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)