| 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 | import sys
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| 7 | from ctypes import *
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| 8 | import numpy as np
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| 9 | import pprint # for SlowData
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| 10 | from scipy import signal
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| 11 |
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| 12 | # get the ROOT stuff + my shared libs
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| 13 | import ROOT
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| 14 | # factfits_h.so is made from factfits.h and is used to access the data
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| 15 | # make sure the location of factfits_h.so is in LD_LIBRARY_PATH.
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| 16 | # having it in PYTHONPATH is *not* sufficient
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| 17 | hostname = ROOT.gSystem.HostName()
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| 18 | libz_path_dict = {
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| 19 | # hostname : /path/to/libz.so
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| 20 | 'isdc' : "/usr/lib64/libz.so",
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| 21 | 'neiseLenovo' : "/usr/lib/libz.so",
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| 22 | 'factcontrol' : "/usr/lib/libz.so",
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| 23 | "max-K50AB" : "/usr/lib/x86_64-linux-gnu/libz.so",
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| 24 | "watz" : "/usr/lib/x86_64-linux-gnu/libz.so",
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| 25 | "grolsch" : "/usr/lib/i386-linux-gnu/libz.so",
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| 26 | }
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| 27 | libz_loaded = False
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| 28 | for my_hostname in libz_path_dict:
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| 29 | if my_hostname in hostname:
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| 30 | ROOT.gSystem.Load(libz_path_dict[my_hostname])
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| 31 | libz_loaded = True
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| 32 | if not libz_loaded:
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| 33 | print """Warning - Warning - Warning - Warning - Warning - Warning - Warning
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| 34 | I most probably need to load libz.so but I don't know where it is.
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| 35 |
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| 36 | Please edit pyfact.py around line 16-24 and insert your hostname and your
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| 37 | path to your libz.so
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| 38 | Sorry for the inconvenience.
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| 39 | """
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| 40 | sys.exit(-1)
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| 41 |
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| 42 |
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| 43 | root_make_string = ROOT.gSystem.GetMakeSharedLib()
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| 44 | if not "-std=c++0x" in root_make_string:
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| 45 | root_make_string = root_make_string.replace('$Opt', '$Opt -std=c++0x -D HAVE_ZLIB')
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| 46 | ROOT.gSystem.SetMakeSharedLib(root_make_string)
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| 47 |
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| 48 | ROOT.gROOT.ProcessLine(".L extern_Mars_mcore/izstream.h+O")
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| 49 | ROOT.gROOT.ProcessLine(".L extern_Mars_mcore/fits.h+O")
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| 50 | ROOT.gROOT.ProcessLine(".L extern_Mars_mcore/zfits.h+O")
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| 51 | ROOT.gROOT.ProcessLine(".L extern_Mars_mcore/factfits.h+O")
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| 52 | ROOT.gROOT.ProcessLine(".L calfactfits.h+O")
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| 53 |
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| 54 | ROOT.gInterpreter.GenerateDictionary("map<string,fits::Entry>","map;string;extern_Mars_mcore/fits.h")
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| 55 | ROOT.gInterpreter.GenerateDictionary("pair<string,fits::Entry>","map;string;extern_Mars_mcore/fits.h")
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| 56 | ROOT.gInterpreter.GenerateDictionary("map<string,fits::Table::Column>","map;string;extern_Mars_mcore/fits.h")
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| 57 | ROOT.gInterpreter.GenerateDictionary("pair<string,fits::Table::Column>","map;string;extern_Mars_mcore/fits.h")
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| 58 |
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| 59 | #ROOT.gSystem.Load('my_string_h.so')
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| 60 | ROOT.gSystem.Load('extern_Mars_mcore/fits_h.so')
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| 61 | ROOT.gSystem.Load('extern_Mars_mcore/izstream_h.so')
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| 62 | ROOT.gSystem.Load('extern_Mars_mcore/zfits_h.so')
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| 63 | ROOT.gSystem.Load('extern_Mars_mcore/factfits_h.so')
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| 64 | ROOT.gSystem.Load('calfactfits_h.so')
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| 65 | from ROOT import *
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| 66 |
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| 67 | class RawDataFeeder( object ):
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| 68 | """ Wrapper class for RawData class
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| 69 | capable of iterating over multiple RawData Files
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| 70 | """
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| 71 |
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| 72 | def __init__(self, filelist):
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| 73 | """ *filelist* list of files to iterate over
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| 74 | the list should contain tuples, or sublists of two filenames
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| 75 | the first should be a data file (\*.fits.gz)
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| 76 | the second should be an amplitude calibration file(\*.drs.fits.gz)
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| 77 | """
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| 78 |
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| 79 | self.__module__ = 'pyfact'
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| 80 |
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| 81 | # sanity check for input
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| 82 | if type(filelist) != type(list()):
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| 83 | raise TypeError('filelist should be a list')
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| 84 | for entry in filelist:
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| 85 | if len(entry) != 2:
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| 86 | raise TypeError('the entries of filelist should have length == 2')
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| 87 | for path in entry:
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| 88 | if type(path) != type(str()):
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| 89 | raise TypeError('the entries of filelist should be path, i.e. of type str()')
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| 90 | #todo check if 'path' is a valid path
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| 91 | # else: throw an Exception, or Warning?
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| 92 |
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| 93 | self.filelist = filelist
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| 94 | self._current_RawData = RawData(filelist[0][0], filelist[0][1], return_dict=True)
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| 95 | del filelist[0]
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| 96 |
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| 97 | def __iter__(self):
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| 98 | return self
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| 99 |
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| 100 | def next():
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| 101 | """ Method being called by the iterator.
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| 102 | Since the RawData Objects are simply looped over, the event_id from the
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| 103 | RawData object will not be unique.
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| 104 | Each RawData obejct will start with event_id = 1 as usual.
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| 105 | """
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| 106 | try:
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| 107 | return self._current_RawData.next()
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| 108 | except StopIteration:
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| 109 | # current_RawData was completely processed
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| 110 | # delete it (I hope this calls the destructor of the fits file and/or closes it)
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| 111 | del self._current_RawData
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| 112 | # and remake it, if possible
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| 113 | if len(self.filelist) > 0:
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| 114 | self._current_RawData = RawData(filelist[0][0], filelist[0][1], return_dict=True)
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| 115 | del filelist[0]
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| 116 | else:
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| 117 | raise
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| 118 |
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| 119 |
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| 120 |
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| 121 | class RawData( object ):
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| 122 | """ raw data access and calibration
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| 123 |
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| 124 | class is **iterable**
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| 125 |
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| 126 | - open raw data file and drs calibration file
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| 127 | - performs amplitude calibration
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| 128 | - performs baseline substraction if wanted
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| 129 | - provides all data in an array:
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| 130 | row = number of pixel
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| 131 | col = length of region of interest
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| 132 |
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| 133 | """
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| 134 |
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| 135 |
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| 136 | def __init__(self, data_file_name, calib_file_name,
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| 137 | baseline_file_name='',
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| 138 | return_dict = True,
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| 139 | use_CalFactFits = True,
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| 140 | do_calibration = True,
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| 141 | user_action_calib=lambda acal_data, data, blm, tom, gm, scells, nroi: None):
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| 142 | """ -constructor-
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| 143 |
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| 144 | - open data file and calibration data file
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| 145 | - get basic information about the data in data_file_name
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| 146 | - allocate buffers for data access
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| 147 |
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| 148 | *data_file_name* : fits or fits.gz file of the data including the path
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| 149 |
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| 150 | *calib_file_name* : fits or fits.gz file containing DRS calibration data
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| 151 |
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| 152 | *baseline_file_name* : npy file containing the baseline values
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| 153 |
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| 154 | *return_dict* : this option will be removed in future releases.
