#!/usr/bin/env python

# Copyright (c) 2015, Ecole Polytechnique Federale de Lausanne, Blue Brain Project
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# This file is part of NeuroM <https://github.com/BlueBrain/NeuroM>
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'''Extract a distribution for the selected feature of the population of neurons among
   the exponential, normal and uniform distribution, according to the minimum ks distance.
   '''

from neurom import ezy
from scipy import stats
import numpy as np
import argparse


def parse_args():
    '''Parse command line arguments'''
    parser = argparse.ArgumentParser(
        description='Morphology fit distribution extractor',
        epilog='Note: Prints the optimal distribution and corresponding parameters.')

    parser.add_argument('datapath',
                        help='Path to morphology data file or directory')

    parser.add_argument('feature',
                        help='Feature available for the ezy.neuron')

    return parser.parse_args()


def distribution_fit(data, distribution='norm'):
    '''Calculates and returns the parameters of a distribution'''
    return getattr(stats, distribution).fit(data)


def distribution_error(data, distribution='norm'):
    '''Calculates and returns the distance of a fitted distribution
       from the initial data.
    '''
    params = distribution_fit(data, distribution=distribution)
    return stats.kstest(data, distribution, params)[0]


def test_multiple_distr(data):
    '''Runs the distribution fit for multiple distributions and returns
       the optimal distribution along with the corresponding parameters.
    '''
    # Create a list of basic distributions
    distr_to_check = ['norm', 'expon', 'uniform']

    # Fit the section lengths of the neuron with a distribution.
    fit_data = {d: distribution_fit(data, d) for d in distr_to_check}

    # Get the error for the fitted data with each distribution.
    fit_error = {distribution_error(data, d): d for d in distr_to_check}

    # Select the distribution with the minimum ks distance from data
    optimal = fit_error.values()[np.argmax(fit_error.iterkeys())]

    return optimal, fit_data[optimal]


if __name__ == '__main__':
    args = parse_args()

    data_path = args.datapath

    feature = args.feature

    population = ezy.load_neurons(data_path)

    feature_data = [getattr(n, 'get_' + feature)() for n in population]

    try:
        result = test_multiple_distr(feature_data)
    except ValueError:
        from itertools import chain
        feature_data = list(chain(*feature_data))
        result = test_multiple_distr(feature_data)

    print "Optimal distribution fit for %s is: %s with parameters %s"\
        % (feature, result[0], result[1])

Add example of distribution fit on any selected feature