schain Commit - r1707:21f41e756eb1 · Jicamarca Repository

Valley Densities Proc

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schainpy/model/graphics/jroplot_voltage_lags.py +83 0

             import os
             import time
             import math
             import datetime
             import numpy
             from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator  #YONG
             from .jroplot_spectra import RTIPlot, NoisePlot
             from schainpy.utils import log
             from .plotting_codes import *
             from schainpy.model.graphics.jroplot_base import Plot, plt
             import matplotlib.pyplot as plt
             import matplotlib.colors as colors
             from matplotlib.ticker import MultipleLocator, LogLocator, NullFormatter
             class RTIDPPlot(RTIPlot):
                 '''
                 Written by R. Flores
                 '''
                 '''Plot for RTI Double Pulse Experiment Using Cross Products Analysis
                 '''
                 CODE = 'RTIDP'
                 colormap = 'jet'
                 plot_name = 'RTI'
                 plot_type = 'pcolorbuffer'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = 3
                     self.nplots = self.nrows
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Time (LT)'
                     self.cb_label = 'Intensity (dB)'
                     self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.1, 'right':0.95})
                     self.titles = ['{} Channel {}'.format(
                         self.plot_name.upper(), '0x1'),'{} Channel {}'.format(
                             self.plot_name.upper(), '0'),'{} Channel {}'.format(
                                 self.plot_name.upper(), '1')]
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['rti'] = dataOut.data_for_RTI_DP
                     data['NDP'] = dataOut.NDP
                     return data, meta
                 def plot(self):
                     NDP = self.data['NDP'][-1]
                     self.x = self.data.times
                     self.y = self.data.yrange[0:NDP]
                     self.z = self.data['rti']
                     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[1][0,12:40])
                         self.zmin = self.zmin if self.zmin is not None else numpy.min(
                             self.z[1][0,12:40])
                         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,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         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,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
             class RTILPPlot(RTIPlot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                    Plot for RTI Long Pulse Using Cross Products Analysis
                 '''
                 CODE = 'RTILP'
                 colormap = 'jet'
                 plot_name = 'RTI LP'
                 plot_type = 'pcolorbuffer'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = 2
                     self.nplots = self.nrows
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Time (LT)'
                     self.cb_label = 'Intensity (dB)'
                     self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.1, 'right':0.95})
                     self.titles = ['{} Channel {}'.format(
                         self.plot_name.upper(), '0'),'{} Channel {}'.format(
                             self.plot_name.upper(), '1'),'{} Channel {}'.format(
                                 self.plot_name.upper(), '2'),'{} Channel {}'.format(
                                     self.plot_name.upper(), '3')]
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['rti'] = dataOut.data_for_RTI_LP
                     data['NRANGE'] = dataOut.NRANGE
                     return data, meta
                 def plot(self):
                     NRANGE = self.data['NRANGE'][-1]
                     self.x = self.data.times
                     self.y = self.data.yrange[0:NRANGE]
                     self.z = self.data['rti']
                     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[1][0,12:40])
                         self.zmin = self.zmin if self.zmin is not None else numpy.min(
                             self.z[1][0,12:40])
                         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,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
                         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,
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=plt.get_cmap(self.colormap)
                                                    )
             class DenRTIPlot(RTIPlot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                    Plot for Den
                 '''
                 CODE = 'denrti'
                 colormap = 'jet'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = self.data.shape(self.CODE)[0]
                     self.nplots = self.nrows
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Time (LT)'
                     self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18})
                     if self.CODE == 'denrti':
                         self.cb_label = r'$\mathrm{N_e}$ Electron Density ($\mathrm{1/cm^3}$)'
                     self.titles = ['Electron Density RTI']
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['denrti'] = dataOut.DensityFinal*1.e-6 #To Plot in cm^-3
                     return data, meta
                 def plot(self):
                     self.x = self.data.times
                     self.y = self.data.yrange
                     self.z = self.data[self.CODE]
                     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]
                             if numpy.log10(self.zmin)<0:
                                 self.zmin=1
                             ax.plt = ax.pcolormesh(x, y, z[n].T * self.factors[n],
                                                    vmin=self.zmin,
                                                    vmax=self.zmax,
                                                    cmap=self.cmaps[n],
                                                    norm=colors.LogNorm()
                                                    )
                         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],
                                                    norm=colors.LogNorm()
                                                    )
             class ETempRTIPlot(RTIPlot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                    Plot for Electron Temperature
                 '''
                 CODE = 'ETemp'
                 colormap = 'jet'
                 def setup(self):
                     self.xaxis = 'time'
                     self.ncols = 1
                     self.nrows = self.data.shape(self.CODE)[0]
                     self.nplots = self.nrows
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Time (LT)'
                     self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18})
                     if self.CODE == 'ETemp':
                         self.cb_label = 'Electron Temperature (K)'
                         self.titles = ['Electron Temperature RTI']
                     if self.CODE == 'ITemp':
                         self.cb_label = 'Ion Temperature (K)'
                         self.titles = ['Ion Temperature RTI']
                     if self.CODE == 'HeFracLP':
                         self.cb_label ='He+ Fraction'
                         self.titles = ['He+ Fraction RTI']
                         self.zmax=0.16
                     if self.CODE == 'HFracLP':
                         self.cb_label ='H+ Fraction'
                         self.titles = ['H+ Fraction RTI']
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['ETemp'] = dataOut.ElecTempFinal
                     return data, meta
                 def plot(self):
                     self.x = self.data.times
                     self.y = self.data.yrange
                     self.z = self.data[self.CODE]
                     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]
                                                    )
                             #plt.tight_layout()
                         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 ITempRTIPlot(ETempRTIPlot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                    Plot for Ion Temperature
                 '''
                 CODE = 'ITemp'
                 colormap = 'jet'
                 plot_name = 'Ion Temperature'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['ITemp'] = dataOut.IonTempFinal
                     return data, meta
             class HFracRTIPlot(ETempRTIPlot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                    Plot for H+ LP
                 '''
                 CODE = 'HFracLP'
                 colormap = 'jet'
                 plot_name = 'H+ Frac'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['HFracLP'] = dataOut.PhyFinal
                     return data, meta
             class HeFracRTIPlot(ETempRTIPlot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                    Plot for He+ LP
                 '''
                 CODE = 'HeFracLP'
                 colormap = 'jet'
                 plot_name = 'He+ Frac'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['HeFracLP'] = dataOut.PheFinal
                     return data, meta
             class TempsDPPlot(Plot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for Electron - Ion Temperatures
                 '''
                 CODE = 'tempsDP'
                 #plot_name = 'Temperatures'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Temperature (K)'
                     self.titles = ['Electron/Ion Temperatures']
                     self.width = 3.5
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['Te'] = dataOut.te2
                     data['Ti'] = dataOut.ti2
                     data['Te_error'] = dataOut.ete2
                     data['Ti_error'] = dataOut.eti2
                     meta['yrange'] = dataOut.heightList[0:dataOut.NSHTS]
                     return data, meta
                 def plot(self):
                     y = self.data.yrange
                     self.xmin = -100
                     self.xmax = 5000
                     ax = self.axes[0]
                     data = self.data[-1]
                     Te = data['Te']
                     Ti = data['Ti']
                     errTe = data['Te_error']
                     errTi = data['Ti_error']
                     if ax.firsttime:
                         ax.errorbar(Te, y, xerr=errTe, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='Te')
                         ax.errorbar(Ti, y, fmt='k^', xerr=errTi,elinewidth=1.0,color='b',linewidth=2.0, label='Ti')
                         plt.legend(loc='lower right')
                         self.ystep_given = 50
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
                     else:
                         self.clear_figures()
                         ax.errorbar(Te, y, xerr=errTe, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='Te')
                         ax.errorbar(Ti, y, fmt='k^', xerr=errTi,elinewidth=1.0,color='b',linewidth=2.0, label='Ti')
                         plt.legend(loc='lower right')
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
             class TempsHPPlot(Plot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for Temperatures Hybrid Experiment
                 '''
                 CODE = 'temps_LP'
                 #plot_name = 'Temperatures'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Temperature (K)'
                     self.titles = ['Electron/Ion Temperatures']
                     self.width = 3.5
                     self.height = 6.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['Te'] = numpy.concatenate((dataOut.te2[:dataOut.cut],dataOut.te[dataOut.cut:]))
                     data['Ti'] = numpy.concatenate((dataOut.ti2[:dataOut.cut],dataOut.ti[dataOut.cut:]))
                     data['Te_error'] = numpy.concatenate((dataOut.ete2[:dataOut.cut],dataOut.ete[dataOut.cut:]))
                     data['Ti_error'] = numpy.concatenate((dataOut.eti2[:dataOut.cut],dataOut.eti[dataOut.cut:]))
                     meta['yrange'] = dataOut.heightList[0:dataOut.NACF]
                     return data, meta
                 def plot(self):
                     self.y = self.data.yrange
                     self.xmin = -100
                     self.xmax = 4500
                     ax = self.axes[0]
                     data = self.data[-1]
                     Te = data['Te']
                     Ti = data['Ti']
                     errTe = data['Te_error']
                     errTi = data['Ti_error']
                     if ax.firsttime:
                         ax.errorbar(Te, self.y, xerr=errTe, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='Te')
                         ax.errorbar(Ti, self.y, fmt='k^', xerr=errTi,elinewidth=1.0,color='b',linewidth=2.0, label='Ti')
                         plt.legend(loc='lower right')
                         self.ystep_given = 200
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
                     else:
                         self.clear_figures()
                         ax.errorbar(Te, self.y, xerr=errTe, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='Te')
                         ax.errorbar(Ti, self.y, fmt='k^', xerr=errTi,elinewidth=1.0,color='b',linewidth=2.0, label='Ti')
                         plt.legend(loc='lower right')
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
             class FracsHPPlot(Plot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for Composition LP
                 '''
                 CODE = 'fracs_LP'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Frac'
                     self.titles = ['Composition']
                     self.width = 3.5
                     self.height = 6.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     #aux_nan=numpy.zeros(dataOut.cut,'float32')
                     #aux_nan[:]=numpy.nan
                     #data['ph'] = numpy.concatenate((aux_nan,dataOut.ph[dataOut.cut:]))
                     #data['eph'] = numpy.concatenate((aux_nan,dataOut.eph[dataOut.cut:]))
                     data['ph'] = dataOut.ph[dataOut.cut:]
                     data['eph'] = dataOut.eph[dataOut.cut:]
                     data['phe'] = dataOut.phe[dataOut.cut:]
                     data['ephe'] = dataOut.ephe[dataOut.cut:]
                     data['cut'] = dataOut.cut
                     meta['yrange'] = dataOut.heightList[0:dataOut.NACF]
                     return data, meta
                 def plot(self):
                     data = self.data[-1]
                     ph = data['ph']
                     eph = data['eph']
                     phe = data['phe']
                     ephe = data['ephe']
                     cut = data['cut']
                     self.y = self.data.yrange
                     self.xmin = 0
                     self.xmax = 1
                     ax = self.axes[0]
                     if ax.firsttime:
                         ax.errorbar(ph, self.y[cut:], xerr=eph, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='H+')
                         ax.errorbar(phe, self.y[cut:], fmt='k^', xerr=ephe,elinewidth=1.0,color='b',linewidth=2.0, label='He+')
                         plt.legend(loc='lower right')
                         self.xstep_given = 0.2
                         self.ystep_given = 200
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
                     else:
                         self.clear_figures()
                         ax.errorbar(ph, self.y[cut:], xerr=eph, fmt='r^',elinewidth=1.0,color='b',linewidth=2.0, label='H+')
