@@ -53,9 +53,8 class CrossSpectraPlot(Figure): | |||
|
53 | 53 | counter += 1 |
|
54 | 54 | |
|
55 | 55 | def run(self, dataOut, idfigure, wintitle="", pairsList=None, showprofile='True', |
|
56 |
xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
57 |
save=False, figpath='./', figfile=None |
|
|
58 | power_cmap='jet', coherence_cmap='jet', phase_cmap='RdBu_r'): | |
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56 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, | |
|
57 | save=False, figpath='./', figfile=None): | |
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59 | 58 | |
|
60 | 59 | """ |
|
61 | 60 | |
@@ -87,15 +86,12 class CrossSpectraPlot(Figure): | |||
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87 | 86 | |
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88 | 87 | if len(pairsIndexList) > 4: |
|
89 | 88 | pairsIndexList = pairsIndexList[0:4] |
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90 | ||
|
91 | factor = 1 | |
|
92 | if normalize: | |
|
93 | factor = dataOut.normFactor | |
|
89 | factor = dataOut.normFactor | |
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94 | 90 | x = dataOut.getVelRange(1) |
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95 | 91 | y = dataOut.getHeiRange() |
|
96 | 92 | z = dataOut.data_spc[:,:,:]/factor |
|
97 | ||
|
98 | avg = numpy.average(z, axis=1) | |
|
93 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) | |
|
94 | avg = numpy.average(numpy.abs(z), axis=1) | |
|
99 | 95 | noise = dataOut.getNoise()/factor |
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100 | 96 | |
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101 | 97 | zdB = 10*numpy.log10(z) |
@@ -137,7 +133,7 class CrossSpectraPlot(Figure): | |||
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137 | 133 | axes0.pcolor(x, y, zdB, |
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138 | 134 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
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139 | 135 | xlabel=xlabel, ylabel=ylabel, title=title, |
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140 |
ticksize=9 |
|
|
136 | ticksize=9, cblabel='') | |
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141 | 137 | |
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142 | 138 | title = "Channel %d: %4.2fdB" %(pair[1], noisedB[pair[1]]) |
|
143 | 139 | zdB = 10.*numpy.log10(dataOut.data_spc[pair[1],:,:]/factor) |
@@ -145,26 +141,26 class CrossSpectraPlot(Figure): | |||
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145 | 141 | axes0.pcolor(x, y, zdB, |
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146 | 142 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
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147 | 143 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
148 |
ticksize=9 |
|
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144 | ticksize=9, cblabel='') | |
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149 | 145 | |
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150 | 146 | coherenceComplex = dataOut.data_cspc[pairsIndexList[i],:,:]/numpy.sqrt(dataOut.data_spc[pair[0],:,:]*dataOut.data_spc[pair[1],:,:]) |
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151 | 147 | coherence = numpy.abs(coherenceComplex) |
|
152 |
|
|
|
153 | phase = numpy.arctan2(coherenceComplex.imag, coherenceComplex.real)*180/numpy.pi | |
|
148 | phase = numpy.arctan(-1*coherenceComplex.imag/coherenceComplex.real)*180/numpy.pi | |
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149 | ||
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154 | 150 | |
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155 | 151 | title = "Coherence %d%d" %(pair[0], pair[1]) |
