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

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_scalable_plot_diagonal.py>`
        to download the full example code

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

.. _sphx_glr_examples_scalable_plot_diagonal.py:


Scalable diagonal discipline
============================

Let us consider the
:class:`~gemseo.problems.mdo.sobieski.disciplines.SobieskiAerodynamics` discipline.
We want to build its :class:`.ScalableDiscipline` counterpart,
using a :class:`.ScalableDiagonalModel`

For that, we can use a 20-length :class:`.DiagonalDOE`
and test different sizes of variables or different settings
for the scalable diagonal discipline.

.. GENERATED FROM PYTHON SOURCE LINES 34-43

.. code-block:: Python


    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_discipline
    from gemseo import create_scalable
    from gemseo import create_scenario
    from gemseo.problems.mdo.sobieski.core.design_space import SobieskiDesignSpace








.. GENERATED FROM PYTHON SOURCE LINES 44-46

Import
------

.. GENERATED FROM PYTHON SOURCE LINES 46-50

.. code-block:: Python


    configure_logger()






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <RootLogger root (INFO)>



.. GENERATED FROM PYTHON SOURCE LINES 51-55

Learning dataset
----------------
The first step is to build an :class:`.AbstractFullCache` dataset
from a :class:`.DiagonalDOE`.

.. GENERATED FROM PYTHON SOURCE LINES 57-62

Instantiate the discipline
~~~~~~~~~~~~~~~~~~~~~~~~~~
For that, we instantiate the
:class:`~gemseo.problems.mdo.sobieski.disciplines.SobieskiAerodynamics` discipline
and set it up to cache all evaluations.

.. GENERATED FROM PYTHON SOURCE LINES 62-64

.. code-block:: Python

    discipline = create_discipline("SobieskiAerodynamics")








.. GENERATED FROM PYTHON SOURCE LINES 65-68

Get the input space
~~~~~~~~~~~~~~~~~~~
We also define the input space on which to sample the discipline.

.. GENERATED FROM PYTHON SOURCE LINES 68-72

.. code-block:: Python

    input_space = SobieskiDesignSpace()
    input_names = [name for name in discipline.get_input_data_names() if name != "c_4"]
    input_space.filter(input_names)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div style='background-color: #fafae2; padding: 10px;'>Sobieski design space:<br/><table>
        <tr>
            <th style='text-align: left;'>Name</th>
            <th style='text-align: left;'>Lower bound</th>
            <th style='text-align: left;'>Value</th>
            <th style='text-align: left;'>Upper bound</th>
            <th style='text-align: left;'>Type</th>
        </tr>
        <tr>
            <td>x_shared[0]</td>
            <td>0.01</td>
            <td>0.05</td>
            <td>0.09</td>
            <td>float</td>
        </tr>
        <tr>
            <td>x_shared[1]</td>
            <td>30000</td>
            <td>45000</td>
            <td>60000</td>
            <td>float</td>
        </tr>
        <tr>
            <td>x_shared[2]</td>
            <td>1.4</td>
            <td>1.6</td>
            <td>1.8</td>
            <td>float</td>
        </tr>
        <tr>
            <td>x_shared[3]</td>
            <td>2.5</td>
            <td>5.5</td>
            <td>8.5</td>
            <td>float</td>
        </tr>
        <tr>
            <td>x_shared[4]</td>
            <td>40</td>
            <td>55</td>
            <td>70</td>
            <td>float</td>
        </tr>
        <tr>
            <td>x_shared[5]</td>
            <td>500</td>
            <td>1000</td>
            <td>1500</td>
            <td>float</td>
        </tr>
        <tr>
            <td>x_2</td>
            <td>0.75</td>
            <td>1</td>
            <td>1.25</td>
            <td>float</td>
        </tr>
        <tr>
            <td>y_32</td>
            <td>0.235</td>
            <td>0.5027962499999999</td>
            <td>0.795</td>
            <td>float</td>
        </tr>
        <tr>
            <td>y_12[0]</td>
            <td>24850</td>
            <td>50606.9742</td>
            <td>77250</td>
            <td>float</td>
        </tr>
        <tr>
            <td>y_12[1]</td>
            <td>0.45</td>
            <td>0.95</td>
            <td>1.5</td>
            <td>float</td>
        </tr>
    </table></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 73-79

