| @@ -116,6 +116,8 class CrossSpectraPlot(Plot): | |||
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116 | 116 | zmax_coh = None |
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117 | 117 | zmin_phase = None |
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118 | 118 | zmax_phase = None |
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119 | realChannels = None | |
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120 | crossPairs = None | |
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119 | 121 | |
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120 | 122 | def setup(self): |
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121 | 123 | |
| @@ -136,11 +138,17 class CrossSpectraPlot(Plot): | |||
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136 | 138 | spc = dataOut.data_spc |
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137 | 139 | cspc = dataOut.data_cspc |
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138 | 140 | meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) |
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139 | meta['pairs'] = dataOut.pairsList | |
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141 | rawPairs = list(combinations(list(range(dataOut.nChannels)), 2)) | |
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142 | #print(rawPairs) | |
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143 | meta['pairs'] = rawPairs | |
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144 | ||
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145 | if self.crossPairs == None: | |
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146 | self.crossPairs = dataOut.pairsList | |
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140 | 147 | |
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141 | 148 | tmp = [] |
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142 | 149 | |
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143 | 150 | for n, pair in enumerate(meta['pairs']): |
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151 | ||
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144 | 152 | out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) |
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145 | 153 | coh = numpy.abs(out) |
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146 | 154 | phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi |
| @@ -170,12 +178,15 class CrossSpectraPlot(Plot): | |||
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170 | 178 | |
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171 | 179 | data = self.data[-1] |
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172 | 180 | cspc = data['cspc'] |
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173 | ||
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181 | #print(self.crossPairs) | |
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174 | 182 | for n in range(len(self.data.pairs)): |
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175 | pair = self.data.pairs[n] | |
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183 | #pair = self.data.pairs[n] | |
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184 | pair = self.crossPairs[n] | |
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185 | ||
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176 | 186 | coh = cspc[n*2] |
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177 | 187 | phase = cspc[n*2+1] |
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178 | 188 | ax = self.axes[2 * n] |
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189 | ||
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179 | 190 | if ax.firsttime: |
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180 | 191 | ax.plt = ax.pcolormesh(x, y, coh.T, |
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181 | 192 | vmin=0, |
| @@ -238,10 +249,14 class RTIPlot(Plot): | |||
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238 | 249 | self.y = self.data.yrange |
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239 | 250 | self.z = self.data[self.CODE] |
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240 | 251 | self.z = numpy.ma.masked_invalid(self.z) |
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252 | try: | |
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241 | 253 | if self.channelList != None: |
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242 | 254 | self.titles = ['{} Channel {}'.format( |
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243 | 255 | self.CODE.upper(), x) for x in self.channelList] |
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244 | ||
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256 | except: | |
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257 | if self.channelList.any() != None: | |
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258 | self.titles = ['{} Channel {}'.format( | |
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259 | self.CODE.upper(), x) for x in self.channelList] | |
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245 | 260 | if self.decimation is None: |
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246 | 261 | x, y, z = self.fill_gaps(self.x, self.y, self.z) |
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247 | 262 | else: |
| @@ -487,6 +487,8 def fit_func( x, a0, a1, a2): #, a3, a4, a5): | |||
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487 | 487 | z = (x - a1) / a2 |
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488 | 488 | y = a0 * numpy.exp(-z**2 / a2) #+ a3 + a4 * x + a5 * x**2 |
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489 | 489 | return y |
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490 | ||
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491 | ||
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490 | 492 | class CleanRayleigh(Operation): |
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491 | 493 | |
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492 | 494 | def __init__(self): |
| @@ -641,9 +643,10 class CleanRayleigh(Operation): | |||
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641 | 643 | |
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642 | 644 | def cleanRayleigh(self,dataOut,spectra,cspectra,factor_stdv): |
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643 | 645 | #print("OP cleanRayleigh") |
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644 |
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646 | import matplotlib.pyplot as plt | |
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645 | 647 | #for k in range(149): |
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646 | ||
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648 | channelsProcssd = [] | |
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649 | channelA_ok = False | |
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647 | 650 | rfunc = cspectra.copy() #self.bloques |
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648 | 651 | #rfunc = cspectra |
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649 | 652 | #val_spc = spectra*0.0 #self.bloque0*0.0 |