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| 155 | formerly the next() method returned only a subset of (important) event information,
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| 156 | and it was not transparent how to retrieve the other (less important) information.
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| 157 | Nowadays next() returns self.__dict__ which contains everything we were able to find in the fits file.
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| 158 |
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| 159 | *use_CalFactFits* : formerly the DRS amplitude calibration was
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| 160 | implemented in python. But for performance reasons this was now moved into
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| 161 | a C++ class called CalFactFits. For test purposes, this option can be set to
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| 162 | False, but this is not really maintained anymore. If DRS the DRS calibration algorithm is
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| 163 | beeing updated in C++ it may not be updated in the python implementation.
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| 164 |
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| 165 | *do_calibration* : In case *use_CalFactFits* is False, one may choose
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| 166 | not to calibrate the data at all, thus safe quite some time.
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| 167 | This is imho only needed in case one is interesting in learning something about the
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| 168 | calibration algorithm itself.
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| 169 |
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| 170 | *user_action_calib* : callback function, intended for tests of the DRS calibration algorithm.
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| 171 | but since this is not done in the Python regime anymore, this function is never called.
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| 172 | (depending on *use_CalFactFits* of course)
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| 173 | """
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| 174 | self.__module__='pyfact'
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| 175 | # manual implementation of default value, but I need to find out
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| 176 | # if the user of this class is aware of the new option
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| 177 | if return_dict == False:
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| 178 | print 'DEPRECATION WARNING:'
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| 179 | print 'you are using RawData in a way, which is nor supported anymore.'
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| 180 | print ' Please set: return_dict = True, in the __init__ call'
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| 181 | self.return_dict = return_dict
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| 182 | self.use_CalFactFits = use_CalFactFits
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| 183 |
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| 184 | self.do_calibration = do_calibration
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| 185 |
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| 186 | self.data_file_name = data_file_name
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| 187 | self.calib_file_name = calib_file_name
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| 188 | self.baseline_file_name = baseline_file_name
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| 189 |
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| 190 | self.user_action_calib = user_action_calib
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| 191 |
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| 192 | # baseline correction: True / False
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| 193 | if len(baseline_file_name) == 0:
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| 194 | self.correct_baseline = False
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| 195 | else:
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| 196 | self.correct_baseline = True
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| 197 |
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| 198 |
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| 199 | # access data file
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| 200 | if use_CalFactFits:
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| 201 | try:
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| 202 | data_file = CalFactFits(data_file_name, calib_file_name)
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| 203 | except IOError:
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| 204 | print 'problem accessing data file: ', data_file_name
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| 205 | raise # stop ! no data
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| 206 |
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| 207 | #: either CalFactFits object or FactFits object, depending on *use_CalFactFits*
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| 208 | self.data_file = data_file
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| 209 | #: 1440x300 nparray containing the event data. pixel sorted according to CHID
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| 210 | self.data = np.empty( data_file.npix * data_file.nroi, np.float64)
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| 211 | data_file.SetNpcaldataPtr(self.data)
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| 212 | self.data = self.data.reshape( data_file.npix, data_file.nroi )
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| 213 | #: copy of data. here for historical reasons
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| 214 | self.acal_data = self.data
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| 215 | #: region of interest. (number of DRS slices read).
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| 216 | # for FACT data mostly 300. for special runs sometimes 1024.
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| 217 | self.nroi = data_file.nroi
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| 218 | #: number of Pixel in FACT. should be 1440
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| 219 | self.npix = data_file.npix
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| 220 | #: the total number of events in the data_file
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| 221 | self.nevents = data_file.nevents
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| 222 |
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| 223 | # Data per event
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| 224 | #: starting at 1
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| 225 | self.event_id = None
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| 226 |
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| 227 | #: data=4 ; the rest I don't know by heart .. should be documented here :-)
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| 228 | self.trigger_type = None
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| 229 | #self.start_cells = None
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| 230 | #self.board_times = None
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| 231 | #: 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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| 232 | self.start_cells = np.zeros( self.npix, np.int16 )
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| 233 | #: 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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| 234 | self.board_times = np.zeros( 40, np.int32 )
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| 235 | self._unixtime_tuple = np.zeros( 2, np.int32 )
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| 236 | self.unixtime = None
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| 237 |
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| 238 | # data_file is a CalFactFits object
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| 239 | # data_file.datafile is one of the two FactFits objects hold by a CalFactFits.
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| 240 | # sorry for the strange naming ..
