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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() |