Build the DOE scenario
~~~~~~~~~~~~~~~~~~~~~~
Lastly, we sample the discipline by means of a :class:`.DOEScenario`
relying on both discipline and input space.
In order to build a diagonal scalable discipline,
a :class:`.DiagonalDOE` must be used.

.. GENERATED FROM PYTHON SOURCE LINES 79-87

.. code-block:: Python

    scenario = create_scenario(
        [discipline], "DisciplinaryOpt", "y_2", input_space, scenario_type="DOE"
    )
    for output_name in discipline.get_output_data_names():
        if output_name != "y_2":
            scenario.add_observable(output_name)
    scenario.execute({"algo": "DiagonalDOE", "n_samples": 20})





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

        INFO - 08:56:36:  
        INFO - 08:56:36: *** Start DOEScenario execution ***
        INFO - 08:56:36: DOEScenario
        INFO - 08:56:36:    Disciplines: SobieskiAerodynamics
        INFO - 08:56:36:    MDO formulation: DisciplinaryOpt
        INFO - 08:56:36: Optimization problem:
        INFO - 08:56:36:    minimize y_2(x_shared, x_2, y_32, y_12)
        INFO - 08:56:36:    with respect to x_2, x_shared, y_12, y_32
        INFO - 08:56:36:    over the design space:
        INFO - 08:56:36:       +-------------+-------------+--------------------+-------------+-------+
        INFO - 08:56:36:       | Name        | Lower bound |       Value        | Upper bound | Type  |
        INFO - 08:56:36:       +-------------+-------------+--------------------+-------------+-------+
        INFO - 08:56:36:       | x_shared[0] |     0.01    |        0.05        |     0.09    | float |
        INFO - 08:56:36:       | x_shared[1] |    30000    |       45000        |    60000    | float |
        INFO - 08:56:36:       | x_shared[2] |     1.4     |        1.6         |     1.8     | float |
        INFO - 08:56:36:       | x_shared[3] |     2.5     |        5.5         |     8.5     | float |
        INFO - 08:56:36:       | x_shared[4] |      40     |         55         |      70     | float |
        INFO - 08:56:36:       | x_shared[5] |     500     |        1000        |     1500    | float |
        INFO - 08:56:36:       | x_2         |     0.75    |         1          |     1.25    | float |
        INFO - 08:56:36:       | y_32        |    0.235    | 0.5027962499999999 |    0.795    | float |
        INFO - 08:56:36:       | y_12[0]     |    24850    |     50606.9742     |    77250    | float |
        INFO - 08:56:36:       | y_12[1]     |     0.45    |        0.95        |     1.5     | float |
        INFO - 08:56:36:       +-------------+-------------+--------------------+-------------+-------+
        INFO - 08:56:36: Solving optimization problem with algorithm DiagonalDOE:
        INFO - 08:56:36:      5%|▌         | 1/20 [00:00<00:00, 164.53 it/sec, obj=[2.48500000e+04 5.75800074e+03 4.31573408e+00]]
        INFO - 08:56:36:     10%|█         | 2/20 [00:00<00:00, 248.22 it/sec, obj=[2.76078947e+04 6.45112022e+03 4.27955049e+00]]
        INFO - 08:56:36:     15%|█▌        | 3/20 [00:00<00:00, 301.92 it/sec, obj=[3.03657895e+04 7.17864545e+03 4.23001661e+00]]
        INFO - 08:56:36:     20%|██        | 4/20 [00:00<00:00, 337.43 it/sec, obj=[3.31236842e+04 7.92362686e+03 4.18036902e+00]]
        INFO - 08:56:36:     25%|██▌       | 5/20 [00:00<00:00, 364.38 it/sec, obj=[3.58815789e+04 8.66958857e+03 4.13878683e+00]]
        INFO - 08:56:36:     30%|███       | 6/20 [00:00<00:00, 383.29 it/sec, obj=[3.86394737e+04 9.40253365e+03 4.10947465e+00]]
        INFO - 08:56:36:     35%|███▌      | 7/20 [00:00<00:00, 399.12 it/sec, obj=[4.13973684e+04 1.01086635e+04 4.09523655e+00]]
