Python PROPER

Implementation of John Krist's PROPER optical propagation library for Python version 3.x. PROPER is a library of optical propagation procedures and functions for the IDL (Interactive Data Language) environment. PROPER is intended for exploring diffraction effects in optical systems. It is a set of wavefront propagation tools – it is not a ray tracing system and thus is not suitable for detailed design work.

• Authors: Navtej Singh, Nikta Amiri, Luis Marchen, A J Eldorado Riggs

• Contact: nsaini@jpl.nasa.gov

• Organization: NASA Jet Propulsion Laboratory California Institute of Technology

• Following requirements should be met to run pyPROPER3 -

  • Numpy >= 1.8
  • Scipy >= 0.14
  • astropy >= 1.3

  To use FFTW, you will also need pyfftw >= 0.1

• To install PROPER, execute the following command in downloaded PyPROPER directory -

  python setup.py install

• To use PROPER without installing, issue following commands in python/ipython shell

  import sys.path.insert(0, /path/to/PROPER)

• To run a prescription in interactive mode -

  1. Open python or ipython shell and change directory to where the prescription is lying.

  2. Import proper package -
     import proper

  3. Execute the prescription -
     (psf, sampling) = proper.prop_run('prescription_name', wavelength, grid_size)

     where the first parameter is name of prescription (without file extension), second parameter is wavelength in micrometer and third argument is grid dimension. You can also pass key-value pairs as optional parameters. To run multiple cases at once in parallel, use prop_run_multi instead.

     Please refer to PROPER user manual for more details.

  4. One can display the generated point spread function (psf) using matplotlib package -
     import numpy as np
     import matplotlib.pylab as plt

     plt.imshow(np.log10(psf), origin = 'lower')
     plt.show()

  5. The PSF can be saved as FITS image -
     proper.prop_fits_write("example.fits", psf)

     where the first parameter is FITS image file name and second parameter is 2D numpy array. prop_fits_write also accepts optional key-value parameters (check doc-string in prop_fits_write for more details). This function will overwrite an existing FITS image with the same name.