Getting Started

Installing and upgrading

You need matplotlib, which is a fairly standard Python module. If you do not have it, installing Anaconda is your best option. Python 3.6 or later is required. There are several ways to install the module:

  1. Simplest: pip install raytracing or pip install --upgrade raytracing
    1. If you need to install pip, download getpip.py and run it with python getpip.py
  2. If you download the source of the module, then you can type: python setup.py install
  3. From GitHub, you can get the latest version (including bugs, which are 153% free!) and then type python setup.py install
  4. If you are completely lost, copying the folder raytracing (the one that includes __init__.py) from the source file into the same directory as your own script will work.
  5. Watch the tutorial with subtitles here.

Getting started

The simplest way to import the package in your own scripts after installing it:

from raytracing import *

This will import Ray , GaussianBeam, and several Matrix elements such as Space, Lens, ThickLens, Aperture, DielectricInterface, but also MatrixGroup (to group elements together), ImagingPath (to ray trace with an object at the front edge), LaserPath (to trace a gaussian laser beam from the front edge) and a few predefined other such as Objective (to create a very thick lens that mimicks an objective).

You create an ImagingPath or a LaserPath, which you then populate with optical elements such as Space, Lens or Aperture or vendor lenses. You can then adjust the path properties (object height in ImagingPath for instance or inputBeam for LaserPath) and display in matplotlib. You can create a group of elements with MatrixGroup for instance a telescope, a retrofocus or any group of optical elements you would like to treat as a “group”. The Thorlabs and Edmund optics lenses, for instance, are defined as MatrixGroups.

This will show you a few examples of things you can do:

python -m raytracing

or request help with:

python -m raytracing -h

In your code, (such as the test.py or demo.py files included in the source), you would do this:

from raytracing import *

path = ImagingPath()
path.append(Space(d=100))
path.append(Lens(f=50, diameter=25))
path.append(Space(d=120))
path.append(Lens(f=70))
path.append(Space(d=100))
path.display()

You can also call display() on an element to see the cardinal points, principal planes, BFL and FFL. You can do it with any single Matrix element but also with MatrixGroup.

from raytracing import *

thorlabs.AC254_050_A().display()
eo.PN_33_921().display()

Finally, an addition as of 1.2.0 is the ability to obtain the intensity profile of a given source from the object plane at the exit plane of an OpticalPath. This is in fact really simple: by tracing a large number of rays, with the number of rays at y and θ being proportionnal to the intensity, one can obtain the intensity profile by plotting the histogram of rays reaching a given height at the image plane. Rays are small classes that return a Ray that satisfies the condition of the class. Currently, there is UniformRays,RandomUniformRays LambertianRays and RandomLambertianRays (a Lambertian distribution follows a cosθ distribution, it is a common diffuse surface source). They appear like iterators and can easily be used like this example script:

from raytracing import *
from numpy import *
import matplotlib.pyplot as plt

# Kohler illumination with these variables
fobj = 5
dObj = 5
f2 = 200
d2 = 50
f3 = 100
d3 = 50

# We build the path (i.e. not an Imaging path)
path = OpticalPath()
path.append(Space(d=f3))
path.append(Lens(f=f3, diameter=d3))
path.append(Space(d=f3))
path.append(Space(d=f2))
path.append(Lens(f=f2, diameter=d2))
path.append(Space(d=f2))
path.append(Space(d=fobj))
path.append(Lens(f=fobj, diameter=dObj))
path.append(Space(d=fobj))

# Obtaining the intensity profile
nRays = 1000000 # Increase for better resolution 
inputRays = RandomLambertianRays(yMax=2.5, maxCount=nRays)
inputRays.display("Input profile")
outputRays = path.traceManyThrough(inputRays, progress=True)
# On macOS and Linux, you can do parallel computations
# outputRays = path.traceManyThroughInParallel(inputRays, progress=True, processes=8) 
outputRays.display("Output profile")

and you will get the following ray histograms:

inputProfileoutputProfile