):
|
|---|
| 520 | self.next()
|
|---|
| 521 | for col in self._registered_cols:
|
|---|
| 522 | if isinstance( self.dict[col], type(np.array('')) ):
|
|---|
| 523 | self.stacked_cols[col] = self.dict[col]
|
|---|
| 524 | else:
|
|---|
| 525 | # elif isinstance(self.dict[col], ctypes._SimpleCData):
|
|---|
| 526 | self.stacked_cols[col] = np.array(self.dict[col])
|
|---|
| 527 | # else:
|
|---|
| 528 | # raise TypeError("I don't know how to stack "+col+". It is of type: "+str(type(self.dict[col])))
|
|---|
| 529 |
|
|---|
| 530 | def register(self, input_str):
|
|---|
| 531 | columns = self.columns
|
|---|
| 532 | if input_str.lower() == 'all':
|
|---|
| 533 | for col in columns:
|
|---|
| 534 | self._register(col)
|
|---|
| 535 | else:
|
|---|
| 536 | #check if colname is in columns:
|
|---|
| 537 | if input_str not in columns:
|
|---|
| 538 | error_msg = 'colname:'+ input_str +' is not a column in the binary table.\n'
|
|---|
| 539 | error_msg+= 'possible colnames are\n'
|
|---|
| 540 | for key in columns:
|
|---|
| 541 | error_msg += key+'\n'
|
|---|
| 542 | raise KeyError(error_msg)
|
|---|
| 543 | else:
|
|---|
| 544 | self._register(input_str)
|
|---|
| 545 |
|
|---|
| 546 | # 'private' method, do not use
|
|---|
| 547 | def _register( self, colname):
|
|---|
| 548 | columns = self.columns
|
|---|
| 549 | local = None
|
|---|
| 550 |
|
|---|
| 551 | number_of_elements = int(columns[colname][0])
|
|---|
| 552 | size_of_elements_in_bytes = int(columns[colname][1])
|
|---|
| 553 | ctypecode_of_elements = columns[colname][2]
|
|---|
| 554 | physical_unit_of_elements = columns[colname][3]
|
|---|
| 555 |
|
|---|
| 556 | # snippet from the C++ source code, or header file to be precise:
|
|---|
| 557 | #case 'L': gLog << "bool(8)"; break;
|
|---|
| 558 | #case 'B': gLog << "byte(8)"; break;
|
|---|
| 559 | #case 'I': gLog << "short(16)"; break;
|
|---|
| 560 | #case 'J': gLog << "int(32)"; break;
|
|---|
| 561 | #case 'K': gLog << "int(64)"; break;
|
|---|
| 562 | #case 'E': gLog << "float(32)"; break;
|
|---|
| 563 | #case 'D': gLog << "double(64)"; break;
|
|---|
| 564 |
|
|---|
| 565 |
|
|---|
| 566 |
|
|---|
| 567 | # the fields inside the columns can either contain single numbers,
|
|---|
| 568 | # or whole arrays of numbers as well.
|
|---|
| 569 | # we treat single elements differently...
