##// END OF EJS Templates
schain
RSS
Remove numpy.complex
Juan C. Espinoza -
r1595:fed0fad1169f
parent child
Show More
Side by Side Unified
Context file:
r1595:fed0fad1169f Loading diff...:
Show file before | Show file after | Hide comments
@@ -1419,8 +1419,8 class SpectralMoments(Operation):
1419 1419
1420 1420 absc = dataOut.abscissaList[:-1]
1421 1421 noise = dataOut.noise
1422 nChannel = data.shape[0]
1423 data_param = numpy.zeros((nChannel, 8, data.shape[2]))
1422 nChannel = dataOut.data_pre[0].shape[0]
1423 data_param = numpy.zeros((nChannel, 4 + proc_type*3, dataOut.data_pre[0].shape[2]))
1424 1424
1425 1425 if proc_type == 1:
1426 1426 fwindow = numpy.zeros(absc.size) + 1
@@ -1449,7 +1449,7 class SpectralMoments(Operation):
1449 1449 smooth=None
1450 1450
1451 1451 for ind in range(nChannel):
1452 data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind], nicoh=nIncohInt, smooth=smooth, type1=type1, fwindow=fwindow, id_ch=ind)
1452 data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind], nicoh=nIncohInt, smooth=smooth, type1=type1, fwindow=fwindow)
1453 1453
1454 1454 if proc_type == 1:
1455 1455 dataOut.moments = data_param[:,1:,:]
Show file before | Show file after | Hide comments
@@ -666,7 +666,7 class removeInterference(Operation):
666 666 median_imag = int(numpy.median(numpy.imag(
667 667 junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
668 668 comp_mask_prof = [int(e) for e in comp_mask_prof]
669 junkcspc_interf[comp_mask_prof, :] = numpy.complex(
669 junkcspc_interf[comp_mask_prof, :] = complex(
670 670 median_real, median_imag)
671 671
672 672 for iprof in range(num_prof):
Show file before | Show file after | Hide comments
@@ -610,7 +610,7 class SpectraAFCProc(ProcessingUnit):
610 610
611 611 median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[list(range(3 * num_prof / 4))]], :]))
612 612 median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[list(range(3 * num_prof / 4))]], :]))
613 junkcspc_interf[comp_mask_prof, :] = numpy.complex(median_real, median_imag)
613 junkcspc_interf[comp_mask_prof, :] = complex(median_real, median_imag)
614 614
615 615 for iprof in range(num_prof):
616 616 ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort()
Show file before | Show file after | Hide comments
@@ -613,7 +613,7 class SpectraLagsProc(ProcessingUnit):
613 613
614 614 median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[list(range(3 * num_prof / 4))]], :]))
615 615 median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[list(range(3 * num_prof / 4))]], :]))
616 junkcspc_interf[comp_mask_prof, :] = numpy.complex(median_real, median_imag)
616 junkcspc_interf[comp_mask_prof, :] = complex(median_real, median_imag)
617 617
618 618 for iprof in range(num_prof):
619 619 ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort()
Show file before | Show file after | Hide comments
@@ -2996,7 +2996,7 class CohInt(Operation):
2996 2996 def integrateByBlock(self, dataOut):
2997 2997
2998 2998 times = int(dataOut.data.shape[1]/self.n)
2999 avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
2999 avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=complex)
3000 3000
3001 3001 id_min = 0
3002 3002 id_max = self.n
@@ -3236,8 +3236,8 class SSheightProfiles(Operation):
3236 3236 self.bufferShape = int(shape[0]), int(numberSamples), int(numberProfile) # nchannels, nsamples , nprofiles
3237 3237 self.profileShape = int(shape[0]), int(numberProfile), int(numberSamples) # nchannels, nprofiles, nsamples
3238 3238
3239 self.buffer = numpy.zeros(self.bufferShape , dtype=numpy.complex)
3240 self.sshProfiles = numpy.zeros(self.profileShape, dtype=numpy.complex)
3239 self.buffer = numpy.zeros(self.bufferShape , dtype=complex)
3240 self.sshProfiles = numpy.zeros(self.profileShape, dtype=complex)
3241 3241
3242 3242 def run(self, dataOut, step, nsamples, code = None, repeat = None):
3243 3243 #print(dataOut.profileIndex)
@@ -3475,7 +3475,7 class Decoder(Operation):
3475 3475 raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud))
3476 3476
3477 3477 #Frequency
3478 __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
3478 __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=complex)
3479 3479
3480 3480 __codeBuffer[:,0:self.nBaud] = self.code
3481 3481
@@ -3485,7 +3485,7 class Decoder(Operation):
3485 3485
3486 3486 self.ndatadec = self.__nHeis #- self.nBaud + 1
3487 3487
3488 self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
3488 self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=complex)
3489 3489
3490 3490 else:
3491 3491
@@ -3493,7 +3493,7 class Decoder(Operation):
3493 3493 self.ndatadec = self.__nHeis #- self.nBaud + 1
3494 3494
3495 3495
3496 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
3496 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=complex)
3497 3497
3498 3498 def __convolutionInFreq(self, data):
3499 3499
@@ -3677,7 +3677,7 class DecoderRoll(Operation):
3677 3677 raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud))
3678 3678
3679 3679 #Frequency
3680 __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
3680 __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=complex)
3681 3681
3682 3682 __codeBuffer[:,0:self.nBaud] = self.code
3683 3683
@@ -3687,7 +3687,7 class DecoderRoll(Operation):
3687 3687
3688 3688 self.ndatadec = self.__nHeis #- self.nBaud + 1
3689 3689
3690 self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
3690 self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=complex)
3691 3691
3692 3692 else:
3693 3693
@@ -3695,7 +3695,7 class DecoderRoll(Operation):
3695 3695 self.ndatadec = self.__nHeis #- self.nBaud + 1
3696 3696
3697 3697
3698 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
3698 self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=complex)
3699 3699
3700 3700 def __convolutionInFreq(self, data):
3701 3701
@@ -4373,7 +4373,7 class CombineProfiles(Operation):
4373 4373 # bufferList = []
4374 4374 #
4375 4375 # for i in range(nChannels):
4376 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
4376 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=complex) + numpy.NAN,
4377 4377 # maxlen = buffer_size*nSamples)
4378 4378 #
4379 4379 # bufferList.append(bufferByChannel)
@@ -4406,7 +4406,7 class CombineProfiles(Operation):
4406 4406 # dataOut.flagNoData = True
4407 4407 # return
4408 4408 #
4409 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
4409 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = complex)
4410 4410 # self.__pulseFound = True
4411 4411 # self.__startIndex = indexFound
4412 4412 #
@@ -6211,7 +6211,7 class PulsePairVoltage(Operation):
6211 6211 # bufferList = []
6212 6212 #
6213 6213 # for i in range(nChannels):
6214 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) + numpy.NAN,
6214 # bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=complex) + numpy.NAN,
6215 6215 # maxlen = buffer_size*nSamples)
6216 6216 #
6217 6217 # bufferList.append(bufferByChannel)
@@ -6244,7 +6244,7 class PulsePairVoltage(Operation):
6244 6244 # dataOut.flagNoData = True
6245 6245 # return
6246 6246 #
6247 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
6247 # self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = complex)
6248 6248 # self.__pulseFound = True
6249 6249 # self.__startIndex = indexFound
6250 6250 #
General Comments 0
You need to be logged in to leave comments. Login now