July 22, 2011 at 03:07 - by eelvex | one comment (Leave a comment)

[ Get the code at github ]

A programming language is low level when its programs require attention to the irrelevant.

Alan J. Perlis

It's not only that physicists can't program (we rarely use version control, we don't plan, we don't test... I suppose most labs score zero on The Joel Test). It is that we program, we program a lot and we still mostly use Fortran!

Now I'm not implying that Fortran is not a good language. Fortran is just fine. What I'm saying is that Fortran is a very bad choice for non-programmers who do a lot of programming.

Over the years some have turned to other languages like C, C++ and Matlab. Some, perhaps more enlightened, have even turned to Python. Naturally, I, too, turned to other languages and soon began wondering about what else might be out there (language-wise) to help a poor physicist do his job.

Could there be a language that won't break everything apart just because a number is consider "special"? (What's a float or a double? Why do I care?) Could there be one that implements a few-lines-of-equations-model in a few lines of code? One that will **help** me in my work not make the problem just ... different?

I've been googling and poking around on stackoverflow and programmers.SE for some answers but nothing particularly interesting came up. It, thus, seems that I have to try them all.

- "Rules"
- no extra packages
- idiomatic code for each language
- efficiency is not so much of an issue
- extendable code

[TOC]

I'll be solving, as an example, the following 2D system of ordinary differential equations:

\begin{align*} \frac{df_1(x)}{dx} =& 1 - f_2(x)\\ & \\

\frac{df_2(x)}{dx} =& f_1(x) - x

\end{align*}

with initial conditions:

\begin{align*}

x_0 &= 0\\

f_1(x_0) &= 1\\

f_2(x_0) &= 0

\end{align*}

in the range [0, 3]

The exact solution is .

To numerically solve the system we treat the function values as parameters of the differential functions:

\begin{align*}

F_1(x,y_1,y_2) &= \frac{df_1(x,y_1,y_2)}{dx} =& 1 - y_2\\ && \\

F_2(x,y_1,y_2) &= \frac{df_2(x,y_1,y_2)}{dx} =& y_1 - x

\end{align*}

We will end up with a list of values that will represent an approximation of the values .

Runge Kutta method iterates from a current known value of to the "next" value at . So, knowing and choosing a small step the method gives us by calculating a "correction" to .

We thus have: where the correction depends on and is given by:

\begin{equation*}

\Delta(\delta x, x_0, f(x_0), f\prime(x,y)) = \frac{1}{6}\delta x (a + 2b + 2c + d)

\end{equation*}

where

\begin{align*}

a =& f\prime(x_0, f(x_0))\\

b = & f\prime(x_0 + \frac{1}{2}\delta x, f(x_0) + \frac{1}{2}a\delta x)\\

c = & f\prime(x_0 + \frac{1}{2}\delta x, f(x_0) + \frac{1}{2}b\delta x)\\

d = & f\prime(x_0 + \delta x, f(x_0) + c\delta x)\\

\end{align*}

We feed with the initial known values.

To apply RK4 to a system of ODEs

for each set of equations we have a different set of parameters and a correction function

\begin{align*}

a_i &= F_i(x_0 ,& y_1,& \ldots ,& y_n) \\

b_i &= F_i(x_0+\frac{1}{2}\delta x ,& y_1 + \frac{1}{2}a_1\delta x ,& \ldots ,& y_n + \frac{1}{2}a_n\delta x)\\

c_i &= F_i(x_0+\frac{1}{2}\delta x ,& y_1 + \frac{1}{2}b_1\delta x ,& \ldots ,& y_n + \frac{1}{2}b_n\delta x)\\

d_i &= F_i(x_0+\delta x ,& y_1 + c_1\delta x ,& \ldots ,& y_n + c_n\delta x)\\

\end{align*}

I'll use a C implementation as a reference.

Adapted from numerical recipes in C:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | void rk4(double y[], double dydx[], int n, double x, double h, double yout[], void (*derivs)(double, double [], double [])) { int i; double xh, hh, h6; double dym[n], dyt[n], yt[n]; hh = h*0.5; h6 = h/6.0; xh = x+hh; for (i=0;i<n;i++) yt[i] = y[i] + hh*dydx[i]; (*derivs)(xh,yt,dyt); for (i=0;i<n;i++) yt[i] = y[i] + hh*dyt[i]; (*derivs)(xh,yt,dym); for (i=0;i<n;i++) { yt[i] = y[i] + h*dym[i]; dym[i] = dym[i] + dyt[i]; } (*derivs)(x+h,yt,dyt); for (i=0;i<n;i++) yout[i] = y[i] + h6*(dydx[i] + dyt[i] + 2.*dym[i]); } void rkdump(double vstart[], int nvar, double t1, double t2, int nstep, void (*derivs)(double, double[], double[])) { int i,k; double x,h; double v[nvar], vout[nvar], dv[nvar]; for (i=0;i<nvar;i++) { v[i] = vstart[i]; } x = t1; h = (t2-t1)/nstep; for (k=0;k<nstep;k++) { (*derivs)(x,v,dv); rk4(v,dv,nvar,x,h,vout,derivs); if ((double)(x+h) == x) printf("Step size too small in routine rkdump\n"); x += h; for (i=0;i<nvar;i++) { v[i] = vout[i]; } solution[0][k] = v[0]; solution[1][k] = v[1]; } } |

for example:

#include <stdlib.h>

#include <stdio.h>

#include <math.h>

#include <complex.h>

#define N 2 //Dimensions

#define x0 0.0

#define x1 3.0

#define dt 0.01

double *solution[N] = {NULL};

void deriv1 (double x, double y[], double dydt[])

{

dydt[0] = 1 - y[1];

dydt[1] = y[0] - x;

}

int main (void)

{

int i;

int steps; // solution steps

double starting_v[N]; // Starting vector

steps = (x1-x0)/dt;

for (i=0;i<N;i++)

solution[i] = (double *)realloc((char *)solution[i], sizeof(double)*steps);

/* -- initial values -- */

starting_v[0] = 1.0;

starting_v[1] = 0.0;

rkdump(starting_v, N, x0, x1, steps, deriv1);

for (i=0;i<steps;i++) {

printf("%4f %4f %4f\n", (i+1)*dt, solution[0][i], solution[1][i]);

}

return 0;

}

#include <stdio.h>

#include <math.h>

#include <complex.h>

#define N 2 //Dimensions

#define x0 0.0

#define x1 3.0

#define dt 0.01

double *solution[N] = {NULL};

void deriv1 (double x, double y[], double dydt[])

{

dydt[0] = 1 - y[1];

dydt[1] = y[0] - x;

}

int main (void)

{

int i;

int steps; // solution steps

double starting_v[N]; // Starting vector

steps = (x1-x0)/dt;

for (i=0;i<N;i++)

solution[i] = (double *)realloc((char *)solution[i], sizeof(double)*steps);

/* -- initial values -- */

starting_v[0] = 1.0;

starting_v[1] = 0.0;

rkdump(starting_v, N, x0, x1, steps, deriv1);

for (i=0;i<steps;i++) {

printf("%4f %4f %4f\n", (i+1)*dt, solution[0][i], solution[1][i]);

}

return 0;

}

Coming up: J, LISP, R, Fortran, Python, Haskell, Pari/GP, maxima.

Any other languages that you would like to see?

## eelvex

on July 29, 2011 at 23:07

These great floating point tips are exactly the kind of things that you shouldn't have to know.

[Reply]