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()