| 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 |
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| 32 | self.__module__ = 'pyfact'
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| 33 |
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| 34 | # sanity check for input
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| 35 | if type(filelist) != type(list()):
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| 36 | raise TypeError('filelist should be a list')
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| 37 | for entry in filelist:
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| 38 | if len(entry) != 2:
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| 39 | raise TypeError('the entries of filelist should have length == 2')
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| 40 | for path in entry:
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| 41 | if type(path) != type(str()):
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| 42 | raise TypeError('the entries of filelist should be path, i.e. of type str()')
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| 43 | #todo check if 'path' is a valid path
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| 44 | # else: throw an Exception, or Warning?
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| 45 |
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| 46 | self.filelist = filelist
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| 47 | self._current_RawData = RawData(filelist[0][0], filelist[0][1], return_dict=True)
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| 48 | del filelist[0]
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| 49 |
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| 50 | def __iter__(self):
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| 51 | return self
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| 52 |
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| 53 | def next():
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| 54 | """ Method being called by the iterator.
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| 55 | Since the RawData Objects are simply looped over, the event_id from the
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| 56 | RawData object will not be unique.
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| 57 | Each RawData obejct will start with event_id = 1 as usual.
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| 58 | """
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| 59 | try:
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| 60 | return self._current_RawData.next()
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| 61 | except StopIteration:
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| 62 | # current_RawData was completely processed
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| 63 | # delete it (I hope this calls the destructor of the fits file and/or closes it)
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| 64 | del self._current_RawData
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| 65 | # and remake it, if possible
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| 66 | if len(self.filelist) > 0:
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| 67 | self._current_RawData = RawData(filelist[0][0], filelist[0][1], return_dict=True)
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| 68 | del filelist[0]
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| 69 | else:
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| 70 | raise
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| 71 |
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| 72 |
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| 73 |
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| 74 | class RawData( object ):
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| 75 | """ raw data access and calibration
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| 76 |
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| 77 | class is **iterable**
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| 78 |
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| 79 | - open raw data file and drs calibration file
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| 80 | - performs amplitude calibration
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| 81 | - performs baseline substraction if wanted
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| 82 | - provides all data in an array:
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| 83 | row = number of pixel
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| 84 | col = length of region of interest
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| 85 |
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| 86 | """
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| 87 |
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| 88 |
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| 89 | def __init__(self, data_file_name, calib_file_name,
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| 90 | baseline_file_name='',
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| 91 | return_dict = True,
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| 92 | use_CalFactFits = True,
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| 93 | do_calibration = True,
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| 94 | user_action_calib=lambda acal_data, data, blm, tom, gm, scells, nroi: None):
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| 95 | """ -constructor-
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| 96 |
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| 97 | - open data file and calibration data file
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| 98 | - get basic information about the data in data_file_name
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| 99 | - allocate buffers for data access
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| 100 |
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| 101 | *data_file_name* : fits or fits.gz file of the data including the path
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| 102 |
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| 103 | *calib_file_name* : fits or fits.gz file containing DRS calibration data
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| 104 |
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| 105 | *baseline_file_name* : npy file containing the baseline values
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| 106 |
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| 107 | *return_dict* : this option will be removed in future releases.
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| 108 | formerly the next() method returned only a subset of (important) event information,
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| 109 | and it was not transparent how to retrieve the other (less important) information.
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| 110 | Nowadays next() returns self.__dict__ which contains everything we were able to find in the fits file.
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| 111 |
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| 112 | *use_CalFactFits* : formerly the DRS amplitude calibration was
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| 113 | implemented in python. But for performance reasons this was now moved into
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| 114 | a C++ class called CalFactFits. For test purposes, this option can be set to
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| 115 | False, but this is not really maintained anymore. If DRS the DRS calibration algorithm is
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| 116 | beeing updated in C++ it may not be updated in the python implementation.
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| 117 |
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| 118 | *do_calibration* : In case *use_CalFactFits* is False, one may choose
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| 119 | not to calibrate the data at all, thus safe quite some time.
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| 120 | This is imho only needed in case one is interesting in learning something about the
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| 121 | calibration algorithm itself.
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| 122 |
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| 123 | *user_action_calib* : callback function, intended for tests of the DRS calibration algorithm.
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| 124 | but since this is not done in the Python regime anymore, this function is never called.
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| 125 | (depending on *use_CalFactFits* of course)
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| 126 | """
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| 127 | self.__module__='pyfact'
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| 128 | # manual implementation of default value, but I need to find out
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| 129 | # if the user of this class is aware of the new option
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| 130 | if return_dict == False:
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| 131 | print 'DEPRECATION WARNING:'
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| 132 | print 'you are using RawData in a way, which is nor supported anymore.'
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| 133 | print ' Please set: return_dict = True, in the __init__ call'
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| 134 | self.return_dict = return_dict
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| 135 | self.use_CalFactFits = use_CalFactFits
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| 136 |
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| 137 | self.do_calibration = do_calibration
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| 138 |
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| 139 | self.data_file_name = data_file_name
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| 140 | self.calib_file_name = calib_file_name
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| 141 | self.baseline_file_name = baseline_file_name
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| 142 |
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| 143 | self.user_action_calib = user_action_calib
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| 144 |
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| 145 | # baseline correction: True / False
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| 146 | if len(baseline_file_name) == 0:
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| 147 | self.correct_baseline = False
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| 148 | else:
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| 149 | self.correct_baseline = True
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| 150 |
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| 151 |
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| 152 | # access data file
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| 153 | if use_CalFactFits:
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| 154 | try:
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| 155 | data_file = CalFactFits(data_file_name, calib_file_name)
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| 156 | except IOError:
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| 157 | print 'problem accessing data file: ', data_file_name
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| 158 | raise # stop ! no data
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| 159 |
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| 160 | #: either CalFactFits object or FactFits object, depending on *use_CalFactFits*
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| 161 | self.data_file = data_file
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| 162 | #: 1440x300 nparray containing the event data. pixel sorted according to CHID
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| 163 | self.data = np.empty( data_file.npix * data_file.nroi, np.float64)
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| 164 | data_file.SetNpcaldataPtr(self.data)
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| 165 | self.data = self.data.reshape( data_file.npix, data_file.nroi )
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| 166 | #: copy of data. here for historical reasons
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| 167 | self.acal_data = self.data
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| 168 | #: region of interest. (number of DRS slices read).
