jroplot_spectra.py
1226 lines
| 41.6 KiB
| text/x-python
|
PythonLexer
Danny Scipión
|
r1358 | # Copyright (c) 2012-2021 Jicamarca Radio Observatory | ||
| r1343 | # All rights reserved. | |||
| # | ||||
| # Distributed under the terms of the BSD 3-clause license. | ||||
| """Classes to plot Spectra data | ||||
Miguel Valdez
|
r568 | |||
| r1343 | """ | |||
Juan C. Espinoza
|
r1285 | |||
Daniel Valdez
|
r487 | import os | ||
| import numpy | ||||
| r1343 | from schainpy.model.graphics.jroplot_base import Plot, plt, log | |||
José Chávez
|
r953 | |||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | class SpectraPlot(Plot): | ||
| ''' | ||||
| Plot for Spectra data | ||||
| ''' | ||||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | CODE = 'spc' | ||
| colormap = 'jet' | ||||
| plot_type = 'pcolor' | ||||
| r1343 | buffering = False | |||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | def setup(self): | ||
| r1377 | ||||
|
Juan C. Espinoza
|
r1285 | self.nplots = len(self.data.channels) | ||
| self.ncols = int(numpy.sqrt(self.nplots) + 0.9) | ||||
| self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) | ||||
| r1334 | self.height = 2.6 * self.nrows | |||
|
Juan C. Espinoza
|
r1285 | self.cb_label = 'dB' | ||
| if self.showprofile: | ||||
| self.width = 4 * self.ncols | ||||
|
Daniel Valdez
|
r487 | else: | ||
|
Juan C. Espinoza
|
r1285 | self.width = 3.5 * self.ncols | ||
| r1377 | self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18}) | |||
|
Juan C. Espinoza
|
r1285 | self.ylabel = 'Range [km]' | ||
| r1343 | def update(self, dataOut): | |||
| data = {} | ||||
| meta = {} | ||||
| r1396 | spc = 10 * numpy.log10(dataOut.data_spc / dataOut.normFactor) | |||
| r1343 | data['spc'] = spc | |||
| data['rti'] = dataOut.getPower() | ||||
| r1396 | data['noise'] = 10 * numpy.log10(dataOut.getNoise() / dataOut.normFactor) | |||
| meta['xrange'] = (dataOut.getFreqRange(1) / 1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) | ||||
| r1343 | if self.CODE == 'spc_moments': | |||
| data['moments'] = dataOut.moments | ||||
|
Danny Scipión
|
r1358 | if self.CODE == 'gaussian_fit': | ||
| data['gaussfit'] = dataOut.DGauFitParams | ||||
| r1343 | return data, meta | |||
|
Juan C. Espinoza
|
r1285 | def plot(self): | ||
| r1377 | ||||
|
Juan C. Espinoza
|
r1285 | if self.xaxis == "frequency": | ||
| x = self.data.xrange[0] | ||||
| self.xlabel = "Frequency (kHz)" | ||||
| elif self.xaxis == "time": | ||||
| x = self.data.xrange[1] | ||||
| self.xlabel = "Time (ms)" | ||||
ebocanegra
|
r965 | else: | ||
|
Juan C. Espinoza
|
r1285 | x = self.data.xrange[2] | ||
| self.xlabel = "Velocity (m/s)" | ||||
|
Danny Scipión
|
r1358 | if (self.CODE == 'spc_moments') | (self.CODE == 'gaussian_fit'): | ||
|
Juan C. Espinoza
|
r1285 | x = self.data.xrange[2] | ||
| self.xlabel = "Velocity (m/s)" | ||||
| self.titles = [] | ||||
| r1343 | y = self.data.yrange | |||
|
Juan C. Espinoza
|
r1285 | self.y = y | ||
| r1343 | ||||
| data = self.data[-1] | ||||
| z = data['spc'] | ||||
|
Juan C. Espinoza
|
r1285 | |||
| for n, ax in enumerate(self.axes): | ||||
| r1343 | noise = data['noise'][n] | |||
| r1396 | ||||
|
Juan C. Espinoza
|
r1285 | if self.CODE == 'spc_moments': | ||
|
Danny Scipión
|
r1358 | mean = data['moments'][n, 1] | ||
| if self.CODE == 'gaussian_fit': | ||||
| gau0 = data['gaussfit'][n][2,:,0] | ||||
| gau1 = data['gaussfit'][n][2,:,1] | ||||
|
Juan C. Espinoza
|
r1285 | if ax.firsttime: | ||
| self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | ||||
| self.xmin = self.xmin if self.xmin else -self.xmax | ||||
| self.zmin = self.zmin if self.zmin else numpy.nanmin(z) | ||||
| self.zmax = self.zmax if self.zmax else numpy.nanmax(z) | ||||
| ax.plt = ax.pcolormesh(x, y, z[n].T, | ||||
| vmin=self.zmin, | ||||
| vmax=self.zmax, | ||||
| cmap=plt.get_cmap(self.colormap) | ||||
| ) | ||||
| if self.showprofile: | ||||
| ax.plt_profile = self.pf_axes[n].plot( | ||||
| r1343 | data['rti'][n], y)[0] | |||
|
Juan C. Espinoza
|
r1285 | ax.plt_noise = self.pf_axes[n].plot(numpy.repeat(noise, len(y)), y, | ||
| color="k", linestyle="dashed", lw=1)[0] | ||||
| if self.CODE == 'spc_moments': | ||||
|
Danny Scipión
|
r1358 | ax.plt_mean = ax.plot(mean, y, color='k', lw=1)[0] | ||
| if self.CODE == 'gaussian_fit': | ||||
| ax.plt_gau0 = ax.plot(gau0, y, color='r', lw=1)[0] | ||||
| ax.plt_gau1 = ax.plot(gau1, y, color='y', lw=1)[0] | ||||
|
Juan C. Espinoza
|
r1285 | else: | ||
| ax.plt.set_array(z[n].T.ravel()) | ||||
| if self.showprofile: | ||||
| r1343 | ax.plt_profile.set_data(data['rti'][n], y) | |||
|
Juan C. Espinoza
|
r1285 | ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) | ||
| if self.CODE == 'spc_moments': | ||||
| ax.plt_mean.set_data(mean, y) | ||||
|
Danny Scipión
|
r1358 | if self.CODE == 'gaussian_fit': | ||
| ax.plt_gau0.set_data(gau0, y) | ||||
| ax.plt_gau1.set_data(gau1, y) | ||||
|
Juan C. Espinoza
|
r1285 | self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) | ||
| r1377 | class SpectraObliquePlot(Plot): | |||
| ''' | ||||
| Plot for Spectra data | ||||
| ''' | ||||
| r1381 | CODE = 'spc_oblique' | |||
| r1377 | colormap = 'jet' | |||
| plot_type = 'pcolor' | ||||
| def setup(self): | ||||
| self.xaxis = "oblique" | ||||
| self.nplots = len(self.data.channels) | ||||
| self.ncols = int(numpy.sqrt(self.nplots) + 0.9) | ||||
| self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) | ||||
| self.height = 2.6 * self.nrows | ||||
| self.cb_label = 'dB' | ||||
| if self.showprofile: | ||||
| self.width = 4 * self.ncols | ||||
| else: | ||||
| self.width = 3.5 * self.ncols | ||||
