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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_spatial_conncon_sources.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_spatial_conncon_sources.py:


Convergent projection and rectangular mask, from target perspective
-------------------------------------------------------------------

Create two populations of iaf_psc_alpha neurons on a 30x30 grid

Connect the two populations with convergent projection and rectangular mask
and visualize connection from target perspective.

BCCN Tutorial @ CNS*09
Hans Ekkehard Plesser, UMB

.. GENERATED FROM PYTHON SOURCE LINES 34-44

.. code-block:: default


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

    nest.ResetKernel()
    nest.set_verbosity('M_WARNING')

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


.. GENERATED FROM PYTHON SOURCE LINES 45-46

create and connect two populations

.. GENERATED FROM PYTHON SOURCE LINES 46-58

.. code-block:: default

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

    nest.Connect(a, b,
                 conn_spec={'rule': 'pairwise_bernoulli',
                            'p': 0.5,
                            'use_on_source': True,
                            'mask': {'rectangular': {'lower_left': [-0.2, -0.5],
                                                     'upper_right': [0.2, 0.5]}}},
                 syn_spec={'weight': nest.random.uniform(0.5, 2.)})
    plt.clf()


.. GENERATED FROM PYTHON SOURCE LINES 59-60

plot sources of neurons in different grid locations

.. GENERATED FROM PYTHON SOURCE LINES 60-98

.. code-block:: default

    for tgt_index in [30 * 15 + 15, 0]:
        # obtain node id for center
        tgt = a[tgt_index:tgt_index + 1]

        # obtain list of outgoing connections for ctr
        spos = nest.GetTargetPositions(tgt, b)[0]

        spos_x = np.array([x for x, y in spos])
        spos_y = np.array([y for x, y in spos])

        print(spos_x)
        print(spos_y)

        # scatter-plot
        plt.scatter(spos_x, spos_y, 20, zorder=10)

        # mark sender position with transparent red circle
        ctrpos = np.array(nest.GetPosition(tgt))
        plt.gca().add_patch(plt.Circle(ctrpos, radius=0.1, zorder=99,
                                       fc='r', alpha=0.4, ec='none'))

        # mark mask position with open red rectangle
        plt.gca().add_patch(
            plt.Rectangle(ctrpos - (0.2, 0.5), 0.4, 1.0, zorder=1,
                          fc='none', ec='r', lw=3))

    # mark layer edge
    plt.gca().add_patch(plt.Rectangle((-1.5, -1.5), 3.0, 3.0, zorder=1,
                                      fc='none', ec='k', lw=3))

    # 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 sources')
    plt.show()


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

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


.. _sphx_glr_download_auto_examples_spatial_conncon_sources.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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