def histogram(self):
"""Return a plain histogram by converting all sub-aggregator values into Counts"""
out = CentrallyBin([c for c, v in self.bins], self.quantity, Count(),
self.nanflow.copy())
out.entries = self.entries
for i, v in self.bins:
out.bins[i] = Count.ed(v.entries)
return out.specialize()
def histogram(self):
"""Return a plain histogram by converting all sub-aggregator values into Counts"""
out = SparselyBin(self.binWidth, self.quantity, Count(),
self.nanflow.copy(), self.origin)
out.entries = float(self.entries)
out.contentType = "Count"
for i, v in self.bins.items():
out.bins[i] = Count.ed(v.entries)
return out.specialize()
def build(*ys):
"""Create a Stack out of pre-existing containers, which might have been aggregated on different streams.
Parameters:
aggregators (list of :doc:`Container <histogrammar.defs.Container>`): this function will attempt to add
them, so they must also have the same binning/bounds/etc.
"""
from functools import reduce
if not all(isinstance(y, Container) for y in ys):
raise TypeError("ys must all be Containers")
entries = sum(y.entries for y in ys)
bins = []
for i in xrange(len(ys)):
bins.append((float("nan"), reduce(lambda a, b: a + b, ys[i:])))
return Stack.ed(entries, bins, Count.ed(0.0))