schain Commit - r1439:14a3ab7942a7 · Jicamarca Repository

Block360Update

rflores -

r1439:14a3ab7942a7

parent child

Context file:

r1439:14a3ab7942a7

schainpy/model/graphics/jroplot_parameters.py +513 0

             import os
             import datetime
             import numpy
+            from mpl_toolkits.axisartist.grid_finder import FixedLocator, DictFormatter
             from schainpy.model.graphics.jroplot_base import Plot, plt
             from schainpy.model.graphics.jroplot_spectra import SpectraPlot, RTIPlot, CoherencePlot, SpectraCutPlot
             from schainpy.utils import log
             # libreria wradlib
             import wradlib as wrl
             EARTH_RADIUS = 6.3710e3
             def ll2xy(lat1, lon1, lat2, lon2):
                 p = 0.017453292519943295
                 a = 0.5 - numpy.cos((lat2 - lat1) * p)/2 + numpy.cos(lat1 * p) * \
                     numpy.cos(lat2 * p) * (1 - numpy.cos((lon2 - lon1) * p)) / 2
                 r = 12742 * numpy.arcsin(numpy.sqrt(a))
                 theta = numpy.arctan2(numpy.sin((lon2-lon1)*p)*numpy.cos(lat2*p), numpy.cos(lat1*p)
                                       * numpy.sin(lat2*p)-numpy.sin(lat1*p)*numpy.cos(lat2*p)*numpy.cos((lon2-lon1)*p))
                 theta = -theta + numpy.pi/2
                 return r*numpy.cos(theta), r*numpy.sin(theta)
             def km2deg(km):
                 '''
                 Convert distance in km to degrees
                 '''
                 return numpy.rad2deg(km/EARTH_RADIUS)
             class SpectralMomentsPlot(SpectraPlot):
                 '''
                 Plot for Spectral Moments
                 '''
                 CODE = 'spc_moments'
                 # colormap = 'jet'
                 # plot_type = 'pcolor'
             class DobleGaussianPlot(SpectraPlot):
                 '''
                 Plot for Double Gaussian Plot
                 '''
                 CODE = 'gaussian_fit'
                 # colormap = 'jet'
                 # plot_type = 'pcolor'
             class DoubleGaussianSpectraCutPlot(SpectraCutPlot):
                 '''
                 Plot SpectraCut with Double Gaussian Fit
                 '''
                 CODE = 'cut_gaussian_fit'
             class SnrPlot(RTIPlot):
                 '''
                 Plot for SNR Data
                 '''
                 CODE = 'snr'
                 colormap = 'jet'
                 def update(self, dataOut):
                     data = {
                         'snr': 10*numpy.log10(dataOut.data_snr)
                     }
                     return data, {}
             class DopplerPlot(RTIPlot):
                 '''
                 Plot for DOPPLER Data (1st moment)
                 '''
                 CODE = 'dop'
                 colormap = 'jet'
                 def update(self, dataOut):
                     data = {
                         'dop': 10*numpy.log10(dataOut.data_dop)
                     }
                     return data, {}
             class PowerPlot(RTIPlot):
                 '''
                 Plot for Power Data (0 moment)
                 '''
                 CODE = 'pow'
                 colormap = 'jet'
                 def update(self, dataOut):
                     data = {
                         'pow': 10*numpy.log10(dataOut.data_pow/dataOut.normFactor)
                     }
                     return data, {}
             class SpectralWidthPlot(RTIPlot):
                 '''
                 Plot for Spectral Width Data (2nd moment)
                 '''
                 CODE = 'width'
                 colormap = 'jet'
                 def update(self, dataOut):
                     data = {
                         'width': dataOut.data_width
                     }
                     return data, {}
             class SkyMapPlot(Plot):
                 '''
                 Plot for meteors detection data
                 '''
                 CODE = 'param'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.width = 7.2
                     self.height = 7.2
                     self.nplots = 1
                     self.xlabel = 'Zonal Zenith Angle (deg)'
                     self.ylabel = 'Meridional Zenith Angle (deg)'
                     self.polar = True
                     self.ymin = -180
                     self.ymax = 180
                     self.colorbar = False
                 def plot(self):
                     arrayParameters = numpy.concatenate(self.data['param'])
                     error = arrayParameters[:, -1]
                     indValid = numpy.where(error == 0)[0]
                     finalMeteor = arrayParameters[indValid, :]
                     finalAzimuth = finalMeteor[:, 3]
                     finalZenith = finalMeteor[:, 4]
                     x = finalAzimuth * numpy.pi / 180
                     y = finalZenith
                     ax = self.axes[0]
                     if ax.firsttime:
                         ax.plot = ax.plot(x, y, 'bo', markersize=5)[0]
                     else:
                         ax.plot.set_data(x, y)
                     dt1 = self.getDateTime(self.data.min_time).strftime('%y/%m/%d %H:%M:%S')
                     dt2 = self.getDateTime(self.data.max_time).strftime('%y/%m/%d %H:%M:%S')
                     title = 'Meteor Detection Sky Map\n %s - %s \n Number of events: %5.0f\n' % (dt1,
                                                                                                  dt2,
                                                                                                  len(x))
                     self.titles[0] = title
             class GenericRTIPlot(Plot):
                 '''
                 Plot for data_xxxx object
                 '''
                 CODE = 'param'
                 colormap = 'viridis'
                 plot_type = 'pcolorbuffer'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = self.data.shape('param')[0]
                     self.nplots = self.nrows
                     self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.08, 'right':0.95, 'top': 0.95})
                     if not self.xlabel:
                         self.xlabel = 'Time'
                     self.ylabel = 'Range [km]'
                     if not self.titles:
                         self.titles = ['Param {}'.format(x) for x in range(self.nrows)]
                 def update(self, dataOut):
                     data = {
                         'param' : numpy.concatenate([getattr(dataOut, attr) for attr in self.attr_data], axis=0)
                     }
                     meta = {}
                     return data, meta
                 def plot(self):
                     # self.data.normalize_heights()
                     self.x = self.data.times
                     self.y = self.data.yrange
                     self.z = self.data['param']
                     self.z = 10*numpy.log10(self.z)
                     self.z = numpy.ma.masked_invalid(self.z)
                     if self.decimation is None:
                         x, y, z = self.fill_gaps(self.x, self.y, self.z)
                     else:
                         x, y, z = self.fill_gaps(*self.decimate())
                     for n, ax in enumerate(self.axes):
                         self.zmax = self.zmax if self.zmax is not None else numpy.max(
                             self.z[n])
                         self.zmin = self.zmin if self.zmin is not None else numpy.min(
                             self.z[n])
                         if ax.firsttime:
                             if self.zlimits is not None:
                                 self.zmin, self.zmax = self.zlimits[n]
                             ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=self.cmaps[n]
                                                    )
                         else:
                             if self.zlimits is not None:
                                 self.zmin, self.zmax = self.zlimits[n]
                             ax.collections.remove(ax.collections[0])
                             ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=self.cmaps[n]
                                                    )
             class PolarMapPlot(Plot):
                 '''
                 Plot for weather radar
                 '''
                 CODE = 'param'
                 colormap = 'seismic'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.width = 9
                     self.height = 8
                     self.mode = self.data.meta['mode']
                     if self.channels is not None:
                         self.nplots = len(self.channels)
                         self.nrows = len(self.channels)
                     else:
                         self.nplots = self.data.shape(self.CODE)[0]
                         self.nrows = self.nplots
                         self.channels = list(range(self.nplots))
                     if self.mode == 'E':
                         self.xlabel = 'Longitude'
                         self.ylabel = 'Latitude'
                     else:
                         self.xlabel = 'Range (km)'
                         self.ylabel = 'Height (km)'
                     self.bgcolor = 'white'
                     self.cb_labels = self.data.meta['units']
                     self.lat = self.data.meta['latitude']
                     self.lon = self.data.meta['longitude']
                     self.xmin, self.xmax = float(
                         km2deg(self.xmin) + self.lon), float(km2deg(self.xmax) + self.lon)
                     self.ymin, self.ymax = float(
                         km2deg(self.ymin) + self.lat), float(km2deg(self.ymax) + self.lat)
                     #  self.polar = True
                 def plot(self):
                     for n, ax in enumerate(self.axes):
                         data = self.data['param'][self.channels[n]]
                         zeniths = numpy.linspace(
 , self.data.meta['max_range'], data.shape[1])
                         if self.mode == 'E':
                             azimuths = -numpy.radians(self.data.yrange)+numpy.pi/2
                             r, theta = numpy.meshgrid(zeniths, azimuths)
                             x, y = r*numpy.cos(theta)*numpy.cos(numpy.radians(self.data.meta['elevation'])), r*numpy.sin(
                                 theta)*numpy.cos(numpy.radians(self.data.meta['elevation']))
                             x = km2deg(x) + self.lon
                             y = km2deg(y) + self.lat
                         else:
                             azimuths = numpy.radians(self.data.yrange)
                             r, theta = numpy.meshgrid(zeniths, azimuths)
                             x, y = r*numpy.cos(theta), r*numpy.sin(theta)
                         self.y = zeniths
                         if ax.firsttime:
                             if self.zlimits is not None:
                                 self.zmin, self.zmax = self.zlimits[n]
                             ax.plt = ax.pcolormesh(  # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
                                 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
                                 vmin=self.zmin,
                                 vmax=self.zmax,
                                 cmap=self.cmaps[n])
                         else:
                             if self.zlimits is not None:
                                 self.zmin, self.zmax = self.zlimits[n]
                             ax.collections.remove(ax.collections[0])
                             ax.plt = ax.pcolormesh(  # r, theta, numpy.ma.array(data, mask=numpy.isnan(data)),
                                 x, y, numpy.ma.array(data, mask=numpy.isnan(data)),
                                 vmin=self.zmin,
                                 vmax=self.zmax,
                                 cmap=self.cmaps[n])
                         if self.mode == 'A':
                             continue
                         # plot district names
                         f = open('/data/workspace/schain_scripts/distrito.csv')
                         for line in f:
                             label, lon, lat = [s.strip() for s in line.split(',') if s]
                             lat = float(lat)
                             lon = float(lon)
                             # ax.plot(lon, lat, '.b', ms=2)
                             ax.text(lon, lat, label.decode('utf8'), ha='center',
                                     va='bottom', size='8', color='black')
                         # plot limites
                         limites = []
                         tmp = []
                         for line in open('/data/workspace/schain_scripts/lima.csv'):
                             if '#' in line:
                                 if tmp:
                                     limites.append(tmp)
                                 tmp = []
                                 continue
                             values = line.strip().split(',')
                             tmp.append((float(values[0]), float(values[1])))
                         for points in limites:
                             ax.add_patch(
                                 Polygon(points, ec='k', fc='none', ls='--', lw=0.5))
                         # plot Cuencas
                         for cuenca in ('rimac', 'lurin', 'mala', 'chillon', 'chilca', 'chancay-huaral'):
                             f = open('/data/workspace/schain_scripts/{}.csv'.format(cuenca))
                             values = [line.strip().split(',') for line in f]
                             points = [(float(s[0]), float(s[1])) for s in values]
                             ax.add_patch(Polygon(points, ec='b', fc='none'))
                         # plot grid
                         for r in (15, 30, 45, 60):
                             ax.add_artist(plt.Circle((self.lon, self.lat),
                                                      km2deg(r), color='0.6', fill=False, lw=0.2))
                             ax.text(
                                 self.lon + (km2deg(r))*numpy.cos(60*numpy.pi/180),
                                 self.lat + (km2deg(r))*numpy.sin(60*numpy.pi/180),
                                 '{}km'.format(r),
                                 ha='center', va='bottom', size='8', color='0.6', weight='heavy')
                     if self.mode == 'E':
                         title = 'El={}$^\circ$'.format(self.data.meta['elevation'])
                         label = 'E{:02d}'.format(int(self.data.meta['elevation']))
                     else:
                         title = 'Az={}$^\circ$'.format(self.data.meta['azimuth'])
                         label = 'A{:02d}'.format(int(self.data.meta['azimuth']))
                     self.save_labels = ['{}-{}'.format(lbl, label) for lbl in self.labels]
                     self.titles = ['{} {}'.format(
                         self.data.parameters[x], title) for x in self.channels]
             class WeatherPlot(Plot):
                 CODE = 'weather'
                 plot_name = 'weather'
                 plot_type = 'ppistyle'
                 buffering = False
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.width   =8
                     self.height  =8
                     self.nplots= 1
                     self.ylabel= 'Range [Km]'
                     self.titles= ['Weather']
                     self.colorbar=False
                     self.ini     =0
                     self.len_azi =0
                     self.buffer_ini  = None
                     self.buffer_azi   = None
                     self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
                     self.flag    =0
                     self.indicador= 0
                     self.last_data_azi = None
                     self.val_mean      = None
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     if hasattr(dataOut, 'dataPP_POWER'):
                         factor = 1
                     if hasattr(dataOut, 'nFFTPoints'):
                         factor = dataOut.normFactor
                     #print("DIME EL SHAPE PORFAVOR",dataOut.data_360.shape)
                     data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
                     data['azi']     = dataOut.data_azi
                     data['ele']     = dataOut.data_ele
                     return data, meta
                 def get2List(self,angulos):
                     list1=[]
                     list2=[]
                     for i in reversed(range(len(angulos))):
                         diff_ = angulos[i]-angulos[i-1]
                         if diff_ >1.5:
                             list1.append(i-1)
                             list2.append(diff_)
                     return list(reversed(list1)),list(reversed(list2))
                 def fixData360(self,list_,ang_):
                     if list_[0]==-1:
                         vec = numpy.where(ang_<ang_[0])
                         ang_[vec] = ang_[vec]+360
                         return ang_
                     return ang_
                 def fixData360HL(self,angulos):
                     vec = numpy.where(angulos>=360)
                     angulos[vec]=angulos[vec]-360
                     return angulos
                 def search_pos(self,pos,list_):
                     for i in range(len(list_)):
                         if pos == list_[i]:
                             return True,i
                     i=None
                     return False,i
                 def fixDataComp(self,ang_,list1_,list2_):
                     size  = len(ang_)
                     size2 = 0
                     for i in range(len(list2_)):
                         size2=size2+round(list2_[i])-1
                     new_size= size+size2
                     ang_new = numpy.zeros(new_size)
                     ang_new2 = numpy.zeros(new_size)
                     tmp = 0
                     c   = 0
                     for i in range(len(ang_)):
                         ang_new[tmp +c] = ang_[i]
                         ang_new2[tmp+c] = ang_[i]
                         condition , value = self.search_pos(i,list1_)
                         if condition:
                             pos = tmp + c + 1
                             for k in range(round(list2_[value])-1):
                                 ang_new[pos+k]  = ang_new[pos+k-1]+1
                                 ang_new2[pos+k] = numpy.nan
                             tmp = pos +k
                             c   = 0
                         c=c+1
                     return ang_new,ang_new2
                 def globalCheckPED(self,angulos):
                     l1,l2 = self.get2List(angulos)
                     if len(l1)>0:
                         angulos2 = self.fixData360(list_=l1,ang_=angulos)
                         l1,l2 = self.get2List(angulos2)
                         ang1_,ang2_ = self.fixDataComp(ang_=angulos2,list1_=l1,list2_=l2)
                         ang1_ = self.fixData360HL(ang1_)
                         ang2_ = self.fixData360HL(ang2_)
                     else:
                         ang1_= angulos
                         ang2_= angulos
                     return ang1_,ang2_
                 def analizeDATA(self,data_azi):
                     list1 = []
                     list2 = []
                     dat   = data_azi
                     for i in reversed(range(1,len(dat))):
                         if dat[i]>dat[i-1]:
                             diff = int(dat[i])-int(dat[i-1])
                         else:
                             diff = 360+int(dat[i])-int(dat[i-1])
                         if diff > 1:
                             list1.append(i-1)
                             list2.append(diff-1)
                     return list1,list2
                 def fixDATANEW(self,data_azi,data_weather):
                     list1,list2 = self.analizeDATA(data_azi)
                     if len(list1)== 0:
                         return data_azi,data_weather
                     else:
                         resize = 0
                         for i in range(len(list2)):
                             resize=  resize + list2[i]
                         new_data_azi = numpy.resize(data_azi,resize)
                         new_data_weather= numpy.resize(date_weather,resize)
                         for i in range(len(list2)):
                             j=0
                             position=list1[i]+1
                             for j in range(list2[i]):
                                 new_data_azi[position+j]=new_data_azi[position+j-1]+1
                     return new_data_azi
                 def fixDATA(self,data_azi):
                     data=data_azi
                     for i in range(len(data)):
                         if numpy.isnan(data[i]):
                            data[i]=data[i-1]+1
                     return data
                 def replaceNAN(self,data_weather,data_azi,val):
                     data= data_azi
                     data_T= data_weather
                     if data.shape[0]> data_T.shape[0]:
                         data_N = numpy.ones( [data.shape[0],data_T.shape[1]])
                         c      = 0
                         for i in range(len(data)):
                             if numpy.isnan(data[i]):
                                 data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
                             else:
                                 data_N[i,:]=data_T[c,:]
                                 c=c+1
                         return data_N
                     else:
                         for i in range(len(data)):
                             if numpy.isnan(data[i]):
                                data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
                         return data_T
                 def const_ploteo(self,data_weather,data_azi,step,res):
                     if self.ini==0:
                         #-------
                         n     = (360/res)-len(data_azi)
                         #--------------------- new -------------------------
                         data_azi_new ,data_azi_old= self.globalCheckPED(data_azi)
                         #------------------------
                         start = data_azi_new[-1] + res
                         end   = data_azi_new[0]  - res
                         #------ new
                         self.last_data_azi = end
                         if start>end:
                             end        = end + 360
                         azi_vacia = numpy.linspace(start,end,int(n))
                         azi_vacia = numpy.where(azi_vacia>360,azi_vacia-360,azi_vacia)
                         data_azi  = numpy.hstack((data_azi_new,azi_vacia))
                         # RADAR
                         val_mean         = numpy.mean(data_weather[:,-1])
                         self.val_mean    = val_mean
                         data_weather_cmp = numpy.ones([(360-data_weather.shape[0]),data_weather.shape[1]])*val_mean
                         data_weather     = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
                         data_weather     = numpy.vstack((data_weather,data_weather_cmp))
                     else:
                         # azimuth
                         flag=0
                         start_azi = self.res_azi[0]
                         #-----------new------------
                         data_azi ,data_azi_old= self.globalCheckPED(data_azi)
                         data_weather = self.replaceNAN(data_weather=data_weather,data_azi=data_azi_old,val=self.val_mean)
                         #--------------------------
                         start = data_azi[0]
                         end   = data_azi[-1]
                         self.last_data_azi= end
                         if start< start_azi:
                             start = start +360
                         if end <start_azi:
                             end  = end +360
                         pos_ini = int((start-start_azi)/res)
                         len_azi = len(data_azi)
                         if (360-pos_ini)<len_azi:
                             if pos_ini+1==360:
                                 pos_ini=0
                             else:
                                 flag=1
                                 dif= 360-pos_ini
                                 comp= len_azi-dif
                         #-----------------
                         if flag==0:
                             # AZIMUTH
                             self.res_azi[pos_ini:pos_ini+len_azi] = data_azi
                             # RADAR
                             self.res_weather[pos_ini:pos_ini+len_azi,:] = data_weather
                         else:
                             # AZIMUTH
                             self.res_azi[pos_ini:pos_ini+dif] = data_azi[0:dif]
                             self.res_azi[0:comp]              = data_azi[dif:]
                             # RADAR
                             self.res_weather[pos_ini:pos_ini+dif,:] = data_weather[0:dif,:]
                             self.res_weather[0:comp,:]              = data_weather[dif:,:]
                             flag=0
                         data_azi                                    = self.res_azi
                         data_weather                                = self.res_weather
                     return data_weather,data_azi
                 def plot(self):
                     thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
                     data         = self.data[-1]
                     r            = self.data.yrange
                     delta_height = r[1]-r[0]
                     r_mask       = numpy.where(r>=0)[0]
                     r            = numpy.arange(len(r_mask))*delta_height
                     self.y       = 2*r
                     # RADAR
                     #data_weather = data['weather']
                     # PEDESTAL
                     #data_azi  = data['azi']
                     res          = 1
                     # STEP
                     step         = (360/(res*data['weather'].shape[0]))
                     self.res_weather, self.res_azi = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_azi=data['azi'],step=step,res=res)
                     self.res_ele = numpy.mean(data['ele'])
                     #################    PLOTEO           ###################
                     for i,ax in enumerate(self.axes):
                         if ax.firsttime:
                             plt.clf()
                             cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=20, vmax=80)
                         else:
                             plt.clf()
                             cgax, pm = wrl.vis.plot_ppi(self.res_weather,r=r,az=self.res_azi,fig=self.figures[0], proj='cg', vmin=20, vmax=80)
                     caax = cgax.parasites[0]
                     paax = cgax.parasites[1]
                     cbar = plt.gcf().colorbar(pm, pad=0.075)
                     caax.set_xlabel('x_range [km]')
                     caax.set_ylabel('y_range [km]')
                     plt.text(1.0, 1.05, 'Azimuth '+str(thisDatetime)+"  Step   "+str(self.ini)+ " Elev: "+str(round(self.res_ele,2)), transform=caax.transAxes, va='bottom',ha='right')
                     self.ini= self.ini+1
             class WeatherRHIPlot(Plot):
                 CODE = 'weather'
                 plot_name = 'weather'
                 plot_type = 'rhistyle'
                 buffering = False
                 data_ele_tmp = None
                 def setup(self):
                     print("********************")
                     print("********************")
                     print("********************")
                     print("SETUP WEATHER PLOT")
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots= 1
                     self.ylabel= 'Range [Km]'
                     self.titles= ['Weather']
                     if self.channels is not None:
                         self.nplots = len(self.channels)
                         self.nrows = len(self.channels)
                     else:
                         self.nplots = self.data.shape(self.CODE)[0]
                         self.nrows = self.nplots
                         self.channels = list(range(self.nplots))
                     print("channels",self.channels)
                     print("que saldra", self.data.shape(self.CODE)[0])
                     self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)]
                     print("self.titles",self.titles)
                     self.colorbar=False
                     self.width   =8
                     self.height  =8
                     self.ini     =0
                     self.len_azi =0
                     self.buffer_ini  = None
                     self.buffer_ele   = None
                     self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
                     self.flag    =0
                     self.indicador= 0
                     self.last_data_ele = None
                     self.val_mean      = None
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     if hasattr(dataOut, 'dataPP_POWER'):
                         factor = 1
                     if hasattr(dataOut, 'nFFTPoints'):
                         factor = dataOut.normFactor
                     print("dataOut",dataOut.data_360.shape)
                     #
                     data['weather'] = 10*numpy.log10(dataOut.data_360/(factor))
                     #
                     #data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
                     data['azi']     = dataOut.data_azi
                     data['ele']     = dataOut.data_ele
                     #print("UPDATE")
                     #print("data[weather]",data['weather'].shape)
                     #print("data[azi]",data['azi'])
                     return data, meta
                 def get2List(self,angulos):
                     list1=[]
                     list2=[]
                     for i in reversed(range(len(angulos))):
                         if not i==0:#el caso de i=0 evalula el primero de la lista con el ultimo y no es relevante
                             diff_ = angulos[i]-angulos[i-1]
                             if abs(diff_) >1.5:
                                 list1.append(i-1)
                                 list2.append(diff_)
                     return list(reversed(list1)),list(reversed(list2))
                 def fixData90(self,list_,ang_):
                     if list_[0]==-1:
                         vec = numpy.where(ang_<ang_[0])
                         ang_[vec] = ang_[vec]+90
                         return ang_
                     return ang_
                 def fixData90HL(self,angulos):
                     vec = numpy.where(angulos>=90)
                     angulos[vec]=angulos[vec]-90
                     return angulos
                 def search_pos(self,pos,list_):
                     for i in range(len(list_)):
                         if pos == list_[i]:
                             return True,i
                     i=None
                     return False,i
                 def fixDataComp(self,ang_,list1_,list2_,tipo_case):
                     size  = len(ang_)
                     size2 = 0
                     for i in range(len(list2_)):
                         size2=size2+round(abs(list2_[i]))-1
                     new_size= size+size2
                     ang_new = numpy.zeros(new_size)
                     ang_new2 = numpy.zeros(new_size)
                     tmp = 0
                     c   = 0
                     for i in range(len(ang_)):
                         ang_new[tmp +c] = ang_[i]
                         ang_new2[tmp+c] = ang_[i]
                         condition , value = self.search_pos(i,list1_)
                         if condition:
                             pos = tmp + c + 1
                             for k in range(round(abs(list2_[value]))-1):
                                 if tipo_case==0 or tipo_case==3:#subida
                                     ang_new[pos+k]  = ang_new[pos+k-1]+1
                                     ang_new2[pos+k] = numpy.nan
                                 elif tipo_case==1 or tipo_case==2:#bajada
                                     ang_new[pos+k]  = ang_new[pos+k-1]-1
                                     ang_new2[pos+k] = numpy.nan
                             tmp = pos +k
                             c   = 0
                         c=c+1
                     return ang_new,ang_new2
                 def globalCheckPED(self,angulos,tipo_case):
                     l1,l2 = self.get2List(angulos)
                     ##print("l1",l1)
                     ##print("l2",l2)
                     if len(l1)>0:
                         #angulos2 = self.fixData90(list_=l1,ang_=angulos)
                         #l1,l2 = self.get2List(angulos2)
                         ang1_,ang2_ = self.fixDataComp(ang_=angulos,list1_=l1,list2_=l2,tipo_case=tipo_case)
                         #ang1_ = self.fixData90HL(ang1_)
                         #ang2_ = self.fixData90HL(ang2_)
                     else:
                         ang1_= angulos
                         ang2_= angulos
                     return ang1_,ang2_
                 def replaceNAN(self,data_weather,data_ele,val):
                     data= data_ele
                     data_T= data_weather
                     if data.shape[0]> data_T.shape[0]:
                         data_N = numpy.ones( [data.shape[0],data_T.shape[1]])
                         c      = 0
                         for i in range(len(data)):
                             if numpy.isnan(data[i]):
                                 data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
                             else:
                                 data_N[i,:]=data_T[c,:]
                                 c=c+1
                         return data_N
                     else:
                         for i in range(len(data)):
                             if numpy.isnan(data[i]):
                                data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
                         return data_T
                 def check_case(self,data_ele,ang_max,ang_min):
                     start  = data_ele[0]
                     end    = data_ele[-1]
                     number = (end-start)
                     len_ang=len(data_ele)
                     print("start",start)
                     print("end",end)
                     print("number",number)
                     print("len_ang",len_ang)
                     #exit(1)
                     if start<end and (round(abs(number)+1)>=len_ang or (numpy.argmin(data_ele)==0)):#caso subida
                         return 0
                     #elif start>end and (round(abs(number)+1)>=len_ang or(numpy.argmax(data_ele)==0)):#caso bajada
                     #    return 1
                     elif round(abs(number)+1)>=len_ang and (start>end or(numpy.argmax(data_ele)==0)):#caso bajada
                         return 1
                     elif round(abs(number)+1)<len_ang and data_ele[-2]>data_ele[-1]:# caso BAJADA CAMBIO ANG MAX
                         return 2
                     elif round(abs(number)+1)<len_ang and data_ele[-2]<data_ele[-1] :# caso SUBIDA CAMBIO ANG MIN
                         return 3
                 def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min):
                     ang_max= ang_max
                     ang_min= ang_min
                     data_weather=data_weather
                     val_ch=val_ch
                     ##print("*********************DATA WEATHER**************************************")
                     ##print(data_weather)
                     if self.ini==0:
                         '''
                         print("**********************************************")
                         print("**********************************************")
                         print("***************ini**************")
                         print("**********************************************")
                         print("**********************************************")
                         '''
                         #print("data_ele",data_ele)
                         #----------------------------------------------------------
                         tipo_case = self.check_case(data_ele,ang_max,ang_min)
                         print("check_case",tipo_case)
                         #exit(1)
                         #--------------------- new -------------------------
                         data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case)
                         #-------------------------CAMBIOS RHI---------------------------------
                         start= ang_min
                         end  = ang_max
                         n= (ang_max-ang_min)/res
                         #------ new
                         self.start_data_ele = data_ele_new[0]
                         self.end_data_ele  = data_ele_new[-1]
                         if tipo_case==0  or tipo_case==3: # SUBIDA
                             n1= round(self.start_data_ele)- start
                             n2= end - round(self.end_data_ele)
                             print(self.start_data_ele)
                             print(self.end_data_ele)
                             if n1>0:
                                 ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
                                 ele1_nan= numpy.ones(n1)*numpy.nan
                                 data_ele = numpy.hstack((ele1,data_ele_new))
                                 print("ele1_nan",ele1_nan.shape)
                                 print("data_ele_old",data_ele_old.shape)
                                 data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
                             if n2>0:
                                 ele2= numpy.linspace(self.end_data_ele+1,end,n2)
                                 ele2_nan= numpy.ones(n2)*numpy.nan
                                 data_ele = numpy.hstack((data_ele,ele2))
                                 print("ele2_nan",ele2_nan.shape)
                                 print("data_ele_old",data_ele_old.shape)
                                 data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
                         if tipo_case==1  or tipo_case==2: # BAJADA
                             data_ele_new       = data_ele_new[::-1] #  reversa
                             data_ele_old   = data_ele_old[::-1]# reversa
                             data_weather   = data_weather[::-1,:]# reversa
                             vec= numpy.where(data_ele_new<ang_max)
                             data_ele_new = data_ele_new[vec]
                             data_ele_old = data_ele_old[vec]
                             data_weather  = data_weather[vec[0]]
                             vec2= numpy.where(0<data_ele_new)
                             data_ele_new = data_ele_new[vec2]
                             data_ele_old = data_ele_old[vec2]
                             data_weather  = data_weather[vec2[0]]
                             self.start_data_ele = data_ele_new[0]
                             self.end_data_ele  = data_ele_new[-1]
                             n1= round(self.start_data_ele)- start
                             n2= end - round(self.end_data_ele)-1
                             print(self.start_data_ele)
                             print(self.end_data_ele)
                             if n1>0:
                                 ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
                                 ele1_nan= numpy.ones(n1)*numpy.nan
                                 data_ele = numpy.hstack((ele1,data_ele_new))
                                 data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
                             if n2>0:
                                 ele2= numpy.linspace(self.end_data_ele+1,end,n2)
                                 ele2_nan= numpy.ones(n2)*numpy.nan
                                 data_ele = numpy.hstack((data_ele,ele2))
                                 data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
                         # RADAR
                         # NOTA data_ele y data_weather es la variable que retorna
                         val_mean         = numpy.mean(data_weather[:,-1])
                         self.val_mean    = val_mean
                         data_weather     = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
                         self.data_ele_tmp[val_ch]= data_ele_old
                     else:
                         #print("**********************************************")
                         #print("****************VARIABLE**********************")
                         #-------------------------CAMBIOS RHI---------------------------------
                         #---------------------------------------------------------------------
                         ##print("INPUT data_ele",data_ele)
                         flag=0
                         start_ele = self.res_ele[0]
                         tipo_case = self.check_case(data_ele,ang_max,ang_min)
                         #print("TIPO DE DATA",tipo_case)
                         #-----------new------------
                         data_ele ,data_ele_old = self.globalCheckPED(data_ele,tipo_case)
                         data_weather           = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
                         #-------------------------------NEW RHI ITERATIVO-------------------------
                         if tipo_case==0 : # SUBIDA
                             vec = numpy.where(data_ele<ang_max)
                             data_ele = data_ele[vec]
                             data_ele_old = data_ele_old[vec]
                             data_weather  = data_weather[vec[0]]
                             vec2 = numpy.where(0<data_ele)
                             data_ele= data_ele[vec2]
                             data_ele_old= data_ele_old[vec2]
                             ##print(data_ele_new)
                             data_weather= data_weather[vec2[0]]
                             new_i_ele  = int(round(data_ele[0]))
                             new_f_ele  = int(round(data_ele[-1]))
                             #print(new_i_ele)
                             #print(new_f_ele)
                             #print(data_ele,len(data_ele))
                             #print(data_ele_old,len(data_ele_old))
                             if new_i_ele< 2:
                                 self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
                                 self.res_weather[val_ch]  = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
                             self.data_ele_tmp[val_ch][new_i_ele:new_i_ele+len(data_ele)]=data_ele_old
                             self.res_ele[new_i_ele:new_i_ele+len(data_ele)]= data_ele
                             self.res_weather[val_ch][new_i_ele:new_i_ele+len(data_ele),:]= data_weather
                             data_ele                                    = self.res_ele
                             data_weather                                = self.res_weather[val_ch]
                         elif tipo_case==1 : #BAJADA
                             data_ele       = data_ele[::-1] #  reversa
                             data_ele_old   = data_ele_old[::-1]# reversa
                             data_weather   = data_weather[::-1,:]# reversa
                             vec= numpy.where(data_ele<ang_max)
                             data_ele = data_ele[vec]
                             data_ele_old = data_ele_old[vec]
                             data_weather  = data_weather[vec[0]]
                             vec2= numpy.where(0<data_ele)
                             data_ele = data_ele[vec2]
                             data_ele_old = data_ele_old[vec2]
                             data_weather  = data_weather[vec2[0]]
                             new_i_ele  = int(round(data_ele[0]))
                             new_f_ele  = int(round(data_ele[-1]))
                             #print(data_ele)
                             #print(ang_max)
                             #print(data_ele_old)
                             if new_i_ele <= 1:
                                 new_i_ele = 1
                             if round(data_ele[-1])>=ang_max-1:
                                 self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
                                 self.res_weather[val_ch]  = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
                             self.data_ele_tmp[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1]=data_ele_old
                             self.res_ele[new_i_ele-1:new_i_ele+len(data_ele)-1]= data_ele
                             self.res_weather[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1,:]= data_weather
                             data_ele     = self.res_ele
                             data_weather = self.res_weather[val_ch]
                         elif tipo_case==2:  #bajada
                             vec = numpy.where(data_ele<ang_max)
                             data_ele = data_ele[vec]
                             data_weather= data_weather[vec[0]]
                             len_vec = len(vec)
                             data_ele_new     = data_ele[::-1] #  reversa
                             data_weather =  data_weather[::-1,:]
                             new_i_ele  = int(data_ele_new[0])
                             new_f_ele  = int(data_ele_new[-1])
                             n1= new_i_ele- ang_min
                             n2= ang_max - new_f_ele-1
                             if n1>0:
                                 ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
                                 ele1_nan= numpy.ones(n1)*numpy.nan
                                 data_ele = numpy.hstack((ele1,data_ele_new))
                                 data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
                             if n2>0:
                                 ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
                                 ele2_nan= numpy.ones(n2)*numpy.nan
                                 data_ele = numpy.hstack((data_ele,ele2))
                                 data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
                             self.data_ele_tmp[val_ch] = data_ele_old
                             self.res_ele      = data_ele
                             self.res_weather[val_ch]  = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
                             data_ele                                    = self.res_ele
                             data_weather                                = self.res_weather[val_ch]
                         elif tipo_case==3:#subida
                             vec = numpy.where(0<data_ele)
                             data_ele= data_ele[vec]
                             data_ele_new = data_ele
                             data_ele_old= data_ele_old[vec]
                             data_weather= data_weather[vec[0]]
                             pos_ini = numpy.argmin(data_ele)
                             if pos_ini>0:
                                 len_vec= len(data_ele)
                                 vec3  = numpy.linspace(pos_ini,len_vec-1,len_vec-pos_ini).astype(int)
                                 #print(vec3)
                                 data_ele= data_ele[vec3]
                                 data_ele_new = data_ele
                                 data_ele_old= data_ele_old[vec3]
                                 data_weather= data_weather[vec3]
                             new_i_ele  = int(data_ele_new[0])
                             new_f_ele  = int(data_ele_new[-1])
                             n1= new_i_ele- ang_min
                             n2= ang_max - new_f_ele-1
                             if n1>0:
                                 ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
                                 ele1_nan= numpy.ones(n1)*numpy.nan
                                 data_ele = numpy.hstack((ele1,data_ele_new))
                                 data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
                             if n2>0:
                                 ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
                                 ele2_nan= numpy.ones(n2)*numpy.nan
                                 data_ele = numpy.hstack((data_ele,ele2))
                                 data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
                             self.data_ele_tmp[val_ch] = data_ele_old
                             self.res_ele      = data_ele
                             self.res_weather[val_ch]  = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
                             data_ele                                    = self.res_ele
                             data_weather                                = self.res_weather[val_ch]
                     #print("self.data_ele_tmp",self.data_ele_tmp)
                     return data_weather,data_ele
                 def plot(self):
                     thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
                     data         = self.data[-1]
                     r            = self.data.yrange
                     delta_height = r[1]-r[0]
                     r_mask       = numpy.where(r>=0)[0]
                     ##print("delta_height",delta_height)
                     #print("r_mask",r_mask,len(r_mask))
                     r            = numpy.arange(len(r_mask))*delta_height
                     self.y       = 2*r
                     res          = 1
                     ###print("data['weather'].shape[0]",data['weather'].shape[0])
                     ang_max = self.ang_max
                     ang_min = self.ang_min
                     var_ang      =ang_max -  ang_min
                     step         = (int(var_ang)/(res*data['weather'].shape[0]))
                     ###print("step",step)
                     #--------------------------------------------------------
                     ##print('weather',data['weather'].shape)
                     ##print('ele',data['ele'].shape)
                     ###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min)
                     ###self.res_azi                   = numpy.mean(data['azi'])
                     ###print("self.res_ele",self.res_ele)
                     plt.clf()
                     subplots = [121, 122]
                     if self.ini==0:
                         self.data_ele_tmp = numpy.ones([self.nplots,int(var_ang)])*numpy.nan
                         self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan
                         print("SHAPE",self.data_ele_tmp.shape)
                     for i,ax in enumerate(self.axes):
                         self.res_weather[i], self.res_ele = self.const_ploteo(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min)
                         self.res_azi                   = numpy.mean(data['azi'])
                         if i==0:
                             print("*****************************************************************************to plot**************************",self.res_weather[i].shape)
                         if ax.firsttime:
                             #plt.clf()
                             cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
                             #fig=self.figures[0]
                         else:
                             #plt.clf()
                             if i==0:
                                 print(self.res_weather[i])
                                 print(self.res_ele)
                             cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
                         caax = cgax.parasites[0]
                         paax = cgax.parasites[1]
                         cbar = plt.gcf().colorbar(pm, pad=0.075)
                         caax.set_xlabel('x_range [km]')
                         caax.set_ylabel('y_range [km]')
                         plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+"  Step   "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right')
                     print("***************************self.ini****************************",self.ini)
                     self.ini= self.ini+1
             class WeatherRHI_vRF2_Plot(Plot):
                 CODE = 'weather'
                 plot_name = 'weather'
                 plot_type = 'rhistyle'
                 buffering = False
                 data_ele_tmp = None
                 def setup(self):
                     print("********************")
                     print("********************")
                     print("********************")
                     print("SETUP WEATHER PLOT")
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots= 1
                     self.ylabel= 'Range [Km]'
                     self.titles= ['Weather']
                     if self.channels is not None:
                         self.nplots = len(self.channels)
                         self.nrows = len(self.channels)
                     else:
                         self.nplots = self.data.shape(self.CODE)[0]
                         self.nrows = self.nplots
                         self.channels = list(range(self.nplots))
                     print("channels",self.channels)
                     print("que saldra", self.data.shape(self.CODE)[0])
                     self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)]
                     print("self.titles",self.titles)
                     self.colorbar=False
                     self.width   =8
                     self.height  =8
                     self.ini     =0
                     self.len_azi =0
                     self.buffer_ini  = None
                     self.buffer_ele   = None
                     self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
                     self.flag    =0
                     self.indicador= 0
                     self.last_data_ele = None
                     self.val_mean      = None
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     if hasattr(dataOut, 'dataPP_POWER'):
                         factor = 1
                     if hasattr(dataOut, 'nFFTPoints'):
                         factor = dataOut.normFactor
                     print("dataOut",dataOut.data_360.shape)
                     #
                     data['weather'] = 10*numpy.log10(dataOut.data_360/(factor))
                     #
                     #data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
                     data['azi']     = dataOut.data_azi
                     data['ele']     = dataOut.data_ele
                     data['case_flag']     = dataOut.case_flag
                     #print("UPDATE")
                     #print("data[weather]",data['weather'].shape)
                     #print("data[azi]",data['azi'])
                     return data, meta
                 def get2List(self,angulos):
                     list1=[]
                     list2=[]
                     for i in reversed(range(len(angulos))):
                         if not i==0:#el caso de i=0 evalula el primero de la lista con el ultimo y no es relevante
                             diff_ = angulos[i]-angulos[i-1]
                             if abs(diff_) >1.5:
                                 list1.append(i-1)
                                 list2.append(diff_)
                     return list(reversed(list1)),list(reversed(list2))
                 def fixData90(self,list_,ang_):
                     if list_[0]==-1:
                         vec = numpy.where(ang_<ang_[0])
                         ang_[vec] = ang_[vec]+90
                         return ang_
                     return ang_
                 def fixData90HL(self,angulos):
                     vec = numpy.where(angulos>=90)
                     angulos[vec]=angulos[vec]-90
                     return angulos
                 def search_pos(self,pos,list_):
                     for i in range(len(list_)):
                         if pos == list_[i]:
                             return True,i
                     i=None
                     return False,i
                 def fixDataComp(self,ang_,list1_,list2_,tipo_case):
                     size  = len(ang_)
                     size2 = 0
                     for i in range(len(list2_)):
                         size2=size2+round(abs(list2_[i]))-1
                     new_size= size+size2
                     ang_new = numpy.zeros(new_size)
                     ang_new2 = numpy.zeros(new_size)
                     tmp = 0
                     c   = 0
                     for i in range(len(ang_)):
                         ang_new[tmp +c] = ang_[i]
                         ang_new2[tmp+c] = ang_[i]
                         condition , value = self.search_pos(i,list1_)
                         if condition:
                             pos = tmp + c + 1
                             for k in range(round(abs(list2_[value]))-1):
                                 if tipo_case==0 or tipo_case==3:#subida
                                     ang_new[pos+k]  = ang_new[pos+k-1]+1
                                     ang_new2[pos+k] = numpy.nan
                                 elif tipo_case==1 or tipo_case==2:#bajada
                                     ang_new[pos+k]  = ang_new[pos+k-1]-1
                                     ang_new2[pos+k] = numpy.nan
                             tmp = pos +k
                             c   = 0
                         c=c+1
                     return ang_new,ang_new2
                 def globalCheckPED(self,angulos,tipo_case):
                     l1,l2 = self.get2List(angulos)
                     ##print("l1",l1)
                     ##print("l2",l2)
                     if len(l1)>0:
                         #angulos2 = self.fixData90(list_=l1,ang_=angulos)
                         #l1,l2 = self.get2List(angulos2)
                         ang1_,ang2_ = self.fixDataComp(ang_=angulos,list1_=l1,list2_=l2,tipo_case=tipo_case)
                         #ang1_ = self.fixData90HL(ang1_)
                         #ang2_ = self.fixData90HL(ang2_)
                     else:
                         ang1_= angulos
                         ang2_= angulos
                     return ang1_,ang2_
                 def replaceNAN(self,data_weather,data_ele,val):
                     data= data_ele
                     data_T= data_weather
                     if data.shape[0]> data_T.shape[0]:
                         data_N = numpy.ones( [data.shape[0],data_T.shape[1]])
                         c      = 0
                         for i in range(len(data)):
                             if numpy.isnan(data[i]):
                                 data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
                             else:
                                 data_N[i,:]=data_T[c,:]
                                 c=c+1
                         return data_N
                     else:
                         for i in range(len(data)):
                             if numpy.isnan(data[i]):
                                data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
                         return data_T
                 def check_case(self,data_ele,ang_max,ang_min):
                     start  = data_ele[0]
                     end    = data_ele[-1]
                     number = (end-start)
                     len_ang=len(data_ele)
                     print("start",start)
                     print("end",end)
