"""

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 :class:`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.

from __future__ import (absolute_import, division, print_function,

unicode_literals)

import six

import numpy as np

from matplotlib._path import (affine_transform, count_bboxes_overlapping_bbox,

update_path_extents)

from numpy.linalg import inv

import weakref

import warnings

from . import cbook

from .path import Path

DEBUG = False

class TransformNode(object):

"""

:class:`TransformNode` is 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.

"""

_gid = 0

# 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):

"""

Creates a new :class:`TransformNode`.

**shorthand_name** - a string representing the "name" of this

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 it's repr

return self._shorthand_name or repr(self)

def __getstate__(self):

d = self.__dict__.copy()

# turn the dictionary with weak values into a normal dictionary

d['_parents'] = dict((k, v()) for (k, v) in

six.iteritems(self._parents))

return d

def __setstate__(self, data_dict):

self.__dict__ = data_dict

# turn the normal dictionary back into a dictionary with weak

# values

self._parents = dict((k, weakref.ref(v)) for (k, v) in

six.iteritems(self._parents) if v is not None)

def __copy__(self, *args):

raise NotImplementedError(

"TransformNode instances can not be copied. " +

"Consider using frozen() instead.")

__deepcopy__ = __copy__

def invalidate(self):

"""

Invalidate this :class:`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(six.itervalues(self._parents)):

# 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:

child._parents[id(self)] = weakref.ref(self)

if DEBUG:

_set_children = set_children

def set_children(self, *children):

self._set_children(*children)

self._children = children

set_children.__doc__ = _set_children.__doc__

def frozen(self):

"""

Returns a frozen copy of this transform node. The frozen copy

will not update when its children change. Useful for storing

a previously known state of a transform where

``copy.deepcopy()`` might normally be used.

"""

return self

if DEBUG:

def write_graphviz(self, fobj, highlight=[]):

"""

For debugging purposes.

Writes the transform tree rooted at 'self' to a graphviz "dot"

format file. This file can be run through the "dot" utility

to produce a graph of the transform tree.

Affine transforms are marked in blue. Bounding boxes are

marked in yellow.

*fobj*: A Python file-like object

Once the "dot" file has been created, it can be turned into a

png easily with::

$> dot -Tpng -o $OUTPUT_FILE $DOT_FILE

"""

seen = set()

def recurse(root):

if root in seen:

return

seen.add(root)

props = {}

label = root.__class__.__name__

if root._invalid:

label = '[%s]' % label

if root in highlight:

props['style'] = 'bold'

props['shape'] = 'box'

props['label'] = '"%s"' % label

props = ' '.join(['%s=%s' % (key, val)

for key, val

in six.iteritems(props)])

fobj.write('%s [%s];\n' %

(hash(root), props))

if hasattr(root, '_children'):

for child in root._children:

name = '?'

for key, val in six.iteritems(root.__dict__):

if val is child:

name = key

break

fobj.write('"%s" -> "%s" [label="%s", fontsize=10];\n'

% (hash(root),

hash(child),

name))

recurse(child)

fobj.write("digraph G {\n")

recurse(self)

fobj.write("}\n")

class BboxBase(TransformNode):

"""

This is the base class of all bounding boxes, and provides

read-only access to its data. A mutable bounding box is provided

by the :class:`Bbox` class.

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:

def _check(points):

if isinstance(points, np.ma.MaskedArray):

warnings.warn("Bbox bounds are a masked array.")

points = np.asarray(points)

if (points[1, 0] - points[0, 0] == 0 or

points[1, 1] - points[0, 1] == 0):

warnings.warn("Singular Bbox.")

_check = staticmethod(_check)

def frozen(self):

return Bbox(self.get_points().copy())

frozen.__doc__ = TransformNode.__doc__

def __array__(self, *args, **kwargs):

return self.get_points()

def is_unit(self):

"""

Returns True if the :class:`Bbox` is the unit bounding box

from (0, 0) to (1, 1).

"""

return list(self.get_points().flatten()) == [0., 0., 1., 1.]

@property

def x0(self):

"""

(property) :attr:`x0` is the first of the pair of *x* coordinates that

define the bounding box. :attr:`x0` is not guaranteed to be less than

:attr:`x1`. If you require that, use :attr:`xmin`.

"""

return self.get_points()[0, 0]

@property

def y0(self):

"""

(property) :attr:`y0` is the first of the pair of *y* coordinates that

define the bounding box. :attr:`y0` is not guaranteed to be less than

:attr:`y1`. If you require that, use :attr:`ymin`.

"""

return self.get_points()[0, 1]

@property

def x1(self):

"""

(property) :attr:`x1` is the second of the pair of *x* coordinates that

define the bounding box. :attr:`x1` is not guaranteed to be greater

than :attr:`x0`. If you require that, use :attr:`xmax`.

