/usr/lib/python3/dist-packages/photutils/morphology/non_parametric.py is in python3-photutils 0.4-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | # Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Functions for measuring non-parametric morphology.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import numpy as np
def gini(data):
"""
Calculate the `Gini coefficient
<https://en.wikipedia.org/wiki/Gini_coefficient>`_ of a 2D array.
The Gini coefficient is calculated using the prescription from `Lotz
et al. 2004 <http://adsabs.harvard.edu/abs/2004AJ....128..163L>`_
as:
.. math::
G = \\frac{1}{\\left | \\bar{x} \\right | n (n - 1)}
\\sum^{n}_{i} (2i - n - 1) \\left | x_i \\right |
where :math:`\\bar{x}` is the mean over all pixel values
:math:`x_i`.
The Gini coefficient is a way of measuring the inequality in a given
set of values. In the context of galaxy morphology, it measures how
the light of a galaxy image is distributed among its pixels. A
``G`` value of 0 corresponds to a galaxy image with the light evenly
distributed over all pixels while a ``G`` value of 1 represents a
galaxy image with all its light concentrated in just one pixel.
Usually Gini's measurement needs some sort of preprocessing for
defining the galaxy region in the image based on the quality of the
input data. As there is not a general standard for doing this, this
is left for the user.
Parameters
----------
data : array-like
The 2D data array or object that can be converted to an array.
Returns
-------
gini : `float`
The Gini coefficient of the input 2D array.
"""
flattened = np.sort(np.ravel(data))
N = np.size(flattened)
normalization = 1. / (np.abs(np.mean(flattened)) * N * (N - 1))
kernel = (2 * np.arange(1, N + 1) - N - 1) * np.abs(flattened)
G = normalization * np.sum(kernel)
return G
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