                         ax.errorbar(phe, self.y[cut:], fmt='k^', xerr=ephe,elinewidth=1.0,color='b',linewidth=2.0, label='He+')
                         plt.legend(loc='lower right')
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
             class EDensityPlot(Plot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for electron density
                 '''
                 CODE = 'den'
                 #plot_name = 'Electron Density'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = r'$\mathrm{N_e}$ Electron Density ($\mathrm{1/cm^3}$)'
                     self.titles = ['Electron Density']
                     self.width = 3.5
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['den_power'] = dataOut.ph2[:dataOut.NSHTS]
                     data['den_Faraday'] = dataOut.dphi[:dataOut.NSHTS]
                     data['den_error'] = dataOut.sdp2[:dataOut.NSHTS]
                     #data['err_Faraday'] = dataOut.sdn1[:dataOut.NSHTS]
                     #print(numpy.shape(data['den_power']))
                     #print(numpy.shape(data['den_Faraday']))
                     #print(numpy.shape(data['den_error']))
                     data['NSHTS'] = dataOut.NSHTS
                     meta['yrange'] = dataOut.heightList[0:dataOut.NSHTS]
                     return data, meta
                 def plot(self):
                     y = self.data.yrange
                     #self.xmin = 1e3
                     #self.xmax = 1e7
                     ax = self.axes[0]
                     data = self.data[-1]
                     DenPow = data['den_power']
                     DenFar = data['den_Faraday']
                     errDenPow = data['den_error']
                     #errFaraday = data['err_Faraday']
                     NSHTS = data['NSHTS']
                     if self.CODE == 'denLP':
                         DenPowLP = data['den_LP']
                         errDenPowLP = data['den_LP_error']
                         cut = data['cut']
                     if ax.firsttime:
                         self.autoxticks=False
                         #ax.errorbar(DenFar, y[:NSHTS], xerr=1, fmt='h-',elinewidth=1.0,color='g',linewidth=1.0, label='Faraday Profile',markersize=2)
                         ax.errorbar(DenFar, y[:NSHTS], xerr=1, fmt='h-',elinewidth=1.0,color='g',linewidth=1.0, label='Faraday',markersize=2,linestyle='-')
                         #ax.errorbar(DenPow, y[:NSHTS], fmt='k^-', xerr=errDenPow,elinewidth=1.0,color='b',linewidth=1.0, label='Power Profile',markersize=2)
                         ax.errorbar(DenPow, y[:NSHTS], fmt='k^-', xerr=errDenPow,elinewidth=1.0,color='b',linewidth=1.0, label='Power',markersize=2,linestyle='-')
                         if self.CODE=='denLP':
                             ax.errorbar(DenPowLP[cut:], y[cut:], xerr=errDenPowLP[cut:], fmt='r^-',elinewidth=1.0,color='r',linewidth=1.0, label='LP Profile',markersize=2)
                         plt.legend(loc='upper left',fontsize=8.5)
                         #plt.legend(loc='lower left',fontsize=8.5)
                         ax.set_xscale("log", nonposx='clip')
                         grid_y_ticks=numpy.arange(numpy.nanmin(y),numpy.nanmax(y),50)
                         self.ystep_given=100
                         if self.CODE=='denLP':
                             self.ystep_given=200
                         ax.set_yticks(grid_y_ticks,minor=True)
                         locmaj = LogLocator(base=10,numticks=12)
                         ax.xaxis.set_major_locator(locmaj)
                         locmin = LogLocator(base=10.0,subs=(0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9),numticks=12)
                         ax.xaxis.set_minor_locator(locmin)
                         ax.xaxis.set_minor_formatter(NullFormatter())
                         ax.grid(which='minor')
                     else:
                         dataBefore = self.data[-2]
                         DenPowBefore = dataBefore['den_power']
                         self.clear_figures()
                         #ax.errorbar(DenFar, y[:NSHTS], xerr=1, fmt='h-',elinewidth=1.0,color='g',linewidth=1.0, label='Faraday Profile',markersize=2)
                         ax.errorbar(DenFar, y[:NSHTS], xerr=1, fmt='h-',elinewidth=1.0,color='g',linewidth=1.0, label='Faraday',markersize=2,linestyle='-')
                         #ax.errorbar(DenPow, y[:NSHTS], fmt='k^-', xerr=errDenPow,elinewidth=1.0,color='b',linewidth=1.0, label='Power Profile',markersize=2)
                         ax.errorbar(DenPow, y[:NSHTS], fmt='k^-', xerr=errDenPow,elinewidth=1.0,color='b',linewidth=1.0, label='Power',markersize=2,linestyle='-')
                         ax.errorbar(DenPowBefore, y[:NSHTS], elinewidth=1.0,color='r',linewidth=0.5,linestyle="dashed")
                         if self.CODE=='denLP':
                             ax.errorbar(DenPowLP[cut:], y[cut:], fmt='r^-', xerr=errDenPowLP[cut:],elinewidth=1.0,color='r',linewidth=1.0, label='LP Profile',markersize=2)
                         ax.set_xscale("log", nonposx='clip')
                         grid_y_ticks=numpy.arange(numpy.nanmin(y),numpy.nanmax(y),50)
                         ax.set_yticks(grid_y_ticks,minor=True)
                         locmaj = LogLocator(base=10,numticks=12)
                         ax.xaxis.set_major_locator(locmaj)
                         locmin = LogLocator(base=10.0,subs=(0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9),numticks=12)
                         ax.xaxis.set_minor_locator(locmin)
                         ax.xaxis.set_minor_formatter(NullFormatter())
                         ax.grid(which='minor')
                         plt.legend(loc='upper left',fontsize=8.5)
                         #plt.legend(loc='lower left',fontsize=8.5)
+            class RelativeDenPlot(Plot):
+                '''
+                Written by R. Flores
+                '''
+                '''
+                Plot for electron density
+                '''
+                CODE = 'den'
+                #plot_name = 'Electron Density'
+                plot_type = 'scatterbuffer'
+                def setup(self):
+                    self.ncols = 1
+                    self.nrows = 1
+                    self.nplots = 1
+                    self.ylabel = 'Range [km]'
+                    self.xlabel = r'$\mathrm{N_e}$ Relative Electron Density ($\mathrm{1/cm^3}$)'
+                    self.titles = ['Electron Density']
+                    self.width = 3.5
+                    self.height = 5.5
+                    self.colorbar = False
+                    self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
+                def update(self, dataOut):
+                    data = {}
+                    meta = {}
+                    data['den_power'] = dataOut.ph2
+                    data['den_error'] = dataOut.sdp2
+                    meta['yrange'] = dataOut.heightList
+                    return data, meta
+                def plot(self):
+                    y = self.data.yrange
+                    ax = self.axes[0]
+                    data = self.data[-1]
+                    DenPow = data['den_power']
+                    errDenPow = data['den_error']
+                    if ax.firsttime:
+                        self.autoxticks=False
+                        ax.errorbar(DenPow, y, fmt='k^-', xerr=errDenPow,elinewidth=1.0,color='b',linewidth=1.0, label='Power',markersize=2,linestyle='-')
+                        plt.legend(loc='upper left',fontsize=8.5)
+                        #plt.legend(loc='lower left',fontsize=8.5)
+                        ax.set_xscale("log", nonposx='clip')
+                        grid_y_ticks=numpy.arange(numpy.nanmin(y),numpy.nanmax(y),50)
+                        self.ystep_given=100
+                        ax.set_yticks(grid_y_ticks,minor=True)
+                        locmaj = LogLocator(base=10,numticks=12)
+                        ax.xaxis.set_major_locator(locmaj)
+                        locmin = LogLocator(base=10.0,subs=(0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9),numticks=12)
+                        ax.xaxis.set_minor_locator(locmin)
+                        ax.xaxis.set_minor_formatter(NullFormatter())
+                        ax.grid(which='minor')
+                    else:
+                        dataBefore = self.data[-2]
+                        DenPowBefore = dataBefore['den_power']
+                        self.clear_figures()
+                        ax.errorbar(DenPow, y, fmt='k^-', xerr=errDenPow,elinewidth=1.0,color='b',linewidth=1.0, label='Power',markersize=2,linestyle='-')
+                        ax.errorbar(DenPowBefore, y, elinewidth=1.0,color='r',linewidth=0.5,linestyle="dashed")
+                        ax.set_xscale("log", nonposx='clip')
+                        grid_y_ticks=numpy.arange(numpy.nanmin(y),numpy.nanmax(y),50)
+                        ax.set_yticks(grid_y_ticks,minor=True)
+                        locmaj = LogLocator(base=10,numticks=12)
+                        ax.xaxis.set_major_locator(locmaj)
+                        locmin = LogLocator(base=10.0,subs=(0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9),numticks=12)
+                        ax.xaxis.set_minor_locator(locmin)
+                        ax.xaxis.set_minor_formatter(NullFormatter())
+                        ax.grid(which='minor')
+                        plt.legend(loc='upper left',fontsize=8.5)
+                        #plt.legend(loc='lower left',fontsize=8.5)
             class FaradayAnglePlot(Plot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for electron density
                 '''
                 CODE = 'angle'
                 plot_name = 'Faraday Angle'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Faraday Angle (º)'
                     self.titles = ['Electron Density']
                     self.width = 3.5
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['angle'] = numpy.degrees(dataOut.phi)
                     #'''
                     #print(dataOut.phi_uwrp)
                     #print(data['angle'])
                     #exit(1)
                     #'''
                     data['dphi'] = dataOut.dphi_uc*10
                     #print(dataOut.dphi)
                     #data['NSHTS'] = dataOut.NSHTS
                     #meta['yrange'] = dataOut.heightList[0:dataOut.NSHTS]
                     return data, meta
                 def plot(self):
                     data = self.data[-1]
                     self.x = data[self.CODE]
                     dphi = data['dphi']
                     self.y = self.data.yrange
                     self.xmin = -360#-180
                     self.xmax = 360#180
                     ax = self.axes[0]
                     if ax.firsttime:
                         self.autoxticks=False
                         #if self.CODE=='den':
                         ax.plot(self.x, self.y,marker='o',color='g',linewidth=1.0,markersize=2)
                         ax.plot(dphi, self.y,marker='o',color='blue',linewidth=1.0,markersize=2)
                         grid_y_ticks=numpy.arange(numpy.nanmin(self.y),numpy.nanmax(self.y),50)
                         self.ystep_given=100
                         if self.CODE=='denLP':
                             self.ystep_given=200
                         ax.set_yticks(grid_y_ticks,minor=True)
                         ax.grid(which='minor')
                         #plt.tight_layout()
                     else:
                         self.clear_figures()
                         #if self.CODE=='den':
                         #print(numpy.shape(self.x))
                         ax.plot(self.x, self.y, marker='o',color='g',linewidth=1.0, markersize=2)
                         ax.plot(dphi, self.y,marker='o',color='blue',linewidth=1.0,markersize=2)
                         grid_y_ticks=numpy.arange(numpy.nanmin(self.y),numpy.nanmax(self.y),50)
                         ax.set_yticks(grid_y_ticks,minor=True)
                         ax.grid(which='minor')
             class EDensityHPPlot(EDensityPlot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                    Plot for Electron Density Hybrid Experiment
                 '''
                 CODE = 'denLP'
                 plot_name = 'Electron Density'
                 plot_type = 'scatterbuffer'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['den_power'] = dataOut.ph2[:dataOut.NSHTS]
                     data['den_Faraday']=dataOut.dphi[:dataOut.NSHTS]
                     data['den_error']=dataOut.sdp2[:dataOut.NSHTS]
                     data['den_LP']=dataOut.ne[:dataOut.NACF]
                     data['den_LP_error']=dataOut.ene[:dataOut.NACF]*dataOut.ne[:dataOut.NACF]*0.434
                     #self.ene=10**dataOut.ene[:dataOut.NACF]
                     data['NSHTS']=dataOut.NSHTS
                     data['cut']=dataOut.cut
                     return data, meta
             class ACFsPlot(Plot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for ACFs Double Pulse Experiment
                 '''
                 CODE = 'acfs'
                 #plot_name = 'ACF'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Lag (ms)'
                     self.titles = ['ACFs']
                     self.width = 3.5
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['ACFs'] = dataOut.acfs_to_plot
                     data['ACFs_error'] = dataOut.acfs_error_to_plot
                     data['lags'] = dataOut.lags_to_plot
                     data['Lag_contaminated_1'] = dataOut.x_igcej_to_plot
                     data['Lag_contaminated_2'] = dataOut.x_ibad_to_plot
                     data['Height_contaminated_1'] = dataOut.y_igcej_to_plot
                     data['Height_contaminated_2'] = dataOut.y_ibad_to_plot
                     meta['yrange'] = numpy.array([])
                     #meta['NSHTS'] = dataOut.NSHTS
                     #meta['DPL'] = dataOut.DPL
                     data['NSHTS'] = dataOut.NSHTS #This is metadata
                     data['DPL'] = dataOut.DPL #This is metadata
                     return data, meta
                 def plot(self):
                     data = self.data[-1]
                     #NSHTS = self.meta['NSHTS']
                     #DPL = self.meta['DPL']
                     NSHTS = data['NSHTS'] #This is metadata
                     DPL = data['DPL'] #This is metadata
                     lags = data['lags']
                     ACFs = data['ACFs']
                     errACFs = data['ACFs_error']
                     BadLag1 = data['Lag_contaminated_1']
                     BadLag2 = data['Lag_contaminated_2']
                     BadHei1 = data['Height_contaminated_1']
                     BadHei2 = data['Height_contaminated_2']
                     self.xmin = 0.0
                     self.xmax = 2.0
                     self.y = ACFs
                     ax = self.axes[0]
                     if ax.firsttime:
                         for i in range(NSHTS):
                             x_aux = numpy.isfinite(lags[i,:])
                             y_aux = numpy.isfinite(ACFs[i,:])
                             yerr_aux = numpy.isfinite(errACFs[i,:])
                             x_igcej_aux = numpy.isfinite(BadLag1[i,:])
                             y_igcej_aux = numpy.isfinite(BadHei1[i,:])
                             x_ibad_aux = numpy.isfinite(BadLag2[i,:])
                             y_ibad_aux = numpy.isfinite(BadHei2[i,:])
                             if lags[i,:][~numpy.isnan(lags[i,:])].shape[0]>2:
                                 ax.errorbar(lags[i,x_aux], ACFs[i,y_aux], yerr=errACFs[i,x_aux],color='b',marker='o',linewidth=1.0,markersize=2)
                             ax.plot(BadLag1[i,x_igcej_aux],BadHei1[i,y_igcej_aux],'x',color='red',markersize=2)
                             ax.plot(BadLag2[i,x_ibad_aux],BadHei2[i,y_ibad_aux],'X',color='red',markersize=2)
