def plot(self, data, notebook=False, show=True, savename=None):
fig = pyplot.figure()
ncenters = len(self.centers)
colorizer = Colorize()
colorizer.get = lambda x: self.colors[int(self.predict(x)[0])]
# plot time series of each center
# TODO move into a time series plotting function in viz.plots
for i, center in enumerate(self.centers):
ax = pyplot.subplot2grid((ncenters, 3), (i, 0))
ax.plot(center, color=self.colors[i], linewidth=5)
fig.add_axes(ax)
# make a scatter plot of the data
ax2 = pyplot.subplot2grid((ncenters, 3), (0, 1), rowspan=ncenters, colspan=2)
ax2, h2 = scatter(data, colormap=colorizer, ax=ax2)
fig.add_axes(ax2)
plugins.connect(fig, HiddenAxes())
if show and notebook is False:
mpld3.show()
if savename is not None:
mpld3.save_html(fig, savename)
elif show is False:
return mpld3.fig_to_html(fig)
def plot(self, data, notebook=False, show=True, savename=None):
fig = pyplot.figure()
ncenters = len(self.centers)
colorizer = Colorize()
colorizer.get = lambda x: self.colors[int(self.predict(x)[0])]
# plot time series of each center
# TODO move into a time series plotting function in viz.plots
for i, center in enumerate(self.centers):
ax = pyplot.subplot2grid((ncenters, 3), (i, 0))
ax.plot(center, color=self.colors[i], linewidth=5)
fig.add_axes(ax)
# make a scatter plot of the data
ax2 = pyplot.subplot2grid((ncenters, 3), (0, 1),
rowspan=ncenters,
colspan=2)
ax2, h2 = scatter(data, colormap=colorizer, ax=ax2)
fig.add_axes(ax2)
plugins.connect(fig, HiddenAxes())
if show and notebook is False:
mpld3.show()
if savename is not None:
mpld3.save_html(fig, savename)
elif show is False:
return mpld3.fig_to_html(fig)
def plot(self, notebook=False, colormap='polar', scale=1, maptype='points', show=True, savename=None):
# make a spatial map based on the scores
fig = pyplot.figure(figsize=(12, 5))
ax1 = pyplot.subplot2grid((2, 3), (0, 1), colspan=2, rowspan=2)
if maptype is 'points':
ax1, h1 = pointmap(self.scores, colormap=colormap, scale=scale, ax=ax1)
elif maptype is 'image':
ax1, h1 = imagemap(self.scores, colormap=colormap, scale=scale, ax=ax1)
fig.add_axes(ax1)
# make a scatter plot of sampled scores
ax2 = pyplot.subplot2grid((2, 3), (1, 0))
ax2, h2, samples = scatter(self.scores, colormap=colormap, scale=scale, thresh=0.01, nsamples=1000, ax=ax2, store=True)
fig.add_axes(ax2)
# make the line plot of reconstructions from principal components for the same samples
ax3 = pyplot.subplot2grid((2, 3), (0, 0))
ax3, h3, linedata = tsrecon(self.comps, samples, ax=ax3)
plugins.connect(fig, LinkedView(h2, h3[0], linedata))
plugins.connect(fig, HiddenAxes())
if show and notebook is False:
mpld3.show()
if savename is not None:
mpld3.save_html(fig, savename)
elif show is False:
return mpld3.fig_to_html(fig)
def plot(self,
notebook=False,
colormap='polar',
scale=1,
maptype='points',
show=True,
savename=None):
# make a spatial map based on the scores
fig = pyplot.figure(figsize=(12, 5))
ax1 = pyplot.subplot2grid((2, 3), (0, 1), colspan=2, rowspan=2)
if maptype is 'points':
ax1, h1 = pointmap(self.scores,
colormap=colormap,
scale=scale,
ax=ax1)
elif maptype is 'image':
ax1, h1 = imagemap(self.scores,
colormap=colormap,
scale=scale,
ax=ax1)
fig.add_axes(ax1)
# make a scatter plot of sampled scores
ax2 = pyplot.subplot2grid((2, 3), (1, 0))
ax2, h2, samples = scatter(self.scores,
colormap=colormap,
scale=scale,
thresh=0.01,
nsamples=1000,
ax=ax2,
store=True)
fig.add_axes(ax2)
# make the line plot of reconstructions from principal components for the same samples
ax3 = pyplot.subplot2grid((2, 3), (0, 0))
ax3, h3, linedata = tsrecon(self.comps, samples, ax=ax3)
plugins.connect(fig, LinkedView(h2, h3[0], linedata))
plugins.connect(fig, HiddenAxes())
if show and notebook is False:
mpld3.show()
if savename is not None:
mpld3.save_html(fig, savename)
elif show is False:
return mpld3.fig_to_html(fig)