                     print("number",number)
                     print("len_ang",len_ang)
                     #exit(1)
                     if start<end and (round(abs(number)+1)>=len_ang or (numpy.argmin(data_ele)==0)):#caso subida
                         return 0
                     #elif start>end and (round(abs(number)+1)>=len_ang or(numpy.argmax(data_ele)==0)):#caso bajada
                     #    return 1
                     elif round(abs(number)+1)>=len_ang and (start>end or(numpy.argmax(data_ele)==0)):#caso bajada
                         return 1
                     elif round(abs(number)+1)<len_ang and data_ele[-2]>data_ele[-1]:# caso BAJADA CAMBIO ANG MAX
                         return 2
                     elif round(abs(number)+1)<len_ang and data_ele[-2]<data_ele[-1] :# caso SUBIDA CAMBIO ANG MIN
                         return 3
                 def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min,case_flag):
                     ang_max= ang_max
                     ang_min= ang_min
                     data_weather=data_weather
                     val_ch=val_ch
                     ##print("*********************DATA WEATHER**************************************")
                     ##print(data_weather)
                     if self.ini==0:
                         '''
                         print("**********************************************")
                         print("**********************************************")
                         print("***************ini**************")
                         print("**********************************************")
                         print("**********************************************")
                         '''
                         #print("data_ele",data_ele)
                         #----------------------------------------------------------
                         tipo_case = case_flag[-1]
                         #tipo_case = self.check_case(data_ele,ang_max,ang_min)
                         print("check_case",tipo_case)
                         #exit(1)
                         #--------------------- new -------------------------
                         data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case)
                         #-------------------------CAMBIOS RHI---------------------------------
                         start= ang_min
                         end  = ang_max
                         n= (ang_max-ang_min)/res
                         #------ new
                         self.start_data_ele = data_ele_new[0]
                         self.end_data_ele  = data_ele_new[-1]
                         if tipo_case==0  or tipo_case==3: # SUBIDA
                             n1= round(self.start_data_ele)- start
                             n2= end - round(self.end_data_ele)
                             print(self.start_data_ele)
                             print(self.end_data_ele)
                             if n1>0:
                                 ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
                                 ele1_nan= numpy.ones(n1)*numpy.nan
                                 data_ele = numpy.hstack((ele1,data_ele_new))
                                 print("ele1_nan",ele1_nan.shape)
                                 print("data_ele_old",data_ele_old.shape)
                                 data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
                             if n2>0:
                                 ele2= numpy.linspace(self.end_data_ele+1,end,n2)
                                 ele2_nan= numpy.ones(n2)*numpy.nan
                                 data_ele = numpy.hstack((data_ele,ele2))
                                 print("ele2_nan",ele2_nan.shape)
                                 print("data_ele_old",data_ele_old.shape)
                                 data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
                         if tipo_case==1  or tipo_case==2: # BAJADA
                             data_ele_new       = data_ele_new[::-1] #  reversa
                             data_ele_old   = data_ele_old[::-1]# reversa
                             data_weather   = data_weather[::-1,:]# reversa
                             vec= numpy.where(data_ele_new<ang_max)
                             data_ele_new = data_ele_new[vec]
                             data_ele_old = data_ele_old[vec]
                             data_weather  = data_weather[vec[0]]
                             vec2= numpy.where(0<data_ele_new)
                             data_ele_new = data_ele_new[vec2]
                             data_ele_old = data_ele_old[vec2]
                             data_weather  = data_weather[vec2[0]]
                             self.start_data_ele = data_ele_new[0]
                             self.end_data_ele  = data_ele_new[-1]
                             n1= round(self.start_data_ele)- start
                             n2= end - round(self.end_data_ele)-1
                             print(self.start_data_ele)
                             print(self.end_data_ele)
                             if n1>0:
                                 ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
                                 ele1_nan= numpy.ones(n1)*numpy.nan
                                 data_ele = numpy.hstack((ele1,data_ele_new))
                                 data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
                             if n2>0:
                                 ele2= numpy.linspace(self.end_data_ele+1,end,n2)
                                 ele2_nan= numpy.ones(n2)*numpy.nan
                                 data_ele = numpy.hstack((data_ele,ele2))
                                 data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
                         # RADAR
                         # NOTA data_ele y data_weather es la variable que retorna
                         val_mean         = numpy.mean(data_weather[:,-1])
                         self.val_mean    = val_mean
                         data_weather     = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
                         print("eleold",data_ele_old)
                         print(self.data_ele_tmp[val_ch])
                         print(data_ele_old.shape[0])
                         print(self.data_ele_tmp[val_ch].shape[0])
                         if (data_ele_old.shape[0]==91 or self.data_ele_tmp[val_ch].shape[0]==91):
                             import sys
                             print("EXIT",self.ini)
                             sys.exit(1)
                         self.data_ele_tmp[val_ch]= data_ele_old
                     else:
                         #print("**********************************************")
                         #print("****************VARIABLE**********************")
                         #-------------------------CAMBIOS RHI---------------------------------
                         #---------------------------------------------------------------------
                         ##print("INPUT data_ele",data_ele)
                         flag=0
                         start_ele = self.res_ele[0]
                         #tipo_case = self.check_case(data_ele,ang_max,ang_min)
                         tipo_case = case_flag[-1]
                         #print("TIPO DE DATA",tipo_case)
                         #-----------new------------
                         data_ele ,data_ele_old = self.globalCheckPED(data_ele,tipo_case)
                         data_weather           = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
                         #-------------------------------NEW RHI ITERATIVO-------------------------
                         if tipo_case==0 : # SUBIDA
                             vec = numpy.where(data_ele<ang_max)
                             data_ele = data_ele[vec]
                             data_ele_old = data_ele_old[vec]
                             data_weather  = data_weather[vec[0]]
                             vec2 = numpy.where(0<data_ele)
                             data_ele= data_ele[vec2]
                             data_ele_old= data_ele_old[vec2]
                             ##print(data_ele_new)
                             data_weather= data_weather[vec2[0]]
                             new_i_ele  = int(round(data_ele[0]))
                             new_f_ele  = int(round(data_ele[-1]))
                             #print(new_i_ele)
                             #print(new_f_ele)
                             #print(data_ele,len(data_ele))
                             #print(data_ele_old,len(data_ele_old))
                             if new_i_ele< 2:
                                 self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
                                 self.res_weather[val_ch]  = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
                             self.data_ele_tmp[val_ch][new_i_ele:new_i_ele+len(data_ele)]=data_ele_old
                             self.res_ele[new_i_ele:new_i_ele+len(data_ele)]= data_ele
                             self.res_weather[val_ch][new_i_ele:new_i_ele+len(data_ele),:]= data_weather
                             data_ele                                    = self.res_ele
                             data_weather                                = self.res_weather[val_ch]
                         elif tipo_case==1 : #BAJADA
                             data_ele       = data_ele[::-1] #  reversa
                             data_ele_old   = data_ele_old[::-1]# reversa
                             data_weather   = data_weather[::-1,:]# reversa
                             vec= numpy.where(data_ele<ang_max)
                             data_ele = data_ele[vec]
                             data_ele_old = data_ele_old[vec]
                             data_weather  = data_weather[vec[0]]
                             vec2= numpy.where(0<data_ele)
                             data_ele = data_ele[vec2]
                             data_ele_old = data_ele_old[vec2]
                             data_weather  = data_weather[vec2[0]]
                             new_i_ele  = int(round(data_ele[0]))
                             new_f_ele  = int(round(data_ele[-1]))
                             #print(data_ele)
                             #print(ang_max)
                             #print(data_ele_old)
                             if new_i_ele <= 1:
                                 new_i_ele = 1
                             if round(data_ele[-1])>=ang_max-1:
                                 self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
                                 self.res_weather[val_ch]  = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
                             self.data_ele_tmp[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1]=data_ele_old
                             self.res_ele[new_i_ele-1:new_i_ele+len(data_ele)-1]= data_ele
                             self.res_weather[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1,:]= data_weather
                             data_ele     = self.res_ele
                             data_weather = self.res_weather[val_ch]
                         elif tipo_case==2:  #bajada
                             vec = numpy.where(data_ele<ang_max)
                             data_ele = data_ele[vec]
                             data_weather= data_weather[vec[0]]
                             len_vec = len(vec)
                             data_ele_new     = data_ele[::-1] #  reversa
                             data_weather =  data_weather[::-1,:]
                             new_i_ele  = int(data_ele_new[0])
                             new_f_ele  = int(data_ele_new[-1])
                             n1= new_i_ele- ang_min
                             n2= ang_max - new_f_ele-1
                             if n1>0:
                                 ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
                                 ele1_nan= numpy.ones(n1)*numpy.nan
                                 data_ele = numpy.hstack((ele1,data_ele_new))
                                 data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
                             if n2>0:
                                 ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
                                 ele2_nan= numpy.ones(n2)*numpy.nan
                                 data_ele = numpy.hstack((data_ele,ele2))
                                 data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
                             self.data_ele_tmp[val_ch] = data_ele_old
                             self.res_ele      = data_ele
                             self.res_weather[val_ch]  = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
                             data_ele                                    = self.res_ele
                             data_weather                                = self.res_weather[val_ch]
                         elif tipo_case==3:#subida
                             vec = numpy.where(0<data_ele)
                             data_ele= data_ele[vec]
                             data_ele_new = data_ele
                             data_ele_old= data_ele_old[vec]
                             data_weather= data_weather[vec[0]]
                             pos_ini = numpy.argmin(data_ele)
                             if pos_ini>0:
                                 len_vec= len(data_ele)
                                 vec3  = numpy.linspace(pos_ini,len_vec-1,len_vec-pos_ini).astype(int)
                                 #print(vec3)
                                 data_ele= data_ele[vec3]
                                 data_ele_new = data_ele
                                 data_ele_old= data_ele_old[vec3]
                                 data_weather= data_weather[vec3]
                             new_i_ele  = int(data_ele_new[0])
                             new_f_ele  = int(data_ele_new[-1])
                             n1= new_i_ele- ang_min
                             n2= ang_max - new_f_ele-1
                             if n1>0:
                                 ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
                                 ele1_nan= numpy.ones(n1)*numpy.nan
                                 data_ele = numpy.hstack((ele1,data_ele_new))
                                 data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
                             if n2>0:
                                 ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
                                 ele2_nan= numpy.ones(n2)*numpy.nan
                                 data_ele = numpy.hstack((data_ele,ele2))
                                 data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
                             self.data_ele_tmp[val_ch] = data_ele_old
                             self.res_ele      = data_ele
                             self.res_weather[val_ch]  = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
                             data_ele                                    = self.res_ele
                             data_weather                                = self.res_weather[val_ch]
                     #print("self.data_ele_tmp",self.data_ele_tmp)
                     return data_weather,data_ele
                 def plot(self):
                     thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
                     data         = self.data[-1]
                     r            = self.data.yrange
                     delta_height = r[1]-r[0]
                     r_mask       = numpy.where(r>=0)[0]
                     ##print("delta_height",delta_height)
                     #print("r_mask",r_mask,len(r_mask))
                     r            = numpy.arange(len(r_mask))*delta_height
                     self.y       = 2*r
                     res          = 1
                     ###print("data['weather'].shape[0]",data['weather'].shape[0])
                     ang_max = self.ang_max
                     ang_min = self.ang_min
                     var_ang      =ang_max -  ang_min
                     step         = (int(var_ang)/(res*data['weather'].shape[0]))
                     ###print("step",step)
                     #--------------------------------------------------------
                     ##print('weather',data['weather'].shape)
                     ##print('ele',data['ele'].shape)
                     ###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min)
                     ###self.res_azi                   = numpy.mean(data['azi'])
                     ###print("self.res_ele",self.res_ele)
                     plt.clf()
                     subplots = [121, 122]
                     try:
                         if self.data[-2]['ele'].max()<data['ele'].max():
                             self.ini=0
                     except:
                         pass
                     if self.ini==0:
                         self.data_ele_tmp = numpy.ones([self.nplots,int(var_ang)])*numpy.nan
                         self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan
                         print("SHAPE",self.data_ele_tmp.shape)
                     for i,ax in enumerate(self.axes):
                         self.res_weather[i], self.res_ele = self.const_ploteo(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min,case_flag=self.data['case_flag'])
                         self.res_azi                   = numpy.mean(data['azi'])
                         if ax.firsttime:
                             #plt.clf()
                             print("Frist Plot")
                             cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
                             #fig=self.figures[0]
                         else:
                             #plt.clf()
                             print("ELSE PLOT")
                             cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
                         caax = cgax.parasites[0]
                         paax = cgax.parasites[1]
                         cbar = plt.gcf().colorbar(pm, pad=0.075)
                         caax.set_xlabel('x_range [km]')
                         caax.set_ylabel('y_range [km]')
                         plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+"  Step   "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right')
                     print("***************************self.ini****************************",self.ini)
                     self.ini= self.ini+1
             class WeatherRHI_vRF_Plot(Plot):
                 CODE = 'weather'
                 plot_name = 'weather'
                 plot_type = 'rhistyle'
                 buffering = False
                 data_ele_tmp = None
                 def setup(self):
                     print("********************")
                     print("********************")
                     print("********************")
                     print("SETUP WEATHER PLOT")
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots= 1
                     self.ylabel= 'Range [Km]'
                     self.titles= ['Weather']
                     if self.channels is not None:
                         self.nplots = len(self.channels)
                         self.nrows = len(self.channels)
                     else:
                         self.nplots = self.data.shape(self.CODE)[0]
                         self.nrows = self.nplots
                         self.channels = list(range(self.nplots))
                     print("channels",self.channels)
                     print("que saldra", self.data.shape(self.CODE)[0])
                     self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)]
                     print("self.titles",self.titles)
                     self.colorbar=False
                     self.width   =8
                     self.height  =8
                     self.ini     =0
                     self.len_azi =0
                     self.buffer_ini  = None
                     self.buffer_ele   = None
                     self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
                     self.flag    =0
                     self.indicador= 0
                     self.last_data_ele = None
                     self.val_mean      = None
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     if hasattr(dataOut, 'dataPP_POWER'):
                         factor = 1
                     if hasattr(dataOut, 'nFFTPoints'):
                         factor = dataOut.normFactor
                     print("dataOut",dataOut.data_360.shape)
                     #
                     data['weather'] = 10*numpy.log10(dataOut.data_360/(factor))
                     #
                     #data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
                     data['azi']     = dataOut.data_azi
                     data['ele']     = dataOut.data_ele
                     data['case_flag']     = dataOut.case_flag
                     #print("UPDATE")
                     #print("data[weather]",data['weather'].shape)
                     #print("data[azi]",data['azi'])
                     return data, meta
                 def get2List(self,angulos):
                     list1=[]
                     list2=[]
                     #print(angulos)
                     #exit(1)
                     for i in reversed(range(len(angulos))):
                         if not i==0:#el caso de i=0 evalula el primero de la lista con el ultimo y no es relevante
                             diff_ = angulos[i]-angulos[i-1]
                             if abs(diff_) >1.5:
                                 list1.append(i-1)
                                 list2.append(diff_)
                     return list(reversed(list1)),list(reversed(list2))
                 def fixData90(self,list_,ang_):
                     if list_[0]==-1:
                         vec = numpy.where(ang_<ang_[0])
                         ang_[vec] = ang_[vec]+90
                         return ang_
                     return ang_
                 def fixData90HL(self,angulos):
                     vec = numpy.where(angulos>=90)
                     angulos[vec]=angulos[vec]-90
                     return angulos
                 def search_pos(self,pos,list_):
                     for i in range(len(list_)):
                         if pos == list_[i]:
                             return True,i
                     i=None
                     return False,i
                 def fixDataComp(self,ang_,list1_,list2_,tipo_case):
                     size  = len(ang_)
                     size2 = 0
                     for i in range(len(list2_)):
                         size2=size2+round(abs(list2_[i]))-1
                     new_size= size+size2
                     ang_new = numpy.zeros(new_size)
                     ang_new2 = numpy.zeros(new_size)
                     tmp = 0
                     c   = 0
                     for i in range(len(ang_)):
                         ang_new[tmp +c] = ang_[i]
                         ang_new2[tmp+c] = ang_[i]
                         condition , value = self.search_pos(i,list1_)
                         if condition:
                             pos = tmp + c + 1
                             for k in range(round(abs(list2_[value]))-1):
                                 if tipo_case==0 or tipo_case==3:#subida
                                     ang_new[pos+k]  = ang_new[pos+k-1]+1
                                     ang_new2[pos+k] = numpy.nan
                                 elif tipo_case==1 or tipo_case==2:#bajada
                                     ang_new[pos+k]  = ang_new[pos+k-1]-1
                                     ang_new2[pos+k] = numpy.nan
                             tmp = pos +k
                             c   = 0
                         c=c+1
                     return ang_new,ang_new2
                 def globalCheckPED(self,angulos,tipo_case):
                     l1,l2 = self.get2List(angulos)
                     print("l1",l1)
                     print("l2",l2)
                     if len(l1)>0:
                         #angulos2 = self.fixData90(list_=l1,ang_=angulos)
                         #l1,l2 = self.get2List(angulos2)
                         ang1_,ang2_ = self.fixDataComp(ang_=angulos,list1_=l1,list2_=l2,tipo_case=tipo_case)
                         #ang1_ = self.fixData90HL(ang1_)
                         #ang2_ = self.fixData90HL(ang2_)
                     else:
                         ang1_= angulos
                         ang2_= angulos
                     return ang1_,ang2_
                 def replaceNAN(self,data_weather,data_ele,val):
                     data= data_ele
                     data_T= data_weather
                     #print(data.shape[0])
                     #print(data_T.shape[0])
                     #exit(1)
                     if data.shape[0]> data_T.shape[0]:
                         data_N = numpy.ones( [data.shape[0],data_T.shape[1]])
                         c      = 0
                         for i in range(len(data)):
                             if numpy.isnan(data[i]):
                                 data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
                             else:
                                 data_N[i,:]=data_T[c,:]
                                 c=c+1
                         return data_N
                     else:
                         for i in range(len(data)):
                             if numpy.isnan(data[i]):
                                data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
                         return data_T
                 def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min,case_flag):
                     ang_max= ang_max
                     ang_min= ang_min
                     data_weather=data_weather
                     val_ch=val_ch
                     ##print("*********************DATA WEATHER**************************************")
                     ##print(data_weather)
                     '''
                     print("**********************************************")
                     print("**********************************************")
                     print("***************ini**************")
                     print("**********************************************")
                     print("**********************************************")
                     '''
                     #print("data_ele",data_ele)
                     #----------------------------------------------------------
                     #exit(1)
                     tipo_case = case_flag[-1]
                     print("tipo_case",tipo_case)
                     #--------------------- new -------------------------
                     data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case)
                     #-------------------------CAMBIOS RHI---------------------------------
                     vec = numpy.where(data_ele<ang_max)
                     data_ele = data_ele[vec]
                     data_weather= data_weather[vec[0]]
                     len_vec = len(vec)
                     data_ele_new     = data_ele[::-1] #  reversa
                     data_weather =  data_weather[::-1,:]
                     new_i_ele  = int(data_ele_new[0])
                     new_f_ele  = int(data_ele_new[-1])
                     n1= new_i_ele- ang_min
                     n2= ang_max - new_f_ele-1
                     if n1>0:
                         ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
                         ele1_nan= numpy.ones(n1)*numpy.nan
                         data_ele = numpy.hstack((ele1,data_ele_new))
                         data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
                     if n2>0:
                         ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
                         ele2_nan= numpy.ones(n2)*numpy.nan
                         data_ele = numpy.hstack((data_ele,ele2))
                         data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
                     print("ele shape",data_ele.shape)
                     print(data_ele)
                     #print("self.data_ele_tmp",self.data_ele_tmp)
                     val_mean         = numpy.mean(data_weather[:,-1])
                     self.val_mean    = val_mean
                     data_weather     = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
                     self.data_ele_tmp[val_ch]= data_ele_old
                     print("data_weather shape",data_weather.shape)
                     print(data_weather)
                     #exit(1)
                     return data_weather,data_ele
                 def plot(self):
                     thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
                     data         = self.data[-1]
                     r            = self.data.yrange
                     delta_height = r[1]-r[0]
                     r_mask       = numpy.where(r>=0)[0]
                     ##print("delta_height",delta_height)
                     #print("r_mask",r_mask,len(r_mask))
                     r            = numpy.arange(len(r_mask))*delta_height
                     self.y       = 2*r
                     res          = 1
                     ###print("data['weather'].shape[0]",data['weather'].shape[0])
                     ang_max = self.ang_max
                     ang_min = self.ang_min
                     var_ang      =ang_max -  ang_min
                     step         = (int(var_ang)/(res*data['weather'].shape[0]))
                     ###print("step",step)
                     #--------------------------------------------------------
                     ##print('weather',data['weather'].shape)
                     ##print('ele',data['ele'].shape)
                     ###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min)
                     ###self.res_azi                   = numpy.mean(data['azi'])
                     ###print("self.res_ele",self.res_ele)
                     plt.clf()
                     subplots = [121, 122]
                     if self.ini==0:
                         self.data_ele_tmp = numpy.ones([self.nplots,int(var_ang)])*numpy.nan
                         self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan
                         print("SHAPE",self.data_ele_tmp.shape)
                     for i,ax in enumerate(self.axes):
                         self.res_weather[i], self.res_ele = self.const_ploteo(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min,case_flag=self.data['case_flag'])
                         self.res_azi                   = numpy.mean(data['azi'])
                         print(self.res_ele)
                         #exit(1)
                         if ax.firsttime:
                             #plt.clf()
                             cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
                             #fig=self.figures[0]
                         else:
                             #plt.clf()
                             cgax, pm = wrl.vis.plot_rhi(self.res_weather[i],r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
                         caax = cgax.parasites[0]
                         paax = cgax.parasites[1]
                         cbar = plt.gcf().colorbar(pm, pad=0.075)
                         caax.set_xlabel('x_range [km]')
                         caax.set_ylabel('y_range [km]')
                         plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+"  Step   "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right')
                     print("***************************self.ini****************************",self.ini)
                     self.ini= self.ini+1
+            class WeatherRHI_vRF3_Plot(Plot):
+                CODE = 'weather'
+                plot_name = 'weather'
+                plot_type = 'rhistyle'
+                buffering = False
+                data_ele_tmp = None
+                def setup(self):
+                    print("********************")
+                    print("********************")
+                    print("********************")
+                    print("SETUP WEATHER PLOT")
+                    self.ncols = 1
+                    self.nrows = 1
+                    self.nplots= 1
+                    self.ylabel= 'Range [Km]'
+                    self.titles= ['Weather']
+                    if self.channels is not None:
+                        self.nplots = len(self.channels)
+                        self.nrows = len(self.channels)
+                    else:
+                        self.nplots = self.data.shape(self.CODE)[0]
+                        self.nrows = self.nplots
+                        self.channels = list(range(self.nplots))
+                    print("channels",self.channels)
+                    print("que saldra", self.data.shape(self.CODE)[0])
+                    self.titles = ['{} Channel {}'.format(self.CODE.upper(), x) for x in range(self.nrows)]
+                    print("self.titles",self.titles)
+                    self.colorbar=False
+                    self.width   =8
+                    self.height  =8
+                    self.ini     =0
+                    self.len_azi =0
+                    self.buffer_ini  = None
+                    self.buffer_ele   = None
+                    self.plots_adjust.update({'wspace': 0.4, 'hspace':0.4, 'left': 0.1, 'right': 0.9, 'bottom': 0.08})
+                    self.flag    =0
+                    self.indicador= 0
+                    self.last_data_ele = None
+                    self.val_mean      = None
+                def update(self, dataOut):
+                    data = {}
+                    meta = {}
+                    if hasattr(dataOut, 'dataPP_POWER'):
+                        factor = 1
+                    if hasattr(dataOut, 'nFFTPoints'):
+                        factor = dataOut.normFactor
+                    print("dataOut",dataOut.data_360.shape)
+                    #
+                    data['weather'] = 10*numpy.log10(dataOut.data_360/(factor))
+                    #
+                    #data['weather'] = 10*numpy.log10(dataOut.data_360[1]/(factor))
+                    data['azi']     = dataOut.data_azi
+                    data['ele']     = dataOut.data_ele
+                    #data['case_flag']     = dataOut.case_flag
+                    #print("UPDATE")
+                    #print("data[weather]",data['weather'].shape)
+                    #print("data[azi]",data['azi'])
+                    return data, meta
+                def get2List(self,angulos):
+                    list1=[]
+                    list2=[]
+                    for i in reversed(range(len(angulos))):
+                        if not i==0:#el caso de i=0 evalula el primero de la lista con el ultimo y no es relevante
+                            diff_ = angulos[i]-angulos[i-1]
+                            if abs(diff_) >1.5:
+                                list1.append(i-1)
+                                list2.append(diff_)
+                    return list(reversed(list1)),list(reversed(list2))
+                def fixData90(self,list_,ang_):
+                    if list_[0]==-1:
+                        vec = numpy.where(ang_<ang_[0])
+                        ang_[vec] = ang_[vec]+90
+                        return ang_
+                    return ang_
+                def fixData90HL(self,angulos):
+                    vec = numpy.where(angulos>=90)
+                    angulos[vec]=angulos[vec]-90
+                    return angulos
+                def search_pos(self,pos,list_):
+                    for i in range(len(list_)):
+                        if pos == list_[i]:
+                            return True,i
+                    i=None
+                    return False,i
+                def fixDataComp(self,ang_,list1_,list2_,tipo_case):
+                    size  = len(ang_)
+                    size2 = 0
+                    for i in range(len(list2_)):
+                        size2=size2+round(abs(list2_[i]))-1
+                    new_size= size+size2
+                    ang_new = numpy.zeros(new_size)
+                    ang_new2 = numpy.zeros(new_size)
+                    tmp = 0
+                    c   = 0
+                    for i in range(len(ang_)):
+                        ang_new[tmp +c] = ang_[i]
+                        ang_new2[tmp+c] = ang_[i]
+                        condition , value = self.search_pos(i,list1_)
+                        if condition:
+                            pos = tmp + c + 1
+                            for k in range(round(abs(list2_[value]))-1):
+                                if tipo_case==0 or tipo_case==3:#subida
+                                    ang_new[pos+k]  = ang_new[pos+k-1]+1
+                                    ang_new2[pos+k] = numpy.nan
+                                elif tipo_case==1 or tipo_case==2:#bajada
+                                    ang_new[pos+k]  = ang_new[pos+k-1]-1
+                                    ang_new2[pos+k] = numpy.nan
+                            tmp = pos +k
+                            c   = 0
+                        c=c+1
+                    return ang_new,ang_new2
+                def globalCheckPED(self,angulos,tipo_case):
+                    l1,l2 = self.get2List(angulos)
+                    ##print("l1",l1)
+                    ##print("l2",l2)
+                    if len(l1)>0:
+                        #angulos2 = self.fixData90(list_=l1,ang_=angulos)
+                        #l1,l2 = self.get2List(angulos2)
+                        ang1_,ang2_ = self.fixDataComp(ang_=angulos,list1_=l1,list2_=l2,tipo_case=tipo_case)
+                        #ang1_ = self.fixData90HL(ang1_)
+                        #ang2_ = self.fixData90HL(ang2_)
+                    else:
+                        ang1_= angulos
+                        ang2_= angulos
+                    return ang1_,ang2_
+                def replaceNAN(self,data_weather,data_ele,val):
+                    data= data_ele
+                    data_T= data_weather
+                    if data.shape[0]> data_T.shape[0]:
+                        print("IF")
+                        data_N = numpy.ones( [data.shape[0],data_T.shape[1]])
+                        c      = 0
+                        for i in range(len(data)):
+                            if numpy.isnan(data[i]):
+                                data_N[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
+                            else:
+                                data_N[i,:]=data_T[c,:]
+                                c=c+1
+                        return data_N
+                    else:
+                        print("else")
+                        for i in range(len(data)):
+                            if numpy.isnan(data[i]):
+                               data_T[i,:]=numpy.ones(data_T.shape[1])*numpy.nan
+                        return data_T
+                def check_case(self,data_ele,ang_max,ang_min):
+                    start  = data_ele[0]
+                    end    = data_ele[-1]
+                    number = (end-start)
+                    len_ang=len(data_ele)
+                    print("start",start)
+                    print("end",end)
+                    print("number",number)
+                    print("len_ang",len_ang)
+                    #exit(1)
+                    if start<end and (round(abs(number)+1)>=len_ang or (numpy.argmin(data_ele)==0)):#caso subida
+                        return 0
+                    #elif start>end and (round(abs(number)+1)>=len_ang or(numpy.argmax(data_ele)==0)):#caso bajada
+                    #    return 1
+                    elif round(abs(number)+1)>=len_ang and (start>end or(numpy.argmax(data_ele)==0)):#caso bajada
+                        return 1
+                    elif round(abs(number)+1)<len_ang and data_ele[-2]>data_ele[-1]:# caso BAJADA CAMBIO ANG MAX
+                        return 2
+                    elif round(abs(number)+1)<len_ang and data_ele[-2]<data_ele[-1] :# caso SUBIDA CAMBIO ANG MIN
+                        return 3
+                def const_ploteo(self,val_ch,data_weather,data_ele,step,res,ang_max,ang_min,case_flag):
+                    ang_max= ang_max
+                    ang_min= ang_min
+                    data_weather=data_weather
+                    val_ch=val_ch
+                    ##print("*********************DATA WEATHER**************************************")
+                    ##print(data_weather)
+                    if self.ini==0:
+                        #--------------------- new -------------------------
+                        data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,tipo_case)
+                        #-------------------------CAMBIOS RHI---------------------------------
+                        start= ang_min
+                        end  = ang_max
+                        n= (ang_max-ang_min)/res
+                        #------ new
+                        self.start_data_ele = data_ele_new[0]
+                        self.end_data_ele  = data_ele_new[-1]
+                        if tipo_case==0  or tipo_case==3: # SUBIDA
+                            n1= round(self.start_data_ele)- start
+                            n2= end - round(self.end_data_ele)
+                            print(self.start_data_ele)
+                            print(self.end_data_ele)
+                            if n1>0:
+                                ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
+                                ele1_nan= numpy.ones(n1)*numpy.nan
+                                data_ele = numpy.hstack((ele1,data_ele_new))
+                                print("ele1_nan",ele1_nan.shape)
+                                print("data_ele_old",data_ele_old.shape)
+                                data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
+                            if n2>0:
+                                ele2= numpy.linspace(self.end_data_ele+1,end,n2)
+                                ele2_nan= numpy.ones(n2)*numpy.nan
+                                data_ele = numpy.hstack((data_ele,ele2))
+                                print("ele2_nan",ele2_nan.shape)
+                                print("data_ele_old",data_ele_old.shape)
+                                data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
+                        if tipo_case==1  or tipo_case==2: # BAJADA
+                            data_ele_new       = data_ele_new[::-1] #  reversa
+                            data_ele_old   = data_ele_old[::-1]# reversa
+                            data_weather   = data_weather[::-1,:]# reversa
+                            vec= numpy.where(data_ele_new<ang_max)
+                            data_ele_new = data_ele_new[vec]
+                            data_ele_old = data_ele_old[vec]
+                            data_weather  = data_weather[vec[0]]
+                            vec2= numpy.where(0<data_ele_new)
+                            data_ele_new = data_ele_new[vec2]
+                            data_ele_old = data_ele_old[vec2]
+                            data_weather  = data_weather[vec2[0]]
+                            self.start_data_ele = data_ele_new[0]
+                            self.end_data_ele  = data_ele_new[-1]
+                            n1= round(self.start_data_ele)- start
+                            n2= end - round(self.end_data_ele)-1
+                            print(self.start_data_ele)
+                            print(self.end_data_ele)
+                            if n1>0:
+                                ele1= numpy.linspace(ang_min+1,self.start_data_ele-1,n1)
+                                ele1_nan= numpy.ones(n1)*numpy.nan
+                                data_ele = numpy.hstack((ele1,data_ele_new))
+                                data_ele_old = numpy.hstack((ele1_nan,data_ele_old))
+                            if n2>0:
+                                ele2= numpy.linspace(self.end_data_ele+1,end,n2)
+                                ele2_nan= numpy.ones(n2)*numpy.nan
+                                data_ele = numpy.hstack((data_ele,ele2))
+                                data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
+                        # RADAR
+                        # NOTA data_ele y data_weather es la variable que retorna
+                        val_mean         = numpy.mean(data_weather[:,-1])
+                        self.val_mean    = val_mean
+                        data_weather     = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
+                        print("eleold",data_ele_old)
+                        print(self.data_ele_tmp[val_ch])
+                        print(data_ele_old.shape[0])
+                        print(self.data_ele_tmp[val_ch].shape[0])
+                        if (data_ele_old.shape[0]==91 or self.data_ele_tmp[val_ch].shape[0]==91):
+                            import sys
+                            print("EXIT",self.ini)
+                            sys.exit(1)
+                        self.data_ele_tmp[val_ch]= data_ele_old
+                    else:
+                        #print("**********************************************")
+                        #print("****************VARIABLE**********************")
+                        #-------------------------CAMBIOS RHI---------------------------------
+                        #---------------------------------------------------------------------
+                        ##print("INPUT data_ele",data_ele)
+                        flag=0
+                        start_ele = self.res_ele[0]
+                        #tipo_case = self.check_case(data_ele,ang_max,ang_min)
+                        tipo_case = case_flag[-1]
+                        #print("TIPO DE DATA",tipo_case)
+                        #-----------new------------
+                        data_ele ,data_ele_old = self.globalCheckPED(data_ele,tipo_case)
+                        data_weather           = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
+                        #-------------------------------NEW RHI ITERATIVO-------------------------
+                        if tipo_case==0 : # SUBIDA
+                            vec = numpy.where(data_ele<ang_max)
+                            data_ele = data_ele[vec]
+                            data_ele_old = data_ele_old[vec]
+                            data_weather  = data_weather[vec[0]]
+                            vec2 = numpy.where(0<data_ele)
+                            data_ele= data_ele[vec2]
+                            data_ele_old= data_ele_old[vec2]
+                            ##print(data_ele_new)
+                            data_weather= data_weather[vec2[0]]
+                            new_i_ele  = int(round(data_ele[0]))
+                            new_f_ele  = int(round(data_ele[-1]))
+                            #print(new_i_ele)
+                            #print(new_f_ele)
+                            #print(data_ele,len(data_ele))
+                            #print(data_ele_old,len(data_ele_old))
+                            if new_i_ele< 2:
+                                self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
+                                self.res_weather[val_ch]  = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
+                            self.data_ele_tmp[val_ch][new_i_ele:new_i_ele+len(data_ele)]=data_ele_old
+                            self.res_ele[new_i_ele:new_i_ele+len(data_ele)]= data_ele
+                            self.res_weather[val_ch][new_i_ele:new_i_ele+len(data_ele),:]= data_weather
+                            data_ele                                    = self.res_ele
+                            data_weather                                = self.res_weather[val_ch]
+                        elif tipo_case==1 : #BAJADA
+                            data_ele       = data_ele[::-1] #  reversa
+                            data_ele_old   = data_ele_old[::-1]# reversa
+                            data_weather   = data_weather[::-1,:]# reversa
+                            vec= numpy.where(data_ele<ang_max)
+                            data_ele = data_ele[vec]
+                            data_ele_old = data_ele_old[vec]
+                            data_weather  = data_weather[vec[0]]
+                            vec2= numpy.where(0<data_ele)
+                            data_ele = data_ele[vec2]
+                            data_ele_old = data_ele_old[vec2]
+                            data_weather  = data_weather[vec2[0]]
+                            new_i_ele  = int(round(data_ele[0]))
+                            new_f_ele  = int(round(data_ele[-1]))
+                            #print(data_ele)
+                            #print(ang_max)
+                            #print(data_ele_old)
+                            if new_i_ele <= 1:
+                                new_i_ele = 1
+                            if round(data_ele[-1])>=ang_max-1:
+                                self.data_ele_tmp[val_ch] = numpy.ones(ang_max-ang_min)*numpy.nan
+                                self.res_weather[val_ch]  = self.replaceNAN(data_weather=self.res_weather[val_ch],data_ele=self.data_ele_tmp[val_ch],val=self.val_mean)
+                            self.data_ele_tmp[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1]=data_ele_old
+                            self.res_ele[new_i_ele-1:new_i_ele+len(data_ele)-1]= data_ele
+                            self.res_weather[val_ch][new_i_ele-1:new_i_ele+len(data_ele)-1,:]= data_weather
+                            data_ele     = self.res_ele
+                            data_weather = self.res_weather[val_ch]
+                        elif tipo_case==2:  #bajada
+                            vec = numpy.where(data_ele<ang_max)
+                            data_ele = data_ele[vec]
+                            data_weather= data_weather[vec[0]]
+                            len_vec = len(vec)
+                            data_ele_new     = data_ele[::-1] #  reversa
+                            data_weather =  data_weather[::-1,:]
+                            new_i_ele  = int(data_ele_new[0])
+                            new_f_ele  = int(data_ele_new[-1])
+                            n1= new_i_ele- ang_min
+                            n2= ang_max - new_f_ele-1
+                            if n1>0:
+                                ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
+                                ele1_nan= numpy.ones(n1)*numpy.nan
+                                data_ele = numpy.hstack((ele1,data_ele_new))
+                                data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
+                            if n2>0:
+                                ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
+                                ele2_nan= numpy.ones(n2)*numpy.nan
+                                data_ele = numpy.hstack((data_ele,ele2))
+                                data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
+                            self.data_ele_tmp[val_ch] = data_ele_old
+                            self.res_ele      = data_ele
+                            self.res_weather[val_ch]  = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
+                            data_ele                                    = self.res_ele
+                            data_weather                                = self.res_weather[val_ch]
+                        elif tipo_case==3:#subida
+                            vec = numpy.where(0<data_ele)
+                            data_ele= data_ele[vec]
+                            data_ele_new = data_ele
+                            data_ele_old= data_ele_old[vec]
+                            data_weather= data_weather[vec[0]]
+                            pos_ini = numpy.argmin(data_ele)
+                            if pos_ini>0:
+                                len_vec= len(data_ele)
+                                vec3  = numpy.linspace(pos_ini,len_vec-1,len_vec-pos_ini).astype(int)
+                                #print(vec3)
+                                data_ele= data_ele[vec3]
+                                data_ele_new = data_ele
+                                data_ele_old= data_ele_old[vec3]
+                                data_weather= data_weather[vec3]
+                            new_i_ele  = int(data_ele_new[0])
+                            new_f_ele  = int(data_ele_new[-1])
+                            n1= new_i_ele- ang_min
+                            n2= ang_max - new_f_ele-1
+                            if n1>0:
+                                ele1= numpy.linspace(ang_min+1,new_i_ele-1,n1)
+                                ele1_nan= numpy.ones(n1)*numpy.nan
+                                data_ele = numpy.hstack((ele1,data_ele_new))
+                                data_ele_old = numpy.hstack((ele1_nan,data_ele_new))
+                            if n2>0:
+                                ele2= numpy.linspace(new_f_ele+1,ang_max,n2)
+                                ele2_nan= numpy.ones(n2)*numpy.nan
+                                data_ele = numpy.hstack((data_ele,ele2))
+                                data_ele_old = numpy.hstack((data_ele_old,ele2_nan))
+                            self.data_ele_tmp[val_ch] = data_ele_old
+                            self.res_ele      = data_ele
+                            self.res_weather[val_ch]  = self.replaceNAN(data_weather=data_weather,data_ele=data_ele_old,val=self.val_mean)
+                            data_ele                                    = self.res_ele
+                            data_weather                                = self.res_weather[val_ch]
+                    #print("self.data_ele_tmp",self.data_ele_tmp)
+                    return data_weather,data_ele
+                def const_ploteo_vRF(self,val_ch,data_weather,data_ele,res,ang_max,ang_min):
+                    data_ele_new ,data_ele_old= self.globalCheckPED(data_ele,1)
+                    data_ele = data_ele_old.copy()
+                    diff_1 = ang_max - data_ele[0]
+                    angles_1_nan = numpy.linspace(ang_max,data_ele[0]+1,int(diff_1)-1)#*numpy.nan
+                    diff_2 = data_ele[-1]-ang_min
+                    angles_2_nan = numpy.linspace(data_ele[-1]-1,ang_min,int(diff_2)-1)#*numpy.nan
+                    angles_filled = numpy.concatenate((angles_1_nan,data_ele,angles_2_nan))
+                    print(angles_filled)
+                    data_1_nan = numpy.ones([angles_1_nan.shape[0],len(self.r_mask)])*numpy.nan
+                    data_2_nan = numpy.ones([angles_2_nan.shape[0],len(self.r_mask)])*numpy.nan
+                    data_filled = numpy.concatenate((data_1_nan,data_weather,data_2_nan),axis=0)
+                    #val_mean         = numpy.mean(data_weather[:,-1])
+                    #self.val_mean    = val_mean
+                    print(data_filled)
+                    data_filled     = self.replaceNAN(data_weather=data_filled,data_ele=angles_filled,val=numpy.nan)
+                    print(data_filled)
+                    print(data_filled.shape)
+                    print(angles_filled.shape)
+                    return data_filled,angles_filled
+                def plot(self):
+                    thisDatetime = datetime.datetime.utcfromtimestamp(self.data.times[-1]).strftime('%Y-%m-%d %H:%M:%S')
+                    data         = self.data[-1]
+                    r            = self.data.yrange
+                    delta_height = r[1]-r[0]
+                    r_mask       = numpy.where(r>=0)[0]
+                    self.r_mask =r_mask
+                    ##print("delta_height",delta_height)
+                    #print("r_mask",r_mask,len(r_mask))
+                    r            = numpy.arange(len(r_mask))*delta_height
+                    self.y       = 2*r
+                    res          = 1
+                    ###print("data['weather'].shape[0]",data['weather'].shape[0])
+                    ang_max = self.ang_max
+                    ang_min = self.ang_min
+                    var_ang      =ang_max -  ang_min
+                    step         = (int(var_ang)/(res*data['weather'].shape[0]))
+                    ###print("step",step)
+                    #--------------------------------------------------------
+                    ##print('weather',data['weather'].shape)
+                    ##print('ele',data['ele'].shape)
+                    ###self.res_weather, self.res_ele = self.const_ploteo(data_weather=data['weather'][:,r_mask],data_ele=data['ele'],step=step,res=res,ang_max=ang_max,ang_min=ang_min)
+                    ###self.res_azi                   = numpy.mean(data['azi'])
+                    ###print("self.res_ele",self.res_ele)
+                    plt.clf()
+                    subplots = [121, 122]
+                    #if self.ini==0:
+                        #self.res_weather= numpy.ones([self.nplots,int(var_ang),len(r_mask)])*numpy.nan
+                        #print("SHAPE",self.data_ele_tmp.shape)
+                    for i,ax in enumerate(self.axes):
+                        res_weather, self.res_ele = self.const_ploteo_vRF(val_ch=i, data_weather=data['weather'][i][:,r_mask],data_ele=data['ele'],res=res,ang_max=ang_max,ang_min=ang_min)
+                        self.res_azi                   = numpy.mean(data['azi'])
+                        if ax.firsttime:
+                            #plt.clf()
+                            print("Frist Plot")
+                            print(data['weather'][i][:,r_mask].shape)
+                            print(data['ele'].shape)
+                            cgax, pm = wrl.vis.plot_rhi(res_weather,r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
+                            #cgax, pm = wrl.vis.plot_rhi(data['weather'][i][:,r_mask],r=r,th=data['ele'],ax=subplots[i], proj='cg',vmin=20, vmax=80)
+                            gh = cgax.get_grid_helper()
+                            locs = numpy.linspace(ang_min,ang_max,var_ang+1)
+                            gh.grid_finder.grid_locator1 = FixedLocator(locs)
+                            gh.grid_finder.tick_formatter1 = DictFormatter(dict([(i, r"${0:.0f}^\circ$".format(i)) for i in locs]))
+                            #fig=self.figures[0]
+                        else:
+                            #plt.clf()
+                            print("ELSE PLOT")
+                            cgax, pm = wrl.vis.plot_rhi(res_weather,r=r,th=self.res_ele,ax=subplots[i], proj='cg',vmin=20, vmax=80)
+                            #cgax, pm = wrl.vis.plot_rhi(data['weather'][i][:,r_mask],r=r,th=data['ele'],ax=subplots[i], proj='cg',vmin=20, vmax=80)
+                            gh = cgax.get_grid_helper()
+                            locs = numpy.linspace(ang_min,ang_max,var_ang+1)
+                            gh.grid_finder.grid_locator1 = FixedLocator(locs)
+                            gh.grid_finder.tick_formatter1 = DictFormatter(dict([(i, r"${0:.0f}^\circ$".format(i)) for i in locs]))
+                        caax = cgax.parasites[0]
+                        paax = cgax.parasites[1]
+                        cbar = plt.gcf().colorbar(pm, pad=0.075)
+                        caax.set_xlabel('x_range [km]')
+                        caax.set_ylabel('y_range [km]')
+                        plt.text(1.0, 1.05, 'Elevacion '+str(thisDatetime)+"  Step   "+str(self.ini)+ " Azi: "+str(round(self.res_azi,2)), transform=caax.transAxes, va='bottom',ha='right')
+                    print("***************************self.ini****************************",self.ini)
+                    self.ini= self.ini+1

