For a while now, I've wanted to make some of the great functionality of things like Octave (and it's parent Matlab) to Common Lisp. Mostly, I want things like the slice assignments (arrays with ranges in the indices) and matrix math. Also, it never hurts to make higher mathematical functions available in simple ways. So, I started writing Map, which I hope will do just that.
Lately, I've been working in singular optics, largely with Spatial Light Modulators, which are like very tiny LCD screens in front of a mirror. They're cool because they can transform the wavefront of a laser beam into any sort of beam you'd like! We use a fair bit of math to pump out the right images to put onto the SLM, and I'd been wanting to make a more general interface for that for a while. I'm not there yet, but I'd like to share the few things I've done so far.
In Octave, you can type
[1:10]and it will automatically be expanded to
[1 2 3 4 5 6 7 8 9 10]. This is great notation and can be used both for generating a vector or setting a range. In map, I've implemented those with
#r(1 10), which creates a 'range' class that just keeps track of the numbers, and #v(1 10), which will expand to
#(1 2 3 4 5 6 7 8 9 10), like the Octave code. This means you can do things like
(reduce #'+ #v(1 100))
To add up the numbers 1 to 100. That, of course, isn't hard, especially when Gauss came up with a much easier way, but you get the picture.
One further thing that's special, is that you can apply a function to the vector you're generating by passing another argument (you can also pass a step size by entering three numbers).
(reduce #' #v(1 .1 10 #'sin))
will give you the sum of the sine of the numbers 1, 1.1, ..., 9.9, 10! Moving on...
This is a much more meaty section, and was really fun for me, since I hadn't really implemented a ODE solver before (that I remember). Map now contains a Runge-Kutta Feldberg 45 solver, which is supposed to have an adaptive step size, and currently doesn't seem to, so I didn't do everything right, but it works to a certain accuracy.
Here's a demonstration of finding the angular position and velocity ($\theta, \omega$) of a damped, driven pendulum, taken from some of my old computational physics notes.
(defun damped-pendulum (ti yi)
(declare (type double-float ti)
(type (array double-float 2))
(vector (+ (* -1d0 (sin (elt yi 1))) (* -1d0 .5d0 (elt yi 0)))
(elt yi 0)))
(map::mute (multiple-value-setq (ts ys)
#r(0d0 1d-7 4d1)
#(0d0 1d0) 1d-10)))
Now, ts holds the time values, and ys is a 2x(length ts) array of position and velocity values! Once I implement array slices, it'll be easy to graph them, but for now, you need to manually extract one of the rows of ys to plot against ts. There's still a lot of work to be done. Anyway, the resulting graphs are:
Thanks for reading, I'll probably post more once I've got slices sorted out.