"""

return self.get_points()[1, 0]

@property

def y1(self):

"""

(property) :attr:`y1` is the second of the pair of *y* coordinates that

define the bounding box. :attr:`y1` is not guaranteed to be greater

than :attr:`y0`. If you require that, use :attr:`ymax`.

"""

return self.get_points()[1, 1]

@property

def p0(self):

"""

(property) :attr:`p0` is the first pair of (*x*, *y*) coordinates that

define the bounding box. It is not guaranteed to be the bottom-left

corner. For that, use :attr:`min`.

"""

return self.get_points()[0]

@property

def p1(self):

"""

(property) :attr:`p1` is the second pair of (*x*, *y*) coordinates that

define the bounding box. It is not guaranteed to be the top-right

corner. For that, use :attr:`max`.

"""

return self.get_points()[1]

@property

def xmin(self):

"""

(property) :attr:`xmin` is the left edge of the bounding box.

"""

return np.min(self.get_points()[:, 0])

@property

def ymin(self):

"""

(property) :attr:`ymin` is the bottom edge of the bounding box.

"""

return np.min(self.get_points()[:, 1])

@property

def xmax(self):

"""

(property) :attr:`xmax` is the right edge of the bounding box.

"""

return np.max(self.get_points()[:, 0])

@property

def ymax(self):

"""

(property) :attr:`ymax` is the top edge of the bounding box.

"""

return np.max(self.get_points()[:, 1])

@property

def min(self):

"""

(property) :attr:`min` is the bottom-left corner of the bounding box.

"""

return np.min(self.get_points(), axis=0)

@property

def max(self):

"""

(property) :attr:`max` is the top-right corner of the bounding box.

"""

return np.max(self.get_points(), axis=0)

@property

def intervalx(self):

"""

(property) :attr:`intervalx` is the pair of *x* coordinates that define

the bounding box. It is not guaranteed to be sorted from left to right.

"""

return self.get_points()[:, 0]

@property

def intervaly(self):

"""

(property) :attr:`intervaly` is the pair of *y* coordinates that define

the bounding box. It is not guaranteed to be sorted from bottom to

top.

"""

return self.get_points()[:, 1]

@property

def width(self):

"""

(property) The width of the bounding box. It may be negative if

:attr:`x1` < :attr:`x0`.

"""

points = self.get_points()

return points[1, 0] - points[0, 0]

@property

def height(self):

"""

(property) The height of the bounding box. It may be negative if

:attr:`y1` < :attr:`y0`.

"""

points = self.get_points()

return points[1, 1] - points[0, 1]

@property

def size(self):

"""

(property) The width and height of the bounding box. May be negative,

in the same way as :attr:`width` and :attr:`height`.

"""

points = self.get_points()

return points[1] - points[0]

@property

def bounds(self):

"""

(property) Returns (:attr:`x0`, :attr:`y0`, :attr:`width`,

:attr:`height`).

"""

x0, y0, x1, y1 = self.get_points().flatten()

return (x0, y0, x1 - x0, y1 - y0)

@property

def extents(self):

"""

(property) Returns (:attr:`x0`, :attr:`y0`, :attr:`x1`,

:attr:`y1`).

"""

return self.get_points().flatten().copy()

def get_points(self):

raise NotImplementedError

def containsx(self, x):

"""

Returns 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):

"""

Returns 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):

"""

Returns whether `x, y` is in the bounding box or on its edge.

"""

return self.containsx(x) and self.containsy(y)

def overlaps(self, other):

"""

Returns whether this bounding box overlaps with the other bounding box.

"""

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):

"""

Returns 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):

"""

Returns 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):

"""

Returns 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):

"""

Returns whether this bounding box overlaps with the other bounding box,

not including the edges.

"""

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):

"""

Return a new :class:`Bbox` object, statically transformed by

the given 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]]])

def inverse_transformed(self, transform):

"""

Return a new :class:`Bbox` object, statically transformed by

the inverse of the given 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 :class:`Bbox`, shifted to position *c*

within a container.

*c*: 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.

Optional argument *container* is the box within which the

:class:`Bbox` is positioned; it defaults to the initial

:class:`Bbox`.

"""

if container is None:

container = self

l, b, w, h = container.bounds

if isinstance(c, six.string_types):

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 :class:`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 :class:`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---that

is, 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):

"""

e.g., ``bbox.splitx(f1, f2, ...)``

Returns a list of new :class:`Bbox` objects formed by

splitting the original one with vertical lines at fractional

positions *f1*, *f2*, ...

"""

xf = [0] + list(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):

"""

e.g., ``bbox.splitx(f1, f2, ...)``

Returns a list of new :class:`Bbox` objects formed by

splitting the original one with horizontal lines at fractional

positions *f1*, *f2*, ...

"""

yf = [0] + list(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 :class:`Bbox`.