                         self.xstep_given = (self.xmax-self.xmin)/(DPL-1)
                         self.ystep_given = 50
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
                     else:
                         self.clear_figures()
                         for i in range(NSHTS):
                             x_aux = numpy.isfinite(lags[i,:])
                             y_aux = numpy.isfinite(ACFs[i,:])
                             yerr_aux = numpy.isfinite(errACFs[i,:])
                             x_igcej_aux = numpy.isfinite(BadLag1[i,:])
                             y_igcej_aux = numpy.isfinite(BadHei1[i,:])
                             x_ibad_aux = numpy.isfinite(BadLag2[i,:])
                             y_ibad_aux = numpy.isfinite(BadHei2[i,:])
                             if lags[i,:][~numpy.isnan(lags[i,:])].shape[0]>2:
                                 ax.errorbar(lags[i,x_aux], ACFs[i,y_aux], yerr=errACFs[i,x_aux],linewidth=1.0,markersize=2,color='b',marker='o')
                             ax.plot(BadLag1[i,x_igcej_aux],BadHei1[i,y_igcej_aux],'x',color='red',markersize=2)
                             ax.plot(BadLag2[i,x_ibad_aux],BadHei2[i,y_ibad_aux],'X',color='red',markersize=2)
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
             class ACFsLPPlot(Plot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for ACFs Double Pulse Experiment
                 '''
                 CODE = 'acfs_LP'
                 #plot_name = 'ACF'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'Lag (ms)'
                     self.titles = ['ACFs']
                     self.width = 3.5
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     aux=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32')
                     errors=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32')
                     lags_LP_to_plot=numpy.zeros((dataOut.NACF,dataOut.IBITS),'float32')
                     for i in range(dataOut.NACF):
                         for j in range(dataOut.IBITS):
                             if numpy.abs(dataOut.errors[j,i]/dataOut.output_LP_integrated.real[0,i,0])<1.0:
                                 aux[i,j]=dataOut.output_LP_integrated.real[j,i,0]/dataOut.output_LP_integrated.real[0,i,0]
                                 aux[i,j]=max(min(aux[i,j],1.0),-1.0)*dataOut.DH+dataOut.heightList[i]
                                 lags_LP_to_plot[i,j]=dataOut.lags_LP[j]
                                 errors[i,j]=dataOut.errors[j,i]/dataOut.output_LP_integrated.real[0,i,0]*dataOut.DH
                             else:
                                 aux[i,j]=numpy.nan
                                 lags_LP_to_plot[i,j]=numpy.nan
                                 errors[i,j]=numpy.nan
                     data['ACFs'] = aux
                     data['ACFs_error'] = errors
                     data['lags'] = lags_LP_to_plot
                     meta['yrange'] = numpy.array([])
                     #meta['NACF'] = dataOut.NACF
                     #meta['NLAG'] = dataOut.NLAG
                     data['NACF'] = dataOut.NACF #This is metadata
                     data['NLAG'] = dataOut.NLAG #This is metadata
                     return data, meta
                 def plot(self):
                     data = self.data[-1]
                     #NACF = self.meta['NACF']
                     #NLAG = self.meta['NLAG']
                     NACF = data['NACF'] #This is metadata
                     NLAG = data['NLAG'] #This is metadata
                     lags = data['lags']
                     ACFs = data['ACFs']
                     errACFs = data['ACFs_error']
                     self.xmin = 0.0
                     self.xmax = 1.5
                     self.y = ACFs
                     ax = self.axes[0]
                     if ax.firsttime:
                         for i in range(NACF):
                             x_aux = numpy.isfinite(lags[i,:])
                             y_aux = numpy.isfinite(ACFs[i,:])
                             yerr_aux = numpy.isfinite(errACFs[i,:])
                             if lags[i,:][~numpy.isnan(lags[i,:])].shape[0]>2:
                                 ax.errorbar(lags[i,x_aux], ACFs[i,y_aux], yerr=errACFs[i,x_aux],color='b',linewidth=1.0,markersize=2,ecolor='r')
                         #self.xstep_given = (self.xmax-self.xmin)/(self.data.NLAG-1)
                         self.xstep_given=0.3
                         self.ystep_given = 200
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
                         ax.grid(which='minor')
                     else:
                         self.clear_figures()
                         for i in range(NACF):
                             x_aux = numpy.isfinite(lags[i,:])
                             y_aux = numpy.isfinite(ACFs[i,:])
                             yerr_aux = numpy.isfinite(errACFs[i,:])
                             if lags[i,:][~numpy.isnan(lags[i,:])].shape[0]>2:
                                 ax.errorbar(lags[i,x_aux], ACFs[i,y_aux], yerr=errACFs[i,x_aux],color='b',linewidth=1.0,markersize=2,ecolor='r')
                         ax.yaxis.set_minor_locator(MultipleLocator(15))
             class CrossProductsPlot(Plot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for cross products
                 '''
                 CODE = 'crossprod'
                 plot_name = 'Cross Products'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 3
                     self.nrows = 1
                     self.nplots = 3
                     self.ylabel = 'Range [km]'
                     self.titles = []
                     self.width = 3.5*self.nplots
                     self.height = 5.5
                     self.colorbar = False
                     self.plots_adjust.update({'wspace':.3, 'left': 0.12, 'right': 0.92, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['crossprod'] = dataOut.crossprods
                     data['NDP'] = dataOut.NDP
                     return data, meta
                 def plot(self):
                     NDP = self.data['NDP'][-1]
                     x = self.data['crossprod'][:,-1,:,:,:,:]
                     y = self.data.yrange[0:NDP]
                     for n, ax in enumerate(self.axes):
                         self.xmin=numpy.min(numpy.concatenate((x[n][0,20:30,0,0],x[n][1,20:30,0,0],x[n][2,20:30,0,0],x[n][3,20:30,0,0])))
                         self.xmax=numpy.max(numpy.concatenate((x[n][0,20:30,0,0],x[n][1,20:30,0,0],x[n][2,20:30,0,0],x[n][3,20:30,0,0])))
                         if ax.firsttime:
                             self.autoxticks=False
                             if n==0:
                                 label1='kax'
                                 label2='kay'
                                 label3='kbx'
                                 label4='kby'
                                 self.xlimits=[(self.xmin,self.xmax)]
                             elif n==1:
                                 label1='kax2'
                                 label2='kay2'
                                 label3='kbx2'
                                 label4='kby2'
                                 self.xlimits.append((self.xmin,self.xmax))
                             elif n==2:
                                 label1='kaxay'
                                 label2='kbxby'
                                 label3='kaxbx'
                                 label4='kaxby'
                                 self.xlimits.append((self.xmin,self.xmax))
                             ax.plotline1 = ax.plot(x[n][0,:,0,0], y, color='r',linewidth=2.0, label=label1)
                             ax.plotline2 = ax.plot(x[n][1,:,0,0], y, color='k',linewidth=2.0, label=label2)
                             ax.plotline3 = ax.plot(x[n][2,:,0,0], y, color='b',linewidth=2.0, label=label3)
                             ax.plotline4 = ax.plot(x[n][3,:,0,0], y, color='m',linewidth=2.0, label=label4)
                             ax.legend(loc='upper right')
                             ax.set_xlim(self.xmin, self.xmax)
                             self.titles.append('{}'.format(self.plot_name.upper()))
                         else:
                             if n==0:
                                 self.xlimits=[(self.xmin,self.xmax)]
                             else:
                                 self.xlimits.append((self.xmin,self.xmax))
                             ax.set_xlim(self.xmin, self.xmax)
                             ax.plotline1[0].set_data(x[n][0,:,0,0],y)
                             ax.plotline2[0].set_data(x[n][1,:,0,0],y)
                             ax.plotline3[0].set_data(x[n][2,:,0,0],y)
                             ax.plotline4[0].set_data(x[n][3,:,0,0],y)
                             self.titles.append('{}'.format(self.plot_name.upper()))
             class CrossProductsLPPlot(Plot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for cross products LP
                 '''
                 CODE = 'crossprodslp'
                 plot_name = 'Cross Products LP'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 2
                     self.nrows = 1
                     self.nplots = 2
                     self.ylabel = 'Range [km]'
                     self.xlabel = 'dB'
                     self.width = 3.5*self.nplots
                     self.height = 5.5
                     self.colorbar = False
                     self.titles = []
                     self.plots_adjust.update({'wspace': .8 ,'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['crossprodslp'] = 10*numpy.log10(numpy.abs(dataOut.output_LP))
                     data['NRANGE'] = dataOut.NRANGE #This is metadata
                     data['NLAG'] = dataOut.NLAG #This is metadata
                     return data, meta
                 def plot(self):
                     NRANGE = self.data['NRANGE'][-1]
                     NLAG = self.data['NLAG'][-1]
                     x = self.data[self.CODE][:,-1,:,:]
                     self.y = self.data.yrange[0:NRANGE]
                     label_array=numpy.array(['lag '+ str(x) for x in range(NLAG)])
                     color_array=['r','k','g','b','c','m','y','orange','steelblue','purple','peru','darksalmon','grey','limegreen','olive','midnightblue']
                     for n, ax in enumerate(self.axes):
                         self.xmin=28#30
                         self.xmax=70#70
                         #self.xmin=numpy.min(numpy.concatenate((self.x[0,:,n],self.x[1,:,n])))
                         #self.xmax=numpy.max(numpy.concatenate((self.x[0,:,n],self.x[1,:,n])))
                         if ax.firsttime:
                             self.autoxticks=False
                             if n == 0:
                                 self.plotline_array=numpy.zeros((2,NLAG),dtype=object)
                             for i in range(NLAG):
                                 self.plotline_array[n,i], = ax.plot(x[i,:,n], self.y, color=color_array[i],linewidth=1.0, label=label_array[i])
                             ax.legend(loc='upper right')
                             ax.set_xlim(self.xmin, self.xmax)
                             if n==0:
                                 self.titles.append('{} CH0'.format(self.plot_name.upper()))
                             if n==1:
                                 self.titles.append('{} CH1'.format(self.plot_name.upper()))
                         else:
                             for i in range(NLAG):
                                 self.plotline_array[n,i].set_data(x[i,:,n],self.y)
                             if n==0:
                                 self.titles.append('{} CH0'.format(self.plot_name.upper()))
                             if n==1:
                                 self.titles.append('{} CH1'.format(self.plot_name.upper()))
             class NoiseDPPlot(NoisePlot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for noise Double Pulse
                 '''
                 CODE = 'noise'
                 #plot_name = 'Noise'
                 #plot_type = 'scatterbuffer'
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     data['noise'] = 10*numpy.log10(dataOut.noise_final)
                     return data, meta
             class XmitWaveformPlot(Plot):
                 '''
                 Written by R. Flores
                 '''
                 '''
                 Plot for xmit waveform
                 '''
                 CODE = 'xmit'
                 plot_name = 'Xmit Waveform'
                 plot_type = 'scatterbuffer'
                 def setup(self):
                     self.ncols = 1
                     self.nrows = 1
                     self.nplots = 1
                     self.ylabel = ''
                     self.xlabel = 'Number of Lag'
                     self.width = 5.5
                     self.height = 3.5
                     self.colorbar = False
                     self.plots_adjust.update({'right': 0.85 })
                     self.titles = [self.plot_name]
                     #self.plots_adjust.update({'left': 0.17, 'right': 0.88, 'bottom': 0.1})
                     #if not self.titles:
                         #self.titles = self.data.parameters \
                             #if self.data.parameters else ['{}'.format(self.plot_name.upper())]
                 def update(self, dataOut):
                     data = {}
                     meta = {}
                     y_1=numpy.arctan2(dataOut.output_LP[:,0,2].imag,dataOut.output_LP[:,0,2].real)* 180 / (numpy.pi*10)
                     y_2=numpy.abs(dataOut.output_LP[:,0,2])
                     norm=numpy.max(y_2)
                     norm=max(norm,0.1)
                     y_2=y_2/norm
                     meta['yrange'] = numpy.array([])
                     data['xmit'] = numpy.vstack((y_1,y_2))
                     data['NLAG'] = dataOut.NLAG
                     return data, meta
                 def plot(self):
                     data = self.data[-1]
                     NLAG = data['NLAG']
                     x = numpy.arange(0,NLAG,1,'float32')
                     y = data['xmit']
                     self.xmin = 0
                     self.xmax = NLAG-1
                     self.ymin = -1.0
                     self.ymax = 1.0
                     ax = self.axes[0]
                     if ax.firsttime:
                         ax.plotline0=ax.plot(x,y[0,:],color='blue')
                         ax.plotline1=ax.plot(x,y[1,:],color='red')
                         secax=ax.secondary_xaxis(location=0.5)
                         secax.xaxis.tick_bottom()
                         secax.tick_params( labelleft=False, labeltop=False,
                                   labelright=False, labelbottom=False)
                         self.xstep_given = 3
                         self.ystep_given = .25
                         secax.set_xticks(numpy.linspace(self.xmin, self.xmax, 6)) #only works on matplotlib.version>3.2
                     else:
                         ax.plotline0[0].set_data(x,y[0,:])
                         ax.plotline1[0].set_data(x,y[1,:])

schainpy/model/io/jroIO_param.py +15 -5

             import os
             import time
             import datetime
             import numpy
             import h5py
             import schainpy.admin
             from schainpy.model.data.jrodata import *
             from schainpy.model.proc.jroproc_base import ProcessingUnit, Operation, MPDecorator
             from schainpy.model.io.jroIO_base import *
             from schainpy.utils import log
             class HDFReader(Reader, ProcessingUnit):
                 """Processing unit to read HDF5 format files
                 This unit reads HDF5 files created with `HDFWriter` operation contains
                 by default two groups Data and Metadata all variables would be saved as `dataOut`
                 attributes.