schainpy/model/proc/jroproc_parameters.py +211 0

             import numpy,os,h5py
             import math
             from scipy import optimize, interpolate, signal, stats, ndimage
             import scipy
             import re
             import datetime
             import copy
             import sys
             import importlib
             import itertools
             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
             from scipy import asarray as ar,exp
             from scipy.optimize import curve_fit
             from schainpy.utils import log
             import warnings
             from numpy import NaN
             from scipy.optimize.optimize import OptimizeWarning
             warnings.filterwarnings('ignore')
             from time import sleep
             import matplotlib.pyplot as plt
             SPEED_OF_LIGHT = 299792458
             '''solving pickling issue'''
             def _pickle_method(method):
                 func_name = method.__func__.__name__
                 obj = method.__self__
                 cls = method.__self__.__class__
                 return _unpickle_method, (func_name, obj, cls)
             def _unpickle_method(func_name, obj, cls):
                 for cls in cls.mro():
                     try:
                         func = cls.__dict__[func_name]
                     except KeyError:
                         pass
                     else:
                         break
                 return func.__get__(obj, cls)
             def isNumber(str):
                 try:
                     float(str)
                     return True
                 except:
                     return False
             class ParametersProc(ProcessingUnit):
                 METHODS = {}
                 nSeconds = None
                 def __init__(self):
                     ProcessingUnit.__init__(self)
             #         self.objectDict = {}
                     self.buffer = None
                     self.firstdatatime = None
                     self.profIndex = 0
                     self.dataOut = Parameters()
                     self.setupReq = False #Agregar a todas las unidades de proc
                 def __updateObjFromInput(self):
                     self.dataOut.inputUnit = self.dataIn.type
                     self.dataOut.timeZone = self.dataIn.timeZone
                     self.dataOut.dstFlag = self.dataIn.dstFlag
                     self.dataOut.errorCount = self.dataIn.errorCount
                     self.dataOut.useLocalTime = self.dataIn.useLocalTime
                     self.dataOut.radarControllerHeaderObj = self.dataIn.radarControllerHeaderObj.copy()
                     self.dataOut.systemHeaderObj = self.dataIn.systemHeaderObj.copy()
                     self.dataOut.channelList = self.dataIn.channelList
                     self.dataOut.heightList = self.dataIn.heightList
                     self.dataOut.dtype = numpy.dtype([('real','<f4'),('imag','<f4')])
             #         self.dataOut.nHeights = self.dataIn.nHeights
             #         self.dataOut.nChannels = self.dataIn.nChannels
                     # self.dataOut.nBaud = self.dataIn.nBaud
                     # self.dataOut.nCode = self.dataIn.nCode
                     # self.dataOut.code = self.dataIn.code
             #        self.dataOut.nProfiles = self.dataOut.nFFTPoints
                     self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
             #         self.dataOut.utctime = self.firstdatatime
                     self.dataOut.utctime = self.dataIn.utctime
                     self.dataOut.flagDecodeData = self.dataIn.flagDecodeData #asumo q la data esta decodificada
                     self.dataOut.flagDeflipData = self.dataIn.flagDeflipData #asumo q la data esta sin flip
                     self.dataOut.nCohInt = self.dataIn.nCohInt
             #        self.dataOut.nIncohInt = 1
             #        self.dataOut.ippSeconds = self.dataIn.ippSeconds
             #        self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
                     self.dataOut.timeInterval1 = self.dataIn.timeInterval
                     self.dataOut.heightList = self.dataIn.heightList
                     self.dataOut.frequency = self.dataIn.frequency
                     # self.dataOut.noise = self.dataIn.noise
                 def run(self):
                     #print("HOLA MUNDO SOY YO")
                     #----------------------    Voltage Data    ---------------------------
                     if self.dataIn.type == "Voltage":
                         self.__updateObjFromInput()
                         self.dataOut.data_pre = self.dataIn.data.copy()
                         self.dataOut.flagNoData = False
                         self.dataOut.utctimeInit = self.dataIn.utctime
                         self.dataOut.paramInterval = self.dataIn.nProfiles*self.dataIn.nCohInt*self.dataIn.ippSeconds
                         if hasattr(self.dataIn, 'flagDataAsBlock'):
                             self.dataOut.flagDataAsBlock = self.dataIn.flagDataAsBlock
                         if hasattr(self.dataIn, 'profileIndex'):
                             self.dataOut.profileIndex = self.dataIn.profileIndex
                         if hasattr(self.dataIn, 'dataPP_POW'):
                             self.dataOut.dataPP_POW = self.dataIn.dataPP_POW
                         if hasattr(self.dataIn, 'dataPP_POWER'):
                             self.dataOut.dataPP_POWER = self.dataIn.dataPP_POWER
                         if hasattr(self.dataIn, 'dataPP_DOP'):
                             self.dataOut.dataPP_DOP = self.dataIn.dataPP_DOP
                         if hasattr(self.dataIn, 'dataPP_SNR'):
                             self.dataOut.dataPP_SNR = self.dataIn.dataPP_SNR
                         if hasattr(self.dataIn, 'dataPP_WIDTH'):
                             self.dataOut.dataPP_WIDTH = self.dataIn.dataPP_WIDTH
                         return
                     #----------------------    Spectra Data    ---------------------------
                     if self.dataIn.type == "Spectra":
                         #print("que paso en spectra")
                         self.dataOut.data_pre = [self.dataIn.data_spc, self.dataIn.data_cspc]
                         self.dataOut.data_spc = self.dataIn.data_spc
                         self.dataOut.data_cspc = self.dataIn.data_cspc
                         self.dataOut.nProfiles = self.dataIn.nProfiles
                         self.dataOut.nIncohInt = self.dataIn.nIncohInt
                         self.dataOut.nFFTPoints = self.dataIn.nFFTPoints
                         self.dataOut.ippFactor = self.dataIn.ippFactor
                         self.dataOut.abscissaList = self.dataIn.getVelRange(1)
                         self.dataOut.spc_noise = self.dataIn.getNoise()
                         self.dataOut.spc_range = (self.dataIn.getFreqRange(1) , self.dataIn.getAcfRange(1) , self.dataIn.getVelRange(1))
                         # self.dataOut.normFactor = self.dataIn.normFactor
                         self.dataOut.pairsList = self.dataIn.pairsList
                         self.dataOut.groupList = self.dataIn.pairsList
                         self.dataOut.flagNoData = False
                         if hasattr(self.dataIn, 'flagDataAsBlock'):
                             self.dataOut.flagDataAsBlock = self.dataIn.flagDataAsBlock
                         if hasattr(self.dataIn, 'ChanDist'): #Distances of receiver channels
                             self.dataOut.ChanDist = self.dataIn.ChanDist
                         else: self.dataOut.ChanDist = None
                         #if hasattr(self.dataIn, 'VelRange'): #Velocities range
                         #    self.dataOut.VelRange = self.dataIn.VelRange
                         #else: self.dataOut.VelRange = None
                         if hasattr(self.dataIn, 'RadarConst'): #Radar Constant
                             self.dataOut.RadarConst = self.dataIn.RadarConst
                         if hasattr(self.dataIn, 'NPW'): #NPW
                             self.dataOut.NPW = self.dataIn.NPW
                         if hasattr(self.dataIn, 'COFA'): #COFA
                             self.dataOut.COFA = self.dataIn.COFA
                     #----------------------    Correlation Data    ---------------------------
                     if self.dataIn.type == "Correlation":
                         acf_ind, ccf_ind, acf_pairs, ccf_pairs, data_acf, data_ccf = self.dataIn.splitFunctions()
                         self.dataOut.data_pre = (self.dataIn.data_cf[acf_ind,:], self.dataIn.data_cf[ccf_ind,:,:])
                         self.dataOut.normFactor = (self.dataIn.normFactor[acf_ind,:], self.dataIn.normFactor[ccf_ind,:])
                         self.dataOut.groupList = (acf_pairs, ccf_pairs)
                         self.dataOut.abscissaList = self.dataIn.lagRange
                         self.dataOut.noise = self.dataIn.noise
                         self.dataOut.data_snr = self.dataIn.SNR
                         self.dataOut.flagNoData = False
                         self.dataOut.nAvg = self.dataIn.nAvg
                     #----------------------    Parameters Data    ---------------------------
                     if self.dataIn.type == "Parameters":
                         self.dataOut.copy(self.dataIn)
                         self.dataOut.flagNoData = False
                         #print("yo si entre")
                         return True
                     self.__updateObjFromInput()
                     #print("yo si entre2")
                     self.dataOut.utctimeInit = self.dataIn.utctime
                     self.dataOut.paramInterval = self.dataIn.timeInterval
                     #print("soy spectra ",self.dataOut.utctimeInit)
                     return
             def target(tups):
                 obj, args = tups
                 return obj.FitGau(args)
             class RemoveWideGC(Operation):
                 ''' This class remove the wide clutter and replace it with a simple interpolation points
                     This mainly applies to CLAIRE radar
                     ClutterWidth :    Width to look for the clutter peak
                     Input:
                     self.dataOut.data_pre :        SPC and CSPC
                     self.dataOut.spc_range :       To select wind and rainfall velocities
                     Affected:
                     self.dataOut.data_pre :        It is used for the new SPC and CSPC ranges of wind
                     Written by D. Scipión 25.02.2021
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self.i = 0
                     self.ich = 0
                     self.ir = 0
                 def run(self, dataOut, ClutterWidth=2.5):
                     # print ('Entering RemoveWideGC ... ')
                     self.spc = dataOut.data_pre[0].copy()
                     self.spc_out = dataOut.data_pre[0].copy()
                     self.Num_Chn = self.spc.shape[0]
                     self.Num_Hei = self.spc.shape[2]
                     VelRange = dataOut.spc_range[2][:-1]
                     dv = VelRange[1]-VelRange[0]
                     # Find the velocities that corresponds to zero
                     gc_values = numpy.squeeze(numpy.where(numpy.abs(VelRange) <= ClutterWidth))
                     # Removing novalid data from the spectra
                     for ich in range(self.Num_Chn) :
                         for ir in range(self.Num_Hei) :
                             # Estimate the noise at each range
                             HSn = hildebrand_sekhon(self.spc[ich,:,ir],dataOut.nIncohInt)
                             # Removing the noise floor at each range
                             novalid = numpy.where(self.spc[ich,:,ir] < HSn)
                             self.spc[ich,novalid,ir] = HSn
                             junk = numpy.append(numpy.insert(numpy.squeeze(self.spc[ich,gc_values,ir]),0,HSn),HSn)
                             j1index = numpy.squeeze(numpy.where(numpy.diff(junk)>0))
                             j2index = numpy.squeeze(numpy.where(numpy.diff(junk)<0))
                             if ((numpy.size(j1index)<=1) | (numpy.size(j2index)<=1)) :
                                 continue
                             junk3 = numpy.squeeze(numpy.diff(j1index))
                             junk4 = numpy.squeeze(numpy.diff(j2index))
                             valleyindex = j2index[numpy.where(junk4>1)]
                             peakindex = j1index[numpy.where(junk3>1)]
                             isvalid = numpy.squeeze(numpy.where(numpy.abs(VelRange[gc_values[peakindex]]) <= 2.5*dv))
                             if numpy.size(isvalid) == 0 :
                                 continue
                             if numpy.size(isvalid) >1 :
                                 vindex = numpy.argmax(self.spc[ich,gc_values[peakindex[isvalid]],ir])
                                 isvalid = isvalid[vindex]
                             # clutter peak
                             gcpeak = peakindex[isvalid]
                             vl = numpy.where(valleyindex < gcpeak)
                             if numpy.size(vl) == 0:
                                 continue
                             gcvl = valleyindex[vl[0][-1]]
                             vr = numpy.where(valleyindex > gcpeak)
                             if numpy.size(vr) == 0:
                                 continue
                             gcvr = valleyindex[vr[0][0]]
                             # Removing the clutter
                             interpindex = numpy.array([gc_values[gcvl], gc_values[gcvr]])
                             gcindex = gc_values[gcvl+1:gcvr-1]
                             self.spc_out[ich,gcindex,ir] = numpy.interp(VelRange[gcindex],VelRange[interpindex],self.spc[ich,interpindex,ir])
                     dataOut.data_pre[0] = self.spc_out
                     #print ('Leaving RemoveWideGC ... ')
                     return dataOut
             class SpectralFilters(Operation):
                 ''' This class allows to replace the novalid values with noise for each channel
                     This applies to CLAIRE RADAR
                     PositiveLimit :    RightLimit of novalid data
                     NegativeLimit :    LeftLimit of novalid data
                     Input:
                     self.dataOut.data_pre :        SPC and CSPC
                     self.dataOut.spc_range :       To select wind and rainfall velocities
                     Affected:
                     self.dataOut.data_pre :        It is used for the new SPC and CSPC ranges of wind
                     Written by D. Scipión 29.01.2021
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self.i = 0
                 def run(self, dataOut, ):
                     self.spc = dataOut.data_pre[0].copy()
                     self.Num_Chn = self.spc.shape[0]
                     VelRange = dataOut.spc_range[2]
                     # novalid corresponds to data within the Negative and PositiveLimit
                     # Removing novalid data from the spectra
                     for i in range(self.Num_Chn):
                         self.spc[i,novalid,:] = dataOut.noise[i]
                     dataOut.data_pre[0] = self.spc
                     return dataOut
             class GaussianFit(Operation):
                 '''
                     Function that fit of one and two generalized gaussians (gg) based
                     on the PSD shape across an "power band" identified from a cumsum of
                     the measured spectrum - noise.
                     Input:
                         self.dataOut.data_pre    :    SelfSpectra
                     Output:
                         self.dataOut.SPCparam :    SPC_ch1, SPC_ch2
                 '''
                 def __init__(self):
                     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
                     input: spc
                     output:
                         noise, amplitude0,shift0,width0,p0,Amplitude1,shift1,width1,p1
                     """
                     print ('Entering ',method,' double Gaussian fit')
                     self.spc = dataOut.data_pre[0].copy()
                     self.Num_Hei = self.spc.shape[2]
                     self.Num_Bin = self.spc.shape[1]
                     self.Num_Chn = self.spc.shape[0]
                     start_time = time.time()
                     pool = Pool(processes=self.Num_Chn)
                     args = [(dataOut.spc_range[2], ich, dataOut.spc_noise[ich], dataOut.nIncohInt, SNRdBlimit) for ich in range(self.Num_Chn)]
                     objs = [self for __ in range(self.Num_Chn)]
                     attrs = list(zip(objs, args))
                     DGauFitParam = pool.map(target, attrs)
                     # Parameters:
                     # 0. Noise, 1. Amplitude, 2. Shift, 3. Width 4. Power
                     dataOut.DGauFitParams = numpy.asarray(DGauFitParam)
                     # Double Gaussian Curves
                     gau0 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei])
                     gau0[:] = numpy.NaN
                     gau1 = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei])
                     gau1[:] = numpy.NaN
                     x_mtr = numpy.transpose(numpy.tile(dataOut.getVelRange(1)[:-1], (self.Num_Hei,1)))
                     for iCh in range(self.Num_Chn):
                         N0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,0]] * self.Num_Bin))
                         N1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][0,:,1]] * self.Num_Bin))
                         A0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,0]] * self.Num_Bin))
                         A1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][1,:,1]] * self.Num_Bin))
                         v0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,0]] * self.Num_Bin))
                         v1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][2,:,1]] * self.Num_Bin))
                         s0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,0]] * self.Num_Bin))
                         s1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][3,:,1]] * self.Num_Bin))
                         if method == 'genealized':
                             p0 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,0]] * self.Num_Bin))
                             p1 = numpy.transpose(numpy.transpose([dataOut.DGauFitParams[iCh][4,:,1]] * self.Num_Bin))
                         elif method == 'squared':
                             p0 = 2.
                             p1 = 2.
                         gau0[iCh] = A0*numpy.exp(-0.5*numpy.abs((x_mtr-v0)/s0)**p0)+N0
                         gau1[iCh] = A1*numpy.exp(-0.5*numpy.abs((x_mtr-v1)/s1)**p1)+N1
                     dataOut.GaussFit0 = gau0
                     dataOut.GaussFit1 = gau1
                     print('Leaving ',method ,' double Gaussian fit')
                     return dataOut
                 def FitGau(self, X):
                     # print('Entering FitGau')
                     # Assigning the variables
                     Vrange, ch, wnoise, num_intg, SNRlimit = X
                     # Noise Limits
                     noisebl = wnoise * 0.9
                     noisebh = wnoise * 1.1
                     # Radar Velocity
                     Va = max(Vrange)
                     deltav = Vrange[1] - Vrange[0]
                     x = numpy.arange(self.Num_Bin)
                     # print ('stop 0')
                     # 5 parameters, 2 Gaussians
                     DGauFitParam = numpy.zeros([5, self.Num_Hei,2])
                     DGauFitParam[:] = numpy.NaN
                     # SPCparam = []
                     # SPC_ch1 = numpy.zeros([self.Num_Bin,self.Num_Hei])
                     # SPC_ch2 = numpy.zeros([self.Num_Bin,self.Num_Hei])
                     # SPC_ch1[:] = 0 #numpy.NaN
                     # SPC_ch2[:] = 0 #numpy.NaN
                     # print ('stop 1')
                     for ht in range(self.Num_Hei):
                         # print (ht)
                         # print ('stop 2')
                         # Spectra at each range
                         spc =  numpy.asarray(self.spc)[ch,:,ht]
                         snr = ( spc.mean() - wnoise ) / wnoise
                         snrdB = 10.*numpy.log10(snr)
                         #print ('stop 3')
                         if snrdB < SNRlimit :
                             # snr = numpy.NaN
                             # SPC_ch1[:,ht] = 0#numpy.NaN
                             # SPC_ch1[:,ht] = 0#numpy.NaN
                             # SPCparam = (SPC_ch1,SPC_ch2)
                             # print ('SNR less than SNRth')
                             continue
                         # wnoise = hildebrand_sekhon(spc,num_intg)
                         # print ('stop 2.01')
                         #############################################
                         # normalizing spc and noise
                         # This part differs from gg1
                         # spc_norm_max = max(spc) #commented by D. Scipión 19.03.2021
                         #spc = spc / spc_norm_max
                         # pnoise = pnoise #/ spc_norm_max #commented by D. Scipión 19.03.2021
                         #############################################
                         # print ('stop 2.1')
                         fatspectra=1.0
                         # noise per channel.... we might want to use the noise at each range
                         # wnoise = noise_ #/ spc_norm_max #commented by D. Scipión 19.03.2021
                             #wnoise,stdv,i_max,index =enoise(spc,num_intg) #noise estimate using Hildebrand Sekhon, only wnoise is used
                             #if wnoise>1.1*pnoise: # to be tested later
                             #    wnoise=pnoise
                         # noisebl = wnoise*0.9
                         # noisebh = wnoise*1.1
                         spc = spc - wnoise # signal
                         # print ('stop 2.2')
                         minx = numpy.argmin(spc)
                         #spcs=spc.copy()
                         spcs = numpy.roll(spc,-minx)
                         cum = numpy.cumsum(spcs)
                         # tot_noise = wnoise * self.Num_Bin  #64;
                         # print ('stop 2.3')
                         # snr = sum(spcs) / tot_noise
                         # snrdB = 10.*numpy.log10(snr)
                         #print ('stop 3')
                         # if snrdB < SNRlimit :
                             # snr = numpy.NaN
                             # SPC_ch1[:,ht] = 0#numpy.NaN
                             # SPC_ch1[:,ht] = 0#numpy.NaN
                             # SPCparam = (SPC_ch1,SPC_ch2)
                             # print ('SNR less than SNRth')
                             # continue
                         #if snrdB<-18 or numpy.isnan(snrdB) or num_intg<4:
                         #    return [None,]*4,[None,]*4,None,snrdB,None,None,[None,]*5,[None,]*9,None
                         # print ('stop 4')
                         cummax = max(cum)
                         epsi = 0.08 * fatspectra # cumsum to narrow down the energy region
                         cumlo = cummax * epsi
                         cumhi = cummax * (1-epsi)
                         powerindex = numpy.array(numpy.where(numpy.logical_and(cum>cumlo, cum<cumhi))[0])
                         # print ('stop 5')
                         if len(powerindex) < 1:# case for powerindex 0
                             # print ('powerindex < 1')
                             continue
                         powerlo = powerindex[0]
                         powerhi = powerindex[-1]
                         powerwidth = powerhi-powerlo
                         if powerwidth <= 1:
                             # print('powerwidth <= 1')
                             continue
                         # print ('stop 6')
                         firstpeak = powerlo + powerwidth/10.# first gaussian energy location
                         secondpeak = powerhi - powerwidth/10. #second gaussian energy location
                         midpeak = (firstpeak + secondpeak)/2.
                         firstamp = spcs[int(firstpeak)]
                         secondamp = spcs[int(secondpeak)]
                         midamp = spcs[int(midpeak)]
                         y_data = spc + wnoise
                         '''    single Gaussian    '''
                         shift0 = numpy.mod(midpeak+minx, self.Num_Bin )
                         width0 = powerwidth/4.#Initialization entire power of spectrum divided by 4
                         power0 = 2.
                         amplitude0 = midamp
                         state0 = [shift0,width0,amplitude0,power0,wnoise]
                         bnds = ((0,self.Num_Bin-1),(1,powerwidth),(0,None),(0.5,3.),(noisebl,noisebh))
                         lsq1 = fmin_l_bfgs_b(self.misfit1, state0, args=(y_data,x,num_intg), bounds=bnds, approx_grad=True)
                         # print ('stop 7.1')
                         # print (bnds)
                         chiSq1=lsq1[1]
                         # print ('stop 8')
                         if fatspectra<1.0 and powerwidth<4:
                                 choice=0
                                 Amplitude0=lsq1[0][2]
                                 shift0=lsq1[0][0]
                                 width0=lsq1[0][1]
                                 p0=lsq1[0][3]
                                 Amplitude1=0.
                                 shift1=0.
                                 width1=0.
                                 p1=0.
                                 noise=lsq1[0][4]
                                 #return (numpy.array([shift0,width0,Amplitude0,p0]),
                                 #        numpy.array([shift1,width1,Amplitude1,p1]),noise,snrdB,chiSq1,6.,sigmas1,[None,]*9,choice)
                         # print ('stop 9')
                         '''    two Gaussians    '''
                         #shift0=numpy.mod(firstpeak+minx,64); shift1=numpy.mod(secondpeak+minx,64)
                         shift0 = numpy.mod(firstpeak+minx, self.Num_Bin )
                         shift1 = numpy.mod(secondpeak+minx, self.Num_Bin )
                         width0 = powerwidth/6.
                         width1 = width0
                         power0 = 2.
                         power1 = power0
                         amplitude0 = firstamp
                         amplitude1 = secondamp
                         state0 = [shift0,width0,amplitude0,power0,shift1,width1,amplitude1,power1,wnoise]
                         #bnds=((0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(0,63),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
                         bnds=((0,self.Num_Bin-1),(1,powerwidth/2.),(0,None),(0.5,3.),(0,self.Num_Bin-1),(1,powerwidth/2.),(0,None),(0.5,3.),(noisebl,noisebh))
                         #bnds=(( 0,(self.Num_Bin-1) ),(1,powerwidth/2.),(0,None),(0.5,3.),( 0,(self.Num_Bin-1)),(1,powerwidth/2.),(0,None),(0.5,3.),(0.1,0.5))
                         # print ('stop 10')
                         lsq2 = fmin_l_bfgs_b( self.misfit2 , state0 , args=(y_data,x,num_intg) , bounds=bnds , approx_grad=True )
                         # print ('stop 11')
                         chiSq2 = lsq2[1]
                         # print ('stop 12')
                         oneG = (chiSq1<5 and chiSq1/chiSq2<2.0) and (abs(lsq2[0][0]-lsq2[0][4])<(lsq2[0][1]+lsq2[0][5])/3. or abs(lsq2[0][0]-lsq2[0][4])<10)
                         # print ('stop 13')
                         if snrdB>-12: # when SNR is strong pick the peak with least shift (LOS velocity) error
                             if oneG:
                                 choice = 0
                             else:
                                 w1 = lsq2[0][1]; w2 = lsq2[0][5]
                                 a1 = lsq2[0][2]; a2 = lsq2[0][6]
                                 p1 = lsq2[0][3]; p2 = lsq2[0][7]
                                 s1 = (2**(1+1./p1))*scipy.special.gamma(1./p1)/p1
                                 s2 = (2**(1+1./p2))*scipy.special.gamma(1./p2)/p2
                                 gp1 = a1*w1*s1; gp2 = a2*w2*s2 # power content of each ggaussian with proper p scaling
                                 if gp1>gp2:
                                     if a1>0.7*a2:
                                         choice = 1
                                     else:
                                         choice = 2
                                 elif gp2>gp1:
                                     if a2>0.7*a1:
                                         choice = 2
                                     else:
                                         choice = 1
                                 else:
                                     choice = numpy.argmax([a1,a2])+1
                                     #else:
                                     #choice=argmin([std2a,std2b])+1
                         else: # with low SNR go to the most energetic peak
                             choice = numpy.argmax([lsq1[0][2]*lsq1[0][1],lsq2[0][2]*lsq2[0][1],lsq2[0][6]*lsq2[0][5]])
                         # print ('stop 14')
                         shift0 = lsq2[0][0]
                         vel0 = Vrange[0] + shift0 * deltav
                         shift1 = lsq2[0][4]
                         # vel1=Vrange[0] + shift1 * deltav
                         # max_vel = 1.0
                         # Va = max(Vrange)
                         # deltav = Vrange[1]-Vrange[0]
                         # print ('stop 15')
                         #first peak will be 0, second peak will be 1
                         # if vel0 > -1.0 and vel0 < max_vel : #first peak is in the correct range # Commented by D.Scipión 19.03.2021
                         if vel0 > -Va and vel0 < Va : #first peak is in the correct range
                             shift0 = lsq2[0][0]
                             width0 = lsq2[0][1]
                             Amplitude0 = lsq2[0][2]
                             p0 = lsq2[0][3]
                             shift1 = lsq2[0][4]
                             width1 = lsq2[0][5]
                             Amplitude1 = lsq2[0][6]
                             p1 = lsq2[0][7]
                             noise = lsq2[0][8]
                         else:
                             shift1 = lsq2[0][0]
                             width1 = lsq2[0][1]
                             Amplitude1 = lsq2[0][2]
                             p1 = lsq2[0][3]
                             shift0 = lsq2[0][4]
                             width0 = lsq2[0][5]
                             Amplitude0 = lsq2[0][6]
                             p0 = lsq2[0][7]
                             noise = lsq2[0][8]
                         if Amplitude0<0.05: # in case the peak is noise
                             shift0,width0,Amplitude0,p0 = 4*[numpy.NaN]
                         if Amplitude1<0.05:
                             shift1,width1,Amplitude1,p1 = 4*[numpy.NaN]
                         # print ('stop 16 ')
                         # SPC_ch1[:,ht] = noise + Amplitude0*numpy.exp(-0.5*(abs(x-shift0)/width0)**p0)
                         # SPC_ch2[:,ht] = noise + Amplitude1*numpy.exp(-0.5*(abs(x-shift1)/width1)**p1)
                         # SPCparam = (SPC_ch1,SPC_ch2)
                         DGauFitParam[0,ht,0] = noise
                         DGauFitParam[0,ht,1] = noise
                         DGauFitParam[1,ht,0] = Amplitude0
                         DGauFitParam[1,ht,1] = Amplitude1
                         DGauFitParam[2,ht,0] = Vrange[0] + shift0 * deltav
                         DGauFitParam[2,ht,1] = Vrange[0] + shift1 * deltav
                         DGauFitParam[3,ht,0] = width0 * deltav
                         DGauFitParam[3,ht,1] = width1 * deltav
                         DGauFitParam[4,ht,0] = p0
                         DGauFitParam[4,ht,1] = p1
                     # print (DGauFitParam.shape)
                     # print ('Leaving FitGau')
                     return DGauFitParam
                     # return SPCparam
                     # return GauSPC
                 def y_model1(self,x,state):
                     shift0, width0, amplitude0, power0, noise = state
                     model0 = amplitude0*numpy.exp(-0.5*abs((x - shift0)/width0)**power0)
                     model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0)
                     model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0)
                     return model0 + model0u + model0d + noise
                 def y_model2(self,x,state): #Equation for two generalized Gaussians with Nyquist
                     shift0, width0, amplitude0, power0, shift1, width1, amplitude1, power1, noise = state
                     model0 = amplitude0*numpy.exp(-0.5*abs((x-shift0)/width0)**power0)
                     model0u = amplitude0*numpy.exp(-0.5*abs((x - shift0 - self.Num_Bin)/width0)**power0)
                     model0d = amplitude0*numpy.exp(-0.5*abs((x - shift0 + self.Num_Bin)/width0)**power0)
                     model1 = amplitude1*numpy.exp(-0.5*abs((x - shift1)/width1)**power1)
                     model1u = amplitude1*numpy.exp(-0.5*abs((x - shift1 - self.Num_Bin)/width1)**power1)
                     model1d = amplitude1*numpy.exp(-0.5*abs((x - shift1 + self.Num_Bin)/width1)**power1)
                     return model0 + model0u + model0d + model1 + model1u + model1d + noise
                 def misfit1(self,state,y_data,x,num_intg): # This function compares how close real data is with the model data, the close it is, the better it is.
                     return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model1(x,state)))**2)#/(64-5.) # /(64-5.) can be commented
                 def misfit2(self,state,y_data,x,num_intg):
                     return num_intg*sum((numpy.log(y_data)-numpy.log(self.y_model2(x,state)))**2)#/(64-9.)
             class PrecipitationProc(Operation):
                 '''
                      Operator that estimates Reflectivity factor (Z), and estimates rainfall Rate (R)
                      Input:
                         self.dataOut.data_pre    :    SelfSpectra
                      Output:
                         self.dataOut.data_output :    Reflectivity factor, rainfall Rate
                      Parameters affected:
                 '''
                 def __init__(self):
                     Operation.__init__(self)
                     self.i=0
                 def run(self, dataOut, radar=None, Pt=5000, Gt=295.1209, Gr=70.7945, Lambda=0.6741, aL=2.5118,
                         tauW=4e-06, ThetaT=0.1656317, ThetaR=0.36774087, Km2 = 0.93, Altitude=3350,SNRdBlimit=-30):
                     # print ('Entering PrecepitationProc ... ')
                     if radar == "MIRA35C" :
                         self.spc = dataOut.data_pre[0].copy()
                         self.Num_Hei = self.spc.shape[2]
                         self.Num_Bin = self.spc.shape[1]
                         self.Num_Chn = self.spc.shape[0]
                         Ze = self.dBZeMODE2(dataOut)
                     else:
                         self.spc = dataOut.data_pre[0].copy()
                         #NOTA SE DEBE REMOVER EL RANGO DEL PULSO TX
                         self.spc[:,:,0:7]= numpy.NaN
                         self.Num_Hei = self.spc.shape[2]
                         self.Num_Bin = self.spc.shape[1]
                         self.Num_Chn = self.spc.shape[0]
                         VelRange = dataOut.spc_range[2]
                         ''' Se obtiene la constante del RADAR '''
                         self.Pt = Pt
                         self.Gt = Gt
                         self.Gr = Gr
                         self.Lambda = Lambda
                         self.aL = aL
                         self.tauW = tauW
                         self.ThetaT = ThetaT
                         self.ThetaR = ThetaR
                         self.GSys = 10**(36.63/10) # Ganancia de los LNA 36.63 dB
                         self.lt = 10**(1.67/10) # Perdida en cables Tx 1.67 dB
                         self.lr = 10**(5.73/10) # Perdida en cables Rx 5.73 dB
                         Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
                         Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * tauW * numpy.pi * ThetaT * ThetaR)
                         RadarConstant = 10e-26 * Numerator / Denominator #
                         ExpConstant = 10**(40/10) #Constante Experimental
                         SignalPower = numpy.zeros([self.Num_Chn,self.Num_Bin,self.Num_Hei])
                         for i in range(self.Num_Chn):
                             SignalPower[i,:,:] = self.spc[i,:,:] - dataOut.noise[i]
                             SignalPower[numpy.where(SignalPower < 0)] = 1e-20
                         SPCmean = numpy.mean(SignalPower, 0)
                         Pr = SPCmean[:,:]/dataOut.normFactor
                         # Declaring auxiliary variables
                         Range = dataOut.heightList*1000. #Range in m
                         # replicate the heightlist to obtain a matrix [Num_Bin,Num_Hei]
                         rMtrx = numpy.transpose(numpy.transpose([dataOut.heightList*1000.] * self.Num_Bin))
                         zMtrx = rMtrx+Altitude
                         # replicate the VelRange to obtain a matrix [Num_Bin,Num_Hei]
                         VelMtrx = numpy.transpose(numpy.tile(VelRange[:-1], (self.Num_Hei,1)))
                         # height dependence to air density Foote and Du Toit (1969)
                         delv_z = 1 + 3.68e-5 * zMtrx + 1.71e-9 * zMtrx**2
                         VMtrx = VelMtrx / delv_z #Normalized velocity
                         VMtrx[numpy.where(VMtrx> 9.6)] = numpy.NaN
                         # Diameter is related to the fall speed of falling drops
                         D_Vz = -1.667 * numpy.log( 0.9369 - 0.097087 * VMtrx ) # D in [mm]
                         # Only valid for D>= 0.16 mm
                         D_Vz[numpy.where(D_Vz < 0.16)] = numpy.NaN
                         #Calculate Radar Reflectivity ETAn
                         ETAn = (RadarConstant *ExpConstant) * Pr * rMtrx**2  #Reflectivity (ETA)
                         ETAd = ETAn * 6.18 * exp( -0.6 * D_Vz ) * delv_z
                         # Radar Cross Section
                         sigmaD = Km2 * (D_Vz * 1e-3 )**6 * numpy.pi**5 / Lambda**4
                         # Drop Size Distribution
                         DSD = ETAn / sigmaD
                         # Equivalente Reflectivy
                         Ze_eqn = numpy.nansum( DSD * D_Vz**6 ,axis=0)
