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2995 lines (2470 loc) · 97.4 KB
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"""
Matplotlib includes a framework for arbitrary geometric transformations that is used to
determine the final position of all elements drawn on the canvas.
Transforms are composed into trees of `TransformNode` objects
whose actual value depends on their children. When the contents of
children change, their parents are automatically invalidated. The
next time an invalidated transform is accessed, it is recomputed to
reflect those changes. This invalidation/caching approach prevents
unnecessary recomputations of transforms, and contributes to better
interactive performance.
For example, here is a graph of the transform tree used to plot data to the figure:
.. graphviz:: /api/transforms.dot
:alt: Diagram of transform tree from data to figure coordinates.
The framework can be used for both affine and non-affine
transformations. However, for speed, we want to use the backend
renderers to perform affine transformations whenever possible.
Therefore, it is possible to perform just the affine or non-affine
part of a transformation on a set of data. The affine is always
assumed to occur after the non-affine. For any transform::
full transform == non-affine part + affine part
The backends are not expected to handle non-affine transformations
themselves.
See the tutorial :ref:`transforms_tutorial` for examples
of how to use transforms.
"""
# Note: There are a number of places in the code where we use `np.min` or
# `np.minimum` instead of the builtin `min`, and likewise for `max`. This is
# done so that `nan`s are propagated, instead of being silently dropped.
import copy
import functools
import itertools
import textwrap
import weakref
import math
import numpy as np
from numpy.linalg import inv
from matplotlib import _api
from matplotlib._path import (
affine_transform, count_bboxes_overlapping_bbox, update_path_extents)
from .path import Path
DEBUG = False
def _make_str_method(*args, **kwargs):
"""
Generate a ``__str__`` method for a `.Transform` subclass.
After ::
class T:
__str__ = _make_str_method("attr", key="other")
``str(T(...))`` will be
.. code-block:: text
{type(T).__name__}(
{self.attr},
key={self.other})
"""
indent = functools.partial(textwrap.indent, prefix=" " * 4)
def strrepr(x): return repr(x) if isinstance(x, str) else str(x)
return lambda self: (
type(self).__name__ + "("
+ ",".join([*(indent("\n" + strrepr(getattr(self, arg)))
for arg in args),
*(indent("\n" + k + "=" + strrepr(getattr(self, arg)))
for k, arg in kwargs.items())])
+ ")")
class TransformNode:
"""
The base class for anything that participates in the transform tree
and needs to invalidate its parents or be invalidated. This includes
classes that are not really transforms, such as bounding boxes, since some
transforms depend on bounding boxes to compute their values.
"""
# Invalidation may affect only the affine part. If the
# invalidation was "affine-only", the _invalid member is set to
# INVALID_AFFINE_ONLY
# Possible values for the _invalid attribute.
_VALID, _INVALID_AFFINE_ONLY, _INVALID_FULL = range(3)
# Some metadata about the transform, used to determine whether an
# invalidation is affine-only
is_affine = False
is_bbox = _api.deprecated("3.9")(_api.classproperty(lambda cls: False))
pass_through = False
"""
If pass_through is True, all ancestors will always be
invalidated, even if 'self' is already invalid.
"""
def __init__(self, shorthand_name=None):
"""
Parameters
----------
shorthand_name : str
A string representing the "name" of the transform. The name carries
no significance other than to improve the readability of
``str(transform)`` when DEBUG=True.
"""
self._parents = {}
# Initially invalid, until first computation.
self._invalid = self._INVALID_FULL
self._shorthand_name = shorthand_name or ''
if DEBUG:
def __str__(self):
# either just return the name of this TransformNode, or its repr
return self._shorthand_name or repr(self)
def __getstate__(self):
# turn the dictionary with weak values into a normal dictionary
return {**self.__dict__,
'_parents': {k: v() for k, v in self._parents.items()}}
def __setstate__(self, data_dict):
self.__dict__ = data_dict
# turn the normal dictionary back into a dictionary with weak values
# The extra lambda is to provide a callback to remove dead
# weakrefs from the dictionary when garbage collection is done.
self._parents = {
k: weakref.ref(v, lambda _, pop=self._parents.pop, k=k: pop(k))
for k, v in self._parents.items() if v is not None}
def __copy__(self):
other = copy.copy(super())
# If `c = a + b; a1 = copy(a)`, then modifications to `a1` do not
# propagate back to `c`, i.e. we need to clear the parents of `a1`.
other._parents = {}
# If `c = a + b; c1 = copy(c)`, then modifications to `a` also need to
# be propagated to `c1`.
for key, val in vars(self).items():
if isinstance(val, TransformNode) and id(self) in val._parents:
other.set_children(val) # val == getattr(other, key)
return other
def invalidate(self):
"""
Invalidate this `TransformNode` and triggers an invalidation of its
ancestors. Should be called any time the transform changes.
