# -*- coding: utf-8 -*-
"""
Created on Mon Jul 12 14:46:44 2021

@author: Ji
"""

import networkx as nx
import numpy as np

def get_separation_within_set(nodes_from, lengths=None):
    values = []
    # Distance to closest node within the set (A or B)
    for source_id in nodes_from:
        inner_values = []
        for target_id in nodes_from:
            if source_id == target_id:
                continue
            d = nx.shortest_path_length(G, source_id, target_id)
            inner_values.append(d)
        values.append(np.min(inner_values))
    return values


def get_separation_between_sets(nodes_from, nodes_to):
    values = []
    for source_id in nodes_from:
        source_to_values = []
        for target_id in nodes_to:
            d = nx.shortest_path_length(G, source_id, target_id)
            source_to_values.append(d)
        values.append(np.min(source_to_values))
    for target_id in nodes_to:
        target_to_values = []
        for source_id in nodes_from:
            d = nx.shortest_path_length(G, target_id, source_id)
            target_to_values.append(d)
        values.append(np.min(source_to_values))

    return values
    

def get_separation(nodes_from, nodes_to):
    dAA = np.mean(get_separation_within_set(nodes_from))
    dBB = np.mean(get_separation_within_set(nodes_to))
    dAB = np.mean(get_separation_between_sets(nodes_from, nodes_to))
    d = dAB - (dAA + dBB) / 2.0
    return d