|
|---|
| 570 | if number_of_elements == 1:
|
|---|
| 571 | # allocate some memory for a single number according to its type
|
|---|
| 572 | if ctypecode_of_elements == 'J': # J is for a 4byte int, i.e. an unsigned long
|
|---|
| 573 | local = ctypes.c_ulong()
|
|---|
| 574 | un_c_type = long
|
|---|
| 575 | elif ctypecode_of_elements == 'I': # I is for a 2byte int, i.e. an unsinged int
|
|---|
| 576 | local = ctypes.c_ushort()
|
|---|
| 577 | un_c_type = int
|
|---|
| 578 | elif ctypecode_of_elements == 'B': # B is for a byte
|
|---|
| 579 | local = ctypes.c_ubyte()
|
|---|
| 580 | un_c_type = int
|
|---|
| 581 | elif ctypecode_of_elements == 'D':
|
|---|
| 582 | local = ctypes.c_double()
|
|---|
| 583 | un_c_type = float
|
|---|
| 584 | elif ctypecode_of_elements == 'E':
|
|---|
| 585 | local = ctypes.c_float()
|
|---|
| 586 | un_c_type = float
|
|---|
| 587 | elif ctypecode_of_elements == 'A':
|
|---|
| 588 | local = ctypes.c_uchar()
|
|---|
| 589 | un_c_type = chr
|
|---|
| 590 | elif ctypecode_of_elements == 'K':
|
|---|
| 591 | local = ctypes.c_ulonglong()
|
|---|
| 592 | un_c_type = long
|
|---|
| 593 | else:
|
|---|
| 594 | raise TypeError('unknown ctypecode_of_elements:',ctypecode_of_elements)
|
|---|
| 595 | else:
|
|---|
| 596 | if ctypecode_of_elements == 'B': # B is for a byte
|
|---|
| 597 | nptype = np.int8
|
|---|
| 598 | elif ctypecode_of_elements == 'A': # A is for a char .. but I don't know how to handle it
|
|---|
| 599 | nptype = np.int8
|
|---|
| 600 | elif ctypecode_of_elements == 'I': # I is for a 2byte int
|
|---|
| 601 | nptype = np.int16
|
|---|
| 602 | elif ctypecode_of_elements == 'J': # J is for a 4byte int
|
|---|
| 603 | nptype = np.int32
|
|---|
| 604 | elif ctypecode_of_elements == 'K': # B is for a byte
|
|---|
| 605 | nptype = np.int64
|
|---|
| 606 | elif ctypecode_of_elements == 'E': # B is for a byte
|
|---|
| 607 | nptype = np.float32
|
|---|
| 608 | elif ctypecode_of_elements == 'D': # B is for a byte
|
|---|
| 609 | nptype = np.float64
|
|---|
| 610 | else:
|
|---|
| 611 | raise TypeError('unknown ctypecode_of_elements:',ctypecode_of_elements)
|
|---|
| 612 | local = np.zeros( number_of_elements, nptype)
|
|---|
| 613 |
|
|---|
| 614 | # Set the Pointer Address
|
|---|
| 615 | self.SetPtrAddress(colname, local)
|
|---|
| 616 | self._table_cols[colname] = local
|
|---|
| 617 | if number_of_elements > 1:
|
|---|
| 618 | self.__dict__[colname] = local
|
|---|
| 619 | self.dict[colname] = local
|
|---|
| 620 | else:
|
|---|
| 621 | # remove any traces of ctypes:
|
|---|
| 622 | self.__dict__[colname] = local.value
|
|---|
| 623 | self.dict[colname] = local.value
|
|---|
| 624 | self._registered_cols.append(colname)
|
|---|
| 625 |
|
|---|
| 626 |
|
|---|
| 627 | def treat_meta_dict(self):
|
|---|
| 628 | """make 'interesting' meta information available like normal members.
|
|---|
| 629 | non interesting are:
|
|---|
| 630 | TFORM, TUNIT, and TTYPE
|
|---|
| 631 | since these are available via the columns dict.