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156 | 152 | axes0 = self.axesList[i*self.__nsubplots+2] |
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157 | 153 | axes0.pcolor(x, y, coherence, |
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158 | 154 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=0, zmax=1, |
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159 | 155 | xlabel=xlabel, ylabel=ylabel, title=title, |
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160 |
ticksize=9 |
|
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156 | ticksize=9, cblabel='') | |
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161 | 157 | |
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162 | 158 | title = "Phase %d%d" %(pair[0], pair[1]) |
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163 | 159 | axes0 = self.axesList[i*self.__nsubplots+3] |
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164 | 160 | axes0.pcolor(x, y, phase, |
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165 | 161 | xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, zmin=-180, zmax=180, |
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166 | 162 | xlabel=xlabel, ylabel=ylabel, title=title, |
|
167 |
ticksize=9, |
|
|
163 | ticksize=9, cblabel='', colormap='RdBu_r') | |
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168 | 164 | |
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169 | 165 | |
|
170 | 166 | |
@@ -182,7 +178,7 class RTIPlot(Figure): | |||
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182 | 178 | |
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183 | 179 | __isConfig = None |
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184 | 180 | __nsubplots = None |
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185 | __missing = 1E30 | |
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181 | ||
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186 | 182 | WIDTHPROF = None |
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187 | 183 | HEIGHTPROF = None |
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188 | 184 | PREFIX = 'rti' |
@@ -194,11 +190,9 class RTIPlot(Figure): | |||
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194 | 190 | self.__nsubplots = 1 |
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195 | 191 | |
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196 | 192 | self.WIDTH = 800 |
|
197 |
self.HEIGHT = |
|
|
193 | self.HEIGHT = 200 | |
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198 | 194 | self.WIDTHPROF = 120 |
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199 | 195 | self.HEIGHTPROF = 0 |
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200 | self.x_buffer = None | |
|
201 | self.avgdB_buffer = None | |
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202 | 196 | |
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203 | 197 | def getSubplots(self): |
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204 | 198 | |
@@ -214,19 +208,15 class RTIPlot(Figure): | |||
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214 | 208 | |
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215 | 209 | ncolspan = 1 |
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216 | 210 | colspan = 1 |
|
217 | widthplot = self.WIDTH | |
|
218 | heightplot = self.HEIGHT | |
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219 | 211 | if showprofile: |
|
220 | 212 | ncolspan = 7 |
|
221 | 213 | colspan = 6 |
|
222 | 214 | self.__nsubplots = 2 |
|
223 | widthplot += self.WIDTHPROF | |
|
224 | heightplot += self.HEIGHTPROF | |
|
225 | 215 | |
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226 | 216 | self.createFigure(idfigure = idfigure, |