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| 169 | # for FACT data mostly 300. for special runs sometimes 1024.
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| 170 | self.nroi = data_file.nroi
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| 171 | #: number of Pixel in FACT. should be 1440
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| 172 | self.npix = data_file.npix
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| 173 | #: the total number of events in the data_file
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| 174 | self.nevents = data_file.nevents
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| 175 |
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| 176 | # Data per event
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| 177 | #: starting at 1
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| 178 | self.event_id = None
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| 179 |
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| 180 | #: data=4 ; the rest I don't know by heart .. should be documented here :-)
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| 181 | self.trigger_type = None
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| 182 | #self.start_cells = None
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| 183 | #self.board_times = None
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| 184 | #: slice where drs readout started for all DRS chips (160) .. but enlarged to the size of 1440 pixel. thus there are always 9 equal numbers inside.
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| 185 | self.start_cells = np.zeros( self.npix, np.int16 )
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| 186 | #: each FAD has an onboard clock running from startup time. Currently I don't know the time unit. However this is an array of 40 times, since we have 40 boards.
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| 187 | self.board_times = np.zeros( 40, np.int32 )
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| 188 | self._unixtime_tuple = np.zeros( 2, np.int32 )
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| 189 | self.unixtime = None
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| 190 |
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| 191 | # data_file is a CalFactFits object
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| 192 | # data_file.datafile is one of the two FactFits objects hold by a CalFactFits.
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| 193 | # sorry for the strange naming ..
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| 194 | data_file.datafile.SetPtrAddress('StartCellData', self.start_cells)
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| 195 | data_file.datafile.SetPtrAddress('BoardTime', self.board_times)
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| 196 | data_file.datafile.SetPtrAddress('UnixTimeUTC', self._unixtime_tuple)
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| 197 |
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| 198 |
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| 199 | else:
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| 200 | try:
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| 201 | data_file = FactFits(self.data_file_name)
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| 202 | except IOError:
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| 203 | print 'problem accessing data file: ', data_file_name
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| 204 | raise # stop ! no data
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| 205 |
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| 206 | self.data_file = data_file
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| 207 |
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| 208 | # get basic information about the data file
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| 209 | self.nroi = data_file.GetUInt('NROI')
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| 210 | self.npix = data_file.GetUInt('NPIX')
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| 211 | self.nevents = data_file.GetNumRows()
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| 212 |
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| 213 | # allocate the data memories
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| 214 | self.event_id = c_ulong()
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| 215 | self.trigger_type = c_ushort()
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| 216 | self.data = np.zeros( self.npix * self.nroi, np.int16 ).reshape(self.npix ,self.nroi)
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| 217 | self.start_cells = np.zeros( self.npix, np.int16 )
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| 218 | self.board_times = np.zeros( 40, np.int32 )
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| 219 | self._unixtime_tuple = np.zeros(2, np.int32 )
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| 220 |
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| 221 | # set the pointers to the data++
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| 222 | data_file.SetPtrAddress('EventNum', self.event_id)
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| 223 | data_file.SetPtrAddress('TriggerType', self.trigger_type)
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| 224 | data_file.SetPtrAddress('StartCellData', self.start_cells)
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| 225 | data_file.SetPtrAddress('Data', self.data)
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| 226 | data_file.SetPtrAddress('BoardTime', self.board_times)
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| 227 | data_file.SetPtrAddress('UnixTimeUTC', self._unixtime_tuple)
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| 228 |
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| 229 | # open the calibration file
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| 230 | try:
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| 231 | calib_file = FactFits(self.calib_file_name)
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| 232 | except IOError:
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| 233 | print 'problem accessing calibration file: ', calib_file_name
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| 234 | raise
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| 235 | #: drs calibration file
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| 236 | self.calib_file = calib_file
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| 237 |
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| 238 | baseline_mean = calib_file.GetN('BaselineMean')
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| 239 | gain_mean = calib_file.GetN('GainMean')
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| 240 | trigger_offset_mean = calib_file.GetN('TriggerOffsetMean')
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| 241 |
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| 242 | self.Nblm = baseline_mean / self.npix
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| 243 | self.Ngm = gain_mean / self.npix
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| 244 | self.Ntom = trigger_offset_mean / self.npix
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| 245 |
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| 246 | self.blm = np.zeros(baseline_mean, np.float32).reshape(self.npix , self.Nblm)
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| 247 | self.gm = np.zeros(gain_mean, np.float32).reshape(self.npix , self.Ngm)
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| 248 | self.tom = np.zeros(trigger_offset_mean, np.float32).reshape(self.npix , self.Ntom)
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| 249 |
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| 250 | calib_file.SetPtrAddress('BaselineMean', self.blm)
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| 251 | calib_file.SetPtrAddress('GainMean', self.gm)
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| 252 | calib_file.SetPtrAddress('TriggerOffsetMean', self.tom)
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| 253 | calib_file.GetRow(0)
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| 254 |
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| 255 | # make calibration constants double, so we never need to roll
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| 256 | self.blm = np.hstack((self.blm, self.blm))
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| 257 | self.gm = np.hstack((self.gm, self.gm))
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| 258 | self.tom = np.hstack((self.tom, self.tom))
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| 259 |
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| 260 | self.v_bsl = np.zeros(self.npix) # array of baseline values (all ZERO)
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| 261 |
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| 262 | def __iter__(self):
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| 263 | """ iterator """
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| 264 | return self
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| 265 |
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| 266 | def next(self):
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| 267 | """ used by __iter__
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| 268 |
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| 269 | returns self.__dict__
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| 270 | """
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| 271 | if self.use_CalFactFits:
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| 272 | if self.data_file.GetCalEvent() == False:
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| 273 | raise StopIteration
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| 274 | else:
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| 275 | self.event_id = self.data_file.event_id
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| 276 | self.trigger_type = self.data_file.event_triggertype
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| 277 | #self.start_cells = self.data_file.event_offset
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| 278 | #self.board_times = self.data_file.event_boardtimes
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| 279 | #self.acal_data = self.data.copy().reshape(self.data_file.npix, self.data_file.nroi)
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| 280 |
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| 281 | self.unixtime = self._unixtime_tuple[0] + self._unixtime_tuple[1]/1.e6
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| 282 |
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| 283 | else:
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| 284 | if self.data_file.GetNextRow() == False:
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| 285 | raise StopIteration
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| 286 | else:
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| 287 | if self.do_calibration == True:
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| 288 | self.calibrate_drs_amplitude()
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| 289 |
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| 290 | #print 'nevents = ', self.nevents, 'event_id = ', self.event_id.value
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| 291 | if self.return_dict:
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| 292 | return self.__dict__
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| 293 | else:
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| 294 | return self.acal_data, self.start_cells, self.trigger_type.value
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| 295 |
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| 296 | def next_event(self):
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| 297 | """ ---- DEPRICATED ----
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| 298 |
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| 299 | load the next event from disk and calibrate it
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| 300 | """
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| 301 | if self.use_CalFactFits:
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| 302 | self.data_file.GetCalEvent()
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| 303 | else:
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| 304 | self.data_file.GetNextRow()
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| 305 | self.calibrate_drs_amplitude()
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| 306 |
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| 307 | def calibrate_drs_amplitude(self):
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| 308 | """ --- DEPRICATED ---
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| 309 |
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| 310 | since the DRS calibration is done by the C++ class CalFactFits
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| 311 |
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| 312 | perform the drs amplitude calibration of the event data
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| 313 | """
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| 314 | # shortcuts
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| 315 | blm = self.blm
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| 316 | gm = self.gm
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| 317 | tom = self.tom
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| 318 |
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| 319 | to_mV = 2000./4096.
