(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)