                         Ze_org = numpy.nansum(ETAn * Lambda**4, axis=0) / (1e-18*numpy.pi**5 * Km2) # [mm^6 /m^3]
                         # RainFall Rate
                         RR = 0.0006*numpy.pi * numpy.nansum( D_Vz**3 * DSD * VelMtrx ,0) #mm/hr
                     # Censoring the data
                     # Removing data with SNRth < 0dB se debe considerar el SNR por canal
                     SNRth = 10**(SNRdBlimit/10) #-30dB
                     novalid = numpy.where((dataOut.data_snr[0,:] <SNRth) | (dataOut.data_snr[1,:] <SNRth) | (dataOut.data_snr[2,:] <SNRth)) # AND condition. Maybe OR condition better
                     W = numpy.nanmean(dataOut.data_dop,0)
                     W[novalid] = numpy.NaN
                     Ze_org[novalid] = numpy.NaN
                     RR[novalid] = numpy.NaN
                     dataOut.data_output = RR[8]
                     dataOut.data_param = numpy.ones([3,self.Num_Hei])
                     dataOut.channelList = [0,1,2]
                     dataOut.data_param[0]=10*numpy.log10(Ze_org)
                     dataOut.data_param[1]=-W
                     dataOut.data_param[2]=RR
                     # print ('Leaving PrecepitationProc ... ')
                     return dataOut
                 def dBZeMODE2(self, dataOut): #    Processing for MIRA35C
                     NPW = dataOut.NPW
                     COFA = dataOut.COFA
                     SNR = numpy.array([self.spc[0,:,:] / NPW[0]]) #, self.spc[1,:,:] / NPW[1]])
                     RadarConst = dataOut.RadarConst
                     #frequency = 34.85*10**9
                     ETA = numpy.zeros(([self.Num_Chn ,self.Num_Hei]))
                     data_output = numpy.ones([self.Num_Chn , self.Num_Hei])*numpy.NaN
                     ETA = numpy.sum(SNR,1)
                     ETA = numpy.where(ETA != 0. , ETA, numpy.NaN)
                     Ze = numpy.ones([self.Num_Chn, self.Num_Hei] )
                     for r in range(self.Num_Hei):
                         Ze[0,r] =  ( ETA[0,r] ) * COFA[0,r][0] * RadarConst * ((r/5000.)**2)
                         #Ze[1,r] =  ( ETA[1,r] ) * COFA[1,r][0] * RadarConst * ((r/5000.)**2)
                     return Ze
             #     def GetRadarConstant(self):
             #
             #         """
             #         Constants:
             #
             #         Pt:     Transmission Power               dB        5kW                5000
             #         Gt:     Transmission Gain                dB        24.7 dB            295.1209
             #         Gr:     Reception Gain                   dB        18.5 dB            70.7945
             #         Lambda: Wavelenght                       m         0.6741 m           0.6741
             #         aL:     Attenuation loses                dB        4dB                2.5118
             #         tauW:   Width of transmission pulse      s         4us                4e-6
             #         ThetaT: Transmission antenna bean angle  rad       0.1656317 rad      0.1656317
             #         ThetaR: Reception antenna beam angle     rad       0.36774087 rad     0.36774087
             #
             #         """
             #
             #         Numerator = ( (4*numpy.pi)**3 * aL**2 * 16 * numpy.log(2) )
             #         Denominator = ( Pt * Gt * Gr * Lambda**2 * SPEED_OF_LIGHT * TauW * numpy.pi * ThetaT * TheraR)
             #         RadarConstant =  Numerator / Denominator
             #
             #         return RadarConstant
             class FullSpectralAnalysis(Operation):
                 """
                     Function that implements Full Spectral Analysis technique.
                     Input:
                         self.dataOut.data_pre    :    SelfSpectra and CrossSpectra data
                         self.dataOut.groupList   :    Pairlist of channels
                         self.dataOut.ChanDist    :    Physical distance between receivers
                     Output:
                         self.dataOut.data_output :    Zonal wind, Meridional wind, and Vertical wind
                     Parameters affected:    Winds, height range, SNR
                 """
                 def run(self, dataOut, Xi01=None, Xi02=None, Xi12=None, Eta01=None, Eta02=None, Eta12=None, SNRdBlimit=-30,
                     minheight=None, maxheight=None, NegativeLimit=None, PositiveLimit=None):
                     spc = dataOut.data_pre[0].copy()
                     cspc = dataOut.data_pre[1]
                     nHeights = spc.shape[2]
                     # first_height = 0.75 #km (ref: data header 20170822)
                     # resolution_height = 0.075 #km
                     '''
                         finding height range. check this when radar parameters are changed!
                     '''
                     if maxheight is not None:
                         # range_max = math.ceil((maxheight - first_height) / resolution_height) # theoretical
                         range_max = math.ceil(13.26 * maxheight - 3) # empirical, works better
                     else:
                         range_max = nHeights
                     if minheight is not None:
                         # range_min = int((minheight - first_height) / resolution_height) # theoretical
                         range_min = int(13.26 * minheight - 5) # empirical, works better
                         if range_min < 0:
                             range_min = 0
                     else:
                         range_min = 0
                     pairsList = dataOut.groupList
                     if dataOut.ChanDist is not None :
                         ChanDist = dataOut.ChanDist
                     else:
                         ChanDist = numpy.array([[Xi01, Eta01],[Xi02,Eta02],[Xi12,Eta12]])
                     # 4 variables: zonal, meridional, vertical, and average SNR
                     data_param = numpy.zeros([4,nHeights]) * numpy.NaN
                     velocityX = numpy.zeros([nHeights]) * numpy.NaN
                     velocityY = numpy.zeros([nHeights]) * numpy.NaN
                     velocityZ = numpy.zeros([nHeights]) * numpy.NaN
                     dbSNR = 10*numpy.log10(numpy.average(dataOut.data_snr,0))
                     '''***********************************************WIND ESTIMATION**************************************'''
                     for Height in range(nHeights):
                         if Height >= range_min and Height < range_max:
                             # error_code will be useful in future analysis
                             [Vzon,Vmer,Vver, error_code] = self.WindEstimation(spc[:,:,Height], cspc[:,:,Height], pairsList,
                                 ChanDist, Height, dataOut.noise, dataOut.spc_range, dbSNR[Height], SNRdBlimit, NegativeLimit, PositiveLimit,dataOut.frequency)
                         if abs(Vzon) < 100. and abs(Vmer) < 100.:
                             velocityX[Height] = Vzon
                             velocityY[Height] = -Vmer
                             velocityZ[Height] = Vver
                     # Censoring data with SNR threshold
                     dbSNR [dbSNR < SNRdBlimit] = numpy.NaN
                     data_param[0] = velocityX
                     data_param[1] = velocityY
                     data_param[2] = velocityZ
                     data_param[3] = dbSNR
                     dataOut.data_param = data_param
                     return dataOut
                 def moving_average(self,x, N=2):
                     """ convolution for smoothenig data. note that last N-1 values are convolution with zeroes """
                     return numpy.convolve(x, numpy.ones((N,))/N)[(N-1):]
                 def gaus(self,xSamples,Amp,Mu,Sigma):
                     return Amp * numpy.exp(-0.5*((xSamples - Mu)/Sigma)**2)
                 def Moments(self, ySamples, xSamples):
                     Power = numpy.nanmean(ySamples)                                 # Power, 0th Moment
                     yNorm = ySamples / numpy.nansum(ySamples)
                     RadVel = numpy.nansum(xSamples * yNorm)                         # Radial Velocity, 1st Moment
                     Sigma2 = numpy.nansum(yNorm * (xSamples - RadVel)**2)      # Spectral Width, 2nd Moment
                     StdDev = numpy.sqrt(numpy.abs(Sigma2))                                            # Desv. Estandar, Ancho espectral
                     return numpy.array([Power,RadVel,StdDev])
                 def StopWindEstimation(self, error_code):
                     Vzon = numpy.NaN
                     Vmer = numpy.NaN
                     Vver = numpy.NaN
                     return Vzon, Vmer, Vver, error_code
                 def AntiAliasing(self, interval, maxstep):
                     """
                         function to prevent errors from aliased values when computing phaseslope
                     """
                     antialiased = numpy.zeros(len(interval))
                     copyinterval = interval.copy()
                     antialiased[0] = copyinterval[0]
                     for i in range(1,len(antialiased)):
                         step = interval[i] - interval[i-1]
                         if step > maxstep:
                             copyinterval -= 2*numpy.pi
                             antialiased[i] = copyinterval[i]
                         elif step < maxstep*(-1):
                             copyinterval += 2*numpy.pi
                             antialiased[i] = copyinterval[i]
                         else:
                             antialiased[i] = copyinterval[i].copy()
                     return antialiased
                 def WindEstimation(self, spc, cspc, pairsList, ChanDist, Height, noise, AbbsisaRange, dbSNR, SNRlimit, NegativeLimit, PositiveLimit, radfreq):
                     """
                         Function that Calculates Zonal, Meridional and Vertical wind velocities.
                         Initial Version by E. Bocanegra updated by J. Zibell until Nov. 2019.
                         Input:
                             spc, cspc       : self spectra and cross spectra data. In Briggs notation something like S_i*(S_i)_conj, (S_j)_conj respectively.
                             pairsList       : Pairlist of channels
                             ChanDist        : array of xi_ij and eta_ij
                             Height          : height at which data is processed
                             noise           : noise in [channels] format for specific height
                             Abbsisarange    : range of the frequencies or velocities
                             dbSNR, SNRlimit : signal to noise ratio in db, lower limit
                         Output:
                             Vzon, Vmer, Vver         : wind velocities
                             error_code               : int that states where code is terminated
 : no error detected
 : Gaussian of mean spc exceeds widthlimit
 : no Gaussian of mean spc found
 : SNR to low or velocity to high -> prec. e.g.
 : at least one Gaussian of cspc exceeds widthlimit
 : zero out of three cspc Gaussian fits converged
 : phase slope fit could not be found
 : arrays used to fit phase have different length
 : frequency range is either too short (len <= 5) or very long (> 30% of cspc)
                     """
                     error_code = 0
                     nChan = spc.shape[0]
                     nProf = spc.shape[1]
                     nPair = cspc.shape[0]
                     SPC_Samples = numpy.zeros([nChan, nProf])           # for normalized spc values for one height
                     CSPC_Samples = numpy.zeros([nPair, nProf], dtype=numpy.complex_)      # for normalized cspc values
                     phase = numpy.zeros([nPair, nProf])                 # phase between channels
                     PhaseSlope = numpy.zeros(nPair)                     # slope of the phases, channelwise
                     PhaseInter = numpy.zeros(nPair)                     # intercept to the slope of the phases, channelwise
                     xFrec = AbbsisaRange[0][:-1]                        # frequency range
                     xVel = AbbsisaRange[2][:-1]                         # velocity range
                     xSamples = xFrec                                    # the frequency range is taken
                     delta_x = xSamples[1] - xSamples[0]                 # delta_f or delta_x
                     # only consider velocities with in NegativeLimit and PositiveLimit
                     if (NegativeLimit is None):
                         NegativeLimit = numpy.min(xVel)
                     if (PositiveLimit is None):
                         PositiveLimit = numpy.max(xVel)
                     xvalid = numpy.where((xVel > NegativeLimit) & (xVel < PositiveLimit))
                     xSamples_zoom = xSamples[xvalid]
                     '''Getting Eij and Nij'''
                     Xi01, Xi02, Xi12 = ChanDist[:,0]
                     Eta01, Eta02, Eta12 = ChanDist[:,1]
                     # spwd limit - updated by D. Scipión 30.03.2021
                     widthlimit = 10
                     '''************************* SPC is normalized ********************************'''
                     spc_norm = spc.copy()
                     # For each channel
                     for i in range(nChan):
                         spc_sub = spc_norm[i,:] - noise[i]  # only the signal power
                         SPC_Samples[i] = spc_sub / (numpy.nansum(spc_sub) * delta_x)
                     '''********************** FITTING MEAN SPC GAUSSIAN **********************'''
                     """ the gaussian of the mean: first subtract noise, then normalize. this is legal because
                         you only fit the curve and don't need the absolute value of height for calculation,
                         only for estimation of width. for normalization of cross spectra, you need initial,
                         unnormalized self-spectra With noise.
                         Technically, you don't even need to normalize the self-spectra, as you only need the
                         width of the peak. However, it was left this way. Note that the normalization has a flaw:
                         due to subtraction of the noise, some values are below zero. Raw "spc" values should be
                         >= 0, as it is the modulus squared of the signals (complex * it's conjugate)
                     """
                     # initial conditions
                     popt = [1e-10,0,1e-10]
                     # Spectra average
                     SPCMean = numpy.average(SPC_Samples,0)
                     # Moments in frequency
                     SPCMoments = self.Moments(SPCMean[xvalid], xSamples_zoom)
                     # Gauss Fit SPC in frequency domain
                     if dbSNR > SNRlimit: # only if SNR > SNRth
                         try:
                             popt,pcov = curve_fit(self.gaus,xSamples_zoom,SPCMean[xvalid],p0=SPCMoments)
                             if popt[2] <= 0 or popt[2] > widthlimit: # CONDITION
                                 return self.StopWindEstimation(error_code = 1)
                             FitGauss = self.gaus(xSamples_zoom,*popt)
                         except :#RuntimeError:
                             return self.StopWindEstimation(error_code = 2)
                     else:
                         return self.StopWindEstimation(error_code = 3)
                     '''***************************** CSPC Normalization *************************
                             The Spc spectra are used to normalize the crossspectra. Peaks from precipitation
                             influence the norm which is not desired. First, a range is identified where the
                             wind peak is estimated -> sum_wind is sum of those frequencies. Next, the area
                             around it gets cut off and values replaced by mean determined by the boundary
                             data -> sum_noise (spc is not normalized here, thats why the noise is important)
                             The sums are then added and multiplied by range/datapoints, because you need
                             an integral and not a sum for normalization.
                             A norm is found according to Briggs 92.
                     '''
                     # for each pair
                     for i in range(nPair):
                         cspc_norm = cspc[i,:].copy()
                         chan_index0 = pairsList[i][0]
                         chan_index1 = pairsList[i][1]
                         CSPC_Samples[i] = cspc_norm / (numpy.sqrt(numpy.nansum(spc_norm[chan_index0])*numpy.nansum(spc_norm[chan_index1])) * delta_x)
                         phase[i] = numpy.arctan2(CSPC_Samples[i].imag, CSPC_Samples[i].real)
                     CSPCmoments = numpy.vstack([self.Moments(numpy.abs(CSPC_Samples[0,xvalid]), xSamples_zoom),
                                                 self.Moments(numpy.abs(CSPC_Samples[1,xvalid]), xSamples_zoom),
                                                 self.Moments(numpy.abs(CSPC_Samples[2,xvalid]), xSamples_zoom)])
                     popt01, popt02, popt12 = [1e-10,0,1e-10], [1e-10,0,1e-10] ,[1e-10,0,1e-10]
                     FitGauss01, FitGauss02, FitGauss12 = numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples)), numpy.zeros(len(xSamples))
                     '''*******************************FIT GAUSS CSPC************************************'''
                     try:
                         popt01,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[0][xvalid]),p0=CSPCmoments[0])
                         if popt01[2] > widthlimit: # CONDITION
                             return self.StopWindEstimation(error_code = 4)
                         popt02,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[1][xvalid]),p0=CSPCmoments[1])
                         if popt02[2] > widthlimit: # CONDITION
                             return self.StopWindEstimation(error_code = 4)
                         popt12,pcov = curve_fit(self.gaus,xSamples_zoom,numpy.abs(CSPC_Samples[2][xvalid]),p0=CSPCmoments[2])
                         if popt12[2] > widthlimit: # CONDITION
                             return self.StopWindEstimation(error_code = 4)
                         FitGauss01 = self.gaus(xSamples_zoom, *popt01)
                         FitGauss02 = self.gaus(xSamples_zoom, *popt02)
                         FitGauss12 = self.gaus(xSamples_zoom, *popt12)
                     except:
                         return self.StopWindEstimation(error_code = 5)
                     '''************* Getting Fij ***************'''
                     # x-axis point of the gaussian where the center is located from GaussFit of spectra
                     GaussCenter = popt[1]
                     ClosestCenter = xSamples_zoom[numpy.abs(xSamples_zoom-GaussCenter).argmin()]
                     PointGauCenter = numpy.where(xSamples_zoom==ClosestCenter)[0][0]
                     # Point where e^-1 is located in the gaussian
                     PeMinus1 = numpy.max(FitGauss) * numpy.exp(-1)
                     FijClosest = FitGauss[numpy.abs(FitGauss-PeMinus1).argmin()] # The closest point to"Peminus1" in "FitGauss"
                     PointFij = numpy.where(FitGauss==FijClosest)[0][0]
                     Fij = numpy.abs(xSamples_zoom[PointFij] - xSamples_zoom[PointGauCenter])
                     '''********** Taking frequency ranges from mean SPCs **********'''
                     GauWidth = popt[2] * 3/2        # Bandwidth of Gau01
                     Range = numpy.empty(2)
                     Range[0] = GaussCenter - GauWidth
                     Range[1] = GaussCenter + GauWidth
                     # Point in x-axis where the bandwidth is located (min:max)
                     ClosRangeMin = xSamples_zoom[numpy.abs(xSamples_zoom-Range[0]).argmin()]
                     ClosRangeMax = xSamples_zoom[numpy.abs(xSamples_zoom-Range[1]).argmin()]
                     PointRangeMin = numpy.where(xSamples_zoom==ClosRangeMin)[0][0]
                     PointRangeMax = numpy.where(xSamples_zoom==ClosRangeMax)[0][0]
                     Range = numpy.array([ PointRangeMin, PointRangeMax ])
                     FrecRange = xSamples_zoom[ Range[0] : Range[1] ]
                     '''************************** Getting Phase Slope ***************************'''
                     for i in range(nPair):
                         if len(FrecRange) > 5:
                             PhaseRange = phase[i, xvalid[0][Range[0]:Range[1]]].copy()
                             mask = ~numpy.isnan(FrecRange) & ~numpy.isnan(PhaseRange)
                             if len(FrecRange) == len(PhaseRange):
                                 try:
                                     slope, intercept, _, _, _ = stats.linregress(FrecRange[mask], self.AntiAliasing(PhaseRange[mask], 4.5))
                                     PhaseSlope[i] = slope
                                     PhaseInter[i] = intercept
                                 except:
                                     return self.StopWindEstimation(error_code = 6)
                             else:
                                 return self.StopWindEstimation(error_code = 7)
                         else:
                             return self.StopWindEstimation(error_code = 8)
                     '''*** Constants A-H correspond to the convention as in Briggs and Vincent 1992 ***'''
                     '''Getting constant C'''
                     cC=(Fij*numpy.pi)**2
                     '''****** Getting constants F and G ******'''
                     MijEijNij = numpy.array([[Xi02,Eta02], [Xi12,Eta12]])
                     # MijEijNij = numpy.array([[Xi01,Eta01], [Xi02,Eta02], [Xi12,Eta12]])
                     # MijResult0 = (-PhaseSlope[0] * cC) / (2*numpy.pi)
                     MijResult1 = (-PhaseSlope[1] * cC) / (2*numpy.pi)
                     MijResult2 = (-PhaseSlope[2] * cC) / (2*numpy.pi)
                     # MijResults = numpy.array([MijResult0, MijResult1, MijResult2])
                     MijResults = numpy.array([MijResult1, MijResult2])
                     (cF,cG) = numpy.linalg.solve(MijEijNij, MijResults)
                     '''****** Getting constants A, B and H ******'''
                     W01 = numpy.nanmax( FitGauss01 )
                     W02 = numpy.nanmax( FitGauss02 )
                     W12 = numpy.nanmax( FitGauss12 )
                     WijResult01 = ((cF * Xi01 + cG * Eta01)**2)/cC - numpy.log(W01 / numpy.sqrt(numpy.pi / cC))
                     WijResult02 = ((cF * Xi02 + cG * Eta02)**2)/cC - numpy.log(W02 / numpy.sqrt(numpy.pi / cC))
                     WijResult12 = ((cF * Xi12 + cG * Eta12)**2)/cC - numpy.log(W12 / numpy.sqrt(numpy.pi / cC))
                     WijResults = numpy.array([WijResult01, WijResult02, WijResult12])
                     WijEijNij = numpy.array([ [Xi01**2, Eta01**2, 2*Xi01*Eta01] , [Xi02**2, Eta02**2, 2*Xi02*Eta02] , [Xi12**2, Eta12**2, 2*Xi12*Eta12] ])
                     (cA,cB,cH) = numpy.linalg.solve(WijEijNij, WijResults)
                     VxVy = numpy.array([[cA,cH],[cH,cB]])
                     VxVyResults = numpy.array([-cF,-cG])
                     (Vmer,Vzon) = numpy.linalg.solve(VxVy, VxVyResults)
                     Vver =  -SPCMoments[1]*SPEED_OF_LIGHT/(2*radfreq)
                     error_code = 0
                     return Vzon, Vmer, Vver, error_code
             class SpectralMoments(Operation):
                 '''
                     Function SpectralMoments()
                     Calculates moments (power, mean, standard deviation) and SNR of the signal
                     Type of dataIn:    Spectra
                     Configuration Parameters:
                         dirCosx    :     Cosine director in X axis
                         dirCosy    :     Cosine director in Y axis
                         elevation  :
                         azimuth    :
                     Input:
                         channelList    :    simple channel list to select e.g. [2,3,7]
                         self.dataOut.data_pre        :    Spectral data
                         self.dataOut.abscissaList    :    List of frequencies
                         self.dataOut.noise           :    Noise level per channel
                     Affected:
                         self.dataOut.moments        :    Parameters per channel
                         self.dataOut.data_snr       :    SNR per channel
                 '''
                 def run(self, dataOut):
                     data = dataOut.data_pre[0]
                     absc = dataOut.abscissaList[:-1]
                     noise = dataOut.noise
                     nChannel = data.shape[0]
                     data_param = numpy.zeros((nChannel, 4, data.shape[2]))
                     for ind in range(nChannel):
                         data_param[ind,:,:] = self.__calculateMoments( data[ind,:,:] , absc , noise[ind] )
                     dataOut.moments = data_param[:,1:,:]
                     dataOut.data_snr = data_param[:,0]
                     dataOut.data_pow = data_param[:,1]
                     dataOut.data_dop = data_param[:,2]
                     dataOut.data_width = data_param[:,3]
                     return dataOut
                 def __calculateMoments(self, oldspec, oldfreq, n0,
                                        nicoh = None, graph = None, smooth = None, type1 = None, fwindow = None, snrth = None, dc = None, aliasing = None, oldfd = None, wwauto = None):
                     if (nicoh is None): nicoh = 1
                     if (graph is None): graph = 0
                     if (smooth is None): smooth = 0
                     elif (self.smooth < 3): smooth = 0
                     if (type1 is None): type1 = 0
                     if (fwindow is None): fwindow = numpy.zeros(oldfreq.size) + 1
                     if (snrth is None): snrth = -3
                     if (dc is None): dc = 0
                     if (aliasing is None): aliasing = 0
                     if (oldfd is None): oldfd = 0
                     if (wwauto is None): wwauto = 0
                     if (n0 < 1.e-20):   n0 = 1.e-20
                     freq = oldfreq
                     vec_power = numpy.zeros(oldspec.shape[1])
                     vec_fd = numpy.zeros(oldspec.shape[1])
                     vec_w = numpy.zeros(oldspec.shape[1])
                     vec_snr = numpy.zeros(oldspec.shape[1])
                     # oldspec = numpy.ma.masked_invalid(oldspec)
                     for ind in range(oldspec.shape[1]):
                         spec = oldspec[:,ind]
                         aux = spec*fwindow
                         max_spec = aux.max()
                         m = aux.tolist().index(max_spec)
                         # Smooth
                         if (smooth == 0):
                             spec2 = spec
                         else:
                             spec2 = scipy.ndimage.filters.uniform_filter1d(spec,size=smooth)
                         # Moments Estimation
                         bb = spec2[numpy.arange(m,spec2.size)]
                         bb = (bb<n0).nonzero()
                         bb = bb[0]
                         ss = spec2[numpy.arange(0,m + 1)]
                         ss = (ss<n0).nonzero()
                         ss = ss[0]
                         if (bb.size == 0):
                             bb0 = spec.size - 1 - m
                         else:
                             bb0 = bb[0] - 1
                             if (bb0 < 0):
                                 bb0 = 0
                         if (ss.size == 0):
                             ss1 = 1
                         else:
                             ss1 = max(ss) + 1
                         if (ss1 > m):
                             ss1 = m
                         #valid = numpy.arange(int(m + bb0 - ss1 + 1)) + ss1
                         valid = numpy.arange(1,oldspec.shape[0])# valid perfil completo igual pulsepair
                         signal_power = ((spec2[valid] - n0) * fwindow[valid]).mean()    # D. Scipión added with correct definition
                         total_power = (spec2[valid] * fwindow[valid]).mean()            # D. Scipión added with correct definition
                         power = ((spec2[valid] - n0) * fwindow[valid]).sum()
                         fd = ((spec2[valid]- n0)*freq[valid] * fwindow[valid]).sum() / power
                         w = numpy.sqrt(((spec2[valid] - n0)*fwindow[valid]*(freq[valid]- fd)**2).sum() / power)
                         snr = (spec2.mean()-n0)/n0
                         if (snr < 1.e-20) :
                             snr = 1.e-20
                         # vec_power[ind] = power    #D. Scipión replaced with the line below
                         vec_power[ind] = total_power
                         vec_fd[ind] = fd
                         vec_w[ind] = w
                         vec_snr[ind] = snr
                     return numpy.vstack((vec_snr, vec_power, vec_fd, vec_w))
                 #------------------    Get SA Parameters    --------------------------
                 def GetSAParameters(self):
                     #SA en frecuencia
                     pairslist = self.dataOut.groupList
                     num_pairs = len(pairslist)
                     vel = self.dataOut.abscissaList
                     spectra = self.dataOut.data_pre
                     cspectra = self.dataIn.data_cspc
                     delta_v = vel[1] - vel[0]
                     #Calculating the power spectrum
                     spc_pow = numpy.sum(spectra, 3)*delta_v
                     #Normalizing Spectra
                     norm_spectra = spectra/spc_pow
                     #Calculating the norm_spectra at peak
                     max_spectra = numpy.max(norm_spectra, 3)
                     #Normalizing Cross Spectra
                     norm_cspectra = numpy.zeros(cspectra.shape)
                     for i in range(num_chan):
                         norm_cspectra[i,:,:] = cspectra[i,:,:]/numpy.sqrt(spc_pow[pairslist[i][0],:]*spc_pow[pairslist[i][1],:])
                     max_cspectra = numpy.max(norm_cspectra,2)
                     max_cspectra_index = numpy.argmax(norm_cspectra, 2)
                     for i in range(num_pairs):
                         cspc_par[i,:,:] = __calculateMoments(norm_cspectra)
                 #-------------------    Get Lags    ----------------------------------
             class SALags(Operation):
                 '''
                 Function GetMoments()
                 Input:
                     self.dataOut.data_pre
                     self.dataOut.abscissaList
                     self.dataOut.noise
                     self.dataOut.normFactor
                     self.dataOut.data_snr
                     self.dataOut.groupList
                     self.dataOut.nChannels
                 Affected:
                     self.dataOut.data_param
                 '''
                 def run(self, dataOut):
                     data_acf = dataOut.data_pre[0]
                     data_ccf = dataOut.data_pre[1]
                     normFactor_acf = dataOut.normFactor[0]
                     normFactor_ccf = dataOut.normFactor[1]
                     pairs_acf = dataOut.groupList[0]
                     pairs_ccf = dataOut.groupList[1]
                     nHeights = dataOut.nHeights
                     absc = dataOut.abscissaList
                     noise = dataOut.noise
                     SNR = dataOut.data_snr
                     nChannels = dataOut.nChannels
             #         pairsList = dataOut.groupList
             #         pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairsList, nChannels)
                     for l in range(len(pairs_acf)):
                         data_acf[l,:,:] = data_acf[l,:,:]/normFactor_acf[l,:]
                     for l in range(len(pairs_ccf)):
                         data_ccf[l,:,:] = data_ccf[l,:,:]/normFactor_ccf[l,:]
                     dataOut.data_param = numpy.zeros((len(pairs_ccf)*2 + 1, nHeights))
                     dataOut.data_param[:-1,:] = self.__calculateTaus(data_acf, data_ccf, absc)
                     dataOut.data_param[-1,:] = self.__calculateLag1Phase(data_acf, absc)
                     return
             #     def __getPairsAutoCorr(self, pairsList, nChannels):
             #
             #         pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
             #
             #         for l in range(len(pairsList)):
             #             firstChannel = pairsList[l][0]
             #             secondChannel = pairsList[l][1]
             #
             #             #Obteniendo pares de Autocorrelacion
             #             if firstChannel == secondChannel:
             #                 pairsAutoCorr[firstChannel] = int(l)
             #
             #         pairsAutoCorr = pairsAutoCorr.astype(int)
             #
             #         pairsCrossCorr = range(len(pairsList))
             #         pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
             #
             #         return pairsAutoCorr, pairsCrossCorr
                 def __calculateTaus(self, data_acf, data_ccf, lagRange):
                     lag0 = data_acf.shape[1]/2
                     #Funcion de Autocorrelacion
                     mean_acf = stats.nanmean(data_acf, axis = 0)
                     #Obtencion Indice de TauCross
                     ind_ccf = data_ccf.argmax(axis = 1)
                     #Obtencion Indice de TauAuto
                     ind_acf = numpy.zeros(ind_ccf.shape,dtype = 'int')
                     ccf_lag0 = data_ccf[:,lag0,:]
                     for i in range(ccf_lag0.shape[0]):
                         ind_acf[i,:] = numpy.abs(mean_acf - ccf_lag0[i,:]).argmin(axis = 0)
                     #Obtencion de TauCross y TauAuto
                     tau_ccf = lagRange[ind_ccf]
                     tau_acf  = lagRange[ind_acf]
                     Nan1, Nan2 = numpy.where(tau_ccf == lagRange[0])
                     tau_ccf[Nan1,Nan2] = numpy.nan
                     tau_acf[Nan1,Nan2] = numpy.nan
                     tau = numpy.vstack((tau_ccf,tau_acf))
                     return tau
                 def __calculateLag1Phase(self, data, lagTRange):
                     data1 = stats.nanmean(data, axis = 0)
                     lag1 = numpy.where(lagTRange == 0)[0][0] + 1
                     phase = numpy.angle(data1[lag1,:])
                     return phase
             class SpectralFitting(Operation):
                 '''
                     Function GetMoments()
                     Input:
                     Output:
                     Variables modified:
                 '''
                 def run(self, dataOut, getSNR = True, path=None, file=None, groupList=None):
                     if path != None:
                         sys.path.append(path)
                     self.dataOut.library = importlib.import_module(file)
                     #To be inserted as a parameter
                     groupArray = numpy.array(groupList)
             #         groupArray = numpy.array([[0,1],[2,3]])
                     self.dataOut.groupList = groupArray
                     nGroups = groupArray.shape[0]
                     nChannels = self.dataIn.nChannels
                     nHeights=self.dataIn.heightList.size
                     #Parameters Array
                     self.dataOut.data_param = None
                     #Set constants
                     constants = self.dataOut.library.setConstants(self.dataIn)
                     self.dataOut.constants = constants
                     M = self.dataIn.normFactor
                     N = self.dataIn.nFFTPoints
                     ippSeconds = self.dataIn.ippSeconds
                     K = self.dataIn.nIncohInt
                     pairsArray = numpy.array(self.dataIn.pairsList)
                     #List of possible combinations
                     listComb = itertools.combinations(numpy.arange(groupArray.shape[1]),2)
                     indCross = numpy.zeros(len(list(listComb)), dtype = 'int')
                     if getSNR:
                         listChannels = groupArray.reshape((groupArray.size))
                         listChannels.sort()
                         noise = self.dataIn.getNoise()
                         self.dataOut.data_snr = self.__getSNR(self.dataIn.data_spc[listChannels,:,:], noise[listChannels])
                     for i in range(nGroups):
                         coord = groupArray[i,:]
                         #Input data array
                         data = self.dataIn.data_spc[coord,:,:]/(M*N)
                         data = data.reshape((data.shape[0]*data.shape[1],data.shape[2]))
                         #Cross Spectra data array for Covariance Matrixes
                         ind = 0
                         for pairs in listComb:
                             pairsSel = numpy.array([coord[x],coord[y]])
                             indCross[ind] = int(numpy.where(numpy.all(pairsArray == pairsSel, axis = 1))[0][0])
                             ind += 1
                         dataCross = self.dataIn.data_cspc[indCross,:,:]/(M*N)
                         dataCross = dataCross**2/K
                         for h in range(nHeights):
                             #Input
                             d = data[:,h]
                             #Covariance Matrix
                             D = numpy.diag(d**2/K)
                             ind = 0
                             for pairs in listComb:
                                 #Coordinates in Covariance Matrix
                                 x = pairs[0]
                                 y = pairs[1]
                                 #Channel Index
                                 S12 = dataCross[ind,:,h]
                                 D12 = numpy.diag(S12)
                                 #Completing Covariance Matrix with Cross Spectras
                                 D[x*N:(x+1)*N,y*N:(y+1)*N] = D12
                                 D[y*N:(y+1)*N,x*N:(x+1)*N] = D12
                                 ind += 1
                             Dinv=numpy.linalg.inv(D)
                             L=numpy.linalg.cholesky(Dinv)
                             LT=L.T
                             dp = numpy.dot(LT,d)
                             #Initial values
                             data_spc = self.dataIn.data_spc[coord,:,h]
                             if (h>0)and(error1[3]<5):
                                 p0 = self.dataOut.data_param[i,:,h-1]
                             else:
                                 p0 = numpy.array(self.dataOut.library.initialValuesFunction(data_spc, constants, i))
                             try:
                                 #Least Squares
