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LALInference 4.1.9.1-6c6b863
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cbcBayesCombinePosteriors.py File Reference

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Namespaces

namespace  cbcBayesCombinePosteriors
 

Variables

string cbcBayesCombinePosteriors.posterior_grp_name = "posterior_samples"
 
 cbcBayesCombinePosteriors.parser = argparse.ArgumentParser(description="Combine some posterior samples.")
 
 cbcBayesCombinePosteriors.dest
 
 cbcBayesCombinePosteriors.default
 
 cbcBayesCombinePosteriors.help
 
 cbcBayesCombinePosteriors.metavar
 
 cbcBayesCombinePosteriors.action
 
 cbcBayesCombinePosteriors.required
 
 cbcBayesCombinePosteriors.type
 
 cbcBayesCombinePosteriors.shuffleGroup = parser.add_mutually_exclusive_group()
 
 cbcBayesCombinePosteriors.fileGroup = parser.add_mutually_exclusive_group()
 
 cbcBayesCombinePosteriors.args = parser.parse_args()
 
 cbcBayesCombinePosteriors.nPos = np.size(args.infilename)
 
 cbcBayesCombinePosteriors.nWeight = np.size(args.weightings)
 
 cbcBayesCombinePosteriors.weightings
 
string cbcBayesCombinePosteriors.combineID = "combined"
 
list cbcBayesCombinePosteriors.samples = []
 
list cbcBayesCombinePosteriors.paramsList = []
 
list cbcBayesCombinePosteriors.sizeList = []
 
dictionary cbcBayesCombinePosteriors.metadata
 
 cbcBayesCombinePosteriors.group = inFile["lalinference"]
 
 cbcBayesCombinePosteriors.run_id = list(group.keys())[0]
 
list cbcBayesCombinePosteriors.posDtype = []
 
 cbcBayesCombinePosteriors.shape = group[key].shape
 
 cbcBayesCombinePosteriors.posData = np.empty(shape, dtype=posDtype)
 
 cbcBayesCombinePosteriors.paramsOut = list(set.intersection(*paramsList))
 
list cbcBayesCombinePosteriors.datatypes = samples[0][paramsOut].dtype
 
 cbcBayesCombinePosteriors.sizeOut = sum(sizeList)
 
 cbcBayesCombinePosteriors.samplesOut = np.empty(sizeOut, dtype=datatypes)
 
list cbcBayesCombinePosteriors.indexSize = sizeList
 
 cbcBayesCombinePosteriors.fracWeight = np.asarray(args.weightings) / float(sum(args.weightings))
 
 cbcBayesCombinePosteriors.testNum = fracWeight * float(sum(sizeList))
 
 cbcBayesCombinePosteriors.minIndex = np.argmin(np.asarray(sizeList) / np.asarray(testNum))
 
list cbcBayesCombinePosteriors.testSize = sizeList[minIndex] / fracWeight[minIndex]
 
 cbcBayesCombinePosteriors.weightNum = np.around(fracWeight * testSize).astype(int)
 
int cbcBayesCombinePosteriors.startIndex = 0
 
int cbcBayesCombinePosteriors.stopIndex = startIndex + indexSize[posIndex]
 
 cbcBayesCombinePosteriors.key
 
 cbcBayesCombinePosteriors.data
 
 cbcBayesCombinePosteriors.shuffle
 
 cbcBayesCombinePosteriors.True
 
 cbcBayesCombinePosteriors.compression
 
string cbcBayesCombinePosteriors.paramHeader = "\t".join(paramsOut)
 
 cbcBayesCombinePosteriors.T
 
 cbcBayesCombinePosteriors.delimiter
 
 cbcBayesCombinePosteriors.header
 
 cbcBayesCombinePosteriors.comments