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| 241 | data_file.datafile.SetPtrAddress('StartCellData', self.start_cells)
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| 242 | data_file.datafile.SetPtrAddress('BoardTime', self.board_times)
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| 243 | data_file.datafile.SetPtrAddress('UnixTimeUTC', self._unixtime_tuple)
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| 244 |
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| 245 |
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| 246 | else:
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| 247 | try:
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| 248 | data_file = factfits(self.data_file_name)
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| 249 | except IOError:
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| 250 | print 'problem accessing data file: ', data_file_name
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| 251 | raise # stop ! no data
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| 252 |
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| 253 | self.data_file = data_file
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| 254 |
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| 255 | # get basic information about the data file
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| 256 | self.nroi = data_file.GetUInt('NROI')
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| 257 | self.npix = data_file.GetUInt('NPIX')
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| 258 | self.nevents = data_file.GetNumRows()
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| 259 |
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| 260 | # allocate the data memories
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| 261 | self.event_id = c_ulong()
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| 262 | self.trigger_type = c_ushort()
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| 263 | self.data = np.zeros( self.npix * self.nroi, np.int16 ).reshape(self.npix ,self.nroi)
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| 264 | self.start_cells = np.zeros( self.npix, np.int16 )
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| 265 | self.board_times = np.zeros( 40, np.int32 )
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| 266 | self._unixtime_tuple = np.zeros(2, np.int32 )
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| 267 |
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| 268 | # set the pointers to the data++
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| 269 | data_file.SetPtrAddress('EventNum', self.event_id)
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| 270 | data_file.SetPtrAddress('TriggerType', self.trigger_type)
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| 271 | data_file.SetPtrAddress('StartCellData', self.start_cells)
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| 272 | data_file.SetPtrAddress('Data', self.data)
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| 273 | data_file.SetPtrAddress('BoardTime', self.board_times)
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| 274 | data_file.SetPtrAddress('UnixTimeUTC', self._unixtime_tuple)
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| 275 |
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| 276 | # open the calibration file
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| 277 | try:
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| 278 | calib_file = factfits(self.calib_file_name)
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| 279 | except IOError:
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| 280 | print 'problem accessing calibration file: ', calib_file_name
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| 281 | raise
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| 282 | #: drs calibration file
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| 283 | self.calib_file = calib_file
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| 284 |
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| 285 | baseline_mean = calib_file.GetN('BaselineMean')
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| 286 | gain_mean = calib_file.GetN('GainMean')
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| 287 | trigger_offset_mean = calib_file.GetN('TriggerOffsetMean')
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| 288 |
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| 289 | self.Nblm = baseline_mean / self.npix
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| 290 | self.Ngm = gain_mean / self.npix
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| 291 | self.Ntom = trigger_offset_mean / self.npix
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| 292 |
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| 293 | self.blm = np.zeros(baseline_mean, np.float32).reshape(self.npix , self.Nblm)
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| 294 | self.gm = np.zeros(gain_mean, np.float32).reshape(self.npix , self.Ngm)
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| 295 | self.tom = np.zeros(trigger_offset_mean, np.float32).reshape(self.npix , self.Ntom)
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| 296 |
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| 297 | calib_file.SetPtrAddress('BaselineMean', self.blm)
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| 298 | calib_file.SetPtrAddress('GainMean', self.gm)
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| 299 | calib_file.SetPtrAddress('TriggerOffsetMean', self.tom)
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| 300 | calib_file.GetRow(0)
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| 301 |
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| 302 | # make calibration constants double, so we never need to roll
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| 303 | self.blm = np.hstack((self.blm, self.blm))
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| 304 | self.gm = np.hstack((self.gm, self.gm))
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| 305 | self.tom = np.hstack((self.tom, self.tom))
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| 306 |
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| 307 | self.v_bsl = np.zeros(self.npix) # array of baseline values (all ZERO)
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| 308 |
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| 309 | def __iter__(self):
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| 310 | """ iterator """
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| 311 | return self
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| 312 |
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| 313 | def next(self):
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| 314 | """ used by __iter__
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| 315 |
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| 316 | returns self.__dict__
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| 317 | """
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| 318 | if self.use_CalFactFits:
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| 319 | if self.data_file.GetCalEvent() == False:
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| 320 | raise StopIteration
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| 321 | else:
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| 322 | self.event_id = self.data_file.event_id
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| 323 | self.trigger_type = self.data_file.event_triggertype
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| 324 | #self.start_cells = self.data_file.event_offset
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| 325 | #self.board_times = self.data_file.event_boardtimes
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| 326 | #self.acal_data = self.data.copy().reshape(self.data_file.npix, self.data_file.nroi)
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| 327 |
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| 328 | self.unixtime = self._unixtime_tuple[0] + self._unixtime_tuple[1]/1.e6
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| 329 |
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| 330 | else:
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| 331 | if self.data_file.GetNextRow() == False:
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| 332 | raise StopIteration
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| 333 | else:
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| 334 | if self.do_calibration == True:
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| 335 | self.calibrate_drs_amplitude()
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| 336 |
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| 337 | #print 'nevents = ', self.nevents, 'event_id = ', self.event_id.value
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| 338 | if self.return_dict:
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| 339 | return self.__dict__
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| 340 | else:
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| 341 | return self.acal_data, self.start_cells, self.trigger_type.value
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| 342 |
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| 343 | def next_event(self):
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| 344 | """ ---- DEPRICATED ----
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| 345 |
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| 346 | load the next event from disk and calibrate it
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| 347 | """
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| 348 | if self.use_CalFactFits:
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| 349 | self.data_file.GetCalEvent()
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| 350 | else:
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| 351 | self.data_file.GetNextRow()
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| 352 | self.calibrate_drs_amplitude()
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| 353 |
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| 354 | def calibrate_drs_amplitude(self):
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| 355 | """ --- DEPRICATED ---
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| 356 |
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| 357 | since the DRS calibration is done by the C++ class CalFactFits
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| 358 |
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| 359 | perform the drs amplitude calibration of the event data
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| 360 | """
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| 361 | # shortcuts
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| 362 | blm = self.blm
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| 363 | gm = self.gm
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| 364 | tom = self.tom
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| 365 |
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| 366 | to_mV = 2000./4096.
|
|---|
| 367 | #: 2D array with amplitude calibrated dat in mV
|
|---|
| 368 | acal_data = self.data * to_mV # convert ADC counts to mV
|
|---|
| 369 |
|
|---|
| 370 |
|
|---|
| 371 | for pixel in range( self.npix ):
|
|---|
| 372 | #shortcuts
|
|---|
| 373 | sc = self.start_cells[pixel]
|
|---|
| 374 | roi = self.nroi
|
|---|
| 375 | # rotate the pixel baseline mean to the Data startCell
|
|---|
| 376 | acal_data[pixel,:] -= blm[pixel,sc:sc+roi]
|
|---|
| 377 | # the 'trigger offset mean' does not need to be rolled
|
|---|
| 378 | # on the contrary, it seems there is an offset in the DRS data,
|
|---|
| 379 | # which is related to its distance to the startCell, not to its
|
|---|
| 380 | # distance to the beginning of the physical pipeline in the DRS chip
|
|---|
| 381 | acal_data[pixel,:] -= tom[pixel,0:roi]
|
|---|
| 382 | # rotate the pixel gain mean to the Data startCell
|
|---|
| 383 | acal_data[pixel,:] /= gm[pixel,sc:sc+roi]
|
|---|
| 384 |
|
|---|
| 385 |
|
|---|
| 386 | self.acal_data = acal_data * 1907.35
|
|---|
| 387 |
|
|---|
| 388 | self.user_action_calib( self.acal_data,
|
|---|
| 389 | np.reshape(self.data, (self.npix, self.nroi) ), blm, tom, gm, self.start_cells, self.nroi)
|
|---|
| 390 |
|
|---|
| 391 |
|
|---|
| 392 | def baseline_read_values(self, file, bsl_hist='bsl_sum/hplt_mean'):
|
|---|
| 393 | """
|
|---|
| 394 | open ROOT file with baseline histogram and read baseline values
|
|---|
| 395 |
|
|---|
| 396 | *file* : name of the root file
|
|---|
| 397 |
|
|---|
| 398 | *bsl_hist* : path to the histogram containing the basline values
|
|---|
| 399 | """
|
|---|
| 400 |
|
|---|
| 401 | try:
|
|---|
| 402 | f = TFile(file)
|
|---|
| 403 | except:
|
|---|
| 404 | print 'Baseline data file could not be read: ', file
|
|---|
| 405 | return
|
|---|
| 406 |
|
|---|
| 407 | h = f.Get(bsl_hist)
|
|---|
| 408 |
|
|---|
| 409 | for i in range(self.npix):
|
|---|
| 410 | self.v_bsl[i] = h.GetBinContent(i+1)
|
|---|
| 411 |
|
|---|
| 412 | f.Close()
|
|---|
| 413 |
|
|---|
| 414 | def baseline_correct(self):
|
|---|
| 415 | """ subtract baseline from the data
|
|---|
| 416 |
|
|---|
| 417 | DN 08.06.2011: I didn't use this function at all so far... don't know how well it works.