                 It is possible to read any HDF5 file by given the structure in the `description`
                 parameter, also you can add extra values to metadata with the parameter `extras`.
                 Parameters:
                 -----------
                 path : str
                     Path where files are located.
                 startDate : date
                     Start date of the files
                 endDate : list
                     End date of the files
                 startTime : time
                     Start time of the files
                 endTime : time
                     End time of the files
                 description : dict, optional
                     Dictionary with the description of the HDF5 file
                 extras : dict, optional
                     Dictionary with extra metadata to be be added to `dataOut`
                 Examples
                 --------
                 desc = {
                     'Data': {
                         'data_output': ['u', 'v', 'w'],
                         'utctime': 'timestamps',
                     }  ,
                     'Metadata': {
                         'heightList': 'heights'
                     }
                 }
                 desc = {
                     'Data': {
                         'data_output': 'winds',
                         'utctime': 'timestamps'
                     },
                     'Metadata': {
                         'heightList': 'heights'
                     }
                 }
                 extras = {
                     'timeZone': 300
                 }
                 reader = project.addReadUnit(
                     name='HDFReader',
                     path='/path/to/files',
                     startDate='2019/01/01',
                     endDate='2019/01/31',
                     startTime='00:00:00',
                     endTime='23:59:59',
                     # description=json.dumps(desc),
                     # extras=json.dumps(extras),
                     )
                 """
                 __attrs__ = ['path', 'startDate', 'endDate', 'startTime', 'endTime', 'description', 'extras']
                 def __init__(self):
                     ProcessingUnit.__init__(self)
                     self.dataOut = Parameters()
                     self.ext = ".hdf5"
                     self.optchar = "D"
                     self.meta = {}
                     self.data = {}
                     self.open_file = h5py.File
                     self.open_mode = 'r'
                     self.description = {}
                     self.extras = {}
                     self.filefmt = "*%Y%j***"
                     self.folderfmt = "*%Y%j"
                     self.utcoffset = 0
                 def setup(self, **kwargs):
                     self.set_kwargs(**kwargs)
                     if not self.ext.startswith('.'):
                         self.ext = '.{}'.format(self.ext)
                     if self.online:
                         log.log("Searching files in online mode...", self.name)
                         for nTries in range(self.nTries):
                             fullpath = self.searchFilesOnLine(self.path, self.startDate,
                                 self.endDate, self.expLabel, self.ext, self.walk,
                                 self.filefmt, self.folderfmt)
                             try:
                                 fullpath = next(fullpath)
                             except:
                                 fullpath = None
                             if fullpath:
                                 break
                             log.warning(
                                 'Waiting {} sec for a valid file in {}: try {} ...'.format(
                                     self.delay, self.path, nTries + 1),
                                 self.name)
                             time.sleep(self.delay)
                         if not(fullpath):
                             raise schainpy.admin.SchainError(
                                 'There isn\'t any valid file in {}'.format(self.path))
                         pathname, filename = os.path.split(fullpath)
                         self.year = int(filename[1:5])
                         self.doy = int(filename[5:8])
                         self.set = int(filename[8:11]) - 1
                     else:
                         log.log("Searching files in {}".format(self.path), self.name)
                         self.filenameList = self.searchFilesOffLine(self.path, self.startDate,
                             self.endDate, self.expLabel, self.ext, self.walk, self.filefmt, self.folderfmt)
                     self.setNextFile()
                     return
                 def readFirstHeader(self):
                     '''Read metadata and data'''
                     self.__readMetadata()
                     self.__readData()
                     self.__setBlockList()
                     if 'type' in self.meta:
                         self.dataOut = eval(self.meta['type'])()
                     for attr in self.meta:
                         setattr(self.dataOut, attr, self.meta[attr])
                     self.blockIndex = 0
                     return
                 def __setBlockList(self):
                     '''
                     Selects the data within the times defined
                     self.fp
                     self.startTime
                     self.endTime
                     self.blockList
                     self.blocksPerFile
                     '''
                     startTime = self.startTime
                     endTime = self.endTime
                     thisUtcTime = self.data['utctime'] + self.utcoffset
                     self.interval = numpy.min(thisUtcTime[1:] - thisUtcTime[:-1])
                     thisDatetime = datetime.datetime.utcfromtimestamp(thisUtcTime[0])
                     thisDate = thisDatetime.date()
                     thisTime = thisDatetime.time()
                     startUtcTime = (datetime.datetime.combine(thisDate, startTime) - datetime.datetime(1970, 1, 1)).total_seconds()
                     endUtcTime = (datetime.datetime.combine(thisDate, endTime) - datetime.datetime(1970, 1, 1)).total_seconds()
                     ind = numpy.where(numpy.logical_and(thisUtcTime >= startUtcTime, thisUtcTime < endUtcTime))[0]
                     self.blockList = ind
                     self.blocksPerFile = len(ind)
                     return
                 def __readMetadata(self):
                     '''
                     Reads Metadata
                     '''
                     meta = {}
                     if self.description:
                         for key, value in self.description['Metadata'].items():
                             meta[key] = self.fp[value][()]
                     else:
                         grp = self.fp['Metadata']
                         for name in grp:
                             meta[name] = grp[name][()]
                     if self.extras:
                         for key, value in self.extras.items():
                             meta[key] = value
                     self.meta = meta
                     return
                 def __readData(self):
                     data = {}
                     if self.description:
                         for key, value in self.description['Data'].items():
                             if isinstance(value, str):
                                 if isinstance(self.fp[value], h5py.Dataset):
                                     data[key] = self.fp[value][()]
                                 elif isinstance(self.fp[value], h5py.Group):
                                     array = []
                                     for ch in self.fp[value]:
                                         array.append(self.fp[value][ch][()])
                                     data[key] = numpy.array(array)
                             elif isinstance(value, list):
                                 array = []
                                 for ch in value:
                                     array.append(self.fp[ch][()])
                                 data[key] = numpy.array(array)
                     else:
                         grp = self.fp['Data']
                         for name in grp:
                             if isinstance(grp[name], h5py.Dataset):
                                 array = grp[name][()]
                             elif isinstance(grp[name], h5py.Group):
                                 array = []
                                 for ch in grp[name]:
                                     array.append(grp[name][ch][()])
                                 array = numpy.array(array)
                             else:
                                 log.warning('Unknown type: {}'.format(name))
                             if name in self.description:
                                 key = self.description[name]
                             else:
                                 key = name
                             data[key] = array
                     self.data = data
                     return
                 def getData(self):
                     for attr in self.data:
                         if self.data[attr].ndim == 1:
                             setattr(self.dataOut, attr, self.data[attr][self.blockIndex])
                         else:
                             setattr(self.dataOut, attr, self.data[attr][:, self.blockIndex])
                     self.dataOut.flagNoData = False
                     self.blockIndex += 1
                     log.log("Block No. {}/{} -> {}".format(
                         self.blockIndex,
                         self.blocksPerFile,
                         self.dataOut.datatime.ctime()), self.name)
                     return
                 def run(self, **kwargs):
                     if not(self.isConfig):
                         self.setup(**kwargs)
                         self.isConfig = True
                     if self.blockIndex == self.blocksPerFile:
                         self.setNextFile()
                     self.getData()
                     return
             @MPDecorator
             class HDFWriter(Operation):
                 """Operation to write HDF5 files.
                 The HDF5 file contains by default two groups Data and Metadata where
                 you can save any `dataOut` attribute specified by `dataList` and `metadataList`
                 parameters, data attributes are normaly time dependent where the metadata
                 are not.
                 It is possible to customize the structure of the HDF5 file with the
                 optional description parameter see the examples.
