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,