schain Commit - r1257:f1a6137b3999 · Jicamarca Repository

update y fix

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r1257:f1a6137b3999

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r1257:f1a6137b3999

schainpy/model/graphics/jroplot_spectra.py +4 -3

                     #z           =  numpy.where(numpy.isfinite(z), z, numpy.NAN)
                     shape        =  dataOut.data_acf.shape
+            	print "shape_plot",shape
                     hei_index    =  numpy.arange(shape[2])
                     hei_plot     =  numpy.arange(nSamples)*resFactor
                         hei_plot = numpy.array(nSampleList)*resFactor
                     if hei_plot[-1] >= hei_index[-1]:
-                        print ("La cantidad de puntos en altura es %d y la resolucion es %d Km"%(hei_plot.shape[0],deltaHeight*resFactor ))
+                        print ("La cantidad de puntos en altura es %d y la resolucion es %f Km"%(hei_plot.shape[0],deltaHeight*resFactor ))
                         raise ValueError, "resFactor %d multiplicado por el valor de %d nSamples  es mayor a %d cantidad total de puntos"%(resFactor,nSamples,hei_index[-1])
                     #escalamiento  -1 a 1 a resolucion (factor de resolucion en altura)* deltaHeight
                         if ymin == None: ymin = numpy.nanmin(zdB)
                         if ymax == None: ymax = numpy.nanmax(zdB)
-                        print ("El parametro resFactor es %d y la resolucion en altura es %d"%(resFactor,deltaHeight ))
+                        print ("El parametro resFactor es %d y la resolucion en altura es %f"%(resFactor,deltaHeight ))
-                        print ("La cantidad de puntos en altura es %d y la nueva resolucion es %d Km"%(hei_plot.shape[0],deltaHeight*resFactor ))
+                        print ("La cantidad de puntos en altura es %d y la nueva resolucion es %f Km"%(hei_plot.shape[0],deltaHeight*resFactor ))
                         print ("La altura maxima es %d Km"%(hei_plot[-1]*deltaHeight ))
                         self.isConfig = True

schainpy/model/proc/jroproc_spectra_acf.py +20 -16

@@ -151,24 +151,28 class SpectraAFCProc(ProcessingUnit):
151	shape = acf.shape # nchannels, nprofiles, nsamples	151	shape = acf.shape # nchannels, nprofiles, nsamples
152		152
153	#import matplotlib.pyplot as plt	153	#import matplotlib.pyplot as plt
154	#acf_tmp=acf[0,:,85]	154	#print "test",acf.shape
		155	#acf_tmp=acf[0,:,]
155	#plt.plot(acf_tmp)	156	#plt.plot(acf_tmp)
156	#plt.show()	157	#plt.show()
157		158	#import time
158	for i in range(shape[1]):	159	#time.sleep(10)
159	tmp = numpy.argmax(acf[0,:,i])	160
160	if i>30:	161	for j in range(shape[0]):
161	value = (acf[0,:,i][tmp+3]+acf[0,:,i][tmp+4])/2.0	162	for i in range(shape[1]):
162	acf[0,:,i][tmp] = value	163	tmp = numpy.argmax(acf[j,:,i])
163	acf[0,:,i][tmp-1] = value	164	if i>30:
164	acf[0,:,i][tmp+1] = value	165	value = (acf[j,:,i][tmp+3]+acf[j,:,i][tmp+4])/2.0
165	acf[0,:,i][tmp-2] = value	166	acf[j,:,i][tmp] = value
166	acf[0,:,i][tmp+2] = value	167	acf[j,:,i][tmp-1] = value
167		168	acf[j,:,i][tmp+1] = value
168	import scipy as sp	169	acf[j,:,i][tmp-2] = value
169	from scipy import signal	170	acf[j,:,i][tmp+2] = value
170	acf[0,:,i] = sp.signal.medfilt(acf[0,:,i],21)	171
171		172	import scipy as sp
		173	from scipy import signal
		174	#acf[3,:,i] = sp.signal.medfilt(acf[3,:,i],21)
		175
172		176
173		177
174	#print numpy.argmax(acf[0,:,85])	178	#print numpy.argmax(acf[0,:,85])

schainpy/model/proc/jroproc_voltage.py +12 -2

                     self.__nProfiles  = dataOut.nProfiles
                     self.__nHeis      = dataOut.nHeights
                     shape             = dataOut.data.shape #nchannels, nprofiles, nsamples
+            	print "input nChannels",self.__nChannels
+            	print "input nProfiles",self.__nProfiles
+            	print "input nHeis",self.__nHeis
+            	print "input Shape",shape
                     residue     =  (shape[1] - self.nsamples) % self.step
                     numberProfile    =  self.nsamples
                     numberSamples    =  (shape[1] - self.nsamples)/self.step
-                    print "New number of profile: %d, number of height: %d, Resolution %d Km"%(numberProfile,numberSamples,deltaHeight*self.step)
+            	print "new numberProfile",numberProfile
+            	print "new numberSamples",numberSamples
+                    print "New number of profile: %d, number of height: %d, Resolution %f Km"%(numberProfile,numberSamples,deltaHeight*self.step)
                     self.bufferShape  = shape[0], numberSamples, numberProfile  # nchannels, nsamples , nprofiles
                     self.profileShape = shape[0], numberProfile, numberSamples  # nchannels, nprofiles, nsamples
                         self.isConfig = True
                     for i in range(self.buffer.shape[1]):
-                        self.buffer[:,i]    = numpy.flip(dataOut.data[:,i*self.step:i*self.step + self.nsamples])
+                        #self.buffer[:,i]    = numpy.flip(dataOut.data[:,i*self.step:i*self.step + self.nsamples])
+                        self.buffer[:,i]    = dataOut.data[:,i*self.step:i*self.step + self.nsamples]
                         #self.buffer[:,j,self.__nHeis-j*self.step - self.nheights:self.__nHeis-j*self.step] = numpy.flip(dataOut.data[:,j*self.step:j*self.step + self.nheights])
                     for j in range(self.buffer.shape[0]):

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