Version 3.0.0
"""
Classes for the efficient drawing of large collections of objects that
share most properties, e.g., a large number of line segments or
polygons.
The classes are not meant to be as flexible as their single element
counterparts (e.g., you may not be able to select all line styles) but
they are meant to be fast for common use cases (e.g., a large set of solid
line segemnts)
"""
import math
from numbers import Number
import warnings
import numpy as np
import matplotlib as mpl
from . import (_path, artist, cbook, cm, colors as mcolors, docstring,
lines as mlines, path as mpath, transforms)
CIRCLE_AREA_FACTOR = 1.0 / np.sqrt(np.pi)
[docs]@cbook._define_aliases({
"antialiased": ["antialiaseds"],
"edgecolor": ["edgecolors"],
"facecolor": ["facecolors"],
"linestyle": ["linestyles", "dashes"],
"linewidth": ["linewidths", "lw"],
})
class Collection(artist.Artist, cm.ScalarMappable):
"""
Base class for Collections. Must be subclassed to be usable.
All properties in a collection must be sequences or scalars;
if scalars, they will be converted to sequences. The
property of the ith element of the collection is::
prop[i % len(props)]
Exceptions are *capstyle* and *joinstyle* properties, these can
only be set globally for the whole collection.
Keyword arguments and default values:
* *edgecolors*: None
* *facecolors*: None
* *linewidths*: None
* *capstyle*: None
* *joinstyle*: None
* *antialiaseds*: None
* *offsets*: None
* *transOffset*: transforms.IdentityTransform()
* *offset_position*: 'screen' (default) or 'data'
* *norm*: None (optional for
:class:`matplotlib.cm.ScalarMappable`)
* *cmap*: None (optional for
:class:`matplotlib.cm.ScalarMappable`)
* *hatch*: None
* *zorder*: 1
*offsets* and *transOffset* are used to translate the patch after
rendering (default no offsets). If offset_position is 'screen'
(default) the offset is applied after the master transform has
been applied, that is, the offsets are in screen coordinates. If
offset_position is 'data', the offset is applied before the master
transform, i.e., the offsets are in data coordinates.
If any of *edgecolors*, *facecolors*, *linewidths*, *antialiaseds*
are None, they default to their :data:`matplotlib.rcParams` patch
setting, in sequence form.
The use of :class:`~matplotlib.cm.ScalarMappable` is optional. If
the :class:`~matplotlib.cm.ScalarMappable` matrix _A is not None
(i.e., a call to set_array has been made), at draw time a call to
scalar mappable will be made to set the face colors.
"""
_offsets = np.zeros((0, 2))
_transOffset = transforms.IdentityTransform()
#: Either a list of 3x3 arrays or an Nx3x3 array of transforms, suitable
#: for the `all_transforms` argument to
#: :meth:`~matplotlib.backend_bases.RendererBase.draw_path_collection`;
#: each 3x3 array is used to initialize an
#: :class:`~matplotlib.transforms.Affine2D` object.
#: Each kind of collection defines this based on its arguments.
_transforms = np.empty((0, 3, 3))
# Whether to draw an edge by default. Set on a
# subclass-by-subclass basis.
_edge_default = False
def __init__(self,
edgecolors=None,
facecolors=None,
linewidths=None,
linestyles='solid',
capstyle=None,
joinstyle=None,
antialiaseds=None,
offsets=None,
transOffset=None,
norm=None, # optional for ScalarMappable
cmap=None, # ditto
pickradius=5.0,
hatch=None,
urls=None,
offset_position='screen',
zorder=1,
**kwargs
):
"""
Create a Collection
%(Collection)s
"""
artist.Artist.__init__(self)
cm.ScalarMappable.__init__(self, norm, cmap)
# list of un-scaled dash patterns
# this is needed scaling the dash pattern by linewidth
self._us_linestyles = [(None, None)]
# list of dash patterns
self._linestyles = [(None, None)]
# list of unbroadcast/scaled linewidths
self._us_lw = [0]
self._linewidths = [0]
self._is_filled = True # May be modified by set_facecolor().
self._hatch_color = mcolors.to_rgba(mpl.rcParams['hatch.color'])
self.set_facecolor(facecolors)
self.set_edgecolor(edgecolors)
self.set_linewidth(linewidths)
self.set_linestyle(linestyles)
self.set_antialiased(antialiaseds)
self.set_pickradius(pickradius)
self.set_urls(urls)
self.set_hatch(hatch)
self.set_offset_position(offset_position)
self.set_zorder(zorder)
if capstyle:
self.set_capstyle(capstyle)
else:
self._capstyle = None
if joinstyle:
self.set_joinstyle(joinstyle)
else:
self._joinstyle = None
self._offsets = np.zeros((1, 2))
self._uniform_offsets = None
if offsets is not None:
offsets = np.asanyarray(offsets, float)
# Broadcast (2,) -> (1, 2) but nothing else.
if offsets.shape == (2,):
offsets = offsets[None, :]
if transOffset is not None:
self._offsets = offsets
self._transOffset = transOffset
else:
self._uniform_offsets = offsets
self._path_effects = None
self.update(kwargs)
self._paths = None
[docs] def get_offset_transform(self):
t = self._transOffset
if (not isinstance(t, transforms.Transform)
and hasattr(t, '_as_mpl_transform')):
t = t._as_mpl_transform(self.axes)
return t
[docs] def get_datalim(self, transData):
transform = self.get_transform()
transOffset = self.get_offset_transform()
offsets = self._offsets
paths = self.get_paths()
if not transform.is_affine:
paths = [transform.transform_path_non_affine(p) for p in paths]
transform = transform.get_affine()
if not transOffset.is_affine:
offsets = transOffset.transform_non_affine(offsets)
transOffset = transOffset.get_affine()
if isinstance(offsets, np.ma.MaskedArray):
offsets = offsets.filled(np.nan)
# get_path_collection_extents handles nan but not masked arrays
if len(paths) and len(offsets):
result = mpath.get_path_collection_extents(
transform.frozen(), paths, self.get_transforms(),
offsets, transOffset.frozen())
result = result.inverse_transformed(transData)
else:
result = transforms.Bbox.null()
return result
[docs] def get_window_extent(self, renderer):
# TODO:check to ensure that this does not fail for
# cases other than scatter plot legend
return self.get_datalim(transforms.IdentityTransform())
def _prepare_points(self):
"""Point prep for drawing and hit testing"""
transform = self.get_transform()
transOffset = self.get_offset_transform()
offsets = self._offsets
paths = self.get_paths()
if self.have_units():
paths = []
for path in self.get_paths():
vertices = path.vertices
xs, ys = vertices[:, 0], vertices[:, 1]
xs = self.convert_xunits(xs)
ys = self.convert_yunits(ys)
paths.append(mpath.Path(np.column_stack([xs, ys]), path.codes))
if offsets.size > 0:
xs = self.convert_xunits(offsets[:, 0])
ys = self.convert_yunits(offsets[:, 1])
offsets = np.column_stack([xs, ys])
if not transform.is_affine:
paths = [transform.transform_path_non_affine(path)
for path in paths]
transform = transform.get_affine()
if not transOffset.is_affine:
offsets = transOffset.transform_non_affine(offsets)
