micro.py

import numpy as np

t_adopt = .5
p_adopt = .9
p_expl = .1

def timestep(state, influence):
    influenced = (np.abs(np.dot(influence,state)) > t_adopt)
    r = np.random.rand(np.size(state))
    # vectorized: 415us
    #state = logic(state, influenced, r)
    
    # loop: 737us
    #for i in range(state.size): 
    #    state[i] = logic1(state[i], influenced[i], r[i])

    #map: 489 us
    state = list(map(logic1, state, influenced, r))

    #numba 180 us
    #state = logicnumba(state, influenced, r)
    return state


def init(n):
    s = np.random.rand(n) > .5
    I = np.random.rand(n,n)
    return s, I

def run(n = 1000, iter=1000):
    s, I = init(n)
    for i in range(iter):
        s = timestep(s,I)
    return s

import numba
@numba.vectorize
def logic(state, influenced, rand):
    if influenced:
        if p_adopt > rand:
            state = not(state)
    else:
        if p_expl > rand:
            state = not(state)
    return state

def logic1(state, influenced, rand):
    if influenced:
        if p_adopt > rand:
            state = not(state)
    else:
        if p_expl > rand:
            state = not(state)
    return state

logic = np.vectorize(logic1)