Delete radar_chart.py

radar_chart.py

@@ -1,202 +0,0 @@
-"""
-======================================
-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 axes.
-.. [1] https://en.wikipedia.org/wiki/Radar_chart
-"""
-
-import numpy as np
-
-import matplotlib.pyplot as plt
-from matplotlib.patches import Circle, RegularPolygon
-from matplotlib.path import Path
-from matplotlib.projections.polar import PolarAxes
-from matplotlib.projections import register_projection
-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 = 8
-    theta = radar_factory(N, frame='polygon')
-
-    # data = example_data()
-    # spoke_labels = data.pop(0)
-    spoke_labels = np.array(['neutral',
-                            'calm',
-                            'happy',
-                            'sad',
-                            'angry',
-                            'fearful',
-                            'disgust',
-                            'surprised'])
-    fig, axs = plt.subplots(figsize=(8, 8), nrows=1, ncols=1,
-                            subplot_kw=dict(projection='radar'))
-    # fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
-    vec = np.array([0.1, 0.05, 0.2, 0.05, 0.3, 0, 0.15, 0.15])
-    axs.plot(vec)
-    axs.set_varlabels(spoke_labels)
-    # 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()