# This might have changed an ndarray into a masked array.
transOffset = transOffset.get_affine()
if isinstance(offsets, np.ma.MaskedArray):
offsets = offsets.filled(np.nan)
# Changing from a masked array to nan-filled ndarray
# is probably most efficient at this point.
return transform, transOffset, offsets, paths
[docs] @artist.allow_rasterization
def draw(self, renderer):
if not self.get_visible():
return
renderer.open_group(self.__class__.__name__, self.get_gid())
self.update_scalarmappable()
transform, transOffset, offsets, paths = self._prepare_points()
gc = renderer.new_gc()
self._set_gc_clip(gc)
gc.set_snap(self.get_snap())
if self._hatch:
gc.set_hatch(self._hatch)
try:
gc.set_hatch_color(self._hatch_color)
except AttributeError:
# if we end up with a GC that does not have this method
warnings.warn("Your backend does not support setting the "
"hatch color.")
if self.get_sketch_params() is not None:
gc.set_sketch_params(*self.get_sketch_params())
if self.get_path_effects():
from matplotlib.patheffects import PathEffectRenderer
renderer = PathEffectRenderer(self.get_path_effects(), renderer)
# If the collection is made up of a single shape/color/stroke,
# it can be rendered once and blitted multiple times, using
# `draw_markers` rather than `draw_path_collection`. This is
# *much* faster for Agg, and results in smaller file sizes in
# PDF/SVG/PS.
trans = self.get_transforms()
facecolors = self.get_facecolor()
edgecolors = self.get_edgecolor()
do_single_path_optimization = False
if (len(paths) == 1 and len(trans) <= 1 and
len(facecolors) == 1 and len(edgecolors) == 1 and
len(self._linewidths) == 1 and
self._linestyles == [(None, None)] and
len(self._antialiaseds) == 1 and len(self._urls) == 1 and
self.get_hatch() is None):
if len(trans):
combined_transform = (transforms.Affine2D(trans[0]) +
transform)
else:
combined_transform = transform
extents = paths[0].get_extents(combined_transform)
width, height = renderer.get_canvas_width_height()
if extents.width < width and extents.height < height:
do_single_path_optimization = True
if self._joinstyle:
gc.set_joinstyle(self._joinstyle)
if self._capstyle:
gc.set_capstyle(self._capstyle)
if do_single_path_optimization:
gc.set_foreground(tuple(edgecolors[0]))
gc.set_linewidth(self._linewidths[0])
gc.set_dashes(*self._linestyles[0])
gc.set_antialiased(self._antialiaseds[0])
gc.set_url(self._urls[0])
renderer.draw_markers(
gc, paths[0], combined_transform.frozen(),
mpath.Path(offsets), transOffset, tuple(facecolors[0]))
else:
renderer.draw_path_collection(
gc, transform.frozen(), paths,
self.get_transforms(), offsets, transOffset,
self.get_facecolor(), self.get_edgecolor(),
self._linewidths, self._linestyles,
self._antialiaseds, self._urls,
self._offset_position)
gc.restore()
renderer.close_group(self.__class__.__name__)
self.stale = False
[docs] def set_pickradius(self, pr):
"""Set the pick radius used for containment tests.
Parameters
----------
d : float
Pick radius, in points.
"""
self._pickradius = pr
[docs] def contains(self, mouseevent):
"""
Test whether the mouse event occurred in the collection.
Returns True | False, ``dict(ind=itemlist)``, where every
item in itemlist contains the event.
"""
if callable(self._contains):
return self._contains(self, mouseevent)
if not self.get_visible():
return False, {}
pickradius = (
float(self._picker)
if isinstance(self._picker, Number) and
self._picker is not True # the bool, not just nonzero or 1
else self._pickradius)
transform, transOffset, offsets, paths = self._prepare_points()
ind = _path.point_in_path_collection(
mouseevent.x, mouseevent.y, pickradius,
transform.frozen(), paths, self.get_transforms(),
offsets, transOffset, pickradius <= 0,
self.get_offset_position())
return len(ind) > 0, dict(ind=ind)
[docs] def set_urls(self, urls):
"""
Parameters
----------
urls : List[str] or None
"""
self._urls = urls if urls is not None else [None]
self.stale = True
[docs] def set_hatch(self, hatch):
r"""
Set the hatching pattern
*hatch* can be one of::
/ - diagonal hatching
\ - back diagonal
| - vertical
- - horizontal
+ - crossed
x - crossed diagonal
o - small circle
O - large circle
. - dots
* - stars
Letters can be combined, in which case all the specified
hatchings are done. If same letter repeats, it increases the
density of hatching of that pattern.
Hatching is supported in the PostScript, PDF, SVG and Agg
backends only.
Unlike other properties such as linewidth and colors, hatching
can only be specified for the collection as a whole, not separately
for each member.
Parameters
----------
hatch : {'/', '\\', '|', '-', '+', 'x', 'o', 'O', '.', '*'}
"""
self._hatch = hatch
self.stale = True
[docs] def set_offsets(self, offsets):
"""
Set the offsets for the collection. *offsets* can be a scalar
or a sequence.
Parameters
----------
offsets : float or sequence of floats
"""
offsets = np.asanyarray(offsets, float)
if offsets.shape == (2,): # Broadcast (2,) -> (1, 2) but nothing else.
offsets = offsets[None, :]
# This decision is based on how they are initialized above in __init__.
if self._uniform_offsets is None:
self._offsets = offsets
else:
self._uniform_offsets = offsets
self.stale = True
[docs] def get_offsets(self):
"""Return the offsets for the collection."""
# This decision is based on how they are initialized above in __init__.
if self._uniform_offsets is None:
return self._offsets
else:
return self._uniform_offsets
[docs] def set_offset_position(self, offset_position):
"""
Set how offsets are applied. If *offset_position* is 'screen'
(default) the offset is applied after the master transform has
been applied, that is, the offsets are in screen coordinates.
If offset_position is 'data', the offset is applied before the
master transform, i.e., the offsets are in data coordinates.
Parameters
----------
offset_position : {'screen', 'data'}
"""
if offset_position not in ('screen', 'data'):
raise ValueError("offset_position must be 'screen' or 'data'")
self._offset_position = offset_position
self.stale = True
[docs] def get_offset_position(self):
"""
Returns how offsets are applied for the collection. If
*offset_position* is 'screen', the offset is applied after the
master transform has been applied, that is, the offsets are in
screen coordinates. If offset_position is 'data', the offset
is applied before the master transform, i.e., the offsets are
in data coordinates.
