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        Click :ref:`here <sphx_glr_download_auto_examples_csa_spatial_example.py>`
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.. _sphx_glr_auto_examples_csa_spatial_example.py:


Using CSA with spatial populations
----------------------------------

This example shows a brute-force way of specifying connections between
NEST populations with spatial data using Connection Set Algebra instead of
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_example`

References
~~~~~~~~~~

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

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First, we import all necessary modules.

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.. code-block:: default


    import nest
    import matplotlib.pyplot as plt


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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:: default


    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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We define a factory that returns a CSA-style geometry function for
the given layer. The function returned will return for each CSA-index
the position in space of the given neuron as a 2- or 3-element list.

This function stores a copy of the neuron positions internally, entailing
memory overhead.

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.. code-block:: default



    def geometryFunction(population):

        positions = nest.GetPosition(population)

        def geometry_function(idx):
            return positions[idx]

        return geometry_function



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We create two spatial populations that have 20x20 neurons of type
``iaf_psc_alpha``.

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.. code-block:: default


    pop1 = nest.Create('iaf_psc_alpha', positions=nest.spatial.grid([20, 20]))
    pop2 = nest.Create('iaf_psc_alpha', positions=nest.spatial.grid([20, 20]))


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For each population, we create a CSA-style geometry function and a CSA metric
based on them.

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.. code-block:: default


    g1 = geometryFunction(pop1)
    g2 = geometryFunction(pop2)
    d = csa.euclidMetric2d(g1, g2)


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The connection set `cg` describes a Gaussian connectivity profile with
``sigma = 0.2`` and cutoff at 0.5, and two values (10000.0 and 1.0) used as
``weight`` and ``delay``, respectively.

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.. code-block:: default


    cg = csa.cset(csa.random * (csa.gaussian(0.2, 0.5) * d), 10000.0, 1.0)


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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 (``pop1`` and ``pop2``), the connection set (``cg``) and a
dictionary that map the parameters weight and delay to positions in
the value set associated with the connection set (``params_map``).

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.. code-block:: default


    params_map = {"weight": 0, "delay": 1}
    connspec = {"rule": "conngen", "cg": cg, "params_map": params_map}
    nest.Connect(pop1, pop2, connspec)


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Finally, we use the ``PlotTargets`` function to show all targets in `pop2`
starting at the center neuron of `pop1`.

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.. code-block:: default


    cntr = nest.FindCenterElement(pop1)
    nest.PlotTargets(cntr, pop2)
    plt.show()


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.. _sphx_glr_download_auto_examples_csa_spatial_example.py:

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