|
|---|
| 418 | """
|
|---|
| 419 |
|
|---|
| 420 | for pixel in range(self.npix):
|
|---|
| 421 | self.acal_data[pixel,:] -= self.v_bsl[pixel]
|
|---|
| 422 |
|
|---|
| 423 | def info(self):
|
|---|
| 424 | """ print run information
|
|---|
| 425 |
|
|---|
| 426 | not very well implemented ... we need more info here.
|
|---|
| 427 | """
|
|---|
| 428 | print 'data file: ', self.data_file_name
|
|---|
| 429 | print 'calib file: ', self.calib_file_name
|
|---|
| 430 | print '... we need more information printed here ... '
|
|---|
| 431 |
|
|---|
| 432 | # -----------------------------------------------------------------------------
|
|---|
| 433 | class RawDataFake( object ):
|
|---|
| 434 | """ raw data FAKE access similar to real RawData access
|
|---|
| 435 |
|
|---|
| 436 | DO NOT USE ... its not working
|
|---|
| 437 | """
|
|---|
| 438 |
|
|---|
| 439 |
|
|---|
| 440 | def __init__(self, data_file_name, calib_file_name,
|
|---|
| 441 | user_action_calib=lambda acal_data, data, blm, tom, gm, scells, nroi: None,
|
|---|
| 442 | baseline_file_name=''):
|
|---|
| 443 | self.__module__='pyfact'
|
|---|
| 444 |
|
|---|
| 445 | self.nroi = 300
|
|---|
| 446 | self.npix = 9
|
|---|
| 447 | self.nevents = 1000
|
|---|
| 448 |
|
|---|
| 449 | self.simulator = None
|
|---|
| 450 |
|
|---|
| 451 | self.time = np.ones(1024) * 0.5
|
|---|
| 452 |
|
|---|
| 453 |
|
|---|
| 454 | self.event_id = c_ulong(0)
|
|---|
| 455 | self.trigger_type = c_ushort(4)
|
|---|
| 456 | self.data = np.zeros( self.npix * self.nroi, np.int16 ).reshape(self.npix ,self.nroi)
|
|---|
| 457 | self.start_cells = np.zeros( self.npix, np.int16 )
|
|---|
| 458 | self.board_times = np.zeros( 40, np.int32 )
|
|---|
| 459 | def __iter__(self):
|
|---|
| 460 | """ iterator """
|
|---|
| 461 | return self
|
|---|
| 462 |
|
|---|
| 463 | def next(self):
|
|---|
| 464 | """ used by __iter__ """
|
|---|
| 465 | self.event_id = c_ulong(self.event_id.value + 1)
|
|---|
| 466 | self.board_times = self.board_times + 42
|
|---|
| 467 |
|
|---|
| 468 | if self.event_id.value >= self.nevents:
|
|---|
| 469 | raise StopIteration
|
|---|
| 470 | else:
|
|---|
| 471 | self._make_event_data()
|
|---|
| 472 |
|
|---|
| 473 | return self.__dict__
|
|---|
| 474 |
|
|---|
| 475 | def _make_event_data(self):
|
|---|
| 476 | sample_times = self.time.cumsum() - time[0]
|
|---|
| 477 |
|
|---|
| 478 | # random start cell
|
|---|
| 479 | self.start_cells = np.ones( self.npix, np.int16 ) * np.random.randint(0,1024)
|
|---|
| 480 |
|
|---|
| 481 | starttime = self.start_cells[0]
|
|---|
| 482 |
|
|---|
| 483 | signal = self._std_sinus_simu(sample_times, starttime)
|
|---|
| 484 |
|
|---|
| 485 | data = np.vstack( (signal,signal) )
|
|---|
| 486 | for i in range(8):
|
|---|
| 487 | data = np.vstack( (data,signal) )
|
|---|
| 488 |
|
|---|
| 489 | self.data = data
|
|---|
| 490 |
|
|---|
| 491 | def _std_sinus_simu(self, times, starttime):
|
|---|
| 492 | period = 10 # in ns
|
|---|
| 493 |
|
|---|
| 494 | # give a jitter on starttime
|
|---|
| 495 | starttime = np.random.normal(startime, 0.05)
|
|---|
| 496 |
|
|---|
| 497 | phase = 0.0
|
|---|
| 498 | signal = 10 * np.sin(times * 2*np.pi/period + starttime + phase)
|
|---|
| 499 |
|
|---|
| 500 | # add some noise
|
|---|
| 501 | noise = np.random.normal(0.0, 0.5, signal.shape)
|
|---|
| 502 | signal += noise
|
|---|
| 503 | return signal
|
|---|
| 504 |
|
|---|
| 505 | def info(self):
|
|---|
| 506 | """ print run information
|
|---|
| 507 |
|
|---|
| 508 | """
|
|---|
| 509 |
|
|---|
| 510 | print 'data file: ', data_file_name
|
|---|
| 511 | print 'calib file: ', calib_file_name
|
|---|
| 512 | print 'calibration file'
|
|---|
| 513 | print 'N baseline_mean: ', self.Nblm
|
|---|
| 514 | print 'N gain mean: ', self.Ngm
|
|---|
| 515 | print 'N TriggeroffsetMean: ', self.Ntom
|
|---|
| 516 |
|
|---|
| 517 | # -----------------------------------------------------------------------------
|
|---|
| 518 | import ctypes
|
|---|
| 519 |
|
|---|
| 520 | class SlowData( object ):
|
|---|
| 521 | """ -Fact SlowData File-
|
|---|
| 522 |
|
|---|
| 523 | A Python wrapper for the fits-class implemented in factfits.h
|
|---|
| 524 | provides easy access to the fits file meta data.
|
|---|
| 525 |
|
|---|
| 526 | * dictionary of file metadata - self.meta
|
|---|
| 527 | * dict of table metadata - self.columns
|
|---|
| 528 | * variable table column access, thus possibly increased speed while looping
|
|---|
| 529 | """
|
|---|
| 530 | def __del__(self):
|
|---|
| 531 | del self.f
|
|---|
| 532 |
|
|---|
| 533 | def __init__(self, path):
|
|---|
| 534 | """ creates meta and columns dictionaries
|
|---|
| 535 | """
|
|---|
| 536 | import os
|
|---|
| 537 |
|
|---|
| 538 | if not os.path.exists(path):
|
|---|
| 539 | raise IOError(path+' was not found')
|
|---|
| 540 | self.path = path
|
|---|
| 541 | self.__module__ = 'pyfact'
|
|---|
| 542 | try:
|
|---|
| 543 | self.f = factfits(path)
|
|---|
| 544 | except IOError:
|
|---|
| 545 | print 'problem accessing data file: ', data_file_name
|
|---|
| 546 | raise # stop ! no data
|
|---|
| 547 |
|
|---|
| 548 | self.meta = self._make_meta_dict()
|
|---|
| 549 | self.columns = self._make_columns_dict()
|
|---|
| 550 |
|
|---|
| 551 | self._treat_meta_dict()
|
|---|
| 552 |
|
|---|
| 553 |
|
|---|
| 554 | # list of columns, which are already registered
|
|---|
| 555 | # see method register()
|
|---|
| 556 | self._registered_cols = []
|
|---|
| 557 | # dict of column data, this is used, in order to be able to remove
|
|---|
| 558 | # the ctypes of
|
|---|
| 559 | self._table_cols = {}
|
|---|
| 560 |
|
|---|
| 561 | # I need to count the rows, since the normal loop mechanism seems not to work.
