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

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

.. _sphx_glr_auto_examples_spatial_gaussex.py:


Spatial networks: Gaussian probabilistic kernel
-----------------------------------------------

.. 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%2Fspatial%2Fgaussex.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.

----


Create two populations on a 30x30 grid and connect them using a Gaussian probabilistic kernel
BCCN Tutorial @ CNS*09
Hans Ekkehard Plesser, UMB

.. GENERATED FROM PYTHON SOURCE LINES 30-37

.. code-block:: Python


    import matplotlib.pyplot as plt
    import nest
    import numpy as np

    nest.ResetKernel()


.. GENERATED FROM PYTHON SOURCE LINES 38-39

create two test layers

.. GENERATED FROM PYTHON SOURCE LINES 39-41

.. code-block:: Python

    pos = nest.spatial.grid(shape=[30, 30], extent=[3.0, 3.0])


.. GENERATED FROM PYTHON SOURCE LINES 42-43

create and connect two populations

.. GENERATED FROM PYTHON SOURCE LINES 43-54

.. code-block:: Python

    a = nest.Create("iaf_psc_alpha", positions=pos)
    b = nest.Create("iaf_psc_alpha", positions=pos)

    cdict = {
        "rule": "pairwise_bernoulli",
        "p": nest.spatial_distributions.gaussian(nest.spatial.distance, std=0.5),
        "mask": {"circular": {"radius": 3.0}},
    }

    nest.Connect(a, b, cdict)


.. GENERATED FROM PYTHON SOURCE LINES 55-59

plot targets of neurons in different grid locations

plot targets of two source neurons into same figure, with mask
use different colors

.. GENERATED FROM PYTHON SOURCE LINES 59-88

.. code-block:: Python


    for src_index, color, cmap in [(30 * 15 + 15, "blue", "Blues"), (0, "green", "Greens")]:
        # obtain node id for center
        src = a[src_index : src_index + 1]
        fig = plt.figure()
        nest.PlotTargets(
            src,
            b,
            mask=cdict["mask"],
            probability_parameter=cdict["p"],
            src_color=color,
            tgt_color=color,
            mask_color=color,
            probability_cmap=cmap,
            src_size=100,
            fig=fig,
        )

        # beautify
        plt.axes().set_xticks(np.arange(-1.5, 1.55, 0.5))
        plt.axes().set_yticks(np.arange(-1.5, 1.55, 0.5))
        plt.grid(True)
        plt.axis([-2.0, 2.0, -2.0, 2.0])
        plt.axes().set_aspect("equal", "box")
        plt.title("Connection targets, Gaussian kernel")

    plt.show()

    # plt.savefig('gaussex.pdf')


.. _sphx_glr_download_auto_examples_spatial_gaussex.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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