| @@ -651,24 +654,40 class CleanRayleigh(Operation): | |||
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651 | 654 | #in_sat_spectra = spectra.copy() #self.bloque0 |
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652 | 655 | #in_sat_cspectra = cspectra.copy() #self.bloques |
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653 | 656 | |
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654 | #raxs = math.ceil(math.sqrt(self.nPairs)) | |
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655 | #caxs = math.ceil(self.nPairs/raxs) | |
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656 | 657 | |
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657 | #print(self.hval) | |
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658 | ###ONLY FOR TEST: | |
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659 | raxs = math.ceil(math.sqrt(self.nPairs)) | |
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660 | caxs = math.ceil(self.nPairs/raxs) | |
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661 | if self.nPairs <4: | |
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662 | raxs = 2 | |
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663 | caxs = 2 | |
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664 | #print(raxs, caxs) | |
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665 | fft_rev = 14 #nFFT to plot | |
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666 | hei_rev = ((self.heights >= 550) & (self.heights <= 551)).nonzero() #hei to plot | |
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667 | hei_rev = hei_rev[0] | |
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668 | #print(hei_rev) | |
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669 | ||
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658 | 670 | #print numpy.absolute(rfunc[:,0,0,14]) |
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671 | ||
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659 | 672 | gauss_fit, covariance = None, None |
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660 | 673 | for ih in range(self.minAltInd,self.maxAltInd): |
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661 | 674 | for ifreq in range(self.nFFTPoints): |
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662 | # fig, axs = plt.subplots(raxs, caxs) | |
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663 | # fig2, axs2 = plt.subplots(raxs, caxs) | |
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664 | # col_ax = 0 | |
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665 | # row_ax = 0 | |
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666 | #print(len(self.nPairs)) | |
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675 | ###ONLY FOR TEST: | |
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676 | if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY | |
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677 | fig, axs = plt.subplots(raxs, caxs) | |
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678 | fig2, axs2 = plt.subplots(raxs, caxs) | |
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679 | col_ax = 0 | |
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680 | row_ax = 0 | |
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681 | #print(self.nPairs) | |
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667 | 682 |
for ii in range(self.nPairs): |
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668 | #print("ii: ",ii) | |
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669 | # if (col_ax%caxs==0 and col_ax!=0): | |
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670 | # col_ax = 0 | |
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671 |
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683 | if self.crosspairs[ii][1]-self.crosspairs[ii][0] > 1: # APLICAR SOLO EN PARES CONTIGUOS | |
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684 | continue | |
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685 | if not self.crosspairs[ii][0] in channelsProcssd: | |
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686 | channelA_ok = True | |
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687 | #print("pair: ",self.crosspairs[ii]) | |
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688 | if (col_ax%caxs==0 and col_ax!=0 and self.nPairs !=1): ###ONLY FOR TEST: | |
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689 | col_ax = 0 | |
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690 | row_ax += 1 | |
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672 | 691 | func2clean = 10*numpy.log10(numpy.absolute(rfunc[:,ii,ifreq,ih])) #Potencia? |
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673 | 692 | #print(func2clean.shape) |
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674 | 693 | val = (numpy.isfinite(func2clean)==True).nonzero() |
| @@ -692,22 +711,26 class CleanRayleigh(Operation): | |||
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692 | 711 | sigma = numpy.sqrt(numpy.sum(y_dist * (x_dist - mean)**2) / numpy.sum(y_dist)) |
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693 | 712 | parg = [numpy.amax(y_dist),mean,sigma] |
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694 | 713 | |
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695 |
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714 | newY = None | |
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696 | 715 | |
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697 | 716 | try : |
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698 | 717 | gauss_fit, covariance = curve_fit(fit_func, x_dist, y_dist,p0=parg) |
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699 | 718 | mode = gauss_fit[1] |
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700 | 719 | stdv = gauss_fit[2] |
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701 | 720 | #print(" FIT OK",gauss_fit) |
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702 | ''' | |
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721 | ||
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722 | ###ONLY FOR TEST: | |
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723 | if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY | |
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703 | 724 | newY = fit_func(x_dist,gauss_fit[0],gauss_fit[1],gauss_fit[2]) |
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704 | 725 | axs[row_ax,col_ax].plot(binstep[:-1],y_dist,color='green') |
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705 | 726 | axs[row_ax,col_ax].plot(binstep[:-1],newY,color='red') |
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706 |
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727 | axs[row_ax,col_ax].set_title("Pair "+str(self.crosspairs[ii])) | |
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728 | ||
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707 | 729 | except: |
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708 | 730 | mode = mean |
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709 | 731 | stdv = sigma |
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710 | 732 | #print("FIT FAIL") |
