schain Commit - r1595:fed0fad1169f · Jicamarca Repository

Remove numpy.complex

Juan C. Espinoza -

r1595:fed0fad1169f

parent child

Context file:

r1595:fed0fad1169f

Collapse all files

schainpy/model/proc/jroproc_parameters.py +3 -3

                     absc = dataOut.abscissaList[:-1]
                     noise = dataOut.noise
-                    nChannel = data.shape[0]
+                    nChannel = dataOut.data_pre[0].shape[0]
-                    data_param = numpy.zeros((nChannel, 8, data.shape[2]))
+                    data_param = numpy.zeros((nChannel, 4 + proc_type*3, dataOut.data_pre[0].shape[2]))
                     if proc_type == 1:
                         fwindow = numpy.zeros(absc.size) + 1
                         smooth=None
                     for ind in range(nChannel):
-                        data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind], nicoh=nIncohInt, smooth=smooth, type1=type1, fwindow=fwindow, id_ch=ind)
+                        data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind], nicoh=nIncohInt, smooth=smooth, type1=type1, fwindow=fwindow)
                     if proc_type == 1:
                         dataOut.moments = data_param[:,1:,:]

schainpy/model/proc/jroproc_spectra.py +1 -1

                         median_imag = int(numpy.median(numpy.imag(
                             junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
                         comp_mask_prof = [int(e) for e in comp_mask_prof]
-                        junkcspc_interf[comp_mask_prof, :] = numpy.complex(
+                        junkcspc_interf[comp_mask_prof, :] = complex(
                             median_real, median_imag)
                         for iprof in range(num_prof):

schainpy/model/proc/jroproc_spectra_acf.py +1 -1

                         median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[list(range(3 * num_prof / 4))]], :]))
                         median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[list(range(3 * num_prof / 4))]], :]))
-                        junkcspc_interf[comp_mask_prof, :] = numpy.complex(median_real, median_imag)
+                        junkcspc_interf[comp_mask_prof, :] = complex(median_real, median_imag)
                         for iprof in range(num_prof):
                             ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort()

schainpy/model/proc/jroproc_spectra_lags.py +1 -1

                         median_real = numpy.median(numpy.real(junkcspc_interf[mask_prof[ind[list(range(3 * num_prof / 4))]], :]))
                         median_imag = numpy.median(numpy.imag(junkcspc_interf[mask_prof[ind[list(range(3 * num_prof / 4))]], :]))
-                        junkcspc_interf[comp_mask_prof, :] = numpy.complex(median_real, median_imag)
+                        junkcspc_interf[comp_mask_prof, :] = complex(median_real, median_imag)
                         for iprof in range(num_prof):
                             ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort()

schainpy/model/proc/jroproc_voltage.py +13 -13

                 def integrateByBlock(self, dataOut):
                     times = int(dataOut.data.shape[1]/self.n)
-                    avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=numpy.complex)
+                    avgdata = numpy.zeros((dataOut.nChannels, times, dataOut.nHeights), dtype=complex)
                     id_min = 0
                     id_max = self.n
                     self.bufferShape  = int(shape[0]), int(numberSamples), int(numberProfile)  # nchannels, nsamples , nprofiles
                     self.profileShape = int(shape[0]), int(numberProfile), int(numberSamples)  # nchannels, nprofiles, nsamples
-                    self.buffer       = numpy.zeros(self.bufferShape , dtype=numpy.complex)
+                    self.buffer       = numpy.zeros(self.bufferShape , dtype=complex)
-                    self.sshProfiles  = numpy.zeros(self.profileShape, dtype=numpy.complex)
+                    self.sshProfiles  = numpy.zeros(self.profileShape, dtype=complex)
                 def run(self, dataOut, step, nsamples, code = None, repeat = None):
                     #print(dataOut.profileIndex)
                         raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud))
                     #Frequency
-                    __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
+                    __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=complex)
                     __codeBuffer[:,0:self.nBaud] = self.code
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
-                        self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
+                        self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=complex)
                     else:
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
-                        self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
+                        self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=complex)
                 def __convolutionInFreq(self, data):
                         raise ValueError('Number of heights (%d) should be greater than number of bauds (%d)' %(self.__nHeis, self.nBaud))
                     #Frequency
-                    __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=numpy.complex)
+                    __codeBuffer = numpy.zeros((self.nCode, self.__nHeis), dtype=complex)
                     __codeBuffer[:,0:self.nBaud] = self.code
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
-                        self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=numpy.complex)
+                        self.datadecTime = numpy.zeros((self.__nChannels, self.__nProfiles, self.ndatadec), dtype=complex)
                     else:
                         self.ndatadec = self.__nHeis #- self.nBaud + 1
-                        self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=numpy.complex)
+                        self.datadecTime = numpy.zeros((self.__nChannels, self.ndatadec), dtype=complex)
                 def __convolutionInFreq(self, data):
             #         bufferList = []
             #
             #         for i in range(nChannels):
-            #             bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) +  numpy.NAN,
+            #             bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=complex) +  numpy.NAN,
             #                                           maxlen = buffer_size*nSamples)
             #
             #             bufferList.append(bufferByChannel)
             #                 dataOut.flagNoData = True
             #                 return
             #
-            #             self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
+            #             self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = complex)
             #             self.__pulseFound = True
             #             self.__startIndex = indexFound
             #
             #         bufferList = []
             #
             #         for i in range(nChannels):
-            #             bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=numpy.complex) +  numpy.NAN,
+            #             bufferByChannel = collections.deque(numpy.zeros( buffer_size*nSamples, dtype=complex) +  numpy.NAN,
             #                                           maxlen = buffer_size*nSamples)
             #
             #             bufferList.append(bufferByChannel)
             #                 dataOut.flagNoData = True
             #                 return
             #
-            #             self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = numpy.complex)
+            #             self.__arrayBuffer = numpy.zeros((self.__nChannels, self.__newNSamples), dtype = complex)
             #             self.__pulseFound = True
             #             self.__startIndex = indexFound
             #

General Comments 0

Write
Preview

You need to be logged in to leave comments. Login now

No TODOs yet

	Site-wide shortcuts
/	Use quick search box
g h	Goto home page
g g	Goto my private gists page
g G	Goto my public gists page
g 0-9	Goto bookmarked items from 0-9
n r	New repository page
n g	New gist page

	Repositories
g s	Goto summary page
g c	Goto changelog page
g f	Goto files page
g F	Goto files page with file search activated
g p	Goto pull requests page
g o	Goto repository settings
g O	Goto repository access permissions settings
t s	Toggle sidebar on some pages