.. _sphx_glr_auto_examples_sudoku:

NEST Sudoku
===========

A PyNEST implementation of Sudoku as a constraint satisfaction problem.

You can run the solver using ``sudoku_solver.py``. It will display the
solution in the end.

To visualize the solution process, you can afterwards run ``plot_progress.py``;
this requires the ``imageio`` package.



.. raw:: html

    <div class="sphx-glr-thumbnails">

.. thumbnail-parent-div-open

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This script instantiates a network and configures it to solve a Sudoku puzzle. It then simulates the network until it either converges on a solution or a maximum simulation time has been reached.">

.. only:: html

  .. image:: /auto_examples/sudoku/images/thumb/sphx_glr_run_example_thumb.png
    :alt:

  :doc:`/auto_examples/sudoku/run_example`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Script controlling the simulation of a single game of Sudoku</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="This script instantiates a network and configures it to solve a Sudoku puzzle. It then simulates the network until it either converges on a solution or a maximum simulation time has been reached.">

.. only:: html

  .. image:: /auto_examples/sudoku/images/thumb/sphx_glr_sudoku_solver_thumb.png
    :alt:

  :doc:`/auto_examples/sudoku/sudoku_solver`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Script controlling the simulation of a single game of Sudoku</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Note that the script generates the images individually, storing them to disk first, assembling them into a GIF and then, by default, deleting the images and folder.">

.. only:: html

  .. image:: /auto_examples/sudoku/images/thumb/sphx_glr_plot_progress_thumb.png
    :alt:

  :doc:`/auto_examples/sudoku/plot_progress`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Generate a GIF of the network solving a Sudoku puzzle</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip=" Notes ~~~~~ The functionality of the network lies entirely within the inhibitory connections between neuron populations. Each population of size n_digit encodes a digit between 1 and 9 in one of the 81 cells and has outgoing inhibitory connections to several other populations. Namely all populations coding for the same digit in the same row, column and 3x3 box of the sudoku field, since a given digit can only ever occur once in any of those three. It also inhibits all populations in the same cell which encode different digits to force the network to converge on a single digit per cell.">

.. only:: html

  .. image:: /auto_examples/sudoku/images/thumb/sphx_glr_sudoku_net_thumb.png
    :alt:

  :doc:`/auto_examples/sudoku/sudoku_net`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Class encapsulating the network for solving Sudoku</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip=":Authors: J Gille, S Furber, A Rowley">

.. only:: html

  .. image:: /auto_examples/sudoku/images/thumb/sphx_glr_helpers_sudoku_thumb.png
    :alt:

  :doc:`/auto_examples/sudoku/helpers_sudoku`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Helper functions for the Sudoku solver</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/sudoku/run_example
   /auto_examples/sudoku/sudoku_solver
   /auto_examples/sudoku/plot_progress
   /auto_examples/sudoku/sudoku_net
   /auto_examples/sudoku/helpers_sudoku