                 Parameters:
                 -----------
                 path : str
                     Path where files will be saved.
                 blocksPerFile : int
                     Number of blocks per file
                 metadataList : list
                     List of the dataOut attributes that will be saved as metadata
                 dataList : int
                     List of the dataOut attributes that will be saved as data
                 setType : bool
                     If True the name of the files corresponds to the timestamp of the data
                 description : dict, optional
                     Dictionary with the desired description of the HDF5 file
                 Examples
                 --------
                 desc = {
                     'data_output': {'winds': ['z', 'w', 'v']},
                     'utctime': 'timestamps',
                     'heightList': 'heights'
                 }
                 desc = {
                     'data_output': ['z', 'w', 'v'],
                     'utctime': 'timestamps',
                     'heightList': 'heights'
                 }
                 desc = {
                     'Data': {
                         'data_output': 'winds',
                         'utctime': 'timestamps'
                     },
                     'Metadata': {
                         'heightList': 'heights'
                     }
                 }
                 writer = proc_unit.addOperation(name='HDFWriter')
                 writer.addParameter(name='path', value='/path/to/file')
                 writer.addParameter(name='blocksPerFile', value='32')
                 writer.addParameter(name='metadataList', value='heightList,timeZone')
                 writer.addParameter(name='dataList',value='data_output,utctime')
                 # writer.addParameter(name='description',value=json.dumps(desc))
                 """
                 ext = ".hdf5"
                 optchar = "D"
                 filename = None
                 path = None
                 setFile = None
                 fp = None
                 firsttime = True
                 #Configurations
                 blocksPerFile = None
                 blockIndex = None
                 dataOut = None
                 #Data Arrays
                 dataList = None
                 metadataList = None
                 currentDay = None
                 lastTime = None
                 def __init__(self):
                     Operation.__init__(self)
                     return
-                def setup(self, path=None, blocksPerFile=10, metadataList=None, dataList=None, setType=None, description=None):
+                def setup(self, path=None, blocksPerFile=10, metadataList=None, dataList=None, setType=None, description=None, uniqueChannel=False):
                     self.path = path
                     self.blocksPerFile = blocksPerFile
                     self.metadataList = metadataList
                     self.dataList = [s.strip() for s in dataList]
                     self.setType = setType
                     self.description = description
+                    self.uniqueChannel = uniqueChannel
                     if self.metadataList is None:
                         self.metadataList = self.dataOut.metadata_list
                     tableList = []
                     dsList = []
                     for i in range(len(self.dataList)):
                         dsDict = {}
                         if hasattr(self.dataOut, self.dataList[i]):
                             dataAux = getattr(self.dataOut, self.dataList[i])
                             dsDict['variable'] = self.dataList[i]
                         else:
                             log.warning('Attribute {} not found in dataOut', self.name)
                             continue
                         if dataAux is None:
                             continue
                         elif isinstance(dataAux, (int, float, numpy.integer, numpy.float)):
                             dsDict['nDim'] = 0
                         else:
+                            if uniqueChannel: #Creates extra dimension to avoid the creation of multiple channels
+                                dataAux = numpy.expand_dims(dataAux, axis=0)
                             dsDict['nDim'] = len(dataAux.shape)
                             dsDict['shape'] = dataAux.shape
                             dsDict['dsNumber'] = dataAux.shape[0]
                             dsDict['dtype'] = dataAux.dtype
                         dsList.append(dsDict)
                     self.dsList = dsList
                     self.currentDay = self.dataOut.datatime.date()
                 def timeFlag(self):
                     currentTime = self.dataOut.utctime
                     timeTuple = time.localtime(currentTime)
                     dataDay = timeTuple.tm_yday
                     if self.lastTime is None:
                         self.lastTime = currentTime
                         self.currentDay = dataDay
                         return False
                     timeDiff = currentTime - self.lastTime
                     #Si el dia es diferente o si la diferencia entre un dato y otro supera la hora
                     if dataDay != self.currentDay:
                         self.currentDay = dataDay
                         return True
                     elif timeDiff > 3*60*60:
                         self.lastTime = currentTime
                         return True
                     else:
                         self.lastTime = currentTime
                         return False
                 def run(self, dataOut, path, blocksPerFile=10, metadataList=None,
-                        dataList=[], setType=None, description={}):
+                        dataList=[], setType=None, description={}, uniqueChannel= False):
                     self.dataOut = dataOut
                     if not(self.isConfig):
                         self.setup(path=path, blocksPerFile=blocksPerFile,
                                    metadataList=metadataList, dataList=dataList,
-                                   setType=setType, description=description)
+                                   setType=setType, description=description, uniqueChannel=uniqueChannel)
                         self.isConfig = True
                         self.setNextFile()
                     self.putData()
                     return
                 def setNextFile(self):
                     ext = self.ext
                     path = self.path
                     setFile = self.setFile
                     timeTuple = time.localtime(self.dataOut.utctime)
                     subfolder = 'd%4.4d%3.3d' % (timeTuple.tm_year,timeTuple.tm_yday)
                     fullpath = os.path.join(path, subfolder)
                     if os.path.exists(fullpath):
                         filesList = os.listdir(fullpath)
                         filesList = [k for k in filesList if k.startswith(self.optchar)]
                         if len( filesList ) > 0:
                             filesList = sorted(filesList, key=str.lower)
                             filen = filesList[-1]
                             # el filename debera tener el siguiente formato
                             # 0 1234 567 89A BCDE (hex)
                             # x YYYY DDD SSS .ext
                             if isNumber(filen[8:11]):
                                 setFile = int(filen[8:11]) #inicializo mi contador de seteo al seteo del ultimo file
                             else:
                                 setFile = -1
                         else:
                             setFile = -1 #inicializo mi contador de seteo
                     else:
                         os.makedirs(fullpath)
                         setFile = -1 #inicializo mi contador de seteo
                     if self.setType is None:
                         setFile += 1
                         file = '%s%4.4d%3.3d%03d%s' % (self.optchar,
                                                        timeTuple.tm_year,
                                                        timeTuple.tm_yday,
                                                        setFile,
                                                        ext )
                     else:
                         setFile = timeTuple.tm_hour*60+timeTuple.tm_min
                         file = '%s%4.4d%3.3d%04d%s' % (self.optchar,
                                                        timeTuple.tm_year,
                                                        timeTuple.tm_yday,
                                                        setFile,
                                                        ext )
                     self.filename = os.path.join( path, subfolder, file )
                     #Setting HDF5 File
                     self.fp = h5py.File(self.filename, 'w')
                     #write metadata
                     self.writeMetadata(self.fp)
                     #Write data
                     self.writeData(self.fp)
                 def getLabel(self, name, x=None):
+                    #print("x: ", x)
                     if x is None:
                         if 'Data' in self.description:
                             data = self.description['Data']
                             if 'Metadata' in self.description:
                                 data.update(self.description['Metadata'])
                         else:
                             data = self.description
                         if name in data:
                             if isinstance(data[name], str):
                                 return data[name]
                             elif isinstance(data[name], list):
                                 return None
                             elif isinstance(data[name], dict):
                                 for key, value in data[name].items():
                                     return key
                         return name
                     else:
                         if 'Metadata' in self.description:
                             meta = self.description['Metadata']
                         else:
                             meta = self.description
                         if name in meta:
                             if isinstance(meta[name], list):
                                 return meta[name][x]
                             elif isinstance(meta[name], dict):
                                 for key, value in meta[name].items():
                                     return value[x]
                         if 'cspc' in name:
                             return 'pair{:02d}'.format(x)
                         else:
                             return 'channel{:02d}'.format(x)
                 def writeMetadata(self, fp):
                     if self.description:
                         if 'Metadata' in self.description:
                             grp = fp.create_group('Metadata')
                         else:
                             grp = fp
                     else:
                         grp = fp.create_group('Metadata')
                     for i in range(len(self.metadataList)):
                         if not hasattr(self.dataOut, self.metadataList[i]):
                             log.warning('Metadata: `{}` not found'.format(self.metadataList[i]), self.name)
                             continue
                         value = getattr(self.dataOut, self.metadataList[i])
                         if isinstance(value, bool):
                             if value is True:
                                 value = 1
                             else:
                                 value = 0
                         grp.create_dataset(self.getLabel(self.metadataList[i]), data=value)
                     return
                 def writeData(self, fp):
                     if self.description:
                         if 'Data' in self.description:
                             grp = fp.create_group('Data')
                         else:
                             grp = fp
                     else:
                         grp = fp.create_group('Data')
                     dtsets = []
                     data = []
+                    #print("self.dsList: ", self.dsList)
                     for dsInfo in self.dsList:
                         if dsInfo['nDim'] == 0:
                             ds = grp.create_dataset(
                                 self.getLabel(dsInfo['variable']),
                                 (self.blocksPerFile, ),
                                 chunks=True,
                                 dtype=numpy.float64)
                             dtsets.append(ds)
                             data.append((dsInfo['variable'], -1))
                         else:
                             label = self.getLabel(dsInfo['variable'])
                             if label is not None:
                                 sgrp = grp.create_group(label)
                             else:
                                 sgrp = grp
                             for i in range(dsInfo['dsNumber']):
                                 ds = sgrp.create_dataset(
                                     self.getLabel(dsInfo['variable'], i),
                                     (self.blocksPerFile, ) + dsInfo['shape'][1:],
                                     chunks=True,
                                     dtype=dsInfo['dtype'])
                                 dtsets.append(ds)
                                 data.append((dsInfo['variable'], i))
+                            if self.uniqueChannel: #Deletes extra dimension created to avoid the creation of multiple channels
+                                dataAux = getattr(self.dataOut, dsInfo['variable'])
+                                dataAux = dataAux[0]
                     fp.flush()
                     log.log('Creating file: {}'.format(fp.filename), self.name)
                     self.ds = dtsets
                     self.data = data
                     self.firsttime = True
                     self.blockIndex = 0
                     return
                 def putData(self):
                     if (self.blockIndex == self.blocksPerFile) or self.timeFlag():
                         self.closeFile()
                         self.setNextFile()
                     for i, ds in enumerate(self.ds):
                         attr, ch = self.data[i]
                         if ch == -1:
                             ds[self.blockIndex] = getattr(self.dataOut, attr)
                         else:
                             ds[self.blockIndex] = getattr(self.dataOut, attr)[ch]
                     self.fp.flush()
                     self.blockIndex += 1
                     log.log('Block No. {}/{}'.format(self.blockIndex, self.blocksPerFile), self.name)
                     return
                 def closeFile(self):
                     if self.blockIndex != self.blocksPerFile:
                         for ds in self.ds:
                             ds.resize(self.blockIndex, axis=0)
                     if self.fp:
                         self.fp.flush()
                         self.fp.close()
                 def close(self):
                     self.closeFile()

schainpy/model/proc/jroproc_spectra.py +67 0