| self.plots_adjust.update({'wspace': 0.8, 'hspace':0.2, 'left': 0.2, 'right': 0.9, 'bottom': 0.18}) | ||||
| self.ylabel = 'Range [km]' | ||||
| r1381 | 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)) | ||||
| data['shift1'] = dataOut.Oblique_params[0][1] | ||||
| data['shift2'] = dataOut.Oblique_params[0][4] | ||||
| data['shift1_error'] = dataOut.Oblique_param_errors[0][1] | ||||
| data['shift2_error'] = dataOut.Oblique_param_errors[0][4] | ||||
| return data, meta | ||||
| r1377 | def plot(self): | |||
| if self.xaxis == "frequency": | ||||
| x = self.data.xrange[0] | ||||
| self.xlabel = "Frequency (kHz)" | ||||
| elif self.xaxis == "time": | ||||
| x = self.data.xrange[1] | ||||
| self.xlabel = "Time (ms)" | ||||
| else: | ||||
| x = self.data.xrange[2] | ||||
| self.xlabel = "Velocity (m/s)" | ||||
| self.titles = [] | ||||
| r1381 | y = self.data.yrange | |||
| r1377 | self.y = y | |||
| z = self.data['spc'] | ||||
| for n, ax in enumerate(self.axes): | ||||
| noise = self.data['noise'][n][-1] | ||||
| r1381 | shift1 = self.data['shift1'] | |||
| shift2 = self.data['shift2'] | ||||
| err1 = self.data['shift1_error'] | ||||
| err2 = self.data['shift2_error'] | ||||
| r1377 | if ax.firsttime: | |||
| self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | ||||
| self.xmin = self.xmin if self.xmin else -self.xmax | ||||
| self.zmin = self.zmin if self.zmin else numpy.nanmin(z) | ||||
| self.zmax = self.zmax if self.zmax else numpy.nanmax(z) | ||||
| ax.plt = ax.pcolormesh(x, y, z[n].T, | ||||
| vmin=self.zmin, | ||||
| vmax=self.zmax, | ||||
| cmap=plt.get_cmap(self.colormap) | ||||
| ) | ||||
| if self.showprofile: | ||||
| ax.plt_profile = self.pf_axes[n].plot( | ||||
| 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] | ||||
| r1381 | ||||
| self.ploterr1 = ax.errorbar(shift1, y, xerr=err1, fmt='k^', elinewidth=0.2, marker='x', linestyle='None',markersize=0.5,capsize=0.3,markeredgewidth=0.2) | ||||
| self.ploterr2 = ax.errorbar(shift2, y, xerr=err2, fmt='m^',elinewidth=0.2,marker='x',linestyle='None',markersize=0.5,capsize=0.3,markeredgewidth=0.2) | ||||
| r1377 | else: | |||
| self.ploterr1.remove() | ||||
| self.ploterr2.remove() | ||||
| ax.plt.set_array(z[n].T.ravel()) | ||||
| if self.showprofile: | ||||
| ax.plt_profile.set_data(self.data['rti'][n][-1], y) | ||||
| ax.plt_noise.set_data(numpy.repeat(noise, len(y)), y) | ||||
| r1381 | self.ploterr1 = ax.errorbar(shift1, y, xerr=err1, fmt='k^',elinewidth=0.2,marker='x',linestyle='None',markersize=0.5,capsize=0.3,markeredgewidth=0.2) | |||
| self.ploterr2 = ax.errorbar(shift2, y, xerr=err2, fmt='m^',elinewidth=0.2,marker='x',linestyle='None',markersize=0.5,capsize=0.3,markeredgewidth=0.2) | ||||
| r1377 | ||||
| self.titles.append('CH {}: {:3.2f}dB'.format(n, noise)) | ||||
| r1381 | ||||
|
Juan C. Espinoza
|
r1285 | |||
| class CrossSpectraPlot(Plot): | ||||
| CODE = 'cspc' | ||||
| colormap = 'jet' | ||||
| plot_type = 'pcolor' | ||||
| zmin_coh = None | ||||
| zmax_coh = None | ||||
| zmin_phase = None | ||||
| zmax_phase = None | ||||
| def setup(self): | ||||
| self.ncols = 4 | ||||
| r1343 | self.nplots = len(self.data.pairs) * 2 | |||
| self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) | ||||
| r1334 | self.width = 3.1 * self.ncols | |||
| r1377 | self.height = 5 * self.nrows | |||
|
Juan C. Espinoza
|
r1285 | self.ylabel = 'Range [km]' | ||
| self.showprofile = False | ||||
| r1334 | self.plots_adjust.update({'left': 0.08, 'right': 0.92, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) | |||
|
Juan C. Espinoza
|
r1285 | |||
| r1343 | def update(self, dataOut): | |||
| data = {} | ||||
| meta = {} | ||||
| spc = dataOut.data_spc | ||||
| cspc = dataOut.data_cspc | ||||
| r1396 | meta['xrange'] = (dataOut.getFreqRange(1) / 1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) | |||
| r1343 | meta['pairs'] = dataOut.pairsList | |||
| tmp = [] | ||||
| for n, pair in enumerate(meta['pairs']): | ||||
| out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) | ||||
| coh = numpy.abs(out) | ||||
| phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi | ||||
| tmp.append(coh) | ||||
| tmp.append(phase) | ||||
| data['cspc'] = numpy.array(tmp) | ||||
| return data, meta | ||||
|
Juan C. Espinoza
|
r1285 | def plot(self): | ||
| if self.xaxis == "frequency": | ||||
| x = self.data.xrange[0] | ||||
| self.xlabel = "Frequency (kHz)" | ||||
| elif self.xaxis == "time": | ||||
| x = self.data.xrange[1] | ||||
| self.xlabel = "Time (ms)" | ||||
|
Miguel Valdez
|
r777 | else: | ||
|
Juan C. Espinoza
|
r1285 | x = self.data.xrange[2] | ||
| self.xlabel = "Velocity (m/s)" | ||||
| r1377 | ||||
|
Juan C. Espinoza
|
r1285 | self.titles = [] | ||
| r1343 | y = self.data.yrange | |||
|
Juan C. Espinoza
|
r1285 | self.y = y | ||
| r1343 | data = self.data[-1] | |||
| cspc = data['cspc'] | ||||
|
Juan C. Espinoza
|
r1285 | |||
| r1343 | for n in range(len(self.data.pairs)): | |||
| pair = self.data.pairs[n] | ||||
| r1396 | coh = cspc[n * 2] | |||
| phase = cspc[n * 2 + 1] | ||||
| r1343 | ax = self.axes[2 * n] | |||
|
Juan C. Espinoza
|
r1285 | if ax.firsttime: | ||
| ax.plt = ax.pcolormesh(x, y, coh.T, | ||||
| vmin=0, | ||||
| vmax=1, | ||||
| cmap=plt.get_cmap(self.colormap_coh) | ||||
| ) | ||||
| else: | ||||
| ax.plt.set_array(coh.T.ravel()) | ||||
| self.titles.append( | ||||
| 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) | ||||
| r1343 | ax = self.axes[2 * n + 1] | |||
|
Juan C. Espinoza
|
r1285 | if ax.firsttime: | ||
| ax.plt = ax.pcolormesh(x, y, phase.T, | ||||
| vmin=-180, | ||||
| vmax=180, | ||||