"""
return self._invalidate_internal(
level=self._INVALID_AFFINE_ONLY if self.is_affine else self._INVALID_FULL,
invalidating_node=self)
def _invalidate_internal(self, level, invalidating_node):
"""
Called by :meth:`invalidate` and subsequently ascends the transform
stack calling each TransformNode's _invalidate_internal method.
"""
# If we are already more invalid than the currently propagated invalidation,
# then we don't need to do anything.
if level <= self._invalid and not self.pass_through:
return
self._invalid = level
for parent in list(self._parents.values()):
parent = parent() # Dereference the weak reference.
if parent is not None:
parent._invalidate_internal(level=level, invalidating_node=self)
def set_children(self, *children):
"""
Set the children of the transform, to let the invalidation
system know which transforms can invalidate this transform.
Should be called from the constructor of any transforms that
depend on other transforms.
"""
# Parents are stored as weak references, so that if the
# parents are destroyed, references from the children won't
# keep them alive.
id_self = id(self)
for child in children:
# Use weak references so this dictionary won't keep obsolete nodes
# alive; the callback deletes the dictionary entry. This is a
# performance improvement over using WeakValueDictionary.
ref = weakref.ref(
self, lambda _, pop=child._parents.pop, k=id_self: pop(k))
child._parents[id_self] = ref
def frozen(self):
"""
Return a frozen copy of this transform node. The frozen copy will not
be updated when its children change. Useful for storing a previously
known state of a transform where ``copy.deepcopy()`` might normally be
used.
"""
return self
class BboxBase(TransformNode):
"""
The base class of all bounding boxes.
This class is immutable; `Bbox` is a mutable subclass.
The canonical representation is as two points, with no
restrictions on their ordering. Convenience properties are
provided to get the left, bottom, right and top edges and width
and height, but these are not stored explicitly.
"""
is_bbox = _api.deprecated("3.9")(_api.classproperty(lambda cls: True))
is_affine = True
if DEBUG:
@staticmethod
def _check(points):
if isinstance(points, np.ma.MaskedArray):
_api.warn_external("Bbox bounds are a masked array.")
points = np.asarray(points)
if any((points[1, :] - points[0, :]) == 0):
_api.warn_external("Singular Bbox.")
def frozen(self):
return Bbox(self.get_points().copy())
frozen.__doc__ = TransformNode.__doc__
def __array__(self, *args, **kwargs):
return self.get_points()
@property
def x0(self):
"""
The first of the pair of *x* coordinates that define the bounding box.
This is not guaranteed to be less than :attr:`x1` (for that, use
:attr:`xmin`).
"""
return self.get_points()[0, 0]
@property
def y0(self):
"""
The first of the pair of *y* coordinates that define the bounding box.
This is not guaranteed to be less than :attr:`y1` (for that, use
:attr:`ymin`).
"""
return self.get_points()[0, 1]
@property
def x1(self):
"""
The second of the pair of *x* coordinates that define the bounding box.
This is not guaranteed to be greater than :attr:`x0` (for that, use
:attr:`xmax`).
"""
return self.get_points()[1, 0]
@property
def y1(self):
"""
The second of the pair of *y* coordinates that define the bounding box.
This is not guaranteed to be greater than :attr:`y0` (for that, use
:attr:`ymax`).
"""
return self.get_points()[1, 1]
@property
def p0(self):
"""
The first pair of (*x*, *y*) coordinates that define the bounding box.
This is not guaranteed to be the bottom-left corner (for that, use
:attr:`min`).
"""
return self.get_points()[0]
@property
def p1(self):
"""
The second pair of (*x*, *y*) coordinates that define the bounding box.
This is not guaranteed to be the top-right corner (for that, use
:attr:`max`).
