schain Commit - r1700:20a5087b4329 · Jicamarca Repository

update jroproc_parameters, cambios JULIADriftsEstimation, referencia v3.0-devel IVAN

Alexander Valdez -

r1700:20a5087b4329

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r1700:20a5087b4329

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schainpy/model/proc/jroproc_parameters.py +36 -21

              from multiprocessing import Pool, TimeoutError
              from multiprocessing.pool import ThreadPool
              import time
              from scipy.optimize import fmin_l_bfgs_b #optimize with bounds on state papameters
              from .jroproc_base import ProcessingUnit, Operation, MPDecorator
              from schainpy.model.data.jrodata import Parameters, hildebrand_sekhon
                      Operation.__init__(self)
                      self.i=0
-                 # 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
                  def run(self, dataOut, SNRdBlimit=-9, method='generalized'):
                      """This routine will find a couple of generalized Gaussians to a power spectrum
                      methods: generalized, squared
                      return gaussian(x, popt[0], popt[1], popt[2], popt[3]), popt[0], popt[1], popt[2], popt[3]
                  def Gauss_fit_2(self,spc,x,nGauss):
                  def run(self, dataOut, zenith, zenithCorrection,heights=None, statistics=0, otype=0):
+                     dataOut.lat=-11.95
+                     dataOut.lon=-76.87
                      nCh=dataOut.spcpar.shape[0]
                      nHei=dataOut.spcpar.shape[1]
                      nParam=dataOut.spcpar.shape[2]
-                     # Solo las alturas de interes
-                     hei=dataOut.heightList
-                     hvalid=numpy.where([hei >= heights[0]][0] & [hei <= heights[1]][0])[0]
-                     nhvalid=len(hvalid)
-                     parm = numpy.zeros((nCh,nhvalid,nParam))
-                     parm = dataOut.spcpar[:,hvalid,:]
+                     # Selección de alturas
+                     if not heights:
+                         parm = numpy.zeros((nCh,nHei,nParam))
+                         parm[:] = dataOut.spcpar[:]
+                     else:
+                         hei=dataOut.heightList
+                         hvalid=numpy.where([hei >= heights[0]][0] & [hei <= heights[1]][0])[0]
+                         nhvalid=len(hvalid)
+                         dataOut.heightList = hei[hvalid]
+                         parm = numpy.zeros((nCh,nhvalid,nParam))
+                         parm[:] = dataOut.spcpar[:,hvalid,:]
                      # Primer filtrado: Umbral de SNR
                      for i in range(nCh):
-                         dataOut.spcpar[i,hvalid,:] = self.data_filter(parm[i,:,:])[0]
+                         parm[i,:,:] = self.data_filter(parm[i,:,:])[0]
                      zenith = numpy.array(zenith)
                      zenith -= zenithCorrection
                      zenith *= numpy.pi/180
                      alpha = zenith[0]
                      beta = zenith[1]
-                     dopplerCH0 = dataOut.spcpar[0,:,0]
-                     dopplerCH1 = dataOut.spcpar[1,:,0]
-                     swCH0 = dataOut.spcpar[0,:,1]
-                     swCH1 = dataOut.spcpar[1,:,1]
-                     snrCH0 = 10*numpy.log10(dataOut.spcpar[0,:,2])
-                     snrCH1 = 10*numpy.log10(dataOut.spcpar[1,:,2])
-                     noiseCH0 = dataOut.spcpar[0,:,3]
-                     noiseCH1 = dataOut.spcpar[1,:,3]
-                     wErrCH0 = dataOut.spcpar[0,:,5]
-                     wErrCH1 = dataOut.spcpar[1,:,5]
+                     dopplerCH0 = parm[0,:,0]
+                     dopplerCH1 = parm[1,:,0]
+                     swCH0 = parm[0,:,1]
+                     swCH1 = parm[1,:,1]
+                     snrCH0 = 10*numpy.log10(parm[0,:,2])
+                     snrCH1 = 10*numpy.log10(parm[1,:,2])
+                     noiseCH0 = parm[0,:,3]
+                     noiseCH1 = parm[1,:,3]
+                     wErrCH0 = parm[0,:,5]
+                     wErrCH1 = parm[1,:,5]
                      # Vertical and zonal calculation according to geometry
                      sinB_A = numpy.sin(beta)*numpy.cos(alpha) - numpy.sin(alpha)* numpy.cos(beta)
                      # for statistics150km
                      if statistics:
-                         print('Implemented offline.')
+                         print('Implemented offline.')
                      if otype == 0:
                          winds = numpy.vstack((snr, drift, zonal, noise, sw, w_w_err, w_e_err)) # to process statistics drifts
                      elif otype == 3:
                      dataOut.utctimeInit = dataOut.utctime
                      dataOut.outputInterval = dataOut.timeInterval
+                     try:
+                         dataOut.flagNoData = numpy.all(numpy.isnan(dataOut.data_output[0]))  # NAN vectors are not written MADRIGAL CASE
+                     except:
+                         print("Check there is no Data")
                      return dataOut

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