"""
return self._offset_position
[docs] def set_linewidth(self, lw):
"""
Set the linewidth(s) for the collection. *lw* can be a scalar
or a sequence; if it is a sequence the patches will cycle
through the sequence
Parameters
----------
lw : float or sequence of floats
"""
if lw is None:
lw = mpl.rcParams['patch.linewidth']
if lw is None:
lw = mpl.rcParams['lines.linewidth']
# get the un-scaled/broadcast lw
self._us_lw = np.atleast_1d(np.asarray(lw))
# scale all of the dash patterns.
self._linewidths, self._linestyles = self._bcast_lwls(
self._us_lw, self._us_linestyles)
self.stale = True
[docs] def set_linestyle(self, ls):
"""
Set the linestyle(s) for the collection.
=========================== =================
linestyle description
=========================== =================
``'-'`` or ``'solid'`` solid line
``'--'`` or ``'dashed'`` dashed line
``'-.'`` or ``'dashdot'`` dash-dotted line
``':'`` or ``'dotted'`` dotted line
=========================== =================
Alternatively a dash tuple of the following form can be provided::
(offset, onoffseq),
where ``onoffseq`` is an even length tuple of on and off ink in points.
Parameters
----------
ls : {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}
The line style.
"""
try:
if isinstance(ls, str):
ls = cbook.ls_mapper.get(ls, ls)
dashes = [mlines._get_dash_pattern(ls)]
else:
try:
dashes = [mlines._get_dash_pattern(ls)]
except ValueError:
dashes = [mlines._get_dash_pattern(x) for x in ls]
except ValueError:
raise ValueError(
'Do not know how to convert {!r} to dashes'.format(ls))
# get the list of raw 'unscaled' dash patterns
self._us_linestyles = dashes
# broadcast and scale the lw and dash patterns
self._linewidths, self._linestyles = self._bcast_lwls(
self._us_lw, self._us_linestyles)
[docs] def set_capstyle(self, cs):
"""
Set the capstyle for the collection. The capstyle can
only be set globally for all elements in the collection
Parameters
----------
cs : {'butt', 'round', 'projecting'}
The capstyle
"""
if cs in ('butt', 'round', 'projecting'):
self._capstyle = cs
else:
raise ValueError('Unrecognized cap style. Found %s' % cs)
[docs] def set_joinstyle(self, js):
"""
Set the joinstyle for the collection. The joinstyle can only be
set globally for all elements in the collection.
Parameters
----------
js : {'miter', 'round', 'bevel'}
The joinstyle
"""
if js in ('miter', 'round', 'bevel'):
self._joinstyle = js
else:
raise ValueError('Unrecognized join style. Found %s' % js)
@staticmethod
def _bcast_lwls(linewidths, dashes):
'''Internal helper function to broadcast + scale ls/lw
In the collection drawing code the linewidth and linestyle are
cycled through as circular buffers (via v[i % len(v)]). Thus,
if we are going to scale the dash pattern at set time (not
draw time) we need to do the broadcasting now and expand both
lists to be the same length.
Parameters
----------
linewidths : list
line widths of collection
dashes : list
dash specification (offset, (dash pattern tuple))
Returns
-------
linewidths, dashes : list
Will be the same length, dashes are scaled by paired linewidth
'''
if mpl.rcParams['_internal.classic_mode']:
return linewidths, dashes
# make sure they are the same length so we can zip them
if len(dashes) != len(linewidths):
l_dashes = len(dashes)
l_lw = len(linewidths)
gcd = math.gcd(l_dashes, l_lw)
dashes = list(dashes) * (l_lw // gcd)
linewidths = list(linewidths) * (l_dashes // gcd)
# scale the dash patters
dashes = [mlines._scale_dashes(o, d, lw)
for (o, d), lw in zip(dashes, linewidths)]
return linewidths, dashes
[docs] def set_antialiased(self, aa):
"""
Set the antialiasing state for rendering.
Parameters
----------
aa : bool or sequence of bools
"""
if aa is None:
aa = mpl.rcParams['patch.antialiased']
self._antialiaseds = np.atleast_1d(np.asarray(aa, bool))
self.stale = True
[docs] def set_color(self, c):
"""
Set both the edgecolor and the facecolor.
.. seealso::
:meth:`set_facecolor`, :meth:`set_edgecolor`
For setting the edge or face color individually.
Parameters
----------
c : matplotlib color arg or sequence of rgba tuples
"""
self.set_facecolor(c)
self.set_edgecolor(c)
def _set_facecolor(self, c):
if c is None:
c = mpl.rcParams['patch.facecolor']
self._is_filled = True
try:
if c.lower() == 'none':
self._is_filled = False
except AttributeError:
pass
self._facecolors = mcolors.to_rgba_array(c, self._alpha)
self.stale = True
[docs] def set_facecolor(self, c):
"""
Set the facecolor(s) of the collection. *c* can be a
matplotlib color spec (all patches have same color), or a
sequence of specs; if it is a sequence the patches will
cycle through the sequence.
If *c* is 'none', the patch will not be filled.
Parameters
----------
c : color or sequence of colors
"""
self._original_facecolor = c
self._set_facecolor(c)
[docs] def get_edgecolor(self):
if cbook._str_equal(self._edgecolors, 'face'):
return self.get_facecolors()
else:
return self._edgecolors
def _set_edgecolor(self, c):
set_hatch_color = True
if c is None:
if (mpl.rcParams['patch.force_edgecolor'] or
not self._is_filled or self._edge_default):
c = mpl.rcParams['patch.edgecolor']
else:
c = 'none'
set_hatch_color = False
self._is_stroked = True
try:
if c.lower() == 'none':
self._is_stroked = False
except AttributeError:
pass
try:
if c.lower() == 'face': # Special case: lookup in "get" method.
self._edgecolors = 'face'
return
except AttributeError:
pass
self._edgecolors = mcolors.to_rgba_array(c, self._alpha)
if set_hatch_color and len(self._edgecolors):
self._hatch_color = tuple(self._edgecolors[0])
self.stale = True
[docs] def set_edgecolor(self, c):
"""
Set the edgecolor(s) of the collection. *c* can be a
matplotlib color spec (all patches have same color), or a
sequence of specs; if it is a sequence the patches will
cycle through the sequence.
If *c* is 'face', the edge color will always be the same as
the face color. If it is 'none', the patch boundary will not
be drawn.
Parameters
----------
c : color or sequence of colors
"""
self._original_edgecolor = c
self._set_edgecolor(c)
[docs] def set_alpha(self, alpha):
"""
Set the alpha tranparencies of the collection. *alpha* must be
a float or *None*.
