matplotlib.transforms
¶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:
The framework can be used for both affine and nonaffine 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 nonaffine part of a transformation on a set of data. The affine is always assumed to occur after the nonaffine. For any transform:
full transform == nonaffine part + affine part
The backends are not expected to handle nonaffine transformations themselves.
matplotlib.transforms.
Affine2D
(matrix=None, **kwargs)[source]¶Bases: matplotlib.transforms.Affine2DBase
A mutable 2D affine transformation.
Initialize an Affine transform from a 3x3 numpy float array:
a c e
b d f
0 0 1
If matrix is None, initialize with the identity transform.
from_values
(a, b, c, d, e, f)[source]¶Create a new Affine2D instance from the given values:
a c e
b d f
0 0 1
.
get_matrix
()[source]¶Get the underlying transformation matrix as a 3x3 numpy array:
a c e
b d f
0 0 1
.
identity
()[source]¶Return a new Affine2D
object that is the identity transform.
Unless this transform will be mutated later on, consider using
the faster IdentityTransform
class instead.
rotate
(theta)[source]¶Add a rotation (in radians) to this transform in place.
Returns self, so this method can easily be chained with more
calls to rotate()
, rotate_deg()
, translate()
and scale()
.
rotate_around
(x, y, theta)[source]¶Add a rotation (in radians) around the point (x, y) in place.
Returns self, so this method can easily be chained with more
calls to rotate()
, rotate_deg()
, translate()
and scale()
.
rotate_deg
(degrees)[source]¶Add a rotation (in degrees) to this transform in place.
Returns self, so this method can easily be chained with more
calls to rotate()
, rotate_deg()
, translate()
and scale()
.
rotate_deg_around
(x, y, degrees)[source]¶Add a rotation (in degrees) around the point (x, y) in place.
Returns self, so this method can easily be chained with more
calls to rotate()
, rotate_deg()
, translate()
and scale()
.
scale
(sx, sy=None)[source]¶Adds a scale in place.
If sy is None, the same scale is applied in both the x and ydirections.
Returns self, so this method can easily be chained with more
calls to rotate()
, rotate_deg()
, translate()
and scale()
.
set
(other)[source]¶Set this transformation from the frozen copy of another
Affine2DBase
object.
set_matrix
(mtx)[source]¶Set the underlying transformation matrix from a 3x3 numpy array:
a c e
b d f
0 0 1
.
skew
(xShear, yShear)[source]¶Adds a skew in place.
xShear and yShear are the shear angles along the x and yaxes, respectively, in radians.
Returns self, so this method can easily be chained with more
calls to rotate()
, rotate_deg()
, translate()
and scale()
.
skew_deg
(xShear, yShear)[source]¶Adds a skew in place.
xShear and yShear are the shear angles along the x and yaxes, respectively, in degrees.
Returns self, so this method can easily be chained with more
calls to rotate()
, rotate_deg()
, translate()
and scale()
.
translate
(tx, ty)[source]¶Adds a translation in place.
Returns self, so this method can easily be chained with more
calls to rotate()
, rotate_deg()
, translate()
and scale()
.
matplotlib.transforms.
Affine2DBase
(*args, **kwargs)[source]¶Bases: matplotlib.transforms.AffineBase
The base class of all 2D affine transformations.
2D affine transformations are performed using a 3x3 numpy array:
a c e
b d f
0 0 1
This class provides the readonly interface. For a mutable 2D
affine transformation, use Affine2D
.
Subclasses of this class will generally only need to override a
constructor and get_matrix()
that generates a custom 3x3 matrix.
frozen
()[source]¶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.
has_inverse
= True¶input_dims
= 2¶inverted
()[source]¶Return the corresponding inverse transformation.
The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.
x === self.inverted().transform(self.transform(x))
is_separable
¶bool(x) > bool
Returns True when the argument x is true, False otherwise. The builtins True and False are the only two instances of the class bool. The class bool is a subclass of the class int, and cannot be subclassed.
matrix_from_values
(a, b, c, d, e, f)[source]¶Create a new transformation matrix as a 3x3 numpy array of the form:
a c e
b d f
0 0 1
output_dims
= 2¶transform_affine
(points)[source]¶Performs only the affine part of this transformation on the given array of values.
transform(values)
is always equivalent to
transform_affine(transform_non_affine(values))
.
In nonaffine transformations, this is generally a noop. In
affine transformations, this is equivalent to
transform(values)
.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_point
(point)[source]¶A convenience function that returns the transformed copy of a single point.
The point is given as a sequence of length input_dims
.
The transformed point is returned as a sequence of length
output_dims
.
matplotlib.transforms.
AffineBase
(*args, **kwargs)[source]¶Bases: matplotlib.transforms.Transform
The base class of all affine transformations of any number of dimensions.
is_affine
= True¶transform
(values)[source]¶Performs the transformation on the given array of values.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_affine
(values)[source]¶Performs only the affine part of this transformation on the given array of values.
transform(values)
is always equivalent to
transform_affine(transform_non_affine(values))
.
In nonaffine transformations, this is generally a noop. In
affine transformations, this is equivalent to
transform(values)
.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_non_affine
(points)[source]¶Performs only the nonaffine part of the transformation.
transform(values)
is always equivalent to
transform_affine(transform_non_affine(values))
.
In nonaffine transformations, this is generally equivalent to
transform(values)
. In affine transformations, this is
always a noop.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_path
(path)[source]¶Returns a transformed path.
path: a Path
instance.
In some cases, this transform may insert curves into the path that began as line segments.
matplotlib.transforms.
Bbox
(points, **kwargs)[source]¶Bases: matplotlib.transforms.BboxBase
A mutable bounding box.
Parameters: 


