def plot(self, ax):
"""Plots a dendrogram of the similarities between data on the axes
Parameters
----------
ax : matplotlib.axes.Axes
Axes object upon which the dendrogram is plotted
"""
line_kwargs = dict(linewidths=.5, colors='k')
if self.rotate and self.axis == 0:
lines = LineCollection([
list(zip(x, y))
for x, y in zip(self.dependent_coord, self.independent_coord)
], **line_kwargs)
else:
lines = LineCollection([
list(zip(x, y))
for x, y in zip(self.independent_coord, self.dependent_coord)
], **line_kwargs)
ax.add_collection(lines)
number_of_leaves = len(self.reordered_ind)
max_dependent_coord = max(map(max, self.dependent_coord))
if self.rotate:
ax.yaxis.set_ticks_position('right')
# Constants 10 and 1.05 come from
# `scipy.cluster.hierarchy._plot_dendrogram`
ax.set_ylim(0, number_of_leaves * 10)
ax.set_xlim(0, max_dependent_coord * 1.05)
ax.invert_xaxis()
ax.invert_yaxis()
else:
# Constants 10 and 1.05 come from
# `scipy.cluster.hierarchy._plot_dendrogram`
ax.set_xlim(0, number_of_leaves * 10)
ax.set_ylim(0, max_dependent_coord * 1.05)
despine(ax=ax, bottom=True, left=True)
ax.set(xticks=self.xticks,
yticks=self.yticks,
xlabel=self.xlabel,
ylabel=self.ylabel)
xtl = ax.set_xticklabels(self.xticklabels)
ytl = ax.set_yticklabels(self.yticklabels, rotation='vertical')
# Force a draw of the plot to avoid matplotlib window error
plt.draw()
if len(ytl) > 0 and axis_ticklabels_overlap(ytl):
plt.setp(ytl, rotation="horizontal")
if len(xtl) > 0 and axis_ticklabels_overlap(xtl):
plt.setp(xtl, rotation="vertical")
return self
def test_ticklabels_overlap():
rcmod.set()
f, ax = plt.subplots(figsize=(2, 2))
f.tight_layout() # This gets the Agg renderer working
assert not utils.axis_ticklabels_overlap(ax.get_xticklabels())
big_strings = "abcdefgh", "ijklmnop"
ax.set_xlim(-.5, 1.5)
ax.set_xticks([0, 1])
ax.set_xticklabels(big_strings)
assert utils.axis_ticklabels_overlap(ax.get_xticklabels())
x, y = utils.axes_ticklabels_overlap(ax)
assert x
assert not y
def plot(self, ax, cax, kws):
"""Draw the heatmap on the provided Axes."""
# Remove all the Axes spines
despine(ax=ax, left=True, bottom=True)
#Annie Yim
# Draw the heatmap
self.mesh = ax.pcolormesh(self.plot_data,
vmin=self.vmin,
vmax=self.vmax,
cmap=self.cmap,
**kws)
# Set the axis limits
ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))
# Add row and column labels
ax.set(xticks=self.xticks, yticks=self.yticks)
xtl = ax.set_xticklabels(self.xticklabels)
ytl = ax.set_yticklabels(self.yticklabels, rotation="vertical")
# Possibly rotate them if they overlap
plt.draw()
if axis_ticklabels_overlap(xtl):
plt.setp(xtl, rotation="vertical")
if axis_ticklabels_overlap(ytl):
plt.setp(ytl, rotation="horizontal")
# Add the axis labels
ax.set(xlabel=self.xlabel, ylabel=self.ylabel)
# Annotate the cells with the formatted values
if self.annot:
self._annotate_heatmap(ax, mesh)
#Annie Yim
# Possibly add a colorbar
if self.cbar:
cb = ax.figure.colorbar(self.mesh, cax, ax, **self.cbar_kws)
cb.outline.set_linewidth(0)
# If rasterized is passed to pcolormesh, also rasterize the
# colorbar to avoid white lines on the PDF rendering
if kws.get('rasterized', False):
cb.solids.set_rasterized(True)
def plot(self, ax, cax):
"""Draw the heatmap on the provided Axes."""
