def write_posterior_to_hdf(posterior_path, headers, posterior, metadata, run_identifier): from lalinference.io import write_samples write_samples(posterior, posterior_path, path='/'.join([ '', 'lalinference', run_identifier, 'posterior_samples' ]), metadata=metadata)
parser.add_option( '-b','--fixedBurnin',dest='fixedBurnin',action="callback", callback=multipleFileCB,help='Fixed number of iteration for burnin.') opts, args = parser.parse_args() datafiles=[] if args: datafiles = datafiles + args if opts.data: datafiles = datafiles + opts.data if opts.fixedBurnin: # If only one value for multiple chains, assume it's to be applied to all chains if len(opts.fixedBurnin) == 1: fixedBurnins = [int(opts.fixedBurnin[0]) for df in datafiles] else: fixedBurnins = [int(fixedBurnin) for fixedBurnin in opts.fixedBurnin] else: fixedBurnins = [None]*len(datafiles) chain_posteriors = [] for i in range(len(datafiles)): chain_posteriors.append(downsample_and_evidence(datafiles[i], deltaLogP=opts.deltaLogP, fixedBurnin=fixedBurnins[i], nDownsample=opts.downsample, verbose=opts.verbose)) final_posterior, metadata = weight_and_combine(chain_posteriors, verbose=opts.verbose) write_samples(final_posterior, opts.pos, path='/'.join(['','lalinference','lalinference_mcmc','posterior_samples']), metadata=metadata)
def write_posterior_to_hdf(posterior_path, headers, posterior, metadata, run_identifier): from lalinference.io import write_samples write_samples(posterior, posterior_path, path='/'.join(['','lalinference',run_identifier,'posterior_samples']), metadata=metadata, overwrite=True)
verbose=opts.verbose, evidence_weighting=not opts.equal_weighting, combine_only=opts.combine_only) for path in datafiles: run_identifier = extract_metadata(path, metadata) # Remove duplicate metadata path_to_samples = '/'.join( ['', 'lalinference', run_identifier, 'posterior_samples']) if path_to_samples in metadata: for colname in final_posterior.columns: metadata[path_to_samples].pop(colname, None) # for metadata which is in a list, take the average. for level in metadata: for key in metadata[level]: #if isinstance(metadata[level][key], list) and all(isinstance(x, (int,float)) for x in metadata[level][key]): # metadata[level][key] = mean(metadata[level][key]) if isinstance(metadata[level][key], list) and all( isinstance(x, (str)) for x in metadata[level][key]): print( "Warning: only printing the first of the %d entries found for metadata %s/%s. You can find the whole list in the headers of individual hdf5 output files\n" % (len(metadata[level][key]), level, key)) metadata[level][key] = metadata[level][key][0] write_samples(final_posterior, opts.pos, path=path_to_samples, metadata=metadata)
parser.add_option( '-b','--fixedBurnin',dest='fixedBurnin',action="callback", callback=multipleFileCB,help='Fixed number of iteration for burnin.') opts, args = parser.parse_args() datafiles=[] if args: datafiles = datafiles + args if opts.data: datafiles = datafiles + opts.data if opts.fixedBurnin: # If only one value for multiple chains, assume it's to be applied to all chains if len(opts.fixedBurnin) == 1: fixedBurnins = [int(opts.fixedBurnin[0]) for df in datafiles] else: fixedBurnins = [int(fixedBurnin) for fixedBurnin in opts.fixedBurnin] else: fixedBurnins = [None]*len(datafiles) chain_posteriors = [] for i in range(len(datafiles)): chain_posteriors.append(downsample_and_evidence(datafiles[i], deltaLogP=opts.deltaLogP, fixedBurnin=fixedBurnins[i], nDownsample=opts.downsample, verbose=opts.verbose)) final_posterior, metadata = weight_and_combine(chain_posteriors, verbose=opts.verbose) write_samples(final_posterior, opts.pos, path='/'.join(['','lalinference','lalinference_mcmc','posterior_samples']), metadata=metadata)