"""
Matplotlib Exporter
===================
This submodule contains tools for crawling a matplotlib figure and exporting
relevant pieces to a renderer.
"""
import warnings
import io
from . import utils
import matplotlib
from matplotlib import transforms, collections
from matplotlib.backends.backend_agg import FigureCanvasAgg
class Exporter(object):
"""Matplotlib Exporter
Parameters
----------
renderer : Renderer object
The renderer object called by the exporter to create a figure
visualization. See mplexporter.Renderer for information on the
methods which should be defined within the renderer.
close_mpl : bool
If True (default), close the matplotlib figure as it is rendered. This
is useful for when the exporter is used within the notebook, or with
an interactive matplotlib backend.
"""
def __init__(self, renderer, close_mpl=True):
self.close_mpl = close_mpl
self.renderer = renderer
def run(self, fig):
"""
Run the exporter on the given figure
Parmeters
---------
fig : matplotlib.Figure instance
The figure to export
"""
# Calling savefig executes the draw() command, putting elements
# in the correct place.
if fig.canvas is None:
canvas = FigureCanvasAgg(fig)
fig.savefig(io.BytesIO(), format='png', dpi=fig.dpi)
if self.close_mpl:
import matplotlib.pyplot as plt
plt.close(fig)
self.crawl_fig(fig)
@staticmethod
def process_transform(transform, ax=None, data=None, return_trans=False,
force_trans=None):
"""Process the transform and convert data to figure or data coordinates
Parameters
----------
transform : matplotlib Transform object
The transform applied to the data
ax : matplotlib Axes object (optional)
The axes the data is associated with
data : ndarray (optional)
The array of data to be transformed.
return_trans : bool (optional)
If true, return the final transform of the data
force_trans : matplotlib.transform instance (optional)
If supplied, first force the data to this transform
Returns
-------
code : string
Code is either "data", "axes", "figure", or "display", indicating
the type of coordinates output.
transform : matplotlib transform
the transform used to map input data to output data.
Returned only if return_trans is True
new_data : ndarray
Data transformed to match the given coordinate code.
Returned only if data is specified
"""
if isinstance(transform, transforms.BlendedGenericTransform):
warnings.warn("Blended transforms not yet supported. "
"Zoom behavior may not work as expected.")
if force_trans is not None:
if data is not None:
data = (transform - force_trans).transform(data)
transform = force_trans
code = "display"
if ax is not None:
for (c, trans) in [("data", ax.transData),
("axes", ax.transAxes),
("figure", ax.figure.transFigure),
("display", transforms.IdentityTransform())]:
if transform.contains_branch(trans):
code, transform = (c, transform - trans)
break
if data is not None:
if return_trans:
return code, transform.transform(data), transform
else:
return code, transform.transform(data)
else:
if return_trans:
return code, transform
else:
return code
def crawl_fig(self, fig):
"""Crawl the figure and process all axes"""
with self.renderer.draw_figure(fig=fig,
props=utils.get_figure_properties(fig)):
for ax in fig.axes:
self.crawl_ax(ax)
def crawl_ax(self, ax):
"""Crawl the axes and process all elements within"""
with self.renderer.draw_axes(ax=ax,
props=utils.get_axes_properties(ax)):
for line in ax.lines:
self.draw_line(ax, line)
for text in ax.texts:
self.draw_text(ax, text)
for (text, ttp) in zip([ax.xaxis.label, ax.yaxis.label, ax.title],
["xlabel", "ylabel", "title"]):
if(hasattr(text, 'get_text') and text.get_text()):
self.draw_text(ax, text, force_trans=ax.transAxes,
text_type=ttp)
for artist in ax.artists:
# TODO: process other artists
if isinstance(artist, matplotlib.text.Text):
self.draw_text(ax, artist)
for patch in ax.patches:
self.draw_patch(ax, patch)
for collection in ax.collections:
self.draw_collection(ax, collection)
for image in ax.images:
self.draw_image(ax, image)
legend = ax.get_legend()
if legend is not None:
props = utils.get_legend_properties(ax, legend)
with self.renderer.draw_legend(legend=legend, props=props):
if props['visible']:
self.crawl_legend(ax, legend)
def crawl_legend(self, ax, legend):
"""
Recursively look through objects in legend children
"""
legendElements = list(utils.iter_all_children(legend._legend_box,
skipContainers=True))
legendElements.append(legend.legendPatch)
for child in legendElements:
# force a large zorder so it appears on top
child.set_zorder(1E6 + child.get_zorder())
# reorder border box to make sure marks are visible
if isinstance(child, matplotlib.patches.FancyBboxPatch):
child.set_zorder(child.get_zorder()-1)
try:
