| @@ -1,6 +1,5 | |||
|
|
1 | 1 | import sys |
|
|
2 | 2 | import numpy |
|
|
3 | from profilehooks import profile | |
|
|
4 | 3 | from scipy import interpolate |
|
|
5 | 4 | from schainpy import cSchain |
|
|
6 | 5 | from jroproc_base import ProcessingUnit, Operation |
| @@ -623,13 +622,6 class Decoder(Operation): | |||
|
|
623 | 622 | |
|
|
624 | 623 | return self.datadecTime |
|
|
625 | 624 | |
|
|
626 | #@profile | |
|
|
627 | def oldCorrelate(self, i, data, code_block): | |
|
|
628 | profilesList = xrange(self.__nProfiles) | |
|
|
629 | for j in profilesList: | |
|
|
630 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] | |
|
|
631 | ||
|
|
632 | #@profile | |
|
|
633 | 625 | def __convolutionByBlockInTime(self, data): |
|
|
634 | 626 | |
|
|
635 | 627 | repetitions = self.__nProfiles / self.nCode |
| @@ -639,25 +631,10 class Decoder(Operation): | |||
|
|
639 | 631 | code_block = numpy.reshape(junk, (self.nCode*repetitions, self.nBaud)) |
|
|
640 | 632 | profilesList = xrange(self.__nProfiles) |
|
|
641 | 633 | |
|
|
642 | # def toVectorize(a,b): | |
|
|
643 | # return numpy.correlate(a,b, mode='full') | |
|
|
644 | # vectorized = numpy.vectorize(toVectorize, signature='(n),(m)->(k)') | |
|
|
645 | for i in range(self.__nChannels): | |
|
|
646 | # self.datadecTime[i,:,:] = numpy.array([numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] for j in profilesList ]) | |
|
|
647 | # def func(i, j): | |
|
|
648 | # self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] | |
|
|
649 | # map(lambda j: func(i, j), range(self.__nProfiles)) | |
|
|
650 | #print data[i,:,:].shape | |
|
|
651 | # self.datadecTime[i,:,:] = vectorized(data[i,:,:], code_block[:,:])[:,self.nBaud-1:] | |
|
|
652 | for j in profilesList: | |
|
|
653 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] | |
|
|
654 | # print data[i,:,:] | |
|
|
655 | # print cSchain.correlateByBlock(data[i,:,:], code_block, 2) | |
|
|
656 | # self.datadecTime[i,:,:] = cSchain.correlateByBlock(data[i,:,:], code_block, 2) | |
|
|
657 | # print self.datadecTime[i,:,:] | |
|
|
658 | #print self.datadecTime[i,:,:].shape | |
|
|
659 | return self.datadecTime | |
|
|
660 | ||
|
|
634 | for i in range(self.__nChannels): | |
|
|
635 | for j in profilesList: | |
|
|
636 | self.datadecTime[i,j,:] = numpy.correlate(data[i,j,:], code_block[j,:], mode='full')[self.nBaud-1:] | |
|
|
637 | return self.datadecTime | |
|
|
661 | 638 | |
|
|
662 | 639 | def __convolutionByBlockInFreq(self, data): |
|
|
663 | 640 | |
General Comments 0
You need to be logged in to leave comments.
Login now