"""
return self.get_points()[1]
@property
def xmin(self):
"""The left edge of the bounding box."""
return np.min(self.get_points()[:, 0])
@property
def ymin(self):
"""The bottom edge of the bounding box."""
return np.min(self.get_points()[:, 1])
@property
def xmax(self):
"""The right edge of the bounding box."""
return np.max(self.get_points()[:, 0])
@property
def ymax(self):
"""The top edge of the bounding box."""
return np.max(self.get_points()[:, 1])
@property
def min(self):
"""The bottom-left corner of the bounding box."""
return np.min(self.get_points(), axis=0)
@property
def max(self):
"""The top-right corner of the bounding box."""
return np.max(self.get_points(), axis=0)
@property
def intervalx(self):
"""
The pair of *x* coordinates that define the bounding box.
This is not guaranteed to be sorted from left to right.
"""
return self.get_points()[:, 0]
@property
def intervaly(self):
"""
The pair of *y* coordinates that define the bounding box.
This is not guaranteed to be sorted from bottom to top.
"""
return self.get_points()[:, 1]
@property
def width(self):
"""The (signed) width of the bounding box."""
points = self.get_points()
return points[1, 0] - points[0, 0]
@property
def height(self):
"""The (signed) height of the bounding box."""
points = self.get_points()
return points[1, 1] - points[0, 1]
@property
def size(self):
"""The (signed) width and height of the bounding box."""
points = self.get_points()
return points[1] - points[0]
@property
def bounds(self):
"""Return (:attr:`x0`, :attr:`y0`, :attr:`width`, :attr:`height`)."""
(x0, y0), (x1, y1) = self.get_points()
return (x0, y0, x1 - x0, y1 - y0)
@property
def extents(self):
"""Return (:attr:`x0`, :attr:`y0`, :attr:`x1`, :attr:`y1`)."""
return self.get_points().flatten() # flatten returns a copy.
def get_points(self):
raise NotImplementedError
def containsx(self, x):
"""
Return whether *x* is in the closed (:attr:`x0`, :attr:`x1`) interval.
"""
x0, x1 = self.intervalx
return x0 <= x <= x1 or x0 >= x >= x1
def containsy(self, y):
"""
Return whether *y* is in the closed (:attr:`y0`, :attr:`y1`) interval.
"""
y0, y1 = self.intervaly
return y0 <= y <= y1 or y0 >= y >= y1
def contains(self, x, y):
"""
Return whether ``(x, y)`` is in the bounding box or on its edge.
"""
return self.containsx(x) and self.containsy(y)
def overlaps(self, other):
"""
Return whether this bounding box overlaps with the other bounding box.
Parameters
----------
other : `.BboxBase`
"""
ax1, ay1, ax2, ay2 = self.extents
bx1, by1, bx2, by2 = other.extents
if ax2 < ax1:
ax2, ax1 = ax1, ax2
if ay2 < ay1:
ay2, ay1 = ay1, ay2
if bx2 < bx1:
bx2, bx1 = bx1, bx2
if by2 < by1:
by2, by1 = by1, by2
return ax1 <= bx2 and bx1 <= ax2 and ay1 <= by2 and by1 <= ay2
def fully_containsx(self, x):
"""
Return whether *x* is in the open (:attr:`x0`, :attr:`x1`) interval.
"""
x0, x1 = self.intervalx
return x0 < x < x1 or x0 > x > x1
def fully_containsy(self, y):
"""
Return whether *y* is in the open (:attr:`y0`, :attr:`y1`) interval.
"""
y0, y1 = self.intervaly
return y0 < y < y1 or y0 > y > y1
def fully_contains(self, x, y):
"""
Return whether ``x, y`` is in the bounding box, but not on its edge.
"""
return self.fully_containsx(x) and self.fully_containsy(y)
def fully_overlaps(self, other):
"""
Return whether this bounding box overlaps with the other bounding box,
not including the edges.
Parameters
----------
other : `.BboxBase`
"""
ax1, ay1, ax2, ay2 = self.extents
bx1, by1, bx2, by2 = other.extents
if ax2 < ax1:
ax2, ax1 = ax1, ax2
if ay2 < ay1:
ay2, ay1 = ay1, ay2
if bx2 < bx1:
bx2, bx1 = bx1, bx2
if by2 < by1:
by2, by1 = by1, by2
return ax1 < bx2 and bx1 < ax2 and ay1 < by2 and by1 < ay2
def transformed(self, transform):
"""
Construct a `Bbox` by statically transforming this one by *transform*.
