.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_tutorials/basics/g_code_listing_01.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_tutorials_basics_g_code_listing_01.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_tutorials_basics_g_code_listing_01.py:


Basic workflow
==============

This examples demonstrates a basic workflow using the `py-fmas` library code.

.. codeauthor:: Oliver Melchert <melchert@iqo.uni-hannover.de>

.. GENERATED FROM PYTHON SOURCE LINES 11-13

We start by simply importing the required `fmas` into the current namespace.


.. GENERATED FROM PYTHON SOURCE LINES 13-16

.. code-block:: default


    import fmas








.. GENERATED FROM PYTHON SOURCE LINES 17-20

If an adequate input file is located within the current working directory,
the function `read_h5`, located in module `data_io`, can be used to read-in
the propagation setting stored in the input file `input_file.h5`:

.. GENERATED FROM PYTHON SOURCE LINES 20-23

.. code-block:: default


    glob = fmas.data_io.read_h5('input_file.h5')








.. GENERATED FROM PYTHON SOURCE LINES 24-26

Next, the problem specific data structures, given by the computational grid 
and the propagation model, can be initialized: 

.. GENERATED FROM PYTHON SOURCE LINES 26-41

.. code-block:: default


    grid = fmas.grid.Grid(
        t_max = glob.t_max,
        t_num = glob.t_num,
        z_max = glob.z_max,
        z_num = glob.z_num)

    model = fmas.models.FMAS_S_R(
        w = grid.w,
        beta_w = glob.beta_w,
        n2 = glob.n2,
        fR = glob.fR,
        tau1 = glob.tau1,
        tau2 = glob.tau2)








.. GENERATED FROM PYTHON SOURCE LINES 42-44

The provided initial condition, which represents the real-valued optical
field can be converted to the complex-valued analytic signal as shown below:

.. GENERATED FROM PYTHON SOURCE LINES 44-47

.. code-block:: default


    ic = fmas.analytic_signal.AS(glob.E_0t)








.. GENERATED FROM PYTHON SOURCE LINES 48-51

Below we implement a user-action function that can be passed to the
propagation algorithm. Upon propagation it will evaluated at every
:math:`z`-step

.. GENERATED FROM PYTHON SOURCE LINES 51-58

.. code-block:: default


    import numpy as np

    def Cp(i, zi, w, uw):
        Iw = np.abs(uw)**2
        return np.sum(Iw[w>0]/w[w>0])








.. GENERATED FROM PYTHON SOURCE LINES 59-62

Next, we initialzize the  :math:`z`-propagation algorithm, given by the
`Runge-Kutta in the interaction picture` (RK4IP) method, set the initial
condition, and perform :math:`z`-propagation:

.. GENERATED FROM PYTHON SOURCE LINES 62-75

.. code-block:: default


    solver = fmas.solver.IFM_RK4IP(
        model.Lw, model.Nw,
        user_action = Cp)

    solver.set_initial_condition(
        grid.w, ic.w_rep)

    solver.propagate(
        z_range = glob.z_max,
        n_steps = glob.z_num,
        n_skip = glob.z_skip)








.. GENERATED FROM PYTHON SOURCE LINES 76-80

After the propagation algorithm has terminated, the generated simulation data
can be stored within an output file in HDF5-format. Therefore, the data is
organized as dictionary with custom keys for the stored data objects, which
is then passed to the function `save_h5` implemented in module `data_io`:

.. GENERATED FROM PYTHON SOURCE LINES 80-90

.. code-block:: default


    res = {
        "t": grid.t,
        "z": solver.z,
        "w": solver.w,
        "u": solver.utz,
        "Cp": solver.ua_vals}

    fmas.data_io.save_h5('out_file.h5', **res)








.. GENERATED FROM PYTHON SOURCE LINES 91-93

A simple plot of the generated data can be obtained using convenience functions
implemented in module `tools`:

.. GENERATED FROM PYTHON SOURCE LINES 93-97

.. code-block:: default


    fmas.tools.plot_evolution(
        solver.z, grid.t, solver.utz,
        t_lim = (-500,2200), w_lim = (1.,4.))



.. image:: /auto_tutorials/basics/images/sphx_glr_g_code_listing_01_001.png
    :alt: $|u|^2/{\rm{max}}\left(|u|^2\right)$, $|u_\omega|^2/{\rm{max}}\left(|u_\omega|^2\right)$
    :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  34.082 seconds)


.. _sphx_glr_download_auto_tutorials_basics_g_code_listing_01.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: g_code_listing_01.py <g_code_listing_01.py>`



  .. container:: sphx-glr-download sphx-glr-download-jupyter

     :download:`Download Jupyter notebook: g_code_listing_01.ipynb <g_code_listing_01.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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