"""

Matplotlib includes a framework for arbitrary geometric

transformations that is used 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 graph:

.. image:: ../_static/transforms.png

The framework can be used for both affine and non-affine

transformations. However, for speed, we want 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.

"""

# 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 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

INVALID_NON_AFFINE = 1

INVALID_AFFINE = 2

INVALID = INVALID_NON_AFFINE | INVALID_AFFINE

# Some metadata about the transform, used to determine whether an

# invalidation is affine-only

is_affine = False

is_bbox = 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 = {}

# TransformNodes start out as invalid until their values are

# computed for the first time.

self._invalid = 1

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 __deepcopy__(self, memo):

# We could deepcopy the entire transform tree, but nothing except

# `self` is accessible publicly, so we may as well just freeze `self`.

other = self.frozen()

if other is not self:

return other

# Some classes implement frozen() as returning self, which is not

# acceptable for deepcopying, so we need to handle them separately.

other = copy.deepcopy(super(), memo)

# 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)`, this creates a separate tree

# (`c1 = a1 + b1`) so nothing needs to be done.

return other

def invalidate(self):

"""

Invalidate this `TransformNode` and triggers an invalidation of its

ancestors. Should be called any time the transform changes.

"""

value = self.INVALID

if self.is_affine:

value = self.INVALID_AFFINE

return self._invalidate_internal(value, invalidating_node=self)

def _invalidate_internal(self, value, invalidating_node):

"""

Called by :meth:`invalidate` and subsequently ascends the transform

stack calling each TransformNode's _invalidate_internal method.

"""

# determine if this call will be an extension to the invalidation

# status. If not, then a shortcut means that we needn't invoke an

# invalidation up the transform stack as it will already have been

# invalidated.

# N.B This makes the invalidation sticky, once a transform has been

# invalidated as NON_AFFINE, then it will always be invalidated as

# NON_AFFINE even when triggered with a AFFINE_ONLY invalidation.

# In most cases this is not a problem (i.e. for interactive panning and

# zooming) and the only side effect will be on performance.

status_changed = self._invalid < value

if self.pass_through or status_changed:

self._invalid = value

for parent in list(self._parents.values()):

# Dereference the weak reference

parent = parent()

if parent is not None:

parent._invalidate_internal(

value=value, 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.

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 = 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]]])

@_api.deprecated("3.3", alternative="transformed(transform.inverted())")

def inverse_transformed(self, transform):

"""

Construct a `Bbox` by statically transforming this one by the inverse

of *transform*.

"""

return self.transformed(transform.inverted())

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=None):

"""

Return a copy of the `Bbox` shifted to position *c* within *container*.

Parameters

----------

c : (float, float) or str

May be either:

* A sequence (*cx*, *cy*) where *cx* and *cy* range from 0

to 1, where 0 is left or bottom and 1 is right or top

* a string:

- 'C' for centered

- 'S' for bottom-center

- 'SE' for bottom-left

- 'E' for left

- etc.

container : `Bbox`, optional

The box within which the `Bbox` is positioned; it defaults

to the initial `Bbox`.

"""

if container is None:

container = self

l, b, w, h = container.bounds

if isinstance(c, str):

cx, cy = self.coefs[c]

else:

cx, cy = c

L, B, W, H = self.bounds

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 zip(xf[:-1], xf[1:])]

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 zip(yf[:-1], yf[1:])]

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 : Nx2 Numpy 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, p):

"""Construct a `Bbox` by padding this one on all four sides by *p*."""

points = self.get_points()

return Bbox(points + [[-p, -p], [p, p]])

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

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 : ndarray

A 2x2 numpy 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 = np.array([np.inf, np.inf])

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()

@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 :meth:`update_from_data_xy`

will ignore the existing bounds of the `Bbox`.

- When ``False``, subsequent calls to :meth:`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_xy(self, xy, ignore=None, updatex=True, updatey=True):

"""

Update the bounds of the `Bbox` based on the passed in

data. After updating, the bounds will have positive *width*

and *height*; *x0* and *y0* will be the minimal values.

Parameters

----------

xy : ndarray

A numpy array of 2D points.

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

as the minimum extent instead of *p0*.

"""

return self._minpos

@property

def minposx(self):

"""

The minimum positive value in the *x*-direction within the Bbox.