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| 320 | #: 2D array with amplitude calibrated dat in mV
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| 321 | acal_data = self.data * to_mV # convert ADC counts to mV
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| 322 |
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| 323 |
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| 324 | for pixel in range( self.npix ):
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| 325 | #shortcuts
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| 326 | sc = self.start_cells[pixel]
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| 327 | roi = self.nroi
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| 328 | # rotate the pixel baseline mean to the Data startCell
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| 329 | acal_data[pixel,:] -= blm[pixel,sc:sc+roi]
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| 330 | # the 'trigger offset mean' does not need to be rolled
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| 331 | # on the contrary, it seems there is an offset in the DRS data,
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| 332 | # which is related to its distance to the startCell, not to its
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| 333 | # distance to the beginning of the physical pipeline in the DRS chip
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| 334 | acal_data[pixel,:] -= tom[pixel,0:roi]
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| 335 | # rotate the pixel gain mean to the Data startCell
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| 336 | acal_data[pixel,:] /= gm[pixel,sc:sc+roi]
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| 337 |
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| 338 |
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| 339 | self.acal_data = acal_data * 1907.35
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| 340 |
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| 341 | self.user_action_calib( self.acal_data,
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| 342 | np.reshape(self.data, (self.npix, self.nroi) ), blm, tom, gm, self.start_cells, self.nroi)
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| 343 |
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| 344 |
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| 345 | def baseline_read_values(self, file, bsl_hist='bsl_sum/hplt_mean'):
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| 346 | """
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| 347 | open ROOT file with baseline histogram and read baseline values
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| 348 |
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| 349 | *file* : name of the root file
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| 350 |
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| 351 | *bsl_hist* : path to the histogram containing the basline values
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| 352 | """
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| 353 |
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| 354 | try:
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| 355 | f = TFile(file)
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| 356 | except:
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| 357 | print 'Baseline data file could not be read: ', file
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| 358 | return
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| 359 |
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| 360 | h = f.Get(bsl_hist)
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| 361 |
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| 362 | for i in range(self.npix):
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| 363 | self.v_bsl[i] = h.GetBinContent(i+1)
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| 364 |
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| 365 | f.Close()
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| 366 |
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| 367 | def baseline_correct(self):
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| 368 | """ subtract baseline from the data
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| 369 |
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| 370 | DN 08.06.2011: I didn't use this function at all so far... don't know how well it works.
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| 371 | """
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| 372 |
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| 373 | for pixel in range(self.npix):
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| 374 | self.acal_data[pixel,:] -= self.v_bsl[pixel]
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| 375 |
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| 376 | def info(self):
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| 377 | """ print run information
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| 378 |
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| 379 | not very well implemented ... we need more info here.
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| 380 | """
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| 381 | print 'data file: ', self.data_file_name