"""
pts = self.get_points()
ll, ul, lr = transform.transform(np.array(
[pts[0], [pts[0, 0], pts[1, 1]], [pts[1, 0], pts[0, 1]]]))
return Bbox([ll, [lr[0], ul[1]]])
coefs = {'C': (0.5, 0.5),
'SW': (0, 0),
'S': (0.5, 0),
'SE': (1.0, 0),
'E': (1.0, 0.5),
'NE': (1.0, 1.0),
'N': (0.5, 1.0),
'NW': (0, 1.0),
'W': (0, 0.5)}
def anchored(self, c, container):
"""
Return a copy of the `Bbox` anchored to *c* within *container*.
Parameters
----------
c : (float, float) or {'C', 'SW', 'S', 'SE', 'E', 'NE', ...}
Either an (*x*, *y*) pair of relative coordinates (0 is left or
bottom, 1 is right or top), 'C' (center), or a cardinal direction
('SW', southwest, is bottom left, etc.).
container : `Bbox`
The box within which the `Bbox` is positioned.
See Also
--------
.Axes.set_anchor
"""
l, b, w, h = container.bounds
L, B, W, H = self.bounds
cx, cy = self.coefs[c] if isinstance(c, str) else c
return Bbox(self._points +
[(l + cx * (w - W)) - L,
(b + cy * (h - H)) - B])
def shrunk(self, mx, my):
"""
Return a copy of the `Bbox`, shrunk by the factor *mx*
in the *x* direction and the factor *my* in the *y* direction.
The lower left corner of the box remains unchanged. Normally
*mx* and *my* will be less than 1, but this is not enforced.
"""
w, h = self.size
return Bbox([self._points[0],
self._points[0] + [mx * w, my * h]])
def shrunk_to_aspect(self, box_aspect, container=None, fig_aspect=1.0):
"""
Return a copy of the `Bbox`, shrunk so that it is as
large as it can be while having the desired aspect ratio,
*box_aspect*. If the box coordinates are relative (i.e.
fractions of a larger box such as a figure) then the
physical aspect ratio of that figure is specified with
*fig_aspect*, so that *box_aspect* can also be given as a
ratio of the absolute dimensions, not the relative dimensions.
"""
if box_aspect <= 0 or fig_aspect <= 0:
raise ValueError("'box_aspect' and 'fig_aspect' must be positive")
if container is None:
container = self
w, h = container.size
H = w * box_aspect / fig_aspect
if H <= h:
W = w
else:
W = h * fig_aspect / box_aspect
H = h
return Bbox([self._points[0],
self._points[0] + (W, H)])
def splitx(self, *args):
"""
Return a list of new `Bbox` objects formed by splitting the original
one with vertical lines at fractional positions given by *args*.
"""
xf = [0, *args, 1]
x0, y0, x1, y1 = self.extents
w = x1 - x0
return [Bbox([[x0 + xf0 * w, y0], [x0 + xf1 * w, y1]])
for xf0, xf1 in itertools.pairwise(xf)]
def splity(self, *args):
"""
Return a list of new `Bbox` objects formed by splitting the original
one with horizontal lines at fractional positions given by *args*.
"""
yf = [0, *args, 1]
x0, y0, x1, y1 = self.extents
h = y1 - y0
return [Bbox([[x0, y0 + yf0 * h], [x1, y0 + yf1 * h]])
for yf0, yf1 in itertools.pairwise(yf)]
def count_contains(self, vertices):
"""
Count the number of vertices contained in the `Bbox`.
Any vertices with a non-finite x or y value are ignored.
Parameters
----------
vertices : (N, 2) array
"""
if len(vertices) == 0:
return 0
vertices = np.asarray(vertices)
with np.errstate(invalid='ignore'):
return (((self.min < vertices) &
(vertices < self.max)).all(axis=1).sum())
def count_overlaps(self, bboxes):
"""
Count the number of bounding boxes that overlap this one.
