Hi everyone, For certain input arrays with small variation the method np.histogram with automatic binning can select an enormous amount of bins and return with an out-of-memory error. A minimal example:
import numpy as np e = 1 + 1e-12 Z = [0,1,1,1,1,1,e,e,e,e,e,e, 2] np.histogram(Z, bins="auto") There is a proposal to change the automatic bin selection to avoid this: https://github.com/numpy/numpy/pull/28426. The aim is to keep close to the original algorithm, but avoid the out-of-memory issues for input with small variance. It passes unit tests, but since this is a user visible change we would like some more input. In particular: * What are expectations of the auto binning algorithm? * What is a reasonable maximum number of bins for a sample of size n? With kind regards, Pieter Eendebak _______________________________________________ NumPy-Discussion mailing list -- numpy-discussion@python.org To unsubscribe send an email to numpy-discussion-le...@python.org https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ Member address: arch...@mail-archive.com