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| 382 | print 'calib file: ', self.calib_file_name
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| 383 | print '... we need more information printed here ... '
|
|---|
| 384 |
|
|---|
| 385 | # -----------------------------------------------------------------------------
|
|---|
| 386 | class RawDataFake( object ):
|
|---|
| 387 | """ raw data FAKE access similar to real RawData access
|
|---|
| 388 |
|
|---|
| 389 | DO NOT USE ... its not working
|
|---|
| 390 | """
|
|---|
| 391 |
|
|---|
| 392 |
|
|---|
| 393 | def __init__(self, data_file_name, calib_file_name,
|
|---|
| 394 | user_action_calib=lambda acal_data, data, blm, tom, gm, scells, nroi: None,
|
|---|
| 395 | baseline_file_name=''):
|
|---|
| 396 | self.__module__='pyfact'
|
|---|
| 397 |
|
|---|
| 398 | self.nroi = 300
|
|---|
| 399 | self.npix = 9
|
|---|
| 400 | self.nevents = 1000
|
|---|
| 401 |
|
|---|
| 402 | self.simulator = None
|
|---|
| 403 |
|
|---|
| 404 | self.time = np.ones(1024) * 0.5
|
|---|
| 405 |
|
|---|
| 406 |
|
|---|
| 407 | self.event_id = c_ulong(0)
|
|---|
| 408 | self.trigger_type = c_ushort(4)
|
|---|
| 409 | self.data = np.zeros( self.npix * self.nroi, np.int16 ).reshape(self.npix ,self.nroi)
|
|---|
| 410 | self.start_cells = np.zeros( self.npix, np.int16 )
|
|---|
| 411 | self.board_times = np.zeros( 40, np.int32 )
|
|---|
| 412 | def __iter__(self):
|
|---|
| 413 | """ iterator """
|
|---|
| 414 | return self
|
|---|
| 415 |
|
|---|
| 416 | def next(self):
|
|---|
| 417 | """ used by __iter__ """
|
|---|
| 418 | self.event_id = c_ulong(self.event_id.value + 1)
|
|---|
| 419 | self.board_times = self.board_times + 42
|
|---|
| 420 |
|
|---|
| 421 | if self.event_id.value >= self.nevents:
|
|---|
| 422 | raise StopIteration
|
|---|
| 423 | else:
|
|---|
| 424 | self._make_event_data()
|
|---|
| 425 |
|
|---|
| 426 | return self.__dict__
|
|---|
| 427 |
|
|---|
| 428 | def _make_event_data(self):
|
|---|
| 429 | sample_times = self.time.cumsum() - time[0]
|
|---|
| 430 |
|
|---|
| 431 | # random start cell
|
|---|
| 432 | self.start_cells = np.ones( self.npix, np.int16 ) * np.random.randint(0,1024)
|
|---|
| 433 |
|
|---|
| 434 | starttime = self.start_cells[0]
|
|---|
| 435 |
|
|---|
| 436 | signal = self._std_sinus_simu(sample_times, starttime)
|
|---|
| 437 |
|
|---|
| 438 | data = np.vstack( (signal,signal) )
|
|---|
| 439 | for i in range(8):
|
|---|
| 440 | data = np.vstack( (data,signal) )
|
|---|
| 441 |
|
|---|
| 442 | self.data = data
|
|---|
| 443 |
|
|---|
| 444 | def _std_sinus_simu(self, times, starttime):
|
|---|
| 445 | period = 10 # in ns
|
|---|
| 446 |
|
|---|
| 447 | # give a jitter on starttime
|
|---|
| 448 | starttime = np.random.normal(startime, 0.05)
|
|---|
| 449 |
|
|---|
| 450 | phase = 0.0
|
|---|
| 451 | signal = 10 * np.sin(times * 2*np.pi/period + starttime + phase)
|
|---|
| 452 |
|
|---|
| 453 | # add some noise
|
|---|
| 454 | noise = np.random.normal(0.0, 0.5, signal.shape)
|
|---|
| 455 | signal += noise
|
|---|
| 456 | return signal
|
|---|
| 457 |
|
|---|
| 458 | def info(self):
|
|---|
| 459 | """ print run information
|
|---|
| 460 |
|
|---|
| 461 | """
|
|---|
| 462 |
|
|---|
| 463 | print 'data file: ', data_file_name
|
|---|
| 464 | print 'calib file: ', calib_file_name
|
|---|
| 465 | print 'calibration file'
|
|---|
| 466 | print 'N baseline_mean: ', self.Nblm
|
|---|
| 467 | print 'N gain mean: ', self.Ngm
|
|---|
| 468 | print 'N TriggeroffsetMean: ', self.Ntom
|
|---|
| 469 |
|
|---|
| 470 | # -----------------------------------------------------------------------------
|
|---|
| 471 | import ctypes
|
|---|
| 472 |
|
|---|
| 473 | class SlowData( object ):
|
|---|
| 474 | """ -Fact SlowData File-
|
|---|
| 475 |
|
|---|
| 476 | A Python wrapper for the fits-class implemented in factfits.h
|
|---|
| 477 | provides easy access to the fits file meta data.
|
|---|
| 478 |
|
|---|
| 479 | * dictionary of file metadata - self.meta
|
|---|
| 480 | * dict of table metadata - self.columns
|
|---|
| 481 | * variable table column access, thus possibly increased speed while looping
|
|---|
| 482 | """
|
|---|
| 483 | def __del__(self):
|
|---|
| 484 | del self.f
|
|---|
| 485 |
|
|---|
| 486 | def __init__(self, path):
|
|---|
| 487 | """ creates meta and columns dictionaries
|
|---|
| 488 | """
|
|---|
| 489 | import os
|
|---|
| 490 |
|
|---|
| 491 | if not os.path.exists(path):
|
|---|
| 492 | raise IOError(path+' was not found')
|
|---|
| 493 | self.path = path
|
|---|
| 494 | self.__module__ = 'pyfact'
|
|---|
| 495 | try:
|
|---|
| 496 | self.f = FactFits(path)
|
|---|
| 497 | except IOError:
|
|---|
| 498 | print 'problem accessing data file: ', data_file_name
|
|---|
| 499 | raise # stop ! no data
|
|---|
| 500 |
|
|---|
| 501 | self.meta = self._make_meta_dict()
|
|---|
| 502 | self.columns = self._make_columns_dict()
|
|---|
| 503 |
|
|---|
| 504 | self._treat_meta_dict()
|
|---|
| 505 |
|
|---|
| 506 |
|
|---|
| 507 | # list of columns, which are already registered
|
|---|
| 508 | # see method register()
|
|---|
| 509 | self._registered_cols = []
|
|---|
| 510 | # dict of column data, this is used, in order to be able to remove
|
|---|
| 511 | # the ctypes of
|
|---|
| 512 | self._table_cols = {}
|
|---|
| 513 |
|
|---|
| 514 | # I need to count the rows, since the normal loop mechanism seems not to work.
