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>>> plt.show()
customize plot
>>> fig, _ = rose.plot()(attribute=y1)
>>> plt.show()
"""
use_splot = False
try:
import splot.giddy
use_splot = True
except ImportError:
warnings.warn('This method relies on importing `splot` in future',
DeprecationWarning)
use_splot = False
if use_splot:
fig, ax = splot.giddy.dynamic_lisa_rose(self, attribute=attribute,
ax=ax, **kwargs)
else:
# This can be removed if splot has been released with support for
# giddy.directional TODO add **kwargs
import matplotlib.cm as cm
import matplotlib.pyplot as plt
ax = plt.subplot(111, projection='polar')
ax.set_rlabel_position(315)
if attribute is None:
c = ax.scatter(self.theta, self.r)
else:
c = ax.scatter(self.theta, self.r, c=attribute)
plt.colorbar(c)
fig = ax.get_figure()
return fig, ax
>>> plt.show()
customize plot
>>> fig, _ = rose.plot_vectors(arrows=False)
>>> plt.show()
"""
use_splot = False
try:
import splot.giddy
use_splot = True
except ImportError:
warnings.warn('This method relies on importing `splot` in future',
DeprecationWarning)
use_splot = False
if use_splot:
fig, ax = splot.giddy.dynamic_lisa_vectors(self, arrows=arrows)
else:
# This can be removed if splot has been released with support for
# giddy.directional TODO add **kwargs, arrow=True
import matplotlib.cm as cm
import matplotlib.pyplot as plt
ax = plt.subplot(111 )
xlim = [self.Y.min(), self.Y.max()]
ylim = [self.wY.min(), self.wY.max()]
for i in range(len(self.Y)):
xs = self.Y[i,:]
ys = self.wY[i,:]
ax.plot(xs,ys, '-b') # TODO change this to scale with attribute
plt.axis('equal')
plt.xlim(xlim)
plt.ylim(ylim)
fig = ax.get_figure()