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

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

.. _sphx_glr_gallery_specialty_plots_radar_chart.py:


======================================
Radar chart (aka spider or star chart)
======================================

This example creates a radar chart, also known as a spider or star chart [1]_.

Although this example allows a frame of either 'circle' or 'polygon', polygon
frames don't have proper gridlines (the lines are circles instead of polygons).
It's possible to get a polygon grid by setting GRIDLINE_INTERPOLATION_STEPS in
`matplotlib.axis` to the desired number of vertices, but the orientation of the
polygon is not aligned with the radial axis.

.. [1] https://en.wikipedia.org/wiki/Radar_chart

.. GENERATED FROM PYTHON SOURCE LINES 16-202

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from matplotlib.patches import Circle, RegularPolygon
    from matplotlib.path import Path
    from matplotlib.projections import register_projection
    from matplotlib.projections.polar import PolarAxes
    from matplotlib.spines import Spine
    from matplotlib.transforms import Affine2D


    def radar_factory(num_vars, frame='circle'):
        """
        Create a radar chart with `num_vars` Axes.

        This function creates a RadarAxes projection and registers it.

        Parameters
        ----------
        num_vars : int
            Number of variables for radar chart.
        frame : {'circle', 'polygon'}
            Shape of frame surrounding Axes.

        """
        # calculate evenly-spaced axis angles
        theta = np.linspace(0, 2*np.pi, num_vars, endpoint=False)

        class RadarTransform(PolarAxes.PolarTransform):

            def transform_path_non_affine(self, path):
                # Paths with non-unit interpolation steps correspond to gridlines,
                # in which case we force interpolation (to defeat PolarTransform's
                # autoconversion to circular arcs).
                if path._interpolation_steps > 1:
                    path = path.interpolated(num_vars)
                return Path(self.transform(path.vertices), path.codes)

        class RadarAxes(PolarAxes):

            name = 'radar'
            PolarTransform = RadarTransform

            def __init__(self, *args, **kwargs):
                super().__init__(*args, **kwargs)
                # rotate plot such that the first axis is at the top
                self.set_theta_zero_location('N')

            def fill(self, *args, closed=True, **kwargs):
                """Override fill so that line is closed by default"""
                return super().fill(closed=closed, *args, **kwargs)

            def plot(self, *args, **kwargs):
                """Override plot so that line is closed by default"""
                lines = super().plot(*args, **kwargs)
                for line in lines:
                    self._close_line(line)

            def _close_line(self, line):
                x, y = line.get_data()
                # FIXME: markers at x[0], y[0] get doubled-up
                if x[0] != x[-1]:
                    x = np.append(x, x[0])
                    y = np.append(y, y[0])
                    line.set_data(x, y)

            def set_varlabels(self, labels):
                self.set_thetagrids(np.degrees(theta), labels)

            def _gen_axes_patch(self):
                # The Axes patch must be centered at (0.5, 0.5) and of radius 0.5
                # in axes coordinates.
                if frame == 'circle':
                    return Circle((0.5, 0.5), 0.5)
                elif frame == 'polygon':
                    return RegularPolygon((0.5, 0.5), num_vars,
                                          radius=.5, edgecolor="k")
                else:
                    raise ValueError("Unknown value for 'frame': %s" % frame)

            def _gen_axes_spines(self):
                if frame == 'circle':
                    return super()._gen_axes_spines()
                elif frame == 'polygon':
                    # spine_type must be 'left'/'right'/'top'/'bottom'/'circle'.
                    spine = Spine(axes=self,
                                  spine_type='circle',
                                  path=Path.unit_regular_polygon(num_vars))
                    # unit_regular_polygon gives a polygon of radius 1 centered at
                    # (0, 0) but we want a polygon of radius 0.5 centered at (0.5,
                    # 0.5) in axes coordinates.
                    spine.set_transform(Affine2D().scale(.5).translate(.5, .5)
                                        + self.transAxes)
                    return {'polar': spine}
                else:
                    raise ValueError("Unknown value for 'frame': %s" % frame)

        register_projection(RadarAxes)
        return theta


    def example_data():
        # The following data is from the Denver Aerosol Sources and Health study.
        # See doi:10.1016/j.atmosenv.2008.12.017
        #
        # The data are pollution source profile estimates for five modeled
        # pollution sources (e.g., cars, wood-burning, etc) that emit 7-9 chemical
        # species. The radar charts are experimented with here to see if we can
        # nicely visualize how the modeled source profiles change across four
        # scenarios:
        #  1) No gas-phase species present, just seven particulate counts on
        #     Sulfate
        #     Nitrate
        #     Elemental Carbon (EC)
        #     Organic Carbon fraction 1 (OC)
        #     Organic Carbon fraction 2 (OC2)
        #     Organic Carbon fraction 3 (OC3)
        #     Pyrolyzed Organic Carbon (OP)
        #  2)Inclusion of gas-phase specie carbon monoxide (CO)
        #  3)Inclusion of gas-phase specie ozone (O3).
        #  4)Inclusion of both gas-phase species is present...
        data = [
            ['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', 'O3'],
            ('Basecase', [
                [0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00],
                [0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00],
                [0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00],
                [0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00],
                [0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00]]),
            ('With CO', [
                [0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00],
                [0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00],
                [0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00],
                [0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00],
                [0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00]]),
            ('With O3', [
                [0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03],
                [0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00],
                [0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00],
                [0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95],
                [0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00]]),
            ('CO & O3', [
                [0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01],
                [0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00],
                [0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00],
                [0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88],
                [0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16]])
        ]
        return data


    if __name__ == '__main__':
        N = 9
        theta = radar_factory(N, frame='polygon')

        data = example_data()
        spoke_labels = data.pop(0)

        fig, axs = plt.subplots(figsize=(9, 9), nrows=2, ncols=2,
                                subplot_kw=dict(projection='radar'))
        fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)

        colors = ['b', 'r', 'g', 'm', 'y']
        # Plot the four cases from the example data on separate Axes
        for ax, (title, case_data) in zip(axs.flat, data):
            ax.set_rgrids([0.2, 0.4, 0.6, 0.8])
            ax.set_title(title, weight='bold', size='medium', position=(0.5, 1.1),
                         horizontalalignment='center', verticalalignment='center')
            for d, color in zip(case_data, colors):
                ax.plot(theta, d, color=color)
                ax.fill(theta, d, facecolor=color, alpha=0.25, label='_nolegend_')
            ax.set_varlabels(spoke_labels)

        # add legend relative to top-left plot
        labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5')
        legend = axs[0, 0].legend(labels, loc=(0.9, .95),
                                  labelspacing=0.1, fontsize='small')

        fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
                 horizontalalignment='center', color='black', weight='bold',
                 size='large')

        plt.show()





.. image-sg:: /gallery/specialty_plots/images/sphx_glr_radar_chart_001.png
   :alt: Basecase, With CO, With O3, CO & O3
   :srcset: /gallery/specialty_plots/images/sphx_glr_radar_chart_001.png, /gallery/specialty_plots/images/sphx_glr_radar_chart_001_2_00x.png 2.00x
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 203-216

.. admonition:: References

   The use of the following functions, methods, classes and modules is shown
   in this example:

   - `matplotlib.path`
   - `matplotlib.path.Path`
   - `matplotlib.spines`
   - `matplotlib.spines.Spine`
   - `matplotlib.projections`
   - `matplotlib.projections.polar`
   - `matplotlib.projections.polar.PolarAxes`
   - `matplotlib.projections.register_projection`


.. _sphx_glr_download_gallery_specialty_plots_radar_chart.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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