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jroproc_voltage.py: ProfileSelector, Decoder, Reshape was tested with nTxs != 1 and getblock = True
Miguel Valdez -
r749:2d4c40d861d3
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@@ -619,6 +619,9 class Decoder(Operation):
619 619
620 620 def __convolutionByBlockInFreq(self, data):
621 621
622 raise NotImplementedError, "Decoder by frequency fro Blocks not implemented"
623
624
622 625 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
623 626
624 627 fft_data = numpy.fft.fft(data, axis=2)
@@ -651,19 +654,21 class Decoder(Operation):
651 654 if mode == 3:
652 655 sys.stderr.write("Decoder Warning: mode=%d is not valid, using mode=0\n" %mode)
653 656
657 if times != None:
658 sys.stderr.write("Decoder Warning: Argument 'times' in not used anymore\n")
654 659
655 660 if self.code is None:
656 661 print "Fail decoding: Code is not defined."
657 662 return
658 663
659 664 datadec = None
665 if mode == 3:
666 mode = 0
660 667
661 668 if dataOut.flagDataAsBlock:
662 669 """
663 670 Decoding when data have been read as block,
664 671 """
665 if mode == 3:
666 mode = 0
667 672
668 673 if mode == 0:
669 674 datadec = self.__convolutionByBlockInTime(dataOut.data)
@@ -812,20 +817,28 class ProfileSelector(Operation):
812 817 """
813 818 if profileList != None:
814 819 dataOut.data = dataOut.data[:,profileList,:]
815 dataOut.nProfiles = len(profileList)
816 dataOut.profileIndex = dataOut.nProfiles - 1
817 820
818 821 if profileRangeList != None:
819 822 minIndex = profileRangeList[0]
820 823 maxIndex = profileRangeList[1]
824 profileList = range(minIndex, maxIndex+1)
821 825
822 826 dataOut.data = dataOut.data[:,minIndex:maxIndex+1,:]
823 dataOut.nProfiles = maxIndex - minIndex + 1
824 dataOut.profileIndex = dataOut.nProfiles - 1
825 827
826 828 if rangeList != None:
827 raise ValueError, "Profile Selector: Invalid argument rangeList. Not implemented for getByBlock yet"
828 829
830 profileList = []
831
832 for thisRange in rangeList:
833 minIndex = thisRange[0]
834 maxIndex = thisRange[1]
835
836 profileList.extend(range(minIndex, maxIndex+1))
837
838 dataOut.data = dataOut.data[:,profileList,:]
839
840 dataOut.nProfiles = len(profileList)
841 dataOut.profileIndex = dataOut.nProfiles - 1
829 842 dataOut.flagNoData = False
830 843
831 844 return True
@@ -834,18 +847,14 class ProfileSelector(Operation):
834 847 data dimension = [nChannels, nHeis]
835 848 """
836 849
837 if nProfiles:
838 self.nProfiles = nProfiles
839 else:
840 self.nProfiles = dataOut.nProfiles
841
842 850 if profileList != None:
843 851
844 dataOut.nProfiles = len(profileList)
845
846 852 if self.isThisProfileInList(dataOut.profileIndex, profileList):
847 dataOut.flagNoData = False
853
854 self.nProfiles = len(profileList)
855 dataOut.nProfiles = self.nProfiles
848 856 dataOut.profileIndex = self.profileIndex
857 dataOut.flagNoData = False
849 858
850 859 self.incIndex()
851 860 return True
@@ -855,11 +864,12 class ProfileSelector(Operation):
855 864 minIndex = profileRangeList[0]
856 865 maxIndex = profileRangeList[1]
857 866
858 dataOut.nProfiles = maxIndex - minIndex + 1
859
860 867 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
861 dataOut.flagNoData = False
868
869 self.nProfiles = maxIndex - minIndex + 1
870 dataOut.nProfiles = self.nProfiles
862 871 dataOut.profileIndex = self.profileIndex
872 dataOut.flagNoData = False
863 873
864 874 self.incIndex()
865 875 return True
@@ -874,8 +884,6 class ProfileSelector(Operation):
874 884
875 885 nProfiles += maxIndex - minIndex + 1
876 886
877 dataOut.nProfiles = nProfiles
878
879 887 for thisRange in rangeList:
880 888
881 889 minIndex = thisRange[0]
@@ -883,13 +891,15 class ProfileSelector(Operation):
883 891
884 892 if self.isThisProfileInRange(dataOut.profileIndex, minIndex, maxIndex):
885 893
886 # print "profileIndex = ", dataOut.profileIndex
887
888 dataOut.flagNoData = False
894 self.nProfiles = nProfiles
