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

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

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

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.. _sphx_glr_auto_examples_csa_example.py:


Using CSA for connection setup
------------------------------

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----

 Run this example as a Jupyter notebook:

  .. card::
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    :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%2Fcsa_example.ipynb&branch=main
    :link-alt: JupyterHub service

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


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

----


This example sets up a simple network in NEST using the Connection Set
Algebra (CSA) instead of using the built-in connection routines.

Using the CSA requires NEST to be compiled with support for
libneurosim. For details, see [1]_.

See Also
~~~~~~~~

:doc:`csa_spatial_example`

References
~~~~~~~~~~

.. [1] Djurfeldt M, Davison AP and Eppler JM (2014). Efficient generation of
       connectivity in neuronal networks from simulator-independent
       descriptions, Front. Neuroinform.
       http://dx.doi.org/10.3389/fninf.2014.00043

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First, we import all necessary modules for simulation and plotting.

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.. code-block:: Python


    import matplotlib.pyplot as plt
    import nest
    from nest import visualization, voltage_trace


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Next, we check for the availability of the CSA Python module. If it does
not import, we exit with an error message.

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.. code-block:: Python


    try:
        import csa

        haveCSA = True
    except ImportError:
        print(
            "This example requires CSA to be installed in order to run.\n"
            + "Please make sure you compiled NEST using\n"
            + "  -Dwith-libneurosim=[OFF|ON|</path/to/libneurosim>]\n"
            + "and CSA and libneurosim are available."
        )
        import sys

        sys.exit(1)


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To set up the connectivity, we create a ``random`` connection set with a
probability of 0.1 and two associated values (10000.0 and 1.0) used as
weight and delay, respectively.

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.. code-block:: Python


    cg = csa.cset(csa.random(0.1), 10000.0, 1.0)


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Using the ``Create`` command from PyNEST, we create the neurons of the pre-
and postsynaptic populations, each of which containing 16 neurons.

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.. code-block:: Python


    pre = nest.Create("iaf_psc_alpha", 16)
    post = nest.Create("iaf_psc_alpha", 16)


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We can now connect the populations using the ``Connect`` function
with the ``conngen`` rule. It takes the IDs of pre- and postsynaptic
neurons (``pre`` and ``post``), the connection set (``cg``) and a
dictionary that maps the parameters weight and delay to positions in
the value set associated with the connection set (``params_map``).

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.. code-block:: Python


    params_map = {"weight": 0, "delay": 1}
    connspec = {"rule": "conngen", "cg": cg, "params_map": params_map}
    nest.Connect(pre, post, connspec)


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To stimulate the network, we create a ``poisson_generator`` and set it up to
fire with a rate of 100000 spikes per second. It is connected to the
neurons of the pre-synaptic population.

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.. code-block:: Python


    pg = nest.Create("poisson_generator", params={"rate": 100000.0})
    nest.Connect(pg, pre, "all_to_all")


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To measure and record the membrane potentials of the neurons, we create a
``voltmeter`` and connect it to all postsynaptic nodes.

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.. code-block:: Python


    vm = nest.Create("voltmeter")
    nest.Connect(vm, post, "all_to_all")


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We save the whole connection graph of the network as a PNG image using the
``plot_network`` function of the ``visualization`` submodule of PyNEST.

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.. code-block:: Python


    allnodes = pg + pre + post + vm
    visualization.plot_network(allnodes, "csa_example_graph.png")


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Finally, we simulate the network for 50 ms. The voltage traces of the
postsynaptic nodes are plotted.

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.. code-block:: Python


    nest.Simulate(50.0)
    voltage_trace.from_device(vm)
    plt.show()


.. _sphx_glr_download_auto_examples_csa_example.py:

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

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

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

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

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


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