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227 | 217 | wintitle = wintitle, |
|
228 |
widthplot = |
|
|
229 |
heightplot = |
|
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218 | widthplot = self.WIDTH + self.WIDTHPROF, | |
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219 | heightplot = self.HEIGHT + self.HEIGHTPROF) | |
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230 | 220 | |
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231 | 221 | nrow, ncol = self.getSubplots() |
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232 | 222 | |
@@ -245,7 +235,7 class RTIPlot(Figure): | |||
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245 | 235 | counter += 1 |
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246 | 236 | |
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247 | 237 | def run(self, dataOut, idfigure, wintitle="", channelList=None, showprofile='True', |
|
248 |
xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
238 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, | |
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249 | 239 | timerange=None, |
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250 | 240 | save=False, figpath='./', figfile=None): |
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251 | 241 | |
@@ -279,18 +269,18 class RTIPlot(Figure): | |||
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279 | 269 | |
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280 | 270 | tmin = None |
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281 | 271 | tmax = None |
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282 |
factor = |
|
|
283 | if normalize: | |
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284 | factor = dataOut.normFactor | |
|
272 | factor = dataOut.normFactor | |
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285 | 273 | x = dataOut.getTimeRange() |
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286 | 274 | y = dataOut.getHeiRange() |
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287 | 275 | |
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288 | 276 | z = dataOut.data_spc[channelIndexList,:,:]/factor |
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289 | 277 | z = numpy.where(numpy.isfinite(z), z, numpy.NAN) |
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290 | 278 | avg = numpy.average(z, axis=1) |
|
279 | noise = dataOut.getNoise()/factor | |
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291 | 280 | |
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281 | # zdB = 10.*numpy.log10(z) | |
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292 | 282 | avgdB = 10.*numpy.log10(avg) |
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293 | ||
|
283 | noisedB = 10.*numpy.log10(noise) | |
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294 | 284 | |
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295 | 285 | thisDatetime = dataOut.datatime |
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296 | 286 | title = "RTI: %s" %(thisDatetime.strftime("%d-%b-%Y")) |
@@ -313,36 +303,16 class RTIPlot(Figure): | |||
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313 | 303 | if zmax == None: zmax = numpy.nanmax(avgdB)*0.9 |
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314 | 304 | |
|
315 | 305 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
316 | self.x_buffer = numpy.array([]) | |
|
317 | self.avgdB_buffer = numpy.array([]) | |
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318 | 306 | self.__isConfig = True |
|
319 | 307 | |
|
320 | 308 | |
|
321 | 309 | self.setWinTitle(title) |
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322 | ||
|
323 | if len(self.avgdB_buffer)==0: | |
|
324 | self.avgdB_buffer = avgdB | |
