.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "generated/gallery/electron_single_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_electron_single_event.py>`
        to download the full example code.

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

.. _sphx_glr_generated_gallery_electron_single_event.py:


Heating only the Electrons with One Triangular Event
====================================================
In this example, only the electrons are heated by a single triangular pulse lasting 500 seconds
and injecting 10 ergs per cubic centimeter into the loop plasma.

.. 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, TriangularHeatingEvent

    quantity_support()






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

 .. code-block:: none


    <astropy.visualization.units.quantity_support.<locals>.MplQuantityConverter object at 0x7890e938a870>



.. GENERATED FROM PYTHON SOURCE LINES 21-25

Set up a single heating event that is triangular
in shape such that it rises linearly for 250 s
and then falls linearly for 250 s starting at
the beginning of the simulation (0 s).

.. GENERATED FROM PYTHON SOURCE LINES 25-29

.. code-block:: Python

    event = TriangularHeatingEvent(0.0*u.s,
                                   500*u.s,
                                   0.04*u.Unit('erg cm-3 s-1'))








.. GENERATED FROM PYTHON SOURCE LINES 30-32

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

.. GENERATED FROM PYTHON SOURCE LINES 32-36

.. code-block:: Python

    heating = HeatingModel(background=3.5e-5*u.Unit('erg cm-3 s-1'),
                           partition=1.0,
                           events=[event])








.. GENERATED FROM PYTHON SOURCE LINES 37-40

Now run the simulation for a 40 Mm loop lasting
a total of 5000 s. We'll also specify that we
want to compute the DEM

.. GENERATED FROM PYTHON SOURCE LINES 40-45

.. code-block:: Python

    result = ebtelplusplus.run(5e3*u.s,
                               40*u.Mm,
                               heating=heating,
                               dem=DemModel(calculate_dem=True))








.. GENERATED FROM PYTHON SOURCE LINES 46-48

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

.. GENERATED FROM PYTHON SOURCE LINES 48-55

.. 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_electron_single_event_001.png
   :alt: electron single event
   :srcset: /generated/gallery/images/sphx_glr_electron_single_event_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    <matplotlib.legend.Legend object at 0x7890e8527470>



.. GENERATED FROM PYTHON SOURCE LINES 56-59

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 59-70

.. 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 71-72

And now we can plot each component

.. GENERATED FROM PYTHON SOURCE LINES 72-84

.. 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_electron_single_event_002.png
   :alt: electron single event
   :srcset: /generated/gallery/images/sphx_glr_electron_single_event_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.407 seconds)


.. _sphx_glr_download_generated_gallery_electron_single_event.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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