Any vertices with a non-finite x or y value are ignored.

*vertices* is a 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.

bboxes is a sequence of :class:`BboxBase` objects

"""

return count_bboxes_overlapping_bbox(

self, np.atleast_3d([np.array(x) for x in bboxes]))

def expanded(self, sw, sh):

"""

Return a new :class:`Bbox` which is this :class:`Bbox`

expanded around its center by the given 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):

"""

Return a new :class:`Bbox` that is padded on all four sides by

the given value.

"""

points = self.get_points()

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

def translated(self, tx, ty):

"""

Return a copy of the :class:`Bbox`, statically translated by

*tx* and *ty*.

"""

return Bbox(self._points + (tx, ty))

def corners(self):

"""

Return an array of points which are the four corners of this

rectangle. For example, if this :class:`Bbox` is defined by

the points (*a*, *b*) and (*c*, *d*), :meth:`corners` returns

(*a*, *b*), (*a*, *d*), (*c*, *b*) and (*c*, *d*).

"""

l, b, r, t = self.get_points().flatten()

return np.array([[l, b], [l, t], [r, b], [r, t]])

def rotated(self, radians):

"""

Return a new bounding box that bounds a rotated version of

this bounding box by the given radians. The new bounding box

is still aligned with the axes, of course.

"""

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 :class:`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 the two bboxes or None

if they do not intersect.

"""

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.

"""

def __init__(self, points, **kwargs):

"""

*points*: a 2x2 numpy array of the form [[x0, y0], [x1, y1]]

If you need to create a :class:`Bbox` object from another form

of data, consider the static methods :meth:`unit`,

:meth:`from_bounds` and :meth:`from_extents`.

"""

BboxBase.__init__(self, **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)

TransformNode.invalidate(self)

@staticmethod

def unit():

"""

(staticmethod) Create a new unit :class:`Bbox` from (0, 0) to

(1, 1).

"""

return Bbox(np.array([[0.0, 0.0], [1.0, 1.0]], float))

@staticmethod

def null():

"""

(staticmethod) Create a new null :class:`Bbox` from (inf, inf) to

(-inf, -inf).

"""

return Bbox(np.array([[np.inf, np.inf], [-np.inf, -np.inf]], float))

@staticmethod

def from_bounds(x0, y0, width, height):

"""

(staticmethod) Create a new :class:`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):

"""

(staticmethod) Create a new Bbox from *left*, *bottom*,

*right* and *top*.

The *y*-axis increases upwards.

"""

points = np.array(args, dtype=float).reshape(2, 2)

return Bbox(points)

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*:

- When True, subsequent calls to :meth:`update_from_data_xy`

will ignore the existing bounds of the :class:`Bbox`.

- When False, subsequent calls to :meth:`update_from_data_xy`

will include the existing bounds of the :class:`Bbox`.

"""

self._ignore = value

@cbook.deprecated('2.0', alternative='update_from_data_xy')

def update_from_data(self, x, y, ignore=None):

"""

Update the bounds of the :class:`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.

*x*: a numpy array of *x*-values

*y*: a numpy array of *y*-values

*ignore*:

- when True, ignore the existing bounds of the :class:`Bbox`.

- when False, include the existing bounds of the :class:`Bbox`.

- when None, use the last value passed to :meth:`ignore`.

"""

warnings.warn(

"update_from_data requires a memory copy -- please replace with "

"update_from_data_xy")

xy = np.hstack((x.reshape((len(x), 1)), y.reshape((len(y), 1))))

return self.update_from_data_xy(xy, ignore)

def update_from_path(self, path, ignore=None, updatex=True, updatey=True):

"""

Update the bounds of the :class:`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.

*path*: a :class:`~matplotlib.path.Path` instance

*ignore*:

- when True, ignore the existing bounds of the :class:`Bbox`.

- when False, include the existing bounds of the :class:`Bbox`.

- when None, use the last value passed to :meth:`ignore`.

*updatex*: when True, update the x values

*updatey*: when True, update the 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 :class:`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.

*xy*: a numpy array of 2D points

*ignore*:

- when True, ignore the existing bounds of the :class:`Bbox`.

- when False, include the existing bounds of the :class:`Bbox`.

- when None, use the last value passed to :meth:`ignore`.

*updatex*: when True, update the x values

*updatey*: when True, update the 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):

return self._minpos

@property

def minposx(self):

return self._minpos[0]

@property

def minposy(self):

return self._minpos[1]

def get_points(self):

"""

Get the points of the bounding box directly as a numpy array

of the form: [[x0, y0], [x1, y1]].

"""

self._invalid = 0

return self._points

def set_points(self, points):

"""

Set the points of the bounding box directly from a numpy array

of the form: [[x0, y0], [x1, y1]]. No error checking is

performed, as this method is mainly for internal use.

"""

if np.any(self._points != points):