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cambios para amisr ISR getNoise
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r1468:1fb12846b034
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@@ -489,7 +489,7 class Spectra(JROData):
489 return noise
489 return noise
490
490
491 def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None):
491 def getNoise(self, xmin_index=None, xmax_index=None, ymin_index=None, ymax_index=None):
492
492
493 if self.noise_estimation is not None:
493 if self.noise_estimation is not None:
494 # this was estimated by getNoise Operation defined in jroproc_spectra.py
494 # this was estimated by getNoise Operation defined in jroproc_spectra.py
495 return self.noise_estimation
495 return self.noise_estimation
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@@ -379,7 +379,8 class NoisePlot(Plot):
379
379
380 data = {}
380 data = {}
381 meta = {}
381 meta = {}
382 data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor).reshape(dataOut.nChannels, 1)
382 noise = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor).reshape(dataOut.nChannels, 1)
383 data['noise'] = noise
383 meta['yrange'] = numpy.array([])
384 meta['yrange'] = numpy.array([])
384
385
385 return data, meta
386 return data, meta
@@ -392,8 +393,8 class NoisePlot(Plot):
392 Y = self.data['noise']
393 Y = self.data['noise']
393
394
394 if self.axes[0].firsttime:
395 if self.axes[0].firsttime:
395 self.ymin = numpy.nanmin(Y) - 5
396 if self.ymin is None: self.ymin = numpy.nanmin(Y) - 5
396 self.ymax = numpy.nanmax(Y) + 5
397 if self.ymax is None: self.ymax = numpy.nanmax(Y) + 5
397 for ch in self.data.channels:
398 for ch in self.data.channels:
398 y = Y[ch]
399 y = Y[ch]
399 self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch))
400 self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch))
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@@ -202,6 +202,7 class SpectraProc(ProcessingUnit):
202 self.dataOut.flagNoData = False
202 self.dataOut.flagNoData = False
203 self.firstdatatime = None
203 self.firstdatatime = None
204 self.profIndex = 0
204 self.profIndex = 0
205 self.dataOut.noise_estimation = None
205 else:
206 else:
206 raise ValueError("The type of input object '%s' is not valid".format(
207 raise ValueError("The type of input object '%s' is not valid".format(
207 self.dataIn.type))
208 self.dataIn.type))
@@ -488,6 +489,131 class removeDC(Operation):
488
489
489 return self.dataOut
490 return self.dataOut
490
491
492 class getNoise(Operation):
493 def __init__(self):
494
495 Operation.__init__(self)
496
497 def run(self, dataOut, minHei=None, maxHei=None, minVel=None, maxVel=None, minFreq= None, maxFreq=None,):
498 self.dataOut = dataOut.copy()
499 print("1: ",dataOut.noise_estimation, dataOut.normFactor)
500
501 if minHei == None:
502 minHei = self.dataOut.heightList[0]
503
504 if maxHei == None:
505 maxHei = self.dataOut.heightList[-1]
506
507 if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
508 print('minHei: %.2f is out of the heights range' % (minHei))
509 print('minHei is setting to %.2f' % (self.dataOut.heightList[0]))
510 minHei = self.dataOut.heightList[0]
511
512 if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei):
513 print('maxHei: %.2f is out of the heights range' % (maxHei))
514 print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1]))
515 maxHei = self.dataOut.heightList[-1]
516
517
518 #indices relativos a los puntos de fft, puede ser de acuerdo a velocidad o frecuencia
519 minIndexFFT = 0
520 maxIndexFFT = 0
521 # validacion de velocidades
522 indminPoint = None
523 indmaxPoint = None
524
525 if minVel == None and maxVel == None:
526
527 freqrange = self.dataOut.getFreqRange(1)
528
529 if minFreq == None:
530 minFreq = freqrange[0]
531
532 if maxFreq == None:
533 maxFreq = freqrange[-1]
534
535 if (minFreq < freqrange[0]) or (minFreq > maxFreq):
536 print('minFreq: %.2f is out of the frequency range' % (minFreq))
537 print('minFreq is setting to %.2f' % (freqrange[0]))
538 minFreq = freqrange[0]
539
540 if (maxFreq > freqrange[-1]) or (maxFreq < minFreq):
541 print('maxFreq: %.2f is out of the frequency range' % (maxFreq))
542 print('maxFreq is setting to %.2f' % (freqrange[-1]))
543 maxFreq = freqrange[-1]
544
545 indminPoint = numpy.where(freqrange >= minFreq)
546 indmaxPoint = numpy.where(freqrange <= maxFreq)
547
548 else:
549 velrange = self.dataOut.getVelRange(1)
550
551 if minVel == None:
552 minVel = velrange[0]
553
554 if maxVel == None:
555 maxVel = velrange[-1]
556
557 if (minVel < velrange[0]) or (minVel > maxVel):
558 print('minVel: %.2f is out of the velocity range' % (minVel))
559 print('minVel is setting to %.2f' % (velrange[0]))
560 minVel = velrange[0]
561
562 if (maxVel > velrange[-1]) or (maxVel < minVel):
563 print('maxVel: %.2f is out of the velocity range' % (maxVel))
564 print('maxVel is setting to %.2f' % (velrange[-1]))
565 maxVel = velrange[-1]
566
567 indminPoint = numpy.where(velrange >= minVel)
568 indmaxPoint = numpy.where(velrange <= maxVel)
569
570
571 # seleccion de indices para rango
572 minIndex = 0
573 maxIndex = 0
574 heights = self.dataOut.heightList
575
576 inda = numpy.where(heights >= minHei)
577 indb = numpy.where(heights <= maxHei)
578
579 try:
580 minIndex = inda[0][0]
581 except:
582 minIndex = 0
583
584 try:
585 maxIndex = indb[0][-1]
586 except:
587 maxIndex = len(heights)
588
589 if (minIndex < 0) or (minIndex > maxIndex):
590 raise ValueError("some value in (%d,%d) is not valid" % (
591 minIndex, maxIndex))
592
593 if (maxIndex >= self.dataOut.nHeights):
594 maxIndex = self.dataOut.nHeights - 1
595 #############################################################3
596 # seleccion de indices para velocidades
597
598 try:
599 minIndexFFT = indminPoint[0][0]
600 except:
601 minIndexFFT = 0
602
603 try:
604 maxIndexFFT = indmaxPoint[0][-1]
605 except:
606 maxIndexFFT = len( self.dataOut.getFreqRange(1))
607
608 #print(minIndex, maxIndex,minIndexVel, maxIndexVel)
609 noise = self.dataOut.getNoise(xmin_index=minIndexFFT, xmax_index=maxIndexFFT, ymin_index=minIndex, ymax_index=maxIndex)
610
611 self.dataOut.noise_estimation = noise.copy()
612 #print("2: ",10*numpy.log10(self.dataOut.noise_estimation/64))
613 return self.dataOut
614
615
616
491 # import matplotlib.pyplot as plt
617 # import matplotlib.pyplot as plt
492
618
493 def fit_func( x, a0, a1, a2): #, a3, a4, a5):
619 def fit_func( x, a0, a1, a2): #, a3, a4, a5):
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