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733 | continue | |
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711 | 734 | |
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712 | 735 | |
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713 | 736 | #print(mode,stdv) |
| @@ -727,36 +750,45 class CleanRayleigh(Operation): | |||
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727 | 750 | # val_spc[novall[0],cross_pairs[1],ifreq,ih] = 1 |
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728 | 751 | # val_cspc[novall[0],ii,ifreq,ih] = 1 |
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729 | 752 | #print("OUT NOVALL 1") |
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730 | #Removing coherent from ISR data | |
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731 | chA = self.channels.index(cross_pairs[0]) | |
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732 | chB = self.channels.index(cross_pairs[1]) | |
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733 | 753 | |
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734 | 754 | new_a = numpy.delete(cspectra[:,ii,ifreq,ih], noval[0]) |
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735 | 755 | cspectra[noval,ii,ifreq,ih] = numpy.mean(new_a) #mean CrossSpectra |
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756 | ||
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757 | if channelA_ok: | |
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758 | chA = self.channels.index(cross_pairs[0]) | |
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736 | 759 | new_b = numpy.delete(spectra[:,chA,ifreq,ih], noval[0]) |
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737 | 760 | spectra[noval,chA,ifreq,ih] = numpy.mean(new_b) #mean Spectra Pair A |
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761 | channelA_ok = False | |
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762 | chB = self.channels.index(cross_pairs[1]) | |
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738 | 763 | new_c = numpy.delete(spectra[:,chB,ifreq,ih], noval[0]) |
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739 | 764 | spectra[noval,chB,ifreq,ih] = numpy.mean(new_c) #mean Spectra Pair B |
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740 | 765 | |
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766 | channelsProcssd.append(self.crosspairs[ii][0]) # save channel A | |
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767 | channelsProcssd.append(self.crosspairs[ii][1]) # save channel B | |
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741 | 768 | |
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742 |
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769 | ###ONLY FOR TEST: | |
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770 | if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY | |
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743 | 771 | func2clean = 10*numpy.log10(numpy.absolute(cspectra[:,ii,ifreq,ih])) |
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744 | 772 | y_dist,binstep = numpy.histogram(func2clean,bins=range(int(min_val),int(max_val+2),step)) |
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745 | 773 | axs2[row_ax,col_ax].plot(binstep[:-1],newY,color='red') |
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746 | 774 | axs2[row_ax,col_ax].plot(binstep[:-1],y_dist,color='green') |
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747 | 775 | axs2[row_ax,col_ax].set_title("Pair "+str(self.crosspairs[ii])) |
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748 | ''' | |
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749 | 776 | |
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750 | #col_ax += 1 #contador de ploteo columnas | |
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777 | ###ONLY FOR TEST: | |
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778 | col_ax += 1 #contador de ploteo columnas | |
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751 | 779 | ##print(col_ax) |
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752 |
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780 | ###ONLY FOR TEST: | |
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781 | if ifreq ==fft_rev and ih==hei_rev: #TO VIEW A SIGNLE FREQUENCY | |
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753 | 782 | title = str(dataOut.datatime)+" nFFT: "+str(ifreq)+" Alt: "+str(self.heights[ih])+ " km" |
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754 | 783 | title2 = str(dataOut.datatime)+" nFFT: "+str(ifreq)+" Alt: "+str(self.heights[ih])+ " km CLEANED" |
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755 | 784 | fig.suptitle(title) |
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756 | 785 | fig2.suptitle(title2) |
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757 |
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786 | plt.show() | |
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787 | ||
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788 | ||
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789 | ''' | |
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758 | 790 |
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759 |
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791 | channels = channels | |
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760 | 792 | cross_pairs = cross_pairs |
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761 | 793 | #print("OUT NOVALL 2") |
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762 | 794 | |
| @@ -786,14 +818,10 class CleanRayleigh(Operation): | |||
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786 | 818 | for ich in range(self.nChan): |
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787 | 819 | tmp = spectra[:,ich,ifreq,ih] |
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788 | 820 | valid = (numpy.isfinite(tmp[:])==True).nonzero() |
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789 | # if ich == 0 and ifreq == 0 and ih == 17 : | |
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790 | # print tmp | |
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791 | # print valid | |
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792 | # print len(valid[0]) | |
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793 | #print('TMP',tmp) | |
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821 | ||
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794 | 822 | if len(valid[0]) >0 : |
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795 | 823 | out_spectra[ich,ifreq,ih] = numpy.nansum(tmp)#/len(valid[0]) |
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796 | #for icr in range(nPairs): | |
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824 | ||
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797 | 825 | for icr in range(self.nPairs): |
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798 | 826 | tmp = numpy.squeeze(cspectra[:,icr,ifreq,ih]) |
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799 | 827 | valid = (numpy.isfinite(tmp)==True).nonzero() |
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