|
|---|
| 515 | self._current_row = 0
|
|---|
| 516 |
|
|---|
| 517 | self.stacked_cols = {}
|
|---|
| 518 |
|
|---|
| 519 | def _make_meta_dict(self):
|
|---|
| 520 | """ This method retrieves meta information about the fits file and
|
|---|
| 521 | stores this information in a dict
|
|---|
| 522 | return: dict
|
|---|
| 523 | key: string - all capital letters
|
|---|
| 524 | value: tuple( numerical value, string comment)
|
|---|
| 525 | """
|
|---|
| 526 | # abbreviation
|
|---|
| 527 | f = self.f
|
|---|
| 528 |
|
|---|
| 529 | # intermediate variables for file metadata dict generation
|
|---|
| 530 |
|
|---|
| 531 | keys=f.GetPy_KeyKeys()
|
|---|
| 532 | values=f.GetPy_KeyValues()
|
|---|
| 533 | comments=f.GetPy_KeyComments()
|
|---|
| 534 | types=f.GetPy_KeyTypes()
|
|---|
| 535 |
|
|---|
| 536 | if len(keys) != len(values):
|
|---|
| 537 | raise TypeError('len(keys)',len(keys),' != len(values)', len(values))
|
|---|
| 538 | if len(keys) != len(types):
|
|---|
| 539 | raise TypeError('len(keys)',len(keys),' != len(types)', len(types))
|
|---|
| 540 | if len(keys) != len(comments):
|
|---|
| 541 | raise TypeError('len(keys)',len(keys),' != len(comments)', len(comments))
|
|---|
| 542 |
|
|---|
| 543 | meta_dict = {}
|
|---|
| 544 | for i in range(len(keys)):
|
|---|
| 545 | type = types[i]
|
|---|
| 546 | if type == 'I':
|
|---|
| 547 | value = int(values[i])
|
|---|
| 548 | elif type == 'F':
|
|---|
| 549 | value = float(values[i])
|
|---|
| 550 | elif type == 'B':
|
|---|
| 551 | if values[i] == 'T':
|
|---|
| 552 | value = True
|
|---|
| 553 | elif values[i] == 'F':
|
|---|
| 554 | value = False
|
|---|
| 555 | else:
|
|---|
| 556 | raise TypeError("meta-type is 'B', but meta-value is neither 'T' nor 'F'. meta-value:",values[i])
|
|---|
| 557 | elif type == 'T':
|
|---|
| 558 | value = values[i]
|
|---|
| 559 | else:
|
|---|
| 560 | raise TypeError("unknown meta-type: known meta types are: I,F,B and T. meta-type:",type)
|
|---|
| 561 | meta_dict[keys[i]]=(value, comments[i])
|
|---|
| 562 | return meta_dict
|
|---|
| 563 |
|
|---|
| 564 |
|
|---|
| 565 | def _make_columns_dict(self):
|
|---|
| 566 | """ This method retrieves information about the columns
|
|---|
| 567 | stored inside the fits files internal binary table.
|
|---|
| 568 | returns: dict
|
|---|
| 569 | key: string column name -- all capital letters
|
|---|
| 570 | values: tuple(
|
|---|
| 571 | number of elements in table field - integer
|
|---|
| 572 | size of element in bytes -- this is not really interesting for any user
|
|---|
| 573 | might be ommited in future versions
|
|---|
| 574 | type - a single character code -- should be translated into
|
|---|
| 575 | a comrehensible word
|
|---|
| 576 | unit - string like 'mV' or 'ADC count'
|
|---|
| 577 | """
|
|---|
| 578 | # abbreviation
|
|---|
| 579 | f = self.f
|
|---|
| 580 |
|
|---|
| 581 | # intermediate variables for file table-metadata dict generation
|
|---|
| 582 | keys=f.GetPy_ColumnKeys()
|
|---|
| 583 | #offsets=self.GetPy_ColumnOffsets() #not needed on python level...
|
|---|
| 584 | nums=f.GetPy_ColumnNums()
|
|---|
| 585 | sizes=f.GetPy_ColumnSizes()
|
|---|
| 586 | types=f.GetPy_ColumnTypes()
|
|---|
| 587 | units=f.GetPy_ColumnUnits()
|
|---|
| 588 |
|
|---|
| 589 | # zip the values
|
|---|
| 590 | values = zip(nums,sizes,types,units)
|
|---|
| 591 | # create the columns dictionary
|
|---|
| 592 | columns = dict(zip(keys ,values))
|
|---|
| 593 | return columns
|
|---|
| 594 |
|
|---|
| 595 | def stack(self, on=True):
|
|---|
| 596 | self.next()
|
|---|
| 597 | for col in self._registered_cols:
|
|---|
| 598 | if isinstance( self.dict[col], type(np.array('')) ):
|
|---|
| 599 | self.stacked_cols[col] = self.dict[col]
|
|---|
| 600 | else:
|
|---|
| 601 | # elif isinstance(self.dict[col], ctypes._SimpleCData):
|
|---|
| 602 | self.stacked_cols[col] = np.array(self.dict[col])
|
|---|
| 603 | # else:
|
|---|
| 604 | # raise TypeError("I don't know how to stack "+col+". It is of type: "+str(type(self.dict[col])))
|
|---|
| 605 |
|
|---|
| 606 | def register(self, col_name):
|
|---|
| 607 | """ register for a column in the fits file
|
|---|
| 608 |
|
|---|
| 609 | after the call, this SlowData object will have a new member variable
|
|---|
| 610 | self.col_name, if col_name is a key in self.colums
|
|---|
| 611 |
|
|---|
| 612 | the value will be updated after each call of next(), or while iterating over self.
|
|---|
| 613 | NB: the initial value is zero(s)
|
|---|
| 614 |
|
|---|
| 615 | *col_name* : name of a key in self.columns, or 'all' to choose all.
|
|---|
| 616 | """
|
|---|
| 617 | columns = self.columns
|
|---|
| 618 | if col_name.lower() == 'all':
|
|---|
| 619 | for col in columns:
|
|---|
| 620 | self._register(col)
|
|---|
| 621 | else:
|
|---|
| 622 | #check if colname is in columns:
|
|---|
| 623 | if col_name not in columns:
|
|---|
| 624 | error_msg = 'colname:'+ col_name +' is not a column in the binary table.\n'
|
|---|
| 625 | error_msg+= 'possible colnames are\n'
|
|---|
| 626 | for key in columns:
|
|---|
| 627 | error_msg += key+' '
|
|---|
| 628 | raise KeyError(error_msg)
|
|---|
| 629 | else:
|
|---|
| 630 | self._register(col_name)
|
|---|
| 631 |
|
|---|
| 632 | # 'private' method, do not use
|
|---|
| 633 | def _register( self, colname):
|
|---|
| 634 |
|
|---|
| 635 | columns = self.columns
|
|---|
| 636 | f = self.f
|
|---|
| 637 | local = None
|
|---|
| 638 |
|
|---|
| 639 | number_of_elements = int(columns[colname][0])
|
|---|
| 640 | size_of_elements_in_bytes = int(columns[colname][1])
|
|---|
| 641 | ctypecode_of_elements = columns[colname][2]
|
|---|
| 642 | physical_unit_of_elements = columns[colname][3]
|
|---|
| 643 |
|
|---|
| 644 | # snippet from the C++ source code, or header file to be precise:
|
|---|
| 645 | #case 'L': gLog << "bool(8)"; break;
|
|---|
| 646 | #case 'B': gLog << "byte(8)"; break;
|
|---|
| 647 | #case 'I': gLog << "short(16)"; break;
|
|---|
| 648 | #case 'J': gLog << "int(32)"; break;
|
|---|
| 649 | #case 'K': gLog << "int(64)"; break;
|
|---|
| 650 | #case 'E': gLog << "float(32)"; break;
|
|---|
| 651 | #case 'D': gLog << "double(64)"; break;
|
|---|
| 652 |
|
|---|
| 653 |
|
|---|
| 654 |
|
|---|
| 655 | # the fields inside the columns can either contain single numbers,
|
|---|
| 656 | # or whole arrays of numbers as well.
