optimal_bins

rubin_sim.maf.utils.optimal_bins(datain, binmin=None, binmax=None, nbin_max=200, nbin_min=1)

Set an ‘optimal’ number of bins using the Freedman-Diaconis rule.

Parameters:

datainnumpy.ndarray or numpy.ma.MaskedArray

The data for which we want to set the bin_size.

binminfloat

The minimum bin value to consider (if None, uses minimum data value).

binmaxfloat

The maximum bin value to consider (if None, uses maximum data value).

nbin_maxint

The maximum number of bins to create. Sometimes the ‘optimal bin_size’ implies an unreasonably large number of bins, if the data distribution is unusual.

nbin_minint

The minimum number of bins to create. Default is 1.

Returns:

int

The number of bins.