schain Commit - r1732:8e0a97a787ce · Jicamarca Repository

plot extra point depending on matplotlib version

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r1732:8e0a97a787ce

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r1732:8e0a97a787ce

schainpy/model/graphics/jroplot_spectra.py +34 -27

              from schainpy.model.graphics.jroplot_base import Plot, plt, log
+             from matplotlib import __version__ as plt_version
+             if plt_version >='3.3.4':
+                 EXTRA_POINTS = 0
+             else:
+                 EXTRA_POINTS = 1
              class SpectraPlot(Plot):
                  '''
                          data['noise'][0] = 10*numpy.log10(dataOut.getNoise(ymin_index=53)[0]/dataOut.normFactor)
                      else:
                          data['noise'] = 10 * numpy.log10(dataOut.getNoise() / dataOut.normFactor)
-                     extrapoints = spc.shape[1] % dataOut.nFFTPoints
-                     extrapoints=1
-                     meta['xrange'] = (dataOut.getFreqRange(extrapoints) / 1000., dataOut.getAcfRange(extrapoints), dataOut.getVelRange(extrapoints))
+                     extrapoints = spc.shape[1] % dataOut.nFFTPoints
+                     extrapoints=1
+                     meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS) / 1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))
                      if self.CODE == 'spc_moments':
                          data['moments'] = dataOut.moments
                      if self.CODE == 'gaussian_fit':
                          data['gaussfit'] = dataOut.DGauFitParams
-                     return data, meta
+                     return data, meta
                  def plot(self):
                      if self.xaxis == "frequency":
                          self.xlabel = "Velocity (m/s)"
                      self.titles = []
                      y = self.data.yrange
                      self.y = y
                          if self.CODE == 'spc_moments':
                              mean = data['moments'][n, 1]
-                         if self.CODE == 'gaussian_fit':
+                         if self.CODE == 'gaussian_fit':
                              gau0 = data['gaussfit'][n][2,:,0]
                              gau1 = data['gaussfit'][n][2,:,1]
                          if ax.firsttime:
                      self.ylabel = 'Range [km]'
                  def update(self, dataOut):
                      data = {}
                      meta = {}
                      spc = 10*numpy.log10(dataOut.data_spc/dataOut.normFactor)
                      data['spc'] = spc
                      data['rti'] = dataOut.getPower()
                      data['noise'] = 10*numpy.log10(dataOut.getNoise()/dataOut.normFactor)
-                     meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
+                     meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS)/1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))
                      data['shift1'] = dataOut.Dop_EEJ_T1[0]
                      data['shift2'] = dataOut.Dop_EEJ_T2[0]
                      data['max_val_2'] = dataOut.Oblique_params[0,-1,:]
                      data['shift1_error'] = dataOut.Err_Dop_EEJ_T1[0]
                      data['shift2_error'] = dataOut.Err_Dop_EEJ_T2[0]
-                     return data, meta
+                     return data, meta
                  def plot(self):
                                      self.data['rti'][n][-1], y)[0]
                                  ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y,
                                                                      color="k", linestyle="dashed", lw=1)[0]
                              self.ploterr1 = ax.errorbar(shift1, y, xerr=err1, fmt='k^', elinewidth=2.2, marker='o', linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2)
                              self.ploterr2 = ax.errorbar(shift2, y, xerr=err2, fmt='m^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2)
                              self.ploterr3 = ax.errorbar(max_val_2, y, xerr=0, fmt='g^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2)
                              self.ploterr1 = ax.errorbar(shift1, y, xerr=err1, fmt='k^', elinewidth=2.2, marker='o', linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2)
                              self.ploterr2 = ax.errorbar(shift2, y, xerr=err2, fmt='m^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2)
                              self.ploterr3 = ax.errorbar(max_val_2, y, xerr=0, fmt='g^',elinewidth=2.2,marker='o',linestyle='None',markersize=2.5,capsize=0.3,markeredgewidth=0.2)
                          self.titles.append('CH {}: {:3.2f}dB'.format(n, noise))
