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update jroproc_parameters, cambios JULIADriftsEstimation, referencia v3.0-devel IVAN
Alexander Valdez -
r1700:20a5087b4329
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@@ -12,6 +12,7 import itertools
12 from multiprocessing import Pool, TimeoutError
12 from multiprocessing import Pool, TimeoutError
13 from multiprocessing.pool import ThreadPool
13 from multiprocessing.pool import ThreadPool
14 import time
14 import time
15
15 from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters
16 from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters
16 from .jroproc_base import ProcessingUnit, Operation, MPDecorator
17 from .jroproc_base import ProcessingUnit, Operation, MPDecorator
17 from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon
18 from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon
@@ -328,8 +329,6 class GaussianFit(Operation):
328 Operation.__init__(self)
329 Operation.__init__(self)
329 self.i=0
330 self.i=0
330
331
331
332 # def run(self, dataOut, num_intg=7, pnoise=1., SNRlimit=-9): #num_intg: Incoherent integrations, pnoise: Noise, vel_arr: range of velocities, similar to the ftt points
333 def run(self, dataOut, SNRdBlimit=-9, method='generalized'):
332 def run(self, dataOut, SNRdBlimit=-9, method='generalized'):
334 """This routine will find a couple of generalized Gaussians to a power spectrum
333 """This routine will find a couple of generalized Gaussians to a power spectrum
335 methods: generalized, squared
334 methods: generalized, squared
@@ -715,6 +714,7 class Oblique_Gauss_Fit(Operation):
715
714
716 return gaussian(x, popt[0], popt[1], popt[2], popt[3]), popt[0], popt[1], popt[2], popt[3]
715 return gaussian(x, popt[0], popt[1], popt[2], popt[3]), popt[0], popt[1], popt[2], popt[3]
717
716
717
718 def Gauss_fit_2(self,spc,x,nGauss):
718 def Gauss_fit_2(self,spc,x,nGauss):
719
719
720
720
@@ -2979,34 +2979,44 class JULIADriftsEstimation(Operation):
2979
2979
2980 def run(self, dataOut, zenith, zenithCorrection,heights=None, statistics=0, otype=0):
2980 def run(self, dataOut, zenith, zenithCorrection,heights=None, statistics=0, otype=0):
2981
2981
2982 dataOut.lat=-11.95
2983 dataOut.lon=-76.87
2982 nCh=dataOut.spcpar.shape[0]
2984 nCh=dataOut.spcpar.shape[0]
2983 nHei=dataOut.spcpar.shape[1]
2985 nHei=dataOut.spcpar.shape[1]
2984 nParam=dataOut.spcpar.shape[2]
2986 nParam=dataOut.spcpar.shape[2]
2985 # Solo las alturas de interes
2987 # SelecciΓ³n de alturas
2986 hei=dataOut.heightList
2988
2987 hvalid=numpy.where([hei >= heights[0]][0] & [hei <= heights[1]][0])[0]
2989 if not heights:
2988 nhvalid=len(hvalid)
2990 parm = numpy.zeros((nCh,nHei,nParam))
2989 parm = numpy.zeros((nCh,nhvalid,nParam))
2991 parm[:] = dataOut.spcpar[:]
2990 parm = dataOut.spcpar[:,hvalid,:]
2992 else:
2993 hei=dataOut.heightList
2994 hvalid=numpy.where([hei >= heights[0]][0] & [hei <= heights[1]][0])[0]
2995 nhvalid=len(hvalid)
2996 dataOut.heightList = hei[hvalid]
2997 parm = numpy.zeros((nCh,nhvalid,nParam))
2998 parm[:] = dataOut.spcpar[:,hvalid,:]
2999
3000
2991 # Primer filtrado: Umbral de SNR
3001 # Primer filtrado: Umbral de SNR
2992 for i in range(nCh):
3002 for i in range(nCh):
2993 dataOut.spcpar[i,hvalid,:] = self.data_filter(parm[i,:,:])[0]
3003 parm[i,:,:] = self.data_filter(parm[i,:,:])[0]
3004
2994 zenith = numpy.array(zenith)
3005 zenith = numpy.array(zenith)
2995 zenith -= zenithCorrection
3006 zenith -= zenithCorrection
2996 zenith *= numpy.pi/180
3007 zenith *= numpy.pi/180
2997 alpha = zenith[0]
3008 alpha = zenith[0]
2998 beta = zenith[1]
3009 beta = zenith[1]
2999
3010 dopplerCH0 = parm[0,:,0]
3000 dopplerCH0 = dataOut.spcpar[0,:,0]
3011 dopplerCH1 = parm[1,:,0]
3001 dopplerCH1 = dataOut.spcpar[1,:,0]
3012 swCH0 = parm[0,:,1]
3002 swCH0 = dataOut.spcpar[0,:,1]
3013 swCH1 = parm[1,:,1]
3003 swCH1 = dataOut.spcpar[1,:,1]
3014 snrCH0 = 10*numpy.log10(parm[0,:,2])
3004 snrCH0 = 10*numpy.log10(dataOut.spcpar[0,:,2])
3015 snrCH1 = 10*numpy.log10(parm[1,:,2])
3005 snrCH1 = 10*numpy.log10(dataOut.spcpar[1,:,2])
3016 noiseCH0 = parm[0,:,3]
3006 noiseCH0 = dataOut.spcpar[0,:,3]
3017 noiseCH1 = parm[1,:,3]
3007 noiseCH1 = dataOut.spcpar[1,:,3]
3018 wErrCH0 = parm[0,:,5]
3008 wErrCH0 = dataOut.spcpar[0,:,5]
3019 wErrCH1 = parm[1,:,5]
3009 wErrCH1 = dataOut.spcpar[1,:,5]
3010
3020
3011 # Vertical and zonal calculation according to geometry
3021 # Vertical and zonal calculation according to geometry
3012 sinB_A = numpy.sin(beta)*numpy.cos(alpha) - numpy.sin(alpha)* numpy.cos(beta)
3022 sinB_A = numpy.sin(beta)*numpy.cos(alpha) - numpy.sin(alpha)* numpy.cos(beta)
@@ -3020,7 +3030,8 class JULIADriftsEstimation(Operation):
3020
3030
3021 # for statistics150km
3031 # for statistics150km
3022 if statistics:
3032 if statistics:
3023 print('Implemented offline.')
3033 print('Implemented offline.')
3034
3024 if otype == 0:
3035 if otype == 0:
3025 winds = numpy.vstack((snr, drift, zonal, noise, sw, w_w_err, w_e_err)) # to process statistics drifts
3036 winds = numpy.vstack((snr, drift, zonal, noise, sw, w_w_err, w_e_err)) # to process statistics drifts
3026 elif otype == 3:
3037 elif otype == 3:
@@ -3034,6 +3045,10 class JULIADriftsEstimation(Operation):
3034
3045
3035 dataOut.utctimeInit = dataOut.utctime
3046 dataOut.utctimeInit = dataOut.utctime
3036 dataOut.outputInterval = dataOut.timeInterval
3047 dataOut.outputInterval = dataOut.timeInterval
3048 try:
3049 dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.data_output[0])) # NAN vectors are not written MADRIGAL CASE
3050 except:
3051 print("Check there is no Data")
3037
3052
3038 return dataOut
3053 return dataOut
3039
3054
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