                                 minp,covp,infodict,mesg,ier = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants),full_output=True)
             #                   minp,covp = optimize.leastsq(self.__residFunction,p0,args=(dp,LT,constants))
                                 #Chi square error
                                 error0 = numpy.sum(infodict['fvec']**2)/(2*N)
                                 #Error with Jacobian
                                 error1 = self.dataOut.library.errorFunction(minp,constants,LT)
                             except:
                                 minp = p0*numpy.nan
                                 error0 = numpy.nan
                                 error1 = p0*numpy.nan
                             #Save
                             if self.dataOut.data_param is None:
                                 self.dataOut.data_param = numpy.zeros((nGroups, p0.size, nHeights))*numpy.nan
                                 self.dataOut.data_error = numpy.zeros((nGroups, p0.size + 1, nHeights))*numpy.nan
                             self.dataOut.data_error[i,:,h] = numpy.hstack((error0,error1))
                             self.dataOut.data_param[i,:,h] = minp
                     return
                 def __residFunction(self, p, dp, LT, constants):
                     fm = self.dataOut.library.modelFunction(p, constants)
                     fmp=numpy.dot(LT,fm)
                     return  dp-fmp
                 def __getSNR(self, z, noise):
                     avg = numpy.average(z, axis=1)
                     SNR = (avg.T-noise)/noise
                     SNR = SNR.T
                     return SNR
                 def __chisq(p,chindex,hindex):
                     #similar to Resid but calculates CHI**2
                     [LT,d,fm]=setupLTdfm(p,chindex,hindex)
                     dp=numpy.dot(LT,d)
                     fmp=numpy.dot(LT,fm)
                     chisq=numpy.dot((dp-fmp).T,(dp-fmp))
                     return chisq
             class WindProfiler(Operation):
                 __isConfig = False
                 __initime = None
                 __lastdatatime = None
                 __integrationtime = None
                 __buffer = None
                 __dataReady = False
                 __firstdata = None
                 n = None
                 def __init__(self):
                     Operation.__init__(self)
                 def __calculateCosDir(self, elev, azim):
                     zen = (90 - elev)*numpy.pi/180
                     azim = azim*numpy.pi/180
                     cosDirX = numpy.sqrt((1-numpy.cos(zen)**2)/((1+numpy.tan(azim)**2)))
                     cosDirY = numpy.sqrt(1-numpy.cos(zen)**2-cosDirX**2)
                     signX = numpy.sign(numpy.cos(azim))
                     signY = numpy.sign(numpy.sin(azim))
                     cosDirX = numpy.copysign(cosDirX, signX)
                     cosDirY = numpy.copysign(cosDirY, signY)
                     return cosDirX, cosDirY
                 def __calculateAngles(self, theta_x, theta_y, azimuth):
                     dir_cosw = numpy.sqrt(1-theta_x**2-theta_y**2)
                     zenith_arr = numpy.arccos(dir_cosw)
                     azimuth_arr = numpy.arctan2(theta_x,theta_y) + azimuth*math.pi/180
                     dir_cosu = numpy.sin(azimuth_arr)*numpy.sin(zenith_arr)
                     dir_cosv = numpy.cos(azimuth_arr)*numpy.sin(zenith_arr)
                     return azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw
                 def __calculateMatA(self, dir_cosu, dir_cosv, dir_cosw, horOnly):
             #
                     if horOnly:
                         A = numpy.c_[dir_cosu,dir_cosv]
                     else:
                         A = numpy.c_[dir_cosu,dir_cosv,dir_cosw]
                     A = numpy.asmatrix(A)
                     A1 = numpy.linalg.inv(A.transpose()*A)*A.transpose()
                     return A1
                 def __correctValues(self, heiRang, phi, velRadial, SNR):
                     listPhi = phi.tolist()
                     maxid = listPhi.index(max(listPhi))
                     minid = listPhi.index(min(listPhi))
                     rango = list(range(len(phi)))
                #     rango = numpy.delete(rango,maxid)
                     heiRang1 = heiRang*math.cos(phi[maxid])
                     heiRangAux = heiRang*math.cos(phi[minid])
                     indOut = (heiRang1 < heiRangAux[0]).nonzero()
                     heiRang1 = numpy.delete(heiRang1,indOut)
                     velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
                     SNR1 = numpy.zeros([len(phi),len(heiRang1)])
                     for i in rango:
                         x = heiRang*math.cos(phi[i])
                         y1 = velRadial[i,:]
                         f1 = interpolate.interp1d(x,y1,kind = 'cubic')
                         x1 = heiRang1
                         y11 = f1(x1)
                         y2 = SNR[i,:]
                         f2 = interpolate.interp1d(x,y2,kind = 'cubic')
                         y21 = f2(x1)
                         velRadial1[i,:] = y11
                         SNR1[i,:] = y21
                     return heiRang1, velRadial1, SNR1
                 def __calculateVelUVW(self, A, velRadial):
                     #Operacion Matricial
             #         velUVW = numpy.zeros((velRadial.shape[1],3))
             #         for ind in range(velRadial.shape[1]):
             #             velUVW[ind,:] = numpy.dot(A,velRadial[:,ind])
             #         velUVW = velUVW.transpose()
                     velUVW = numpy.zeros((A.shape[0],velRadial.shape[1]))
                     velUVW[:,:] = numpy.dot(A,velRadial)
                     return velUVW
             #     def techniqueDBS(self, velRadial0, dirCosx, disrCosy, azimuth, correct, horizontalOnly, heiRang, SNR0):
                 def techniqueDBS(self, kwargs):
                     """
                     Function that implements Doppler Beam Swinging (DBS) technique.
                     Input:    Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
                                 Direction correction (if necessary), Ranges and SNR
                     Output:    Winds estimation (Zonal, Meridional and Vertical)
                     Parameters affected:    Winds, height range, SNR
                     """
                     velRadial0 = kwargs['velRadial']
                     heiRang = kwargs['heightList']
                     SNR0 = kwargs['SNR']
                     if 'dirCosx' in kwargs and 'dirCosy' in kwargs:
                         theta_x = numpy.array(kwargs['dirCosx'])
                         theta_y = numpy.array(kwargs['dirCosy'])
                     else:
                         elev = numpy.array(kwargs['elevation'])
                         azim = numpy.array(kwargs['azimuth'])
                         theta_x, theta_y = self.__calculateCosDir(elev, azim)
                     azimuth = kwargs['correctAzimuth']
                     if 'horizontalOnly' in kwargs:
                         horizontalOnly = kwargs['horizontalOnly']
                     else:   horizontalOnly = False
                     if 'correctFactor' in kwargs:
                         correctFactor = kwargs['correctFactor']
                     else:   correctFactor = 1
                     if 'channelList' in kwargs:
                         channelList = kwargs['channelList']
                         if len(channelList) == 2:
                             horizontalOnly = True
                         arrayChannel = numpy.array(channelList)
                         param = param[arrayChannel,:,:]
                         theta_x = theta_x[arrayChannel]
                         theta_y = theta_y[arrayChannel]
                     azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
                     heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, zenith_arr, correctFactor*velRadial0, SNR0)
                     A = self.__calculateMatA(dir_cosu, dir_cosv, dir_cosw, horizontalOnly)
                     #Calculo de Componentes de la velocidad con DBS
                     winds = self.__calculateVelUVW(A,velRadial1)
                     return winds, heiRang1, SNR1
                 def __calculateDistance(self, posx, posy, pairs_ccf, azimuth = None):
                     nPairs = len(pairs_ccf)
                     posx = numpy.asarray(posx)
                     posy = numpy.asarray(posy)
                     #Rotacion Inversa para alinear con el azimuth
                     if azimuth!= None:
                         azimuth = azimuth*math.pi/180
                         posx1 = posx*math.cos(azimuth) + posy*math.sin(azimuth)
                         posy1 = -posx*math.sin(azimuth) + posy*math.cos(azimuth)
                     else:
                         posx1 = posx
                         posy1 = posy
                     #Calculo de Distancias
                     distx = numpy.zeros(nPairs)
                     disty = numpy.zeros(nPairs)
                     dist = numpy.zeros(nPairs)
                     ang = numpy.zeros(nPairs)
                     for i in range(nPairs):
                         distx[i] = posx1[pairs_ccf[i][1]] - posx1[pairs_ccf[i][0]]
                         disty[i] = posy1[pairs_ccf[i][1]] - posy1[pairs_ccf[i][0]]
                         dist[i] = numpy.sqrt(distx[i]**2 + disty[i]**2)
                         ang[i] = numpy.arctan2(disty[i],distx[i])
                     return distx, disty, dist, ang
                     #Calculo de Matrices
             #         nPairs = len(pairs)
             #         ang1 = numpy.zeros((nPairs, 2, 1))
             #         dist1 = numpy.zeros((nPairs, 2, 1))
             #
             #         for j in range(nPairs):
             #             dist1[j,0,0] = dist[pairs[j][0]]
             #             dist1[j,1,0] = dist[pairs[j][1]]
             #             ang1[j,0,0] = ang[pairs[j][0]]
             #             ang1[j,1,0] = ang[pairs[j][1]]
             #
             #         return distx,disty, dist1,ang1
                 def __calculateVelVer(self, phase, lagTRange, _lambda):
                     Ts = lagTRange[1] - lagTRange[0]
                     velW = -_lambda*phase/(4*math.pi*Ts)
                     return velW
                 def __calculateVelHorDir(self, dist, tau1, tau2, ang):
                     nPairs = tau1.shape[0]
                     nHeights = tau1.shape[1]
                     vel = numpy.zeros((nPairs,3,nHeights))
                     dist1 = numpy.reshape(dist, (dist.size,1))
                     angCos = numpy.cos(ang)
                     angSin = numpy.sin(ang)
                     vel0 = dist1*tau1/(2*tau2**2)
                     vel[:,0,:] = (vel0*angCos).sum(axis = 1)
                     vel[:,1,:] = (vel0*angSin).sum(axis = 1)
                     ind = numpy.where(numpy.isinf(vel))
                     vel[ind] = numpy.nan
                     return vel
             #     def __getPairsAutoCorr(self, pairsList, nChannels):
             #
             #         pairsAutoCorr = numpy.zeros(nChannels, dtype = 'int')*numpy.nan
             #
             #         for l in range(len(pairsList)):
             #             firstChannel = pairsList[l][0]
             #             secondChannel = pairsList[l][1]
             #
             #             #Obteniendo pares de Autocorrelacion
             #             if firstChannel == secondChannel:
             #                 pairsAutoCorr[firstChannel] = int(l)
             #
             #         pairsAutoCorr = pairsAutoCorr.astype(int)
             #
             #         pairsCrossCorr = range(len(pairsList))
             #         pairsCrossCorr = numpy.delete(pairsCrossCorr,pairsAutoCorr)
             #
             #         return pairsAutoCorr, pairsCrossCorr
             #     def techniqueSA(self, pairsSelected, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, lagTRange, correctFactor):
                 def techniqueSA(self, kwargs):
                     """
                     Function that implements Spaced Antenna (SA) technique.
                     Input:    Radial velocities, Direction cosines (x and y) of the Beam, Antenna azimuth,
                                 Direction correction (if necessary), Ranges and SNR
                     Output:    Winds estimation (Zonal, Meridional and Vertical)
                     Parameters affected:    Winds
                     """
                     position_x = kwargs['positionX']
                     position_y = kwargs['positionY']
                     azimuth = kwargs['azimuth']
                     if 'correctFactor' in kwargs:
                         correctFactor = kwargs['correctFactor']
                     else:
                         correctFactor = 1
                     groupList = kwargs['groupList']
                     pairs_ccf = groupList[1]
                     tau = kwargs['tau']
                     _lambda = kwargs['_lambda']
                     #Cross Correlation pairs obtained
             #         pairsAutoCorr, pairsCrossCorr = self.__getPairsAutoCorr(pairssList, nChannels)
             #         pairsArray = numpy.array(pairsList)[pairsCrossCorr]
             #         pairsSelArray = numpy.array(pairsSelected)
             #         pairs = []
             #
             #         #Wind estimation pairs obtained
             #         for i in range(pairsSelArray.shape[0]/2):
             #             ind1 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i], axis = 1))[0][0]
             #             ind2 = numpy.where(numpy.all(pairsArray == pairsSelArray[2*i + 1], axis = 1))[0][0]
             #             pairs.append((ind1,ind2))
                     indtau = tau.shape[0]/2
                     tau1 = tau[:indtau,:]
                     tau2 = tau[indtau:-1,:]
             #         tau1 = tau1[pairs,:]
             #         tau2 = tau2[pairs,:]
                     phase1 = tau[-1,:]
                     #---------------------------------------------------------------------
                     #Metodo Directo
                     distx, disty, dist, ang = self.__calculateDistance(position_x, position_y, pairs_ccf,azimuth)
                     winds = self.__calculateVelHorDir(dist, tau1, tau2, ang)
                     winds = stats.nanmean(winds, axis=0)
                     #---------------------------------------------------------------------
                     #Metodo General
             #         distx, disty, dist = self.calculateDistance(position_x,position_y,pairsCrossCorr, pairsList, azimuth)
             #         #Calculo Coeficientes de Funcion de Correlacion
             #         F,G,A,B,H = self.calculateCoef(tau1,tau2,distx,disty,n)
             #         #Calculo de Velocidades
             #         winds = self.calculateVelUV(F,G,A,B,H)
                     #---------------------------------------------------------------------
                     winds[2,:] = self.__calculateVelVer(phase1, lagTRange, _lambda)
                     winds = correctFactor*winds
                     return winds
                 def __checkTime(self, currentTime, paramInterval, outputInterval):
                     dataTime = currentTime + paramInterval
                     deltaTime = dataTime - self.__initime
                     if deltaTime >= outputInterval or deltaTime < 0:
                         self.__dataReady = True
                     return
                 def techniqueMeteors(self, arrayMeteor, meteorThresh, heightMin, heightMax):
                     '''
                     Function that implements winds estimation technique with detected meteors.
                     Input:    Detected meteors, Minimum meteor quantity to wind estimation
                     Output:    Winds estimation (Zonal and Meridional)
                     Parameters affected:    Winds
                     '''
                     #Settings
                     nInt = (heightMax - heightMin)/2
                     nInt = int(nInt)
                     winds = numpy.zeros((2,nInt))*numpy.nan
                     #Filter errors
                     error = numpy.where(arrayMeteor[:,-1] == 0)[0]
                     finalMeteor = arrayMeteor[error,:]
                     #Meteor Histogram
                     finalHeights = finalMeteor[:,2]
                     hist = numpy.histogram(finalHeights, bins = nInt, range = (heightMin,heightMax))
                     nMeteorsPerI = hist[0]
                     heightPerI = hist[1]
                     #Sort of meteors
                     indSort = finalHeights.argsort()
                     finalMeteor2 = finalMeteor[indSort,:]
                     #    Calculating winds
                     ind1 = 0
                     ind2 = 0
                     for i in range(nInt):
                         nMet = nMeteorsPerI[i]
                         ind1 = ind2
                         ind2 = ind1 + nMet
                         meteorAux = finalMeteor2[ind1:ind2,:]
                         if meteorAux.shape[0] >= meteorThresh:
                             vel = meteorAux[:, 6]
                             zen = meteorAux[:, 4]*numpy.pi/180
                             azim = meteorAux[:, 3]*numpy.pi/180
                             n = numpy.cos(zen)
                     #         m = (1 - n**2)/(1 - numpy.tan(azim)**2)
                     #         l = m*numpy.tan(azim)
                             l = numpy.sin(zen)*numpy.sin(azim)
                             m = numpy.sin(zen)*numpy.cos(azim)
                             A = numpy.vstack((l, m)).transpose()
                             A1 = numpy.dot(numpy.linalg.inv( numpy.dot(A.transpose(),A) ),A.transpose())
                             windsAux = numpy.dot(A1, vel)
                             winds[0,i] = windsAux[0]
                             winds[1,i] = windsAux[1]
                     return winds, heightPerI[:-1]
                 def techniqueNSM_SA(self, **kwargs):
                     metArray = kwargs['metArray']
                     heightList = kwargs['heightList']
                     timeList = kwargs['timeList']
                     rx_location = kwargs['rx_location']
                     groupList = kwargs['groupList']
                     azimuth = kwargs['azimuth']
                     dfactor = kwargs['dfactor']
                     k = kwargs['k']
                     azimuth1, dist = self.__calculateAzimuth1(rx_location, groupList, azimuth)
                     d = dist*dfactor
                     #Phase calculation
                     metArray1 = self.__getPhaseSlope(metArray, heightList, timeList)
                     metArray1[:,-2] = metArray1[:,-2]*metArray1[:,2]*1000/(k*d[metArray1[:,1].astype(int)]) #angles into velocities
                     velEst = numpy.zeros((heightList.size,2))*numpy.nan
                     azimuth1 = azimuth1*numpy.pi/180
                     for i in range(heightList.size):
                         h = heightList[i]
                         indH = numpy.where((metArray1[:,2] == h)&(numpy.abs(metArray1[:,-2]) < 100))[0]
                         metHeight = metArray1[indH,:]
                         if metHeight.shape[0] >= 2:
                             velAux = numpy.asmatrix(metHeight[:,-2]).T    #Radial Velocities
                             iazim = metHeight[:,1].astype(int)
                             azimAux = numpy.asmatrix(azimuth1[iazim]).T    #Azimuths
                             A = numpy.hstack((numpy.cos(azimAux),numpy.sin(azimAux)))
                             A = numpy.asmatrix(A)
                             A1 = numpy.linalg.pinv(A.transpose()*A)*A.transpose()
                             velHor = numpy.dot(A1,velAux)
                             velEst[i,:] = numpy.squeeze(velHor)
                     return velEst
                 def __getPhaseSlope(self, metArray, heightList, timeList):
                     meteorList = []
                     #utctime sec1 height SNR velRad ph0 ph1 ph2 coh0 coh1 coh2
                     #Putting back together the meteor matrix
                     utctime = metArray[:,0]
                     uniqueTime = numpy.unique(utctime)
                     phaseDerThresh = 0.5
                     ippSeconds = timeList[1] - timeList[0]
                     sec = numpy.where(timeList>1)[0][0]
                     nPairs = metArray.shape[1] - 6
                     nHeights = len(heightList)
                     for t in uniqueTime:
                         metArray1 = metArray[utctime==t,:]
             #         phaseDerThresh = numpy.pi/4 #reducir Phase thresh
                         tmet = metArray1[:,1].astype(int)
                         hmet = metArray1[:,2].astype(int)
                         metPhase = numpy.zeros((nPairs, heightList.size, timeList.size - 1))
                         metPhase[:,:] = numpy.nan
                         metPhase[:,hmet,tmet] = metArray1[:,6:].T
                         #Delete short trails
                         metBool = ~numpy.isnan(metPhase[0,:,:])
                         heightVect = numpy.sum(metBool, axis = 1)
                         metBool[heightVect<sec,:] = False
                         metPhase[:,heightVect<sec,:] = numpy.nan
                         #Derivative
                         metDer = numpy.abs(metPhase[:,:,1:] - metPhase[:,:,:-1])
                         phDerAux = numpy.dstack((numpy.full((nPairs,nHeights,1), False, dtype=bool),metDer > phaseDerThresh))
                         metPhase[phDerAux] = numpy.nan
                         #--------------------------METEOR DETECTION    -----------------------------------------
                         indMet = numpy.where(numpy.any(metBool,axis=1))[0]
                         for p in numpy.arange(nPairs):
                             phase = metPhase[p,:,:]
                             phDer = metDer[p,:,:]
                             for h in indMet:
                                 height = heightList[h]
                                 phase1 = phase[h,:] #82
                                 phDer1 = phDer[h,:]
                                 phase1[~numpy.isnan(phase1)] = numpy.unwrap(phase1[~numpy.isnan(phase1)])   #Unwrap
                                 indValid = numpy.where(~numpy.isnan(phase1))[0]
                                 initMet = indValid[0]
                                 endMet = 0
                                 for i in range(len(indValid)-1):
                                     #Time difference
                                     inow = indValid[i]
                                     inext = indValid[i+1]
                                     idiff = inext - inow
                                     #Phase difference
                                     phDiff = numpy.abs(phase1[inext] - phase1[inow])
                                     if idiff>sec or phDiff>numpy.pi/4 or inext==indValid[-1]:   #End of Meteor
                                         sizeTrail = inow - initMet + 1
                                         if sizeTrail>3*sec:  #Too short meteors
                                             x = numpy.arange(initMet,inow+1)*ippSeconds
                                             y = phase1[initMet:inow+1]
                                             ynnan = ~numpy.isnan(y)
                                             x = x[ynnan]
                                             y = y[ynnan]
                                             slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
                                             ylin = x*slope + intercept
                                             rsq = r_value**2
                                             if rsq > 0.5:
                                                 vel = slope#*height*1000/(k*d)
                                                 estAux = numpy.array([utctime,p,height, vel, rsq])
                                                 meteorList.append(estAux)
                                         initMet = inext
                     metArray2 = numpy.array(meteorList)
                     return metArray2
                 def __calculateAzimuth1(self, rx_location, pairslist, azimuth0):
                     azimuth1 = numpy.zeros(len(pairslist))
                     dist = numpy.zeros(len(pairslist))
                     for i in range(len(rx_location)):
                         ch0 = pairslist[i][0]
                         ch1 = pairslist[i][1]
                         diffX = rx_location[ch0][0] - rx_location[ch1][0]
                         diffY = rx_location[ch0][1] - rx_location[ch1][1]
                         azimuth1[i] = numpy.arctan2(diffY,diffX)*180/numpy.pi
                         dist[i] = numpy.sqrt(diffX**2 + diffY**2)
                     azimuth1 -= azimuth0
                     return azimuth1, dist
                 def techniqueNSM_DBS(self, **kwargs):
                     metArray = kwargs['metArray']
                     heightList = kwargs['heightList']
                     timeList = kwargs['timeList']
                     azimuth = kwargs['azimuth']
                     theta_x = numpy.array(kwargs['theta_x'])
                     theta_y = numpy.array(kwargs['theta_y'])
                     utctime = metArray[:,0]
                     cmet = metArray[:,1].astype(int)
                     hmet = metArray[:,3].astype(int)
                     SNRmet = metArray[:,4]
                     vmet = metArray[:,5]
                     spcmet = metArray[:,6]
                     nChan = numpy.max(cmet) + 1
                     nHeights = len(heightList)
                     azimuth_arr, zenith_arr, dir_cosu, dir_cosv, dir_cosw = self.__calculateAngles(theta_x, theta_y, azimuth)
                     hmet = heightList[hmet]
                     h1met = hmet*numpy.cos(zenith_arr[cmet])      #Corrected heights
                     velEst = numpy.zeros((heightList.size,2))*numpy.nan
                     for i in range(nHeights - 1):
                         hmin = heightList[i]
                         hmax = heightList[i + 1]
                         thisH = (h1met>=hmin) & (h1met<hmax) & (cmet!=2) & (SNRmet>8) & (vmet<50) & (spcmet<10)
                         indthisH = numpy.where(thisH)
                         if numpy.size(indthisH) > 3:
                             vel_aux = vmet[thisH]
                             chan_aux = cmet[thisH]
                             cosu_aux = dir_cosu[chan_aux]
                             cosv_aux = dir_cosv[chan_aux]
                             cosw_aux = dir_cosw[chan_aux]
                             nch = numpy.size(numpy.unique(chan_aux))
                             if  nch > 1:
                                 A = self.__calculateMatA(cosu_aux, cosv_aux, cosw_aux, True)
                                 velEst[i,:] = numpy.dot(A,vel_aux)
                     return velEst
                 def run(self, dataOut, technique, nHours=1, hmin=70, hmax=110, **kwargs):
                     param = dataOut.data_param
                     if dataOut.abscissaList != None:
                         absc = dataOut.abscissaList[:-1]
                     # noise = dataOut.noise
                     heightList = dataOut.heightList
                     SNR = dataOut.data_snr
                     if technique == 'DBS':
                         kwargs['velRadial'] = param[:,1,:] #Radial velocity
                         kwargs['heightList'] = heightList
                         kwargs['SNR'] = SNR
                         dataOut.data_output, dataOut.heightList, dataOut.data_snr = self.techniqueDBS(kwargs) #DBS Function
                         dataOut.utctimeInit = dataOut.utctime
                         dataOut.outputInterval = dataOut.paramInterval
                     elif technique == 'SA':
                         #Parameters
             #             position_x = kwargs['positionX']
             #             position_y = kwargs['positionY']
             #             azimuth = kwargs['azimuth']
             #
             #             if kwargs.has_key('crosspairsList'):
             #                 pairs = kwargs['crosspairsList']
             #             else:
             #                 pairs = None
             #
             #             if kwargs.has_key('correctFactor'):
             #                 correctFactor = kwargs['correctFactor']
             #             else:
             #                 correctFactor = 1
             #             tau = dataOut.data_param
             #             _lambda = dataOut.C/dataOut.frequency
             #             pairsList = dataOut.groupList
             #             nChannels = dataOut.nChannels
                         kwargs['groupList'] = dataOut.groupList
                         kwargs['tau'] = dataOut.data_param
                         kwargs['_lambda'] = dataOut.C/dataOut.frequency
             #             dataOut.data_output = self.techniqueSA(pairs, pairsList, nChannels, tau, azimuth, _lambda, position_x, position_y, absc, correctFactor)
                         dataOut.data_output = self.techniqueSA(kwargs)
                         dataOut.utctimeInit = dataOut.utctime
                         dataOut.outputInterval = dataOut.timeInterval
                     elif technique == 'Meteors':
                         dataOut.flagNoData = True
                         self.__dataReady = False
                         if 'nHours' in kwargs:
                             nHours = kwargs['nHours']
                         else:
                             nHours = 1
                         if 'meteorsPerBin' in kwargs:
                             meteorThresh = kwargs['meteorsPerBin']
                         else:
                             meteorThresh = 6
                         if 'hmin' in kwargs:
                             hmin = kwargs['hmin']
                         else:   hmin = 70
                         if 'hmax' in kwargs:
                             hmax = kwargs['hmax']
                         else:   hmax = 110
                         dataOut.outputInterval = nHours*3600
                         if self.__isConfig == False:
             #                 self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
                             #Get Initial LTC time
                             self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
                             self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
                             self.__isConfig = True
                         if self.__buffer is None:
                             self.__buffer = dataOut.data_param
                             self.__firstdata = copy.copy(dataOut)
                         else:
                             self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
                         self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
                         if self.__dataReady:
                             dataOut.utctimeInit = self.__initime
                             self.__initime += dataOut.outputInterval #to erase time offset
                             dataOut.data_output, dataOut.heightList = self.techniqueMeteors(self.__buffer, meteorThresh, hmin, hmax)
                             dataOut.flagNoData = False
                             self.__buffer = None
                     elif technique == 'Meteors1':
                         dataOut.flagNoData = True
                         self.__dataReady = False
                         if 'nMins' in kwargs:
                             nMins = kwargs['nMins']
                         else: nMins = 20
                         if 'rx_location' in kwargs:
                             rx_location = kwargs['rx_location']
                         else: rx_location = [(0,1),(1,1),(1,0)]
                         if 'azimuth' in kwargs:
                             azimuth = kwargs['azimuth']
                         else: azimuth = 51.06
                         if 'dfactor' in kwargs:
                             dfactor = kwargs['dfactor']
                         if 'mode' in kwargs:
                             mode = kwargs['mode']
                         if 'theta_x' in kwargs:
                             theta_x = kwargs['theta_x']
                         if 'theta_y' in kwargs:
                             theta_y = kwargs['theta_y']
                         else: mode = 'SA'
                         #Borrar luego esto
                         if dataOut.groupList is None:
                             dataOut.groupList = [(0,1),(0,2),(1,2)]
                         groupList = dataOut.groupList
                         C = 3e8
                         freq = 50e6
                         lamb = C/freq
                         k = 2*numpy.pi/lamb
                         timeList = dataOut.abscissaList
                         heightList = dataOut.heightList
                         if self.__isConfig == False:
                             dataOut.outputInterval = nMins*60
             #                 self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
                             #Get Initial LTC time
                             initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
                             minuteAux = initime.minute
                             minuteNew = int(numpy.floor(minuteAux/nMins)*nMins)
                             self.__initime = (initime.replace(minute = minuteNew, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
                             self.__isConfig = True
                         if self.__buffer is None:
                             self.__buffer = dataOut.data_param
                             self.__firstdata = copy.copy(dataOut)
                         else:
                             self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
                         self.__checkTime(dataOut.utctime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
                         if self.__dataReady:
                             dataOut.utctimeInit = self.__initime
                             self.__initime += dataOut.outputInterval #to erase time offset
                             metArray = self.__buffer
                             if mode == 'SA':
                                 dataOut.data_output = self.techniqueNSM_SA(rx_location=rx_location, groupList=groupList, azimuth=azimuth, dfactor=dfactor, k=k,metArray=metArray, heightList=heightList,timeList=timeList)
                             elif mode == 'DBS':
                                 dataOut.data_output = self.techniqueNSM_DBS(metArray=metArray,heightList=heightList,timeList=timeList, azimuth=azimuth, theta_x=theta_x, theta_y=theta_y)
                             dataOut.data_output = dataOut.data_output.T
                             dataOut.flagNoData = False
                             self.__buffer = None
                     return
             class EWDriftsEstimation(Operation):
                 def __init__(self):
                     Operation.__init__(self)
                 def __correctValues(self, heiRang, phi, velRadial, SNR):
                     listPhi = phi.tolist()
                     maxid = listPhi.index(max(listPhi))
                     minid = listPhi.index(min(listPhi))
                     rango = list(range(len(phi)))
                #     rango = numpy.delete(rango,maxid)
                     heiRang1 = heiRang*math.cos(phi[maxid])
                     heiRangAux = heiRang*math.cos(phi[minid])
                     indOut = (heiRang1 < heiRangAux[0]).nonzero()
                     heiRang1 = numpy.delete(heiRang1,indOut)
                     velRadial1 = numpy.zeros([len(phi),len(heiRang1)])
                     SNR1 = numpy.zeros([len(phi),len(heiRang1)])
                     for i in rango:
                         x = heiRang*math.cos(phi[i])
                         y1 = velRadial[i,:]
                         f1 = interpolate.interp1d(x,y1,kind = 'cubic')
                         x1 = heiRang1
                         y11 = f1(x1)
                         y2 = SNR[i,:]
                         f2 = interpolate.interp1d(x,y2,kind = 'cubic')
                         y21 = f2(x1)
                         velRadial1[i,:] = y11
                         SNR1[i,:] = y21
                     return heiRang1, velRadial1, SNR1
                 def run(self, dataOut, zenith, zenithCorrection):
                     heiRang = dataOut.heightList
                     velRadial = dataOut.data_param[:,3,:]
                     SNR = dataOut.data_snr
                     zenith = numpy.array(zenith)
                     zenith -= zenithCorrection
                     zenith *= numpy.pi/180
                     heiRang1, velRadial1, SNR1 = self.__correctValues(heiRang, numpy.abs(zenith), velRadial, SNR)
                     alp = zenith[0]
                     bet = zenith[1]
                     w_w = velRadial1[0,:]
                     w_e = velRadial1[1,:]
                     w = (w_w*numpy.sin(bet) - w_e*numpy.sin(alp))/(numpy.cos(alp)*numpy.sin(bet) - numpy.cos(bet)*numpy.sin(alp))
                     u = (w_w*numpy.cos(bet) - w_e*numpy.cos(alp))/(numpy.sin(alp)*numpy.cos(bet) - numpy.sin(bet)*numpy.cos(alp))
                     winds = numpy.vstack((u,w))
                     dataOut.heightList = heiRang1
                     dataOut.data_output = winds
                     dataOut.data_snr = SNR1
                     dataOut.utctimeInit = dataOut.utctime
                     dataOut.outputInterval = dataOut.timeInterval
                     return
             #---------------    Non Specular Meteor    ----------------
             class NonSpecularMeteorDetection(Operation):
                 def run(self, dataOut, mode, SNRthresh=8, phaseDerThresh=0.5, cohThresh=0.8, allData = False):
                     data_acf = dataOut.data_pre[0]
                     data_ccf = dataOut.data_pre[1]
                     pairsList = dataOut.groupList[1]
                     lamb = dataOut.C/dataOut.frequency
                     tSamp = dataOut.ippSeconds*dataOut.nCohInt
                     paramInterval = dataOut.paramInterval
                     nChannels = data_acf.shape[0]
                     nLags = data_acf.shape[1]
                     nProfiles = data_acf.shape[2]
                     nHeights = dataOut.nHeights
                     nCohInt = dataOut.nCohInt
                     sec = numpy.round(nProfiles/dataOut.paramInterval)
                     heightList = dataOut.heightList
                     ippSeconds = dataOut.ippSeconds*dataOut.nCohInt*dataOut.nAvg
                     utctime = dataOut.utctime
                     dataOut.abscissaList = numpy.arange(0,paramInterval+ippSeconds,ippSeconds)
                     #------------------------    SNR    --------------------------------------
                     power = data_acf[:,0,:,:].real
                     noise = numpy.zeros(nChannels)
