def binned_to_binned(self, key, new_binning):
'''
resample a binned key into a different binning
Parameters
----------
key : str
new_binning : MultiDimBinning
the new binning
'''
logging.debug('Resampling %s' % (key))
old_binning, hist = self.binned_data[key]
sample = [
self.get_binned_data(name, old_binning)
for name in old_binning.names
]
new_sample = [
SmartArray(self.unroll_binning(name, new_binning))
for name in new_binning.names
]
hist = resample(hist, sample, old_binning, new_sample, new_binning)
self.add_binned_data(key, (new_binning, hist))
def get_binned_data(self, key, out_binning=None):
"""Get data array from binned data:
if the key is a binning dimensions, then unroll the binning
otherwise return the corresponding flattened array
"""
if out_binning is not None:
# check if key is binning dimension
if key in out_binning.names:
return self.unroll_binning(key, out_binning)
binning, data = self.binned_data[key]
if out_binning is not None:
if not binning == out_binning:
logging.warning('Automatically re-beinning data %s'%key)
sample = [SmartArray(self.unroll_binning(name, binning)) for name in binning.names]
new_sample = [SmartArray(self.unroll_binning(name, out_binning)) for name in out_binning.names]
return resample(data, sample, binning, new_sample, out_binning)
return data
def resample(self, key, src_representation, dest_representation):
"""Resample a binned key into a different binning
Parameters
----------
key : str
src_representation : MultiDimBinning
dest_representation : MultiDimBinning
"""
logging.debug('Resampling %s' % (key))
self.representation = src_representation
sample = [self[name] for name in src_representation.names]
weights = self[key]
self.representation = dest_representation
new_sample = [self[name] for name in dest_representation.names]
new_hist = resample(weights, sample, src_representation, new_sample,
dest_representation)
return new_hist