Ray transfer matrix calculations

This example expects basic knowledge of the ray transfer matrix concept.

First we define the transfer matrix form for free space of length s:

 def FreeSpaceABCD(s):
 	return Matrix([[1,s],[0,1]])

... and for a lens of with focal length f (f positive for convex lens):

 def LensABCD(f):
 	return Matrix([[1,0],[-1/f,1]])

Suppose that we want to focus a source to its image with two lenses. As both focal lengths are given, this is a nonlinear algebraic problem of two equations with three variables (s_1, s_2 and s_3).

 f1,f2=symbols(["f1","f2"])
 L1=LensABCD(f1)
 L2=LensABCD(f2)
 s1,s2,s3=symbols(["s1", "s2", "s3"])
 S1=FreeSpaceABCD(s1)
 S2=FreeSpaceABCD(s2)
 S3=FreeSpaceABCD(s3)
 M=(S3*L2*S2*L1*S1).applyfunc(simplify)

First equation

To make the system imaging, we need the "B" element of M to be zero. This constitutes the first equation, which allows us to express s_3 as a function of s_1 and s_2:

 s3_by_s12 = solve(Eq(M[1],0), s3)[0]

Now we can eliminate s3 from the transfer matrix:

 M2=M.subs(s3, s3_by_s12).applyfunc(simplify)
 assert M2[1]==0

Second equation

The zoom is given, too. This gives us the value of the "A" element, for example A := -1/10:

 s1_by_s2 = solve(Eq(1/M2[0],-10),s1)[0]
 s3_by_s2 = simplify(s3_by_s12.subs(s1, s1_by_s2))
 M3=M2.subs(s1,s1_by_s2).applyfunc(simplify)

Now both s_1 and s_3 are expressed as functions of s_2 (the spacing of the two lenses).

Conclusion

Symbolic manipulation with the matrix saved us a lot of symbolic calculations.

We may test the Liouville theorem for the imaging system now:

 assert  M3[0]*M3[3] == 1, "Error: Liouville theorem not held!"

Optionally we can calculate the constraints for example presented by the limited radii of lenses.

• • TODO** Plot the functions here!