motif-aware-network-inference/util.py

import os
import numpy as np


# implemented with list because of memory issues
def read_cascades(name: str, with_cascade_id=False):
    num_of_nodes = -1
    cascades = []
    finish_time = 0
    if os.path.isfile('./{}'.format(name)):
        with open(name, 'r') as outfile:
            for line in outfile:
                if line.strip() == '':
                    pass
                elif ';' in line:
                    cascade = []
                    if with_cascade_id:
                        stripped = line.strip().split(';')[1].split(',')
                        for i in range(len(stripped)):
                            if i % 2 == 0:
                                cascade += [(int(stripped[i]), float(stripped[i + 1]))]
                                if cascade[-1][1] > finish_time:
                                    finish_time = cascade[-1][1]
                    else:
                        # cascades += [[(int(event.split(',')[0]), float(event.split(',')[1])) for event in
                        #               line.strip().split(';')]]
                        for event in line.strip().split(';'):
                            cascade += [(int(event.split(',')[0]), float(event.split(',')[1]))]
                            if cascade[-1][1] > finish_time:
                                finish_time = cascade[-1][1]
                    cascades += [cascade]
                else:
                    num_of_nodes += 1
    print(cascades)
    print(num_of_nodes)
    print(finish_time)
    return num_of_nodes, finish_time, cascades


def read_result(name: str, num_of_nodes: int = 32):
    mat = np.zeros([num_of_nodes, num_of_nodes])
    if os.path.isfile('./{}'.format(name)):
        with open(name, 'r') as outfile:
            for line in outfile:
                if '.' in line:
                    stripped = line.split(',')
                    i = int(stripped[0])
                    j = int(stripped[1])
                    mat[i][j] = float(stripped[-1])
    return mat


def print_mat(mat, num_of_nodes=32):
    for i in range(num_of_nodes):
        for j in range(num_of_nodes):
            if mat[i][j] != 0:
                print((i, j, mat[i][j]), end=' ')
        print()


def new_print(mat):
    for x in mat:
        print(x)


def normalize(mat):
    mean = np.mean(mat)
    return np.vectorize(lambda val: 1. if val >= mean else 0.)(np.repeat(mat, 1, axis=1))