(powerRec, compPowerRec) = ergoPlot.spectralRecPower(angFreq, f, g, eigValGen, weights, statDist, norm=True) # Plot correlation reconstruction ergoPlot.plotRecCorrelation(lags, corrSample, corrRec, plotPositive=True, ylabel=corrLabel, xlabel=xlabelCorr) plt.savefig('%s/spectrum/reconstruction/%sRec_lag%d_nev%d%s.%s'\ % (cfg.general.plotDir, corrName, int(cfg.stat.lagMax), nev, postfix, ergoPlot.figFormat), dpi=ergoPlot.dpi, bbox_inches=ergoPlot.bbox_inches) # PLot spectrum, powerSampledogram and spectral reconstruction weights /= cfg0 msize = np.zeros((weights.shape[0])) msize[weights.real > 0] = np.log10(weights[weights.real > 0].real) msize[weights.real > 0] = (msize[weights.real > 0] + 8) * 10 # msize[weights.real > 0] = (msize[weights.real > 0] + 6) * 3 msize[msize < 0] = 0. ergoPlot.plotEigPowerRec(angFreq, eigValGen, powerSample, powerRec, markersize=msize, condition=condition, xlabel=realLabel, ylabel=imagLabel, zlabel=powerLabel, xlim=xlimEig, ylim=ylimEig, zlim=zlimEig, xticks=xticks, yticks=yticks, zticks=zticks) plt.savefig('%s/spectrum/reconstruction/%sRec_chunk%d_nev%d%s.%s'\ % (cfg.general.plotDir, powerName, int(cfg.stat.chunkWidth), nev, postfix, ergoPlot.figFormat), dpi=ergoPlot.dpi, bbox_inches=ergoPlot.bbox_inches)
statDist, skipMean=True, norm=True) (powerRec, compPowerRec) = ergoPlot.spectralRecPower(angFreq, f, g, eigValGen, weights, statDist, norm=True) # Plot correlation reconstruction ergoPlot.plotRecCorrelation(lags, corrSample, corrRec, plotPositive=True, ylabel=corrLabel) plt.savefig('%s/spectrum/reconstruction/%sRec_lag%d_nev%d%s.%s'\ % (cfg.general.plotDir, corrName, int(lagMax), nev, postfix, ergoPlot.figFormat), dpi=ergoPlot.dpi, bbox_inches=ergoPlot.bbox_inches) # PLot spectrum, powerSampledogram and spectral reconstruction zmin = cfg.stat.yminPower zmax = cfg.stat.ymaxPower # PLot spectrum, powerSampledogram and spectral reconstruction msizeWeight = np.zeros((weights.shape[0])) msizeWeight[weights.real > 0] = np.log(weights[weights.real > 0].real) msizeWeight[weights.real > 0] = (msizeWeight[weights.real > 0] + 15) * 10 # msizeWeight[weights.real > 0] = (msizeWeight[weights.real > 0] + 6) * 3 msizeWeight[msizeWeight < 0] = 0. ergoPlot.plotEigPowerRec(angFreq, eigValGen, msizeWeight, powerSample, powerSampleSTD, powerRec, xlabel=realLabel, ylabel=imagLabel, zlabel=powerLabel, xlim=[xminEigVal, -xminEigVal/100], ylim=[yminEigVal, -yminEigVal], zlim=[zmin, zmax]) plt.savefig('%s/spectrum/reconstruction/%sRec_nev%d%s.%s'\ % (cfg.general.plotDir, powerName, nev, postfix, ergoPlot.figFormat), dpi=ergoPlot.dpi, bbox_inches=ergoPlot.bbox_inches)
eigenCondition[eigenCondition > maxCondition] = maxCondition (corrRec, compCorrRec) = ergoPlot.spectralRecCorrelation(lags, f, g, eigValGen, weights, statDist, skipMean=True, norm=True) (powerRec, compPowerRec) = ergoPlot.spectralRecPower(angFreq, f, g, eigValGen, weights, statDist, norm=True) # Plot correlation reconstruction ergoPlot.plotRecCorrelation(lags, corrSample, corrRec, plotPositive=True, ylabel=corrLabel) plt.savefig('%s/spectrum/reconstruction/%sRec_lag%d_nev%d%s.%s'\ % (cfg.general.plotDir, corrName, int(cfg.stat.lagMax), nev, postfix, ergoPlot.figFormat), dpi=ergoPlot.dpi, bbox_inches=ergoPlot.bbox_inches) # PLot spectrum, powerSampledogram and spectral reconstruction msize = np.zeros((weights.shape[0])) msize[weights.real > 0] = np.log(weights[weights.real > 0].real) msize[weights.real > 0] = (msize[weights.real > 0] + 15) * 10 # msize[weights.real > 0] = (msize[weights.real > 0] + 6) * 3 msize[msize < 0] = 0. #msize = 20 ergoPlot.plotEigPowerRec(angFreq, eigValGen, powerSample, powerRec, markersize=msize, condition=eigenCondition, xlabel=realLabel, ylabel=imagLabel, zlabel=powerLabel, xlim=xlimEig, ylim=ylimEig, zlim=zlimEig, xticks=xticks, yticks=yticks, zticks=zticks) plt.savefig('%s/spectrum/reconstruction/%sRec_nev%d%s.%s'\ % (cfg.general.plotDir, powerName, nev, postfix, ergoPlot.figFormat), dpi=ergoPlot.dpi, bbox_inches=ergoPlot.bbox_inches)