Parameters
----------
bboxes : sequence of `.BboxBase`
"""
return count_bboxes_overlapping_bbox(
self, np.atleast_3d([np.array(x) for x in bboxes]))
def expanded(self, sw, sh):
"""
Construct a `Bbox` by expanding this one around its center by the
factors *sw* and *sh*.
"""
width = self.width
height = self.height
deltaw = (sw * width - width) / 2.0
deltah = (sh * height - height) / 2.0
a = np.array([[-deltaw, -deltah], [deltaw, deltah]])
return Bbox(self._points + a)
def padded(self, w_pad, h_pad=None):
"""
Construct a `Bbox` by padding this one on all four sides.
Parameters
----------
w_pad : float
Width pad
h_pad : float, optional
Height pad. Defaults to *w_pad*.
"""
points = self.get_points()
if h_pad is None:
h_pad = w_pad
return Bbox(points + [[-w_pad, -h_pad], [w_pad, h_pad]])
def translated(self, tx, ty):
"""Construct a `Bbox` by translating this one by *tx* and *ty*."""
return Bbox(self._points + (tx, ty))
def corners(self):
"""
Return the corners of this rectangle as an array of points.
Specifically, this returns the array
``[[x0, y0], [x0, y1], [x1, y0], [x1, y1]]``.
"""
(x0, y0), (x1, y1) = self.get_points()
return np.array([[x0, y0], [x0, y1], [x1, y0], [x1, y1]])
def rotated(self, radians):
"""
Return the axes-aligned bounding box that bounds the result of rotating
this `Bbox` by an angle of *radians*.
"""
corners = self.corners()
corners_rotated = Affine2D().rotate(radians).transform(corners)
bbox = Bbox.unit()
bbox.update_from_data_xy(corners_rotated, ignore=True)
return bbox
@staticmethod
def union(bboxes):
"""Return a `Bbox` that contains all of the given *bboxes*."""
if not len(bboxes):
raise ValueError("'bboxes' cannot be empty")
x0 = np.min([bbox.xmin for bbox in bboxes])
x1 = np.max([bbox.xmax for bbox in bboxes])
y0 = np.min([bbox.ymin for bbox in bboxes])
y1 = np.max([bbox.ymax for bbox in bboxes])
return Bbox([[x0, y0], [x1, y1]])
@staticmethod
def intersection(bbox1, bbox2):
"""
Return the intersection of *bbox1* and *bbox2* if they intersect, or
None if they don't.
"""
x0 = np.maximum(bbox1.xmin, bbox2.xmin)
x1 = np.minimum(bbox1.xmax, bbox2.xmax)
y0 = np.maximum(bbox1.ymin, bbox2.ymin)
y1 = np.minimum(bbox1.ymax, bbox2.ymax)
return Bbox([[x0, y0], [x1, y1]]) if x0 <= x1 and y0 <= y1 else None
_default_minpos = np.array([np.inf, np.inf])
class Bbox(BboxBase):
"""
A mutable bounding box.
Examples
--------
**Create from known bounds**
The default constructor takes the boundary "points" ``[[xmin, ymin],
[xmax, ymax]]``.
>>> Bbox([[1, 1], [3, 7]])
Bbox([[1.0, 1.0], [3.0, 7.0]])
Alternatively, a Bbox can be created from the flattened points array, the
so-called "extents" ``(xmin, ymin, xmax, ymax)``
>>> Bbox.from_extents(1, 1, 3, 7)
Bbox([[1.0, 1.0], [3.0, 7.0]])
or from the "bounds" ``(xmin, ymin, width, height)``.
>>> Bbox.from_bounds(1, 1, 2, 6)
Bbox([[1.0, 1.0], [3.0, 7.0]])
**Create from collections of points**
The "empty" object for accumulating Bboxs is the null bbox, which is a
stand-in for the empty set.
>>> Bbox.null()
Bbox([[inf, inf], [-inf, -inf]])
Adding points to the null bbox will give you the bbox of those points.
>>> box = Bbox.null()
>>> box.update_from_data_xy([[1, 1]])
>>> box
Bbox([[1.0, 1.0], [1.0, 1.0]])
>>> box.update_from_data_xy([[2, 3], [3, 2]], ignore=False)
>>> box
Bbox([[1.0, 1.0], [3.0, 3.0]])
Setting ``ignore=True`` is equivalent to starting over from a null bbox.
