----------- SNOPROP ------------
SNOPROP: Solver for Nonlinear Optical Propagation is a Python-based simulation code.
Includes
	Linear laser propagation
	Nonlinear self-phase and cross-phase modulation
	Group velocity dispersion
	Multiphoton ionization
	Collisional ionization
	Stimulated Raman Scattering
	Four-wave mixing
	Laser pulse defined in time-radius coordinates and advanced in z.

All units are SI. Numbers are stored as doubles or complex doubles in the simulation where applicable. The output data (scalar data, 1d data, and 2d arrays) are saved as single-precision floats.


----------- License -------------
The source code has been authored by federal and non-federal employees. To the extent that a federal employee is an author of a portion of this software or a derivative work thereof, no copyright is claimed by the United States Government, as represented by the Secretary of the Navy ("GOVERNMENT") under Title 17, U.S. Code. All Other Rights Reserved. To the extent that a non-federal employee is an author of a portion of this software or a derivative work thereof, the work was funded in whole or in part by the U.S. Government, and is, therefore, subject to the following license: The Government has unlimited rights to use, modify, reproduce, release, perform, display, or disclose the computer software and computer software documentation in whole or in part, in any manner and for any purpose whatsoever, and to have or authorize others to do so. Any other rights are reserved by the copyright owner.

Neither the name of NRL or its contributors, nor any entity of the United States Government may be used to endorse or promote products derived from this software, nor does the inclusion of the NRL written and developed software directly or indirectly suggest NRL's or the United States Government's endorsement of this product. 

THIS SOFTWARE IS PROVIDED "AS IS" AND WITHOUT ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.


----------- Installation ----------
Install SNOPROP by entering the root snoprop directory and running the command "pip install ."

The program uses Numpy, Scipy, and Matplotlib extensively. In addition, the program uses Numba (version >=0.42), a JIT compiler, to accelerate the math.
All of these are included by default with the Anaconda python distribution. This is the easiest way to use SNOPROP.


------------ Testing ---------------
Tests can be run with the snoprop.run_tests(v) command, where v is optional boolean for more verbose logging.
The constants used for the tests are saved in test_constants.py


----------- Use ----------------
Create a python file and import snoprop. Then define a dictionary with the desired simulation parameters.
The shortest possible input deck might look something like this:
    import snoprop
    params = {
        # Specify simulation parameters here
    }
    sim = snoprop.Simulation(params)
    sim.run()
    
You can specify the number of threads to use with syntax like the following:
    NUMBA_NUM_THREADS=4 python simulate.py

You may also run it step-by-step with the sim.move() function
You can retrieve the simulation position with sim.getZ() and the field intensities with sim.getIS(), sim.getIL(), sim.getIA()

If you save restarts, you can just load the object from the restart file as so

sim = pickle.load('restart_file','rb')
    sim.run()

Input parameters are detailed in the NRL memo report user guide.