.. 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

        :ref:`Go to the end <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:


Spatial networks: Convergent projection and rectangular mask, from target perspective
-------------------------------------------------------------------------------------

.. 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%2Fconncon_sources.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 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:: Python


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

    nest.ResetKernel()
    nest.set_verbosity("M_WARNING")

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


.. GENERATED FROM PYTHON SOURCE LINES 45-46

create and connect two populations

.. GENERATED FROM PYTHON SOURCE LINES 46-62

.. code-block:: Python

    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, 2.0)},
    )
    plt.clf()


.. GENERATED FROM PYTHON SOURCE LINES 63-64

plot sources of neurons in different grid locations

.. GENERATED FROM PYTHON SOURCE LINES 64-98

.. code-block:: Python

    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()


.. _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-jupyter

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

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

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


.. only:: html

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

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