diff --git a/examples/api/radar_chart.py b/examples/api/radar_chart.py index 5c985fa1530d..96e8d1b69bd5 100644 --- a/examples/api/radar_chart.py +++ b/examples/api/radar_chart.py @@ -1,77 +1,124 @@ +""" +Example of creating a radar chart (a.k.a. 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] http://en.wikipedia.org/wiki/Radar_chart +""" import numpy as np import matplotlib.pyplot as plt -from matplotlib.projections.polar import PolarAxes -from matplotlib.projections import register_projection - -def radar_factory(num_vars, frame='circle'): - """Create a radar chart with `num_vars` axes.""" - # calculate evenly-spaced axis angles - theta = 2*np.pi * np.linspace(0, 1-1./num_vars, num_vars) - # rotate theta such that the first axis is at the top - theta += np.pi/2 - - def draw_poly_frame(self, x0, y0, r): - # TODO: use transforms to convert (x, y) to (r, theta) - verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta] - return plt.Polygon(verts, closed=True, edgecolor='k') - - def draw_circle_frame(self, x0, y0, r): - return plt.Circle((x0, y0), r) - - frame_dict = {'polygon': draw_poly_frame, 'circle': draw_circle_frame} - if frame not in frame_dict: - raise ValueError, 'unknown value for `frame`: %s' % frame - - class RadarAxes(PolarAxes): - """Class for creating a radar chart (a.k.a. a spider or star chart) - - http://en.wikipedia.org/wiki/Radar_chart - """ - name = 'radar' - # use 1 line segment to connect specified points - RESOLUTION = 1 - # define draw_frame method - draw_frame = frame_dict[frame] - - def fill(self, *args, **kwargs): - """Override fill so that line is closed by default""" - closed = kwargs.pop('closed', True) - return super(RadarAxes, self).fill(closed=closed, *args, **kwargs) - - def plot(self, *args, **kwargs): - """Override plot so that line is closed by default""" - lines = super(RadarAxes, self).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.concatenate((x, [x[0]])) - y = np.concatenate((y, [y[0]])) - line.set_data(x, y) - - def set_varlabels(self, labels): - self.set_thetagrids(theta * 180/np.pi, labels) - - def _gen_axes_patch(self): - x0, y0 = (0.5, 0.5) - r = 0.5 - return self.draw_frame(x0, y0, r) - - register_projection(RadarAxes) - return theta - - -if __name__ == '__main__': - #The following data is from the Denver Aerosol Sources and Health study. - #See doi:10.1016/j.atmosenv.2008.12.017 +from matplotlib.path import Path +from matplotlib.spines import Spine +from matplotlib.projections.polar import PolarAxes +from matplotlib.projections import register_projection + + +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 = 2*np.pi * np.linspace(0, 1-1./num_vars, num_vars) + # rotate theta such that the first axis is at the top + theta += np.pi/2 + + def draw_poly_patch(self): + verts = unit_poly_verts(theta) + return plt.Polygon(verts, closed=True, edgecolor='k') + + def draw_circle_patch(self): + # unit circle centered on (0.5, 0.5) + return plt.Circle((0.5, 0.5), 0.5) + + patch_dict = {'polygon': draw_poly_patch, 'circle': draw_circle_patch} + if frame not in patch_dict: + raise ValueError, 'unknown value for `frame`: %s' % frame + + class RadarAxes(PolarAxes): + + name = 'radar' + # use 1 line segment to connect specified points + RESOLUTION = 1 + # define draw_frame method + draw_patch = patch_dict[frame] + + def fill(self, *args, **kwargs): + """Override fill so that line is closed by default""" + closed = kwargs.pop('closed', True) + return super(RadarAxes, self).fill(closed=closed, *args, **kwargs) + + def plot(self, *args, **kwargs): + """Override plot so that line is closed by default""" + lines = super(RadarAxes, self).