|
|---|
| 632 | """
|
|---|
| 633 |
|
|---|
| 634 | self.number_of_rows = self.meta['NAXIS2'][0]
|
|---|
| 635 | self.number_of_columns = self.meta['TFIELDS'][0]
|
|---|
| 636 |
|
|---|
| 637 | # there are some information in the meta dict, which are alsways there:
|
|---|
| 638 | # there are regarded as not interesting:
|
|---|
| 639 | uninteresting_meta = {}
|
|---|
| 640 | uninteresting_meta['arraylike'] = {}
|
|---|
| 641 | uninteresting = ['NAXIS', 'NAXIS1', 'NAXIS2',
|
|---|
| 642 | 'TFIELDS',
|
|---|
| 643 | 'XTENSION','EXTNAME','EXTREL',
|
|---|
| 644 | 'BITPIX', 'PCOUNT', 'GCOUNT',
|
|---|
| 645 | 'ORIGIN',
|
|---|
| 646 | 'PACKAGE', 'COMPILED', 'CREATOR',
|
|---|
| 647 | 'TELESCOP','TIMESYS','TIMEUNIT','VERSION']
|
|---|
| 648 | for key in uninteresting:
|
|---|
| 649 | if key in self.meta:
|
|---|
| 650 | uninteresting_meta[key]=self.meta[key]
|
|---|
| 651 | del self.meta[key]
|
|---|
| 652 |
|
|---|
| 653 | # the table meta data contains
|
|---|
| 654 |
|
|---|
| 655 |
|
|---|
| 656 | # shortcut to access the meta dict. But this needs to
|
|---|
| 657 | # be cleaned up quickly!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
|---|
| 658 | meta = self.meta
|
|---|
| 659 |
|
|---|
| 660 | # loop over keys:
|
|---|
| 661 | # * try to find array-like keys
|
|---|
| 662 | arraylike = {}
|
|---|
| 663 | singlelike = []
|
|---|
| 664 | for key in self.meta:
|
|---|
| 665 | stripped = key.rstrip('1234567890')
|
|---|
| 666 | if stripped == key:
|
|---|
| 667 | singlelike.append(key)
|
|---|
| 668 | else:
|
|---|
| 669 | if stripped not in arraylike:
|
|---|
| 670 | arraylike[stripped] = 0
|
|---|
| 671 | else:
|
|---|
| 672 | arraylike[stripped] += 1
|
|---|
| 673 | newmeta = {}
|
|---|
| 674 | for key in singlelike:
|
|---|
| 675 | newmeta[key.lower()] = meta[key]
|
|---|
| 676 | for key in arraylike:
|
|---|
| 677 | uninteresting_meta['arraylike'][key.lower()] = []
|
|---|
| 678 | for i in range(arraylike[key]+1):
|
|---|
| 679 | if key+str(i) in meta:
|
|---|
| 680 | uninteresting_meta['arraylike'][key.lower()].append(meta[key+str(i)])
|
|---|
| 681 | self.ui_meta = uninteresting_meta
|
|---|
| 682 | # make newmeta self
|
|---|
| 683 | for key in newmeta:
|
|---|
| 684 | self.__dict__[key]=newmeta[key]
|
|---|
| 685 |
|
|---|
| 686 | dict = self.__dict__.copy()
|
|---|
| 687 | del dict['meta']
|
|---|
| 688 | del dict['ui_meta']
|
|---|
| 689 | self.dict = dict
|
|---|
| 690 |
|
|---|
| 691 | def __iter__(self):
|
|---|
| 692 | """ iterator """
|
|---|
| 693 | return self
|
|---|
| 694 |
|
|---|
| 695 | def next(self):
|
|---|
| 696 | """ used by __iter__ """
|
|---|
| 697 | # Here one might check, if looping makes any sense, and if not
|
|---|
| 698 | # one could stop looping or so...