|
|---|
| 562 | self._current_row = 0
|
|---|
| 563 |
|
|---|
| 564 | self.stacked_cols = {}
|
|---|
| 565 |
|
|---|
| 566 | def _make_meta_dict__old(self):
|
|---|
| 567 | """ This method retrieves meta information about the fits file and
|
|---|
| 568 | stores this information in a dict
|
|---|
| 569 | return: dict
|
|---|
| 570 | key: string - all capital letters
|
|---|
| 571 | value: tuple( numerical value, string comment)
|
|---|
| 572 | """
|
|---|
| 573 | # abbreviation
|
|---|
| 574 | f = self.f
|
|---|
| 575 |
|
|---|
| 576 | # intermediate variables for file metadata dict generation
|
|---|
| 577 |
|
|---|
| 578 | keys=f.GetPy_KeyKeys()
|
|---|
| 579 | values=f.GetPy_KeyValues()
|
|---|
| 580 | comments=f.GetPy_KeyComments()
|
|---|
| 581 | types=f.GetPy_KeyTypes()
|
|---|
| 582 |
|
|---|
| 583 | if len(keys) != len(values):
|
|---|
| 584 | raise TypeError('len(keys)',len(keys),' != len(values)', len(values))
|
|---|
| 585 | if len(keys) != len(types):
|
|---|
| 586 | raise TypeError('len(keys)',len(keys),' != len(types)', len(types))
|
|---|
| 587 | if len(keys) != len(comments):
|
|---|
| 588 | raise TypeError('len(keys)',len(keys),' != len(comments)', len(comments))
|
|---|
| 589 |
|
|---|
| 590 | meta_dict = {}
|
|---|
| 591 | for i in range(len(keys)):
|
|---|
| 592 | type = types[i]
|
|---|
| 593 | if type == 'I':
|
|---|
| 594 | value = int(values[i])
|
|---|
| 595 | elif type == 'F':
|
|---|
| 596 | value = float(values[i])
|
|---|
| 597 | elif type == 'B':
|
|---|
| 598 | if values[i] == 'T':
|
|---|
| 599 | value = True
|
|---|
| 600 | elif values[i] == 'F':
|
|---|
| 601 | value = False
|
|---|
| 602 | else:
|
|---|
| 603 | raise TypeError("meta-type is 'B', but meta-value is neither 'T' nor 'F'. meta-value:",values[i])
|
|---|
| 604 | elif type == 'T':
|
|---|
| 605 | value = values[i]
|
|---|
| 606 | else:
|
|---|
| 607 | raise TypeError("unknown meta-type: known meta types are: I,F,B and T. meta-type:",type)
|
|---|
| 608 | meta_dict[keys[i]]=(value, comments[i])
|
|---|
| 609 | return meta_dict
|
|---|
| 610 |
|
|---|
| 611 | def _make_meta_dict(self):
|
|---|
| 612 | meta_dict = {}
|
|---|
| 613 | for key,entry in self.f.GetKeys():
|
|---|
| 614 | type = entry.type
|
|---|
| 615 | fitsString = entry.fitsString # the original 80-char line from the FITS header
|
|---|
| 616 | comment = entry.comment
|
|---|
| 617 | value = entry.value
|
|---|
| 618 |
|
|---|
| 619 | if type == 'I':
|
|---|
| 620 | value = int(value)
|
|---|
| 621 | elif type == 'F':
|
|---|
| 622 | value = float(value)
|
|---|
| 623 | elif type == 'B':
|
|---|
| 624 | if value == 'T':
|
|---|
| 625 | value = True
|
|---|
| 626 | elif value == 'F':
|
|---|
| 627 | value = False
|
|---|
| 628 | else:
|
|---|
| 629 | raise TypeError("meta-type is 'B', but meta-value is neither 'T' nor 'F'. meta-value:",value)
|
|---|
| 630 | elif type == 'T':
|
|---|
| 631 | value = value
|
|---|
| 632 | else:
|
|---|
| 633 | raise TypeError("unknown meta-type: known meta types are: I,F,B and T. meta-type:",type)
|
|---|
| 634 | meta_dict[key]=(value, comment)
|
|---|
| 635 | return meta_dict
|
|---|
| 636 |
|
|---|
| 637 |
|
|---|
| 638 |
|
|---|
| 639 | def _make_columns_dict(self):
|
|---|
| 640 | """ This method retrieves information about the columns
|
|---|
| 641 | stored inside the fits files internal binary table.
|
|---|
| 642 | returns: dict
|
|---|
| 643 | key: string column name -- all capital letters
|
|---|
| 644 | values: tuple(
|
|---|
| 645 | number of elements in table field - integer
|
|---|
| 646 | size of element in bytes -- this is not really interesting for any user
|
|---|
| 647 | might be ommited in future versions
|
|---|
| 648 | type - a single character code -- should be translated into
|
|---|
| 649 | a comrehensible word
|
|---|
| 650 | unit - string like 'mV' or 'ADC count'
|
|---|
| 651 | """
|
|---|
| 652 | ## abbreviation
|
|---|
| 653 | #f = self.f
|
|---|
| 654 | #
|
|---|
| 655 | ## intermediate variables for file table-metadata dict generation
|
|---|
| 656 | #keys=f.GetPy_ColumnKeys()
|
|---|
| 657 | ##offsets=self.GetPy_ColumnOffsets() #not needed on python level...
|
|---|
| 658 | #nums=f.GetPy_ColumnNums()
|
|---|
| 659 | #sizes=f.GetPy_ColumnSizes()
|
|---|
| 660 | #types=f.GetPy_ColumnTypes()
|
|---|
| 661 | #units=f.GetPy_ColumnUnits()
|
|---|
| 662 |
|
|---|
| 663 | ## zip the values
|
|---|
| 664 | #values = zip(nums,sizes,types,units)
|
|---|
| 665 | ## create the columns dictionary
|
|---|
| 666 | #columns = dict(zip(keys ,values))
|
|---|
| 667 |
|
|---|
| 668 |
|
|---|
| 669 | columns = {}
|
|---|
| 670 | for key,col in self.f.GetColumns():
|
|---|
| 671 | columns[key]=( col.num, col.size, col.type, col.unit)
|
|---|
| 672 | return columns
|
|---|
| 673 |
|
|---|
| 674 | def stack(self, on=True):
|
|---|
| 675 | self.next()
|
|---|
| 676 | for col in self._registered_cols:
|
|---|
| 677 | if isinstance( self.dict[col], type(np.array('')) ):
|
|---|
| 678 | self.stacked_cols[col] = self.dict[col]
|
|---|
| 679 | else:
|
|---|
| 680 | # elif isinstance(self.dict[col], ctypes._SimpleCData):
|
|---|
| 681 | self.stacked_cols[col] = np.array(self.dict[col])
|
|---|
| 682 | # else:
|
|---|
| 683 | # raise TypeError("I don't know how to stack "+col+". It is of type: "+str(type(self.dict[col])))
|
|---|
| 684 |
|
|---|
| 685 | def register(self, col_name):
|
|---|
| 686 | """ register for a column in the fits file
|
|---|
| 687 |
|
|---|
| 688 | after the call, this SlowData object will have a new member variable
|
|---|
| 689 | self.col_name, if col_name is a key in self.colums
|
|---|
| 690 |
|
|---|
| 691 | the value will be updated after each call of next(), or while iterating over self.
|
|---|
| 692 | NB: the initial value is zero(s)
|
|---|
| 693 |
|
|---|
| 694 | *col_name* : name of a key in self.columns, or 'all' to choose all.
|
|---|
| 695 | """
|
|---|
| 696 | columns = self.columns
|
|---|
| 697 | if col_name.lower() == 'all':
|
|---|
| 698 | for col in columns:
|
|---|
| 699 | self._register(col)
|
|---|
| 700 | else:
|
|---|
| 701 | #check if colname is in columns:
|
|---|
| 702 | if col_name not in columns:
|
|---|
| 703 | error_msg = 'colname:'+ col_name +' is not a column in the binary table.\n'
|
|---|
| 704 | error_msg+= 'possible colnames are\n'
|
|---|
| 705 | for key in columns:
|
|---|
| 706 | error_msg += key+' '
|
|---|
| 707 | raise KeyError(error_msg)
|
|---|
| 708 | else:
|
|---|
| 709 | self._register(col_name)
|
|---|
| 710 |
|
|---|
| 711 | # 'private' method, do not use
|
|---|
| 712 | def _register( self, colname):
|
|---|
| 713 |
|
|---|
| 714 | columns = self.columns
|
|---|
| 715 | f = self.f
|
|---|
| 716 | local = None
|
|---|
| 717 |
|
|---|
| 718 | number_of_elements = int(columns[colname][0])
|
|---|
| 719 | size_of_elements_in_bytes = int(columns[colname][1])
|
|---|
| 720 | ctypecode_of_elements = columns[colname][2]
|
|---|
| 721 | physical_unit_of_elements = columns[colname][3]
|
|---|
| 722 |
|
|---|
| 723 | # snippet from the C++ source code, or header file to be precise:
|
|---|
| 724 | #case 'L': gLog << "bool(8)"; break;
|
|---|
| 725 | #case 'B': gLog << "byte(8)"; break;
|
|---|
| 726 | #case 'I': gLog << "short(16)"; break;
|
|---|
| 727 | #case 'J': gLog << "int(32)"; break;
|
|---|
| 728 | #case 'K': gLog << "int(64)"; break;
|
|---|
| 729 | #case 'E': gLog << "float(32)"; break;
|
|---|
| 730 | #case 'D': gLog << "double(64)"; break;
|
|---|
| 731 |
|
|---|
| 732 |
|
|---|
| 733 |
|
|---|
| 734 | # the fields inside the columns can either contain single numbers,
|
|---|
| 735 | # or whole arrays of numbers as well.
|
|---|
| 736 | # we treat single elements differently...
|
|---|
| 737 | if number_of_elements == 0:
|
|---|
| 738 | return
|
|---|
| 739 | if number_of_elements == 1:
|
|---|
| 740 | # allocate some memory for a single number according to its type
|
|---|
| 741 | if ctypecode_of_elements == 'J': # J is for a 4byte int, i.e. an unsigned long
|
|---|
| 742 | local = ctypes.c_ulong()
|
|---|
| 743 | un_c_type = long
|
|---|
| 744 | elif ctypecode_of_elements == 'I': # I is for a 2byte int, i.e. an unsinged int
|
|---|
| 745 | local = ctypes.c_ushort()
|
|---|
| 746 | un_c_type = int
|
|---|
| 747 | elif ctypecode_of_elements == 'B': # B is for a byte
|
|---|
| 748 | local = ctypes.c_ubyte()
|
|---|
| 749 | un_c_type = int
|
|---|
| 750 | elif ctypecode_of_elements == 'D':
|
|---|
| 751 | local = ctypes.c_double()
|
|---|
| 752 | un_c_type = float
|
|---|
| 753 | elif ctypecode_of_elements == 'E':
|
|---|
| 754 | local = ctypes.c_float()
|
|---|
| 755 | un_c_type = float
|
|---|
| 756 | elif ctypecode_of_elements == 'A':
|
|---|
| 757 | local = ctypes.c_uchar()
|
|---|
| 758 | un_c_type = chr
|
|---|
| 759 | elif ctypecode_of_elements == 'K':
|
|---|
| 760 | local = ctypes.c_ulonglong()
|
|---|
| 761 | un_c_type = long
|
|---|
| 762 | else:
|
|---|
| 763 | raise TypeError('unknown ctypecode_of_elements:',ctypecode_of_elements)
|
|---|
| 764 | else:
|
|---|
| 765 | if ctypecode_of_elements == 'B': # B is for a byte
|
|---|
| 766 | nptype = np.int8
|
|---|
| 767 | elif ctypecode_of_elements == 'A': # A is for a char .. but I don't know how to handle it
|
|---|
| 768 | nptype = np.int8
|
|---|
| 769 | elif ctypecode_of_elements == 'I': # I is for a 2byte int
|
|---|
| 770 | nptype = np.int16
|
|---|
| 771 | elif ctypecode_of_elements == 'J': # J is for a 4byte int
|
|---|
| 772 | nptype = np.int32
|
|---|
| 773 | elif ctypecode_of_elements == 'K': # B is for a byte
|
|---|
| 774 | nptype = np.int64
|
|---|
| 775 | elif ctypecode_of_elements == 'E': # B is for a byte
|
|---|
| 776 | nptype = np.float32
|
|---|
| 777 | elif ctypecode_of_elements == 'D': # B is for a byte
|
|---|
| 778 | nptype = np.float64
|
|---|
| 779 | else:
|
|---|
| 780 | raise TypeError('unknown ctypecode_of_elements:',ctypecode_of_elements)
|
|---|
| 781 | local = np.zeros( number_of_elements, nptype)
|
|---|
| 782 |
|
|---|
| 783 | # Set the Pointer Address
|
|---|
| 784 | try:
|
|---|
| 785 | f.SetPtrAddress(colname, local)
|
|---|
| 786 | except TypeError:
|
|---|
| 787 | print 'something was wrong with SetPtrAddress()'
|
|---|
| 788 | print 'Type of colname', type(colname)
|
|---|
| 789 | print 'colname:', colname
|
|---|
| 790 | print 'Type of local', type(local)
|
|---|
| 791 | print 'length of local', len(local)
|
|---|
| 792 | print 'local should be alle zeros, since "local = np.zeros( number_of_elements, nptype)" '
|
|---|
| 793 | raise
|
|---|
| 794 |
|
|---|
| 795 | self._table_cols[colname] = local
|
|---|
| 796 | if number_of_elements > 1:
|
|---|
| 797 | self.__dict__[colname] = local
|
|---|
| 798 | self.dict[colname] = local
|
|---|
| 799 | else:
|
|---|
| 800 | # remove any traces of ctypes:
|
|---|
| 801 | self.__dict__[colname] = local.value
|
|---|
| 802 | self.dict[colname] = local.value
|
|---|
| 803 | self._registered_cols.append(colname)
|
|---|
| 804 |
|
|---|
| 805 |
|
|---|
| 806 | def _treat_meta_dict(self):
|
|---|
| 807 | """make 'interesting' meta information available like normal members.