|
325 | newxdim = 1 | |
|
326 | newydim = -1 | |
|
327 | else: | |
|
328 | if x[0]>self.x_buffer[-1]: | |
|
329 | gap = avgdB.copy() | |
|
330 | gap[:] = self.__missing | |
|
331 | self.avgdB_buffer = numpy.hstack((self.avgdB_buffer, gap)) | |
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332 | 310 | |
|
333 | self.avgdB_buffer = numpy.hstack((self.avgdB_buffer, avgdB)) | |
|
334 | newxdim = -1 | |
|
335 | newydim = len(y) | |
|
336 | ||
|
337 | self.x_buffer = numpy.hstack((self.x_buffer, x)) | |
|
338 | ||
|
339 | self.avgdB_buffer = numpy.ma.masked_inside(self.avgdB_buffer,0.99*self.__missing,1.01*self.__missing) | |
|
340 | ||
|
341 | 311 | for i in range(self.nplots): |
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342 | 312 | title = "Channel %d: %s" %(dataOut.channelList[i], thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) |
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343 | 313 | axes = self.axesList[i*self.__nsubplots] |
|
344 |
zdB = |
|
|
345 |
axes.pcolor( |
|
|
314 | zdB = avgdB[i].reshape((1,-1)) | |
|
315 | axes.pcolor(x, y, zdB, | |
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346 | 316 | xmin=tmin, xmax=tmax, ymin=ymin, ymax=ymax, zmin=zmin, zmax=zmax, |
|
347 | 317 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
348 | 318 | ticksize=9, cblabel='', cbsize="1%") |
@@ -381,7 +351,7 class SpectraPlot(Figure): | |||
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381 | 351 | self.__isConfig = False |
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382 | 352 | self.__nsubplots = 1 |
|
383 | 353 | |
|
384 |
self.WIDTH = 2 |
|
|
354 | self.WIDTH = 230 | |
|
385 | 355 | self.HEIGHT = 250 |
|
386 | 356 | self.WIDTHPROF = 120 |
|
387 | 357 | self.HEIGHTPROF = 0 |
@@ -400,19 +370,15 class SpectraPlot(Figure): | |||
|
400 | 370 | |
|
401 | 371 | ncolspan = 1 |
|
402 | 372 | colspan = 1 |
|
403 | widthplot = self.WIDTH | |
|
404 | heightplot = self.HEIGHT | |
|
405 | 373 | if showprofile: |
|
406 | 374 | ncolspan = 3 |
|
407 | 375 | colspan = 2 |
|
408 | 376 | self.__nsubplots = 2 |
|
409 | widthplot += self.WIDTHPROF | |
|
410 | heightplot += self.HEIGHTPROF | |
|
411 | ||
|
377 | ||
|
412 | 378 | self.createFigure(idfigure = idfigure, |
|
413 | 379 | wintitle = wintitle, |
|
414 |
widthplot = |
|
|
415 |
heightplot = |
|
|
380 | widthplot = self.WIDTH + self.WIDTHPROF, | |
|
381 | heightplot = self.HEIGHT + self.HEIGHTPROF) | |
|
416 | 382 | |
|
417 | 383 | nrow, ncol = self.getSubplots() |
|
418 | 384 | |
@@ -431,7 +397,7 class SpectraPlot(Figure): | |||
|
431 | 397 | counter += 1 |
|
432 | 398 | |
|
433 | 399 | def run(self, dataOut, idfigure, wintitle="", channelList=None, showprofile='True', |
|
434 |
xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
|
400 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, | |
|
435 | 401 | save=False, figpath='./', figfile=None): |
|
436 | 402 | |
|
437 | 403 | """ |
@@ -458,9 +424,7 class SpectraPlot(Figure): | |||
|
458 | 424 | if channel not in dataOut.channelList: |
|
459 | 425 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
460 | 426 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
461 |
factor = |
|
|
462 | if normalize: | |
|
463 | factor = dataOut.normFactor | |
|
427 | factor = dataOut.normFactor | |
|
464 | 428 | x = dataOut.getVelRange(1) |
|
465 | 429 | y = dataOut.getHeiRange() |
|
466 | 430 | |
@@ -666,7 +630,7 class ProfilePlot(Figure): | |||
|
666 | 630 | self.addAxes(nrow, ncol*ncolspan, y, x*ncolspan, colspan, 1) |
|
667 | 631 | |
|
668 | 632 | def run(self, dataOut, idfigure, wintitle="", channelList=None, |
|
669 |
xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
633 | xmin=None, xmax=None, ymin=None, ymax=None, | |
|
