scott_bin_width¶
-
astropy.stats.
scott_bin_width
(data, return_bins=False)[source] [edit on github]¶ Return the optimal histogram bin width using Scott’s rule
Scott’s rule is a normal reference rule: it minimizes the integrated mean squared error in the bin approximation under the assumption that the data is approximately Gaussian.
Parameters: - data : array-like, ndim=1
observed (one-dimensional) data
- return_bins : bool (optional)
if True, then return the bin edges
Returns: - width : float
optimal bin width using Scott’s rule
- bins : ndarray
bin edges: returned if
return_bins
is True
See also
knuth_bin_width
,freedman_bin_width
,bayesian_blocks
,histogram
Notes
The optimal bin width is
\[\Delta_b = \frac{3.5\sigma}{n^{1/3}}\]where \(\sigma\) is the standard deviation of the data, and \(n\) is the number of data points [1].
References
[1] (1, 2) Scott, David W. (1979). “On optimal and data-based histograms”. Biometricka 66 (3): 605-610