             # Copyright (c) 2012-2020 Jicamarca Radio Observatory
             # All rights reserved.
             #
             # Distributed under the terms of the BSD 3-clause license.
             """Spectra processing Unit and operations
             Here you will find the processing unit `SpectraProc` and several operations
             to work with Spectra data type
             """
             import time
             import itertools
             import numpy
             from schainpy.model.proc.jroproc_base import ProcessingUnit, MPDecorator, Operation
             from schainpy.model.data.jrodata import Spectra
             from schainpy.model.data.jrodata import hildebrand_sekhon
             from schainpy.utils import log
             class SpectraProc(ProcessingUnit):
                 def __init__(self):
                     ProcessingUnit.__init__(self)
                     self.buffer = None
                     self.firstdatatime = None
                     self.profIndex = 0
                     self.dataOut = Spectra()
                     self.id_min = None
                     self.id_max = None
                     self.setupReq = False #Agregar a todas las unidades de proc
                 def __updateSpecFromVoltage(self):
                     self.dataOut.timeZone = self.dataIn.timeZone
                     self.dataOut.dstFlag = self.dataIn.dstFlag
                     self.dataOut.errorCount = self.dataIn.errorCount
                     self.dataOut.useLocalTime = self.dataIn.useLocalTime
                     try:
                         self.dataOut.processingHeaderObj = self.dataIn.processingHeaderObj.copy()
                     except:
                         pass
                     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.nProfiles = self.dataOut.nFFTPoints
                     self.dataOut.flagDiscontinuousBlock = self.dataIn.flagDiscontinuousBlock
                     self.dataOut.utctime = self.firstdatatime
                     self.dataOut.flagDecodeData = self.dataIn.flagDecodeData
                     self.dataOut.flagDeflipData = self.dataIn.flagDeflipData
                     self.dataOut.flagShiftFFT = False
                     self.dataOut.nCohInt = self.dataIn.nCohInt
                     self.dataOut.nIncohInt = 1
                     self.dataOut.windowOfFilter = self.dataIn.windowOfFilter
                     self.dataOut.frequency = self.dataIn.frequency
                     self.dataOut.realtime = self.dataIn.realtime
                     self.dataOut.azimuth = self.dataIn.azimuth
                     self.dataOut.zenith = self.dataIn.zenith
                     self.dataOut.beam.codeList = self.dataIn.beam.codeList
                     self.dataOut.beam.azimuthList = self.dataIn.beam.azimuthList
                     self.dataOut.beam.zenithList = self.dataIn.beam.zenithList
                     self.dataOut.runNextUnit = self.dataIn.runNextUnit
                     try:
                         self.dataOut.step = self.dataIn.step
                     except:
                         pass
                 def __getFft(self):
                     """
                     Convierte valores de Voltaje a Spectra
                     Affected:
                         self.dataOut.data_spc
                         self.dataOut.data_cspc
                         self.dataOut.data_dc
                         self.dataOut.heightList
                         self.profIndex
                         self.buffer
                         self.dataOut.flagNoData
                     """
                     fft_volt = numpy.fft.fft(
                         self.buffer, n=self.dataOut.nFFTPoints, axis=1)
                     fft_volt = fft_volt.astype(numpy.dtype('complex'))
                     dc = fft_volt[:, 0, :]
                     # calculo de self-spectra
                     fft_volt = numpy.fft.fftshift(fft_volt, axes=(1,))
                     spc = fft_volt * numpy.conjugate(fft_volt)
                     spc = spc.real
                     blocksize = 0
                     blocksize += dc.size
                     blocksize += spc.size
                     cspc = None
                     pairIndex = 0
                     if self.dataOut.pairsList != None:
                         # calculo de cross-spectra
                         cspc = numpy.zeros(
                             (self.dataOut.nPairs, self.dataOut.nFFTPoints, self.dataOut.nHeights), dtype='complex')
                         for pair in self.dataOut.pairsList:
                             if pair[0] not in self.dataOut.channelList:
                                 raise ValueError("Error getting CrossSpectra: pair 0 of %s is not in channelList = %s" % (
                                     str(pair), str(self.dataOut.channelList)))
                             if pair[1] not in self.dataOut.channelList:
                                 raise ValueError("Error getting CrossSpectra: pair 1 of %s is not in channelList = %s" % (
                                     str(pair), str(self.dataOut.channelList)))
                             cspc[pairIndex, :, :] = fft_volt[pair[0], :, :] * \
                                 numpy.conjugate(fft_volt[pair[1], :, :])
                             pairIndex += 1
                         blocksize += cspc.size
                     self.dataOut.data_spc = spc
                     self.dataOut.data_cspc = cspc
                     self.dataOut.data_dc = dc
                     self.dataOut.blockSize = blocksize
                     self.dataOut.flagShiftFFT = False
                 def run(self, nProfiles=None, nFFTPoints=None, pairsList=None, ippFactor=None, shift_fft=False, runNextUnit = 0):
                     self.dataIn.runNextUnit = runNextUnit
                     if self.dataIn.type == "Spectra":
                         self.dataOut.copy(self.dataIn)
                         if shift_fft:
                             #desplaza a la derecha en el eje 2 determinadas posiciones
                             shift = int(self.dataOut.nFFTPoints/2)
                             self.dataOut.data_spc = numpy.roll(self.dataOut.data_spc, shift , axis=1)
                             if self.dataOut.data_cspc is not None:
                                 #desplaza a la derecha en el eje 2 determinadas posiciones
                                 self.dataOut.data_cspc = numpy.roll(self.dataOut.data_cspc, shift, axis=1)
                         if pairsList:
                             self.__selectPairs(pairsList)
                     elif self.dataIn.type == "Voltage":
                         self.dataOut.flagNoData = True
                         if nFFTPoints == None:
                             raise ValueError("This SpectraProc.run() need nFFTPoints input variable")
                         if nProfiles == None:
                             nProfiles = nFFTPoints
                         #print(self.dataOut.ipp)
                         #exit(1)
                         if ippFactor == None:
                             self.dataOut.ippFactor = 1
                         #if ippFactor is not None:
                             #self.dataOut.ippFactor = ippFactor
                         #print(ippFactor)
                         #print(self.dataOut.ippFactor)
                         #exit(1)
                         self.dataOut.nFFTPoints = nFFTPoints
                         if self.buffer is None:
                             self.buffer = numpy.zeros((self.dataIn.nChannels,
                                                        nProfiles,
                                                        self.dataIn.nHeights),
                                                       dtype='complex')
                         if self.dataIn.flagDataAsBlock:
                             nVoltProfiles = self.dataIn.data.shape[1]
                             if nVoltProfiles == nProfiles:
                                 self.buffer = self.dataIn.data.copy()
                                 self.profIndex = nVoltProfiles
                             elif nVoltProfiles < nProfiles:
                                 if self.profIndex == 0:
                                     self.id_min = 0
                                     self.id_max = nVoltProfiles
                                 #print(self.id_min)
                                 #print(self.id_max)
                                 #print(numpy.shape(self.buffer))
                                 self.buffer[:, self.id_min:self.id_max,
                                             :] = self.dataIn.data
                                 self.profIndex += nVoltProfiles
                                 self.id_min += nVoltProfiles
                                 self.id_max += nVoltProfiles
                             else:
                                 raise ValueError("The type object %s has %d profiles, it should just has %d profiles" % (
                                     self.dataIn.type, self.dataIn.data.shape[1], nProfiles))
                                 self.dataOut.flagNoData = True
                         else:
                             self.buffer[:, self.profIndex, :] = self.dataIn.data.copy()
                             self.profIndex += 1
                         if self.firstdatatime == None:
                             self.firstdatatime = self.dataIn.utctime
                         if self.profIndex == nProfiles:
                             self.__updateSpecFromVoltage()
                             if pairsList == None:
                                 self.dataOut.pairsList = [pair for pair in itertools.combinations(self.dataOut.channelList, 2)]
                             else:
                                 self.dataOut.pairsList = pairsList
                             self.__getFft()
                             self.dataOut.flagNoData = False
                             self.firstdatatime = None
                             self.profIndex = 0
                     else:
                         raise ValueError("The type of input object '%s' is not valid".format(
                             self.dataIn.type))
                 def __selectPairs(self, pairsList):
                     if not pairsList:
                         return
                     pairs = []
                     pairsIndex = []
                     for pair in pairsList:
                         if pair[0] not in self.dataOut.channelList or pair[1] not in self.dataOut.channelList:
                             continue
                         pairs.append(pair)
                         pairsIndex.append(pairs.index(pair))
                     self.dataOut.data_cspc = self.dataOut.data_cspc[pairsIndex]
                     self.dataOut.pairsList = pairs
                     return
                 def selectFFTs(self, minFFT, maxFFT ):
                     """
                     Selecciona un bloque de datos en base a un grupo de valores de puntos FFTs segun el rango
                     minFFT<= FFT <= maxFFT
                     """
                     if (minFFT > maxFFT):
                         raise ValueError("Error selecting heights: Height range (%d,%d) is not valid" % (minFFT, maxFFT))
                     if (minFFT < self.dataOut.getFreqRange()[0]):
                         minFFT = self.dataOut.getFreqRange()[0]
                     if (maxFFT > self.dataOut.getFreqRange()[-1]):
                         maxFFT = self.dataOut.getFreqRange()[-1]
                     minIndex = 0
                     maxIndex = 0
                     FFTs = self.dataOut.getFreqRange()
                     inda = numpy.where(FFTs >= minFFT)
                     indb = numpy.where(FFTs <= maxFFT)
                     try:
                         minIndex = inda[0][0]
                     except:
                         minIndex = 0
                     try:
                         maxIndex = indb[0][-1]
                     except:
                         maxIndex = len(FFTs)
                     self.selectFFTsByIndex(minIndex, maxIndex)
                     return 1
                 def getBeaconSignal(self, tauindex=0, channelindex=0, hei_ref=None):
                     newheis = numpy.where(
                         self.dataOut.heightList > self.dataOut.radarControllerHeaderObj.Taus[tauindex])
                     if hei_ref != None:
                         newheis = numpy.where(self.dataOut.heightList > hei_ref)
                     minIndex = min(newheis[0])
                     maxIndex = max(newheis[0])
                     data_spc = self.dataOut.data_spc[:, :, minIndex:maxIndex + 1]
                     heightList = self.dataOut.heightList[minIndex:maxIndex + 1]
                     # determina indices
                     nheis = int(self.dataOut.radarControllerHeaderObj.txB /
                                 (self.dataOut.heightList[1] - self.dataOut.heightList[0]))
                     avg_dB = 10 * \
                         numpy.log10(numpy.sum(data_spc[channelindex, :, :], axis=0))
                     beacon_dB = numpy.sort(avg_dB)[-nheis:]
                     beacon_heiIndexList = []
                     for val in avg_dB.tolist():
                         if val >= beacon_dB[0]:
                             beacon_heiIndexList.append(avg_dB.tolist().index(val))
                     #data_spc = data_spc[:,:,beacon_heiIndexList]
                     data_cspc = None
                     if self.dataOut.data_cspc is not None:
                         data_cspc = self.dataOut.data_cspc[:, :, minIndex:maxIndex + 1]
                         #data_cspc = data_cspc[:,:,beacon_heiIndexList]
                     data_dc = None
                     if self.dataOut.data_dc is not None:
                         data_dc = self.dataOut.data_dc[:, minIndex:maxIndex + 1]
                         #data_dc = data_dc[:,beacon_heiIndexList]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_cspc = data_cspc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.heightList = heightList
                     self.dataOut.beacon_heiIndexList = beacon_heiIndexList
                     return 1
                 def selectFFTsByIndex(self, minIndex, maxIndex):
                     """
                     """
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError("Error selecting heights: Index range (%d,%d) is not valid" % (minIndex, maxIndex))
                     if (maxIndex >= self.dataOut.nProfiles):
                         maxIndex = self.dataOut.nProfiles-1
                     #Spectra
                     data_spc = self.dataOut.data_spc[:,minIndex:maxIndex+1,:]
                     data_cspc = None
                     if self.dataOut.data_cspc is not None:
                         data_cspc = self.dataOut.data_cspc[:,minIndex:maxIndex+1,:]
                     data_dc = None
                     if self.dataOut.data_dc is not None:
                         data_dc = self.dataOut.data_dc[minIndex:maxIndex+1,:]
                     self.dataOut.data_spc = data_spc
                     self.dataOut.data_cspc = data_cspc
                     self.dataOut.data_dc = data_dc
                     self.dataOut.ippSeconds = self.dataOut.ippSeconds*(self.dataOut.nFFTPoints / numpy.shape(data_cspc)[1])
                     self.dataOut.nFFTPoints = numpy.shape(data_cspc)[1]
                     self.dataOut.profilesPerBlock = numpy.shape(data_cspc)[1]
                     return 1
                 def getNoise(self, minHei=None, maxHei=None, minVel=None, maxVel=None):
                     # validacion de rango
                     print("NOISeeee")
                     if minHei == None:
                         minHei = self.dataOut.heightList[0]
                     if maxHei == None:
                         maxHei = self.dataOut.heightList[-1]
                     if (minHei < self.dataOut.heightList[0]) or (minHei > maxHei):
                         print('minHei: %.2f is out of the heights range' % (minHei))
                         print('minHei is setting to %.2f' % (self.dataOut.heightList[0]))
                         minHei = self.dataOut.heightList[0]
                     if (maxHei > self.dataOut.heightList[-1]) or (maxHei < minHei):
                         print('maxHei: %.2f is out of the heights range' % (maxHei))
                         print('maxHei is setting to %.2f' % (self.dataOut.heightList[-1]))
                         maxHei = self.dataOut.heightList[-1]
                     # validacion de velocidades
                     velrange = self.dataOut.getVelRange(1)
                     if minVel == None:
                         minVel = velrange[0]
                     if maxVel == None:
                         maxVel = velrange[-1]
                     if (minVel < velrange[0]) or (minVel > maxVel):
                         print('minVel: %.2f is out of the velocity range' % (minVel))
                         print('minVel is setting to %.2f' % (velrange[0]))
                         minVel = velrange[0]
                     if (maxVel > velrange[-1]) or (maxVel < minVel):
                         print('maxVel: %.2f is out of the velocity range' % (maxVel))
                         print('maxVel is setting to %.2f' % (velrange[-1]))
                         maxVel = velrange[-1]
                     # seleccion de indices para rango
                     minIndex = 0
                     maxIndex = 0
                     heights = self.dataOut.heightList
                     inda = numpy.where(heights >= minHei)
                     indb = numpy.where(heights <= maxHei)
                     try:
                         minIndex = inda[0][0]
                     except:
                         minIndex = 0
                     try:
                         maxIndex = indb[0][-1]
                     except:
                         maxIndex = len(heights)
                     if (minIndex < 0) or (minIndex > maxIndex):
                         raise ValueError("some value in (%d,%d) is not valid" % (
                             minIndex, maxIndex))
                     if (maxIndex >= self.dataOut.nHeights):
                         maxIndex = self.dataOut.nHeights - 1
                     # seleccion de indices para velocidades
                     indminvel = numpy.where(velrange >= minVel)
                     indmaxvel = numpy.where(velrange <= maxVel)
                     try:
                         minIndexVel = indminvel[0][0]
                     except:
                         minIndexVel = 0
                     try:
                         maxIndexVel = indmaxvel[0][-1]
                     except:
                         maxIndexVel = len(velrange)
                     # seleccion del espectro
                     data_spc = self.dataOut.data_spc[:,
                                                      minIndexVel:maxIndexVel + 1, minIndex:maxIndex + 1]
                     # estimacion de ruido
                     noise = numpy.zeros(self.dataOut.nChannels)
                     for channel in range(self.dataOut.nChannels):
                         daux = data_spc[channel, :, :]
                         sortdata = numpy.sort(daux, axis=None)
                         noise[channel] = hildebrand_sekhon(sortdata, self.dataOut.nIncohInt)
                     self.dataOut.noise_estimation = noise.copy()
                     return 1
             class GetSNR(Operation):
                 '''
                 Written by R. Flores
                 '''
                 """Operation to get SNR.