|
|---|
| 657 | # we treat single elements differently...
|
|---|
| 658 | if number_of_elements == 0:
|
|---|
| 659 | return
|
|---|
| 660 | if number_of_elements == 1:
|
|---|
| 661 | # allocate some memory for a single number according to its type
|
|---|
| 662 | if ctypecode_of_elements == 'J': # J is for a 4byte int, i.e. an unsigned long
|
|---|
| 663 | local = ctypes.c_ulong()
|
|---|
| 664 | un_c_type = long
|
|---|
| 665 | elif ctypecode_of_elements == 'I': # I is for a 2byte int, i.e. an unsinged int
|
|---|
| 666 | local = ctypes.c_ushort()
|
|---|
| 667 | un_c_type = int
|
|---|
| 668 | elif ctypecode_of_elements == 'B': # B is for a byte
|
|---|
| 669 | local = ctypes.c_ubyte()
|
|---|
| 670 | un_c_type = int
|
|---|
| 671 | elif ctypecode_of_elements == 'D':
|
|---|
| 672 | local = ctypes.c_double()
|
|---|
| 673 | un_c_type = float
|
|---|
| 674 | elif ctypecode_of_elements == 'E':
|
|---|
| 675 | local = ctypes.c_float()
|
|---|
| 676 | un_c_type = float
|
|---|
| 677 | elif ctypecode_of_elements == 'A':
|
|---|
| 678 | local = ctypes.c_uchar()
|
|---|
| 679 | un_c_type = chr
|
|---|
| 680 | elif ctypecode_of_elements == 'K':
|
|---|
| 681 | local = ctypes.c_ulonglong()
|
|---|
| 682 | un_c_type = long
|
|---|
| 683 | else:
|
|---|
| 684 | raise TypeError('unknown ctypecode_of_elements:',ctypecode_of_elements)
|
|---|
| 685 | else:
|
|---|
| 686 | if ctypecode_of_elements == 'B': # B is for a byte
|
|---|
| 687 | nptype = np.int8
|
|---|
| 688 | elif ctypecode_of_elements == 'A': # A is for a char .. but I don't know how to handle it
|
|---|
| 689 | nptype = np.int8
|
|---|
| 690 | elif ctypecode_of_elements == 'I': # I is for a 2byte int
|
|---|
| 691 | nptype = np.int16
|
|---|
| 692 | elif ctypecode_of_elements == 'J': # J is for a 4byte int
|
|---|
| 693 | nptype = np.int32
|
|---|
| 694 | elif ctypecode_of_elements == 'K': # B is for a byte
|
|---|
| 695 | nptype = np.int64
|
|---|
| 696 | elif ctypecode_of_elements == 'E': # B is for a byte
|
|---|
| 697 | nptype = np.float32
|
|---|
| 698 | elif ctypecode_of_elements == 'D': # B is for a byte
|
|---|
| 699 | nptype = np.float64
|
|---|
| 700 | else:
|
|---|
| 701 | raise TypeError('unknown ctypecode_of_elements:',ctypecode_of_elements)
|
|---|
| 702 | local = np.zeros( number_of_elements, nptype)
|
|---|
| 703 |
|
|---|
| 704 | # Set the Pointer Address
|
|---|
| 705 | try:
|
|---|
| 706 | f.SetPtrAddress(colname, local)
|
|---|
| 707 | except TypeError:
|
|---|
| 708 | print 'something was wrong with SetPtrAddress()'
|
|---|
| 709 | print 'Type of colname', type(colname)
|
|---|
| 710 | print 'colname:', colname
|
|---|
| 711 | print 'Type of local', type(local)
|
|---|
| 712 | print 'length of local', len(local)
|
|---|
| 713 | print 'local should be alle zeros, since "local = np.zeros( number_of_elements, nptype)" '
|
|---|
| 714 | raise
|
|---|
| 715 |
|
|---|
| 716 | self._table_cols[colname] = local
|
|---|
| 717 | if number_of_elements > 1:
|
|---|
| 718 | self.__dict__[colname] = local
|
|---|
| 719 | self.dict[colname] = local
|
|---|
| 720 | else:
|
|---|
| 721 | # remove any traces of ctypes:
|
|---|
| 722 | self.__dict__[colname] = local.value
|
|---|
| 723 | self.dict[colname] = local.value
|
|---|
| 724 | self._registered_cols.append(colname)
|
|---|
| 725 |
|
|---|
| 726 |
|
|---|
| 727 | def _treat_meta_dict(self):
|
|---|
| 728 | """make 'interesting' meta information available like normal members.
|
|---|
| 729 | non interesting are:
|
|---|
| 730 | TFORM, TUNIT, and TTYPE
|
|---|
| 731 | since these are available via the columns dict.
