.. 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

        Click :ref:`here <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:


Gaussian probabilistic kernel
-----------------------------

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:: default


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

    nest.ResetKernel()


.. GENERATED FROM PYTHON SOURCE LINES 38-39

create two test layers

.. GENERATED FROM PYTHON SOURCE LINES 39-41

.. code-block:: default

    pos = nest.spatial.grid(shape=[30, 30], extent=[3., 3.])


.. GENERATED FROM PYTHON SOURCE LINES 42-43

create and connect two populations

.. GENERATED FROM PYTHON SOURCE LINES 43-53

.. code-block:: default

    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 54-58

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 58-79

.. code-block:: default


    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')


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _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-python

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

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

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


.. only:: html

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

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