.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/matrix_plot/nonsquare_plot.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_matrix_plot_nonsquare_plot.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_matrix_plot_nonsquare_plot.py:


Non-Square Matrix Plots
=======================
This example demonstrates how to create a non-square matrix of correlation plots. This is useful for comparing a single application
(or perhaps a few) with a suite of experiments (like those from the ICSBEP handbook [Bess2019]_).

.. GENERATED FROM PYTHON SOURCE LINES 9-15

Generating the Plot
-------------------
The steps for creating the matrix plot are just as described in :ref:`sphx_glr_auto_examples_matrix_plot_matrix_of_contribution_plots.py`
except that the application files and experiment files lists are not the same length. Recall, first the uncertainty contributions
must be generated from the given SDF profiles using :func:`tsunami_ip_utils.integral_indices.get_uncertainty_contributions`. In this
example, we will choose a single application from the MCT directory, and compare it to all other SDFS in the MCT directory.

.. GENERATED FROM PYTHON SOURCE LINES 15-32

.. code-block:: Python


    from tsunami_ip_utils.viz.viz import matrix_plot
    from tsunami_ip_utils.viz.plot_utils import generate_plot_objects_array_from_contributions
    from tsunami_ip_utils.integral_indices import get_uncertainty_contributions
    from paths import EXAMPLES
    import os

    # Get the filenames of all of the SDF profiles in the MCT directory
    all_mct_sdfs = os.listdir(EXAMPLES / 'data' / 'example_sdfs' / 'MCT')

    # Generate the lists of application and experiment SDF file paths
    application_files = [ EXAMPLES / 'data' / 'example_sdfs' / 'MCT' / f'MIX-COMP-THERM-001-001.sdf' ]
    experiment_files = [ EXAMPLES / 'data' / 'example_sdfs' / 'MCT' / filename for filename in all_mct_sdfs 
                        if filename != 'MIX-COMP-THERM-001-001.sdf' ]

    contributions_nuclide, contributions_nuclide_reaction = get_uncertainty_contributions(application_files, experiment_files, variance=True)








.. GENERATED FROM PYTHON SOURCE LINES 33-36

Then the matrix of plot objects (that is plotted using :func:`tsunami_ip_utils.viz.viz.matrix_plot`) can be generated using
:func:`tsunami_ip_utils.viz.plot_utils.generate_plot_objects_array_from_contributions` (note we also label the matrix cells by the
application and experiment sdf filenames by explicitly passing the ``labels`` dictionary as an argument).

.. GENERATED FROM PYTHON SOURCE LINES 36-47

.. code-block:: Python


    labels = {
        'applications': [ application_file.name for application_file in application_files ],
        'experiments': [ experiment_file.name for experiment_file in experiment_files ],
    }

    plot_objects_array = generate_plot_objects_array_from_contributions(
        contributions_nuclide,
        integral_index_name='(Δk/k)^2'
    )








.. GENERATED FROM PYTHON SOURCE LINES 48-49

Finally, the matrix plot can be generated and displayed.

.. GENERATED FROM PYTHON SOURCE LINES 49-53

.. code-block:: Python


    fig = matrix_plot(plot_objects_array, plot_type='interactive', labels=labels)
    fig.show()




.. raw:: html

    <div class="zoomControls">
        <button type="button" onclick="zoom(1, this)">- Zoom Out</button>
        <button type="button" onclick="zoom(-1, this)">+ Zoom In</button>
    </div>
    <div class="iframe_container">
        <iframe src="../../_static/plot_44a328f1-8560-4d20-bf20-122ee4d35813.html" width="100%" height="100%" frameborder="0" class="myiframe"></iframe>
    </div>
    <script>
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            const zoomContainer = element.closest('.zoomControls');
            const iframeContainer = zoomContainer.nextElementSibling;
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            // Initialize or retrieve the current scale
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            let originalWidth = parseFloat($(iframe).data('originalWidth')) || $(iframe).width();
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            // Store the updated scale
            $(iframe).data('scale', currentScale);

            // Apply the new scale as a CSS transform
            $(iframe).css('transform', `scale(${currentScale})`);
            $(iframe).css('width', `${scaledWidth}px`);
            $(iframe).css('height', `${scaledHeight}px`);
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        function applyInitialScale() {
            document.querySelectorAll('.myiframe').forEach(iframe => {
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                $(iframe).data('originalWidth', originalWidth);
                $(iframe).data('originalHeight', originalHeight);

                let scaledWidth = originalWidth / initialScale;
                let scaledHeight = originalHeight / initialScale;

                iframe.style.width = `${scaledWidth}px`;
                iframe.style.height = `${scaledHeight}px`;
                iframe.style.transform = 'scale(0.5)'; // Apply a neutral scale since size is already adjusted
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        window.onload = function() {
            // Apply initial scale to all iframes when the page loads
            applyInitialScale();
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.. GENERATED FROM PYTHON SOURCE LINES 54-61

Sorting the Matrix
------------------
If you're comparins a single application to multiple experiments, it may be useful to sort the experiments by the computed correlation
coefficient (pearson, spearman, etc.). Since a plot objects array is first generated, then plotted using the 
:func:`tsunami_ip_utils.viz.viz.matrix_plot` function, we may arbitrarily reorder the plot objects array before plotting. However,
we must be careful to reorder the labels dictionary in the same way as the plot objects array. The following code demonstrates how
to sort the experiments by the pearson correlation coefficient.