| r1377 | cmap=plt.get_cmap(self.colormap_phase) | |||
|
Juan C. Espinoza
|
r1285 | ) | ||
| else: | ||||
| ax.plt.set_array(phase.T.ravel()) | ||||
| self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) | ||||
| r1377 | class CrossSpectra4Plot(Plot): | |||
| CODE = 'cspc' | ||||
| colormap = 'jet' | ||||
| plot_type = 'pcolor' | ||||
| zmin_coh = None | ||||
| zmax_coh = None | ||||
| zmin_phase = None | ||||
| zmax_phase = None | ||||
| def setup(self): | ||||
| self.ncols = 4 | ||||
| self.nrows = len(self.data.pairs) | ||||
| self.nplots = self.nrows * 4 | ||||
| self.width = 3.1 * self.ncols | ||||
| self.height = 5 * self.nrows | ||||
| self.ylabel = 'Range [km]' | ||||
| self.showprofile = False | ||||
| self.plots_adjust.update({'left': 0.08, 'right': 0.92, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) | ||||
| def plot(self): | ||||
| if self.xaxis == "frequency": | ||||
| x = self.data.xrange[0] | ||||
| self.xlabel = "Frequency (kHz)" | ||||
| elif self.xaxis == "time": | ||||
| x = self.data.xrange[1] | ||||
| self.xlabel = "Time (ms)" | ||||
| else: | ||||
| x = self.data.xrange[2] | ||||
| self.xlabel = "Velocity (m/s)" | ||||
| self.titles = [] | ||||
| y = self.data.heights | ||||
| self.y = y | ||||
| nspc = self.data['spc'] | ||||
| #print(numpy.shape(self.data['spc'])) | ||||
| spc = self.data['cspc'][0] | ||||
| #print(numpy.shape(nspc)) | ||||
| #exit() | ||||
| #nspc[1,:,:] = numpy.flip(nspc[1,:,:],axis=0) | ||||
| #print(numpy.shape(spc)) | ||||
| #exit() | ||||
| cspc = self.data['cspc'][1] | ||||
| #xflip=numpy.flip(x) | ||||
| #print(numpy.shape(cspc)) | ||||
| #exit() | ||||
| for n in range(self.nrows): | ||||
| noise = self.data['noise'][:,-1] | ||||
| pair = self.data.pairs[n] | ||||
| #print(pair) | ||||
| #exit() | ||||
| ax = self.axes[4 * n] | ||||
| if ax.firsttime: | ||||
| self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | ||||
| self.xmin = self.xmin if self.xmin else -self.xmax | ||||
| self.zmin = self.zmin if self.zmin else numpy.nanmin(nspc) | ||||
| self.zmax = self.zmax if self.zmax else numpy.nanmax(nspc) | ||||
| ax.plt = ax.pcolormesh(x , y , nspc[pair[0]].T, | ||||
| vmin=self.zmin, | ||||
| vmax=self.zmax, | ||||
| cmap=plt.get_cmap(self.colormap) | ||||
| ) | ||||
| else: | ||||
| #print(numpy.shape(nspc[pair[0]].T)) | ||||
| #exit() | ||||
| ax.plt.set_array(nspc[pair[0]].T.ravel()) | ||||
| self.titles.append('CH {}: {:3.2f}dB'.format(pair[0], noise[pair[0]])) | ||||
| ax = self.axes[4 * n + 1] | ||||
| if ax.firsttime: | ||||
| ax.plt = ax.pcolormesh(x , y, numpy.flip(nspc[pair[1]],axis=0).T, | ||||
| vmin=self.zmin, | ||||
| vmax=self.zmax, | ||||
| cmap=plt.get_cmap(self.colormap) | ||||
| ) | ||||
| else: | ||||
| ax.plt.set_array(numpy.flip(nspc[pair[1]],axis=0).T.ravel()) | ||||
| self.titles.append('CH {}: {:3.2f}dB'.format(pair[1], noise[pair[1]])) | ||||
| out = cspc[n] / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) | ||||
| coh = numpy.abs(out) | ||||
| phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi | ||||
| ax = self.axes[4 * n + 2] | ||||
| if ax.firsttime: | ||||
| ax.plt = ax.pcolormesh(x, y, numpy.flip(coh,axis=0).T, | ||||
| vmin=0, | ||||
| vmax=1, | ||||
| cmap=plt.get_cmap(self.colormap_coh) | ||||
| ) | ||||
| else: | ||||
| ax.plt.set_array(numpy.flip(coh,axis=0).T.ravel()) | ||||
| self.titles.append( | ||||
| 'Coherence Ch{} * Ch{}'.format(pair[0], pair[1])) | ||||
| ax = self.axes[4 * n + 3] | ||||
| if ax.firsttime: | ||||
| ax.plt = ax.pcolormesh(x, y, numpy.flip(phase,axis=0).T, | ||||
| vmin=-180, | ||||
| vmax=180, | ||||
| cmap=plt.get_cmap(self.colormap_phase) | ||||
| ) | ||||
| else: | ||||
| ax.plt.set_array(numpy.flip(phase,axis=0).T.ravel()) | ||||
| self.titles.append('Phase CH{} * CH{}'.format(pair[0], pair[1])) | ||||
| class CrossSpectra2Plot(Plot): | ||||
| CODE = 'cspc' | ||||
| colormap = 'jet' | ||||
| plot_type = 'pcolor' | ||||
| zmin_coh = None | ||||
| zmax_coh = None | ||||
| zmin_phase = None | ||||
| zmax_phase = None | ||||
| def setup(self): | ||||
| self.ncols = 1 | ||||
| self.nrows = len(self.data.pairs) | ||||
| self.nplots = self.nrows * 1 | ||||
| self.width = 3.1 * self.ncols | ||||
| self.height = 5 * self.nrows | ||||
| self.ylabel = 'Range [km]' | ||||
| self.showprofile = False | ||||
| self.plots_adjust.update({'left': 0.22, 'right': .90, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) | ||||
| def plot(self): | ||||
| if self.xaxis == "frequency": | ||||
| x = self.data.xrange[0] | ||||
| self.xlabel = "Frequency (kHz)" | ||||
| elif self.xaxis == "time": | ||||
| x = self.data.xrange[1] | ||||
| self.xlabel = "Time (ms)" | ||||
| else: | ||||
| x = self.data.xrange[2] | ||||
| self.xlabel = "Velocity (m/s)" | ||||
| self.titles = [] | ||||
| y = self.data.heights | ||||
| self.y = y | ||||
| #nspc = self.data['spc'] | ||||
| #print(numpy.shape(self.data['spc'])) | ||||
| #spc = self.data['cspc'][0] | ||||
| #print(numpy.shape(spc)) | ||||
| #exit() | ||||
| cspc = self.data['cspc'][1] | ||||
| #print(numpy.shape(cspc)) | ||||
| #exit() | ||||
| for n in range(self.nrows): | ||||
| noise = self.data['noise'][:,-1] | ||||
| pair = self.data.pairs[n] | ||||
| #print(pair) #exit() | ||||
| out = cspc[n]# / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) | ||||
| #print(out[:,53]) | ||||
| #exit() | ||||
| cross = numpy.abs(out) | ||||
| z = cross/self.data.nFactor | ||||
| #print("here") | ||||
| #print(dataOut.data_spc[0,0,0]) | ||||
| #exit() | ||||
| cross = 10*numpy.log10(z) | ||||
| #print(numpy.shape(cross)) | ||||
| #print(cross[0,:]) | ||||