        INFO - 08:56:36:     40%|████      | 8/20 [00:00<00:00, 411.32 it/sec, obj=[4.41552632e+04 1.07748595e+04 4.09798968e+00]]
        INFO - 08:56:36:     45%|████▌     | 9/20 [00:00<00:00, 421.83 it/sec, obj=[4.69131579e+04 1.13905076e+04 4.11861872e+00]]
        INFO - 08:56:36:     50%|█████     | 10/20 [00:00<00:00, 428.96 it/sec, obj=[4.96710526e+04 1.19440252e+04 4.15865270e+00]]
        INFO - 08:56:36:     55%|█████▌    | 11/20 [00:00<00:00, 436.67 it/sec, obj=[5.24289474e+04 1.24253852e+04 4.21950279e+00]]
        INFO - 08:56:36:     60%|██████    | 12/20 [00:00<00:00, 442.43 it/sec, obj=[5.51868421e+04 1.28262516e+04 4.30264771e+00]]
        INFO - 08:56:36:     65%|██████▌   | 13/20 [00:00<00:00, 448.00 it/sec, obj=[5.79447368e+04 1.31400641e+04 4.40977580e+00]]
        INFO - 08:56:36:     70%|███████   | 14/20 [00:00<00:00, 452.06 it/sec, obj=[6.07026316e+04 1.33620772e+04 4.54290381e+00]]
        INFO - 08:56:36:     75%|███████▌  | 15/20 [00:00<00:00, 456.74 it/sec, obj=[6.34605263e+04 1.34893612e+04 4.70448714e+00]]
        INFO - 08:56:36:     80%|████████  | 16/20 [00:00<00:00, 459.88 it/sec, obj=[6.62184211e+04 1.35166373e+04 4.89903071e+00]]
        INFO - 08:56:36:     85%|████████▌ | 17/20 [00:00<00:00, 463.64 it/sec, obj=[6.89763158e+04 1.34436796e+04 5.13076164e+00]]
        INFO - 08:56:36:     90%|█████████ | 18/20 [00:00<00:00, 466.28 it/sec, obj=[7.17342105e+04 1.32763007e+04 5.40317758e+00]]
        INFO - 08:56:36:     95%|█████████▌| 19/20 [00:00<00:00, 469.44 it/sec, obj=[7.44921053e+04 1.30407235e+04 5.71226782e+00]]
        INFO - 08:56:36:    100%|██████████| 20/20 [00:00<00:00, 471.96 it/sec, obj=[7.72500000e+04 1.27392070e+04 6.06395673e+00]]
        INFO - 08:56:36: Optimization result:
        INFO - 08:56:36:    Optimizer info:
        INFO - 08:56:36:       Status: None
        INFO - 08:56:36:       Message: None
        INFO - 08:56:36:       Number of calls to the objective function by the optimizer: 20
        INFO - 08:56:36:    Solution:
        INFO - 08:56:36:       Objective: 25508.372961119574
        INFO - 08:56:36:       Design space:
        INFO - 08:56:36:          +-------------+-------------+-------+-------------+-------+
        INFO - 08:56:36:          | Name        | Lower bound | Value | Upper bound | Type  |
        INFO - 08:56:36:          +-------------+-------------+-------+-------------+-------+
        INFO - 08:56:36:          | x_shared[0] |     0.01    |  0.01 |     0.09    | float |
        INFO - 08:56:36:          | x_shared[1] |    30000    | 30000 |    60000    | float |
        INFO - 08:56:36:          | x_shared[2] |     1.4     |  1.4  |     1.8     | float |
        INFO - 08:56:36:          | x_shared[3] |     2.5     |  2.5  |     8.5     | float |
        INFO - 08:56:36:          | x_shared[4] |      40     |   40  |      70     | float |
        INFO - 08:56:36:          | x_shared[5] |     500     |  500  |     1500    | float |
        INFO - 08:56:36:          | x_2         |     0.75    |  0.75 |     1.25    | float |
        INFO - 08:56:36:          | y_32        |    0.235    | 0.235 |    0.795    | float |
        INFO - 08:56:36:          | y_12[0]     |    24850    | 24850 |    77250    | float |
        INFO - 08:56:36:          | y_12[1]     |     0.45    |  0.45 |     1.5     | float |
        INFO - 08:56:36:          +-------------+-------------+-------+-------------+-------+
        INFO - 08:56:36: *** End DOEScenario execution (time: 0:00:00.058856) ***

    {'eval_jac': False, 'n_samples': 20, 'algo': 'DiagonalDOE'}



