weight = 1. allvalues.append(value) allweights.append(weight) allmontevales.append([batch, gcm, iam]) #print filestuff, rowstuff, allvalues if len(allvalues) == 0: continue if output_format == 'edfcsv': if region == 'all': # still set from before assert 'all' in rowstuff allvalues = np.array(allvalues) for ii in range(allvalues.shape[1]): distribution = weights.WeightedECDF( allvalues[:, ii], allweights) myrowstuff = list(rowstuff) myrowstuff[rownames.index( 'region')] = config['regionorder'][ii] writer.writerow(myrowstuff + list(distribution.inverse(evalqvals))) else: distribution = weights.WeightedECDF(allvalues, allweights) writer.writerow( list(rowstuff) + list(distribution.inverse(evalqvals))) elif output_format == 'valuescsv': for ii in range(len(allvalues)): writer.writerow( list(rowstuff) + allmontevales[ii] + [allvalues[ii], allweights[ii]])
if output_format == 'edfcsv': if configs.is_allregions(config): assert 'all' in rowstuff allvalues = np.array(allvalues) if configs.is_parallel_deltamethod(config): allvariances = np.array(allvariances) for ii in range(allvalues.shape[1]): if configs.is_parallel_deltamethod(config): distribution = weights_vcv.WeightedGMCDF( allvalues[:, ii], allvariances[:, ii], allweights) else: distribution = weights.WeightedECDF( allvalues[:, ii], allweights, ignore_missing=config.get( 'ignore-missing', False)) myrowstuff = list(rowstuff) myrowstuff[rownames.index( 'region')] = config['regionorder'][ii] writer.writerow( myrowstuff + list(distribution.inverse(encoded_evalqvals))) else: if configs.is_parallel_deltamethod(config): distribution = weights_vcv.WeightedGMCDF( allvalues, allvariances, allweights) else: distribution = weights.WeightedECDF( allvalues,