| #print(self.data.nFactor) | ||||
| #exit() | ||||
| #phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi | ||||
| ax = self.axes[1 * n] | ||||
| if ax.firsttime: | ||||
| self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | ||||
| self.xmin = self.xmin if self.xmin else -self.xmax | ||||
| self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) | ||||
| self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) | ||||
| ax.plt = ax.pcolormesh(x, y, cross.T, | ||||
| vmin=self.zmin, | ||||
| vmax=self.zmax, | ||||
| cmap=plt.get_cmap(self.colormap) | ||||
| ) | ||||
| else: | ||||
| ax.plt.set_array(cross.T.ravel()) | ||||
| self.titles.append( | ||||
| 'Cross Spectra Power Ch{} * Ch{}'.format(pair[0], pair[1])) | ||||
| class CrossSpectra3Plot(Plot): | ||||
| CODE = 'cspc' | ||||
| colormap = 'jet' | ||||
| plot_type = 'pcolor' | ||||
| zmin_coh = None | ||||
| zmax_coh = None | ||||
| zmin_phase = None | ||||
| zmax_phase = None | ||||
| def setup(self): | ||||
| self.ncols = 3 | ||||
| self.nrows = len(self.data.pairs) | ||||
| self.nplots = self.nrows * 3 | ||||
| self.width = 3.1 * self.ncols | ||||
| self.height = 5 * self.nrows | ||||
| self.ylabel = 'Range [km]' | ||||
| self.showprofile = False | ||||
| self.plots_adjust.update({'left': 0.22, 'right': .90, 'wspace': 0.5, 'hspace':0.4, 'top':0.95, 'bottom': 0.08}) | ||||
| def plot(self): | ||||
| if self.xaxis == "frequency": | ||||
| x = self.data.xrange[0] | ||||
| self.xlabel = "Frequency (kHz)" | ||||
| elif self.xaxis == "time": | ||||
| x = self.data.xrange[1] | ||||
| self.xlabel = "Time (ms)" | ||||
| else: | ||||
| x = self.data.xrange[2] | ||||
| self.xlabel = "Velocity (m/s)" | ||||
| self.titles = [] | ||||
| y = self.data.heights | ||||
| self.y = y | ||||
| #nspc = self.data['spc'] | ||||
| #print(numpy.shape(self.data['spc'])) | ||||
| #spc = self.data['cspc'][0] | ||||
| #print(numpy.shape(spc)) | ||||
| #exit() | ||||
| cspc = self.data['cspc'][1] | ||||
| #print(numpy.shape(cspc)) | ||||
| #exit() | ||||
| for n in range(self.nrows): | ||||
| noise = self.data['noise'][:,-1] | ||||
| pair = self.data.pairs[n] | ||||
| #print(pair) #exit() | ||||
| out = cspc[n]# / numpy.sqrt(spc[pair[0]] * spc[pair[1]]) | ||||
| #print(out[:,53]) | ||||
| #exit() | ||||
| cross = numpy.abs(out) | ||||
| z = cross/self.data.nFactor | ||||
| cross = 10*numpy.log10(z) | ||||
| out_r= out.real/self.data.nFactor | ||||
| #out_r = 10*numpy.log10(out_r) | ||||
| out_i= out.imag/self.data.nFactor | ||||
| #out_i = 10*numpy.log10(out_i) | ||||
| #print(numpy.shape(cross)) | ||||
| #print(cross[0,:]) | ||||
| #print(self.data.nFactor) | ||||
| #exit() | ||||
| #phase = numpy.arctan2(out.imag, out.real) * 180 / numpy.pi | ||||
| ax = self.axes[3 * n] | ||||
| if ax.firsttime: | ||||
| self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | ||||
| self.xmin = self.xmin if self.xmin else -self.xmax | ||||
| self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) | ||||
| self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) | ||||
| ax.plt = ax.pcolormesh(x, y, cross.T, | ||||
| vmin=self.zmin, | ||||
| vmax=self.zmax, | ||||
| cmap=plt.get_cmap(self.colormap) | ||||
| ) | ||||
| else: | ||||
| ax.plt.set_array(cross.T.ravel()) | ||||
| self.titles.append( | ||||
| 'Cross Spectra Power Ch{} * Ch{}'.format(pair[0], pair[1])) | ||||
| ax = self.axes[3 * n + 1] | ||||
| if ax.firsttime: | ||||
| self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | ||||
| self.xmin = self.xmin if self.xmin else -self.xmax | ||||
| self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) | ||||
| self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) | ||||
| ax.plt = ax.pcolormesh(x, y, out_r.T, | ||||
| vmin=-1.e6, | ||||
| vmax=0, | ||||
| cmap=plt.get_cmap(self.colormap) | ||||
| ) | ||||
| else: | ||||
| ax.plt.set_array(out_r.T.ravel()) | ||||
| self.titles.append( | ||||
| 'Cross Spectra Real Ch{} * Ch{}'.format(pair[0], pair[1])) | ||||
| ax = self.axes[3 * n + 2] | ||||
| if ax.firsttime: | ||||
| self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | ||||
| self.xmin = self.xmin if self.xmin else -self.xmax | ||||
| self.zmin = self.zmin if self.zmin else numpy.nanmin(cross) | ||||
| self.zmax = self.zmax if self.zmax else numpy.nanmax(cross) | ||||
| ax.plt = ax.pcolormesh(x, y, out_i.T, | ||||
| vmin=-1.e6, | ||||
| vmax=1.e6, | ||||
| cmap=plt.get_cmap(self.colormap) | ||||
| ) | ||||
| else: | ||||
| ax.plt.set_array(out_i.T.ravel()) | ||||
| self.titles.append( | ||||
| 'Cross Spectra Imag Ch{} * Ch{}'.format(pair[0], pair[1])) | ||||
|
Juan C. Espinoza
|
r1285 | class RTIPlot(Plot): | ||
| ''' | ||||
| Plot for RTI data | ||||
| ''' | ||||
| CODE = 'rti' | ||||
| colormap = 'jet' | ||||
| plot_type = 'pcolorbuffer' | ||||
| def setup(self): | ||||
| self.xaxis = 'time' | ||||
| self.ncols = 1 | ||||
| self.nrows = len(self.data.channels) | ||||
| self.nplots = len(self.data.channels) | ||||
| self.ylabel = 'Range [km]' | ||||
| self.xlabel = 'Time' | ||||
| self.cb_label = 'dB' | ||||
| r1377 | self.plots_adjust.update({'hspace':0.8, 'left': 0.1, 'bottom': 0.1, 'right':0.95}) | |||
|
Juan C. Espinoza
|
r1285 | self.titles = ['{} Channel {}'.format( | ||
| self.CODE.upper(), x) for x in range(self.nrows)] | ||||
| r1343 | def update(self, dataOut): | |||
| data = {} | ||||
| meta = {} | ||||
| data['rti'] = dataOut.getPower() | ||||
| r1396 | data['noise'] = 10 * numpy.log10(dataOut.getNoise() / dataOut.normFactor) | |||
| r1343 | ||||
| return data, meta | ||||
|
Juan C. Espinoza
|
r1285 | def plot(self): | ||