                 Parameters:
                 -----------
                 Example
                 --------
                 op = proc_unit.addOperation(name='GetSNR', optype='other')
                 """
                 def __init__(self, **kwargs):
                     Operation.__init__(self, **kwargs)
                 def run(self,dataOut):
                     #noise = dataOut.getNoise()
                     noise = dataOut.getNoise(ymin_index=-10) #Región superior donde solo debería de haber ruido
                     #print("Noise: ", noise)
                     #print("Noise_dB: ", 10*numpy.log10(noise/dataOut.normFactor))
                     #print("Heights: ", dataOut.heightList)
                     #dataOut.data_snr = (dataOut.data_spc.sum(axis=1))/(noise[:,None]*dataOut.normFactor)
                     ################dataOut.data_snr = (dataOut.data_spc.sum(axis=1))/(noise[:,None]*dataOut.nFFTPoints) #Before 12Jan2023
                     #dataOut.data_snr = (dataOut.data_spc.sum(axis=1)-noise[:,None])/(noise[:,None])
                     dataOut.data_snr = (dataOut.data_spc.sum(axis=1)-noise[:,None]*dataOut.nFFTPoints)/(noise[:,None]*dataOut.nFFTPoints) #It works apparently
                     dataOut.snl = numpy.log10(dataOut.data_snr)
                     #print("snl: ", dataOut.snl)
                     #exit(1)
                     #print(dataOut.heightList[-11])
                     #print(numpy.shape(dataOut.heightList))
                     #print(dataOut.data_snr)
                     #print(dataOut.data_snr[0,-11])
                     #exit(1)
                     #dataOut.data_snr = numpy.where(10*numpy.log10(dataOut.data_snr)<.5, numpy.nan, dataOut.data_snr)
                     #dataOut.data_snr = numpy.where(10*numpy.log10(dataOut.data_snr)<.1, numpy.nan, dataOut.data_snr)
                     #dataOut.data_snr = numpy.where(10*numpy.log10(dataOut.data_snr)<.0, numpy.nan, dataOut.data_snr)
                     #dataOut.data_snr = numpy.where(dataOut.data_snr<.05, numpy.nan, dataOut.data_snr)
                     #dataOut.snl = numpy.where(dataOut.data_snr<.01, numpy.nan, dataOut.snl)
                     dataOut.snl = numpy.where(dataOut.snl<-1, numpy.nan, dataOut.snl)
                     '''
                     import matplotlib.pyplot as plt
                     #plt.plot(10*numpy.log10(dataOut.data_snr[0]),dataOut.heightList)
                     plt.plot(dataOut.data_snr[0],dataOut.heightList)#,marker='*')
                     plt.xlim(-1,10)
                     plt.axvline(1,color='k')
                     plt.axvline(.1,color='k',linestyle='--')
                     plt.grid()
                     plt.show()
                     '''
                     #dataOut.data_snr = 10*numpy.log10(dataOut.data_snr)
                     #dataOut.data_snr = numpy.expand_dims(dataOut.data_snr,axis=0)
                     #print(dataOut.data_snr.shape)
                     #exit(1)
                     #print("Before: ", dataOut.data_snr[0])
                     return dataOut
             class removeDC(Operation):
                 def run(self, dataOut, mode=2):
                     self.dataOut = dataOut
                     jspectra = self.dataOut.data_spc
                     jcspectra = self.dataOut.data_cspc
                     num_chan = jspectra.shape[0]
                     num_hei = jspectra.shape[2]
                     if jcspectra is not None:
                         jcspectraExist = True
                         num_pairs = jcspectra.shape[0]
                     else:
                         jcspectraExist = False
                     freq_dc = int(jspectra.shape[1] / 2)
                     ind_vel = numpy.array([-2, -1, 1, 2]) + freq_dc
                     ind_vel = ind_vel.astype(int)
                     if ind_vel[0] < 0:
                         ind_vel[list(range(0, 1))] = ind_vel[list(range(0, 1))] + self.num_prof
                     if mode == 1:
                         jspectra[:, freq_dc, :] = (
                             jspectra[:, ind_vel[1], :] + jspectra[:, ind_vel[2], :]) / 2  # CORRECCION
                         if jcspectraExist:
                             jcspectra[:, freq_dc, :] = (
                                 jcspectra[:, ind_vel[1], :] + jcspectra[:, ind_vel[2], :]) / 2
                     if mode == 2:
                         vel = numpy.array([-2, -1, 1, 2])
                         xx = numpy.zeros([4, 4])
                         for fil in range(4):
                             xx[fil, :] = vel[fil]**numpy.asarray(list(range(4)))
                         xx_inv = numpy.linalg.inv(xx)
                         xx_aux = xx_inv[0, :]
                         for ich in range(num_chan):
                             yy = jspectra[ich, ind_vel, :]
                             jspectra[ich, freq_dc, :] = numpy.dot(xx_aux, yy)
                             junkid = jspectra[ich, freq_dc, :] <= 0
                             cjunkid = sum(junkid)
                             if cjunkid.any():
                                 jspectra[ich, freq_dc, junkid.nonzero()] = (
                                     jspectra[ich, ind_vel[1], junkid] + jspectra[ich, ind_vel[2], junkid]) / 2
                         if jcspectraExist:
                             for ip in range(num_pairs):
                                 yy = jcspectra[ip, ind_vel, :]
                                 jcspectra[ip, freq_dc, :] = numpy.dot(xx_aux, yy)
                     self.dataOut.data_spc = jspectra
                     self.dataOut.data_cspc = jcspectra
                     return self.dataOut
             class removeInterference(Operation):
                 def removeInterference2(self):
                     cspc = self.dataOut.data_cspc
                     spc = self.dataOut.data_spc
                     Heights = numpy.arange(cspc.shape[2])
                     realCspc = numpy.abs(cspc)
                     for i in range(cspc.shape[0]):
                         LinePower= numpy.sum(realCspc[i], axis=0)
                         Threshold = numpy.amax(LinePower)-numpy.sort(LinePower)[len(Heights)-int(len(Heights)*0.1)]
                         SelectedHeights = Heights[ numpy.where( LinePower < Threshold ) ]
                         InterferenceSum = numpy.sum( realCspc[i,:,SelectedHeights], axis=0 )
                         InterferenceThresholdMin = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.98)]
                         InterferenceThresholdMax = numpy.sort(InterferenceSum)[int(len(InterferenceSum)*0.99)]
                         InterferenceRange = numpy.where( ([InterferenceSum > InterferenceThresholdMin]))# , InterferenceSum < InterferenceThresholdMax]) )
                         #InterferenceRange = numpy.where( ([InterferenceRange < InterferenceThresholdMax]))
                         if len(InterferenceRange)<int(cspc.shape[1]*0.3):
                             cspc[i,InterferenceRange,:] = numpy.NaN
                     self.dataOut.data_cspc = cspc
                 def removeInterference(self, interf = 2, hei_interf = None, nhei_interf = None, offhei_interf = None):
                     jspectra = self.dataOut.data_spc
                     jcspectra = self.dataOut.data_cspc
                     jnoise = self.dataOut.getNoise()
                     num_incoh = self.dataOut.nIncohInt
                     num_channel = jspectra.shape[0]
                     num_prof = jspectra.shape[1]
                     num_hei = jspectra.shape[2]
                     # hei_interf
                     if hei_interf is None:
                         count_hei = int(num_hei / 2)
                         hei_interf = numpy.asmatrix(list(range(count_hei))) + num_hei - count_hei
                         hei_interf = numpy.asarray(hei_interf)[0]
                     # nhei_interf
                     if (nhei_interf == None):
                         nhei_interf = 5
                     if (nhei_interf < 1):
                         nhei_interf = 1
                     if (nhei_interf > count_hei):
                         nhei_interf = count_hei
                     if (offhei_interf == None):
                         offhei_interf = 0
                     ind_hei = list(range(num_hei))
             #         mask_prof = numpy.asarray(range(num_prof - 2)) + 1
             #         mask_prof[range(num_prof/2 - 1,len(mask_prof))] += 1
                     mask_prof = numpy.asarray(list(range(num_prof)))
                     num_mask_prof = mask_prof.size
                     comp_mask_prof = [0, num_prof / 2]
                     # noise_exist:    Determina si la variable jnoise ha sido definida y contiene la informacion del ruido de cada canal
                     if (jnoise.size < num_channel or numpy.isnan(jnoise).any()):
                         jnoise = numpy.nan
                     noise_exist = jnoise[0] < numpy.Inf
                     # Subrutina de Remocion de la Interferencia
                     for ich in range(num_channel):
                         # Se ordena los espectros segun su potencia (menor a mayor)
                         power = jspectra[ich, mask_prof, :]
                         power = power[:, hei_interf]
                         power = power.sum(axis=0)
                         psort = power.ravel().argsort()
                         # Se estima la interferencia promedio en los Espectros de Potencia empleando
                         junkspc_interf = jspectra[ich, :, hei_interf[psort[list(range(
                             offhei_interf, nhei_interf + offhei_interf))]]]
                         if noise_exist:
                             #    tmp_noise = jnoise[ich] / num_prof
                             tmp_noise = jnoise[ich]
                         junkspc_interf = junkspc_interf - tmp_noise
                         #junkspc_interf[:,comp_mask_prof] = 0
                         jspc_interf = junkspc_interf.sum(axis=0) / nhei_interf
                         jspc_interf = jspc_interf.transpose()
                         # Calculando el espectro de interferencia promedio
                         noiseid = numpy.where(
                             jspc_interf <= tmp_noise / numpy.sqrt(num_incoh))
                         noiseid = noiseid[0]
                         cnoiseid = noiseid.size
                         interfid = numpy.where(
                             jspc_interf > tmp_noise / numpy.sqrt(num_incoh))
                         interfid = interfid[0]
                         cinterfid = interfid.size
                         if (cnoiseid > 0):
                             jspc_interf[noiseid] = 0
                         # Expandiendo los perfiles a limpiar
                         if (cinterfid > 0):
                             new_interfid = (
                                 numpy.r_[interfid - 1, interfid, interfid + 1] + num_prof) % num_prof
                             new_interfid = numpy.asarray(new_interfid)
                             new_interfid = {x for x in new_interfid}
                             new_interfid = numpy.array(list(new_interfid))
                             new_cinterfid = new_interfid.size
                         else:
                             new_cinterfid = 0
                         for ip in range(new_cinterfid):
                             ind = junkspc_interf[:, new_interfid[ip]].ravel().argsort()
                             jspc_interf[new_interfid[ip]
                                         ] = junkspc_interf[ind[nhei_interf // 2], new_interfid[ip]]
                         jspectra[ich, :, ind_hei] = jspectra[ich, :,
                                                              ind_hei] - jspc_interf  # Corregir indices
                         # Removiendo la interferencia del punto de mayor interferencia
                         ListAux = jspc_interf[mask_prof].tolist()
                         maxid = ListAux.index(max(ListAux))
                         if cinterfid > 0:
                             for ip in range(cinterfid * (interf == 2) - 1):
                                 ind = (jspectra[ich, interfid[ip], :] < tmp_noise *
                                        (1 + 1 / numpy.sqrt(num_incoh))).nonzero()
                                 cind = len(ind)
                                 if (cind > 0):
                                     jspectra[ich, interfid[ip], ind] = tmp_noise * \
                                         (1 + (numpy.random.uniform(cind) - 0.5) /
                                          numpy.sqrt(num_incoh))
                             ind = numpy.array([-2, -1, 1, 2])
                             xx = numpy.zeros([4, 4])
                             for id1 in range(4):
                                 xx[:, id1] = ind[id1]**numpy.asarray(list(range(4)))
                             xx_inv = numpy.linalg.inv(xx)
                             xx = xx_inv[:, 0]
                             ind = (ind + maxid + num_mask_prof) % num_mask_prof
                             yy = jspectra[ich, mask_prof[ind], :]
                             jspectra[ich, mask_prof[maxid], :] = numpy.dot(
                                 yy.transpose(), xx)
                         indAux = (jspectra[ich, :, :] < tmp_noise *
                                   (1 - 1 / numpy.sqrt(num_incoh))).nonzero()
                         jspectra[ich, indAux[0], indAux[1]] = tmp_noise * \
