schain Commit - r1799:72fde67b5cb4 · Jicamarca Repository

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def statistics150km(self, veloc , sigma , threshold , old_veloc=None, count=0, \

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def statistics150km(self, veloc , sigma , threshold , old_veloc=None, count=0, \

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currTime=None, oldTime=None, amountdata=2, clearAll = None, timeFactor=1800, debug = False):

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currTime=None, oldTime=None, amountdata=3, clearAll = None, timeFactor=1800, debug = False):

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if oldTime == None:

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if oldTime == None:

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oldTime = currTime

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oldTime = currTime

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print('numpy.where(numpy.abs(veloc-veloc_prof) < threshold/factor)[0]:', numpy.where(numpy.abs(veloc-veloc_prof) < threshold/factor)[0])

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print('numpy.where(numpy.abs(veloc-veloc_prof) < threshold/factor)[0]:', numpy.where(numpy.abs(veloc-veloc_prof) < threshold/factor)[0])

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junk = numpy.where(numpy.abs(veloc-veloc_prof) < threshold/factor)[0]

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junk = numpy.where(numpy.abs(veloc-veloc_prof) < threshold/factor)[0]

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if junk.size > 2:

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if junk.size >= amountdata:

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veloc_prof = self.newtotal(veloc[junk]/numpy.power(sigma[junk],2))/self.newtotal(1/numpy.power(sigma[junk],2))

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veloc_prof = self.newtotal(veloc[junk]/numpy.power(sigma[junk],2))/self.newtotal(1/numpy.power(sigma[junk],2))

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sigma_prof1 = numpy.sqrt(1/self.newtotal(1/numpy.power(sigma[junk],2)))

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sigma_prof1 = numpy.sqrt(1/self.newtotal(1/numpy.power(sigma[junk],2)))

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sigma_prof2 = numpy.sqrt(self.newtotal(numpy.power(veloc[junk]-veloc_prof,2)/numpy.power(sigma[junk],2)))*sigma_prof1

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sigma_prof2 = numpy.sqrt(self.newtotal(numpy.power(veloc[junk]-veloc_prof,2)/numpy.power(sigma[junk],2)))*sigma_prof1

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return sets, old_veloc, count, oldTime, aver_veloc, aver_sigma, clearAll

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return sets, old_veloc, count, oldTime, aver_veloc, aver_sigma, clearAll

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def run(self, dataOut, zenith, zenithCorrection=0.0, heights=[125, 185], nchan=2, chan=0, clean=False, driftstdv_th=100, zonalstdv_th=200):

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def run(self, dataOut, zenith, zenithCorrection=0.0, heights=[125, 185], nchan=2, chan=0, clean=False, driftstdv_th=100, zonalstdv_th=200, amountdata=3):

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dataOut.lat=-11.95

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dataOut.lat=-11.95

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dataOut.lon=-76.87

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dataOut.lon=-76.87

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# Vertical

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# Vertical

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sets1, self.old_drift, self.count_drift, self.oldTime_drift, aver_veloc, aver_sigma, clearAll = self.statistics150km(drift, w_w_err, driftstdv_th, \

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sets1, self.old_drift, self.count_drift, self.oldTime_drift, aver_veloc, aver_sigma, clearAll = self.statistics150km(drift, w_w_err, driftstdv_th, \

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old_veloc=self.old_drift, count=self.count_drift, currTime=dataOut.utctime, \

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old_veloc=self.old_drift, count=self.count_drift, currTime=dataOut.utctime, \

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oldTime=self.oldTime_drift, timeFactor=120)

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oldTime=self.oldTime_drift, amountdata = amountdata, timeFactor=120, debug = False)

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if clearAll == 1:

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if clearAll == 1:

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mean_zonal = numpy.nan

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mean_zonal = numpy.nan

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sigma_zonal = numpy.nan

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sigma_zonal = numpy.nan

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if nchan == 2:

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if nchan == 2:

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sets2, self.old_zonal, self.count_zonal, self.oldTime_zonal, aver_veloc, aver_sigma, clearAll = self.statistics150km(zonal, w_e_err, zonalstdv_th, \

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sets2, self.old_zonal, self.count_zonal, self.oldTime_zonal, aver_veloc, aver_sigma, clearAll = self.statistics150km(zonal, w_e_err, zonalstdv_th, \

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old_veloc=self.old_zonal, count=self.count_zonal, currTime=dataOut.utctime, \

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old_veloc=self.old_zonal, count=self.count_zonal, currTime=dataOut.utctime, \

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oldTime=self.oldTime_zonal, timeFactor=600)

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oldTime=self.oldTime_zonal, amountdata = amountdata, timeFactor=600, debug = False)

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if clearAll == 1:

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if clearAll == 1:

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mean_zonal = numpy.nan

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mean_zonal = numpy.nan

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sigma_zonal = numpy.nan

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sigma_zonal = numpy.nan

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                 def statistics150km(self, veloc , sigma , threshold , old_veloc=None, count=0, \
-                                    currTime=None, oldTime=None, amountdata=2, clearAll = None, timeFactor=1800, debug = False):
+                                    currTime=None, oldTime=None, amountdata=3, clearAll = None, timeFactor=1800, debug = False):
                     if oldTime == None:
                         oldTime = currTime
                         print('numpy.where(numpy.abs(veloc-veloc_prof) < threshold/factor)[0]:', numpy.where(numpy.abs(veloc-veloc_prof) < threshold/factor)[0])
                     junk = numpy.where(numpy.abs(veloc-veloc_prof) < threshold/factor)[0]
-                    if junk.size > 2:
+                    if junk.size >= amountdata:
                         veloc_prof = self.newtotal(veloc[junk]/numpy.power(sigma[junk],2))/self.newtotal(1/numpy.power(sigma[junk],2))
                         sigma_prof1 = numpy.sqrt(1/self.newtotal(1/numpy.power(sigma[junk],2)))
                         sigma_prof2 = numpy.sqrt(self.newtotal(numpy.power(veloc[junk]-veloc_prof,2)/numpy.power(sigma[junk],2)))*sigma_prof1
                     return sets, old_veloc, count, oldTime, aver_veloc, aver_sigma, clearAll
-                def run(self, dataOut, zenith, zenithCorrection=0.0, heights=[125, 185], nchan=2, chan=0, clean=False, driftstdv_th=100, zonalstdv_th=200):
+                def run(self, dataOut, zenith, zenithCorrection=0.0, heights=[125, 185], nchan=2, chan=0, clean=False, driftstdv_th=100, zonalstdv_th=200, amountdata=3):
                     dataOut.lat=-11.95
                     dataOut.lon=-76.87
                     # Vertical
                     sets1, self.old_drift, self.count_drift, self.oldTime_drift, aver_veloc, aver_sigma, clearAll = self.statistics150km(drift, w_w_err, driftstdv_th, \
                                                                     old_veloc=self.old_drift, count=self.count_drift, currTime=dataOut.utctime, \
-                                                                    oldTime=self.oldTime_drift, timeFactor=120)
+                                                                    oldTime=self.oldTime_drift, amountdata = amountdata, timeFactor=120, debug = False)
                     if clearAll == 1:
                         mean_zonal = numpy.nan
                         sigma_zonal = numpy.nan
                     if nchan == 2:
                         sets2, self.old_zonal, self.count_zonal, self.oldTime_zonal, aver_veloc, aver_sigma, clearAll = self.statistics150km(zonal, w_e_err, zonalstdv_th, \
                                                                     old_veloc=self.old_zonal, count=self.count_zonal, currTime=dataOut.utctime, \
-                                                                    oldTime=self.oldTime_zonal, timeFactor=600)
+                                                                    oldTime=self.oldTime_zonal, amountdata = amountdata, timeFactor=600, debug = False)
                         if clearAll == 1:
                             mean_zonal = numpy.nan
                             sigma_zonal = numpy.nan