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radar_chart.py

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+"""
+======================================
+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(['kata_benda',
+                            'kata_kerja',
+                            'kata_keterangan',
+                            'kata_sifat])
+    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()