| self.x = self.data.times | ||||
| r1343 | self.y = self.data.yrange | |||
|
Juan C. Espinoza
|
r1285 | self.z = self.data[self.CODE] | ||
| r1377 | ||||
| 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.zmin = self.zmin if self.zmin else numpy.min(self.z) | ||||
| self.zmax = self.zmax if self.zmax else numpy.max(self.z) | ||||
| if ax.firsttime: | ||||
| ax.plt = ax.pcolormesh(x, y, z[n].T, | ||||
| vmin=self.zmin, | ||||
| vmax=self.zmax, | ||||
| cmap=plt.get_cmap(self.colormap) | ||||
| ) | ||||
| if self.showprofile: | ||||
| ax.plot_profile = self.pf_axes[n].plot( | ||||
| self.data['rti'][n][-1], self.y)[0] | ||||
| ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(self.data['noise'][n][-1], len(self.y)), self.y, | ||||
| color="k", linestyle="dashed", lw=1)[0] | ||||
| else: | ||||
| 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) | ||||
| ) | ||||
| if self.showprofile: | ||||
| ax.plot_profile.set_data(self.data['rti'][n][-1], self.y) | ||||
| ax.plot_noise.set_data(numpy.repeat( | ||||
| self.data['noise'][n][-1], len(self.y)), self.y) | ||||
| class SpectrogramPlot(Plot): | ||||
| ''' | ||||
| Plot for Spectrogram data | ||||
| ''' | ||||
| CODE = 'spectrogram' | ||||
| colormap = 'binary' | ||||
| plot_type = 'pcolorbuffer' | ||||
| def setup(self): | ||||
| self.xaxis = 'time' | ||||
| self.ncols = 1 | ||||
| self.nrows = len(self.data.channels) | ||||
| self.nplots = len(self.data.channels) | ||||
| self.xlabel = 'Time' | ||||
| self.cb_label = 'dB' | ||||
| self.plots_adjust.update({'hspace':1.2, 'left': 0.1, 'bottom': 0.12, 'right':0.95}) | ||||
| self.titles = ['{} Channel {} \n H = {} km ({} - {})'.format( | ||||
| self.CODE.upper(), x, self.data.heightList[self.data.hei], self.data.heightList[self.data.hei],self.data.heightList[self.data.hei]+(self.data.DH*self.data.nProfiles)) for x in range(self.nrows)] | ||||
| def plot(self): | ||||
| r1381 | ||||
| r1377 | self.x = self.data.times | |||
| self.z = self.data[self.CODE] | ||||
| self.y = self.data.xrange[0] | ||||
| r1381 | ||||
| r1377 | self.ylabel = "Frequency (kHz)" | |||
|
Juan C. Espinoza
|
r1285 | 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) | ||||
|
ebocanegra
|
r965 | else: | ||
|
Juan C. Espinoza
|
r1285 | x, y, z = self.fill_gaps(*self.decimate()) | ||
| for n, ax in enumerate(self.axes): | ||||
| self.zmin = self.zmin if self.zmin else numpy.min(self.z) | ||||
| self.zmax = self.zmax if self.zmax else numpy.max(self.z) | ||||
| r1343 | data = self.data[-1] | |||
|
Juan C. Espinoza
|
r1285 | if ax.firsttime: | ||
| ax.plt = ax.pcolormesh(x, y, z[n].T, | ||||
| vmin=self.zmin, | ||||
| vmax=self.zmax, | ||||
| cmap=plt.get_cmap(self.colormap) | ||||
| ) | ||||
| if self.showprofile: | ||||
| ax.plot_profile = self.pf_axes[n].plot( | ||||
| r1343 | data['rti'][n], self.y)[0] | |||
| ax.plot_noise = self.pf_axes[n].plot(numpy.repeat(data['noise'][n], len(self.y)), self.y, | ||||
|
Juan C. Espinoza
|
r1285 | color="k", linestyle="dashed", lw=1)[0] | ||
| else: | ||||
| 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) | ||||
| ) | ||||
| if self.showprofile: | ||||
| r1343 | ax.plot_profile.set_data(data['rti'][n], self.y) | |||
|
Juan C. Espinoza
|
r1285 | ax.plot_noise.set_data(numpy.repeat( | ||
| r1343 | data['noise'][n], len(self.y)), self.y) | |||
|
Juan C. Espinoza
|
r1285 | |||
| class CoherencePlot(RTIPlot): | ||||
| ''' | ||||
| Plot for Coherence data | ||||
| ''' | ||||
| CODE = 'coh' | ||||
| def setup(self): | ||||
| self.xaxis = 'time' | ||||
| self.ncols = 1 | ||||
| self.nrows = len(self.data.pairs) | ||||
| self.nplots = len(self.data.pairs) | ||||
| self.ylabel = 'Range [km]' | ||||
| self.xlabel = 'Time' | ||||
| r1396 | self.plots_adjust.update({'hspace':0.6, 'left': 0.1, 'bottom': 0.1, 'right':0.95}) | |||
|
Juan C. Espinoza
|
r1285 | if self.CODE == 'coh': | ||
| self.cb_label = '' | ||||
| self.titles = [ | ||||
| 'Coherence Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] | ||||
|
Miguel Valdez
|
r825 | else: | ||
|
Juan C. Espinoza
|
r1285 | self.cb_label = 'Degrees' | ||
| self.titles = [ | ||||
| 'Phase Map Ch{} * Ch{}'.format(x[0], x[1]) for x in self.data.pairs] | ||||
|
Juan C. Valdez
|
r858 | |||
| r1343 | def update(self, dataOut): | |||
| data = {} | ||||
| meta = {} | ||||
| data['coh'] = dataOut.getCoherence() | ||||
| meta['pairs'] = dataOut.pairsList | ||||
| return data, meta | ||||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | class PhasePlot(CoherencePlot): | ||
| ''' | ||||
| Plot for Phase map data | ||||
| ''' | ||||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | CODE = 'phase' | ||
| colormap = 'seismic' | ||||
|
Juan C. Valdez
|
r858 | |||
| r1343 | def update(self, dataOut): | |||
| data = {} | ||||
| meta = {} | ||||
| data['phase'] = dataOut.getCoherence(phase=True) | ||||
| meta['pairs'] = dataOut.pairsList | ||||
| return data, meta | ||||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | class NoisePlot(Plot): | ||
| ''' | ||||
| r1377 | Plot for noise | |||
|
Juan C. Espinoza
|
r1285 | ''' | ||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | CODE = 'noise' | ||
| plot_type = 'scatterbuffer' | ||||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | def setup(self): | ||
| self.xaxis = 'time' | ||||
| self.ncols = 1 | ||||
| self.nrows = 1 | ||||
| self.nplots = 1 | ||||
| self.ylabel = 'Intensity [dB]' | ||||
|
Juan C. Espinoza
|
r1308 | self.xlabel = 'Time' | ||
|
Juan C. Espinoza
|
r1285 | self.titles = ['Noise'] | ||
| self.colorbar = False | ||||
| r1343 | self.plots_adjust.update({'right': 0.85 }) | |||
| def update(self, dataOut): | ||||