|
|---|
| 699 | # like this:
|
|---|
| 700 | #
|
|---|
| 701 | # if len(self._registered_cols) == 0:
|
|---|
| 702 | # print 'warning: looping without any registered columns'
|
|---|
| 703 | if self._current_row < self.number_of_rows:
|
|---|
| 704 | if self.GetNextRow() == False:
|
|---|
| 705 | raise StopIteration
|
|---|
| 706 | for col in self._registered_cols:
|
|---|
| 707 | if isinstance(self._table_cols[col], ctypes._SimpleCData):
|
|---|
| 708 | self.__dict__[col] = self._table_cols[col].value
|
|---|
| 709 | self.dict[col] = self._table_cols[col].value
|
|---|
| 710 |
|
|---|
| 711 | for col in self.stacked_cols:
|
|---|
| 712 | if isinstance(self.dict[col], type(np.array(''))):
|
|---|
| 713 | self.stacked_cols[col] = np.vstack( (self.stacked_cols[col],self.dict[col]) )
|
|---|
| 714 | else:
|
|---|
| 715 | self.stacked_cols[col] = np.vstack( (self.stacked_cols[col],np.array(self.dict[col])) )
|
|---|
| 716 | self._current_row += 1
|
|---|
| 717 | else:
|
|---|
| 718 | raise StopIteration
|
|---|
| 719 | return self
|
|---|
| 720 |
|
|---|
| 721 | def show(self):
|
|---|
| 722 | pprint.pprint(self.dict)
|
|---|
| 723 |
|
|---|
| 724 |
|
|---|
| 725 |
|
|---|
| 726 |
|
|---|
| 727 | class fnames( object ):
|
|---|
| 728 | """ organize file names of a FACT data run
|
|---|
| 729 |
|
|---|
| 730 | """
|
|---|
| 731 |
|
|---|
| 732 | def __init__(self, specifier = ['012', '023', '2011', '11', '24'],
|
|---|
| 733 | rpath = '/scratch_nfs/res/bsl/',
|
|---|
| 734 | zipped = True):
|
|---|
| 735 | """
|
|---|
| 736 | specifier : list of strings defined as:
|
|---|
| 737 | [ 'DRS calibration file', 'Data file', 'YYYY', 'MM', 'DD']
|
|---|
| 738 |
|
|---|
| 739 | rpath : directory path for the results; YYYYMMDD will be appended to rpath
|
|---|
| 740 | zipped : use zipped (True) or unzipped (Data)
|
|---|
| 741 |
|
|---|
| 742 | """
|
|---|
| 743 |
|
|---|
| 744 | self.specifier = specifier
|
|---|
| 745 | self.rpath = rpath
|
|---|
| 746 | self.zipped = zipped
|
|---|
| 747 |
|
|---|
| 748 | self.make( self.specifier, self.rpath, self.zipped )
|
|---|
| 749 |
|
|---|
| 750 |
|
|---|
| 751 | def make( self, specifier, rpath, zipped ):
|
|---|
| 752 | """ create (make) the filenames
|
|---|
| 753 |
|
|---|
| 754 | names : dictionary of filenames, tags { 'data', 'drscal', 'results' }
|
|---|
| 755 | data : name of the data file
|
|---|
| 756 | drscal : name of the drs calibration file
|
|---|
| 757 | results : radikal of file name(s) for results (to be completed by suffixes)
|
|---|
| 758 | """
|
|---|
| 759 |
|
|---|
| 760 | self.specifier = specifier
|
|---|
| 761 |
|
|---|
| 762 | if zipped:
|
|---|
| 763 | dpath = '/data00/fact-construction/raw/'
|
|---|
| 764 | ext = '.fits.gz'
|
|---|
| 765 | else:
|
|---|
| 766 | dpath = '/data03/fact-construction/raw/'
|
|---|
| 767 | ext = '.fits'
|
|---|
| 768 |
|
|---|
| 769 | year = specifier[2]
|
|---|
| 770 | month = specifier[3]
|
|---|
| 771 | day = specifier[4]
|
|---|
| 772 |
|
|---|
| 773 | yyyymmdd = year + month + day
|
|---|
| 774 | dfile = specifier[1]
|
|---|
| 775 | cfile = specifier[0]
|
|---|
| 776 |
|
|---|
| 777 | rpath = rpath + yyyymmdd + '/'
|
|---|
| 778 | self.rpath = rpath
|
|---|
| 779 | self.names = {}
|
|---|
| 780 |
|
|---|
| 781 | tmp = dpath + year + '/' + month + '/' + day + '/' + yyyymmdd + '_'
|
|---|
| 782 | self.names['data'] = tmp + dfile + ext
|
|---|
| 783 | self.names['drscal'] = tmp + cfile + '.drs' + ext
