def PlotGapDist():
with dedata.dEData('./f0postProcess.cfg') as data:
print "Running with {}...".format(type(data))
sidechain = data.GetModes_hdf()
print "Plotting..."
legend = None
for i in xrange(1,10):
subsamples = pca.GetSubsamples(sidechain[:,0,i], .05, 10)
print subsamples.shape
print subsamples[0,:]
pca.Plot1DHist(subsamples, legend=legend, displace_by=0.0)
pca_h5file = config.get('sidechain','pcafile')
sc_file = h5py.File(sc_h5file)
print sc_file.keys()
sc_ds = sc_file[time_h5tag]
stat_file = h5py.File(h5stats)
print "Loading covariance and averages '{},{}' from hdf5 file {}...".format(h5corrtag,h5eavtag,h5stats)
corr = stat_file[h5corrtag]
Eav_ij = stat_file[h5eavtag]
print "Computing Modes..."
eigval_in, eigvec_inj, impact_in = sc.ComputeModes(corr)
print "Eigenvector dimension: {}".format(eigvec_inj.shape)
conv.ApplyPCA_hdf5(sc_ds, Eav_ij, eigvec_inj, pca_h5file, time_h5tag, site=0, overwrite=True)
sc_file.close()
stat_file.close()
if __name__ == '__main__':
with dedata.dEData('./f0postProcess.cfg') as data:
data.InitSidechain_hdf()
data.ExamineSidechain_hdf()
dat = data.GetSidechain_hdf(1)
print dat.shape
#ComputeCorrelation()
#ComputePCA()