895 dataOut.nProfiles = self.nProfiles
889 896 dataOut.profileIndex = self.profileIndex
897 dataOut.flagNoData = False
890 898
891 899 self.incIndex()
900
892 901 break
902
893 903 return True
894 904
895 905
@@ -913,35 +923,102 class Reshaper(Operation):
913 923 def __init__(self):
914 924
915 925 Operation.__init__(self)
916 self.updateNewHeights = True
917 926
918 def run(self, dataOut, shape):
927 self.__buffer = None
928 self.__nitems = 0
929
930 def __appendProfile(self, dataOut, nTxs):
931
932 if self.__buffer is None:
933 shape = (dataOut.nChannels, int(dataOut.nHeights/nTxs) )
934 self.__buffer = numpy.empty(shape, dtype = dataOut.data.dtype)
935
936 ini = dataOut.nHeights * self.__nitems
937 end = ini + dataOut.nHeights
938
939 self.__buffer[:, ini:end] = dataOut.data
940
941 self.__nitems += 1
942
943 return int(self.__nitems*nTxs)
944
945 def __getBuffer(self):
946
947 if self.__nitems == int(1./self.__nTxs):
919 948
920 if not dataOut.flagDataAsBlock:
921 raise ValueError, "Reshaper can only be used when voltage have been read as Block, getBlock = True"
949 self.__nitems = 0
922 950
923 if len(shape) != 3:
924 raise ValueError, "shape len should be equal to 3, (nChannels, nProfiles, nHeis)"
951 return self.__buffer.copy()
952
953 return None
954
955 def __checkInputs(self, dataOut, shape, nTxs):
956
957 if shape is None and nTxs is None:
958 raise ValueError, "Reshaper: shape of factor should be defined"
959
960 if nTxs:
961 if nTxs < 0:
962 raise ValueError, "nTxs should be greater than 0"
963
964 if nTxs < 1 and dataOut.nProfiles % (1./nTxs) != 0:
965 raise ValueError, "nProfiles= %d is not divisibled by (1./nTxs) = %f" %(dataOut.nProfiles, (1./nTxs))
966
967 shape = [dataOut.nChannels, dataOut.nProfiles*nTxs, dataOut.nHeights/nTxs]
968
969 if len(shape) != 2 and len(shape) != 3:
970 raise ValueError, "shape dimension should be equal to 2 or 3. shape = (nProfiles, nHeis) or (nChannels, nProfiles, nHeis). Actually shape = (%d, %d, %d)" %(dataOut.nChannels, dataOut.nProfiles, dataOut.nHeights)
971
972 if len(shape) == 2:
973 shape_tuple = [dataOut.nChannels]
974 shape_tuple.extend(shape)
975 else:
976 shape_tuple = list(shape)
977
978 if not nTxs:
979 nTxs = int(shape_tuple[1]/dataOut.nProfiles)
980
981 return shape_tuple, nTxs
982
983 def run(self, dataOut, shape=None, nTxs=None):
984
985 shape_tuple, self.__nTxs = self.__checkInputs(dataOut, shape, nTxs)
986
987 dataOut.flagNoData = True
988 profileIndex = None
989
990 if dataOut.flagDataAsBlock:
925 991
926 shape_tuple = tuple(shape)
927 992 dataOut.data = numpy.reshape(dataOut.data, shape_tuple)
928 993 dataOut.flagNoData = False
929 994
930 if self.updateNewHeights:
995 profileIndex = int(dataOut.nProfiles*nTxs) - 1
931 996
932 old_nheights = dataOut.nHeights
933 new_nheights = dataOut.data.shape[2]
934 factor = 1.0*new_nheights / old_nheights
997 else:
998
999 if self.__nTxs < 1:
1000
1001 self.__appendProfile(dataOut, self.__nTxs)
1002 new_data = self.__getBuffer()
1003
1004 if new_data is not None:
1005 dataOut.data = new_data
1006 dataOut.flagNoData = False
1007
1008 profileIndex = dataOut.profileIndex*nTxs
1009
1010 else:
1011 raise ValueError, "nTxs should be greater than 0 and lower than 1, or use VoltageReader(..., getblock=True)"
935 1012
936 1013 deltaHeight = dataOut.heightList[1] - dataOut.heightList[0]
937 1014
938 xf = dataOut.heightList[0] + dataOut.nHeights * deltaHeight * factor
1015 dataOut.heightList = numpy.arange(dataOut.nHeights/self.__nTxs) * deltaHeight + dataOut.heightList[0]
939 1016
940 dataOut.heightList = numpy.arange(dataOut.heightList[0], xf, deltaHeight)
1017 dataOut.nProfiles = int(dataOut.nProfiles*self.__nTxs)
941 1018
942 dataOut.nProfiles = dataOut.data.shape[1]
1019 dataOut.profileIndex = profileIndex
943 1020
944 dataOut.ippSeconds *= factor
1021 dataOut.ippSeconds /= self.__nTxs
945 1022 #
946 1023 # import collections
947 1024 # from scipy.stats import mode
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