.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/Potjans_2014/run_microcircuit.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_Potjans_2014_run_microcircuit.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_Potjans_2014_run_microcircuit.py:

PyNEST Microcircuit: 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%2FPotjans_2014%2Frun_microcircuit.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 microcircuit model and generating
basic plots of the network activity.

.. GENERATED FROM PYTHON SOURCE LINES 31-32

Import the necessary modules and start the time measurements.

.. GENERATED FROM PYTHON SOURCE LINES 32-44

.. code-block:: Python


    import time

    import nest
    import network
    import numpy as np
    from network_params import net_dict
    from sim_params import sim_dict
    from stimulus_params import stim_dict

    time_start = time.time()


.. GENERATED FROM PYTHON SOURCE LINES 45-53

Initialize the network with simulation, network and stimulation parameters,
then create and connect all nodes, and finally simulate.
The times for a presimulation and the main simulation are taken
independently. A presimulation is useful because the spike activity typically
exhibits a startup transient. In benchmark simulations, this transient should
be excluded from a time measurement of the state propagation phase. Besides,
statistical measures of the spike activity should only be computed after the
transient has passed.

.. GENERATED FROM PYTHON SOURCE LINES 53-69

.. code-block:: Python


    net = network.Network(sim_dict, net_dict, stim_dict)
    time_network = time.time()

    net.create()
    time_create = time.time()

    net.connect()
    time_connect = time.time()

    net.simulate(sim_dict["t_presim"])
    time_presimulate = time.time()

    net.simulate(sim_dict["t_sim"])
    time_simulate = time.time()


.. GENERATED FROM PYTHON SOURCE LINES 70-76

Plot a spike raster of the simulated neurons and a box plot of the firing
rates for each population.
For visual purposes only, spikes 100 ms before and 100 ms after the thalamic
stimulus time are plotted here by default.
The computation of spike rates discards the presimulation time to exclude
initialization artifacts.

.. GENERATED FROM PYTHON SOURCE LINES 76-82

.. code-block:: Python


    raster_plot_interval = np.array([stim_dict["th_start"] - 100.0, stim_dict["th_start"] + 100.0])
    firing_rates_interval = np.array([sim_dict["t_presim"], sim_dict["t_presim"] + sim_dict["t_sim"]])
    net.evaluate(raster_plot_interval, firing_rates_interval)
    time_evaluate = time.time()


.. GENERATED FROM PYTHON SOURCE LINES 83-85

Summarize time measurements. Rank 0 usually takes longest because of the
data evaluation and print calls.

.. GENERATED FROM PYTHON SOURCE LINES 85-96

.. code-block:: Python


    print(
        "\nTimes of Rank {}:\n".format(nest.Rank())
        + "  Total time:          {:.3f} s\n".format(time_evaluate - time_start)
        + "  Time to initialize:  {:.3f} s\n".format(time_network - time_start)
        + "  Time to create:      {:.3f} s\n".format(time_create - time_network)
        + "  Time to connect:     {:.3f} s\n".format(time_connect - time_create)
        + "  Time to presimulate: {:.3f} s\n".format(time_presimulate - time_connect)
        + "  Time to simulate:    {:.3f} s\n".format(time_simulate - time_presimulate)
        + "  Time to evaluate:    {:.3f} s\n".format(time_evaluate - time_simulate)
    )


.. _sphx_glr_download_auto_examples_Potjans_2014_run_microcircuit.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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