670 | 634 | save=False, figpath='./', figfile=None): |
|
671 | 635 | |
|
672 | 636 | if channelList == None: |
@@ -679,9 +643,7 class ProfilePlot(Figure): | |||
|
679 | 643 | raise ValueError, "Channel %d is not in dataOut.channelList" |
|
680 | 644 | channelIndexList.append(dataOut.channelList.index(channel)) |
|
681 | 645 | |
|
682 |
factor = |
|
|
683 | if normalize: | |
|
684 | factor = dataOut.normFactor | |
|
646 | factor = dataOut.normFactor | |
|
685 | 647 | y = dataOut.getHeiRange() |
|
686 | 648 | x = dataOut.data_spc[channelIndexList,:,:]/factor |
|
687 | 649 | x = numpy.where(numpy.isfinite(x), x, numpy.NAN) |
@@ -737,8 +699,7 class CoherenceMap(Figure): | |||
|
737 | 699 | |
|
738 | 700 | WIDTHPROF = None |
|
739 | 701 | HEIGHTPROF = None |
|
740 | PREFIX = 'cmap' | |
|
741 | __missing = 1E30 | |
|
702 | PREFIX = 'coherencemap' | |
|
742 | 703 | |
|
743 | 704 | def __init__(self): |
|
744 | 705 | self.timerange = 2*60*60 |
@@ -749,9 +710,6 class CoherenceMap(Figure): | |||
|
749 | 710 | self.HEIGHT = 200 |
|
750 | 711 | self.WIDTHPROF = 120 |
|
751 | 712 | self.HEIGHTPROF = 0 |
|
752 | self.x_buffer = None | |
|
753 | self.coherence_buffer = None | |
|
754 | self.phase_buffer = None | |
|
755 | 713 | |
|
756 | 714 | def getSubplots(self): |
|
757 | 715 | ncol = 1 |
@@ -788,8 +746,7 class CoherenceMap(Figure): | |||
|
788 | 746 | def run(self, dataOut, idfigure, wintitle="", pairsList=None, showprofile='True', |
|
789 | 747 | xmin=None, xmax=None, ymin=None, ymax=None, zmin=None, zmax=None, |
|
790 | 748 | timerange=None, |
|
791 |
save=False, figpath='./', figfile=None |
|
|
792 | coherence_cmap='jet', phase_cmap='RdBu_r'): | |
|
749 | save=False, figpath='./', figfile=None): | |
|
793 | 750 | |
|
794 | 751 | if pairsList == None: |
|
795 | 752 | pairsIndexList = dataOut.pairsIndexList |
@@ -831,82 +788,53 class CoherenceMap(Figure): | |||
|
831 | 788 | if ymax == None: ymax = numpy.nanmax(y) |
|
832 | 789 | |
|
833 | 790 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") |
|
834 | self.x_buffer = numpy.array([]) | |
|
835 | self.coherence_buffer = numpy.array([]) | |
|
836 | self.phase_buffer = numpy.array([]) | |
|
791 | ||
|
837 | 792 | self.__isConfig = True |
|
838 | 793 | |
|
839 | 794 | self.setWinTitle(title) |
|
840 | 795 | |
|
796 | for i in range(self.nplots): | |
|
797 | ||
|
798 | pair = dataOut.pairsList[pairsIndexList[i]] | |
|
799 | coherenceComplex = dataOut.data_cspc[pairsIndexList[i],:,:]/numpy.sqrt(dataOut.data_spc[pair[0],:,:]*dataOut.data_spc[pair[1],:,:]) | |
|
800 | coherence = numpy.abs(coherenceComplex) | |
|
801 | avg = numpy.average(coherence, axis=0) | |
|
802 | z = avg.reshape((1,-1)) | |
|
841 | 803 | |
|
842 | pairArray = numpy.array(dataOut.pairsList) | |
|
843 | pairArray = pairArray[pairsIndexList] | |
|
844 | pair0ids = pairArray[:,0] | |
|
845 | pair1ids = pairArray[:,1] | |
|
846 | ||
|
847 | coherenceComplex = dataOut.data_cspc[pairsIndexList,:,:]/numpy.sqrt(dataOut.data_spc[pair0ids,:,:]*dataOut.data_spc[pair1ids,:,:]) | |
|
848 | avgcoherenceComplex = numpy.average(coherenceComplex, axis=1) | |
|
849 | coherence = numpy.abs(avgcoherenceComplex) | |
|
850 | ||
|
851 | phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real)*180/numpy.pi | |
|
852 | ||
|
853 | if len(self.coherence_buffer)==0: | |
|
854 | self.coherence_buffer = coherence | |
|
855 | self.phase_buffer = phase | |
|
856 | newxdim = 1 | |
|
857 | newydim = -1 | |
|
858 | else: | |
|
859 | if x[0]>self.x_buffer[-1]: | |
|
860 | gap = coherence.copy() | |
|
861 | gap[:] = self.__missing | |
|
862 | self.coherence_buffer = numpy.hstack((self.coherence_buffer, gap)) | |
|
863 | self.phase_buffer = numpy.hstack((self.phase_buffer, gap)) | |
|
804 | counter = 0 | |
|
864 | 805 | |
|