                     SNR = numpy.zeros(power.shape)
                     for i in range(nChannels):
                         noise[i] = hildebrand_sekhon(power[i,:], nCohInt)
                         SNR[i] = (power[i]-noise[i])/noise[i]
                     SNRm = numpy.nanmean(SNR, axis = 0)
                     SNRdB = 10*numpy.log10(SNR)
                     if mode == 'SA':
                         dataOut.groupList = dataOut.groupList[1]
                         nPairs = data_ccf.shape[0]
                         #----------------------    Coherence and Phase   --------------------------
                         phase = numpy.zeros(data_ccf[:,0,:,:].shape)
             #             phase1 = numpy.copy(phase)
                         coh1 = numpy.zeros(data_ccf[:,0,:,:].shape)
                         for p in range(nPairs):
                             ch0 = pairsList[p][0]
                             ch1 = pairsList[p][1]
                             ccf = data_ccf[p,0,:,:]/numpy.sqrt(data_acf[ch0,0,:,:]*data_acf[ch1,0,:,:])
                             phase[p,:,:] = ndimage.median_filter(numpy.angle(ccf), size = (5,1)) #median filter
             #                 phase1[p,:,:] = numpy.angle(ccf) #median filter
                             coh1[p,:,:] = ndimage.median_filter(numpy.abs(ccf), 5) #median filter
             #                 coh1[p,:,:] = numpy.abs(ccf) #median filter
                         coh = numpy.nanmax(coh1, axis = 0)
             #             struc = numpy.ones((5,1))
             #             coh = ndimage.morphology.grey_dilation(coh, size=(10,1))
                         #----------------------    Radial Velocity    ----------------------------
                         phaseAux = numpy.mean(numpy.angle(data_acf[:,1,:,:]), axis = 0)
                         velRad = phaseAux*lamb/(4*numpy.pi*tSamp)
                         if allData:
                             boolMetFin = ~numpy.isnan(SNRm)
             #                 coh[:-1,:] = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
                         else:
                             #------------------------    Meteor mask    ---------------------------------
             #                 #SNR mask
             #                 boolMet = (SNRdB>SNRthresh)#|(~numpy.isnan(SNRdB))
             #
             #                 #Erase small objects
             #                 boolMet1 = self.__erase_small(boolMet, 2*sec, 5)
             #
             #                 auxEEJ = numpy.sum(boolMet1,axis=0)
             #                 indOver = auxEEJ>nProfiles*0.8  #Use this later
             #                 indEEJ = numpy.where(indOver)[0]
             #                 indNEEJ = numpy.where(~indOver)[0]
             #
             #                 boolMetFin = boolMet1
             #
             #                 if indEEJ.size > 0:
             #                     boolMet1[:,indEEJ] = False  #Erase heights with EEJ
             #
             #                     boolMet2 = coh > cohThresh
             #                     boolMet2 = self.__erase_small(boolMet2, 2*sec,5)
             #
             #                     #Final Meteor mask
             #                     boolMetFin = boolMet1|boolMet2
                             #Coherence mask
                             boolMet1 = coh > 0.75
                             struc = numpy.ones((30,1))
                             boolMet1 = ndimage.morphology.binary_dilation(boolMet1, structure=struc)
                             #Derivative mask
                             derPhase = numpy.nanmean(numpy.abs(phase[:,1:,:] - phase[:,:-1,:]),axis=0)
                             boolMet2 = derPhase < 0.2
             #                 boolMet2 = ndimage.morphology.binary_opening(boolMet2)
             #                 boolMet2 = ndimage.morphology.binary_closing(boolMet2, structure = numpy.ones((10,1)))
                             boolMet2 = ndimage.median_filter(boolMet2,size=5)
                             boolMet2 = numpy.vstack((boolMet2,numpy.full((1,nHeights), True, dtype=bool)))
             #                 #Final mask
             #                 boolMetFin = boolMet2
                             boolMetFin = boolMet1&boolMet2
             #                 boolMetFin = ndimage.morphology.binary_dilation(boolMetFin)
                         #Creating data_param
                         coordMet = numpy.where(boolMetFin)
                         tmet = coordMet[0]
                         hmet = coordMet[1]
                         data_param = numpy.zeros((tmet.size, 6 + nPairs))
                         data_param[:,0] = utctime
                         data_param[:,1] = tmet
                         data_param[:,2] = hmet
                         data_param[:,3] = SNRm[tmet,hmet]
                         data_param[:,4] = velRad[tmet,hmet]
                         data_param[:,5] = coh[tmet,hmet]
                         data_param[:,6:] = phase[:,tmet,hmet].T
                     elif mode == 'DBS':
                         dataOut.groupList = numpy.arange(nChannels)
                         #Radial Velocities
                         phase = numpy.angle(data_acf[:,1,:,:])
             #             phase = ndimage.median_filter(numpy.angle(data_acf[:,1,:,:]), size = (1,5,1))
                         velRad = phase*lamb/(4*numpy.pi*tSamp)
                         #Spectral width
             #             acf1 = ndimage.median_filter(numpy.abs(data_acf[:,1,:,:]), size = (1,5,1))
             #             acf2 = ndimage.median_filter(numpy.abs(data_acf[:,2,:,:]), size = (1,5,1))
                         acf1 = data_acf[:,1,:,:]
                         acf2 = data_acf[:,2,:,:]
                         spcWidth = (lamb/(2*numpy.sqrt(6)*numpy.pi*tSamp))*numpy.sqrt(numpy.log(acf1/acf2))
             #             velRad = ndimage.median_filter(velRad, size = (1,5,1))
                         if allData:
                             boolMetFin = ~numpy.isnan(SNRdB)
                         else:
                             #SNR
                             boolMet1 = (SNRdB>SNRthresh) #SNR mask
                             boolMet1 = ndimage.median_filter(boolMet1, size=(1,5,5))
                             #Radial velocity
                             boolMet2 = numpy.abs(velRad) < 20
                             boolMet2 = ndimage.median_filter(boolMet2, (1,5,5))
                             #Spectral Width
                             boolMet3 = spcWidth < 30
                             boolMet3 = ndimage.median_filter(boolMet3, (1,5,5))
             #                 boolMetFin = self.__erase_small(boolMet1, 10,5)
                             boolMetFin = boolMet1&boolMet2&boolMet3
                         #Creating data_param
                         coordMet = numpy.where(boolMetFin)
                         cmet = coordMet[0]
                         tmet = coordMet[1]
                         hmet = coordMet[2]
                         data_param = numpy.zeros((tmet.size, 7))
                         data_param[:,0] = utctime
                         data_param[:,1] = cmet
                         data_param[:,2] = tmet
                         data_param[:,3] = hmet
                         data_param[:,4] = SNR[cmet,tmet,hmet].T
                         data_param[:,5] = velRad[cmet,tmet,hmet].T
                         data_param[:,6] = spcWidth[cmet,tmet,hmet].T
             #         self.dataOut.data_param = data_int
                     if len(data_param) == 0:
                         dataOut.flagNoData = True
                     else:
                         dataOut.data_param = data_param
                 def __erase_small(self, binArray, threshX, threshY):
                     labarray, numfeat = ndimage.measurements.label(binArray)
                     binArray1 = numpy.copy(binArray)
                     for i in range(1,numfeat + 1):
                         auxBin = (labarray==i)
                         auxSize = auxBin.sum()
                         x,y = numpy.where(auxBin)
                         widthX = x.max() - x.min()
                         widthY = y.max() - y.min()
                         #width X: 3 seg -> 12.5*3
                         #width Y:
                         if (auxSize < 50) or (widthX < threshX) or (widthY < threshY):
                             binArray1[auxBin] = False
                     return binArray1
             #---------------    Specular Meteor    ----------------
             class SMDetection(Operation):
                 '''
                     Function DetectMeteors()
                         Project developed with paper:
                         HOLDSWORTH ET AL. 2004
                     Input:
                         self.dataOut.data_pre
                         centerReceiverIndex:      From the channels, which is the center receiver
                         hei_ref:                  Height reference for the Beacon signal extraction
                         tauindex:
                         predefinedPhaseShifts:    Predefined phase offset for the voltge signals
                         cohDetection:             Whether to user Coherent detection or not
                         cohDet_timeStep:          Coherent Detection calculation time step
                         cohDet_thresh:            Coherent Detection phase threshold to correct phases
                         noise_timeStep:           Noise calculation time step
                         noise_multiple:           Noise multiple to define signal threshold
                         multDet_timeLimit:        Multiple Detection Removal time limit in seconds
                         multDet_rangeLimit:       Multiple Detection Removal range limit in km
                         phaseThresh:              Maximum phase difference between receiver to be consider a meteor
                         SNRThresh:                Minimum SNR threshold of the meteor signal to be consider a meteor
                         hmin:                     Minimum Height of the meteor to use it in the further wind estimations
                         hmax:                     Maximum Height of the meteor to use it in the further wind estimations
                         azimuth:                  Azimuth angle correction
                     Affected:
                         self.dataOut.data_param
                     Rejection Criteria (Errors):
 : No error; analysis OK
 : SNR < SNR threshold
 : angle of arrival (AOA) ambiguously determined
 : AOA estimate not feasible
 : Large difference in AOAs obtained from different antenna baselines
 : echo at start or end of time series
 : echo less than 5 examples long; too short for analysis
 : echo rise exceeds 0.3s
 : echo decay time less than twice rise time
 : large power level before echo
 : large power level after echo
 : poor fit to amplitude for estimation of decay time
 : poor fit to CCF phase variation for estimation of radial drift velocity
 : height unresolvable echo: not valid height within 70 to 110 km
 : height ambiguous echo: more then one possible height within 70 to 110 km
 : radial drift velocity or projected horizontal velocity exceeds 200 m/s
 : oscilatory echo, indicating event most likely not an underdense echo
 : phase difference in meteor Reestimation
                     Data Storage:
                         Meteors for Wind Estimation   (8):
                         Utc Time   |    Range    Height
                         Azimuth    Zenith    errorCosDir
                         VelRad    errorVelRad
                         Phase0 Phase1 Phase2 Phase3
                         TypeError
                      '''
                 def run(self, dataOut, hei_ref = None, tauindex = 0,
                                   phaseOffsets = None,
                                   cohDetection = False, cohDet_timeStep = 1, cohDet_thresh = 25,
                                   noise_timeStep = 4, noise_multiple = 4,
                                   multDet_timeLimit = 1, multDet_rangeLimit = 3,
                                   phaseThresh = 20, SNRThresh = 5,
                                   hmin = 50, hmax=150, azimuth = 0,
                                   channelPositions = None) :
                     #Getting Pairslist
                     if channelPositions is None:
             #             channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)]   #T
                         channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)]   #Estrella
                     meteorOps = SMOperations()
                     pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
                     heiRang = dataOut.heightList
                     #Get Beacon signal - No Beacon signal anymore
             #         newheis = numpy.where(self.dataOut.heightList>self.dataOut.radarControllerHeaderObj.Taus[tauindex])
             #
             #         if hei_ref != None:
             #             newheis = numpy.where(self.dataOut.heightList>hei_ref)
             #
                     #****************REMOVING HARDWARE PHASE DIFFERENCES***************
                     # see if the user put in pre defined phase shifts
                     voltsPShift = dataOut.data_pre.copy()
             #         if predefinedPhaseShifts != None:
             #             hardwarePhaseShifts = numpy.array(predefinedPhaseShifts)*numpy.pi/180
             #
             # #         elif beaconPhaseShifts:
             # #             #get hardware phase shifts using beacon signal
             # #             hardwarePhaseShifts = self.__getHardwarePhaseDiff(self.dataOut.data_pre, pairslist, newheis, 10)
             # #             hardwarePhaseShifts = numpy.insert(hardwarePhaseShifts,centerReceiverIndex,0)
             #
             #         else:
             #             hardwarePhaseShifts = numpy.zeros(5)
             #
             #         voltsPShift = numpy.zeros((self.dataOut.data_pre.shape[0],self.dataOut.data_pre.shape[1],self.dataOut.data_pre.shape[2]), dtype = 'complex')
             #         for i in range(self.dataOut.data_pre.shape[0]):
             #             voltsPShift[i,:,:] = self.__shiftPhase(self.dataOut.data_pre[i,:,:], hardwarePhaseShifts[i])
                     #******************END OF REMOVING HARDWARE PHASE DIFFERENCES*********
                     #Remove DC
                     voltsDC = numpy.mean(voltsPShift,1)
                     voltsDC = numpy.mean(voltsDC,1)
                     for i in range(voltsDC.shape[0]):
                         voltsPShift[i] = voltsPShift[i] - voltsDC[i]
                     #Don't considerate last heights, theyre used to calculate Hardware Phase Shift
             #         voltsPShift = voltsPShift[:,:,:newheis[0][0]]
                     #************ FIND POWER OF DATA W/COH OR NON COH DETECTION (3.4) **********
                     #Coherent Detection
                     if cohDetection:
                         #use coherent detection to get the net power
                         cohDet_thresh = cohDet_thresh*numpy.pi/180
                         voltsPShift = self.__coherentDetection(voltsPShift, cohDet_timeStep, dataOut.timeInterval, pairslist0, cohDet_thresh)
                     #Non-coherent detection!
                     powerNet = numpy.nansum(numpy.abs(voltsPShift[:,:,:])**2,0)
                     #********** END OF COH/NON-COH POWER CALCULATION**********************
                     #********** FIND THE NOISE LEVEL AND POSSIBLE METEORS ****************
                     #Get noise
                     noise, noise1 = self.__getNoise(powerNet, noise_timeStep, dataOut.timeInterval)
             #         noise = self.getNoise1(powerNet, noise_timeStep, self.dataOut.timeInterval)
                     #Get signal threshold
                     signalThresh = noise_multiple*noise
                     #Meteor echoes detection
                     listMeteors = self.__findMeteors(powerNet, signalThresh)
                     #******* END OF NOISE LEVEL AND POSSIBLE METEORS CACULATION **********
                     #************** REMOVE MULTIPLE DETECTIONS (3.5) ***************************
                     #Parameters
                     heiRange = dataOut.heightList
                     rangeInterval = heiRange[1] - heiRange[0]
                     rangeLimit = multDet_rangeLimit/rangeInterval
                     timeLimit = multDet_timeLimit/dataOut.timeInterval
                     #Multiple detection removals
                     listMeteors1 = self.__removeMultipleDetections(listMeteors, rangeLimit, timeLimit)
                     #************ END OF REMOVE MULTIPLE DETECTIONS **********************
                     #*********************     METEOR REESTIMATION  (3.7, 3.8, 3.9, 3.10)   ********************
                     #Parameters
                     phaseThresh = phaseThresh*numpy.pi/180
                     thresh = [phaseThresh, noise_multiple, SNRThresh]
                     #Meteor reestimation  (Errors N 1, 6, 12, 17)
                     listMeteors2, listMeteorsPower, listMeteorsVolts = self.__meteorReestimation(listMeteors1, voltsPShift, pairslist0, thresh, noise, dataOut.timeInterval, dataOut.frequency)
             #         listMeteors2, listMeteorsPower, listMeteorsVolts = self.meteorReestimation3(listMeteors2, listMeteorsPower, listMeteorsVolts, voltsPShift, pairslist, thresh, noise)
                     #Estimation of decay times (Errors N 7, 8, 11)
                     listMeteors3 = self.__estimateDecayTime(listMeteors2, listMeteorsPower, dataOut.timeInterval, dataOut.frequency)
                     #*******************     END OF METEOR REESTIMATION    *******************
                     #*********************    METEOR PARAMETERS CALCULATION (3.11, 3.12, 3.13)    **************************
                     #Calculating Radial Velocity (Error N 15)
                     radialStdThresh = 10
                     listMeteors4 = self.__getRadialVelocity(listMeteors3, listMeteorsVolts, radialStdThresh, pairslist0, dataOut.timeInterval)
                     if len(listMeteors4) > 0:
                         #Setting New Array
                         date = dataOut.utctime
                         arrayParameters = self.__setNewArrays(listMeteors4, date, heiRang)
                         #Correcting phase offset
                         if phaseOffsets != None:
                             phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
                             arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
                         #Second Pairslist
                         pairsList = []
                         pairx = (0,1)
                         pairy = (2,3)
                         pairsList.append(pairx)
                         pairsList.append(pairy)
                         jph = numpy.array([0,0,0,0])
                         h = (hmin,hmax)
                         arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
             #             #Calculate AOA (Error N 3, 4)
             #             #JONES ET AL. 1998
             #             error = arrayParameters[:,-1]
             #             AOAthresh = numpy.pi/8
             #             phases = -arrayParameters[:,9:13]
             #             arrayParameters[:,4:7], arrayParameters[:,-1] = meteorOps.getAOA(phases, pairsList, error, AOAthresh, azimuth)
             #
             #             #Calculate Heights (Error N 13 and 14)
             #             error = arrayParameters[:,-1]
             #             Ranges = arrayParameters[:,2]
             #             zenith = arrayParameters[:,5]
             #             arrayParameters[:,3], arrayParameters[:,-1] = meteorOps.getHeights(Ranges, zenith, error, hmin, hmax)
             #             error = arrayParameters[:,-1]
                     #*********************    END OF PARAMETERS CALCULATION    **************************
                     #***************************+     PASS DATA TO NEXT STEP    **********************
             #             arrayFinal = arrayParameters.reshape((1,arrayParameters.shape[0],arrayParameters.shape[1]))
                         dataOut.data_param = arrayParameters
                         if arrayParameters is None:
                             dataOut.flagNoData = True
                     else:
                         dataOut.flagNoData = True
                     return
                 def __getHardwarePhaseDiff(self, voltage0, pairslist, newheis, n):
                     minIndex = min(newheis[0])
                     maxIndex = max(newheis[0])
                     voltage = voltage0[:,:,minIndex:maxIndex+1]
                     nLength = voltage.shape[1]/n
                     nMin = 0
                     nMax = 0
                     phaseOffset = numpy.zeros((len(pairslist),n))
                     for i in range(n):
                         nMax += nLength
                         phaseCCF = -numpy.angle(self.__calculateCCF(voltage[:,nMin:nMax,:], pairslist, [0]))
                         phaseCCF = numpy.mean(phaseCCF, axis = 2)
                         phaseOffset[:,i] = phaseCCF.transpose()
                         nMin = nMax
             #         phaseDiff, phaseArrival = self.estimatePhaseDifference(voltage, pairslist)
                     #Remove Outliers
                     factor = 2
                     wt = phaseOffset - signal.medfilt(phaseOffset,(1,5))
                     dw = numpy.std(wt,axis = 1)
                     dw = dw.reshape((dw.size,1))
                     ind = numpy.where(numpy.logical_or(wt>dw*factor,wt<-dw*factor))
                     phaseOffset[ind] = numpy.nan
                     phaseOffset = stats.nanmean(phaseOffset, axis=1)
                     return phaseOffset
                 def __shiftPhase(self, data, phaseShift):
                     #this will shift the phase of a complex number
                     dataShifted = numpy.abs(data) * numpy.exp((numpy.angle(data)+phaseShift)*1j)
                     return dataShifted
                 def __estimatePhaseDifference(self, array, pairslist):
                     nChannel = array.shape[0]
                     nHeights = array.shape[2]
                     numPairs = len(pairslist)
             #         phaseCCF = numpy.zeros((nChannel, 5, nHeights))
                     phaseCCF = numpy.angle(self.__calculateCCF(array, pairslist, [-2,-1,0,1,2]))
                     #Correct phases
                     derPhaseCCF = phaseCCF[:,1:,:] - phaseCCF[:,0:-1,:]
                     indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
                     if indDer[0].shape[0] > 0:
                         for i in range(indDer[0].shape[0]):
                             signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i],indDer[2][i]])
                             phaseCCF[indDer[0][i],indDer[1][i]+1:,:] += signo*2*numpy.pi
             #         for j in range(numSides):
             #             phaseCCFAux = self.calculateCCF(arrayCenter, arraySides[j,:,:], [-2,1,0,1,2])
             #             phaseCCF[j,:,:] = numpy.angle(phaseCCFAux)
             #
                     #Linear
                     phaseInt = numpy.zeros((numPairs,1))
                     angAllCCF = phaseCCF[:,[0,1,3,4],0]
                     for j in range(numPairs):
                         fit = stats.linregress([-2,-1,1,2],angAllCCF[j,:])
                         phaseInt[j] = fit[1]
                     #Phase Differences
                     phaseDiff = phaseInt - phaseCCF[:,2,:]
                     phaseArrival = phaseInt.reshape(phaseInt.size)
                     #Dealias
                     phaseArrival = numpy.angle(numpy.exp(1j*phaseArrival))
             #         indAlias = numpy.where(phaseArrival > numpy.pi)
             #         phaseArrival[indAlias] -= 2*numpy.pi
             #         indAlias = numpy.where(phaseArrival < -numpy.pi)
             #         phaseArrival[indAlias] += 2*numpy.pi
                     return phaseDiff, phaseArrival
                 def __coherentDetection(self, volts, timeSegment, timeInterval, pairslist, thresh):
                     #this function will run the coherent detection used in Holdworth et al. 2004 and return the net power
                     #find the phase shifts of each channel over 1 second intervals
                     #only look at ranges below the beacon signal
                     numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
                     numBlocks = int(volts.shape[1]/numProfPerBlock)
                     numHeights = volts.shape[2]
                     nChannel = volts.shape[0]
                     voltsCohDet = volts.copy()
                     pairsarray = numpy.array(pairslist)
                     indSides = pairsarray[:,1]
             #         indSides = numpy.array(range(nChannel))
             #         indSides = numpy.delete(indSides, indCenter)
             #
             #         listCenter = numpy.array_split(volts[indCenter,:,:], numBlocks, 0)
                     listBlocks = numpy.array_split(volts, numBlocks, 1)
                     startInd = 0
                     endInd = 0
                     for i in range(numBlocks):
                         startInd = endInd
                         endInd = endInd + listBlocks[i].shape[1]
                         arrayBlock = listBlocks[i]
             #             arrayBlockCenter = listCenter[i]
                         #Estimate the Phase Difference
                         phaseDiff, aux = self.__estimatePhaseDifference(arrayBlock, pairslist)
                         #Phase Difference RMS
                         arrayPhaseRMS = numpy.abs(phaseDiff)
                         phaseRMSaux = numpy.sum(arrayPhaseRMS < thresh,0)
                         indPhase = numpy.where(phaseRMSaux==4)
                         #Shifting
                         if indPhase[0].shape[0] > 0:
                             for j in range(indSides.size):
                                 arrayBlock[indSides[j],:,indPhase] = self.__shiftPhase(arrayBlock[indSides[j],:,indPhase], phaseDiff[j,indPhase].transpose())
                             voltsCohDet[:,startInd:endInd,:] = arrayBlock
                     return voltsCohDet
                 def __calculateCCF(self, volts, pairslist ,laglist):
                     nHeights = volts.shape[2]
                     nPoints = volts.shape[1]
                     voltsCCF = numpy.zeros((len(pairslist), len(laglist), nHeights),dtype = 'complex')
                     for i in range(len(pairslist)):
                         volts1 = volts[pairslist[i][0]]
                         volts2 = volts[pairslist[i][1]]
                         for t in range(len(laglist)):
                             idxT = laglist[t]
                             if idxT >= 0:
                                 vStacked = numpy.vstack((volts2[idxT:,:],
                                                        numpy.zeros((idxT, nHeights),dtype='complex')))
                             else:
                                 vStacked = numpy.vstack((numpy.zeros((-idxT, nHeights),dtype='complex'),
                                                        volts2[:(nPoints + idxT),:]))
                             voltsCCF[i,t,:] = numpy.sum((numpy.conjugate(volts1)*vStacked),axis=0)
                             vStacked = None
                     return voltsCCF
                 def __getNoise(self, power, timeSegment, timeInterval):
                     numProfPerBlock = numpy.ceil(timeSegment/timeInterval)
                     numBlocks = int(power.shape[0]/numProfPerBlock)
                     numHeights = power.shape[1]
                     listPower = numpy.array_split(power, numBlocks, 0)
                     noise = numpy.zeros((power.shape[0], power.shape[1]))
                     noise1 = numpy.zeros((power.shape[0], power.shape[1]))
                     startInd = 0
                     endInd = 0
                     for i in range(numBlocks):             #split por canal
                         startInd = endInd
                         endInd = endInd + listPower[i].shape[0]
                         arrayBlock = listPower[i]
                         noiseAux = numpy.mean(arrayBlock, 0)
             #             noiseAux = numpy.median(noiseAux)
             #             noiseAux = numpy.mean(arrayBlock)
                         noise[startInd:endInd,:] = noise[startInd:endInd,:] + noiseAux
                         noiseAux1 = numpy.mean(arrayBlock)
                         noise1[startInd:endInd,:] = noise1[startInd:endInd,:] + noiseAux1
                     return noise, noise1
                 def __findMeteors(self, power, thresh):
                     nProf = power.shape[0]
                     nHeights = power.shape[1]
                     listMeteors = []
                     for i in range(nHeights):
                         powerAux = power[:,i]
                         threshAux = thresh[:,i]
                         indUPthresh = numpy.where(powerAux > threshAux)[0]
                         indDNthresh = numpy.where(powerAux <= threshAux)[0]
                         j = 0
                         while (j < indUPthresh.size - 2):
                             if (indUPthresh[j + 2] == indUPthresh[j] + 2):
                                 indDNAux = numpy.where(indDNthresh > indUPthresh[j])
                                 indDNthresh = indDNthresh[indDNAux]
                                 if (indDNthresh.size > 0):
                                     indEnd = indDNthresh[0] - 1
                                     indInit = indUPthresh[j]
                                     meteor = powerAux[indInit:indEnd + 1]
                                     indPeak = meteor.argmax() + indInit
                                     FLA = sum(numpy.conj(meteor)*numpy.hstack((meteor[1:],0)))
                                     listMeteors.append(numpy.array([i,indInit,indPeak,indEnd,FLA])) #CHEQUEAR!!!!!
                                     j = numpy.where(indUPthresh == indEnd)[0] + 1
                                 else: j+=1
                             else: j+=1
                     return listMeteors
                 def __removeMultipleDetections(self,listMeteors, rangeLimit, timeLimit):
                     arrayMeteors = numpy.asarray(listMeteors)
                     listMeteors1 = []
                     while arrayMeteors.shape[0] > 0:
                         FLAs = arrayMeteors[:,4]
                         maxFLA = FLAs.argmax()
                         listMeteors1.append(arrayMeteors[maxFLA,:])
                         MeteorInitTime = arrayMeteors[maxFLA,1]
                         MeteorEndTime = arrayMeteors[maxFLA,3]
                         MeteorHeight = arrayMeteors[maxFLA,0]
                         #Check neighborhood
                         maxHeightIndex = MeteorHeight + rangeLimit
                         minHeightIndex = MeteorHeight - rangeLimit
                         minTimeIndex = MeteorInitTime - timeLimit
                         maxTimeIndex = MeteorEndTime + timeLimit
                         #Check Heights
                         indHeight = numpy.logical_and(arrayMeteors[:,0] >= minHeightIndex, arrayMeteors[:,0] <= maxHeightIndex)
                         indTime = numpy.logical_and(arrayMeteors[:,3] >= minTimeIndex, arrayMeteors[:,1] <= maxTimeIndex)
                         indBoth = numpy.where(numpy.logical_and(indTime,indHeight))
                         arrayMeteors = numpy.delete(arrayMeteors, indBoth, axis = 0)
                     return listMeteors1
                 def __meteorReestimation(self, listMeteors, volts, pairslist, thresh, noise, timeInterval,frequency):
                     numHeights = volts.shape[2]
                     nChannel = volts.shape[0]
                     thresholdPhase = thresh[0]
                     thresholdNoise = thresh[1]
                     thresholdDB = float(thresh[2])
                     thresholdDB1 = 10**(thresholdDB/10)
                     pairsarray = numpy.array(pairslist)
                     indSides = pairsarray[:,1]
                     pairslist1 = list(pairslist)
                     pairslist1.append((0,1))
                     pairslist1.append((3,4))
                     listMeteors1 = []
                     listPowerSeries = []
                     listVoltageSeries = []
                     #volts has the war data
                     if frequency == 30e6:
                         timeLag = 45*10**-3
                     else:
                         timeLag = 15*10**-3
                     lag = numpy.ceil(timeLag/timeInterval)
                     for i in range(len(listMeteors)):
                         ######################   3.6 - 3.7 PARAMETERS REESTIMATION    #########################
                         meteorAux = numpy.zeros(16)
                         #Loading meteor Data (mHeight, mStart, mPeak, mEnd)
                         mHeight = listMeteors[i][0]
                         mStart = listMeteors[i][1]
                         mPeak = listMeteors[i][2]
                         mEnd = listMeteors[i][3]
                         #get the volt data between the start and end times of the meteor
                         meteorVolts = volts[:,mStart:mEnd+1,mHeight]
                         meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
                         #3.6. Phase Difference estimation
                         phaseDiff, aux = self.__estimatePhaseDifference(meteorVolts, pairslist)
                         #3.7. Phase difference removal & meteor start, peak and end times reestimated
                         #meteorVolts0.- all Channels, all Profiles
                         meteorVolts0 = volts[:,:,mHeight]
                         meteorThresh = noise[:,mHeight]*thresholdNoise
                         meteorNoise = noise[:,mHeight]
                         meteorVolts0[indSides,:] = self.__shiftPhase(meteorVolts0[indSides,:], phaseDiff) #Phase Shifting
                         powerNet0 = numpy.nansum(numpy.abs(meteorVolts0)**2, axis = 0)  #Power
                         #Times reestimation
                         mStart1 = numpy.where(powerNet0[:mPeak] < meteorThresh[:mPeak])[0]
                         if mStart1.size > 0:
                             mStart1 = mStart1[-1] + 1
                         else:
                             mStart1 = mPeak
                         mEnd1 = numpy.where(powerNet0[mPeak:] < meteorThresh[mPeak:])[0][0] + mPeak - 1
                         mEndDecayTime1 = numpy.where(powerNet0[mPeak:] < meteorNoise[mPeak:])[0]
                         if mEndDecayTime1.size == 0:
                             mEndDecayTime1 = powerNet0.size
                         else:
                             mEndDecayTime1 = mEndDecayTime1[0] + mPeak - 1
             #             mPeak1 = meteorVolts0[mStart1:mEnd1 + 1].argmax()
                         #meteorVolts1.- all Channels, from start to end
                         meteorVolts1 = meteorVolts0[:,mStart1:mEnd1 + 1]
                         meteorVolts2 = meteorVolts0[:,mPeak + lag:mEnd1 + 1]
                         if meteorVolts2.shape[1] == 0:
                             meteorVolts2 = meteorVolts0[:,mPeak:mEnd1 + 1]
                         meteorVolts1 = meteorVolts1.reshape(meteorVolts1.shape[0], meteorVolts1.shape[1], 1)
                         meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1], 1)
                         #####################    END PARAMETERS REESTIMATION    #########################
                         #####################   3.8 PHASE DIFFERENCE REESTIMATION  ########################
             #             if mEnd1 - mStart1 > 4:       #Error Number 6: echo less than 5 samples long; too short for analysis
                         if meteorVolts2.shape[1] > 0:
                             #Phase Difference re-estimation
                             phaseDiff1, phaseDiffint = self.__estimatePhaseDifference(meteorVolts2, pairslist1)   #Phase Difference Estimation
             #                 phaseDiff1, phaseDiffint = self.estimatePhaseDifference(meteorVolts2, pairslist)
                             meteorVolts2 = meteorVolts2.reshape(meteorVolts2.shape[0], meteorVolts2.shape[1])
                             phaseDiff11 = numpy.reshape(phaseDiff1, (phaseDiff1.shape[0],1))
                             meteorVolts2[indSides,:] = self.__shiftPhase(meteorVolts2[indSides,:], phaseDiff11[0:4])     #Phase Shifting
                             #Phase Difference RMS
                             phaseRMS1 = numpy.sqrt(numpy.mean(numpy.square(phaseDiff1)))
                             powerNet1 = numpy.nansum(numpy.abs(meteorVolts1[:,:])**2,0)
                             #Data from Meteor
                             mPeak1 = powerNet1.argmax() + mStart1
                             mPeakPower1 = powerNet1.max()
                             noiseAux = sum(noise[mStart1:mEnd1 + 1,mHeight])
                             mSNR1 = (sum(powerNet1)-noiseAux)/noiseAux
                             Meteor1 = numpy.array([mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1])
                             Meteor1 = numpy.hstack((Meteor1,phaseDiffint))
                             PowerSeries  = powerNet0[mStart1:mEndDecayTime1 + 1]
                             #Vectorize
                             meteorAux[0:7] = [mHeight, mStart1, mPeak1, mEnd1, mPeakPower1, mSNR1, phaseRMS1]
                             meteorAux[7:11] = phaseDiffint[0:4]
                             #Rejection Criterions
                             if phaseRMS1 > thresholdPhase:  #Error Number 17: Phase variation
                                 meteorAux[-1] = 17
                             elif mSNR1 < thresholdDB1:      #Error Number 1: SNR < threshold dB
                                 meteorAux[-1] = 1
                         else:
                             meteorAux[0:4] = [mHeight, mStart, mPeak, mEnd]
                             meteorAux[-1] = 6 #Error Number 6: echo less than 5 samples long; too short for analysis
                             PowerSeries = 0
                         listMeteors1.append(meteorAux)
                         listPowerSeries.append(PowerSeries)
                         listVoltageSeries.append(meteorVolts1)
                     return listMeteors1, listPowerSeries, listVoltageSeries
                 def __estimateDecayTime(self, listMeteors, listPower, timeInterval, frequency):
                     threshError = 10
                     #Depending if it is 30 or 50 MHz
                     if frequency == 30e6:
                         timeLag = 45*10**-3
                     else:
                         timeLag = 15*10**-3
                     lag = numpy.ceil(timeLag/timeInterval)
                     listMeteors1 = []
                     for i in range(len(listMeteors)):