.. GENERATED FROM PYTHON SOURCE LINES 88-90

Scalable diagonal discipline
----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 92-97

Build the scalable discipline
-----------------------------
The second step is to build a :class:`.ScalableDiscipline`,
using a :class:`.ScalableDiagonalModel` and the database
converted to a :class:`.Dataset`.

.. GENERATED FROM PYTHON SOURCE LINES 97-100

.. code-block:: Python

    dataset = scenario.to_dataset(opt_naming=False)
    scalable = create_scalable("ScalableDiagonalModel", dataset)








.. GENERATED FROM PYTHON SOURCE LINES 101-112

Visualize the input-output dependencies
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
We can easily access the underlying :class:`.ScalableDiagonalModel`
and plot the corresponding input-output dependency matrix
where the level of gray and the number (in [0,100]) represent
the degree of dependency between inputs and outputs.
Input are on the left while outputs are at the top.
More precisely, for a given output component located at the top of the graph,
these degrees are contributions to the output component and they add up to 1.
In other words, a degree expresses this contribution in percentage
and for a given column, the elements add up to 100.

.. GENERATED FROM PYTHON SOURCE LINES 112-114

.. code-block:: Python

    scalable.scalable_model.plot_dependency(save=False, show=True)




.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_001.png
   :alt: plot diagonal
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'None'



.. GENERATED FROM PYTHON SOURCE LINES 115-119

Visualize the 1D interpolations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
For every output, we can also visualize a spline interpolation of the output
samples over the diagonal of the input space.

.. GENERATED FROM PYTHON SOURCE LINES 119-121

.. code-block:: Python

    scalable.scalable_model.plot_1d_interpolations(save=False, show=True)




.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_002.png
         :alt: 1D interpolation of sdm_DOEScenario.g_2
         :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_003.png
         :alt: 1D interpolation of sdm_DOEScenario.y_2
         :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_004.png
         :alt: 1D interpolation of sdm_DOEScenario.y_2
         :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_004.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_005.png
         :alt: 1D interpolation of sdm_DOEScenario.y_2
         :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_005.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_006.png
         :alt: 1D interpolation of sdm_DOEScenario.y_21
         :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_006.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_007.png
         :alt: 1D interpolation of sdm_DOEScenario.y_23
         :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_007.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_008.png
         :alt: 1D interpolation of sdm_DOEScenario.y_24
         :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_008.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    []



.. GENERATED FROM PYTHON SOURCE LINES 122-126

Increased problem dimension
---------------------------
We can repeat the construction of the scalable discipline for different sizes
of variables and visualize the input-output dependency matrices.

.. GENERATED FROM PYTHON SOURCE LINES 128-131

Twice as many inputs
~~~~~~~~~~~~~~~~~~~~
For example, we can increase the size of each input by a factor of 2.

