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An optical wavefront propagation utility

Project description


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:

  • 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 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')

    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.

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