|
|---|
| 732 | """
|
|---|
| 733 |
|
|---|
| 734 | self.number_of_rows = self.meta['NAXIS2'][0]
|
|---|
| 735 | self.number_of_columns = self.meta['TFIELDS'][0]
|
|---|
| 736 |
|
|---|
| 737 | # there are some information in the meta dict, which are alsways there:
|
|---|
| 738 | # there are regarded as not interesting:
|
|---|
| 739 | uninteresting_meta = {}
|
|---|
| 740 | uninteresting_meta['arraylike'] = {}
|
|---|
| 741 | uninteresting = ['NAXIS', 'NAXIS1', 'NAXIS2',
|
|---|
| 742 | 'TFIELDS',
|
|---|
| 743 | 'XTENSION','EXTNAME','EXTREL',
|
|---|
| 744 | 'BITPIX', 'PCOUNT', 'GCOUNT',
|
|---|
| 745 | 'ORIGIN',
|
|---|
| 746 | 'PACKAGE', 'COMPILED', 'CREATOR',
|
|---|
| 747 | 'TELESCOP','TIMESYS','TIMEUNIT','VERSION']
|
|---|
| 748 | for key in uninteresting:
|
|---|
| 749 | if key in self.meta:
|
|---|
| 750 | uninteresting_meta[key]=self.meta[key]
|
|---|
| 751 | del self.meta[key]
|
|---|
| 752 |
|
|---|
| 753 | # the table meta data contains
|
|---|
| 754 |
|
|---|
| 755 |
|
|---|
| 756 | # shortcut to access the meta dict. But this needs to
|
|---|
| 757 | # be cleaned up quickly!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
|---|
| 758 | meta = self.meta
|
|---|
| 759 |
|
|---|
| 760 | # loop over keys:
|
|---|
| 761 | # * try to find array-like keys
|
|---|
| 762 | arraylike = {}
|
|---|
| 763 | singlelike = []
|
|---|
| 764 | for key in self.meta:
|
|---|
| 765 | stripped = key.rstrip('1234567890')
|
|---|
| 766 | if stripped == key:
|
|---|
| 767 | singlelike.append(key)
|
|---|
| 768 | else:
|
|---|
| 769 | if stripped not in arraylike:
|
|---|
| 770 | arraylike[stripped] = 0
|
|---|
| 771 | else:
|
|---|
| 772 | arraylike[stripped] += 1
|
|---|
| 773 | newmeta = {}
|
|---|
| 774 | for key in singlelike:
|
|---|
| 775 | newmeta[key.lower()] = meta[key]
|
|---|
| 776 | for key in arraylike:
|
|---|
| 777 | uninteresting_meta['arraylike'][key.lower()] = []
|
|---|
| 778 | for i in range(arraylike[key]+1):
|
|---|
| 779 | if key+str(i) in meta:
|
|---|
| 780 | uninteresting_meta['arraylike'][key.lower()].append(meta[key+str(i)])
|
|---|
| 781 | self.ui_meta = uninteresting_meta
|
|---|
| 782 | # make newmeta self
|
|---|
| 783 | for key in newmeta:
|
|---|
| 784 | self.__dict__[key]=newmeta[key]
|
|---|
| 785 |
|
|---|
| 786 | dict = self.__dict__.copy()
|
|---|
| 787 | del dict['meta']
|
|---|
| 788 | del dict['ui_meta']
|
|---|
| 789 | self.dict = dict
|
|---|
| 790 |
|
|---|
| 791 | def __iter__(self):
|
|---|
| 792 | """ iterator """
|
|---|
| 793 | return self
|
|---|
| 794 |
|
|---|
| 795 | def next(self):
|
|---|
| 796 | """ use to iterate over the file
|
|---|
| 797 |
|
|---|
| 798 | do not forget to call register() before iterating over the file
|
|---|
| 799 | call show() in order to find out, what parameters register() accepts.
|
|---|
| 800 | or just call register('all') in case you are unsure.
|
|---|
| 801 |
|
|---|
| 802 | returns self
|
|---|
| 803 | """
|
|---|
| 804 | # abbreviaition
|
|---|
| 805 | f = self.f
|
|---|
| 806 |
|
|---|
| 807 | # Here one might check, if looping makes any sense, and if not
|
|---|
| 808 | # one could stop looping or so...
|
|---|
| 809 | # like this:
|
|---|
| 810 | #
|
|---|
| 811 | # if len(self._registered_cols) == 0:
|
|---|
| 812 | # print 'warning: looping without any registered columns'
|
|---|
| 813 | if self._current_row < self.number_of_rows:
|
|---|
| 814 | if f.GetNextRow() == False:
|
|---|
| 815 | raise StopIteration
|
|---|
| 816 | for col in self._registered_cols:
|
|---|
| 817 | if isinstance(self._table_cols[col], ctypes._SimpleCData):
|
|---|
| 818 | self.__dict__[col] = self._table_cols[col].value
|
|---|
| 819 | self.dict[col] = self._table_cols[col].value
|
|---|
| 820 |
|
|---|
| 821 | for col in self.stacked_cols:
|
|---|
| 822 | if isinstance(self.dict[col], type(np.array(''))):
|
|---|
| 823 | self.stacked_cols[col] = np.vstack( (self.stacked_cols[col],self.dict[col]) )
|
|---|
| 824 | else:
|
|---|
| 825 | self.stacked_cols[col] = np.vstack( (self.stacked_cols[col],np.array(self.dict[col])) )
|
|---|
| 826 | self._current_row += 1
|
|---|
| 827 | else:
|
|---|
| 828 | raise StopIteration
|
|---|
| 829 | return self
|
|---|
| 830 |
|
|---|
| 831 | def show(self):
|
|---|
| 832 | """
|
|---|
| 833 | """
|
|---|
| 834 | pprint.pprint(self.dict)
|
|---|
| 835 |
|
|---|
| 836 |
|
|---|
| 837 |
|
|---|
| 838 |
|
|---|
| 839 | class fnames( object ):
|
|---|
| 840 | """ organize file names of a FACT data run
|
|---|
| 841 |
|
|---|
| 842 | """
|
|---|
| 843 |
|
|---|
| 844 | def __init__(self, specifier = ['012', '023', '2011', '11', '24'],
|
|---|
| 845 | rpath = '/scratch_nfs/res/bsl/',
|
|---|
| 846 | zipped = True):
|
|---|
| 847 | """
|
|---|
| 848 | specifier : list of strings defined as:
|
|---|
| 849 | [ 'DRS calibration file', 'Data file', 'YYYY', 'MM', 'DD']
|
|---|
| 850 |
|
|---|
| 851 | rpath : directory path for the results; YYYYMMDD will be appended to rpath
|
|---|
| 852 | zipped : use zipped (True) or unzipped (Data)
|
|---|
| 853 |
|
|---|