.. GENERATED FROM PYTHON SOURCE LINES 131-135

.. code-block:: Python

    sizes = {name: dataset.variable_names_to_n_components[name] * 2 for name in input_names}
    scalable = create_scalable("ScalableDiagonalModel", dataset, sizes)
    scalable.scalable_model.plot_dependency(save=False, show=True)




.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_009.png
   :alt: plot diagonal
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_009.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'None'



.. GENERATED FROM PYTHON SOURCE LINES 136-139

Twice as many outputs
~~~~~~~~~~~~~~~~~~~~~
Or we can increase the size of each output by a factor of 2.

.. GENERATED FROM PYTHON SOURCE LINES 139-146

.. code-block:: Python

    sizes = {
        name: discipline.cache.names_to_sizes[name] * 2
        for name in discipline.get_output_data_names()
    }
    scalable = create_scalable("ScalableDiagonalModel", dataset, sizes)
    scalable.scalable_model.plot_dependency(save=False, show=True)




.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_010.png
   :alt: plot diagonal
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_010.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'None'



.. GENERATED FROM PYTHON SOURCE LINES 147-150

Twice as many variables
~~~~~~~~~~~~~~~~~~~~~~~
Or we can increase the size of each input and each output by a factor of 2.

.. GENERATED FROM PYTHON SOURCE LINES 150-155

.. code-block:: Python

    names = input_names + list(discipline.get_output_data_names())
    sizes = {name: dataset.variable_names_to_n_components[name] * 2 for name in names}
    scalable = create_scalable("ScalableDiagonalModel", dataset, sizes)
    scalable.scalable_model.plot_dependency(save=False, show=True)




.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_011.png
   :alt: plot diagonal
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_011.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'None'



.. GENERATED FROM PYTHON SOURCE LINES 156-169

Binary IO dependencies
----------------------
By default, any output component depends on any input component
with a random level.
We can also consider sparser input-output dependency
by means of binary input-output dependency matrices.
For that, we have to set the value of the fill factor
which represents the part of connection between inputs and outputs.
Then, a connection is represented by a black square
while an absence of connection is presented by a white one.
When the fill factor is equal to 1, any input is connected to any output.
Conversely, when the fill factor is equal to 0,
there is not a single connection between inputs and outputs.

.. GENERATED FROM PYTHON SOURCE LINES 171-173

Fill factor = 0.2
~~~~~~~~~~~~~~~~~

.. GENERATED FROM PYTHON SOURCE LINES 173-176

.. code-block:: Python

    scalable = create_scalable("ScalableDiagonalModel", dataset, sizes, fill_factor=0.2)
    scalable.scalable_model.plot_dependency(save=False, show=True)




.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_012.png
   :alt: plot diagonal
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_012.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'None'



.. GENERATED FROM PYTHON SOURCE LINES 177-179

Fill factor = 0.5
~~~~~~~~~~~~~~~~~

.. GENERATED FROM PYTHON SOURCE LINES 179-182

.. code-block:: Python

    scalable = create_scalable("ScalableDiagonalModel", dataset, sizes, fill_factor=0.5)
    scalable.scalable_model.plot_dependency(save=False, show=True)




.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_013.png
   :alt: plot diagonal
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_013.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'None'



.. GENERATED FROM PYTHON SOURCE LINES 183-185

Fill factor = 0.8
~~~~~~~~~~~~~~~~~

.. GENERATED FROM PYTHON SOURCE LINES 185-188

.. code-block:: Python

    scalable = create_scalable("ScalableDiagonalModel", dataset, sizes, fill_factor=0.8)
    scalable.scalable_model.plot_dependency(save=False, show=True)




.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_014.png
   :alt: plot diagonal
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_014.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'None'



.. GENERATED FROM PYTHON SOURCE LINES 189-191

Heterogeneous dependencies
--------------------------

.. GENERATED FROM PYTHON SOURCE LINES 191-196

.. code-block:: Python

    scalable = create_scalable(
        "ScalableDiagonalModel", dataset, sizes, fill_factor={"y_2": 0.2}
    )
    scalable.scalable_model.plot_dependency(save=False, show=True)




.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_015.png
   :alt: plot diagonal
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_015.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'None'



.. GENERATED FROM PYTHON SOURCE LINES 197-199

Group dependencies
------------------

.. GENERATED FROM PYTHON SOURCE LINES 199-203

.. code-block:: Python

    scalable = create_scalable(
        "ScalableDiagonalModel", dataset, sizes, group_dep={"y_2": ["x_shared"]}
    )
    scalable.scalable_model.plot_dependency(save=False, show=True)



.. image-sg:: /examples/scalable/images/sphx_glr_plot_diagonal_016.png
   :alt: plot diagonal
   :srcset: /examples/scalable/images/sphx_glr_plot_diagonal_016.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    'None'




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

   **Total running time of the script:** (0 minutes 3.544 seconds)


.. _sphx_glr_download_examples_scalable_plot_diagonal.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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