| @@ -636,17 +636,20 class Spectra(JROData): | |||
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636 | 636 | return freqrange |
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637 | 637 | |
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638 | 638 | def getAcfRange(self, extrapoints=0): |
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639 | #print "GET ACF RANGE" | |
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639 | 640 | #print "NFFTPoints",self.nFFTPoints |
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640 | 641 | #print "IPPFactor", self.ippFactor |
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641 | deltafreq = 10. / ( self.getFmax() / (self.nFFTPoints * self.ippFactor) ) | |
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642 | #print "deltafreq",deltafreq | |
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642 | #deltafreq = 10. / ( self.getFmax() / (self.nFFTPoints * self.ippFactor) ) | |
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643 | deltatime = 1./(self.getFmax()/ self.ippFactor) | |
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644 | #print "getFmax",self.getFmax() | |
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643 | 645 | #import time |
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644 | 646 | #time.sleep(30) |
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645 |
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647 | timerange = deltatime * \ | |
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646 | 648 | (numpy.arange(self.nFFTPoints + extrapoints) - |
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647 |
self.nFFTPoints / 2.) |
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648 | ||
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649 | return freqrange | |
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649 | self.nFFTPoints / 2.) | |
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650 | #- deltafreq / 2 | |
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651 | #print "timerange",timerange | |
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652 | return timerange | |
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650 | 653 | |
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651 | 654 | def getFreqRange(self, extrapoints=0): |
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652 | 655 | |
| @@ -318,7 +318,7 class ACFPlot(Figure): | |||
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318 | 318 | |
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319 | 319 | #z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
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320 | 320 | shape = dataOut.data_acf.shape |
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321 | print "shape_plot",shape | |
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321 | #print "shape_plot",shape | |
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322 | 322 | hei_index = numpy.arange(shape[2]) |
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323 | 323 | hei_plot = numpy.arange(nSamples)*resFactor |
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324 | 324 | |
| @@ -348,8 +348,9 class ACFPlot(Figure): | |||
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348 | 348 | ylabel = "Power (dB)" |
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349 | 349 | |
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350 | 350 | elif xaxis == "time": |
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351 | delta= dataOut.getAcfRange()[1]-dataOut.getAcfRange()[0] | |
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352 | x = dataOut.getAcfRange()+delta/2.0 | |
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351 | #delta= dataOut.getAcfRange()[1]-dataOut.getAcfRange()[0] | |
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352 | #x = dataOut.getAcfRange()+delta/2.0 | |
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353 | x = dataOut.getAcfRange()*1000 | |
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353 | 354 | zdB = z[channel0,:,hei_plot] |
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354 | 355 | xlabel = "Time (ms)" |
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355 | 356 | ylabel = "ACF" |
| @@ -372,8 +373,8 class ACFPlot(Figure): | |||
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372 | 373 | wintitle=wintitle, |
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373 | 374 | show=show) |
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374 | 375 | |
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375 | if xmin == None: xmin = numpy.nanmin(x)*0.9 | |
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376 | if xmax == None: xmax = numpy.nanmax(x)*1.1 | |
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376 | if xmin == None: xmin = numpy.nanmin(x)#*0.9 | |
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377 | if xmax == None: xmax = numpy.nanmax(x)#*1.1 | |
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377 | 378 | if ymin == None: ymin = numpy.nanmin(zdB) |
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378 | 379 | if ymax == None: ymax = numpy.nanmax(zdB) |
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379 | 380 | |
| @@ -141,63 +141,26 class SpectraAFCProc(ProcessingUnit): | |||
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141 | 141 | if self.dataIn.type == "Spectra": |
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142 | 142 | self.dataOut.copy(self.dataIn) |
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143 | 143 | spc = self.dataOut.data_spc |
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144 |
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145 |
data = numpy.fft. |
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144 | data = numpy.fft.fftshift( spc, axes=(1,)) | |
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145 | data = numpy.fft.ifft(data, axis=1) | |
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146 | data = numpy.fft.fftshift( data, axes=(1,)) | |
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146 | 147 | acf = numpy.abs(data) # Autocorrelacion LLAMAR A ESTE VALOR ACF |
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147 | 148 | if real: |
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148 | 149 | acf = data.real |
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149 | 150 | if imag: |
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150 | 151 | acf = data.imag |
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151 | shape = acf.shape # nchannels, nprofiles, nsamples | |
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152 | ||
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153 | #import matplotlib.pyplot as plt | |
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154 | #print "test",acf.shape | |
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155 | #acf_tmp=acf[0,:,] | |
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156 | #plt.plot(acf_tmp) | |
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157 | #plt.show() | |
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158 | #import time | |
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159 | #time.sleep(10) | |
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160 | ||
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152 | shape = acf.shape # nchannels, nprofiles, nsamples //nchannles, lags , alturas | |
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153 | ||
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161 | 154 | for j in range(shape[0]): |
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162 |
for i in range(shape[ |
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163 | tmp = numpy.argmax(acf[j,:,i]) | |
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164 | if i>30: | |
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165 | value = (acf[j,:,i][tmp+3]+acf[j,:,i][tmp+4])/2.0 | |
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166 | acf[j,:,i][tmp] = value | |