| data = {} | ||||
| meta = {} | ||||
| r1396 | data['noise'] = 10 * numpy.log10(dataOut.getNoise() / dataOut.normFactor).reshape(dataOut.nChannels, 1) | |||
| r1343 | meta['yrange'] = numpy.array([]) | |||
| return data, meta | ||||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | def plot(self): | ||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | x = self.data.times | ||
| xmin = self.data.min_time | ||||
| xmax = xmin + self.xrange * 60 * 60 | ||||
| r1343 | Y = self.data['noise'] | |||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | if self.axes[0].firsttime: | ||
| r1343 | self.ymin = numpy.nanmin(Y) - 5 | |||
| self.ymax = numpy.nanmax(Y) + 5 | ||||
|
Juan C. Espinoza
|
r1285 | for ch in self.data.channels: | ||
| y = Y[ch] | ||||
| self.axes[0].plot(x, y, lw=1, label='Ch{}'.format(ch)) | ||||
| r1343 | plt.legend(bbox_to_anchor=(1.18, 1.0)) | |||
Julio Valdez
|
r832 | else: | ||
|
Juan C. Espinoza
|
r1285 | for ch in self.data.channels: | ||
| y = Y[ch] | ||||
| self.axes[0].lines[ch].set_data(x, y) | ||||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | self.ymin = numpy.nanmin(Y) - 5 | ||
| r1377 | self.ymax = numpy.nanmax(Y) + 10 | |||
|
Daniel Valdez
|
r487 | |||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | class PowerProfilePlot(Plot): | ||
|
Juan C. Valdez
|
r858 | |||
| r1343 | CODE = 'pow_profile' | |||
|
Juan C. Espinoza
|
r1285 | plot_type = 'scatter' | ||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | def setup(self): | ||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | self.ncols = 1 | ||
| self.nrows = 1 | ||||
| self.nplots = 1 | ||||
| self.height = 4 | ||||
| self.width = 3 | ||||
| self.ylabel = 'Range [km]' | ||||
| self.xlabel = 'Intensity [dB]' | ||||
| self.titles = ['Power Profile'] | ||||
| self.colorbar = False | ||||
|
Juan C. Valdez
|
r858 | |||
| r1343 | def update(self, dataOut): | |||
| data = {} | ||||
| meta = {} | ||||
| data[self.CODE] = dataOut.getPower() | ||||
| return data, meta | ||||
|
Juan C. Espinoza
|
r1285 | def plot(self): | ||
|
Juan C. Valdez
|
r858 | |||
| r1343 | y = self.data.yrange | |||
|
Juan C. Espinoza
|
r1285 | self.y = y | ||
|
Juan C. Valdez
|
r858 | |||
| r1343 | x = self.data[-1][self.CODE] | |||
George Yong
|
r1171 | |||
| r1396 | if self.xmin is None: self.xmin = numpy.nanmin(x) * 0.9 | |||
| if self.xmax is None: self.xmax = numpy.nanmax(x) * 1.1 | ||||
|
Juan C. Espinoza
|
r1285 | if self.axes[0].firsttime: | ||
| for ch in self.data.channels: | ||||
| self.axes[0].plot(x[ch], y, lw=1, label='Ch{}'.format(ch)) | ||||
| plt.legend() | ||||
|
Miguel Valdez
|
r777 | else: | ||
|
Juan C. Espinoza
|
r1285 | for ch in self.data.channels: | ||
| self.axes[0].lines[ch].set_data(x[ch], y) | ||||
| class SpectraCutPlot(Plot): | ||||
| CODE = 'spc_cut' | ||||
| plot_type = 'scatter' | ||||
| buffering = False | ||||
| def setup(self): | ||||
| self.nplots = len(self.data.channels) | ||||
| self.ncols = int(numpy.sqrt(self.nplots) + 0.9) | ||||
| self.nrows = int((1.0 * self.nplots / self.ncols) + 0.9) | ||||
| self.width = 3.4 * self.ncols + 1.5 | ||||
| self.height = 3 * self.nrows | ||||
| self.ylabel = 'Power [dB]' | ||||
| self.colorbar = False | ||||
| self.plots_adjust.update({'left':0.1, 'hspace':0.3, 'right': 0.75, 'bottom':0.08}) | ||||
| r1343 | def update(self, dataOut): | |||
| data = {} | ||||
| meta = {} | ||||
| r1396 | spc = 10 * numpy.log10(dataOut.data_pre[0] / dataOut.normFactor) | |||
| r1343 | data['spc'] = spc | |||
| r1396 | meta['xrange'] = (dataOut.getFreqRange(1) / 1000., dataOut.getAcfRange(1), dataOut.getVelRange(1)) | |||
|
Danny Scipión
|
r1358 | if self.CODE == 'cut_gaussian_fit': | ||
| r1396 | data['gauss_fit0'] = 10 * numpy.log10(dataOut.GaussFit0 / dataOut.normFactor) | |||
| data['gauss_fit1'] = 10 * numpy.log10(dataOut.GaussFit1 / dataOut.normFactor) | ||||
| r1343 | return data, meta | |||
|
Juan C. Espinoza
|
r1285 | def plot(self): | ||
| if self.xaxis == "frequency": | ||||
| x = self.data.xrange[0][1:] | ||||
| self.xlabel = "Frequency (kHz)" | ||||
| elif self.xaxis == "time": | ||||
| x = self.data.xrange[1] | ||||
| self.xlabel = "Time (ms)" | ||||
|
Daniel Valdez
|
r487 | else: | ||
|
Danny Scipión
|
r1358 | x = self.data.xrange[2][:-1] | ||
| self.xlabel = "Velocity (m/s)" | ||||
| if self.CODE == 'cut_gaussian_fit': | ||||
| x = self.data.xrange[2][:-1] | ||||
|
Juan C. Espinoza
|
r1285 | self.xlabel = "Velocity (m/s)" | ||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | self.titles = [] | ||
|
Juan C. Valdez
|
r858 | |||
| r1343 | y = self.data.yrange | |||
|
Danny Scipión
|
r1358 | data = self.data[-1] | ||
| z = data['spc'] | ||||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | if self.height_index: | ||
| index = numpy.array(self.height_index) | ||||
|
Daniel Valdez
|
r487 | else: | ||
| r1396 | index = numpy.arange(0, len(y), int((len(y)) / 9)) | |||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1285 | for n, ax in enumerate(self.axes): | ||
|
Danny Scipión
|
r1358 | if self.CODE == 'cut_gaussian_fit': | ||
| gau0 = data['gauss_fit0'] | ||||
| gau1 = data['gauss_fit1'] | ||||
|
Juan C. Espinoza
|
r1285 | if ax.firsttime: | ||
| self.xmax = self.xmax if self.xmax else numpy.nanmax(x) | ||||
| self.xmin = self.xmin if self.xmin else -self.xmax | ||||
| self.ymin = self.ymin if self.ymin else numpy.nanmin(z) | ||||
| self.ymax = self.ymax if self.ymax else numpy.nanmax(z) | ||||
|
Danny Scipión
|
r1358 | 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()) | ||||
| 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()) | ||||