865 | self.coherence_buffer = numpy.hstack((self.coherence_buffer, coherence)) | |
|
866 | self.phase_buffer = numpy.hstack((self.phase_buffer, phase)) | |
|
867 | newxdim = -1 | |
|
868 | newydim = len(y) | |
|
869 | ||
|
870 | self.x_buffer = numpy.hstack((self.x_buffer, x)) | |
|
871 | ||
|
872 | self.coherence_buffer = numpy.ma.masked_inside(self.coherence_buffer,0.99*self.__missing,1.01*self.__missing) | |
|
873 | self.phase_buffer = numpy.ma.masked_inside(self.phase_buffer,0.99*self.__missing,1.01*self.__missing) | |
|
874 | ||
|
875 | ||
|
876 | for i in range(self.nplots): | |
|
877 | counter = 0 | |
|
878 | z = self.coherence_buffer[i,:].reshape((newxdim,newydim)) | |
|
879 | title = "Coherence %d%d: %s" %(pair0ids[i], pair1ids[i], thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) | |
|
806 | title = "Coherence %d%d: %s" %(pair[0], pair[1], thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) | |
|
880 | 807 | axes = self.axesList[i*self.__nsubplots*2] |
|
881 |
axes.pcolor( |
|
|
808 | axes.pcolor(x, y, z, | |
|
882 | 809 | xmin=tmin, xmax=tmax, ymin=ymin, ymax=ymax, zmin=0, zmax=1, |
|
883 | 810 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
884 |
ticksize=9, cblabel='', |
|
|
811 | ticksize=9, cblabel='', cbsize="1%") | |
|
885 | 812 | |
|
886 | 813 | if self.__showprofile: |
|
887 | 814 | counter += 1 |
|
888 | 815 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
889 |
axes.pline( |
|
|
816 | axes.pline(avg, y, | |
|
890 | 817 | xmin=0, xmax=1, ymin=ymin, ymax=ymax, |
|
891 | 818 | xlabel='', ylabel='', title='', ticksize=7, |
|
892 | 819 | ytick_visible=False, nxticks=5, |
|
893 | 820 | grid='x') |
|
894 | 821 | |
|
895 | 822 | counter += 1 |
|
896 | ||
|
897 | z = self.phase_buffer[i,:].reshape((newxdim,newydim)) | |
|
823 | phase = numpy.arctan(-1*coherenceComplex.imag/coherenceComplex.real)*180/numpy.pi | |
|
824 | avg = numpy.average(phase, axis=0) | |
|
825 | z = avg.reshape((1,-1)) | |
|
898 | 826 | |
|
899 |
title = "Phase %d%d: %s" %(pair |
|
|
827 | title = "Phase %d%d: %s" %(pair[0], pair[1], thisDatetime.strftime("%d-%b-%Y %H:%M:%S")) | |
|
900 | 828 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
901 |
axes.pcolor( |
|
|
829 | axes.pcolor(x, y, z, | |
|
902 | 830 | xmin=tmin, xmax=tmax, ymin=ymin, ymax=ymax, zmin=-180, zmax=180, |
|
903 | 831 | xlabel=xlabel, ylabel=ylabel, title=title, rti=True, XAxisAsTime=True, |
|
904 |
ticksize=9, cblabel='', colormap= |
|
|
832 | ticksize=9, cblabel='', colormap='RdBu', cbsize="1%") | |
|
905 | 833 | |
|
906 | 834 | if self.__showprofile: |
|
907 | 835 | counter += 1 |
|
908 | 836 | axes = self.axesList[i*self.__nsubplots*2 + counter] |
|
909 |
axes.pline( |
|
|
837 | axes.pline(avg, y, | |
|
910 | 838 | xmin=-180, xmax=180, ymin=ymin, ymax=ymax, |
|
911 | 839 | xlabel='', ylabel='', title='', ticksize=7, |
|
912 | 840 | ytick_visible=False, nxticks=4, |
@@ -971,7 +899,7 class RTIfromNoise(Figure): | |||
|
971 | 899 | |
|
972 | 900 | |
|
973 | 901 | def run(self, dataOut, idfigure, wintitle="", channelList=None, showprofile='True', |
|
974 |
xmin=None, xmax=None, ymin=None, ymax=None, |
|
|
902 | xmin=None, xmax=None, ymin=None, ymax=None, | |
|
975 | 903 | timerange=None, |
|
976 | 904 | save=False, figpath='./', figfile=None): |
|
977 | 905 | |
@@ -992,9 +920,7 class RTIfromNoise(Figure): | |||
|
992 | 920 | tmax = None |
|
993 | 921 | x = dataOut.getTimeRange() |
|
994 | 922 | y = dataOut.getHeiRange() |
|
995 |
factor = |
|
|
996 | if normalize: | |
|
997 | factor = dataOut.normFactor | |
|
923 | factor = dataOut.normFactor | |
|
998 | 924 | noise = dataOut.getNoise()/factor |
|
999 | 925 | noisedB = 10*numpy.log10(noise) |
|
1000 | 926 |
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