def radar_plot(labels, data_plots, out_fpath):
theta = radar_factory(len(labels))
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection="radar")
for data_plot, color, class_num in data_plots:
ax.plot(theta, data_plot, color=color, label="C%d" % class_num)
ax.fill(theta, data_plot, facecolor=color, alpha=0.25)
ax.set_varlabels(labels)
plt.legend(frameon=False, ncol=4, bbox_to_anchor=(0.5, -0.15), loc="lower center")
plt.savefig(out_fpath)
def radar_plot(labels, data_plots, out_fpath):
theta = radar_factory(len(labels))
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection='radar')
for data_plot, color, class_num in data_plots:
ax.plot(theta, data_plot, color=color, label='C%d'%class_num)
ax.fill(theta, data_plot, facecolor=color, alpha=0.25)
ax.set_varlabels(labels)
plt.legend(frameon=False, ncol=4, bbox_to_anchor=(0.5, -0.15),
loc='lower center')
plt.savefig(out_fpath)
def create_radar(
spoke_labels,
data, # Should be a list of tuples with title and LOL data
labels,
save_path=os.path.join(RADAR_IMAGE_FOLDER, 'Mol0.jpg'),
rgrids=[10, 50, 100, 500, 1000]):
N = len(data[0][1][0])
theta = radar_factory(N, frame='polygon')
fig, axes = plt.subplots(figsize=(10, 10),
nrows=len(data),
ncols=1,
subplot_kw=dict(projection='radar'))
fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
if len(data) == 1:
axes = np.array([[axes]])
colors = ['b', 'r', 'g', 'm', 'y']
colors = colors[:len(data[0][1])]
# Plot the four cases from the example data on separate axes
for ax, (title, case_data) in zip(axes.flatten(), data):
ax.set_rgrids(rgrids)
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)
ax.set_varlabels(spoke_labels)
# add legend relative to top-left plot
ax = axes[0, 0]
ax.legend(labels, loc=(0.9, .95), labelspacing=0.1, fontsize='large')
# fig.text(0.5, 0.965, '5-Factor Solution Profiles Across Four Scenarios',
# horizontalalignment='center', color='black', weight='bold',
# size='large')
plt.savefig(save_path)
ax.fill(theta, d, facecolor=color, alpha=0.25)
return g,
anim = animation.FuncAnimation(fig, step, init_func=init, frames=20, interval=1000, blit=True)
plt.show()"""
interval = 20
for i in range(2000, 2020, interval):
N = len(features)
theta = radar_factory(N, frame='polygon')
data = []
for chart in sorted(charts.keys()):
genre = []
for feature in features:
v = charts[chart][feature].loc['%d-01-01' % i:'%d-01-01' %
(i + interval)].mean()
genre.append(v)
data.append(genre)
spoke_labels = features
fig = plt.figure()
ax = fig.add_subplot(projection='radar')
#fig.subplots_adjust(wspace=0.25, hspace=0.20, top=0.85, bottom=0.05)
colors = ['b', 'r', 'g', 'm', 'y', 'c', 'lime']
# Plot the four cases from the example data on separate axes