corrSample = np.loadtxt('%s/corrSample%s_nSeeds%d_lagMax%dyr.txt' \
% (corrSamplePath, corrSamplePostfix, nSeeds, cfg.stat.lagMax))
lags = np.loadtxt('%s/lags%s_nSeeds%d_lagMax%dyr.txt' \
% (corrSamplePath, corrSamplePostfix, nSeeds, cfg.stat.lagMax))
powerSample = np.loadtxt('%s/powerSample%s_nSeeds%d_chunk%dyr.txt' \
% (corrSamplePath, corrSamplePostfix, nSeeds, cfg.stat.chunkWidth))
freq = np.loadtxt('%s/freq%s_nSeeds%d_chunk%dyr.txt' \
% (corrSamplePath, corrSamplePostfix, nSeeds, cfg.stat.chunkWidth))
angFreq = freq * 2*np.pi
cfg0 = ((f - (f * statDist).sum()) * statDist * (g - (g * statDist).sum())).sum()
powerSample /= 2 * np.pi * cfg0
# Reconstruct correlation and power spectrum
# Get normalized weights
weights = ergoPlot.getSpectralWeights(f, g, eigVecForward, eigVecBackward, statDist, skipMean=True)
# Remove components with heigh condition number
weights[eigenCondition > maxCondition] = 0.
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)
lags = np.loadtxt('%s/lags%s_nSeeds%d_lagMax%dyr.txt' % (corrSamplePath, corrSamplePostfix, nSeeds, lagMax))
powerSample = np.loadtxt('%s/powerSample%s_nSeeds%d_chunk%dyr.txt' % (corrSamplePath, corrSamplePostfix, nSeeds, chunkWidth))
powerSampleSTD = np.loadtxt('%s/powerSampleSTD%s_nSeeds%d_chunk%dyr.txt' % (corrSamplePath, corrSamplePostfix, nSeeds, chunkWidth))
freq = np.loadtxt('%s/freq%s_nSeeds%d_chunk%dyr.txt' % (corrSamplePath, corrSamplePostfix, nSeeds, chunkWidth))
angFreq = freq * 2*np.pi
cfg0 = ((f - (f * statDist).sum()) * statDist * (g - (g * statDist).sum())).sum()
powerSample /= 2 * np.pi * cfg0
powerSampleSTD /= 2 * np.pi * cfg0
powerSampleDown = powerSample - powerSampleSTD / 2
powerSampleUp = powerSample + powerSampleSTD / 2
# Reconstruct correlation and power spectrum
# Get normalized weights
weights = ergoPlot.getSpectralWeights(f, g, eigVecForward, eigVecBackward, statDist, skipMean=True)
# Remove components with heigh condition number
weights[eigenCondition > 5] = 0.
(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(lagMax),
nev, postfix, ergoPlot.figFormat),
dpi=ergoPlot.dpi, bbox_inches=ergoPlot.bbox_inches)
# PLot spectrum, powerSampledogram and spectral reconstruction
obsLabel = [r'$S_{x, x}$', r'$S_{x^2, x^2}$', r'$S_{x^3, x^3}$']
g = f
nObs = len(f)
angFreq = np.linspace(ylim[0], ylim[1], 1000)
weights = []
power = []
cPow = rcParams['axes.prop_cycle'].by_key()['color']
while len(cPow) < nObs:
cPow = np.concatenate((cPow, rcParams['axes.prop_cycle'].by_key()['color']))
lsPow = ['-'] * nObs
lwPow = [2] * nObs
for obs in np.arange(nObs):
# Get weights of the spectral projections of the observables
# let the forward eigenvectors carry the measure.
weightsObs = ergoPlot.getSpectralWeights(f[obs], g[obs],
eigVecForward[:, :nevRec],
eigVecBackward[:, :nevRec])
weights.append(weightsObs)
# Get power spectrum
powerObs, powerCompObs = ergoPlot.spectralRecPower(angFreq, f[obs], g[obs],
eigValForward[:nevRec],
weights[obs], norm=True,
statDist=statDist)
power.append(powerObs)
# Calculate analytical eigenvalues
if mu <= 0:
eigValAnaPoint = (J + J.T) * mu \
+ 1j * (J - J.T) * gam
if mu > 0: