#!/usr/bin/env python

# Copyright (c) 2015, Ecole Polytechnique Federale de Lausanne, Blue Brain Project
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"""Extract a distribution for the selected feature of the population of morphologies among
   the exponential, normal and uniform distribution, according to the minimum ks distance.
   """

from itertools import chain
import argparse
import json

import neurom as nm
from neurom import stats


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

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

    parser.add_argument('feature',
                        help='Feature to be extracted with neurom.get')

    return parser.parse_args()


def extract_data(data_path, feature):
    """Loads a list of morphologies, extracts feature
       and transforms the fitted distribution in the correct format.
       Returns the optimal distribution, corresponding parameters,
       minimun and maximum values.
    """
    population = nm.load_morphologies(data_path)

    feature_data = [nm.get(feature, n) for n in population]
    feature_data = list(chain(*feature_data))

    return stats.optimal_distribution(feature_data)


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

    d_path = args.datapath

    feat = args.feature

    _result = stats.fit_results_to_dict(extract_data(d_path, feat))

    print(json.dumps(_result, indent=2, separators=(',', ': ')))
