Metadata-Version: 2.1
Name: PyPROPER
Version: 3.2.5
Summary: An optical wavefront propagation utility
Home-page: UNKNOWN
Author: Navtej Saini, Nikta Amiri, Luis Marchen
License: BSD
Description: 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.
        
Platform: any
Description-Content-Type: text/markdown
Provides-Extra: pyfftw
