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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_EI_clustered_network_run_simulation_EI.py:

PyNEST EI-clustered network: Run Simulation
-----------------------------------------------

.. only:: html

----

 Run this example as a Jupyter notebook:

  .. card::
    :width: 25%
    :margin: 2
    :text-align: center
    :link: https://lab.ebrains.eu/hub/user-redirect/git-pull?repo=https%3A%2F%2Fgithub.com%2Fnest%2Fnest-simulator-examples&urlpath=lab%2Ftree%2Fnest-simulator-examples%2Fnotebooks%2Fnotebooks%2FEI_clustered_network%2Frun_simulation_EI.ipynb&branch=main
    :link-alt: JupyterHub service

    .. image:: https://nest-simulator.org/TryItOnEBRAINS.png


.. grid:: 1 1 1 1
   :padding: 0 0 2 0

   .. grid-item::
     :class: sd-text-muted
     :margin: 0 0 3 0
     :padding: 0 0 3 0
     :columns: 4

     See :ref:`our guide <run_jupyter>` for more information and troubleshooting.

----


This is an example script for running the EI-clustered model
with two stimulations and generating a raster plot.

.. GENERATED FROM PYTHON SOURCE LINES 28-57

.. code-block:: Python


    import matplotlib.pyplot as plt
    import network
    from helper import raster_plot
    from network_params import net_dict
    from sim_params import sim_dict
    from stimulus_params import stim_dict

    if __name__ == "__main__":
        # Creates object which creates the EI clustered network in NEST
        ei_network = network.ClusteredNetwork(sim_dict, net_dict, stim_dict)

        # Runs the simulation and returns the spiketimes
        # get simulation initializes the network in NEST
        # and runs the simulation
        # it returns a dict with the average rates,
        # the spiketimes and the used parameters
        result = ei_network.get_simulation()
        ax = raster_plot(
            result["spiketimes"],
            tlim=(0, sim_dict["simtime"]),
            colorgroups=[
                ("k", 0, net_dict["N_E"]),
                ("darkred", net_dict["N_E"], net_dict["N_E"] + net_dict["N_I"]),
            ],
        )
        plt.savefig("clustered_ei_raster.png")
        print(f"Firing rate of excitatory neurons: {result['e_rate']:6.2f} spikes/s")
        print(f"Firing rate of inhibitory neurons: {result['i_rate']:6.2f} spikes/s")


.. _sphx_glr_download_auto_examples_EI_clustered_network_run_simulation_EI.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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