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def calc_energy_and_gradient(X, Y):
    """ Calculate the Lennard-Jones energy and
        Energy gradient between particles.
        X and Y are lists containing x and y
        coordinates, respectively.
    """

    # Holds the energy and the energy gradient
    energy = 0.0
    gradient = numpy.zeros((2, len(X)))

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import numpy
import random

from numba import double   # Add this
from numba.decorators import autojit, jit # Add this

#@jit(argtypes=[double[:], double[:]]) # Add this if you want more speed than autojit
@autojit # Add this
def calc_energy_and_gradient(X, Y):
    """ Calculate the Lennard-Jones energy and

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C THE LENNARD-JONES POTENTIAL, GRADIENT AND THE VIRIAL
      subroutine LennardJones(U,box,Epot,F,Vir,N)
cf2py intent(in) U
cf2py intent(in) box
cf2py intent(out) Epot
cf2py intent(out) F
cf2py intent(out) Vir
      implicit none
      double precision U,Epot,F,box,Vir
      integer N

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import numpy
from numba import double, jit

rc2 = 6.25
rc2i=1.0/rc2
rc6i=rc2i*rc2i*rc2i
ecut=rc6i*(rc6i-1.0)

@jit(argtypes=[double[:,:], double[:]])
def lennardjones(U, box):

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import numpy
from forces import lennardjones

# Initialize simulations (code not shown)
temperature = 2.0
n_atoms = 100
rho = 0.01

U = initialize_positions(n_atoms, rho)
box = initialize_box(n_atoms, rho)

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#include <sys/time.h>
#include <stdlib.h>
#include <iostream>
#include <math.h>

static unsigned int g_seed;

inline int fastrand() {
  g_seed = (214013*g_seed+2531011);
  return (g_seed>>16)&0x7FFF;

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# A simple energy minimization program that uses steepest descent 
# and a force field to minimize the energy of water in internal coordinates.
# Written by Jan H. Jensen, 2013


def Eandg(rOH,thetaHOH):

    """"
    Arguments: (internal coordinates of the water molecule)

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def implementation_1(P):

    P = [1, 1]


def implementation_a(P):

    P[0] = 1
    P[1] = 1

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# The MIT License
# 
# Copyright (c) 2010-2016 Anders S. Christensen
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:

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import pylab 
import numpy 
import os

# Make a simple plot
x = numpy.arange(1, 100, 1)
y = x**2
pylab.plot(x, y)

# Define a filename (remember the .pdf)