|
Juan C. Espinoza
|
r1285 | labels = ['Range = {:2.1f}km'.format(y[i]) for i in index] | ||
| self.figures[0].legend(ax.plt, labels, loc='center right') | ||||
| else: | ||||
| for i, line in enumerate(ax.plt): | ||||
|
Danny Scipión
|
r1358 | line.set_data(x, z[n, :, index[i]].T) | ||
| for i, line in enumerate(ax.plt_gau0): | ||||
| line.set_data(x, gau0[n, :, index[i]].T) | ||||
| line.set_color(ax.plt[i].get_color()) | ||||
| for i, line in enumerate(ax.plt_gau1): | ||||
| line.set_data(x, gau1[n, :, index[i]].T) | ||||
| line.set_color(ax.plt[i].get_color()) | ||||
|
Juan C. Espinoza
|
r1285 | self.titles.append('CH {}'.format(n)) | ||
|
Juan C. Valdez
|
r858 | |||
|
George Yong
|
r1171 | |||
|
Juan C. Espinoza
|
r1285 | class BeaconPhase(Plot): | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | __isConfig = None | ||
| __nsubplots = None | ||||
| PREFIX = 'beacon_phase' | ||||
|
Juan C. Valdez
|
r858 | |||
|
George Yong
|
r1179 | def __init__(self): | ||
|
Juan C. Espinoza
|
r1285 | Plot.__init__(self) | ||
| r1396 | self.timerange = 24 * 60 * 60 | |||
|
Miguel Valdez
|
r760 | self.isConfig = False | ||
|
Daniel Valdez
|
r494 | self.__nsubplots = 1 | ||
| self.counter_imagwr = 0 | ||||
|
Miguel Valdez
|
r760 | self.WIDTH = 800 | ||
| self.HEIGHT = 400 | ||||
|
Daniel Valdez
|
r494 | self.WIDTHPROF = 120 | ||
| self.HEIGHTPROF = 0 | ||||
| self.xdata = None | ||||
| self.ydata = None | ||||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r573 | self.PLOT_CODE = BEACON_CODE | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | self.FTP_WEI = None | ||
| self.EXP_CODE = None | ||||
| self.SUB_EXP_CODE = None | ||||
| self.PLOT_POS = None | ||||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | self.filename_phase = None | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | self.figfile = None | ||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r568 | self.xmin = None | ||
| self.xmax = None | ||||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | def getSubplots(self): | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | ncol = 1 | ||
| nrow = 1 | ||||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | return nrow, ncol | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | def setup(self, id, nplots, wintitle, showprofile=True, show=True): | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | self.__showprofile = showprofile | ||
| self.nplots = nplots | ||||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | ncolspan = 7 | ||
| colspan = 6 | ||||
| self.__nsubplots = 2 | ||||
|
Juan C. Valdez
|
r858 | |||
| r1396 | self.createFigure(id=id, | |||
| wintitle=wintitle, | ||||
| widthplot=self.WIDTH + self.WIDTHPROF, | ||||
| heightplot=self.HEIGHT + self.HEIGHTPROF, | ||||
|
Daniel Valdez
|
r494 | show=show) | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | nrow, ncol = self.getSubplots() | ||
|
Juan C. Valdez
|
r858 | |||
| r1396 | self.addAxes(nrow, ncol * ncolspan, 0, 0, colspan, 1) | |||
|
Daniel Valdez
|
r494 | |||
| def save_phase(self, filename_phase): | ||||
| r1396 | f = open(filename_phase, 'w+') | |||
|
Daniel Valdez
|
r494 | f.write('\n\n') | ||
| f.write('JICAMARCA RADIO OBSERVATORY - Beacon Phase \n') | ||||
| r1396 | f.write('DD MM YYYY HH MM SS pair(2,0) pair(2,1) pair(2,3) pair(2,4)\n\n') | |||
|
Daniel Valdez
|
r494 | f.close() | ||
| def save_data(self, filename_phase, data, data_datetime): | ||||
| r1396 | f = open(filename_phase, 'a') | |||
|
Daniel Valdez
|
r494 | timetuple_data = data_datetime.timetuple() | ||
| day = str(timetuple_data.tm_mday) | ||||
| month = str(timetuple_data.tm_mon) | ||||
| year = str(timetuple_data.tm_year) | ||||
| hour = str(timetuple_data.tm_hour) | ||||
| minute = str(timetuple_data.tm_min) | ||||
| second = str(timetuple_data.tm_sec) | ||||
| r1396 | f.write(day + ' ' + month + ' ' + year + ' ' + hour + ' ' + minute + ' ' + second + ' ' + str(data[0]) + ' ' + str(data[1]) + ' ' + str(data[2]) + ' ' + str(data[3]) + '\n') | |||
|
Daniel Valdez
|
r494 | f.close() | ||
|
Juan C. Espinoza
|
r1285 | def plot(self): | ||
| log.warning('TODO: Not yet implemented...') | ||||
|
Daniel Valdez
|
r494 | |||
| def run(self, dataOut, id, wintitle="", pairsList=None, showprofile='True', | ||||
|
Miguel Valdez
|
r760 | xmin=None, xmax=None, ymin=None, ymax=None, hmin=None, hmax=None, | ||
|
Daniel Valdez
|
r494 | timerange=None, | ||
|
Miguel Valdez
|
r568 | save=False, figpath='./', figfile=None, show=True, ftp=False, wr_period=1, | ||
|
Daniel Valdez
|
r494 | server=None, folder=None, username=None, password=None, | ||
| ftp_wei=0, exp_code=0, sub_exp_code=0, plot_pos=0): | ||||
|
Juan C. Valdez
|
r858 | |||
| r1377 | if dataOut.flagNoData: | |||
|
George Yong
|
r1171 | return dataOut | ||
|
Miguel Valdez
|
r760 | if not isTimeInHourRange(dataOut.datatime, xmin, xmax): | ||
| return | ||||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | if pairsList == None: | ||
|
Miguel Valdez
|
r760 | pairsIndexList = dataOut.pairsIndexList[:10] | ||
|
Daniel Valdez
|
r494 | else: | ||
| pairsIndexList = [] | ||||
| for pair in pairsList: | ||||
| if pair not in dataOut.pairsList: | ||||
| r1396 | raise ValueError("Pair %s is not in dataOut.pairsList" % (pair)) | |||
|
Daniel Valdez
|
r494 | pairsIndexList.append(dataOut.pairsList.index(pair)) | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | if pairsIndexList == []: | ||
| return | ||||
|
Juan C. Valdez
|
r858 | |||
|
Juan C. Espinoza
|
r1207 | # if len(pairsIndexList) > 4: | ||
| # pairsIndexList = pairsIndexList[0:4] | ||||
|
Miguel Valdez
|
r760 | |||
| hmin_index = None | ||||