|
|---|
| 784 | self.names['results'] = rpath + yyyymmdd + '_' + dfile + '_' + cfile
|
|---|
| 785 |
|
|---|
| 786 | self.data = self.names['data']
|
|---|
| 787 | self.drscal = self.names['drscal']
|
|---|
| 788 | self.results = self.names['results']
|
|---|
| 789 |
|
|---|
| 790 | def info( self ):
|
|---|
| 791 | """ print complete filenames
|
|---|
| 792 |
|
|---|
| 793 | """
|
|---|
| 794 |
|
|---|
| 795 | print 'file names:'
|
|---|
| 796 | print 'data: ', self.names['data']
|
|---|
| 797 | print 'drs-cal: ', self.names['drscal']
|
|---|
| 798 | print 'results: ', self.names['results']
|
|---|
| 799 |
|
|---|
| 800 | # end of class definition: fnames( object )
|
|---|
| 801 |
|
|---|
| 802 | def _test_SlowData( filename ):
|
|---|
| 803 | print '-'*70
|
|---|
| 804 | print "opened :", filename, " as 'file'"
|
|---|
| 805 | print
|
|---|
| 806 | print '-'*70
|
|---|
| 807 | print 'type file.show() to look at its contents'
|
|---|
| 808 | print "type file.register( columnname ) or file.register('all') in order to register columns"
|
|---|
| 809 | print
|
|---|
| 810 | print " due column-registration you declare, that you would like to retrieve the contents of one of the columns"
|
|---|
| 811 | print " after column-registration, the 'file' has new member variables, they are named like the columns"
|
|---|
| 812 | print " PLEASE NOTE: immediatly after registration, the members exist, but they are empty."
|
|---|
| 813 | print " the values are assigned only, when you call file.next() or when you loop over the 'file'"
|
|---|
| 814 | print
|
|---|
| 815 | print "in order to loop over it, just go like this:"
|
|---|
| 816 | print "for row in file:"
|
|---|
| 817 | print " print row.columnname_one, row.columnname_two"
|
|---|
| 818 | print
|
|---|
| 819 | print ""
|
|---|
| 820 | print '-'*70
|
|---|
| 821 |
|
|---|
| 822 |
|
|---|
| 823 |
|
|---|
| 824 | def _test_iter( nevents ):
|
|---|
| 825 | """ test for function __iter__ """
|
|---|
| 826 |
|
|---|
| 827 | data_file_name = '/data00/fact-construction/raw/2011/11/24/20111124_117.fits.gz'
|
|---|
| 828 | calib_file_name = '/data00/fact-construction/raw/2011/11/24/20111124_114.drs.fits.gz'
|
|---|
| 829 | # data_file_name = '/home/luster/win7/FACT/data/raw/20120114/20120114_028.fits.gz'
|
|---|
| 830 | # calib_file_name = '/home/luster/win7/FACT/data/raw/20120114/20120114_022.drs.fits.gz'
|
|---|
| 831 | run = RawData( data_file_name, calib_file_name , return_dict=True)
|
|---|
| 832 |
|
|---|
| 833 | for event in run:
|
|---|
| 834 | print 'ev ', event['event_id'], 'data[0,0] = ', event['acal_data'][0,0], 'start_cell[0] = ', event['start_cells'][0], 'trigger type = ', event['trigger_type']
|
|---|
| 835 | if run.event_id == nevents:
|
|---|
| 836 | break
|
|---|
| 837 |
|
|---|
| 838 | if __name__ == '__main__':
|
|---|
| 839 | """ tests """
|
|---|
| 840 | import sys
|
|---|
| 841 | if len(sys.argv) == 1:
|
|---|
| 842 | print 'showing test of iterator of RawData class'
|
|---|
| 843 | print 'in order to test the SlowData classe please use:', sys.argv[0], 'fits-file-name'
|
|---|
| 844 | _test_iter(10)
|
|---|
| 845 |
|
|---|
| 846 |
|
|---|
| 847 | else:
|
|---|
| 848 | print 'showing test of SlowData class'
|
|---|
| 849 | print 'in case you wanted to test the RawData class, please give no commandline arguments'
|
|---|
| 850 | file = SlowData(sys.argv[1])
|
|---|
| 851 | _test_SlowData(sys.argv[1])
|
|---|
