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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_generated_gallery_ion_multi_event.py>`
        to download the full example code.

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

.. _sphx_glr_generated_gallery_ion_multi_event.py:


Heating only the Ions with Multiple Square Events
=================================================
In this example, only the ions are heated by multiple square pulses each lasting
200 seconds with heating rates chosen from a uniform distribution.

.. GENERATED FROM PYTHON SOURCE LINES 7-20

.. code-block:: Python

    import astropy.units as u
    import matplotlib.pyplot as plt
    import numpy as np

    from astropy.visualization import quantity_support

    import ebtelplusplus

    from ebtelplusplus.models import DemModel, HeatingModel, SquareHeatingEvent

    quantity_support()






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <astropy.visualization.units.quantity_support.<locals>.MplQuantityConverter object at 0x714089832a20>



.. GENERATED FROM PYTHON SOURCE LINES 21-26

Set up a series of 5 square heating events each
lasting 200 s with the first starting at 1500 s.
The rates are chosen from a uniform distribution
between 0.001 and 0.1 erg per cubic centimeter
per second.

.. GENERATED FROM PYTHON SOURCE LINES 26-35

.. code-block:: Python

    events = []
    time_start = 1500 * u.s
    for i in range(5):
        events.append(SquareHeatingEvent(
            (i+1)*time_start,
            200*u.s,
            np.random.uniform(0.001, 0.1)*u.Unit('erg cm-3 s-1')
        ))








.. GENERATED FROM PYTHON SOURCE LINES 36-38

Let's add this to a heating model in which we
partition all of the energy into the ions

.. GENERATED FROM PYTHON SOURCE LINES 38-42

.. code-block:: Python

    heating = HeatingModel(background=3.5e-5*u.Unit('erg cm-3 s-1'),
                           partition=0,
                           events=events)








.. GENERATED FROM PYTHON SOURCE LINES 43-46

Now run the simulation for a 40 Mm loop lasting
a total of 3 h. We'll also specify that we
want to compute the DEM

.. GENERATED FROM PYTHON SOURCE LINES 46-51

.. code-block:: Python

    result = ebtelplusplus.run(3*u.h,
                               40*u.Mm,
                               heating=heating,
                               dem=DemModel(calculate_dem=True))








.. GENERATED FROM PYTHON SOURCE LINES 52-54

Let's visualize the heating profile, temperature,
and density as a function of time.

.. GENERATED FROM PYTHON SOURCE LINES 54-61

.. code-block:: Python

    fig, axes = plt.subplots(3, 1, sharex=True)
    axes[0].plot(result.time, result.heat)
    axes[1].plot(result.time, result.electron_temperature, label='electron')
    axes[1].plot(result.time, result.ion_temperature, label='ion')
    axes[2].plot(result.time, result.density)
    axes[1].legend()




.. image-sg:: /generated/gallery/images/sphx_glr_ion_multi_event_001.png
   :alt: ion multi event
   :srcset: /generated/gallery/images/sphx_glr_ion_multi_event_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <matplotlib.legend.Legend object at 0x714089833830>



.. GENERATED FROM PYTHON SOURCE LINES 62-65

Finally, let's visualize the DEM distribution.
We'll first time-average each component over the
duration of the simulation.

.. GENERATED FROM PYTHON SOURCE LINES 65-76

.. code-block:: Python

    delta_t = np.gradient(result.time)
    dem_avg_total = np.average(result.dem_tr+result.dem_corona,
                               axis=0,
                               weights=delta_t)
    dem_avg_tr = np.average(result.dem_tr,
                            axis=0,
                            weights=delta_t)
    dem_avg_corona = np.average(result.dem_corona,
                                axis=0,
                                weights=delta_t)








.. GENERATED FROM PYTHON SOURCE LINES 77-78

And now we can plot each component

.. GENERATED FROM PYTHON SOURCE LINES 78-90

.. code-block:: Python

    fig = plt.figure()
    ax = fig.add_subplot()
    ax.plot(result.dem_temperature, dem_avg_total, label='Total')
    ax.plot(result.dem_temperature, dem_avg_tr, label='TR')
    ax.plot(result.dem_temperature, dem_avg_corona, label='Corona')
    ax.set_xlim([10**(4.5), 10**(7.5)]*u.K)
    ax.set_ylim([10**(20.0), 10**(23.5)]*u.Unit('cm-5 K-1'))
    ax.set_xscale('log')
    ax.set_yscale('log')
    ax.legend()

    plt.show()



.. image-sg:: /generated/gallery/images/sphx_glr_ion_multi_event_002.png
   :alt: ion multi event
   :srcset: /generated/gallery/images/sphx_glr_ion_multi_event_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.298 seconds)


.. _sphx_glr_download_generated_gallery_ion_multi_event.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: ion_multi_event.zip <ion_multi_event.zip>`


.. only:: html

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

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_