                         meteorPower = listPower[i]
                         meteorAux = listMeteors[i]
                         if meteorAux[-1] == 0:
                             try:
                                 indmax = meteorPower.argmax()
                                 indlag = indmax + lag
                                 y = meteorPower[indlag:]
                                 x = numpy.arange(0, y.size)*timeLag
                                 #first guess
                                 a = y[0]
                                 tau = timeLag
                                 #exponential fit
                                 popt, pcov = optimize.curve_fit(self.__exponential_function, x, y, p0 = [a, tau])
                                 y1 = self.__exponential_function(x, *popt)
                                 #error estimation
                                 error = sum((y - y1)**2)/(numpy.var(y)*(y.size - popt.size))
                                 decayTime = popt[1]
                                 riseTime = indmax*timeInterval
                                 meteorAux[11:13] = [decayTime, error]
                                 #Table items 7, 8 and 11
                                 if (riseTime > 0.3):            #Number 7: Echo rise exceeds 0.3s
                                     meteorAux[-1] = 7
                                 elif (decayTime < 2*riseTime) : #Number 8: Echo decay time less than than twice rise time
                                     meteorAux[-1] = 8
                                 if (error > threshError):       #Number 11: Poor fit to amplitude for estimation of decay time
                                     meteorAux[-1] = 11
                             except:
                                 meteorAux[-1] = 11
                         listMeteors1.append(meteorAux)
                     return listMeteors1
                 #Exponential Function
                 def __exponential_function(self, x, a, tau):
                     y = a*numpy.exp(-x/tau)
                     return y
                 def __getRadialVelocity(self, listMeteors, listVolts, radialStdThresh, pairslist,  timeInterval):
                     pairslist1 = list(pairslist)
                     pairslist1.append((0,1))
                     pairslist1.append((3,4))
                     numPairs = len(pairslist1)
                     #Time Lag
                     timeLag = 45*10**-3
                     c = 3e8
                     lag = numpy.ceil(timeLag/timeInterval)
                     freq = 30e6
                     listMeteors1 = []
                     for i in range(len(listMeteors)):
                         meteorAux = listMeteors[i]
                         if meteorAux[-1] == 0:
                             mStart = listMeteors[i][1]
                             mPeak = listMeteors[i][2]
                             mLag = mPeak - mStart + lag
                             #get the volt data between the start and end times of the meteor
                             meteorVolts = listVolts[i]
                             meteorVolts = meteorVolts.reshape(meteorVolts.shape[0], meteorVolts.shape[1], 1)
                             #Get CCF
                             allCCFs = self.__calculateCCF(meteorVolts, pairslist1, [-2,-1,0,1,2])
                             #Method 2
                             slopes = numpy.zeros(numPairs)
                             time = numpy.array([-2,-1,1,2])*timeInterval
                             angAllCCF = numpy.angle(allCCFs[:,[0,1,3,4],0])
                             #Correct phases
                             derPhaseCCF = angAllCCF[:,1:] - angAllCCF[:,0:-1]
                             indDer = numpy.where(numpy.abs(derPhaseCCF) > numpy.pi)
                             if indDer[0].shape[0] > 0:
                                 for i in range(indDer[0].shape[0]):
                                     signo = -numpy.sign(derPhaseCCF[indDer[0][i],indDer[1][i]])
                                     angAllCCF[indDer[0][i],indDer[1][i]+1:] += signo*2*numpy.pi
             #                     fit = scipy.stats.linregress(numpy.array([-2,-1,1,2])*timeInterval, numpy.array([phaseLagN2s[i],phaseLagN1s[i],phaseLag1s[i],phaseLag2s[i]]))
                             for j in range(numPairs):
                                 fit = stats.linregress(time, angAllCCF[j,:])
                                 slopes[j] = fit[0]
                             #Remove Outlier
             #                 indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
             #                 slopes = numpy.delete(slopes,indOut)
             #                 indOut = numpy.argmax(numpy.abs(slopes - numpy.mean(slopes)))
             #                 slopes = numpy.delete(slopes,indOut)
                             radialVelocity = -numpy.mean(slopes)*(0.25/numpy.pi)*(c/freq)
                             radialError = numpy.std(slopes)*(0.25/numpy.pi)*(c/freq)
                             meteorAux[-2] = radialError
                             meteorAux[-3] = radialVelocity
                             #Setting Error
                             #Number 15: Radial Drift velocity or projected horizontal velocity exceeds 200 m/s
                             if numpy.abs(radialVelocity) > 200:
                                 meteorAux[-1] = 15
                             #Number 12: Poor fit to CCF variation for estimation of radial drift velocity
                             elif radialError > radialStdThresh:
                                 meteorAux[-1] = 12
                         listMeteors1.append(meteorAux)
                     return listMeteors1
                 def __setNewArrays(self, listMeteors, date, heiRang):
                     #New arrays
                     arrayMeteors = numpy.array(listMeteors)
                     arrayParameters = numpy.zeros((len(listMeteors), 13))
                     #Date inclusion
             #         date = re.findall(r'\((.*?)\)', date)
             #         date = date[0].split(',')
             #         date = map(int, date)
             #
             #         if len(date)<6:
             #             date.append(0)
             #
             #         date = [date[0]*10000 + date[1]*100 + date[2], date[3]*10000 + date[4]*100 + date[5]]
             #         arrayDate = numpy.tile(date, (len(listMeteors), 1))
                     arrayDate = numpy.tile(date, (len(listMeteors)))
                     #Meteor array
             #         arrayMeteors[:,0] = heiRang[arrayMeteors[:,0].astype(int)]
             #         arrayMeteors = numpy.hstack((arrayDate, arrayMeteors))
                     #Parameters Array
                     arrayParameters[:,0] = arrayDate #Date
                     arrayParameters[:,1] = heiRang[arrayMeteors[:,0].astype(int)] #Range
                     arrayParameters[:,6:8] = arrayMeteors[:,-3:-1] #Radial velocity and its error
                     arrayParameters[:,8:12] = arrayMeteors[:,7:11]  #Phases
                     arrayParameters[:,-1] = arrayMeteors[:,-1]  #Error
                     return arrayParameters
             class CorrectSMPhases(Operation):
                 def run(self, dataOut, phaseOffsets, hmin = 50, hmax = 150, azimuth = 45, channelPositions = None):
                     arrayParameters = dataOut.data_param
                     pairsList = []
                     pairx = (0,1)
                     pairy = (2,3)
                     pairsList.append(pairx)
                     pairsList.append(pairy)
                     jph = numpy.zeros(4)
                     phaseOffsets = numpy.array(phaseOffsets)*numpy.pi/180
                 #         arrayParameters[:,8:12] = numpy.unwrap(arrayParameters[:,8:12] + phaseOffsets)
                     arrayParameters[:,8:12] = numpy.angle(numpy.exp(1j*(arrayParameters[:,8:12] + phaseOffsets)))
                     meteorOps = SMOperations()
                     if channelPositions is None:
                 #             channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)]   #T
                         channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)]   #Estrella
                     pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
                     h = (hmin,hmax)
                     arrayParameters = meteorOps.getMeteorParams(arrayParameters, azimuth, h, pairsList, distances, jph)
                     dataOut.data_param = arrayParameters
                     return
             class SMPhaseCalibration(Operation):
                 __buffer = None
                 __initime = None
                 __dataReady = False
                 __isConfig = False
                 def __checkTime(self, currentTime, initTime, paramInterval, outputInterval):
                     dataTime = currentTime + paramInterval
                     deltaTime = dataTime - initTime
                     if deltaTime >= outputInterval or deltaTime < 0:
                         return True
                     return False
                 def __getGammas(self, pairs, d, phases):
                     gammas = numpy.zeros(2)
                     for i in range(len(pairs)):
                         pairi = pairs[i]
                         phip3 = phases[:,pairi[0]]
                         d3 = d[pairi[0]]
                         phip2 = phases[:,pairi[1]]
                         d2 = d[pairi[1]]
                         #Calculating gamma
             #             jdcos = alp1/(k*d1)
             #             jgamma = numpy.angle(numpy.exp(1j*(d0*alp1/d1 - alp0)))
                         jgamma = -phip2*d3/d2 - phip3
                         jgamma = numpy.angle(numpy.exp(1j*jgamma))
             #             jgamma[jgamma>numpy.pi] -= 2*numpy.pi
             #             jgamma[jgamma<-numpy.pi] += 2*numpy.pi
                         #Revised distribution
                         jgammaArray = numpy.hstack((jgamma,jgamma+0.5*numpy.pi,jgamma-0.5*numpy.pi))
                         #Histogram
                         nBins = 64
                         rmin = -0.5*numpy.pi
                         rmax = 0.5*numpy.pi
                         phaseHisto = numpy.histogram(jgammaArray, bins=nBins, range=(rmin,rmax))
                         meteorsY = phaseHisto[0]
                         phasesX = phaseHisto[1][:-1]
                         width = phasesX[1] - phasesX[0]
                         phasesX += width/2
                         #Gaussian aproximation
                         bpeak = meteorsY.argmax()
                         peak = meteorsY.max()
                         jmin = bpeak - 5
                         jmax = bpeak + 5 + 1
                         if jmin<0:
                             jmin = 0
                             jmax = 6
                         elif jmax > meteorsY.size:
                             jmin = meteorsY.size - 6
                             jmax = meteorsY.size
                         x0 = numpy.array([peak,bpeak,50])
                         coeff = optimize.leastsq(self.__residualFunction, x0, args=(meteorsY[jmin:jmax], phasesX[jmin:jmax]))
                         #Gammas
                         gammas[i] = coeff[0][1]
                     return gammas
                 def __residualFunction(self, coeffs, y, t):
                     return y - self.__gauss_function(t, coeffs)
                 def __gauss_function(self, t, coeffs):
                     return coeffs[0]*numpy.exp(-0.5*((t - coeffs[1]) / coeffs[2])**2)
                 def __getPhases(self, azimuth, h, pairsList, d, gammas, meteorsArray):
                     meteorOps = SMOperations()
                     nchan = 4
                     pairx = pairsList[0] #x es 0
                     pairy = pairsList[1] #y es 1
                     center_xangle = 0
                     center_yangle = 0
                     range_angle = numpy.array([10*numpy.pi,numpy.pi,numpy.pi/2,numpy.pi/4])
                     ntimes = len(range_angle)
                     nstepsx = 20
                     nstepsy = 20
                     for iz in range(ntimes):
                         min_xangle = -range_angle[iz]/2 + center_xangle
                         max_xangle = range_angle[iz]/2 + center_xangle
                         min_yangle = -range_angle[iz]/2 + center_yangle
                         max_yangle = range_angle[iz]/2 + center_yangle
                         inc_x = (max_xangle-min_xangle)/nstepsx
                         inc_y = (max_yangle-min_yangle)/nstepsy
                         alpha_y = numpy.arange(nstepsy)*inc_y + min_yangle
                         alpha_x = numpy.arange(nstepsx)*inc_x + min_xangle
                         penalty = numpy.zeros((nstepsx,nstepsy))
                         jph_array = numpy.zeros((nchan,nstepsx,nstepsy))
                         jph = numpy.zeros(nchan)
                         # Iterations looking for the offset
                         for iy in range(int(nstepsy)):
                             for ix in range(int(nstepsx)):
                                 d3 = d[pairsList[1][0]]
                                 d2 = d[pairsList[1][1]]
                                 d5 = d[pairsList[0][0]]
                                 d4 = d[pairsList[0][1]]
                                 alp2 = alpha_y[iy]  #gamma 1
                                 alp4 = alpha_x[ix]  #gamma 0
                                 alp3 = -alp2*d3/d2 - gammas[1]
                                 alp5 = -alp4*d5/d4 - gammas[0]
             #                     jph[pairy[1]] = alpha_y[iy]
             #                     jph[pairy[0]] = -gammas[1] - alpha_y[iy]*d[pairy[1]]/d[pairy[0]]
             #                     jph[pairx[1]] = alpha_x[ix]
             #                     jph[pairx[0]] = -gammas[0] - alpha_x[ix]*d[pairx[1]]/d[pairx[0]]
                                 jph[pairsList[0][1]] = alp4
                                 jph[pairsList[0][0]] = alp5
                                 jph[pairsList[1][0]] = alp3
                                 jph[pairsList[1][1]] = alp2
                                 jph_array[:,ix,iy] = jph
             #                     d = [2.0,2.5,2.5,2.0]
                                 #falta chequear si va a leer bien  los meteoros
                                 meteorsArray1 = meteorOps.getMeteorParams(meteorsArray, azimuth, h, pairsList, d, jph)
                                 error = meteorsArray1[:,-1]
                                 ind1 = numpy.where(error==0)[0]
                                 penalty[ix,iy] = ind1.size
                         i,j = numpy.unravel_index(penalty.argmax(), penalty.shape)
                         phOffset = jph_array[:,i,j]
                         center_xangle = phOffset[pairx[1]]
                         center_yangle = phOffset[pairy[1]]
                     phOffset = numpy.angle(numpy.exp(1j*jph_array[:,i,j]))
                     phOffset = phOffset*180/numpy.pi
                     return phOffset
                 def run(self, dataOut, hmin, hmax, channelPositions=None, nHours = 1):
                     dataOut.flagNoData = True
                     self.__dataReady = False
                     dataOut.outputInterval = nHours*3600
                     if self.__isConfig == False:
             #                 self.__initime = dataOut.datatime.replace(minute = 0, second = 0, microsecond = 03)
                         #Get Initial LTC time
                         self.__initime = datetime.datetime.utcfromtimestamp(dataOut.utctime)
                         self.__initime = (self.__initime.replace(minute = 0, second = 0, microsecond = 0) - datetime.datetime(1970, 1, 1)).total_seconds()
                         self.__isConfig = True
                     if self.__buffer is None:
                         self.__buffer = dataOut.data_param.copy()
                     else:
                         self.__buffer = numpy.vstack((self.__buffer, dataOut.data_param))
                     self.__dataReady = self.__checkTime(dataOut.utctime, self.__initime, dataOut.paramInterval, dataOut.outputInterval) #Check if the buffer is ready
                     if self.__dataReady:
                         dataOut.utctimeInit = self.__initime
                         self.__initime += dataOut.outputInterval #to erase time offset
                         freq = dataOut.frequency
                         c = dataOut.C #m/s
                         lamb = c/freq
                         k = 2*numpy.pi/lamb
                         azimuth = 0
                         h = (hmin, hmax)
             #             pairs = ((0,1),(2,3)) #Estrella
             #             pairs = ((1,0),(2,3)) #T
                         if channelPositions is None:
             #             channelPositions = [(2.5,0), (0,2.5), (0,0), (0,4.5), (-2,0)]   #T
                             channelPositions = [(4.5,2), (2,4.5), (2,2), (2,0), (0,2)]   #Estrella
                         meteorOps = SMOperations()
                         pairslist0, distances = meteorOps.getPhasePairs(channelPositions)
                         #Checking correct order of pairs
                         pairs = []
                         if distances[1] > distances[0]:
                             pairs.append((1,0))
                         else:
                             pairs.append((0,1))
                         if distances[3] > distances[2]:
                             pairs.append((3,2))
                         else:
                             pairs.append((2,3))
             #             distances1 = [-distances[0]*lamb, distances[1]*lamb, -distances[2]*lamb, distances[3]*lamb]
                         meteorsArray = self.__buffer
                         error = meteorsArray[:,-1]
                         boolError = (error==0)|(error==3)|(error==4)|(error==13)|(error==14)
                         ind1 = numpy.where(boolError)[0]
                         meteorsArray = meteorsArray[ind1,:]
                         meteorsArray[:,-1] = 0
                         phases = meteorsArray[:,8:12]
                         #Calculate Gammas
                         gammas = self.__getGammas(pairs, distances, phases)
             #             gammas = numpy.array([-21.70409463,45.76935864])*numpy.pi/180
                         #Calculate Phases
                         phasesOff = self.__getPhases(azimuth, h, pairs, distances, gammas, meteorsArray)
                         phasesOff = phasesOff.reshape((1,phasesOff.size))
                         dataOut.data_output = -phasesOff
                         dataOut.flagNoData = False
                         self.__buffer = None
                     return
             class SMOperations():
                 def __init__(self):
                     return
                 def getMeteorParams(self, arrayParameters0, azimuth, h, pairsList, distances, jph):
                     arrayParameters = arrayParameters0.copy()
                     hmin = h[0]
                     hmax = h[1]
                     #Calculate AOA (Error N 3, 4)
                     #JONES ET AL. 1998
                     AOAthresh = numpy.pi/8
                     error = arrayParameters[:,-1]
                     phases = -arrayParameters[:,8:12] + jph
             #         phases = numpy.unwrap(phases)
                     arrayParameters[:,3:6], arrayParameters[:,-1] = self.__getAOA(phases, pairsList, distances, error, AOAthresh, azimuth)
                     #Calculate Heights (Error N 13 and 14)
                     error = arrayParameters[:,-1]
                     Ranges = arrayParameters[:,1]
                     zenith = arrayParameters[:,4]
                     arrayParameters[:,2], arrayParameters[:,-1] = self.__getHeights(Ranges, zenith, error, hmin, hmax)
                     #----------------------- Get Final data    ------------------------------------
             #         error = arrayParameters[:,-1]
             #         ind1 = numpy.where(error==0)[0]
             #         arrayParameters = arrayParameters[ind1,:]
                     return arrayParameters
                 def __getAOA(self, phases, pairsList, directions, error, AOAthresh, azimuth):
                     arrayAOA = numpy.zeros((phases.shape[0],3))
                     cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList,directions)
                     arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
                     cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
                     arrayAOA[:,2] = cosDirError
                     azimuthAngle = arrayAOA[:,0]
                     zenithAngle = arrayAOA[:,1]
                     #Setting Error
                     indError = numpy.where(numpy.logical_or(error == 3, error == 4))[0]
                     error[indError] = 0
                     #Number 3: AOA not fesible
                     indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
                     error[indInvalid] = 3
                     #Number 4: Large difference in AOAs obtained from different antenna baselines
                     indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
                     error[indInvalid] = 4
                     return arrayAOA, error
                 def __getDirectionCosines(self, arrayPhase, pairsList, distances):
                     #Initializing some variables
                     ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
                     ang_aux = ang_aux.reshape(1,ang_aux.size)
                     cosdir = numpy.zeros((arrayPhase.shape[0],2))
                     cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
                     for i in range(2):
                         ph0 = arrayPhase[:,pairsList[i][0]]
                         ph1 = arrayPhase[:,pairsList[i][1]]
                         d0 = distances[pairsList[i][0]]
                         d1 = distances[pairsList[i][1]]
                         ph0_aux = ph0 + ph1
                         ph0_aux = numpy.angle(numpy.exp(1j*ph0_aux))
             #             ph0_aux[ph0_aux > numpy.pi] -= 2*numpy.pi
             #             ph0_aux[ph0_aux < -numpy.pi] += 2*numpy.pi
                         #First Estimation
                         cosdir0[:,i] = (ph0_aux)/(2*numpy.pi*(d0 - d1))
                         #Most-Accurate Second Estimation
                         phi1_aux =  ph0 - ph1
                         phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
                         #Direction Cosine 1
                         cosdir1 = (phi1_aux + ang_aux)/(2*numpy.pi*(d0 + d1))
                         #Searching the correct Direction Cosine
                         cosdir0_aux = cosdir0[:,i]
                         cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
                         #Minimum Distance
                         cosDiff = (cosdir1 - cosdir0_aux)**2
                         indcos = cosDiff.argmin(axis = 1)
                         #Saving Value obtained
                         cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
                     return cosdir0, cosdir
                 def __calculateAOA(self, cosdir, azimuth):
                     cosdirX = cosdir[:,0]
                     cosdirY = cosdir[:,1]
                     zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
                     azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth#0 deg north, 90 deg east
                     angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
                     return angles
                 def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
                     Ramb = 375  #Ramb = c/(2*PRF)
                     Re = 6371   #Earth Radius
                     heights = numpy.zeros(Ranges.shape)
                     R_aux = numpy.array([0,1,2])*Ramb
                     R_aux = R_aux.reshape(1,R_aux.size)
                     Ranges = Ranges.reshape(Ranges.size,1)
                     Ri = Ranges + R_aux
                     hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
                     #Check if there is a height between 70 and 110 km
                     h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
                     ind_h = numpy.where(h_bool == 1)[0]
                     hCorr = hi[ind_h, :]
                     ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
                     hCorr = hi[ind_hCorr][:len(ind_h)]
                     heights[ind_h] = hCorr
                     #Setting Error
                     #Number 13: Height unresolvable echo: not valid height within 70 to 110 km
                     #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
                     indError = numpy.where(numpy.logical_or(error == 13, error == 14))[0]
                     error[indError] = 0
                     indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
                     error[indInvalid2] = 14
                     indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
                     error[indInvalid1] = 13
                     return heights, error
                 def getPhasePairs(self, channelPositions):
                     chanPos = numpy.array(channelPositions)
                     listOper = list(itertools.combinations(list(range(5)),2))
                     distances = numpy.zeros(4)
                     axisX = []
                     axisY = []
                     distX = numpy.zeros(3)
                     distY = numpy.zeros(3)
                     ix = 0
                     iy = 0
                     pairX = numpy.zeros((2,2))
                     pairY = numpy.zeros((2,2))
                     for i in range(len(listOper)):
                         pairi = listOper[i]
                         posDif = numpy.abs(chanPos[pairi[0],:] - chanPos[pairi[1],:])
                         if posDif[0] == 0:
                             axisY.append(pairi)
                             distY[iy] = posDif[1]
                             iy += 1
                         elif posDif[1] == 0:
                             axisX.append(pairi)
                             distX[ix] = posDif[0]
                             ix += 1
                     for i in range(2):
                         if i==0:
                             dist0 = distX
                             axis0 = axisX
                         else:
                             dist0 = distY
                             axis0 = axisY
                         side = numpy.argsort(dist0)[:-1]
                         axis0 = numpy.array(axis0)[side,:]
                         chanC = int(numpy.intersect1d(axis0[0,:], axis0[1,:])[0])
                         axis1 = numpy.unique(numpy.reshape(axis0,4))
                         side = axis1[axis1 != chanC]
                         diff1 = chanPos[chanC,i] - chanPos[side[0],i]
                         diff2 = chanPos[chanC,i] - chanPos[side[1],i]
                         if diff1<0:
                             chan2 = side[0]
                             d2 = numpy.abs(diff1)
                             chan1 = side[1]
                             d1 = numpy.abs(diff2)
                         else:
                             chan2 = side[1]
                             d2 = numpy.abs(diff2)
                             chan1 = side[0]
                             d1 = numpy.abs(diff1)
                         if i==0:
                             chanCX = chanC
                             chan1X = chan1
                             chan2X = chan2
                             distances[0:2] = numpy.array([d1,d2])
                         else:
                             chanCY = chanC
                             chan1Y = chan1
                             chan2Y = chan2
                             distances[2:4] = numpy.array([d1,d2])
             #         axisXsides = numpy.reshape(axisX[ix,:],4)
             #
             #         channelCentX = int(numpy.intersect1d(pairX[0,:], pairX[1,:])[0])
             #         channelCentY = int(numpy.intersect1d(pairY[0,:], pairY[1,:])[0])
             #
             #         ind25X = numpy.where(pairX[0,:] != channelCentX)[0][0]
             #         ind20X = numpy.where(pairX[1,:] != channelCentX)[0][0]
             #         channel25X = int(pairX[0,ind25X])
             #         channel20X = int(pairX[1,ind20X])
             #         ind25Y = numpy.where(pairY[0,:] != channelCentY)[0][0]
             #         ind20Y = numpy.where(pairY[1,:] != channelCentY)[0][0]
             #         channel25Y = int(pairY[0,ind25Y])
             #         channel20Y = int(pairY[1,ind20Y])
             #         pairslist = [(channelCentX, channel25X),(channelCentX, channel20X),(channelCentY,channel25Y),(channelCentY, channel20Y)]
                     pairslist = [(chanCX, chan1X),(chanCX, chan2X),(chanCY,chan1Y),(chanCY, chan2Y)]
                     return pairslist, distances
             #     def __getAOA(self, phases, pairsList, error, AOAthresh, azimuth):
             #
             #         arrayAOA = numpy.zeros((phases.shape[0],3))
             #         cosdir0, cosdir = self.__getDirectionCosines(phases, pairsList)
             #
             #         arrayAOA[:,:2] = self.__calculateAOA(cosdir, azimuth)
             #         cosDirError = numpy.sum(numpy.abs(cosdir0 - cosdir), axis = 1)
             #         arrayAOA[:,2] = cosDirError
             #
             #         azimuthAngle = arrayAOA[:,0]
             #         zenithAngle = arrayAOA[:,1]
             #
             #         #Setting Error
             #         #Number 3: AOA not fesible
             #         indInvalid = numpy.where(numpy.logical_and((numpy.logical_or(numpy.isnan(zenithAngle), numpy.isnan(azimuthAngle))),error == 0))[0]
             #         error[indInvalid] = 3
             #         #Number 4: Large difference in AOAs obtained from different antenna baselines
             #         indInvalid = numpy.where(numpy.logical_and(cosDirError > AOAthresh,error == 0))[0]
             #         error[indInvalid] = 4
             #         return arrayAOA, error
             #
             #     def __getDirectionCosines(self, arrayPhase, pairsList):
             #
             #         #Initializing some variables
             #         ang_aux = numpy.array([-8,-7,-6,-5,-4,-3,-2,-1,0,1,2,3,4,5,6,7,8])*2*numpy.pi
             #         ang_aux = ang_aux.reshape(1,ang_aux.size)
             #
             #         cosdir = numpy.zeros((arrayPhase.shape[0],2))
             #         cosdir0 = numpy.zeros((arrayPhase.shape[0],2))
             #
             #
             #         for i in range(2):
             #             #First Estimation
             #             phi0_aux = arrayPhase[:,pairsList[i][0]] + arrayPhase[:,pairsList[i][1]]
             #             #Dealias
             #             indcsi = numpy.where(phi0_aux > numpy.pi)
             #             phi0_aux[indcsi] -= 2*numpy.pi
             #             indcsi = numpy.where(phi0_aux < -numpy.pi)
             #             phi0_aux[indcsi] += 2*numpy.pi
             #             #Direction Cosine 0
             #             cosdir0[:,i] = -(phi0_aux)/(2*numpy.pi*0.5)
             #
             #             #Most-Accurate Second Estimation
             #             phi1_aux =  arrayPhase[:,pairsList[i][0]] - arrayPhase[:,pairsList[i][1]]
             #             phi1_aux = phi1_aux.reshape(phi1_aux.size,1)
             #             #Direction Cosine 1
             #             cosdir1 = -(phi1_aux + ang_aux)/(2*numpy.pi*4.5)
             #
             #             #Searching the correct Direction Cosine
             #             cosdir0_aux = cosdir0[:,i]
             #             cosdir0_aux = cosdir0_aux.reshape(cosdir0_aux.size,1)
             #             #Minimum Distance
             #             cosDiff = (cosdir1 - cosdir0_aux)**2
             #             indcos = cosDiff.argmin(axis = 1)
             #             #Saving Value obtained
             #             cosdir[:,i] = cosdir1[numpy.arange(len(indcos)),indcos]
             #
             #         return cosdir0, cosdir
             #
             #     def __calculateAOA(self, cosdir, azimuth):
             #         cosdirX = cosdir[:,0]
             #         cosdirY = cosdir[:,1]
             #
             #         zenithAngle = numpy.arccos(numpy.sqrt(1 - cosdirX**2 - cosdirY**2))*180/numpy.pi
             #         azimuthAngle = numpy.arctan2(cosdirX,cosdirY)*180/numpy.pi + azimuth #0 deg north, 90 deg east
             #         angles = numpy.vstack((azimuthAngle, zenithAngle)).transpose()
             #
             #         return angles
             #
             #     def __getHeights(self, Ranges, zenith, error, minHeight, maxHeight):
             #
             #         Ramb = 375  #Ramb = c/(2*PRF)
             #         Re = 6371   #Earth Radius
             #         heights = numpy.zeros(Ranges.shape)
             #
             #         R_aux = numpy.array([0,1,2])*Ramb
             #         R_aux = R_aux.reshape(1,R_aux.size)
             #
             #         Ranges = Ranges.reshape(Ranges.size,1)
             #
             #         Ri = Ranges + R_aux
             #         hi = numpy.sqrt(Re**2 + Ri**2 + (2*Re*numpy.cos(zenith*numpy.pi/180)*Ri.transpose()).transpose()) - Re
             #
             #         #Check if there is a height between 70 and 110 km
             #         h_bool = numpy.sum(numpy.logical_and(hi > minHeight, hi < maxHeight), axis = 1)
             #         ind_h = numpy.where(h_bool == 1)[0]
             #
             #         hCorr = hi[ind_h, :]
             #         ind_hCorr = numpy.where(numpy.logical_and(hi > minHeight, hi < maxHeight))
             #
             #         hCorr = hi[ind_hCorr]
             #         heights[ind_h] = hCorr
             #
             #         #Setting Error
             #         #Number 13: Height unresolvable echo: not valid height within 70 to 110 km
             #         #Number 14: Height ambiguous echo: more than one possible height within 70 to 110 km
             #
             #         indInvalid2 = numpy.where(numpy.logical_and(h_bool > 1, error == 0))[0]
             #         error[indInvalid2] = 14
             #         indInvalid1 = numpy.where(numpy.logical_and(h_bool == 0, error == 0))[0]
             #         error[indInvalid1] = 13
             #
             #         return heights, error
             class WeatherRadar(Operation):
                 '''
                 Function tat implements Weather Radar operations-
                 Input:
                 Output:
                 Parameters affected:
                 '''
                 isConfig  = False
                 variableList = None
                 def __init__(self):
                     Operation.__init__(self)
                 def setup(self,dataOut,variableList= None,Pt=0,Gt=0,Gr=0,lambda_=0, aL=0,
                             tauW= 0,thetaT=0,thetaR=0,Km =0):
                     self.nCh      = dataOut.nChannels
                     self.nHeis    = dataOut.nHeights
                     deltaHeight   = dataOut.heightList[1] - dataOut.heightList[0]
                     self.Range    = numpy.arange(dataOut.nHeights)*deltaHeight + dataOut.heightList[0]
                     self.Range    = self.Range.reshape(1,self.nHeis)
                     self.Range    = numpy.tile(self.Range,[self.nCh,1])
                     '''-----------1 Constante del Radar----------'''
                     self.Pt       = Pt
                     self.Gt       = Gt
                     self.Gr       = Gr
                     self.lambda_  = lambda_
                     self.aL       = aL
                     self.tauW     = tauW
                     self.thetaT   = thetaT
                     self.thetaR   = thetaR
                     self.Km       = Km
                     Numerator     = ((4*numpy.pi)**3 * aL**2 * 16 *numpy.log(2))
                     Denominator   = (Pt * Gt * Gr * lambda_**2 * SPEED_OF_LIGHT * tauW * numpy.pi*thetaT*thetaR)
                     self.RadarConstant = Numerator/Denominator
                     self.variableList= variableList
                 def setMoments(self,dataOut,i):
                     type  = dataOut.inputUnit
                     nCh   = dataOut.nChannels
                     nHeis = dataOut.nHeights
                     data_param = numpy.zeros((nCh,4,nHeis))
                     if type == "Voltage":
                         factor            = dataOut.normFactor
                         data_param[:,0,:] = dataOut.dataPP_POW/(factor)
                         data_param[:,1,:] = dataOut.dataPP_DOP
                         data_param[:,2,:] = dataOut.dataPP_WIDTH
                         data_param[:,3,:] = dataOut.dataPP_SNR
                     if type == "Spectra":
                         data_param[:,0,:] = dataOut.data_POW
                         data_param[:,1,:] = dataOut.data_DOP
                         data_param[:,2,:] = dataOut.data_WIDTH
                         data_param[:,3,:] = dataOut.data_SNR
                     return data_param[:,i,:]
                 def getCoeficienteCorrelacionROhv_R(self,dataOut):
                     type  = dataOut.inputUnit
                     nHeis = dataOut.nHeights
                     data_RhoHV_R = numpy.zeros((nHeis))
                     if type == "Voltage":
                         powa  = dataOut.dataPP_POWER[0]
                         powb = dataOut.dataPP_POWER[1]
                         ccf   = dataOut.dataPP_CCF
                         avgcoherenceComplex = ccf / numpy.sqrt(powa * powb)
                         data_RhoHV_R = numpy.abs(avgcoherenceComplex)
                     if type == "Spectra":
                         data_RhoHV_R = dataOut.getCoherence()
                     return data_RhoHV_R
                 def getFasediferencialPhiD_P(self,dataOut,phase= True):
                     type  = dataOut.inputUnit
                     nHeis = dataOut.nHeights
                     data_PhiD_P = numpy.zeros((nHeis))
                     if type == "Voltage":
                         powa  = dataOut.dataPP_POWER[0]
                         powb = dataOut.dataPP_POWER[1]
                         ccf   = dataOut.dataPP_CCF
                         avgcoherenceComplex = ccf / numpy.sqrt(powa * powb)
                         if phase:
                             data_PhiD_P = numpy.arctan2(avgcoherenceComplex.imag,
                                                  avgcoherenceComplex.real) * 180 / numpy.pi
                     if type == "Spectra":
                         data_PhiD_P = dataOut.getCoherence(phase = phase)
                     return data_PhiD_P
                 def getReflectividad_D(self,dataOut):
                     '''-----------------------------Potencia de Radar -Signal S-----------------------------'''
                     Pr               = self.setMoments(dataOut,0)