| 854 | """
|
|---|
| 855 |
|
|---|
| 856 | self.specifier = specifier
|
|---|
| 857 | self.rpath = rpath
|
|---|
| 858 | self.zipped = zipped
|
|---|
| 859 |
|
|---|
| 860 | self.make( self.specifier, self.rpath, self.zipped )
|
|---|
| 861 |
|
|---|
| 862 |
|
|---|
| 863 | def make( self, specifier, rpath, zipped ):
|
|---|
| 864 | """ create (make) the filenames
|
|---|
| 865 |
|
|---|
| 866 | names : dictionary of filenames, tags { 'data', 'drscal', 'results' }
|
|---|
| 867 | data : name of the data file
|
|---|
| 868 | drscal : name of the drs calibration file
|
|---|
| 869 | results : radikal of file name(s) for results (to be completed by suffixes)
|
|---|
| 870 | """
|
|---|
| 871 |
|
|---|
| 872 | self.specifier = specifier
|
|---|
| 873 |
|
|---|
| 874 | if zipped:
|
|---|
| 875 | dpath = '/data00/fact-construction/raw/'
|
|---|
| 876 | ext = '.fits.gz'
|
|---|
| 877 | else:
|
|---|
| 878 | dpath = '/data03/fact-construction/raw/'
|
|---|
| 879 | ext = '.fits'
|
|---|
| 880 |
|
|---|
| 881 | year = specifier[2]
|
|---|
| 882 | month = specifier[3]
|
|---|
| 883 | day = specifier[4]
|
|---|
| 884 |
|
|---|
| 885 | yyyymmdd = year + month + day
|
|---|
| 886 | dfile = specifier[1]
|
|---|
| 887 | cfile = specifier[0]
|
|---|
| 888 |
|
|---|
| 889 | rpath = rpath + yyyymmdd + '/'
|
|---|
| 890 | self.rpath = rpath
|
|---|
| 891 | self.names = {}
|
|---|
| 892 |
|
|---|
| 893 | tmp = dpath + year + '/' + month + '/' + day + '/' + yyyymmdd + '_'
|
|---|
| 894 | self.names['data'] = tmp + dfile + ext
|
|---|
| 895 | self.names['drscal'] = tmp + cfile + '.drs' + ext
|
|---|
| 896 | self.names['results'] = rpath + yyyymmdd + '_' + dfile + '_' + cfile
|
|---|
| 897 |
|
|---|
| 898 | self.data = self.names['data']
|
|---|
| 899 | self.drscal = self.names['drscal']
|
|---|
| 900 | self.results = self.names['results']
|
|---|
| 901 |
|
|---|
| 902 | def info( self ):
|
|---|
| 903 | """ print complete filenames
|
|---|
| 904 |
|
|---|
| 905 | """
|
|---|
| 906 |
|
|---|
| 907 | print 'file names:'
|
|---|
| 908 | print 'data: ', self.names['data']
|
|---|
| 909 | print 'drs-cal: ', self.names['drscal']
|
|---|
| 910 | print 'results: ', self.names['results']
|
|---|
| 911 |
|
|---|
| 912 | # end of class definition: fnames( object )
|
|---|
| 913 |
|
|---|
| 914 | def _test_SlowData( filename ):
|
|---|
| 915 | print '-'*70
|
|---|
| 916 | print "opened :", filename, " as 'file'"
|
|---|
| 917 | print
|
|---|
| 918 | print '-'*70
|
|---|
| 919 | print 'type file.show() to look at its contents'
|
|---|
| 920 | print "type file.register( columnname ) or file.register('all') in order to register columns"
|
|---|
| 921 | print
|
|---|
| 922 | print " due column-registration you declare, that you would like to retrieve the contents of one of the columns"
|
|---|
| 923 | print " after column-registration, the 'file' has new member variables, they are named like the columns"
|
|---|
| 924 | print " PLEASE NOTE: immediatly after registration, the members exist, but they are empty."
|
|---|
| 925 | print " the values are assigned only, when you call file.next() or when you loop over the 'file'"
|
|---|
| 926 | print
|
|---|
| 927 | print "in order to loop over it, just go like this:"
|
|---|
| 928 | print "for row in file:"
|
|---|
| 929 | print " print row.columnname_one, row.columnname_two"
|
|---|
| 930 | print
|
|---|
| 931 | print ""
|
|---|
| 932 | print '-'*70
|
|---|
| 933 |
|
|---|
| 934 |
|
|---|
| 935 |
|
|---|
| 936 | def _test_iter( nevents ):
|
|---|
| 937 | """ test for function __iter__ """
|
|---|
| 938 |
|
|---|
| 939 | data_file_name = '/fact/raw/2011/11/24/20111124_117.fits.gz'
|
|---|
| 940 | calib_file_name = '/fact/raw/2011/11/24/20111124_114.drs.fits.gz'
|
|---|
| 941 | print 'the files for this test are:'
|
|---|
| 942 | print 'data file:', data_file_name
|
|---|
| 943 | print 'calib file:', calib_file_name
|
|---|
| 944 | # data_file_name = '/home/luster/win7/FACT/data/raw/20120114/20120114_028.fits.gz'
|
|---|
| 945 | # calib_file_name = '/home/luster/win7/FACT/data/raw/20120114/20120114_022.drs.fits.gz'
|
|---|
| 946 | run = RawData( data_file_name, calib_file_name , return_dict=True)
|
|---|
| 947 |
|
|---|
| 948 | for event in run:
|
|---|
| 949 | 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']
|
|---|
| 950 | if run.event_id == nevents:
|
|---|
| 951 | break
|
|---|
| 952 |
|
|---|
| 953 | if __name__ == '__main__':
|
|---|
| 954 | """ tests """
|
|---|
| 955 | import sys
|
|---|
| 956 | if len(sys.argv) == 1:
|
|---|
| 957 | print 'showing test of iterator of RawData class'
|
|---|
| 958 | print 'in order to test the SlowData classe please use:', sys.argv[0], 'fits-file-name'
|
|---|
| 959 | _test_iter(10)
|
|---|
| 960 |
|
|---|
| 961 |
|
|---|
| 962 | else:
|
|---|
| 963 | print 'showing test of SlowData class'
|
|---|
| 964 | print 'in case you wanted to test the RawData class, please give no commandline arguments'
|
|---|
| 965 | file = SlowData(sys.argv[1])
|
|---|
| 966 | _test_SlowData(sys.argv[1])
|
|---|