# What kind of object...
if isinstance(child, matplotlib.patches.Patch):
self.draw_patch(ax, child, force_trans=ax.transAxes)
elif isinstance(child, matplotlib.text.Text):
if child.get_text() != 'None':
self.draw_text(ax, child, force_trans=ax.transAxes)
elif isinstance(child, matplotlib.lines.Line2D):
self.draw_line(ax, child, force_trans=ax.transAxes)
elif isinstance(child, matplotlib.collections.Collection):
self.draw_collection(ax, child,
force_offsettrans=ax.transAxes)
else:
warnings.warn("Legend element %s not impemented" % child)
except NotImplementedError:
warnings.warn("Legend element %s not impemented" % child)
def draw_line(self, ax, line, force_trans=None):
"""Process a matplotlib line and call renderer.draw_line"""
coordinates, data = self.process_transform(line.get_transform(),
ax, line.get_xydata(),
force_trans=force_trans)
linestyle = utils.get_line_style(line)
if (linestyle['dasharray'] is None
and linestyle['drawstyle'] == 'default'):
linestyle = None
markerstyle = utils.get_marker_style(line)
if (markerstyle['marker'] in ['None', 'none', None]
or markerstyle['markerpath'][0].size == 0):
markerstyle = None
label = line.get_label()
if markerstyle or linestyle:
self.renderer.draw_marked_line(data=data, coordinates=coordinates,
linestyle=linestyle,
markerstyle=markerstyle,
label=label,
mplobj=line)
def draw_text(self, ax, text, force_trans=None, text_type=None):
"""Process a matplotlib text object and call renderer.draw_text"""
content = text.get_text()
if content:
transform = text.get_transform()
position = text.get_position()
coords, position = self.process_transform(transform, ax,
position,
force_trans=force_trans)
style = utils.get_text_style(text)
self.renderer.draw_text(text=content, position=position,
coordinates=coords,
text_type=text_type,
style=style, mplobj=text)
def draw_patch(self, ax, patch, force_trans=None):
"""Process a matplotlib patch object and call renderer.draw_path"""
vertices, pathcodes = utils.SVG_path(patch.get_path())
transform = patch.get_transform()
coordinates, vertices = self.process_transform(transform,
ax, vertices,
force_trans=force_trans)
linestyle = utils.get_path_style(patch, fill=patch.get_fill())
self.renderer.draw_path(data=vertices,
coordinates=coordinates,
pathcodes=pathcodes,
style=linestyle,
mplobj=patch)
def draw_collection(self, ax, collection,
force_pathtrans=None,
force_offsettrans=None):
"""Process a matplotlib collection and call renderer.draw_collection"""
(transform, transOffset,
offsets, paths) = collection._prepare_points()
offset_coords, offsets = self.process_transform(
transOffset, ax, offsets, force_trans=force_offsettrans)
path_coords = self.process_transform(
transform, ax, force_trans=force_pathtrans)
processed_paths = [utils.SVG_path(path) for path in paths]
processed_paths = [(self.process_transform(
transform, ax, path[0], force_trans=force_pathtrans)[1], path[1])
for path in processed_paths]
path_transforms = collection.get_transforms()
try:
# matplotlib 1.3: path_transforms are transform objects.
# Convert them to numpy arrays.
path_transforms = [t.get_matrix() for t in path_transforms]
except AttributeError:
# matplotlib 1.4: path transforms are already numpy arrays.
pass
styles = {'linewidth': collection.get_linewidths(),
'facecolor': collection.get_facecolors(),
'edgecolor': collection.get_edgecolors(),
'alpha': collection._alpha,
'zorder': collection.get_zorder()}
offset_dict = {"data": "before",
"screen": "after"}
offset_order = offset_dict[collection.get_offset_position()]
self.renderer.draw_path_collection(paths=processed_paths,
path_coordinates=path_coords,
path_transforms=path_transforms,
offsets=offsets,
offset_coordinates=offset_coords,
offset_order=offset_order,
styles=styles,
mplobj=collection)
def draw_image(self, ax, image):
"""Process a matplotlib image object and call renderer.draw_image"""
self.renderer.draw_image(imdata=utils.image_to_base64(image),
extent=image.get_extent(),
coordinates="data",
style={"alpha": image.get_alpha(),
"zorder": image.get_zorder()},
mplobj=image)
