DavidButts

import time
import numpy as np
from numba import jit

x_dim = 1000
y_dim = 1000
x_min = -1.8
x_max = 1.8
y_min = -1.8j
y_max = 1.8j

DavidButts

import time
import numpy as np
from numba import njit, prange

x_dim = 1000
y_dim = 1000
x_min = -1.8
x_max = 1.8
y_min = -1.8j
y_max = 1.8j

DavidButts

import time #timing
import numpy as np #arrays
import numba as nb #speed

x_dim = 1000
y_dim = 1000
x_min = -1.8
x_max = 1.8
y_min = -1.8j
y_max = 1.8j

DavidButts

import time #timing
import numpy as np #arrays
import numba as nb #speed

x_dim = 1000
y_dim = 1000
x_min = -1.8
x_max = 1.8
y_min = -1.8j
y_max = 1.8j

DavidButts

//Written by David Butts
#include<time.h>
#include<chrono>
#include<thread>
#include<iomanip>
int next_val(const vector<int> &v){
    if (v == vector<int>{0,0,0})
        return 0;
    else if (v == vector<int>{0,0,1})
        return 0;

DavidButts

//Written by Bill Punch
#include<iostream>
using std::cout; using std::endl; using std::boolalpha;
#include<chrono>
#include<iomanip>
#include<bitset>
using std::bitset;

int main() {
  const size_t sz = 100000;

DavidButts

#Written by David Butts
#This code is an implementation of a Rule 30 Wolfram model written in Python.
import numpy as np
import time
import numba
@nb.jit #numba the function
def Rule30_code():
    Rule30 = np.zeros((1000,100000)) #initilize an array to run on (timesteps, width)
    Rule30[0,50] = 1
    for y in range(Rule30.shape[0]-1): #iterate through grid

DavidButts

#Written by David Butts
#This code is an implementation of a Rule 30 Wolfram model written in Python.
import numpy as np
import time
def Rule30_code():
    Rule30 = np.zeros((1000,100000)) #initilize an array to run on (timesteps, width)
    Rule30[0,50] = 1
    for y in range(Rule30.shape[0]-1): #iterate through grid
        for x in range(Rule30.shape[1]):
            #update the next rows values according to neighbor & self value