>>> box.update_from_data_xy([[1, 1]], ignore=True)
>>> box
Bbox([[1.0, 1.0], [1.0, 1.0]])
.. warning::
It is recommended to always specify ``ignore`` explicitly. If not, the
default value of ``ignore`` can be changed at any time by code with
access to your Bbox, for example using the method `~.Bbox.ignore`.
**Properties of the ``null`` bbox**
.. note::
The current behavior of `Bbox.null()` may be surprising as it does
not have all of the properties of the "empty set", and as such does
not behave like a "zero" object in the mathematical sense. We may
change that in the future (with a deprecation period).
The null bbox is the identity for intersections
>>> Bbox.intersection(Bbox([[1, 1], [3, 7]]), Bbox.null())
Bbox([[1.0, 1.0], [3.0, 7.0]])
except with itself, where it returns the full space.
>>> Bbox.intersection(Bbox.null(), Bbox.null())
Bbox([[-inf, -inf], [inf, inf]])
A union containing null will always return the full space (not the other
set!)
>>> Bbox.union([Bbox([[0, 0], [0, 0]]), Bbox.null()])
Bbox([[-inf, -inf], [inf, inf]])
"""
def __init__(self, points, **kwargs):
"""
Parameters
----------
points : `~numpy.ndarray`
A (2, 2) array of the form ``[[x0, y0], [x1, y1]]``.
"""
super().__init__(**kwargs)
points = np.asarray(points, float)
if points.shape != (2, 2):
raise ValueError('Bbox points must be of the form '
'"[[x0, y0], [x1, y1]]".')
self._points = points
self._minpos = _default_minpos.copy()
self._ignore = True
# it is helpful in some contexts to know if the bbox is a
# default or has been mutated; we store the orig points to
# support the mutated methods
self._points_orig = self._points.copy()
if DEBUG:
___init__ = __init__
def __init__(self, points, **kwargs):
self._check(points)
self.___init__(points, **kwargs)
def invalidate(self):
self._check(self._points)
super().invalidate()
def frozen(self):
# docstring inherited
frozen_bbox = super().frozen()
frozen_bbox._minpos = self.minpos.copy()
return frozen_bbox
@staticmethod
def unit():
"""Create a new unit `Bbox` from (0, 0) to (1, 1)."""
return Bbox([[0, 0], [1, 1]])
@staticmethod
def null():
"""Create a new null `Bbox` from (inf, inf) to (-inf, -inf)."""
return Bbox([[np.inf, np.inf], [-np.inf, -np.inf]])
@staticmethod
def from_bounds(x0, y0, width, height):
"""
Create a new `Bbox` from *x0*, *y0*, *width* and *height*.
*width* and *height* may be negative.
"""
return Bbox.from_extents(x0, y0, x0 + width, y0 + height)
@staticmethod
def from_extents(*args, minpos=None):
"""
Create a new Bbox from *left*, *bottom*, *right* and *top*.
The *y*-axis increases upwards.
Parameters
----------
left, bottom, right, top : float
The four extents of the bounding box.
minpos : float or None
If this is supplied, the Bbox will have a minimum positive value
set. This is useful when dealing with logarithmic scales and other
scales where negative bounds result in floating point errors.
"""
bbox = Bbox(np.reshape(args, (2, 2)))
if minpos is not None:
bbox._minpos[:] = minpos
return bbox
def __format__(self, fmt):
return (
'Bbox(x0={0.x0:{1}}, y0={0.y0:{1}}, x1={0.x1:{1}}, y1={0.y1:{1}})'.
format(self, fmt))
def __str__(self):
return format(self, '')
def __repr__(self):
return 'Bbox([[{0.x0}, {0.y0}], [{0.x1}, {0.y1}]])'.format(self)
def ignore(self, value):
"""
Set whether the existing bounds of the box should be ignored
by subsequent calls to :meth:`update_from_data_xy`.
value : bool
- When ``True``, subsequent calls to `update_from_data_xy` will
ignore the existing bounds of the `Bbox`.
- When ``False``, subsequent calls to `update_from_data_xy` will
include the existing bounds of the `Bbox`.
