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

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

.. _sphx_glr_auto_examples_plot_bivariate_dist.py:


Bivariate Distribution
=======================

.. GENERATED FROM PYTHON SOURCE LINES 6-14

.. code-block:: Python


    import numpy as np
    import seaborn as sns
    from matplotlib import pyplot as plt
    from scipy.stats import gaussian_kde

    import marsilea as ma








.. GENERATED FROM PYTHON SOURCE LINES 15-16

Create datasets

.. GENERATED FROM PYTHON SOURCE LINES 16-36

.. code-block:: Python


    np.random.seed(0)
    rs = np.random.RandomState(50)
    x, y = rs.normal(size=(2, 50))
    xmin, ymin, xmax, ymax = x.min(), y.min(), x.max(), y.max()

    X, Y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
    positions = np.vstack([X.ravel(), Y.ravel()])
    values = np.vstack([x, y])

    main_kernel = gaussian_kde(values)

    Z = np.reshape(main_kernel(positions), X.shape)

    x_kernel = gaussian_kde(x)
    zx = x_kernel(np.mgrid[xmin:xmax:100j])

    y_kernel = gaussian_kde(x)
    zy = y_kernel(np.mgrid[ymin:ymax:100j])








.. GENERATED FROM PYTHON SOURCE LINES 37-38

Create skeletons and add contents

.. GENERATED FROM PYTHON SOURCE LINES 38-84

.. code-block:: Python


    wb = ma.WhiteBoard(width=3, height=3)
    # Reserve empty canvas for drawing latter
    wb.add_canvas("top", size=.4, pad=.1, name="x1")
    wb.add_canvas("bottom", size=.4, pad=.1, name="x2")
    wb.add_canvas("left", size=.4, pad=.1, name="y1")
    wb.add_canvas("right", size=.4, pad=.1, name="y2")
    # Add title
    wb.add_title(left="Y-axis distribution", top="X-axis distribution")
    # Add padding
    wb.add_pad("left", size=.3)
    wb.add_pad("right", size=.3)
    # Initiate the axes
    wb.render()

    main_ax = wb.get_main_ax()
    main_ax.set_axis_off()
    main_ax.pcolormesh(Z, cmap="Greens")

    x1_ax = wb.get_ax("x1")
    sns.lineplot(x=np.arange(len(zy)), y=zy,
                 ax=x1_ax, color="b", alpha=.7)
    x1_ax.set_xlim(0, len(zy))
    x1_ax.tick_params(bottom=False, labelbottom=False)
    sns.despine(ax=x1_ax)

    x2_ax = wb.get_ax("x2")
    x2_ax.set_axis_off()
    x2_ax.pcolormesh(zy.reshape(1, -1), cmap="Blues")

    y1_ax = wb.get_ax("y1")
    sns.lineplot(y=np.arange(len(zx)), x=zx, ax=y1_ax,
                 orient="y", color="r", alpha=.7)
    y1_ax.set_ylim(0, len(zx))
    sns.despine(ax=y1_ax, left=True, right=False)
    y1_ax.tick_params(right=False, labelright=False)
    for tick in y1_ax.get_xticklabels():
        tick.set_rotation(90)
    y1_ax.invert_xaxis()

    y2_ax = wb.get_ax("y2")
    y2_ax.set_axis_off()
    y2_ax.pcolormesh(zy.reshape(-1, 1), cmap="Reds")
    y2_ax.invert_yaxis()

    plt.show()



.. image-sg:: /auto_examples/images/sphx_glr_plot_bivariate_dist_001.png
   :alt: plot bivariate dist
   :srcset: /auto_examples/images/sphx_glr_plot_bivariate_dist_001.png, /auto_examples/images/sphx_glr_plot_bivariate_dist_001_2_00x.png 2.00x
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.848 seconds)


.. _sphx_glr_download_auto_examples_plot_bivariate_dist.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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