                      spc = dataOut.data_spc
                      cspc = dataOut.data_cspc
                      extrapoints = spc.shape[1] % dataOut.nFFTPoints
-                     meta['xrange'] = (dataOut.getFreqRange(extrapoints) / 1000., dataOut.getAcfRange(extrapoints), dataOut.getVelRange(extrapoints))
+                     meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS) / 1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))
                      meta['pairs'] = dataOut.pairsList
                      tmp = []
                      data['cspc'] = numpy.array(tmp)
-                     return data, meta
+                     return data, meta
                  def plot(self):
                      if self.xaxis == "frequency":
                              ax.plt.set_array(coh.T.ravel())
                          self.titles.append(
                              'Coherence Ch{} * Ch{}'.format(pair[0], pair[1]))
                          ax = self.axes[2 * n + 1]
                          if ax.firsttime:
                              ax.plt = ax.pcolormesh(x, y, phase.T,
                      z = numpy.where(numpy.isfinite(z), z, numpy.NAN)
                      #buffer = 10 * numpy.log10(z)
-                     meta['xrange'] = (dataOut.getFreqRange(1)/1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
+                     meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS)/1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))
                      #self.hei = hei
                      self.y = y
                      x = self.data[-1][self.CODE]
                      if self.xmin is None: self.xmin = numpy.nanmin(x) * 0.9
                      if self.xmax is None: self.xmax = numpy.nanmax(x) * 1.1
                      if self.axes[0].firsttime:
                          for ch in self.data.channels:
                              self.axes[0].plot(x[ch], y, lw=1, label='Ch{}'.format(ch))
                      meta = {}
                      spc = 10 * numpy.log10(dataOut.data_pre[0] / dataOut.normFactor)
                      data['spc'] = spc
-                     meta['xrange'] = (dataOut.getFreqRange(1) / 1000., dataOut.getAcfRange(1), dataOut.getVelRange(1))
+                     meta['xrange'] = (dataOut.getFreqRange(EXTRA_POINTS) / 1000., dataOut.getAcfRange(EXTRA_POINTS), dataOut.getVelRange(EXTRA_POINTS))
                      if self.CODE == 'cut_gaussian_fit':
                          data['gauss_fit0'] = 10 * numpy.log10(dataOut.GaussFit0 / dataOut.normFactor)
                          data['gauss_fit1'] = 10 * numpy.log10(dataOut.GaussFit1 / dataOut.normFactor)
                      else:
                          x = self.data.xrange[2][:-1]
                          self.xlabel = "Velocity (m/s)"
                      if self.CODE == 'cut_gaussian_fit':
                          x = self.data.xrange[2][:-1]
                          self.xlabel = "Velocity (m/s)"
                          index = numpy.arange(0, len(y), int((len(y)) / 9))
                      for n, ax in enumerate(self.axes):
-                         if self.CODE == 'cut_gaussian_fit':
+                         if self.CODE == 'cut_gaussian_fit':
                              gau0 = data['gauss_fit0']
                              gau1 = data['gauss_fit1']
                          if ax.firsttime:
                              self.xmin = self.xmin if self.xmin else -self.xmax
                              self.ymin = self.ymin if self.ymin else numpy.nanmin(z[:,:,index])
                              self.ymax = self.ymax if self.ymax else numpy.nanmax(z[:,:,index])
                              ax.plt = ax.plot(x, z[n, :, index].T, lw=0.25)
                              if self.CODE == 'cut_gaussian_fit':
                                  ax.plt_gau0 = ax.plot(x, gau0[n, :, index].T, lw=1, linestyle='-.')
                                  for i, line in enumerate(ax.plt_gau0):
-                                     line.set_color(ax.plt[i].get_color())
+                                     line.set_color(ax.plt[i].get_color())
                                  ax.plt_gau1 = ax.plot(x, gau1[n, :, index].T, lw=1, linestyle='--')
                                  for i, line in enumerate(ax.plt_gau1):
-                                     line.set_color(ax.plt[i].get_color())
+                                     line.set_color(ax.plt[i].get_color())
                              labels = ['Range = {:2.1f}km'.format(y[i]) for i in index]
                              self.figures[0].legend(ax.plt, labels, loc='center right')
                          else:

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