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""" 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) """
lines as mlines, path as mpath, transforms)
"antialiased": ["antialiaseds"], "edgecolor": ["edgecolors"], "facecolor": ["facecolors"], "linestyle": ["linestyles", "dashes"], "linewidth": ["linewidths", "lw"], }) """ 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. """ #: 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.
# Whether to draw an edge by default. Set on a # subclass-by-subclass basis.
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 """ # list of un-scaled dash patterns # this is needed scaling the dash pattern by linewidth # list of dash patterns # list of unbroadcast/scaled linewidths
self.set_capstyle(capstyle) else:
self.set_joinstyle(joinstyle) else:
# Broadcast (2,) -> (1, 2) but nothing else. offsets = offsets[None, :] else: self._uniform_offsets = offsets
raise NotImplementedError
and hasattr(t, '_as_mpl_transform')): t = t._as_mpl_transform(self.axes)
paths = [transform.transform_path_non_affine(p) for p in paths] transform = transform.get_affine()
offsets = offsets.filled(np.nan) # get_path_collection_extents handles nan but not masked arrays
transform.frozen(), paths, self.get_transforms(), offsets, transOffset.frozen()) else: result = transforms.Bbox.null()
# TODO:check to ensure that this does not fail for # cases other than scatter plot legend return self.get_datalim(transforms.IdentityTransform())
"""Point prep for drawing and hit testing"""
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])
for path in paths] # This might have changed an ndarray into a masked array.
offsets = offsets.filled(np.nan) # Changing from a masked array to nan-filled ndarray # is probably most efficient at this point.
def draw(self, renderer): return
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.")
gc.set_sketch_params(*self.get_sketch_params())
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.
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): transform) else: combined_transform = transform
gc.set_joinstyle(self._joinstyle)
gc.set_capstyle(self._capstyle)
gc, paths[0], combined_transform.frozen(), mpath.Path(offsets), transOffset, tuple(facecolors[0])) else: 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)
"""Set the pick radius used for containment tests.
Parameters ---------- d : float Pick radius, in points. """
return self._pickradius
""" 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)
""" Parameters ---------- urls : List[str] or None """
return self._urls
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', '.', '*'} """
"""Return the current hatching pattern."""
""" 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
"""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
""" 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'} """ raise ValueError("offset_position must be 'screen' or 'data'")
""" 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
""" 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 """ lw = mpl.rcParams['lines.linewidth'] # get the un-scaled/broadcast lw
# scale all of the dash patterns. self._us_lw, self._us_linestyles)
""" 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. """ else:
except ValueError: raise ValueError( 'Do not know how to convert {!r} to dashes'.format(ls))
# get the list of raw 'unscaled' dash patterns
# broadcast and scale the lw and dash patterns self._us_lw, self._us_linestyles)
""" 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)
return self._capstyle
""" 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)
return self._joinstyle
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
''' return linewidths, dashes # make sure they are the same length so we can zip them
# scale the dash patters for (o, d), lw in zip(dashes, linewidths)]
""" Set the antialiasing state for rendering.
Parameters ---------- aa : bool or sequence of bools """
""" 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)
""" 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 """
else:
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
""" 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 """
""" Set the alpha tranparencies of the collection. *alpha* must be a float or *None*.
Parameters ---------- alpha : float or None """ except TypeError: raise TypeError('alpha must be a float or None')
return self._linestyles
""" If the scalar mappable array is not none, update colors from scalar data """ raise ValueError('Collections can only map rank 1 arrays') return elif self._is_stroked: self._edgecolors = self.to_rgba(self._A, self._alpha)
'return whether fill is set' return self._is_filled
'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 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. """)
""" Base class for collections that have an array of sizes. """
""" 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
""" 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. """ else:
def draw(self, renderer):
""" This is the most basic :class:`Collection` subclass. """ """ *paths* is a sequence of :class:`matplotlib.path.Path` instances.
%(Collection)s """
""" *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
'''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
'''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
""" A collection of horizontal bars spanning *yrange* with a sequence of *xranges*. """ 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)
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
"""Draw a collection of regular polygons with *numsides*."""
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())
return self._numsides
return self._rotation
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)
""" Draw a collection of regular stars with *numsides* points."""
""" Draw a collection of regular asterisks with *numsides* points."""
""" 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. """
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. """ linewidths = (mpl.rcParams['lines.linewidth'],)
self, edgecolors=colors, facecolors=facecolors, linewidths=linewidths, linestyles=linestyles, antialiaseds=antialiaseds, offsets=offsets, transOffset=transOffset, norm=norm, cmap=cmap, pickradius=pickradius, zorder=zorder, **kwargs)
return
_segments = self._add_offsets(_segments)
""" 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
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
""" 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. """
return self._edgecolors
''' 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. '''
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
''' 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
''' 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)
''' 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)
''' True if the eventcollection is horizontal, False if vertical ''' return self._is_horizontal
''' get the orientation of the event line, may be: [ 'horizontal' | 'vertical' ] ''' return 'horizontal' if self.is_horizontal() else 'vertical'
''' 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
''' 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()
''' get the length of the lines used to mark each event ''' return 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
''' get the offset of the lines used to mark each event ''' return 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
"""Get the width of the lines used to mark each event.""" return super(EventCollection, self).get_linewidth()[0]
return super(EventCollection, self).get_linewidth()
''' get the color of the lines used to mark each event ''' return self.get_colors()[0]
""" A collection of circles, drawn using splines. """
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()]
""" A collection of ellipses, drawn using splines. """ """ 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()]
""" 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))
def draw(self, renderer): self._set_transforms() Collection.draw(self, renderer)
""" 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. """
""" *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)
paths = [p.get_transform().transform_path(p.get_path()) for p in patches] self._paths = paths
""" Class for the efficient drawing of a triangular mesh using Gouraud shading.
A triangular mesh is a :class:`~matplotlib.tri.Triangulation` object. """ 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)
if self._paths is None: self.set_paths() return self._paths
self._paths = self.convert_mesh_to_paths(self._triangulation)
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]
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__)
""" 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' """ antialiased=True, shading='flat', **kwargs): # By converting to floats now, we can avoid that on every draw. (meshHeight + 1, meshWidth + 1, 2))
((meshWidth + 1) * (meshHeight + 1), 2)))
if self._paths is None: self.set_paths() return self._paths
self._paths = self.convert_mesh_to_paths( self._meshWidth, self._meshHeight, self._coordinates) self.stale = True
return (self.get_transform() - transData).transform_bbox(self._bbox)
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]
""" 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
def draw(self, renderer): return
if len(self._offsets): xs = self.convert_xunits(self._offsets[:, 0]) ys = self.convert_yunits(self._offsets[:, 1]) offsets = np.column_stack([xs, ys])
coordinates = self._coordinates.reshape((-1, 2)) coordinates = transform.transform(coordinates) coordinates = coordinates.reshape(self._coordinates.shape) transform = transforms.IdentityTransform() else:
offsets = transOffset.transform_non_affine(offsets) transOffset = transOffset.get_affine()
triangles, colors = self.convert_mesh_to_triangles( self._meshWidth, self._meshHeight, coordinates) renderer.draw_gouraud_triangles( gc, triangles, colors, transform.frozen()) else: gc, transform.frozen(), self._meshWidth, self._meshHeight, coordinates, offsets, transOffset, self.get_facecolor(), self._antialiased, self.get_edgecolors())
'RegularPolyCollection', 'PathCollection', 'StarPolygonCollection', 'PatchCollection', 'CircleCollection', 'Collection',): |