Parameters
----------
alpha : float or None
"""
if alpha is not None:
try:
float(alpha)
except TypeError:
raise TypeError('alpha must be a float or None')
self.update_dict['array'] = True
artist.Artist.set_alpha(self, alpha)
self._set_facecolor(self._original_facecolor)
self._set_edgecolor(self._original_edgecolor)
[docs] def update_scalarmappable(self):
"""
If the scalar mappable array is not none, update colors
from scalar data
"""
if self._A is None:
return
if self._A.ndim > 1:
raise ValueError('Collections can only map rank 1 arrays')
if not self.check_update("array"):
return
if self._is_filled:
self._facecolors = self.to_rgba(self._A, self._alpha)
elif self._is_stroked:
self._edgecolors = self.to_rgba(self._A, self._alpha)
self.stale = True
[docs] def update_from(self, other):
'copy properties from other to self'
artist.Artist.update_from(self, other)
self._antialiaseds = other._antialiaseds
self._original_edgecolor = other._original_edgecolor
self._edgecolors = other._edgecolors
self._original_facecolor = other._original_facecolor
self._facecolors = other._facecolors
self._linewidths = other._linewidths
self._linestyles = other._linestyles
self._us_linestyles = other._us_linestyles
self._pickradius = other._pickradius
self._hatch = other._hatch
# update_from for scalarmappable
self._A = other._A
self.norm = other.norm
self.cmap = other.cmap
# self.update_dict = other.update_dict # do we need to copy this? -JJL
self.stale = True
# these are not available for the object inspector until after the
# class is built so we define an initial set here for the init
# function and they will be overridden after object defn
docstring.interpd.update(Collection="""\
Valid Collection keyword arguments:
* *edgecolors*: None
* *facecolors*: None
* *linewidths*: None
* *antialiaseds*: None
* *offsets*: None
* *transOffset*: transforms.IdentityTransform()
* *norm*: None (optional for
:class:`matplotlib.cm.ScalarMappable`)
* *cmap*: None (optional for
:class:`matplotlib.cm.ScalarMappable`)
*offsets* and *transOffset* are used to translate the patch after
rendering (default no offsets)
If any of *edgecolors*, *facecolors*, *linewidths*, *antialiaseds*
are None, they default to their :data:`matplotlib.rcParams` patch
setting, in sequence form.
""")
class _CollectionWithSizes(Collection):
"""
Base class for collections that have an array of sizes.
"""
_factor = 1.0
def get_sizes(self):
"""
Returns the sizes of the elements in the collection. The
value represents the 'area' of the element.
Returns
-------
sizes : array
The 'area' of each element.
"""
return self._sizes
def set_sizes(self, sizes, dpi=72.0):
"""
Set the sizes of each member of the collection.
Parameters
----------
sizes : ndarray or None
The size to set for each element of the collection. The
value is the 'area' of the element.
dpi : float
The dpi of the canvas. Defaults to 72.0.
"""
if sizes is None:
self._sizes = np.array([])
self._transforms = np.empty((0, 3, 3))
else:
self._sizes = np.asarray(sizes)
self._transforms = np.zeros((len(self._sizes), 3, 3))
scale = np.sqrt(self._sizes) * dpi / 72.0 * self._factor
self._transforms[:, 0, 0] = scale
self._transforms[:, 1, 1] = scale
self._transforms[:, 2, 2] = 1.0
self.stale = True
@artist.allow_rasterization
def draw(self, renderer):
self.set_sizes(self._sizes, self.figure.dpi)
Collection.draw(self, renderer)
[docs]class PathCollection(_CollectionWithSizes):
"""
This is the most basic :class:`Collection` subclass.
"""
@docstring.dedent_interpd
def __init__(self, paths, sizes=None, **kwargs):
"""
*paths* is a sequence of :class:`matplotlib.path.Path`
instances.
%(Collection)s
"""
Collection.__init__(self, **kwargs)
self.set_paths(paths)
self.set_sizes(sizes)
self.stale = True
[docs]class PolyCollection(_CollectionWithSizes):
@docstring.dedent_interpd
def __init__(self, verts, sizes=None, closed=True, **kwargs):
"""
*verts* is a sequence of ( *verts0*, *verts1*, ...) where
*verts_i* is a sequence of *xy* tuples of vertices, or an
equivalent :mod:`numpy` array of shape (*nv*, 2).
*sizes* is *None* (default) or a sequence of floats that
scale the corresponding *verts_i*. The scaling is applied
before the Artist master transform; if the latter is an identity
transform, then the overall scaling is such that if
*verts_i* specify a unit square, then *sizes_i* is the area
of that square in points^2.
If len(*sizes*) < *nv*, the additional values will be
taken cyclically from the array.
*closed*, when *True*, will explicitly close the polygon.
%(Collection)s
"""
Collection.__init__(self, **kwargs)
self.set_sizes(sizes)
self.set_verts(verts, closed)
self.stale = True
[docs] def set_verts(self, verts, closed=True):
'''This allows one to delay initialization of the vertices.'''
if isinstance(verts, np.ma.MaskedArray):
verts = verts.astype(float).filled(np.nan)
# This is much faster than having Path do it one at a time.
if closed:
self._paths = []
for xy in verts:
if len(xy):
if isinstance(xy, np.ma.MaskedArray):
xy = np.ma.concatenate([xy, xy[0:1]])
else:
xy = np.asarray(xy)
xy = np.concatenate([xy, xy[0:1]])
codes = np.empty(xy.shape[0], dtype=mpath.Path.code_type)
codes[:] = mpath.Path.LINETO
codes[0] = mpath.Path.MOVETO
codes[-1] = mpath.Path.CLOSEPOLY
self._paths.append(mpath.Path(xy, codes))
else:
self._paths.append(mpath.Path(xy))
else:
self._paths = [mpath.Path(xy) for xy in verts]
self.stale = True
set_paths = set_verts
[docs] def set_verts_and_codes(self, verts, codes):
'''This allows one to initialize vertices with path codes.'''
if len(verts) != len(codes):
raise ValueError("'codes' must be a 1D list or array "
"with the same length of 'verts'")
self._paths = []
for xy, cds in zip(verts, codes):
if len(xy):
self._paths.append(mpath.Path(xy, cds))
else:
self._paths.append(mpath.Path(xy))
self.stale = True
[docs]class BrokenBarHCollection(PolyCollection):
"""
A collection of horizontal bars spanning *yrange* with a sequence of
*xranges*.
"""
@docstring.dedent_interpd
def __init__(self, xranges, yrange, **kwargs):
"""
*xranges*
sequence of (*xmin*, *xwidth*)
*yrange*
*ymin*, *ywidth*
%(Collection)s
"""
ymin, ywidth = yrange
ymax = ymin + ywidth
verts = [[(xmin, ymin),
(xmin, ymax),
(xmin + xwidth, ymax),
(xmin + xwidth, ymin),
(xmin, ymin)] for xmin, xwidth in xranges]
PolyCollection.__init__(self, verts, **kwargs)
[docs] @staticmethod
def span_where(x, ymin, ymax, where, **kwargs):
"""
Create a BrokenBarHCollection to plot horizontal bars from
over the regions in *x* where *where* is True. The bars range
on the y-axis from *ymin* to *ymax*
A :class:`BrokenBarHCollection` is returned. *kwargs* are
passed on to the collection.