|
|---|
| 808 | non interesting are:
|
|---|
| 809 | TFORM, TUNIT, and TTYPE
|
|---|
| 810 | since these are available via the columns dict.
|
|---|
| 811 | """
|
|---|
| 812 |
|
|---|
| 813 | self.number_of_rows = self.meta['NAXIS2'][0]
|
|---|
| 814 | self.number_of_columns = self.meta['TFIELDS'][0]
|
|---|
| 815 |
|
|---|
| 816 | # there are some information in the meta dict, which are alsways there:
|
|---|
| 817 | # there are regarded as not interesting:
|
|---|
| 818 | uninteresting_meta = {}
|
|---|
| 819 | uninteresting_meta['arraylike'] = {}
|
|---|
| 820 | uninteresting = ['NAXIS', 'NAXIS1', 'NAXIS2',
|
|---|
| 821 | 'TFIELDS',
|
|---|
| 822 | 'XTENSION','EXTNAME','EXTREL',
|
|---|
| 823 | 'BITPIX', 'PCOUNT', 'GCOUNT',
|
|---|
| 824 | 'ORIGIN',
|
|---|
| 825 | 'PACKAGE', 'COMPILED', 'CREATOR',
|
|---|
| 826 | 'TELESCOP','TIMESYS','TIMEUNIT','VERSION']
|
|---|
| 827 | for key in uninteresting:
|
|---|
| 828 | if key in self.meta:
|
|---|
| 829 | uninteresting_meta[key]=self.meta[key]
|
|---|
| 830 | del self.meta[key]
|
|---|
| 831 |
|
|---|
| 832 | # the table meta data contains
|
|---|
| 833 |
|
|---|
| 834 |
|
|---|
| 835 | # shortcut to access the meta dict. But this needs to
|
|---|
| 836 | # be cleaned up quickly!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
|
|---|
| 837 | meta = self.meta
|
|---|
| 838 |
|
|---|
| 839 | # loop over keys:
|
|---|
| 840 | # * try to find array-like keys
|
|---|
| 841 | arraylike = {}
|
|---|
| 842 | singlelike = []
|
|---|
| 843 | for key in self.meta:
|
|---|
| 844 | stripped = key.rstrip('1234567890')
|
|---|
| 845 | if stripped == key:
|
|---|
| 846 | singlelike.append(key)
|
|---|
| 847 | else:
|
|---|
| 848 | if stripped not in arraylike:
|
|---|
| 849 | arraylike[stripped] = 0
|
|---|
| 850 | else:
|
|---|
| 851 | arraylike[stripped] += 1
|
|---|
| 852 | newmeta = {}
|
|---|
| 853 | for key in singlelike:
|
|---|
| 854 | newmeta[key.lower()] = meta[key]
|
|---|
| 855 | for key in arraylike:
|
|---|
| 856 | uninteresting_meta['arraylike'][key.lower()] = []
|
|---|
| 857 | for i in range(arraylike[key]+1):
|
|---|
| 858 | if key+str(i) in meta:
|
|---|
| 859 | uninteresting_meta['arraylike'][key.lower()].append(meta[key+str(i)])
|
|---|
| 860 | self.ui_meta = uninteresting_meta
|
|---|
| 861 | # make newmeta self
|
|---|
| 862 | for key in newmeta:
|
|---|
| 863 | self.__dict__[key]=newmeta[key]
|
|---|
| 864 |
|
|---|
| 865 | dict = self.__dict__.copy()
|
|---|
| 866 | del dict['meta']
|
|---|
| 867 | del dict['ui_meta']
|
|---|
| 868 | self.dict = dict
|
|---|
| 869 |
|
|---|
| 870 | def __iter__(self):
|
|---|
| 871 | """ iterator """
|
|---|
| 872 | return self
|
|---|
| 873 |
|
|---|
| 874 | def next(self):
|
|---|
| 875 | """ use to iterate over the file
|
|---|
| 876 |
|
|---|
| 877 | do not forget to call register() before iterating over the file
|
|---|
| 878 | call show() in order to find out, what parameters register() accepts.
|
|---|
| 879 | or just call register('all') in case you are unsure.
|
|---|
| 880 |
|
|---|
| 881 | returns self
|
|---|
| 882 | """
|
|---|
| 883 | # abbreviaition
|
|---|
| 884 | f = self.f
|
|---|
| 885 |
|
|---|
| 886 | # Here one might check, if looping makes any sense, and if not
|
|---|
| 887 | # one could stop looping or so...