# Remove all the Axes spines
despine(ax=ax, left=True, bottom=True)
# Draw the heatmap and annotate
height, width = self.plot_data.shape
xpos, ypos = np.meshgrid(np.arange(width) + .5, np.arange(height) + .5)
data = self.plot_data.data
cellsize = self.cellsize
mask = self.plot_data.mask
if not isinstance(mask, np.ndarray) and not mask:
mask = np.zeros(self.plot_data.shape, np.bool)
annot_data = self.annot_data
if not self.annot:
annot_data = np.zeros(self.plot_data.shape)
# Draw rectangles instead of using pcolormesh
# Might be slower than original heatmap
for x, y, m, val, s, an_val in zip(xpos.flat, ypos.flat, mask.flat,
data.flat, cellsize.flat,
annot_data.flat):
if not m:
vv = (val - self.vmin) / (self.vmax - self.vmin)
size = np.clip(s / self.cellsize_vmax, 0.1, 1.0)
color = self.cmap(vv)
rect = plt.Rectangle([x - size / 2, y - size / 2],
size,
size,
facecolor=color,
**self.rect_kws)
ax.add_patch(rect)
if self.annot:
annotation = ("{:" + self.fmt + "}").format(an_val)
text = ax.text(x, y, annotation, **self.annot_kws)
# add edge to text
text_luminance = relative_luminance(text.get_color())
text_edge_color = ".15" if text_luminance > .408 else "w"
text.set_path_effects([
mpl.patheffects.withStroke(linewidth=1,
foreground=text_edge_color)
])
# Set the axis limits
ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))
# Set other attributes
ax.set(**self.ax_kws)
if self.cbar:
norm = mpl.colors.Normalize(vmin=self.vmin, vmax=self.vmax)
scalar_mappable = mpl.cm.ScalarMappable(cmap=self.cmap, norm=norm)
scalar_mappable.set_array(self.plot_data.data)
cb = ax.figure.colorbar(scalar_mappable, cax, ax, **self.cbar_kws)
cb.outline.set_linewidth(0)
# if kws.get('rasterized', False):
# cb.solids.set_rasterized(True)
# Add row and column labels
if isinstance(self.xticks, string_types) and self.xticks == "auto":
xticks, xticklabels = self._auto_ticks(ax, self.xticklabels, 0)
else:
xticks, xticklabels = self.xticks, self.xticklabels
if isinstance(self.yticks, string_types) and self.yticks == "auto":
yticks, yticklabels = self._auto_ticks(ax, self.yticklabels, 1)
else:
yticks, yticklabels = self.yticks, self.yticklabels
ax.set(xticks=xticks, yticks=yticks)
xtl = ax.set_xticklabels(xticklabels)
ytl = ax.set_yticklabels(yticklabels, rotation="vertical")
# Possibly rotate them if they overlap
ax.figure.draw(ax.figure.canvas.get_renderer())
if axis_ticklabels_overlap(xtl):
plt.setp(xtl, rotation="vertical")
if axis_ticklabels_overlap(ytl):
plt.setp(ytl, rotation="horizontal")
# Add the axis labels
ax.set(xlabel=self.xlabel, ylabel=self.ylabel)
# Invert the y axis to show the plot in matrix form
ax.invert_yaxis()
def plot(self, ax, cax, kws):
"""Draw the scattermap on the provided Axes."""
# Remove all the Axes spines
despine(ax=ax, left=True, bottom=True)
# Draw the heatmap
data = self.plot_data
range_y = np.arange(data.shape[0], dtype=int) + 0.5
range_x = np.arange(data.shape[1], dtype=int) + 0.5
x, y = np.meshgrid(range_x, range_y)
hmap = ax.scatter(x,
y,
c=data,
marker=self.marker,
cmap=self.cmap,
vmin=self.vmin,
vmax=self.vmax,
s=self.marker_size,
**kws)
# Set the axis limits
ax.set(xlim=(0, self.data.shape[1]), ylim=(0, self.data.shape[0]))
# Possibly add a colorbar
if self.cbar:
cb = ax.figure.colorbar(hmap, cax, ax, **self.cbar_kws)
cb.outline.set_linewidth(0)
# If rasterized is passed to pcolormesh, also rasterize the
# colorbar to avoid white lines on the PDF rendering
if kws.get('rasterized', False):
cb.solids.set_rasterized(True)
# Add row and column labels
if isinstance(self.xticks, string_types) and self.xticks == "auto":
xticks, xticklabels = self._auto_ticks(ax, self.xticklabels, 0)
else:
xticks, xticklabels = self.xticks, self.xticklabels
if isinstance(self.yticks, string_types) and self.yticks == "auto":
yticks, yticklabels = self._auto_ticks(ax, self.yticklabels, 1)
else:
yticks, yticklabels = self.yticks, self.yticklabels
ax.set(xticks=xticks, yticks=yticks)
xtl = ax.set_xticklabels(xticklabels)
ytl = ax.set_yticklabels(yticklabels, rotation="vertical")
# Possibly rotate them if they overlap
ax.figure.draw(ax.figure.canvas.get_renderer())
if axis_ticklabels_overlap(xtl):
plt.setp(xtl, rotation="vertical")
if axis_ticklabels_overlap(ytl):
plt.setp(ytl, rotation="horizontal")
# Add the axis labels
ax.set(xlabel=self.xlabel, ylabel=self.ylabel)
# Annotate the cells with the formatted values
if self.annot:
self._annotate_heatmap(ax, hmap)
# Invert the y axis to show the plot in matrix form
ax.invert_yaxis()