"""
xranges = []
for ind0, ind1 in cbook.contiguous_regions(where):
xslice = x[ind0:ind1]
if not len(xslice):
continue
xranges.append((xslice[0], xslice[-1] - xslice[0]))
collection = BrokenBarHCollection(
xranges, [ymin, ymax - ymin], **kwargs)
return collection
[docs]class RegularPolyCollection(_CollectionWithSizes):
"""Draw a collection of regular polygons with *numsides*."""
_path_generator = mpath.Path.unit_regular_polygon
_factor = CIRCLE_AREA_FACTOR
@docstring.dedent_interpd
def __init__(self,
numsides,
rotation=0,
sizes=(1,),
**kwargs):
"""
*numsides*
the number of sides of the polygon
*rotation*
the rotation of the polygon in radians
*sizes*
gives the area of the circle circumscribing the
regular polygon in points^2
%(Collection)s
Example: see :doc:`/gallery/event_handling/lasso_demo` for a
complete example::
offsets = np.random.rand(20,2)
facecolors = [cm.jet(x) for x in np.random.rand(20)]
black = (0,0,0,1)
collection = RegularPolyCollection(
numsides=5, # a pentagon
rotation=0, sizes=(50,),
facecolors=facecolors,
edgecolors=(black,),
linewidths=(1,),
offsets=offsets,
transOffset=ax.transData,
)
"""
Collection.__init__(self, **kwargs)
self.set_sizes(sizes)
self._numsides = numsides
self._paths = [self._path_generator(numsides)]
self._rotation = rotation
self.set_transform(transforms.IdentityTransform())
[docs] @artist.allow_rasterization
def draw(self, renderer):
self.set_sizes(self._sizes, self.figure.dpi)
self._transforms = [
transforms.Affine2D(x).rotate(-self._rotation).get_matrix()
for x in self._transforms
]
Collection.draw(self, renderer)
[docs]class StarPolygonCollection(RegularPolyCollection):
"""
Draw a collection of regular stars with *numsides* points."""
_path_generator = mpath.Path.unit_regular_star
[docs]class AsteriskPolygonCollection(RegularPolyCollection):
"""
Draw a collection of regular asterisks with *numsides* points."""
_path_generator = mpath.Path.unit_regular_asterisk
[docs]class LineCollection(Collection):
"""
All parameters must be sequences or scalars; if scalars, they will
be converted to sequences. The property of the ith line
segment is::
prop[i % len(props)]
i.e., the properties cycle if the ``len`` of props is less than the
number of segments.
"""
_edge_default = True
def __init__(self, segments, # Can be None.
linewidths=None,
colors=None,
antialiaseds=None,
linestyles='solid',
offsets=None,
transOffset=None,
norm=None,
cmap=None,
pickradius=5,
zorder=2,
facecolors='none',
**kwargs
):
"""
Parameters
----------
segments :
A sequence of (*line0*, *line1*, *line2*), where::
linen = (x0, y0), (x1, y1), ... (xm, ym)
or the equivalent numpy array with two columns. Each line
can be a different length.
colors : sequence, optional
A sequence of RGBA tuples (e.g., arbitrary color
strings, etc, not allowed).
antialiaseds : sequence, optional
A sequence of ones or zeros.
linestyles : string, tuple, optional
Either one of [ 'solid' | 'dashed' | 'dashdot' | 'dotted' ], or
a dash tuple. The dash tuple is::
(offset, onoffseq)
where ``onoffseq`` is an even length tuple of on and off ink
in points.
norm : Normalize, optional
`~.colors.Normalize` instance.
cmap : string or Colormap, optional
Colormap name or `~.colors.Colormap` instance.
pickradius : float, optional
The tolerance in points for mouse clicks picking a line.
Default is 5 pt.
zorder : int, optional
zorder of the LineCollection. Default is 2.
facecolors : optional
The facecolors of the LineCollection. Default is 'none'.
Setting to a value other than 'none' will lead to a filled
polygon being drawn between points on each line.
Notes
-----
If *linewidths*, *colors*, or *antialiaseds* is None, they
default to their rcParams setting, in sequence form.
If *offsets* and *transOffset* are not None, then
*offsets* are transformed by *transOffset* and applied after
the segments have been transformed to display coordinates.
If *offsets* is not None but *transOffset* is None, then the
*offsets* are added to the segments before any transformation.
In this case, a single offset can be specified as::
offsets=(xo,yo)
and this value will be added cumulatively to each successive
segment, so as to produce a set of successively offset curves.
The use of :class:`~matplotlib.cm.ScalarMappable` is optional.
If the :class:`~matplotlib.cm.ScalarMappable` array
:attr:`~matplotlib.cm.ScalarMappable._A` is not None (i.e., a call to
:meth:`~matplotlib.cm.ScalarMappable.set_array` has been made), at
draw time a call to scalar mappable will be made to set the colors.
"""
if colors is None:
colors = mpl.rcParams['lines.color']
if linewidths is None:
linewidths = (mpl.rcParams['lines.linewidth'],)
if antialiaseds is None:
antialiaseds = (mpl.rcParams['lines.antialiased'],)
colors = mcolors.to_rgba_array(colors)
Collection.__init__(
self,
edgecolors=colors,
facecolors=facecolors,
linewidths=linewidths,
linestyles=linestyles,
antialiaseds=antialiaseds,
offsets=offsets,
transOffset=transOffset,
norm=norm,
cmap=cmap,
pickradius=pickradius,
zorder=zorder,
**kwargs)
self.set_segments(segments)
[docs] def set_segments(self, segments):
if segments is None:
return
_segments = []
for seg in segments:
if not isinstance(seg, np.ma.MaskedArray):
seg = np.asarray(seg, float)
_segments.append(seg)
if self._uniform_offsets is not None:
_segments = self._add_offsets(_segments)
self._paths = [mpath.Path(_seg) for _seg in _segments]
self.stale = True
set_verts = set_segments # for compatibility with PolyCollection
set_paths = set_segments
[docs] def get_segments(self):
"""
Returns
-------
segments : list
List of segments in the LineCollection. Each list item contains an
array of vertices.
"""
segments = []
for path in self._paths:
vertices = [vertex for vertex, _ in path.iter_segments()]
vertices = np.asarray(vertices)
segments.append(vertices)
return segments
def _add_offsets(self, segs):
offsets = self._uniform_offsets
Nsegs = len(segs)
Noffs = offsets.shape[0]
if Noffs == 1:
for i in range(Nsegs):
segs[i] = segs[i] + i * offsets
else:
for i in range(Nsegs):
io = i % Noffs
segs[i] = segs[i] + offsets[io:io + 1]
return segs
[docs] def set_color(self, c):
"""
Set the color(s) of the LineCollection.