                             (1 - 1 / numpy.sqrt(num_incoh))
                     # Remocion de Interferencia en el Cross Spectra
                     if jcspectra is None:
                         return jspectra, jcspectra
                     num_pairs = int(jcspectra.size / (num_prof * num_hei))
                     jcspectra = jcspectra.reshape(num_pairs, num_prof, num_hei)
                     for ip in range(num_pairs):
                         #-------------------------------------------
                         cspower = numpy.abs(jcspectra[ip, mask_prof, :])
                         cspower = cspower[:, hei_interf]
                         cspower = cspower.sum(axis=0)
                         cspsort = cspower.ravel().argsort()
                         junkcspc_interf = jcspectra[ip, :, hei_interf[cspsort[list(range(
                             offhei_interf, nhei_interf + offhei_interf))]]]
                         junkcspc_interf = junkcspc_interf.transpose()
                         jcspc_interf = junkcspc_interf.sum(axis=1) / nhei_interf
                         ind = numpy.abs(jcspc_interf[mask_prof]).ravel().argsort()
                         median_real = int(numpy.median(numpy.real(
                             junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
                         median_imag = int(numpy.median(numpy.imag(
                             junkcspc_interf[mask_prof[ind[list(range(3 * num_prof // 4))]], :])))
                         comp_mask_prof = [int(e) for e in comp_mask_prof]
                         junkcspc_interf[comp_mask_prof, :] = numpy.complex(
                             median_real, median_imag)
                         for iprof in range(num_prof):
                             ind = numpy.abs(junkcspc_interf[iprof, :]).ravel().argsort()
                             jcspc_interf[iprof] = junkcspc_interf[iprof, ind[nhei_interf // 2]]
                         # Removiendo la Interferencia
                         jcspectra[ip, :, ind_hei] = jcspectra[ip,
                                                               :, ind_hei] - jcspc_interf
                         ListAux = numpy.abs(jcspc_interf[mask_prof]).tolist()
                         maxid = ListAux.index(max(ListAux))
                         ind = numpy.array([-2, -1, 1, 2])
                         xx = numpy.zeros([4, 4])
                         for id1 in range(4):
                             xx[:, id1] = ind[id1]**numpy.asarray(list(range(4)))
                         xx_inv = numpy.linalg.inv(xx)
                         xx = xx_inv[:, 0]
                         ind = (ind + maxid + num_mask_prof) % num_mask_prof
                         yy = jcspectra[ip, mask_prof[ind], :]
                         jcspectra[ip, mask_prof[maxid], :] = numpy.dot(yy.transpose(), xx)
                     # Guardar Resultados
                     self.dataOut.data_spc = jspectra
                     self.dataOut.data_cspc = jcspectra
                     return 1
                 def run(self, dataOut, interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None, mode=1):
                     self.dataOut = dataOut
                     if mode == 1:
                         self.removeInterference(interf = 2,hei_interf = None, nhei_interf = None, offhei_interf = None)
                     elif mode == 2:
                         self.removeInterference2()
                     return self.dataOut
+            class removeInterferenceAtFreq(Operation):
+                '''
+                Written by R. Flores
+                '''
+                """Operation to remove interfernce at a known frequency(s).
+                Parameters:
+                -----------
+                None
+                Example
+                --------
+                op = proc_unit.addOperation(name='removeInterferenceAtFreq')
+                """
+                def __init__(self):
+                    Operation.__init__(self)
+                def run(self, dataOut, freq = None, freqList = None):
+                    VelRange = dataOut.getVelRange()
+                    #print("VelRange: ", VelRange)
+                    freq_ids = []
+                    if freq is not None:
+                        #print("freq")
+                        #if freq < 0:
+                        inda = numpy.where(VelRange >= freq)
+                        minIndex = inda[0][0]
+                        #print(numpy.shape(dataOut.dataLag_spc))
+                        dataOut.data_spc[:,minIndex,:] = numpy.nan
+                        #inda = numpy.where(VelRange >= ymin_noise)
+                        #indb = numpy.where(VelRange <= ymax_noise)
+                        #minIndex = inda[0][0]
+                        #maxIndex = indb[0][-1]
+                    elif freqList is not None:
+                        #print("freqList")
+                        for freq in freqList:
+                            #if freq < 0:
+                            inda = numpy.where(VelRange >= freq)
+                            minIndex = inda[0][0]
+                            #print(numpy.shape(dataOut.dataLag_spc))
+                            if freq > 0:
+                                #dataOut.data_spc[:,minIndex-1,:] = numpy.nan
+                                freq_ids.append(minIndex-1)
+                            else:
+                                #dataOut.data_spc[:,minIndex,:] = numpy.nan
+                                freq_ids.append(minIndex)
+                    else:
+                        raise ValueError("freq or freqList should be specified ...")
+                    #freq_ids = numpy.array(freq_ids).flatten()
+                    avg = numpy.mean(dataOut.data_spc[:,[t for t in range(dataOut.data_spc.shape[0]) if t not in freq_ids],:],axis=1)
+                    for p in list(freq_ids):
+                        dataOut.data_spc[:,p,:] = avg#numpy.nan
+                    return dataOut
             class IncohInt(Operation):
                 __profIndex = 0
                 __withOverapping = False
                 __byTime = False
                 __initime = None
                 __lastdatatime = None
                 __integrationtime = None
                 __buffer_spc = None
                 __buffer_cspc = None
                 __buffer_dc = None
                 __dataReady = False
                 __timeInterval = None
                 n = None
                 def __init__(self):
                     Operation.__init__(self)
                 def setup(self, n=None, timeInterval=None, overlapping=False):
                     """
                     Set the parameters of the integration class.
                     Inputs:
                         n        :    Number of coherent integrations
                         timeInterval   :    Time of integration. If the parameter "n" is selected this one does not work
                         overlapping    :
                     """
                     self.__initime = None
                     self.__lastdatatime = 0
                     self.__buffer_spc = 0
                     self.__buffer_cspc = 0
                     self.__buffer_dc = 0
                     self.__profIndex = 0
                     self.__dataReady = False
                     self.__byTime = False
                     if n is None and timeInterval is None:
                         raise ValueError("n or timeInterval should be specified ...")
                     if n is not None:
                         self.n = int(n)
                     else:
                         self.__integrationtime = int(timeInterval)
                         self.n = None
                         self.__byTime = True
                 def putData(self, data_spc, data_cspc, data_dc):
                     """
                     Add a profile to the __buffer_spc and increase in one the __profileIndex
                     """
                     self.__buffer_spc += data_spc
                     if data_cspc is None:
                         self.__buffer_cspc = None
                     else:
                         self.__buffer_cspc += data_cspc
                     if data_dc is None:
                         self.__buffer_dc = None
                     else:
                         self.__buffer_dc += data_dc
                     self.__profIndex += 1
                     return
                 def pushData(self):
                     """
                     Return the sum of the last profiles and the profiles used in the sum.
                     Affected:
                     self.__profileIndex
                     """
                     data_spc = self.__buffer_spc
                     data_cspc = self.__buffer_cspc
                     data_dc = self.__buffer_dc
                     n = self.__profIndex
                     self.__buffer_spc = 0
                     self.__buffer_cspc = 0
                     self.__buffer_dc = 0
                     self.__profIndex = 0
                     return data_spc, data_cspc, data_dc, n
                 def byProfiles(self, *args):
                     self.__dataReady = False
                     avgdata_spc = None
                     avgdata_cspc = None
                     avgdata_dc = None
                     self.putData(*args)
                     if self.__profIndex == self.n:
                         avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
                         self.n = n
                         self.__dataReady = True
                     return avgdata_spc, avgdata_cspc, avgdata_dc
                 def byTime(self, datatime, *args):
                     self.__dataReady = False
                     avgdata_spc = None
                     avgdata_cspc = None
                     avgdata_dc = None
                     self.putData(*args)
                     if (datatime - self.__initime) >= self.__integrationtime:
                         avgdata_spc, avgdata_cspc, avgdata_dc, n = self.pushData()
                         self.n = n
                         self.__dataReady = True
                     return avgdata_spc, avgdata_cspc, avgdata_dc
                 def integrate(self, datatime, *args):
                     if self.__profIndex == 0:
                         self.__initime = datatime
                     if self.__byTime:
                         avgdata_spc, avgdata_cspc, avgdata_dc = self.byTime(
                             datatime, *args)
                     else:
                         avgdata_spc, avgdata_cspc, avgdata_dc = self.byProfiles(*args)
                     if not self.__dataReady:
                         return None, None, None, None
                     return self.__initime, avgdata_spc, avgdata_cspc, avgdata_dc
                 def run(self, dataOut, n=None, timeInterval=None, overlapping=False):
                     if n == 1:
                         return dataOut
                     print("JERE")
                     dataOut.flagNoData = True
                     if not self.isConfig:
                         self.setup(n, timeInterval, overlapping)
                         self.isConfig = True
                     avgdatatime, avgdata_spc, avgdata_cspc, avgdata_dc = self.integrate(dataOut.utctime,
                                                                                         dataOut.data_spc,
                                                                                         dataOut.data_cspc,
                                                                                         dataOut.data_dc)
                     if self.__dataReady:
                         dataOut.data_spc = avgdata_spc
                         print(numpy.sum(dataOut.data_spc))
                         exit(1)
                         dataOut.data_cspc = avgdata_cspc
                         dataOut.data_dc = avgdata_dc
                         dataOut.nIncohInt *= self.n
                         dataOut.utctime = avgdatatime
                         dataOut.flagNoData = False
                     return dataOut
             class dopplerFlip(Operation):
                 def run(self, dataOut, chann = None):
                     # arreglo 1: (num_chan, num_profiles, num_heights)
                     self.dataOut = dataOut
                     # JULIA-oblicua, indice 2
                     # arreglo 2: (num_profiles, num_heights)
                     jspectra = self.dataOut.data_spc[chann]
                     jspectra_tmp = numpy.zeros(jspectra.shape)
                     num_profiles = jspectra.shape[0]
                     freq_dc = int(num_profiles / 2)
                     # Flip con for
                     for j in range(num_profiles):
                         jspectra_tmp[num_profiles-j-1]= jspectra[j]
                     # Intercambio perfil de DC con perfil inmediato anterior
                     jspectra_tmp[freq_dc-1]= jspectra[freq_dc-1]
                     jspectra_tmp[freq_dc]= jspectra[freq_dc]
                     # canal modificado es re-escrito en el arreglo de canales
                     self.dataOut.data_spc[chann] = jspectra_tmp
                     return self.dataOut

schainpy/model/proc/jroproc_spectra_lags_faraday.py 0 0

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schainpy/model/proc/jroproc_voltage.py 0 0

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