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

.. only:: html

    .. meta::
        :keywords: codex

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

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

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

.. _sphx_glr_gallery_lines_bars_and_markers_multivariate_marker_plot.py:


==============================================
Mapping marker properties to multivariate data
==============================================

This example shows how to use different properties of markers to plot
multivariate datasets. Here we represent a successful baseball throw as a
smiley face with marker size mapped to the skill of thrower, marker rotation to
the take-off angle, and thrust to the marker color.

.. GENERATED FROM PYTHON SOURCE LINES 11-48



.. image-sg:: /gallery/lines_bars_and_markers/images/sphx_glr_multivariate_marker_plot_001.png
   :alt: Throwing success
   :srcset: /gallery/lines_bars_and_markers/images/sphx_glr_multivariate_marker_plot_001.png, /gallery/lines_bars_and_markers/images/sphx_glr_multivariate_marker_plot_001_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from matplotlib.colors import Normalize
    from matplotlib.markers import MarkerStyle
    from matplotlib.text import TextPath
    from matplotlib.transforms import Affine2D

    SUCCESS_SYMBOLS = [
        TextPath((0, 0), "☹"),
        TextPath((0, 0), "😒"),
        TextPath((0, 0), "☺"),
    ]

    N = 25
    np.random.seed(42)
    skills = np.random.uniform(5, 80, size=N) * 0.1 + 5
    takeoff_angles = np.random.normal(0, 90, N)
    thrusts = np.random.uniform(size=N)
    successful = np.random.randint(0, 3, size=N)
    positions = np.random.normal(size=(N, 2)) * 5
    data = zip(skills, takeoff_angles, thrusts, successful, positions)

    cmap = plt.colormaps["plasma"]
    fig, ax = plt.subplots()
    fig.suptitle("Throwing success", size=14)
    for skill, takeoff, thrust, mood, pos in data:
        t = Affine2D().scale(skill).rotate_deg(takeoff)
        m = MarkerStyle(SUCCESS_SYMBOLS[mood], transform=t)
        ax.plot(pos[0], pos[1], marker=m, color=cmap(thrust))
    fig.colorbar(plt.cm.ScalarMappable(norm=Normalize(0, 1), cmap=cmap),
                 ax=ax, label="Normalized Thrust [a.u.]")
    ax.set_xlabel("X position [m]")
    ax.set_ylabel("Y position [m]")

    plt.show()


.. _sphx_glr_download_gallery_lines_bars_and_markers_multivariate_marker_plot.py:

.. only:: html

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

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

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

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

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


.. only:: html

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

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