Parameters
----------
c :
Matplotlib color argument (all patches have same color), or a
sequence or rgba tuples; if it is a sequence the patches will
cycle through the sequence.
"""
self.set_edgecolor(c)
self.stale = True
get_colors = get_color # for compatibility with old versions
[docs]class EventCollection(LineCollection):
'''
A collection of discrete events.
The events are given by a 1-dimensional array, usually the position of
something along an axis, such as time or length. They do not have an
amplitude and are displayed as vertical or horizontal parallel bars.
'''
_edge_default = True
def __init__(self,
positions, # Cannot be None.
orientation=None,
lineoffset=0,
linelength=1,
linewidth=None,
color=None,
linestyle='solid',
antialiased=None,
**kwargs
):
"""
Parameters
----------
positions : 1D array-like object
Each value is an event.
orientation : {None, 'horizontal', 'vertical'}, optional
The orientation of the **collection** (the event bars are along
the orthogonal direction). Defaults to 'horizontal' if not
specified or None.
lineoffset : scalar, optional, default: 0
The offset of the center of the markers from the origin, in the
direction orthogonal to *orientation*.
linelength : scalar, optional, default: 1
The total height of the marker (i.e. the marker stretches from
``lineoffset - linelength/2`` to ``lineoffset + linelength/2``).
linewidth : scalar or None, optional, default: None
If it is None, defaults to its rcParams setting, in sequence form.
color : color, sequence of colors or None, optional, default: None
If it is None, defaults to its rcParams setting, in sequence form.
linestyle : str or tuple, optional, default: 'solid'
Valid strings are ['solid', 'dashed', 'dashdot', 'dotted',
'-', '--', '-.', ':']. Dash tuples should be of the form::
(offset, onoffseq),
where *onoffseq* is an even length tuple of on and off ink
in points.
antialiased : {None, 1, 2}, optional
If it is None, defaults to its rcParams setting, in sequence form.
**kwargs : optional
Other keyword arguments are line collection properties. See
:class:`~matplotlib.collections.LineCollection` for a list of
the valid properties.
Examples
--------
.. plot:: gallery/lines_bars_and_markers/eventcollection_demo.py
"""
segment = (lineoffset + linelength / 2.,
lineoffset - linelength / 2.)
if positions is None or len(positions) == 0:
segments = []
elif hasattr(positions, 'ndim') and positions.ndim > 1:
raise ValueError('positions cannot be an array with more than '
'one dimension.')
elif (orientation is None or orientation.lower() == 'none' or
orientation.lower() == 'horizontal'):
positions.sort()
segments = [[(coord1, coord2) for coord2 in segment] for
coord1 in positions]
self._is_horizontal = True
elif orientation.lower() == 'vertical':
positions.sort()
segments = [[(coord2, coord1) for coord2 in segment] for
coord1 in positions]
self._is_horizontal = False
else:
raise ValueError("orientation must be 'horizontal' or 'vertical'")
LineCollection.__init__(self,
segments,
linewidths=linewidth,
colors=color,
antialiaseds=antialiased,
linestyles=linestyle,
**kwargs)
self._linelength = linelength
self._lineoffset = lineoffset
[docs] def get_positions(self):
'''
return an array containing the floating-point values of the positions
'''
segments = self.get_segments()
pos = 0 if self.is_horizontal() else 1
positions = []
for segment in segments:
positions.append(segment[0, pos])
return positions
[docs] def set_positions(self, positions):
'''
set the positions of the events to the specified value
'''
if positions is None or (hasattr(positions, 'len') and
len(positions) == 0):
self.set_segments([])
return
lineoffset = self.get_lineoffset()
linelength = self.get_linelength()
segment = (lineoffset + linelength / 2.,
lineoffset - linelength / 2.)
positions = np.asanyarray(positions)
positions.sort()
if self.is_horizontal():
segments = [[(coord1, coord2) for coord2 in segment] for
coord1 in positions]
else:
segments = [[(coord2, coord1) for coord2 in segment] for
coord1 in positions]
self.set_segments(segments)
[docs] def add_positions(self, position):
'''
add one or more events at the specified positions
'''
if position is None or (hasattr(position, 'len') and
len(position) == 0):
return
positions = self.get_positions()
positions = np.hstack([positions, np.asanyarray(position)])
self.set_positions(positions)
extend_positions = append_positions = add_positions
[docs] def is_horizontal(self):
'''
True if the eventcollection is horizontal, False if vertical
'''
return self._is_horizontal
[docs] def get_orientation(self):
'''
get the orientation of the event line, may be:
[ 'horizontal' | 'vertical' ]
'''
return 'horizontal' if self.is_horizontal() else 'vertical'
[docs] def switch_orientation(self):
'''
switch the orientation of the event line, either from vertical to
horizontal or vice versus
'''
segments = self.get_segments()
for i, segment in enumerate(segments):
segments[i] = np.fliplr(segment)
self.set_segments(segments)
self._is_horizontal = not self.is_horizontal()
self.stale = True
[docs] def set_orientation(self, orientation=None):
'''
set the orientation of the event line
[ 'horizontal' | 'vertical' | None ]
defaults to 'horizontal' if not specified or None
'''
if (orientation is None or orientation.lower() == 'none' or
orientation.lower() == 'horizontal'):
is_horizontal = True
elif orientation.lower() == 'vertical':
is_horizontal = False
else:
raise ValueError("orientation must be 'horizontal' or 'vertical'")
if is_horizontal == self.is_horizontal():
return
self.switch_orientation()
[docs] def get_linelength(self):
'''
get the length of the lines used to mark each event
'''
return self._linelength
[docs] def set_linelength(self, linelength):
'''
set the length of the lines used to mark each event
'''
if linelength == self.get_linelength():
return
lineoffset = self.get_lineoffset()
segments = self.get_segments()
pos = 1 if self.is_horizontal() else 0
for segment in segments:
segment[0, pos] = lineoffset + linelength / 2.
segment[1, pos] = lineoffset - linelength / 2.
self.set_segments(segments)
self._linelength = linelength
[docs] def get_lineoffset(self):
'''
get the offset of the lines used to mark each event
'''
return self._lineoffset
[docs] def set_lineoffset(self, lineoffset):
'''
set the offset of the lines used to mark each event
'''
if lineoffset == self.get_lineoffset():
return
linelength = self.get_linelength()
segments = self.get_segments()
pos = 1 if self.is_horizontal() else 0
for segment in segments:
segment[0, pos] = lineoffset + linelength / 2.
segment[1, pos] = lineoffset - linelength / 2.
self.set_segments(segments)
self._lineoffset = lineoffset
[docs] def get_linewidth(self):
"""Get the width of the lines used to mark each event."""
return super(EventCollection, self).get_linewidth()[0]
[docs] def get_color(self):
'''
get the color of the lines used to mark each event
'''
return self.get_colors()[0]
[docs]class CircleCollection(_CollectionWithSizes):
"""
A collection of circles, drawn using splines.
"""
_factor = CIRCLE_AREA_FACTOR
@docstring.dedent_interpd
def __init__(self, sizes, **kwargs):
"""
*sizes*
Gives the area of the circle in points^2
%(Collection)s
"""
Collection.__init__(self, **kwargs)
self.set_sizes(sizes)
self.set_transform(transforms.IdentityTransform())
self._paths = [mpath.Path.unit_circle()]
[docs]class EllipseCollection(Collection):
"""
A collection of ellipses, drawn using splines.
"""
@docstring.dedent_interpd
def __init__(self, widths, heights, angles, units='points', **kwargs):
"""
Parameters
----------
widths : array-like
The lengths of the first axes (e.g., major axis lengths).
heights : array-like
The lengths of second axes.
angles : array-like
The angles of the first axes, degrees CCW from the x-axis.
units : {'points', 'inches', 'dots', 'width', 'height', 'x', 'y', 'xy'}
The units in which majors and minors are given; 'width' and
'height' refer to the dimensions of the axes, while 'x'
and 'y' refer to the *offsets* data units. 'xy' differs
from all others in that the angle as plotted varies with
the aspect ratio, and equals the specified angle only when
the aspect ratio is unity. Hence it behaves the same as
the :class:`~matplotlib.patches.Ellipse` with
``axes.transData`` as its transform.
Other Parameters
----------------
**kwargs
Additional kwargs inherited from the base :class:`Collection`.
%(Collection)s
"""
Collection.__init__(self, **kwargs)
self._widths = 0.5 * np.asarray(widths).ravel()
self._heights = 0.5 * np.asarray(heights).ravel()
self._angles = np.deg2rad(angles).ravel()
self._units = units
self.set_transform(transforms.IdentityTransform())
self._transforms = np.empty((0, 3, 3))
self._paths = [mpath.Path.unit_circle()]
def _set_transforms(self):
"""
Calculate transforms immediately before drawing.
"""
ax = self.axes
fig = self.figure
if self._units == 'xy':
sc = 1
elif self._units == 'x':
sc = ax.bbox.width / ax.viewLim.width
elif self._units == 'y':
sc = ax.bbox.height / ax.viewLim.height
elif self._units == 'inches':
sc = fig.dpi
elif self._units == 'points':
sc = fig.dpi / 72.0
elif self._units == 'width':
sc = ax.bbox.width
elif self._units == 'height':
sc = ax.bbox.height
elif self._units == 'dots':
sc = 1.0
else:
raise ValueError('unrecognized units: %s' % self._units)
self._transforms = np.zeros((len(self._widths), 3, 3))
widths = self._widths * sc
heights = self._heights * sc
sin_angle = np.sin(self._angles)
cos_angle = np.cos(self._angles)
self._transforms[:, 0, 0] = widths * cos_angle
self._transforms[:, 0, 1] = heights * -sin_angle
self._transforms[:, 1, 0] = widths * sin_angle
self._transforms[:, 1, 1] = heights * cos_angle
self._transforms[:, 2, 2] = 1.0
_affine = transforms.Affine2D
if self._units == 'xy':
m = ax.transData.get_affine().get_matrix().copy()
m[:2, 2:] = 0
self.set_transform(_affine(m))
[docs] @artist.allow_rasterization
def draw(self, renderer):
self._set_transforms()
Collection.draw(self, renderer)
[docs]class PatchCollection(Collection):
"""
A generic collection of patches.
This makes it easier to assign a color map to a heterogeneous
collection of patches.
This also may improve plotting speed, since PatchCollection will
draw faster than a large number of patches.
"""
def __init__(self, patches, match_original=False, **kwargs):
"""
*patches*
a sequence of Patch objects. This list may include
a heterogeneous assortment of different patch types.
*match_original*
If True, use the colors and linewidths of the original
patches. If False, new colors may be assigned by
providing the standard collection arguments, facecolor,
edgecolor, linewidths, norm or cmap.
If any of *edgecolors*, *facecolors*, *linewidths*,
*antialiaseds* are None, they default to their
:data:`matplotlib.rcParams` patch setting, in sequence form.
The use of :class:`~matplotlib.cm.ScalarMappable` is optional.
If the :class:`~matplotlib.cm.ScalarMappable` matrix _A is not
None (i.e., a call to set_array has been made), at draw time a
call to scalar mappable will be made to set the face colors.
"""
if match_original:
def determine_facecolor(patch):
if patch.get_fill():
return patch.get_facecolor()
return [0, 0, 0, 0]
kwargs['facecolors'] = [determine_facecolor(p) for p in patches]
kwargs['edgecolors'] = [p.get_edgecolor() for p in patches]
kwargs['linewidths'] = [p.get_linewidth() for p in patches]
kwargs['linestyles'] = [p.get_linestyle() for p in patches]
kwargs['antialiaseds'] = [p.get_antialiased() for p in patches]
Collection.__init__(self, **kwargs)
self.set_paths(patches)
[docs] def set_paths(self, patches):
paths = [p.get_transform().transform_path(p.get_path())
for p in patches]
self._paths = paths
[docs]class TriMesh(Collection):
"""
Class for the efficient drawing of a triangular mesh using
Gouraud shading.
A triangular mesh is a :class:`~matplotlib.tri.Triangulation`
object.
"""
def __init__(self, triangulation, **kwargs):
Collection.__init__(self, **kwargs)
self._triangulation = triangulation
self._shading = 'gouraud'
self._is_filled = True
self._bbox = transforms.Bbox.unit()
# Unfortunately this requires a copy, unless Triangulation
# was rewritten.
xy = np.hstack((triangulation.x.reshape(-1, 1),
triangulation.y.reshape(-1, 1)))
self._bbox.update_from_data_xy(xy)
[docs] @staticmethod
def convert_mesh_to_paths(tri):
"""
Converts a given mesh into a sequence of
:class:`matplotlib.path.Path` objects for easier rendering by
backends that do not directly support meshes.
This function is primarily of use to backend implementers.
"""
triangles = tri.get_masked_triangles()
verts = np.stack((tri.x[triangles], tri.y[triangles]), axis=-1)
return [mpath.Path(x) for x in verts]
[docs] @artist.allow_rasterization
def draw(self, renderer):
if not self.get_visible():
return
renderer.open_group(self.__class__.__name__)
transform = self.get_transform()
# Get a list of triangles and the color at each vertex.
tri = self._triangulation
triangles = tri.get_masked_triangles()
verts = np.stack((tri.x[triangles], tri.y[triangles]), axis=-1)
self.update_scalarmappable()
colors = self._facecolors[triangles]
gc = renderer.new_gc()
self._set_gc_clip(gc)
gc.set_linewidth(self.get_linewidth()[0])
renderer.draw_gouraud_triangles(gc, verts, colors, transform.frozen())
gc.restore()
renderer.close_group(self.__class__.__name__)
[docs]class QuadMesh(Collection):
"""
Class for the efficient drawing of a quadrilateral mesh.
A quadrilateral mesh consists of a grid of vertices. The
dimensions of this array are (*meshWidth* + 1, *meshHeight* +
1). Each vertex in the mesh has a different set of "mesh
coordinates" representing its position in the topology of the
mesh. For any values (*m*, *n*) such that 0 <= *m* <= *meshWidth*
and 0 <= *n* <= *meshHeight*, the vertices at mesh coordinates
(*m*, *n*), (*m*, *n* + 1), (*m* + 1, *n* + 1), and (*m* + 1, *n*)
form one of the quadrilaterals in the mesh. There are thus
(*meshWidth* * *meshHeight*) quadrilaterals in the mesh. The mesh
need not be regular and the polygons need not be convex.
A quadrilateral mesh is represented by a (2 x ((*meshWidth* + 1) *
(*meshHeight* + 1))) numpy array *coordinates*, where each row is
the *x* and *y* coordinates of one of the vertices. To define the
function that maps from a data point to its corresponding color,
use the :meth:`set_cmap` method. Each of these arrays is indexed in
row-major order by the mesh coordinates of the vertex (or the mesh
coordinates of the lower left vertex, in the case of the
colors).
For example, the first entry in *coordinates* is the
coordinates of the vertex at mesh coordinates (0, 0), then the one
at (0, 1), then at (0, 2) .. (0, meshWidth), (1, 0), (1, 1), and
so on.
*shading* may be 'flat', or 'gouraud'
"""
def __init__(self, meshWidth, meshHeight, coordinates,
antialiased=True, shading='flat', **kwargs):
Collection.__init__(self, **kwargs)
self._meshWidth = meshWidth
self._meshHeight = meshHeight
# By converting to floats now, we can avoid that on every draw.
self._coordinates = np.asarray(coordinates, float).reshape(
(meshHeight + 1, meshWidth + 1, 2))
self._antialiased = antialiased
self._shading = shading
self._bbox = transforms.Bbox.unit()
self._bbox.update_from_data_xy(coordinates.reshape(
((meshWidth + 1) * (meshHeight + 1), 2)))
[docs] def set_paths(self):
self._paths = self.convert_mesh_to_paths(
self._meshWidth, self._meshHeight, self._coordinates)
self.stale = True
[docs] def get_datalim(self, transData):
return (self.get_transform() - transData).transform_bbox(self._bbox)
[docs] @staticmethod
def convert_mesh_to_paths(meshWidth, meshHeight, coordinates):
"""
Converts a given mesh into a sequence of
:class:`matplotlib.path.Path` objects for easier rendering by
backends that do not directly support quadmeshes.
This function is primarily of use to backend implementers.
"""
if isinstance(coordinates, np.ma.MaskedArray):
c = coordinates.data
else:
c = coordinates
points = np.concatenate((
c[:-1, :-1],
c[:-1, 1:],
c[1:, 1:],
c[1:, :-1],
c[:-1, :-1]
), axis=2)
points = points.reshape((meshWidth * meshHeight, 5, 2))
return [mpath.Path(x) for x in points]
[docs] def convert_mesh_to_triangles(self, meshWidth, meshHeight, coordinates):
"""
Converts a given mesh into a sequence of triangles, each point
with its own color. This is useful for experiments using
`draw_qouraud_triangle`.
"""
if isinstance(coordinates, np.ma.MaskedArray):
p = coordinates.data
else:
p = coordinates
p_a = p[:-1, :-1]
p_b = p[:-1, 1:]
p_c = p[1:, 1:]
p_d = p[1:, :-1]
p_center = (p_a + p_b + p_c + p_d) / 4.0
triangles = np.concatenate((
p_a, p_b, p_center,
p_b, p_c, p_center,
p_c, p_d, p_center,
p_d, p_a, p_center,
), axis=2)
triangles = triangles.reshape((meshWidth * meshHeight * 4, 3, 2))
c = self.get_facecolor().reshape((meshHeight + 1, meshWidth + 1, 4))
c_a = c[:-1, :-1]
c_b = c[:-1, 1:]
c_c = c[1:, 1:]
c_d = c[1:, :-1]
c_center = (c_a + c_b + c_c + c_d) / 4.0
colors = np.concatenate((
c_a, c_b, c_center,
c_b, c_c, c_center,
c_c, c_d, c_center,
c_d, c_a, c_center,
), axis=2)
colors = colors.reshape((meshWidth * meshHeight * 4, 3, 4))
return triangles, colors
[docs] @artist.allow_rasterization
def draw(self, renderer):
if not self.get_visible():
return
renderer.open_group(self.__class__.__name__, self.get_gid())
transform = self.get_transform()
transOffset = self.get_offset_transform()
offsets = self._offsets
if self.have_units():
if len(self._offsets):
xs = self.convert_xunits(self._offsets[:, 0])
ys = self.convert_yunits(self._offsets[:, 1])
offsets = np.column_stack([xs, ys])
self.update_scalarmappable()
if not transform.is_affine:
coordinates = self._coordinates.reshape((-1, 2))
coordinates = transform.transform(coordinates)
coordinates = coordinates.reshape(self._coordinates.shape)
transform = transforms.IdentityTransform()
else:
coordinates = self._coordinates
if not transOffset.is_affine:
offsets = transOffset.transform_non_affine(offsets)
transOffset = transOffset.get_affine()
gc = renderer.new_gc()
self._set_gc_clip(gc)
gc.set_linewidth(self.get_linewidth()[0])
if self._shading == 'gouraud':
triangles, colors = self.convert_mesh_to_triangles(
self._meshWidth, self._meshHeight, coordinates)
renderer.draw_gouraud_triangles(
gc, triangles, colors, transform.frozen())
else:
renderer.draw_quad_mesh(
gc, transform.frozen(), self._meshWidth, self._meshHeight,
coordinates, offsets, transOffset, self.get_facecolor(),
self._antialiased, self.get_edgecolors())
gc.restore()
renderer.close_group(self.__class__.__name__)
self.stale = False
patchstr = artist.kwdoc(Collection)
for k in ('QuadMesh', 'TriMesh', 'PolyCollection', 'BrokenBarHCollection',
'RegularPolyCollection', 'PathCollection',
'StarPolygonCollection', 'PatchCollection',
'CircleCollection', 'Collection',):
docstring.interpd.update({k: patchstr})
docstring.interpd.update(LineCollection=artist.kwdoc(LineCollection))