##// END OF EJS Templates
schain
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Test codificacion
Miguel Valdez -
r308:92c20fb0e516
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@@ -596,73 +596,60 class Decoder(Operation):
596 596
597 597 self.__nChannels, self.__nHeis = shape
598 598
599 self.__codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.float32)
599 __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.float32)
600 600
601 self.__codeBuffer[:,0:self.nBaud] = self.code
601 __codeBuffer[:,0:self.nBaud] = self.code
602 602
603 self.fft_code = numpy.conj(numpy.fft.fft(self.__codeBuffer, axis=1))
603 self.fft_code = numpy.conj(numpy.fft.fft(__codeBuffer, axis=1))
604 604
605 self.ndatadec = __nHeis - nBaud + 1
606
607 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
605 608
606 609 def convolutionInFreq(self, data):
607 610
611 ini = time.time()
612
608 613 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
609 614
610 615 fft_data = numpy.fft.fft(data, axis=1)
611 616
612
613 # conv = fft_data.copy()
614 # conv.fill(0)
615
616 617 conv = fft_data*fft_code
617 618
618 619 data = numpy.fft.ifft(conv,axis=1)
619 620
620 621 datadec = data[:,:-self.nBaud+1]
621 ndatadec = self.__nHeis - self.nBaud + 1
622 622
623 if self.__profIndex == self.nCode-1:
624 self.__profIndex = 0
625 return ndatadec, datadec
626
627 self.__profIndex += 1
623 print "Freq ", time.time() - ini
628 624
629 return ndatadec, datadec
625 return datadec
630 626
631 627 def convolutionInFreqOpt(self, data):
632 628
629 ini = time.time()
630
633 631 fft_code = self.fft_code[self.__profIndex].reshape(1,-1)
634 632
635 633 data = cfunctions.decoder(fft_code, data)
636 634
637 635 datadec = data[:,:-self.nBaud+1]
638 ndatadec = self.__nHeis - self.nBaud + 1
639 636
640 if self.__profIndex == self.nCode-1:
641 self.__profIndex = 0
642 return ndatadec, datadec
643
644 self.__profIndex += 1
637 print "OptFreq ", time.time() - ini
645 638
646 return ndatadec, datadec
639 return datadec
647 640
648 641 def convolutionInTime(self, data):
649 642
650 self.__nChannels, self.__nHeis = data.shape
651 self.__codeBuffer = self.code[self.__profIndex]
652 ndatadec = self.__nHeis - self.nBaud + 1
653
654 datadec = numpy.zeros((self.__nChannels, ndatadec))
643 ini = time.time()
655 644
656 for i in range(self.__nChannels):
657 datadec[i,:] = numpy.correlate(data[i,:], self.__codeBuffer)
645 code = self.code[self.__profIndex].reshape(1,-1)
658 646
659 if self.__profIndex == self.nCode-1:
660 self.__profIndex = 0
661 return ndatadec, datadec
647 for i in range(__nChannels):
648 self.datadecTime[i,:] = numpy.correlate(data[i,:], code, mode='valid')
662 649
663 self.__profIndex += 1
650 print "Time ", time.time() - ini
664 651
665 return ndatadec, datadec
652 return self.datadecTime
666 653
667 654 def run(self, dataOut, code=None, nCode=None, nBaud=None, mode = 0):
668 655 ini = time.time()
@@ -691,7 +678,9 class Decoder(Operation):
691 678
692 679 if mode == 2:
693 680 ndatadec, datadec = self.convolutionInFreqOpt(dataOut.data)
694
681
682
683
695 684 dataOut.data = datadec
696 685
697 686 dataOut.heightList = dataOut.heightList[0:ndatadec]
@@ -699,8 +688,17 class Decoder(Operation):
699 688 dataOut.flagDecodeData = True #asumo q la data no esta decodificada
700 689
701 690 print time.time() - ini, "prof = %d, nCode=%d" %(self.__profIndex, self.nCode)
702 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
691
692 if self.__profIndex == self.nCode-1:
693 self.__profIndex = 0
694 return 1
695
696 self.__profIndex += 1
703 697
698 return 1
699 # dataOut.flagDeflipData = True #asumo q la data no esta sin flip
700
701
704 702
705 703 class SpectraProc(ProcessingUnit):
706 704
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