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167 | acf[j,:,i][tmp-1] = value | |
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168 | acf[j,:,i][tmp+1] = value | |
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169 | acf[j,:,i][tmp-2] = value | |
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170 | acf[j,:,i][tmp+2] = value | |
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171 | ||
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172 | import scipy as sp | |
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173 | from scipy import signal | |
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174 | #acf[3,:,i] = sp.signal.medfilt(acf[3,:,i],21) | |
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175 | ||
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176 | ||
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177 | ||
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178 | #print numpy.argmax(acf[0,:,85]) | |
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179 | #import matplotlib.pyplot as plt | |
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180 | #plt.plot(acf[0,:,85]) | |
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181 | #a= acf[0,:,85] | |
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182 | #b= acf[0,:,0] | |
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183 | #print a[200],a[198],a[199], a[201],a[202],a[203] | |
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184 | #plt.show() | |
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185 | #import time | |
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186 | #time.sleep(10) | |
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187 | ||
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188 | ||
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155 | for i in range(shape[2]): | |
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156 | tmp = shape[1]/2 | |
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157 | value = (acf[j,:,i][tmp-1]+acf[j,:,i][tmp+1])/2.0 | |
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158 | acf[j,:,i][tmp] = value | |
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189 | 159 | # Normalizando |
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190 | 160 | for i in range(shape[0]): |
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191 | 161 | for j in range(shape[2]): |
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192 | 162 | acf[i,:,j]= acf[i,:,j] / numpy.max(numpy.abs(acf[i,:,j])) |
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193 | 163 | |
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194 | #import matplotlib.pyplot as plt | |
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195 | #plt.plot(acf[0,:,85]) | |
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196 | #plt.show() | |
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197 | #import time | |
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198 | #time.sleep(20) | |
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199 | ||
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200 | ||
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201 | 164 | self.dataOut.data_acf = acf |
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202 | 165 | return True |
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203 | 166 | |
| @@ -1316,8 +1316,7 class SSheightProfiles(Operation): | |||
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1316 | 1316 | for i in range(self.buffer.shape[1]): |
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1317 | 1317 | #self.buffer[:,i] = numpy.flip(dataOut.data[:,i*self.step:i*self.step + self.nsamples]) |
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1318 | 1318 | self.buffer[:,i] = dataOut.data[:,i*self.step:i*self.step + self.nsamples] |
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1319 | ||
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1320 | #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]) | |
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1319 | #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]) | |
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1321 | 1320 | |
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1322 | 1321 | for j in range(self.buffer.shape[0]): |
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1323 | 1322 | self.sshProfiles[j] = numpy.transpose(self.buffer[j]) |
| @@ -1325,6 +1324,7 class SSheightProfiles(Operation): | |||
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1325 | 1324 | profileIndex = self.nsamples |
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1326 | 1325 | deltaHeight = dataOut.heightList[1] - dataOut.heightList[0] |
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1327 | 1326 | ippSeconds = (deltaHeight*1.0e-6)/(0.15) |
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1327 | #print "ippSeconds",ippSeconds | |
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1328 | 1328 | try: |
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1329 | 1329 | if dataOut.concat_m is not None: |
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1330 | 1330 | ippSeconds= ippSeconds/float(dataOut.concat_m) |
| @@ -74,9 +74,10 if __name__ == '__main__': | |||
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74 | 74 | # procUnitConfObj1SPC.addParameter(name='pairsList', value='(1,0),(3,2),(5,4),(7,6)', format='pairsList') |
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75 | 75 | |
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76 | 76 | opObj11 = procUnitConfObj1SPC.addOperation(name='IncohInt', optype='other') |
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77 |
opObj11.addParameter(name='timeInterval', value=' |
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77 | opObj11.addParameter(name='timeInterval', value='10', format='int') | |
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78 | 78 | |
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79 | 79 | procUnitConfObj2SPC = controllerObj.addProcUnit(datatype='SpectraAFCProc', inputId=procUnitConfObj1SPC.getId()) |
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80 | procUnitConfObj2SPC.addParameter(name='real', value='1', format='int') | |
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80 | 81 | |
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81 | 82 | opObj11 = procUnitConfObj1SPC.addOperation(name='SpectraPlot', optype='other') |
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82 | 83 | opObj11.addParameter(name='id', value='1', format='int') |
| @@ -113,8 +114,8 if __name__ == '__main__': | |||
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113 | 114 | opObj11.addParameter(name='wintitle', value = 'October 2019', format='str') |
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114 | 115 | opObj11.addParameter(name='xaxis', value='time', format='str') |
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115 | 116 | opObj11.addParameter(name='channel', value='0', format='int') |
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116 |
opObj11.addParameter(name='nSampleList', value='( |
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117 |
opObj11.addParameter(name='resolutionFactor', value=' |
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117 | #opObj11.addParameter(name='nSampleList', value='(50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70)', format='intList') | |
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118 | opObj11.addParameter(name='resolutionFactor', value='15', format='int') | |
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118 | 119 | #opObj11.addParameter(name='zmin', value=0.5, format='int') |
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119 | 120 | #opObj11.addParameter(name='zmax', value=-0.5, format='int') |
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120 | 121 | #opObj11.addParameter(name='ymin', value='0', format='int') |
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