|
|---|
| 888 | # like this:
|
|---|
| 889 | #
|
|---|
| 890 | # if len(self._registered_cols) == 0:
|
|---|
| 891 | # print 'warning: looping without any registered columns'
|
|---|
| 892 | if self._current_row < self.number_of_rows:
|
|---|
| 893 | if f.GetNextRow() == False:
|
|---|
| 894 | raise StopIteration
|
|---|
| 895 | for col in self._registered_cols:
|
|---|
| 896 | if isinstance(self._table_cols[col], ctypes._SimpleCData):
|
|---|
| 897 | self.__dict__[col] = self._table_cols[col].value
|
|---|
| 898 | self.dict[col] = self._table_cols[col].value
|
|---|
| 899 |
|
|---|
| 900 | for col in self.stacked_cols:
|
|---|
| 901 | if isinstance(self.dict[col], type(np.array(''))):
|
|---|
| 902 | self.stacked_cols[col] = np.vstack( (self.stacked_cols[col],self.dict[col]) )
|
|---|
| 903 | else:
|
|---|
| 904 | self.stacked_cols[col] = np.vstack( (self.stacked_cols[col],np.array(self.dict[col])) )
|
|---|
| 905 | self._current_row += 1
|
|---|
| 906 | else:
|
|---|
| 907 | raise StopIteration
|
|---|
| 908 | return self
|
|---|
| 909 |
|
|---|
| 910 | def show(self):
|
|---|
| 911 | """
|
|---|
| 912 | """
|
|---|
| 913 | pprint.pprint(self.dict)
|
|---|
| 914 |
|
|---|
| 915 |
|
|---|
| 916 |
|
|---|
| 917 |
|
|---|
| 918 | class fnames( object ):
|
|---|
| 919 | """ organize file names of a FACT data run
|
|---|
| 920 |
|
|---|
| 921 | """
|
|---|
| 922 |
|
|---|
| 923 | def __init__(self, specifier = ['012', '023', '2011', '11', '24'],
|
|---|
| 924 | rpath = '/scratch_nfs/res/bsl/',
|
|---|
| 925 | zipped = True):
|
|---|
| 926 | """
|
|---|
| 927 | specifier : list of strings defined as:
|
|---|
| 928 | [ 'DRS calibration file', 'Data file', 'YYYY', 'MM', 'DD']
|
|---|
| 929 |
|
|---|
| 930 | rpath : directory path for the results; YYYYMMDD will be appended to rpath
|
|---|
| 931 | zipped : use zipped (True) or unzipped (Data)
|
|---|
| 932 |
|
|---|
| 933 | """
|
|---|
| 934 |
|
|---|
| 935 | self.specifier = specifier
|
|---|
| 936 | self.rpath = rpath
|
|---|
| 937 | self.zipped = zipped
|
|---|
| 938 |
|
|---|
| 939 | self.make( self.specifier, self.rpath, self.zipped )
|
|---|
| 940 |
|
|---|
| 941 |
|
|---|
| 942 | def make( self, specifier, rpath, zipped ):
|
|---|
| 943 | """ create (make) the filenames
|
|---|
| 944 |
|
|---|
| 945 | names : dictionary of filenames, tags { 'data', 'drscal', 'results' }
|
|---|
| 946 | data : name of the data file
|
|---|
| 947 | drscal : name of the drs calibration file
|
|---|
| 948 | results : radikal of file name(s) for results (to be completed by suffixes)
|
|---|
| 949 | """
|
|---|
| 950 |
|
|---|
| 951 | self.specifier = specifier
|
|---|
| 952 |
|
|---|
| 953 | if zipped:
|
|---|
| 954 | dpath = '/data00/fact-construction/raw/'
|
|---|
| 955 | ext = '.fits.gz'
|
|---|
| 956 | else:
|
|---|
| 957 | dpath = '/data03/fact-construction/raw/'
|
|---|
| 958 | ext = '.fits'
|
|---|
| 959 |
|
|---|
| 960 | year = specifier[2]
|
|---|
| 961 | month = specifier[3]
|
|---|
| 962 | day = specifier[4]
|
|---|
| 963 |
|
|---|
| 964 | yyyymmdd = year + month + day
|
|---|
| 965 | dfile = specifier[1]
|
|---|
| 966 | cfile = specifier[0]
|
|---|
| 967 |
|
|---|
| 968 | rpath = rpath + yyyymmdd + '/'
|
|---|
| 969 | self.rpath = rpath
|
|---|
| 970 | self.names = {}
|
|---|
| 971 |
|
|---|
| 972 | tmp = dpath + year + '/' + month + '/' + day + '/' + yyyymmdd + '_'
|
|---|
| 973 | self.names['data'] = tmp + dfile + ext
|
|---|
| 974 | self.names['drscal'] = tmp + cfile + '.drs' + ext
|
|---|
| 975 | self.names['results'] = rpath + yyyymmdd + '_' + dfile + '_' + cfile
|
|---|
| 976 |
|
|---|
| 977 | self.data = self.names['data']
|
|---|
| 978 | self.drscal = self.names['drscal']
|
|---|
| 979 | self.results = self.names['results']
|
|---|
| 980 |
|
|---|
| 981 | def info( self ):
|
|---|
| 982 | """ print complete filenames
|
|---|
| 983 |
|
|---|
| 984 | """
|
|---|
| 985 |
|
|---|
| 986 | print 'file names:'
|
|---|
| 987 | print 'data: ', self.names['data']
|
|---|
| 988 | print 'drs-cal: ', self.names['drscal']
|
|---|
| 989 | print 'results: ', self.names['results']
|
|---|
| 990 |
|
|---|
| 991 | # end of class definition: fnames( object )
|
|---|
| 992 |
|
|---|
| 993 | def _test_SlowData( filename ):
|
|---|
| 994 | print '-'*70
|
|---|
| 995 | print "opened :", filename, " as 'file'"
|
|---|
| 996 | print
|
|---|
| 997 | print '-'*70
|
|---|
| 998 | print 'type file.show() to look at its contents'
|
|---|
| 999 | print "type file.register( columnname ) or file.register('all') in order to register columns"
|
|---|
| 1000 | print
|
|---|
| 1001 | print " due column-registration you declare, that you would like to retrieve the contents of one of the columns"
|
|---|
| 1002 | print " after column-registration, the 'file' has new member variables, they are named like the columns"
|
|---|
| 1003 | print " PLEASE NOTE: immediatly after registration, the members exist, but they are empty."
|
|---|
| 1004 | print " the values are assigned only, when you call file.next() or when you loop over the 'file'"
|
|---|
| 1005 | print
|
|---|
| 1006 | print "in order to loop over it, just go like this:"
|
|---|
| 1007 | print "for row in file:"
|
|---|
| 1008 | print " print row.columnname_one, row.columnname_two"
|
|---|
| 1009 | print
|
|---|
| 1010 | print ""
|
|---|
| 1011 | print '-'*70
|
|---|
| 1012 |
|
|---|
| 1013 |
|
|---|
| 1014 |
|
|---|
| 1015 | def _test_iter( nevents ):
|
|---|
| 1016 | """ test for function __iter__ """
|
|---|
| 1017 |
|
|---|
| 1018 | data_file_name = '/fact/raw/2011/11/24/20111124_117.fits.gz'
|
|---|
| 1019 | calib_file_name = '/fact/raw/2011/11/24/20111124_114.drs.fits.gz'
|
|---|
| 1020 | print 'the files for this test are:'
|
|---|
| 1021 | print 'data file:', data_file_name
|
|---|
| 1022 | print 'calib file:', calib_file_name
|
|---|
| 1023 | run = RawData( data_file_name, calib_file_name , return_dict=True)
|
|---|
| 1024 |
|
|---|
| 1025 | for event in run:
|
|---|
| 1026 | 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']
|
|---|
| 1027 | if run.event_id == nevents:
|
|---|
| 1028 | break
|
|---|
| 1029 |
|
|---|
| 1030 | if __name__ == '__main__':
|
|---|
| 1031 | """ tests """
|
|---|
| 1032 |
|
|---|
| 1033 | f = fits(sys.argv[1])
|
|---|
| 1034 | test_m1 = ROOT.std.map(str,ROOT.fits.Entry)()
|
|---|
| 1035 | test_m2 = ROOT.std.map(str,ROOT.fits.Table.Column)()
|
|---|
| 1036 | print "len(test_m1)", len(test_m1)
|
|---|
| 1037 | print "len(test_m2)", len(test_m2)
|
|---|
| 1038 |
|
|---|
| 1039 | for k1 in f.GetKeys():
|
|---|
| 1040 | pass
|
|---|
| 1041 | print k1
|
|---|
| 1042 | for k2 in f.GetColumns():
|
|---|
| 1043 | pass
|
|---|
| 1044 | print k2
|
|---|
| 1045 |
|
|---|
| 1046 | sd = SlowData(sys.argv[1])
|
|---|
