N = 1
for d in np.arange(dimObs):
N *= cfg.grid.nx[d]
if (hasattr(cfg.grid, 'nSTDLow') & hasattr(cfg.grid, 'nSTDHigh')):
gridPostfix = "%s_n%dl%dh%d" % (gridPostfix, cfg.grid.nx[d],
cfg.grid.nSTDLow[d], cfg.grid.nSTDHigh[d])
else:
gridPostfix = "%s_n%dminmax" % (gridPostfix, cfg.grid.nx[d])
gridPostfix = "%s%s%s" % (srcPostfix, obsName, gridPostfix)
mifName = 'mif'
mifLabel = r'$MI(t)$'
# Read ccf
print 'Reading mutual information function...'
mif = np.loadtxt('%s/mutual_information/mif_lag%d%s.txt'\
% (cfg.general.resDir, int(cfg.stat.lagMax),
gridPostfix))
lags = np.loadtxt('%s/mutual_information/lags_lag%d%s.txt'\
% (cfg.general.resDir, int(cfg.stat.lagMax),
gridPostfix))
# Plot MIF
print 'Plotting mutual information function...'
(fig, ax) = ergoPlot.plotCCF(mif, lags, ylabel=mifLabel, plotPositive=False,
ylim=[0., mif.max()])
plt.savefig('%s/plot/mutual_information/%s_lag%d%s.%s'\
% (cfg.general.resDir, mifName, int(cfg.stat.lagMax),
gridPostfix, ergoPlot.figFormat),
dpi=ergoPlot.dpi, bbox_inches=ergoPlot.bbox_inches)
lags = np.loadtxt('%s/correlation/lags_lag%d%s.txt'\
% (cfg.general.resDir, int(cfg.stat.lagMax),
srcPostfix))
perio = np.loadtxt('%s/power/%s_chunk%d%s.txt'\
% (cfg.general.resDir, powerName, int(cfg.stat.chunkWidth),
srcPostfix))
perioSTD = np.loadtxt('%s/power/%sSTD_chunk%d%s.txt' \
% (cfg.general.resDir, powerName, int(cfg.stat.chunkWidth),
srcPostfix))
freq = np.loadtxt('%s/power/freq_chunk%d%s.txt' \
% (cfg.general.resDir, cfg.stat.chunkWidth,
srcPostfix))
# Plot CCF
print 'Plotting correlation function...'
(fig, ax) = ergoPlot.plotCCF(ccf, lags, ylabel=corrLabel, plotPositive=True)
plt.savefig('%s/plot/correlation/%s_lag%d%s.%s'\
% (cfg.general.resDir, corrName, int(cfg.stat.lagMax),
srcPostfix, ergoPlot.figFormat),
dpi=ergoPlot.dpi, bbox_inches=ergoPlot.bbox_inches)
# Plot perio
print 'Plotting periodogram...'
angFreq = freq * 2 * np.pi
(fig, ax) = ergoPlot.plotPerio(perio, perioSTD=perioSTD, freq=angFreq,
ylabel=powerLabel, plotPositive=True,
absUnit='', yscale='log',
xlim=(0, cfg.stat.angFreqMax),
ylim=(cfg.stat.powerMin, cfg.stat.powerMax))
plt.savefig('%s/plot/power/%s_chunk%d%s.%s'\
% (cfg.general.resDir, powerName, int(cfg.stat.chunkWidth),
np.savetxt('%s/correlation/lags%s_lagMax%dyr.txt' \
% (cfg.general.resDir, dstPostfix,
cfg.stat.lagMax), lags)
np.savetxt('%s/power/powerSample%s_chunk%dyr.txt' \
% (cfg.general.resDir, dstPostfix,
cfg.stat.chunkWidth), powerSample)
np.savetxt('%s/power/powerSampleSTD%s_chunk%dyr.txt' \
% (cfg.general.resDir, dstPostfix,
cfg.stat.chunkWidth), powerSampleSTD)
np.savetxt('%s/power/freq%s_chunk%dyr.txt' \
% (cfg.general.resDir, dstPostfix,
cfg.stat.chunkWidth), freq)
# Plot corrSample
print 'Plotting correlation function...'
(fig, ax) = ergoPlot.plotCCF(corrSample, lags, plotPositive=True)
plt.savefig('%s/correlation/corrSample%s_lagMax%dyr.%s'\
% (cfg.general.plotDir, dstPostfix,
cfg.stat.lagMax, ergoPlot.figFormat),
dpi=ergoPlot.dpi, bbox_inches=ergoPlot.bbox_inches)
# Plot powerSample
print 'Plotting powerSampledogram...'
angFreq = freq * 2 * np.pi
(fig, ax) = ergoPlot.plotPerio(powerSample,
perioSTD=powerSampleSTD,
freq=angFreq,
plotPositive=True,
absUnit='',
yscale='log',
xlim=(0, cfg.stat.angFreqMax),
% (cfg.general.resDir, outDir, postfix, nSeeds, cfg.stat.chunkWidth),
powerSample,
)
np.savetxt(
"%s/%s/powerSampleSTD%s_nSeeds%d_chunk%dyr.txt"
% (cfg.general.resDir, outDir, postfix, nSeeds, cfg.stat.chunkWidth),
powerSampleSTD,
)
np.savetxt(
"%s/%s/freq%s_nSeeds%d_chunk%dyr.txt" % (cfg.general.resDir, outDir, postfix, nSeeds, cfg.stat.chunkWidth),
freq,
)
# Plot CORRSAMPLE
print "Plotting correlation function..."
(fig, ax) = ergoPlot.plotCCF(corrSample, lags, plotPositive=True)
plt.savefig(
"%s/%s/corrSample%s_nSeeds%d_lagMax%dyr.%s"
% (cfg.general.plotDir, outDir, postfix, nSeeds, cfg.stat.lagMax, ergoPlot.figFormat),
dpi=ergoPlot.dpi,
bbox_inches=ergoPlot.bbox_inches,
)
# Plot powerSample
print "Plotting powerSampledogram..."
angFreq = freq * 2 * np.pi
(fig, ax) = ergoPlot.plotPerio(
powerSample,
perioSTD=powerSampleSTD,
freq=angFreq,
plotPositive=True,