"""
self._ignore = value
def update_from_path(self, path, ignore=None, updatex=True, updatey=True):
"""
Update the bounds of the `Bbox` to contain the vertices of the
provided path. After updating, the bounds will have positive *width*
and *height*; *x0* and *y0* will be the minimal values.
Parameters
----------
path : `~matplotlib.path.Path`
ignore : bool, optional
- When ``True``, ignore the existing bounds of the `Bbox`.
- When ``False``, include the existing bounds of the `Bbox`.
- When ``None``, use the last value passed to :meth:`ignore`.
updatex, updatey : bool, default: True
When ``True``, update the x/y values.
"""
if ignore is None:
ignore = self._ignore
if path.vertices.size == 0:
return
points, minpos, changed = update_path_extents(
path, None, self._points, self._minpos, ignore)
if changed:
self.invalidate()
if updatex:
self._points[:, 0] = points[:, 0]
self._minpos[0] = minpos[0]
if updatey:
self._points[:, 1] = points[:, 1]
self._minpos[1] = minpos[1]
def update_from_data_x(self, x, ignore=None):
"""
Update the x-bounds of the `Bbox` based on the passed in data. After
updating, the bounds will have positive *width*, and *x0* will be the
minimal value.
Parameters
----------
x : `~numpy.ndarray`
Array of x-values.
ignore : bool, optional
- When ``True``, ignore the existing bounds of the `Bbox`.
- When ``False``, include the existing bounds of the `Bbox`.
- When ``None``, use the last value passed to :meth:`ignore`.
"""
x = np.ravel(x)
self.update_from_data_xy(np.column_stack([x, np.ones(x.size)]),
ignore=ignore, updatey=False)
def update_from_data_y(self, y, ignore=None):
"""
Update the y-bounds of the `Bbox` based on the passed in data. After
updating, the bounds will have positive *height*, and *y0* will be the
minimal value.
Parameters
----------
y : `~numpy.ndarray`
Array of y-values.
ignore : bool, optional
- When ``True``, ignore the existing bounds of the `Bbox`.
- When ``False``, include the existing bounds of the `Bbox`.
- When ``None``, use the last value passed to :meth:`ignore`.
"""
y = np.ravel(y)
self.update_from_data_xy(np.column_stack([np.ones(y.size), y]),
ignore=ignore, updatex=False)
def update_from_data_xy(self, xy, ignore=None, updatex=True, updatey=True):
"""
Update the `Bbox` bounds based on the passed in *xy* coordinates.
After updating, the bounds will have positive *width* and *height*;
*x0* and *y0* will be the minimal values.
Parameters
----------
xy : (N, 2) array-like
The (x, y) coordinates.
ignore : bool, optional
- When ``True``, ignore the existing bounds of the `Bbox`.
- When ``False``, include the existing bounds of the `Bbox`.
- When ``None``, use the last value passed to :meth:`ignore`.
updatex, updatey : bool, default: True
When ``True``, update the x/y values.
"""
if len(xy) == 0:
return
path = Path(xy)
self.update_from_path(path, ignore=ignore,
updatex=updatex, updatey=updatey)
@BboxBase.x0.setter
def x0(self, val):
self._points[0, 0] = val
self.invalidate()
@BboxBase.y0.setter
def y0(self, val):
self._points[0, 1] = val
self.invalidate()
@BboxBase.x1.setter
def x1(self, val):
self._points[1, 0] = val
self.invalidate()
@BboxBase.y1.setter
def y1(self, val):
self._points[1, 1] = val
self.invalidate()
@BboxBase.p0.setter
def p0(self, val):
self._points[0] = val
self.invalidate()
@BboxBase.p1.setter
def p1(self, val):
self._points[1] = val
self.invalidate()
@BboxBase.intervalx.setter
def intervalx(self, interval):
self._points[:, 0] = interval
self.invalidate()
@BboxBase.intervaly.setter
def intervaly(self, interval):
self._points[:, 1] = interval
self.invalidate()
@BboxBase.bounds.setter
def bounds(self, bounds):
l, b, w, h = bounds
points = np.array([[l, b], [l + w, b + h]], float)
if np.any(self._points != points):
self._points = points
self.invalidate()
@property
def minpos(self):
"""
The minimum positive value in both directions within the Bbox.
This is useful when dealing with logarithmic scales and other scales
where negative bounds result in floating point errors, and will be used