| hmax_index = None | ||||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r760 | if hmin != None and hmax != None: | ||
| indexes = numpy.arange(dataOut.nHeights) | ||||
| hmin_list = indexes[dataOut.heightList >= hmin] | ||||
| hmax_list = indexes[dataOut.heightList <= hmax] | ||||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r760 | if hmin_list.any(): | ||
| hmin_index = hmin_list[0] | ||||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r760 | if hmax_list.any(): | ||
| r1396 | hmax_index = hmax_list[-1] + 1 | |||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | x = dataOut.getTimeRange() | ||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r760 | thisDatetime = dataOut.datatime | ||
|
Juan C. Valdez
|
r858 | |||
| r1396 | title = wintitle + " Signal Phase" # : %s" %(thisDatetime.strftime("%d-%b-%Y")) | |||
|
Daniel Valdez
|
r494 | xlabel = "Local Time" | ||
|
Miguel Valdez
|
r760 | ylabel = "Phase (degrees)" | ||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r760 | update_figfile = False | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | nplots = len(pairsIndexList) | ||
| r1396 | # phase = numpy.zeros((len(pairsIndexList),len(dataOut.beacon_heiIndexList))) | |||
|
Daniel Valdez
|
r494 | phase_beacon = numpy.zeros(len(pairsIndexList)) | ||
| for i in range(nplots): | ||||
| pair = dataOut.pairsList[pairsIndexList[i]] | ||||
|
Miguel Valdez
|
r760 | ccf = numpy.average(dataOut.data_cspc[pairsIndexList[i], :, hmin_index:hmax_index], axis=0) | ||
| powa = numpy.average(dataOut.data_spc[pair[0], :, hmin_index:hmax_index], axis=0) | ||||
| powb = numpy.average(dataOut.data_spc[pair[1], :, hmin_index:hmax_index], axis=0) | ||||
| r1396 | avgcoherenceComplex = ccf / numpy.sqrt(powa * powb) | |||
| phase = numpy.arctan2(avgcoherenceComplex.imag, avgcoherenceComplex.real) * 180 / numpy.pi | ||||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r760 | if dataOut.beacon_heiIndexList: | ||
| phase_beacon[i] = numpy.average(phase[dataOut.beacon_heiIndexList]) | ||||
| else: | ||||
| phase_beacon[i] = numpy.average(phase) | ||||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r760 | if not self.isConfig: | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | nplots = len(pairsIndexList) | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | self.setup(id=id, | ||
| nplots=nplots, | ||||
| wintitle=wintitle, | ||||
| showprofile=showprofile, | ||||
| show=show) | ||||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r568 | if timerange != None: | ||
| self.timerange = timerange | ||||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r568 | self.xmin, self.xmax = self.getTimeLim(x, xmin, xmax, timerange) | ||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r760 | if ymin == None: ymin = 0 | ||
| if ymax == None: ymax = 360 | ||||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | self.FTP_WEI = ftp_wei | ||
| self.EXP_CODE = exp_code | ||||
| self.SUB_EXP_CODE = sub_exp_code | ||||
| self.PLOT_POS = plot_pos | ||||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | self.name = thisDatetime.strftime("%Y%m%d_%H%M%S") | ||
|
Miguel Valdez
|
r760 | self.isConfig = True | ||
|
Daniel Valdez
|
r494 | self.figfile = figfile | ||
| self.xdata = numpy.array([]) | ||||
| self.ydata = numpy.array([]) | ||||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r760 | update_figfile = True | ||
|
Juan C. Valdez
|
r858 | |||
| r1396 | # open file beacon phase | |||
| path = '%s%03d' % (self.PREFIX, self.id) | ||||
| beacon_file = os.path.join(path, '%s.txt' % self.name) | ||||
| self.filename_phase = os.path.join(figpath, beacon_file) | ||||
| # self.save_phase(self.filename_phase) | ||||
|
Juan C. Valdez
|
r858 | |||
| r1396 | # store data beacon phase | |||
| # self.save_data(self.filename_phase, phase_beacon, thisDatetime) | ||||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | self.setWinTitle(title) | ||
|
Juan C. Valdez
|
r858 | |||
| r1396 | title = "Phase Plot %s" % (thisDatetime.strftime("%Y/%m/%d %H:%M:%S")) | |||
|
Juan C. Valdez
|
r858 | |||
| r1396 | legendlabels = ["Pair (%d,%d)" % (pair[0], pair[1]) for pair in dataOut.pairsList] | |||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | axes = self.axesList[0] | ||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | self.xdata = numpy.hstack((self.xdata, x[0:1])) | ||
|
Juan C. Valdez
|
r858 | |||
| r1396 | if len(self.ydata) == 0: | |||
| self.ydata = phase_beacon.reshape(-1, 1) | ||||
|
Daniel Valdez
|
r494 | else: | ||
| r1396 | self.ydata = numpy.hstack((self.ydata, phase_beacon.reshape(-1, 1))) | |||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | axes.pmultilineyaxis(x=self.xdata, y=self.ydata, | ||
|
Miguel Valdez
|
r568 | xmin=self.xmin, xmax=self.xmax, ymin=ymin, ymax=ymax, | ||
|
Daniel Valdez
|
r494 | xlabel=xlabel, ylabel=ylabel, title=title, legendlabels=legendlabels, marker='x', markersize=8, linestyle="solid", | ||
| XAxisAsTime=True, grid='both' | ||||
| ) | ||||
|
Juan C. Valdez
|
r858 | |||
|
Daniel Valdez
|
r494 | self.draw() | ||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r760 | if dataOut.ltctime >= self.xmax: | ||
|
Daniel Valdez
|
r494 | self.counter_imagwr = wr_period | ||
|
Miguel Valdez
|
r760 | self.isConfig = False | ||
| update_figfile = True | ||||
|
Juan C. Valdez
|
r858 | |||
|
Miguel Valdez
|
r573 | self.save(figpath=figpath, | ||
| figfile=figfile, | ||||
| save=save, | ||||
| ftp=ftp, | ||||
| wr_period=wr_period, | ||||
| thisDatetime=thisDatetime, | ||||
|
George Yong
|
r1171 | update_figfile=update_figfile) | ||
| r1377 | return dataOut | |||