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.concatenate((x, [x[0]])) + y = np.concatenate((y, [y[0]])) + line.set_data(x, y) + + def set_varlabels(self, labels): + self.set_thetagrids(theta * 180/np.pi, labels) + + def _gen_axes_patch(self): + return self.draw_patch() + + def _gen_axes_spines(self): + if frame == 'circle': + return PolarAxes._gen_axes_spines(self) + # The following is a hack to get the spines (i.e. the axes frame) + # to draw correctly for a polygon frame. + + # spine_type must be 'left', 'right', 'top', 'bottom', or `circle`. + spine_type = 'circle' + verts = unit_poly_verts(theta) + # close off polygon by repeating first vertex + verts.append(verts[0]) + path = Path(verts) + + spine = Spine(self, spine_type, path) + spine.set_transform(self.transAxes) + return {'polar': spine} + + register_projection(RadarAxes) + return theta + + +def unit_poly_verts(theta): + """Return vertices of polygon for subplot axes. + + This polygon is circumscribed by a unit circle centered at (0.5, 0.5) + """ + x0, y0, r = [0.5] * 3 + verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta] + return verts + + +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 + #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 @@ -81,64 +128,69 @@ def _gen_axes_patch(self): # Organic Carbon fraction 2 (OC2) # Organic Carbon fraction 3 (OC3) # Pyrolized Organic Carbon (OP) - # 2)Inclusion of gas-phase specie carbon monoxide (CO) - # 3)Inclusion of gas-phase specie ozone (O3). + # 2)Inclusion of gas-phase specie carbon monoxide (CO) + # 3)Inclusion of gas-phase specie ozone (O3). # 4)Inclusion of both gas-phase speciesis present... + data = { + 'column names': + ['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) - spoke_labels = ['Sulfate', 'Nitrate', 'EC', 'OC1', 'OC2', 'OC3', 'OP', 'CO', - 'O3'] - f1_base = [0.88, 0.01, 0.03, 0.03, 0.00, 0.06, 0.01, 0.00, 0.00] - f1_CO = [0.88, 0.02, 0.02, 0.02, 0.00, 0.05, 0.00, 0.05, 0.00] - f1_O3 = [0.89, 0.01, 0.07, 0.00, 0.00, 0.05, 0.00, 0.00, 0.03] - f1_both = [0.87, 0.01, 0.08, 0.00, 0.00, 0.04, 0.00, 0.00, 0.01] - - f2_base = [0.07, 0.95, 0.04, 0.05, 0.00, 0.02, 0.01, 0.00, 0.00] - f2_CO = [0.08, 0.94, 0.04, 0.02, 0.00, 0.01, 0.12, 0.04, 0.00] - f2_O3 = [0.07, 0.95, 0.05, 0.04, 0.00, 0.02, 0.12, 0.00, 0.00] - f2_both = [0.09, 0.95, 0.02, 0.03, 0.00, 0.01, 0.13, 0.06, 0.00] - - f3_base = [0.01, 0.02, 0.85, 0.19, 0.05, 0.10, 0.00, 0.00, 0.00] - f3_CO = [0.01, 0.01, 0.79, 0.10, 0.00, 0.05, 0.00, 0.31, 0.00] - f3_O3 = [0.01, 0.02, 0.86, 0.27, 0.16, 0.19, 0.00, 0.00, 0.00] - f3_both = [0.01, 0.02, 0.71, 0.24, 0.13, 0.16, 0.00, 0.50, 0.00] - - f4_base = [0.02, 0.01, 0.07, 0.01, 0.21, 0.12, 0.98, 0.00, 0.00] - f4_CO = [0.00, 0.02, 0.03, 0.38, 0.31, 0.31, 0.00, 0.59, 0.00] - f4_O3 = [0.01, 0.03, 0.00, 0.32, 0.29, 0.27, 0.00, 0.00, 0.95] - f4_both = [0.01, 0.03, 0.00, 0.28, 0.24, 0.23, 0.00, 0.44, 0.88] - - f5_base = [0.01, 0.01, 0.02, 0.71, 0.74, 0.70, 0.00, 0.00, 0.00] - f5_CO = [0.02, 0.02, 0.11, 0.47, 0.69, 0.58, 0.88, 0.00, 0.00] - f5_O3 = [0.02, 0.00, 0.03, 0.37, 0.56, 0.47, 0.87, 0.00, 0.00] - f5_both = [0.02, 0.00, 0.18, 0.45, 0.64, 0.55, 0.86, 0.00, 0.16] - - fig = plt.figure(figsize=(9,9)) - # adjust spacing around the subplots + theta = radar_factory(N, frame='polygon') + + data = example_data() + spoke_labels = data.pop('column names') + + fig = plt.figure(figsize=(9, 9)) fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05) - title_list = ['Basecase', 'With CO', 'With O3', 'CO & O3'] - data = {'Basecase': [f1_base, f2_base, f3_base, f4_base, f5_base], - 'With CO': [f1_CO, f2_CO, f3_CO, f4_CO, f5_CO], - 'With O3': [f1_O3, f2_O3, f3_O3, f4_O3, f5_O3], - 'CO & O3': [f1_both, f2_both, f3_both, f4_both, f5_both]} + colors = ['b', 'r', 'g', 'm', 'y'] - # chemicals range from 0 to 1 - radial_grid = [0.2, 0.4, 0.6, 0.8] - # If you don't care about the order, you can loop over data_dict.items() - for n, title in enumerate(title_list): + # Plot the four cases from the example data on separate axes + for n, title in enumerate(data.keys()): ax = fig.add_subplot(2, 2, n+1, projection='radar') - plt.rgrids(radial_grid) + plt.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(data[title], colors): - ax.plot(theta, d, color=color) - ax.fill(theta, d, facecolor=color, alpha=0.25) + ax.plot(theta, d, color=color) + ax.fill(theta, d, facecolor=color, alpha=0.25) ax.set_varlabels(spoke_labels) + # add legend relative to top-left plot plt.subplot(2,2,1) labels = ('Factor 1', 'Factor 2', 'Factor 3', 'Factor 4', 'Factor 5') legend = plt.legend(labels, loc=(0.9, .95), labelspacing=0.1) plt.setp(legend.get_texts(), fontsize='small') - plt.figtext(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios', - ha='center', color='black', weight='bold', size='large') + + plt.figtext(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios', + ha='center', color='black', weight='bold', size='large') plt.show() +