.. GENERATED FROM PYTHON SOURCE LINES 61-71

.. code-block:: Python


    import numpy as np

    # Sort the plot objects array by the pearson correlation coefficient using numpy.argsort and get the indices representing the sorted
    # ordering of plot objects.

    sorted_indices = np.argsort([ plot_object.statistics['pearson'] for plot_object in plot_objects_array[:, 0] ])

    # Note [:,0] indexes the fist column of the matrix plot, i.e. the column corresponding to the first application.








.. GENERATED FROM PYTHON SOURCE LINES 72-74

Note that by default :func:`numpy.argsort` returns the indices that would sort the array in ascending order. If you want to sort in
descending order, you can reverse the order of the indices as follows:

.. GENERATED FROM PYTHON SOURCE LINES 74-87

.. code-block:: Python


    sorted_indices = sorted_indices[::-1]

    # Sort the plot objects by the indices
    sorted_plot_objects_array = plot_objects_array[sorted_indices]

    # Sort the experiment labels by the indices
    labels['experiments'] = [ labels['experiments'][index] for index in sorted_indices ]

    # now plot the sorted matrix
    fig = matrix_plot(sorted_plot_objects_array, plot_type='interactive', labels=labels)
    fig.show()




.. raw:: html

    <div class="zoomControls">
        <button type="button" onclick="zoom(1, this)">- Zoom Out</button>
        <button type="button" onclick="zoom(-1, this)">+ Zoom In</button>
    </div>
    <div class="iframe_container">
        <iframe src="../../_static/plot_b7f379c5-752e-4396-a26a-5286f4521329.html" width="100%" height="100%" frameborder="0" class="myiframe"></iframe>
    </div>
    <script>
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            const zoomContainer = element.closest('.zoomControls');
            const iframeContainer = zoomContainer.nextElementSibling;
            const iframe = iframeContainer.querySelector('.myiframe');

            // Initialize or retrieve the current scale
            let currentScale = parseFloat($(iframe).data('scale')) || 0.5;
            let originalWidth = parseFloat($(iframe).data('originalWidth')) || $(iframe).width();
            let originalHeight = parseFloat($(iframe).data('originalHeight')) || $(iframe).height();

            if (direction === 1) {
                currentScale /= 1.1;
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            let scaledHeight = originalHeight / currentScale;

            // Store the updated scale
            $(iframe).data('scale', currentScale);

            // Apply the new scale as a CSS transform
            $(iframe).css('transform', `scale(${currentScale})`);
            $(iframe).css('width', `${scaledWidth}px`);
            $(iframe).css('height', `${scaledHeight}px`);
        }

        function applyInitialScale() {
            document.querySelectorAll('.myiframe').forEach(iframe => {
                let initialScale = 0.5;
                let originalWidth = $(iframe).width();
                let originalHeight = $(iframe).height();

                $(iframe).data('scale', initialScale);
                $(iframe).data('originalWidth', originalWidth);
                $(iframe).data('originalHeight', originalHeight);

                let scaledWidth = originalWidth / initialScale;
                let scaledHeight = originalHeight / initialScale;

                iframe.style.width = `${scaledWidth}px`;
                iframe.style.height = `${scaledHeight}px`;
                iframe.style.transform = 'scale(0.5)'; // Apply a neutral scale since size is already adjusted
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        window.onload = function() {
            // Apply initial scale to all iframes when the page loads
            applyInitialScale();
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    </script>
            




.. GENERATED FROM PYTHON SOURCE LINES 88-89

A static image of the plot can be saved using the :meth:`tsunami_ip_utils.viz.matrix_plot.InteractiveMatrixPlot.to_image` method

.. GENERATED FROM PYTHON SOURCE LINES 89-93

.. code-block:: Python


    fig = matrix_plot(plot_objects_array[:4, :], plot_type='interactive') # Note we're only saving a portion of the matrix to get a nice image
    fig.to_image( EXAMPLES / '_static' / 'nonsquare_matrix.png' )

    # sphinx_gallery_thumbnail_path = '../../examples/_static/nonsquare_matrix.png'


.. raw:: html

    <div class="zoomControls">
        <button type="button" onclick="zoom(1, this)">- Zoom Out</button>
        <button type="button" onclick="zoom(-1, this)">+ Zoom In</button>
    </div>
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        <iframe src="../../_static/plot_1492cafc-b14e-4499-9c12-1102e9d85d71.html" width="100%" height="100%" frameborder="0" class="myiframe"></iframe>
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            let scaledHeight = originalHeight / currentScale;

            // Store the updated scale
            $(iframe).data('scale', currentScale);

            // Apply the new scale as a CSS transform
            $(iframe).css('transform', `scale(${currentScale})`);
            $(iframe).css('width', `${scaledWidth}px`);
            $(iframe).css('height', `${scaledHeight}px`);
        }

        function applyInitialScale() {
            document.querySelectorAll('.myiframe').forEach(iframe => {
                let initialScale = 0.5;
                let originalWidth = $(iframe).width();
                let originalHeight = $(iframe).height();

                $(iframe).data('scale', initialScale);
                $(iframe).data('originalWidth', originalWidth);
                $(iframe).data('originalHeight', originalHeight);

                let scaledWidth = originalWidth / initialScale;
                let scaledHeight = originalHeight / initialScale;

                iframe.style.width = `${scaledWidth}px`;
                iframe.style.height = `${scaledHeight}px`;
                iframe.style.transform = 'scale(0.5)'; // Apply a neutral scale since size is already adjusted
                iframe.style.transformOrigin = '0 0'; // Transform from top left corner
            });
        }

        window.onload = function() {
            // Apply initial scale to all iframes when the page loads
            applyInitialScale();
        };
    </script>
            





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

   **Total running time of the script:** (1 minutes 0.247 seconds)


.. _sphx_glr_download_auto_examples_matrix_plot_nonsquare_plot.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: nonsquare_plot.zip <nonsquare_plot.zip>`


.. only:: html

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

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