# spacepy.toolbox.binHisto

spacepy.toolbox.binHisto(data, verbose=False)

  Calculates bin width and number of bins for histogram using Freedman-Diaconis rule, if rule fails, defaults to square-root method

  The Freedman-Diaconis method is detailed in:

    Freedman, D., and P. Diaconis (1981), On the histogram as a density estimator: L2 theory, Z. Wahrscheinlichkeitstheor. Verw. Geb., 57, 453–476

  and is also described by:

    Wilks, D. S. (2006), Statistical Methods in the Atmospheric Sciences, 2nd ed.

  Parameters
    data array_like

      list/array of data values

    verbose boolean (optional)

      print out some more information

  Returns
    out tuple

      calculated width of bins using F-D rule, number of bins (nearest integer) to use for histogram

  See also

  `matplotlib.pyplot.hist`

  Examples

      >>> import numpy, spacepy
      >>> import matplotlib.pyplot as plt
      >>> numpy.random.seed(8675301)
      >>> data = numpy.random.randn(1000)
      >>> binw, nbins = spacepy.toolbox.binHisto(data)
      >>> print(nbins)
      19
      >>> p = plt.hist(data, bins=nbins, histtype='step', density=True)