                     '''-----------2 Reflectividad del Radar y Factor de Reflectividad------'''
                     self.n_radar       = numpy.zeros((self.nCh,self.nHeis))
                     self.Z_radar       = numpy.zeros((self.nCh,self.nHeis))
                     for R in range(self.nHeis):
                         self.n_radar[:,R] = self.RadarConstant*Pr[:,R]* (self.Range[:,R])**2
                         self.Z_radar[:,R] = self.n_radar[:,R]* self.lambda_**4/( numpy.pi**5 * self.Km**2)
                     '''----------- Factor de Reflectividad Equivalente lamda_ < 10 cm , lamda_= 3.2cm-------'''
                     Zeh  =  self.Z_radar
                     dBZeh = 10*numpy.log10(Zeh)
                     Zdb_D = dBZeh[0] - dBZeh[1]
                     return Zdb_D
                 def getRadialVelocity_V(self,dataOut):
                     velRadial_V = self.setMoments(dataOut,1)
                     return velRadial_V
                 def getAnchoEspectral_W(self,dataOut):
                     Sigmav_W = self.setMoments(dataOut,2)
                     return Sigmav_W
                 def run(self,dataOut,variableList=None,Pt=25,Gt=200.0,Gr=50.0,lambda_=0.32, aL=2.5118,
                             tauW= 4.0e-6,thetaT=0.165,thetaR=0.367,Km =0.93):
                     if not self.isConfig:
                         self.setup(dataOut= dataOut,variableList=None,Pt=25,Gt=200.0,Gr=50.0,lambda_=0.32, aL=2.5118,
                                     tauW= 4.0e-6,thetaT=0.165,thetaR=0.367,Km =0.93)
                         self.isConfig = True
                     for i in range(len(self.variableList)):
                         if self.variableList[i]=='ReflectividadDiferencial':
                             dataOut.Zdb_D =self.getReflectividad_D(dataOut=dataOut)
                         if self.variableList[i]=='FaseDiferencial':
                             dataOut.PhiD_P =self.getFasediferencialPhiD_P(dataOut=dataOut, phase=True)
                         if self.variableList[i] == "CoeficienteCorrelacion":
                             dataOut.RhoHV_R = self.getCoeficienteCorrelacionROhv_R(dataOut)
                         if self.variableList[i] =="VelocidadRadial":
                             dataOut.velRadial_V = self.getRadialVelocity_V(dataOut)
                         if self.variableList[i] =="AnchoEspectral":
                             dataOut.Sigmav_W = self.getAnchoEspectral_W(dataOut)
                     return dataOut
             class PedestalInformation(Operation):
                 path_ped     = None
                 path_adq     = None
                 samp_rate_ped= None
                 t_Interval_p = None
                 n_Muestras_p = None
                 isConfig     = False
                 blocksPerfile= None
                 f_a_p        = None
                 online       = None
                 angulo_adq   = None
                 nro_file     = None
                 nro_key_p    = None
                 tmp          = None
                 def __init__(self):
                     Operation.__init__(self)
                 def getAnguloProfile(self,utc_adq,utc_ped_list):
                     utc_adq       = utc_adq
                     ##list_pedestal = list_pedestal
                     utc_ped_list  = utc_ped_list
                     #for i in range(len(list_pedestal)):
                     #    #print(i)# OJO IDENTIFICADOR DE SINCRONISMO
                     #    utc_ped_list.append(self.gettimeutcfromDirFilename(path=self.path_ped,file=list_pedestal[i]))
                     nro_file,utc_ped,utc_ped_1  =self.getNROFile(utc_adq,utc_ped_list)
                     #print("NROFILE************************************", nro_file,utc_ped)
                     #print(nro_file)
                     if nro_file < 0:
                         return numpy.NaN,numpy.NaN
                     else:
                         nro_key_p    = int((utc_adq-utc_ped)/self.t_Interval_p)-1 # ojito al -1 estimado alex
                         #print("nro_key_p",nro_key_p)
                         ff_pedestal  = self.list_pedestal[nro_file]
                         #angulo       = self.getDatavaluefromDirFilename(path=self.path_ped,file=ff_pedestal,value="azimuth")
                         angulo       = self.getDatavaluefromDirFilename(path=self.path_ped,file=ff_pedestal,value="azi_pos")
                         angulo_ele   = self.getDatavaluefromDirFilename(path=self.path_ped,file=ff_pedestal,value="ele_pos")
                         #-----Adicion de filtro........................
                         vel_ele   = self.getDatavaluefromDirFilename(path=self.path_ped,file=ff_pedestal,value="ele_speed")## ele_speed
                         '''
                         vel_mean = numpy.mean(vel_ele)
                         print("#############################################################")
                         print("VEL MEAN----------------:",vel_mean)
                         f vel_mean<7.7 or vel_mean>8.3:
                             return numpy.NaN,numpy.NaN
                         #------------------------------------------------------------------------------------------------------
                         '''
                         #print(int(self.samp_rate_ped))
                         #print(nro_key_p)
                         if int(self.samp_rate_ped)-1>=nro_key_p>0:
                             #print("angulo_array                  :",angulo[nro_key_p])
                             return angulo[nro_key_p],angulo_ele[nro_key_p]
                         else:
                             #print("-----------------------------------------------------------------")
                             return numpy.NaN,numpy.NaN
                 def getfirstFilefromPath(self,path,meta,ext):
                     validFilelist = []
                     #("SEARH",path)
                     try:
                         fileList      = os.listdir(path)
                     except:
                         print("check path - fileList")
                     if len(fileList)<1:
                      return None
                     # meta    1234 567 8-18 BCDE
                     # H,D,PE  YYYY DDD EPOC .ext
                     for thisFile in fileList:
                         #print("HI",thisFile)
                         if meta =="PE":
                             try:
                                 number= int(thisFile[len(meta)+7:len(meta)+17])
                             except:
                                  print("There is a file or folder with different format")
                         if meta =="pos@":
                             try:
                                 number= int(thisFile[len(meta):len(meta)+10])
                             except:
                                  print("There is a file or folder with different format")
                         if meta == "D":
                             try:
                                 number= int(thisFile[8:11])
                             except:
                                 print("There is a file or folder with different format")
                         if not isNumber(str=number):
                             continue
                         if (os.path.splitext(thisFile)[-1].lower() != ext.lower()):
                             continue
                         validFilelist.sort()
                         validFilelist.append(thisFile)
                     if len(validFilelist)>0:
                         validFilelist = sorted(validFilelist,key=str.lower)
                         #print(validFilelist)
                         return validFilelist
                     return None
                 def gettimeutcfromDirFilename(self,path,file):
                     dir_file= path+"/"+file
                     fp      = h5py.File(dir_file,'r')
                     #epoc    = fp['Metadata'].get('utctimeInit')[()]
                     epoc    = fp['Data'].get('utc')[()]
                     epoc    = epoc[0]
                     #print("hola",epoc)
                     fp.close()
                     return epoc
                 def gettimeutcadqfromDirFilename(self,path,file):
                     pass
                 def getDatavaluefromDirFilename(self,path,file,value):
                     dir_file= path+"/"+file
                     fp      = h5py.File(dir_file,'r')
                     array    = fp['Data'].get(value)[()]
                     fp.close()
                     return array
                 def getNROFile(self,utc_adq,utc_ped_list):
                     c=0
                     #print(utc_adq)
                     #print(len(utc_ped_list))
                     ###print(utc_ped_list)
                     if utc_adq<utc_ped_list[0]:
                         pass
                     else:
                         for i in range(len(utc_ped_list)):
                             if utc_adq>utc_ped_list[i]:
                                 #print("mayor")
                                 #print("utc_ped_list",utc_ped_list[i])
                                 c +=1
                     return c-1,utc_ped_list[c-1],utc_ped_list[c]
                 def verificarNROFILE(self,dataOut,utc_ped,f_a_p,n_Muestras_p):
                     pass
                 def setup_offline(self,dataOut,list_pedestal):
                     pass
                 def setup_online(self,dataOut):
                     pass
                     #def setup(self,dataOut,path_ped,path_adq,t_Interval_p,n_Muestras_p,blocksPerfile,f_a_p,online):
                 def setup(self,dataOut,path_ped,samp_rate_ped,t_Interval_p,wr_exp):
                     #print("**************SETUP******************")
                     self.__dataReady      = False
                     self.path_ped     = path_ped
                     self.samp_rate_ped= samp_rate_ped
                     self.t_Interval_p = t_Interval_p
                     self.list_pedestal = self.getfirstFilefromPath(path=self.path_ped,meta="pos@",ext=".h5")
                     self.utc_ped_list= []
                     for i in range(len(self.list_pedestal)):
                         #print(i,self.gettimeutcfromDirFilename(path=self.path_ped,file=self.list_pedestal[i]))# OJO IDENTIFICADOR DE SINCRONISMO
                         self.utc_ped_list.append(self.gettimeutcfromDirFilename(path=self.path_ped,file=self.list_pedestal[i]))
                         #print(self.utc_ped_list)
                         #exit(1)
                     #print("que paso")
                     dataOut.wr_exp     = wr_exp
                     #print("SETUP READY")
                 def setNextFileP(self,dataOut):
                     pass
                 def checkPedFile(self,path,nro_file):
                     pass
                 def setNextFileoffline(self,dataOut):
                     pass
                 def setNextFileonline(self):
                     pass
                 def run(self, dataOut,path_ped,samp_rate_ped,t_Interval_p,wr_exp):
                     #print("INTEGRATION -----")
                     #print("PEDESTAL")
                     if not self.isConfig:
                         self.setup(dataOut, path_ped,samp_rate_ped,t_Interval_p,wr_exp)
                         self.__dataReady = True
                         self.isConfig   = True
                         #print("config TRUE")
                     utc_adq       = dataOut.utctime
                     #print("utc_adq---------------",utc_adq)
                     list_pedestal = self.list_pedestal
                     #print("list_pedestal",list_pedestal[:20])
                     angulo,angulo_ele = self.getAnguloProfile(utc_adq=utc_adq,utc_ped_list=self.utc_ped_list)
                     #print("angulo**********",angulo)
                     dataOut.flagNoData      = False
                     if numpy.isnan(angulo) or numpy.isnan(angulo_ele) :
                         #print("PEDESTAL 3")
                         #exit(1)
                         dataOut.flagNoData = True
                         return dataOut
                     dataOut.azimuth         = angulo
                     dataOut.elevation       = angulo_ele
                     #print("PEDESTAL END")
                     #print(dataOut.azimuth)
                     #print(dataOut.elevation)
                     #exit(1)
                     return dataOut
             class Block360(Operation):
                 '''
                 '''
                 isConfig       = False
                 __profIndex    = 0
                 __initime      = None
                 __lastdatatime = None
                 __buffer       = None
                 __dataReady    = False
                 n              = None
                 __nch          = 0
                 __nHeis        = 0
                 index          = 0
                 mode           = 0
                 def __init__(self,**kwargs):
                     Operation.__init__(self,**kwargs)
                 def setup(self, dataOut, n = None, mode = None):
                     '''
                     n= Numero de PRF's de entrada
                     '''
                     self.__initime        = None
                     self.__lastdatatime   = 0
                     self.__dataReady      = False
                     self.__buffer         = 0
                     self.__buffer_1D      = 0
                     self.__profIndex      = 0
                     self.index            = 0
                     self.__nch            = dataOut.nChannels
                     self.__nHeis          = dataOut.nHeights
                     ##print("ELVALOR DE n es:", n)
                     if n == None:
                         raise ValueError("n should be specified.")
                     if mode == None:
                         raise ValueError("mode should be specified.")
                     if n != None:
                         if n<1:
                             print("n should be greater than 2")
                             raise ValueError("n should be greater than 2")
                     self.n       = n
                     self.mode    = mode
                     #print("self.mode",self.mode)
                     #print("nHeights")
                     self.__buffer  = numpy.zeros(( dataOut.nChannels,n, dataOut.nHeights))
                     self.__buffer2 = numpy.zeros(n)
                     self.__buffer3 = numpy.zeros(n)
                 def putData(self,data,mode):
                     '''
                     Add a profile to he __buffer and increase in one the __profiel Index
                     '''
                     #print("line 4049",data.dataPP_POW.shape,data.dataPP_POW[:10])
                     #print("line 4049",data.azimuth.shape,data.azimuth)
                     if self.mode==0:
                         self.__buffer[:,self.__profIndex,:]= data.dataPP_POWER# PRIMER MOMENTO
                     if self.mode==1:
                         self.__buffer[:,self.__profIndex,:]= data.data_pow
                     #print("me casi",self.index,data.azimuth[self.index])
                     #print(self.__profIndex, self.index , data.azimuth[self.index] )
                     #print("magic",data.profileIndex)
                     #print(data.azimuth[self.index])
                     #print("index",self.index)
                     #####self.__buffer2[self.__profIndex] = data.azimuth[self.index]
                     self.__buffer2[self.__profIndex] = data.azimuth
                     self.__buffer3[self.__profIndex] = data.elevation
                     #print("q pasa")
                     #####self.index+=1
                     #print("index",self.index,data.azimuth[:10])
                     self.__profIndex      += 1
                     return        #················· Remove DC···································
                 def pushData(self,data):
                     '''
                     Return the PULSEPAIR and the profiles used in the operation
                     Affected :  self.__profileIndex
                     '''
                     #print("pushData")
                     data_360 = self.__buffer
                     data_p   = self.__buffer2
                     data_e   = self.__buffer3
                     n                = self.__profIndex
                     self.__buffer    = numpy.zeros((self.__nch, self.n,self.__nHeis))
                     self.__buffer2 = numpy.zeros(self.n)
                     self.__buffer3 = numpy.zeros(self.n)
                     self.__profIndex = 0
                     #print("pushData")
                     return data_360,n,data_p,data_e
                 def byProfiles(self,dataOut):
                     self.__dataReady     =  False
                     data_360           =  None
                     data_p             = None
                     data_e             = None
                     #print("dataOu",dataOut.dataPP_POW)
                     self.putData(data=dataOut,mode = self.mode)
                     ##### print("profIndex",self.__profIndex)
                     if self.__profIndex  == self.n:
                         data_360,n,data_p,data_e  = self.pushData(data=dataOut)
                         self.__dataReady                   = True
                     return data_360,data_p,data_e
                 def blockOp(self, dataOut, datatime= None):
                     if self.__initime == None:
                         self.__initime = datatime
                     data_360,data_p,data_e = self.byProfiles(dataOut)
                     self.__lastdatatime           = datatime
                     if data_360 is None:
                         return None, None,None,None
                     avgdatatime    = self.__initime
                     if self.n==1:
                         avgdatatime = datatime
                     deltatime      = datatime - self.__lastdatatime
                     self.__initime = datatime
                     #print(data_360.shape,avgdatatime,data_p.shape)
                     return data_360,avgdatatime,data_p,data_e
                 def run(self, dataOut,n = None,mode=None,**kwargs):
                     #print("BLOCK 360 HERE WE GO MOMENTOS")
                     print("Block 360")
                     #exit(1)
                     if not self.isConfig:
                         self.setup(dataOut = dataOut, n    = n ,mode= mode ,**kwargs)
                         ####self.index = 0
                         #print("comova",self.isConfig)
                         self.isConfig   = True
                     ####if self.index==dataOut.azimuth.shape[0]:
                     ####    self.index=0
                     data_360, avgdatatime,data_p,data_e = self.blockOp(dataOut, dataOut.utctime)
                     dataOut.flagNoData                         = True
                     if self.__dataReady:
                         dataOut.data_360         = data_360 # S
                         #print("DATA 360")
                         #print(dataOut.data_360)
                         #print("---------------------------------------------------------------------------------")
                         print("---------------------------DATAREADY---------------------------------------------")
                         #print("---------------------------------------------------------------------------------")
                         #print("data_360",dataOut.data_360.shape)
                         dataOut.data_azi         = data_p
                         dataOut.data_ele         = data_e
                         ###print("azi:    ",dataOut.data_azi)
                         #print("ele:    ",dataOut.data_ele)
                         #print("jroproc_parameters",data_p[0],data_p[-1])#,data_360.shape,avgdatatime)
                         dataOut.utctime         = avgdatatime
                         dataOut.flagNoData      = False
                     return dataOut
             class Block360_vRF(Operation):
                 '''
                 '''
                 isConfig       = False
                 __profIndex    = 0
                 __initime      = None
                 __lastdatatime = None
                 __buffer       = None
                 __dataReady    = False
                 n              = None
                 __nch          = 0
                 __nHeis        = 0
                 index          = 0
                 mode           = 0
                 def __init__(self,**kwargs):
                     Operation.__init__(self,**kwargs)
                 def setup(self, dataOut, n = None, mode = None):
                     '''
                     n= Numero de PRF's de entrada
                     '''
                     self.__initime        = None
                     self.__lastdatatime   = 0
                     self.__dataReady      = False
                     self.__buffer         = 0
                     self.__buffer_1D      = 0
                     self.__profIndex      = 0
                     self.index            = 0
                     self.__nch            = dataOut.nChannels
                     self.__nHeis          = dataOut.nHeights
                     ##print("ELVALOR DE n es:", n)
                     if n == None:
                         raise ValueError("n should be specified.")
                     if mode == None:
                         raise ValueError("mode should be specified.")
                     if n != None:
                         if n<1:
                             print("n should be greater than 2")
                             raise ValueError("n should be greater than 2")
                     self.n       = n
                     self.mode    = mode
                     #print("self.mode",self.mode)
                     #print("nHeights")
                     self.__buffer  = numpy.zeros(( dataOut.nChannels,n, dataOut.nHeights))
                     self.__buffer2 = numpy.zeros(n)
                     self.__buffer3 = numpy.zeros(n)
                 def putData(self,data,mode):
                     '''
                     Add a profile to he __buffer and increase in one the __profiel Index
                     '''
                     #print("line 4049",data.dataPP_POW.shape,data.dataPP_POW[:10])
                     #print("line 4049",data.azimuth.shape,data.azimuth)
                     if self.mode==0:
                         self.__buffer[:,self.__profIndex,:]= data.dataPP_POWER# PRIMER MOMENTO
                     if self.mode==1:
                         self.__buffer[:,self.__profIndex,:]= data.data_pow
                     #print("me casi",self.index,data.azimuth[self.index])
                     #print(self.__profIndex, self.index , data.azimuth[self.index] )
                     #print("magic",data.profileIndex)
                     #print(data.azimuth[self.index])
                     #print("index",self.index)
                     #####self.__buffer2[self.__profIndex] = data.azimuth[self.index]
                     self.__buffer2[self.__profIndex] = data.azimuth
                     self.__buffer3[self.__profIndex] = data.elevation
                     #print("q pasa")
                     #####self.index+=1
                     #print("index",self.index,data.azimuth[:10])
                     self.__profIndex      += 1
                     return        #················· Remove DC···································
                 def pushData(self,data):
                     '''
                     Return the PULSEPAIR and the profiles used in the operation
                     Affected :  self.__profileIndex
                     '''
                     #print("pushData")
                     data_360 = self.__buffer
                     data_p   = self.__buffer2
                     data_e   = self.__buffer3
                     n                = self.__profIndex
                     self.__buffer    = numpy.zeros((self.__nch, self.n,self.__nHeis))
                     self.__buffer2 = numpy.zeros(self.n)
                     self.__buffer3 = numpy.zeros(self.n)
                     self.__profIndex = 0
                     #print("pushData")
                     return data_360,n,data_p,data_e
                 def byProfiles(self,dataOut):
                     self.__dataReady     =  False
                     data_360           =  None
                     data_p             = None
                     data_e             = None
                     #print("dataOu",dataOut.dataPP_POW)
                     self.putData(data=dataOut,mode = self.mode)
                     ##### print("profIndex",self.__profIndex)
                     if self.__profIndex  == self.n:
                         data_360,n,data_p,data_e  = self.pushData(data=dataOut)
                         self.__dataReady                   = True
                     return data_360,data_p,data_e
                 def blockOp(self, dataOut, datatime= None):
                     if self.__initime == None:
                         self.__initime = datatime
                     data_360,data_p,data_e = self.byProfiles(dataOut)
                     self.__lastdatatime           = datatime
                     if data_360 is None:
                         return None, None,None,None
                     avgdatatime    = self.__initime
                     if self.n==1:
                         avgdatatime = datatime
                     deltatime      = datatime - self.__lastdatatime
                     self.__initime = datatime
                     #print(data_360.shape,avgdatatime,data_p.shape)
                     return data_360,avgdatatime,data_p,data_e
                 def checkcase(self,data_ele):
                     start  = data_ele[0]
                     end    = data_ele[-1]
                     diff_angle = (end-start)
                     len_ang=len(data_ele)
                     print("start",start)
                     print("end",end)
                     print("number",diff_angle)
                     print("len_ang",len_ang)
                     aux = (data_ele<0).any(axis=0)
                     #exit(1)
                     if diff_angle<0 and aux!=1: #Bajada
                         return 1
                     elif diff_angle<0 and aux==1: #Bajada con angulos negativos
                         return 0
                     elif diff_angle == 0: # This case happens when the angle reaches the max_angle if n = 2
                         self.flagEraseFirstData = 1
                         print("ToDO this case")
                         exit(1)
                     elif diff_angle>0: #Subida
                         return 0
                 def run(self, dataOut,n = None,mode=None,**kwargs):
                     #print("BLOCK 360 HERE WE GO MOMENTOS")
                     print("Block 360")
                     #exit(1)
                     if not self.isConfig:
                         if n == 1:
                             print("*******************Min Value is 2. Setting n = 2*******************")
                             n = 2
                         #exit(1)
                         print(n)
                         self.setup(dataOut = dataOut, n    = n ,mode= mode ,**kwargs)
                         ####self.index = 0
                         #print("comova",self.isConfig)
                         self.isConfig   = True
                     ####if self.index==dataOut.azimuth.shape[0]:
                     ####    self.index=0
                     data_360, avgdatatime,data_p,data_e = self.blockOp(dataOut, dataOut.utctime)
                     dataOut.flagNoData                         = True
                     if self.__dataReady:
                         dataOut.data_360         = data_360 # S
                         #print("DATA 360")
                         #print(dataOut.data_360)
                         #print("---------------------------------------------------------------------------------")
                         print("---------------------------DATAREADY---------------------------------------------")
                         #print("---------------------------------------------------------------------------------")
                         #print("data_360",dataOut.data_360.shape)
                         dataOut.data_azi         = data_p
                         dataOut.data_ele         = data_e
                         ###print("azi:    ",dataOut.data_azi)
                         #print("ele:    ",dataOut.data_ele)
                         #print("jroproc_parameters",data_p[0],data_p[-1])#,data_360.shape,avgdatatime)
                         dataOut.utctime         = avgdatatime
                         dataOut.case_flag = self.checkcase(dataOut.data_ele)
                         if dataOut.case_flag: #Si está de bajada empieza a plotear
                             print("INSIDE CASE FLAG BAJADA")
                             dataOut.flagNoData  = False
                         else:
                             print("CASE SUBIDA")
                             dataOut.flagNoData  = True
                         #dataOut.flagNoData      = False
                     return dataOut
+            class Block360_vRF2(Operation):
+                '''
+                '''
+                isConfig       = False
+                __profIndex    = 0
+                __initime      = None
+                __lastdatatime = None
+                __buffer       = None
+                __dataReady    = False
+                n              = None
+                __nch          = 0
+                __nHeis        = 0
+                index          = 0
+                mode           = 0
+                def __init__(self,**kwargs):
+                    Operation.__init__(self,**kwargs)
+                def setup(self, dataOut, n = None, mode = None):
+                    '''
+                    n= Numero de PRF's de entrada
+                    '''
+                    self.__initime        = None
+                    self.__lastdatatime   = 0
+                    self.__dataReady      = False
+                    self.__buffer         = 0
+                    self.__buffer_1D      = 0
+                    #self.__profIndex      = 0
+                    self.index            = 0
+                    self.__nch            = dataOut.nChannels
+                    self.__nHeis          = dataOut.nHeights
+                    self.mode    = mode
+                    #print("self.mode",self.mode)
+                    #print("nHeights")
+                    self.__buffer  = []
+                    self.__buffer2 = []
+                    self.__buffer3 = []
+                def putData(self,data,mode):
+                    '''
+                    Add a profile to he __buffer and increase in one the __profiel Index
+                    '''
+                    #print("line 4049",data.dataPP_POW.shape,data.dataPP_POW[:10])
+                    #print("line 4049",data.azimuth.shape,data.azimuth)
+                    if self.mode==0:
+                        self.__buffer.append(data.dataPP_POWER)# PRIMER MOMENTO
+                    if self.mode==1:
+                        self.__buffer.append(data.data_pow)
+                    #print("me casi",self.index,data.azimuth[self.index])
+                    #print(self.__profIndex, self.index , data.azimuth[self.index] )
+                    #print("magic",data.profileIndex)
+                    #print(data.azimuth[self.index])
+                    #print("index",self.index)
+                    #####self.__buffer2[self.__profIndex] = data.azimuth[self.index]
+                    self.__buffer2.append(data.azimuth)
+                    self.__buffer3.append(data.elevation)
+                    self.__profIndex      += 1
+                    #print("q pasa")
+                    return numpy.array(self.__buffer3)        #················· Remove DC···································
+                def pushData(self,data):
+                    '''
+                    Return the PULSEPAIR and the profiles used in the operation
+                    Affected :  self.__profileIndex
+                    '''
+                    #print("pushData")
+                    data_360 = numpy.array(self.__buffer).transpose(1,0,2)
+                    data_p   = numpy.array(self.__buffer2)
+                    data_e   = numpy.array(self.__buffer3)
+                    n                = self.__profIndex
+                    self.__buffer    = []
+                    self.__buffer2 = []
+                    self.__buffer3 = []
+                    self.__profIndex = 0
+                    #print("pushData")
+                    return data_360,n,data_p,data_e
+                def byProfiles(self,dataOut):
+                    self.__dataReady     =  False
+                    data_360           =  None
+                    data_p             = None
+                    data_e             = None
+                    #print("dataOu",dataOut.dataPP_POW)
+                    elevations = self.putData(data=dataOut,mode = self.mode)
+                    ##### print("profIndex",self.__profIndex)
+                    if self.__profIndex > 1:
+                        case_flag = self.checkcase(elevations)
+                        if case_flag == 0: #Subida
+                            #Se borra el dato anterior para liberar buffer y comparar el dato actual con el siguiente
+                            if len(self.__buffer) == 2: #Cuando está de subida
+                                self.__buffer.pop(0) #Erase first data
+                                self.__buffer2.pop(0)
+                                self.__buffer3.pop(0)
+                                self.__profIndex -= 1
+                            else: #Cuando ha estado de bajada y ha vuelto a subir
+                                #print("else",self.__buffer3)
+                                self.__buffer.pop() #Erase last data
+                                self.__buffer2.pop()
+                                self.__buffer3.pop()
+                                data_360,n,data_p,data_e  = self.pushData(data=dataOut)
+                                #print(data_360.shape)
+                                #print(data_e.shape)
+                                #exit(1)
+                                self.__dataReady = True
+                        '''
+                        elif elevations[-1]<0.:
+                            if len(self.__buffer) == 2:
+                                self.__buffer.pop(0) #Erase first data
+                                self.__buffer2.pop(0)
+                                self.__buffer3.pop(0)
+                                self.__profIndex -= 1
+                            else:
+                                self.__buffer.pop() #Erase last data
+                                self.__buffer2.pop()
+                                self.__buffer3.pop()
+                                data_360,n,data_p,data_e  = self.pushData(data=dataOut)
+                                self.__dataReady = True
+                                '''
+                    '''
+                    if self.__profIndex  == self.n:
+                        data_360,n,data_p,data_e  = self.pushData(data=dataOut)
+                        self.__dataReady                   = True
+                        '''
+                    return data_360,data_p,data_e
+                def blockOp(self, dataOut, datatime= None):
+                    if self.__initime == None:
+                        self.__initime = datatime
+                    data_360,data_p,data_e = self.byProfiles(dataOut)
+                    self.__lastdatatime           = datatime
+                    if data_360 is None:
+                        return None, None,None,None
+                    avgdatatime    = self.__initime
+                    if self.n==1:
+                        avgdatatime = datatime
+                    deltatime      = datatime - self.__lastdatatime
+                    self.__initime = datatime
+                    #print(data_360.shape,avgdatatime,data_p.shape)
+                    return data_360,avgdatatime,data_p,data_e
+                def checkcase(self,data_ele):
+                    print(data_ele)
+                    start  = data_ele[-2]
+                    end    = data_ele[-1]
+                    diff_angle = (end-start)
+                    len_ang=len(data_ele)
+                    if diff_angle > 0: #Subida
+                        return 0
+                def run(self, dataOut,n = None,mode=None,**kwargs):
+                    #print("BLOCK 360 HERE WE GO MOMENTOS")
+                    print("Block 360")
+                    #exit(1)
+                    if not self.isConfig:
+                        print(n)
+                        self.setup(dataOut = dataOut ,mode= mode ,**kwargs)
+                        ####self.index = 0
+                        #print("comova",self.isConfig)
+                        self.isConfig   = True
+                    ####if self.index==dataOut.azimuth.shape[0]:
+                    ####    self.index=0
+                    data_360, avgdatatime,data_p,data_e = self.blockOp(dataOut, dataOut.utctime)
+                    dataOut.flagNoData                         = True
+                    if self.__dataReady:
+                        dataOut.data_360         = data_360 # S
+                        #print("DATA 360")
+                        #print(dataOut.data_360)
+                        #print("---------------------------------------------------------------------------------")
+                        print("---------------------------DATAREADY---------------------------------------------")
+                        #print("---------------------------------------------------------------------------------")
+                        #print("data_360",dataOut.data_360.shape)
+                        print(data_e)
+                        #exit(1)
+                        dataOut.data_azi         = data_p
+                        dataOut.data_ele         = data_e
+                        ###print("azi:    ",dataOut.data_azi)
+                        #print("ele:    ",dataOut.data_ele)
+                        #print("jroproc_parameters",data_p[0],data_p[-1])#,data_360.shape,avgdatatime)
+                        dataOut.utctime         = avgdatatime
+                        dataOut.flagNoData      = False
+                    return dataOut

General Comments 0

Write
Preview

You need to be logged in to leave comments. Login now

No TODOs yet

	Site-wide shortcuts
/	Use quick search box
g h	Goto home page
g g	Goto my private gists page
g G	Goto my public gists page
g 0-9	Goto bookmarked items from 0-9
n r	New repository page
n g	New gist page

	Repositories
g s	Goto summary page
g c	Goto changelog page
g f	Goto files page
g F	Goto files page with file search activated
g p	Goto pull requests page
g o	Goto repository settings
g O	Goto repository access permissions settings
t s	Toggle sidebar on some pages