Notes
If you need to create a Bbox
object from another form
of data, consider the static methods unit()
,
from_bounds()
and from_extents()
.
from_bounds
(x0, y0, width, height)[source]¶Create a new Bbox
from x0, y0, width and height.
width and height may be negative.
from_extents
(*args)[source]¶Create a new Bbox from left, bottom, right and top.
The yaxis increases upwards.
get_points
()[source]¶Get the points of the bounding box directly as a numpy array
of the form: [[x0, y0], [x1, y1]]
.
ignore
(value)[source]¶Set whether the existing bounds of the box should be ignored
by subsequent calls to update_from_data_xy()
.
True
, subsequent calls to update_from_data_xy()
will ignore the existing bounds of the Bbox
.False
, subsequent calls to update_from_data_xy()
will include the existing bounds of the Bbox
.intervalx
¶The pair of x coordinates that define the bounding box.
This is not guaranteed to be sorted from left to right.
intervaly
¶The pair of y coordinates that define the bounding box.
This is not guaranteed to be sorted from bottom to top.
minpos
¶minposx
¶minposy
¶p0
¶The first pair of (x, y) coordinates that define the bounding box.
This is not guaranteed to be the bottomleft corner (for that, use
min
).
p1
¶The second pair of (x, y) coordinates that define the bounding box.
This is not guaranteed to be the topright corner (for that, use
max
).
set_points
(points)[source]¶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.
update_from_data_xy
(xy, ignore=None, updatex=True, updatey=True)[source]¶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: 

update_from_path
(path, ignore=None, updatex=True, updatey=True)[source]¶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: 

x0
¶The first of the pair of x coordinates that define the bounding box.
This is not guaranteed to be less than x1
(for that, use
xmin
).
x1
¶The second of the pair of x coordinates that define the bounding box.
This is not guaranteed to be greater than x0
(for that, use
xmax
).
matplotlib.transforms.
BboxBase
(shorthand_name=None)[source]¶Bases: matplotlib.transforms.TransformNode
This is the base class of all bounding boxes, and provides readonly access
to its data. A mutable bounding box is provided by the 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.
Creates a new TransformNode
.
Parameters: 


anchored
(c, container=None)[source]¶Return a copy of the Bbox
shifted to position c within container.
Parameters: 


coefs
= {'C': (0.5, 0.5), 'E': (1.0, 0.5), 'N': (0.5, 1.0), 'NE': (1.0, 1.0), 'NW': (0, 1.0), 'S': (0.5, 0), 'SE': (1.0, 0), 'SW': (0, 0), 'W': (0, 0.5)}¶corners
()[source]¶Return the corners of this rectangle as an array of points.
Specifically, this returns the array
[[x0, y0], [x0, y1], [x1, y0], [x1, y1]]
.
count_contains
(vertices)[source]¶Count the number of vertices contained in the Bbox
.
Any vertices with a nonfinite x or y value are ignored.
Parameters: 


count_overlaps
(bboxes)[source]¶Count the number of bounding boxes that overlap this one.
Parameters: 


expanded
(sw, sh)[source]¶Construct a Bbox
by expanding this one around its center by the
factors sw and sh.
frozen
()[source]¶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.
fully_overlaps
(other)[source]¶Return whether this bounding box overlaps with the other bounding box, not including the edges.
Parameters: 


height
¶The (signed) height of the bounding box.
intersection
(bbox1, bbox2)[source]¶Return the intersection of bbox1 and bbox2 if they intersect, or None if they don't.
intervalx
¶The pair of x coordinates that define the bounding box.
This is not guaranteed to be sorted from left to right.
intervaly
¶The pair of y coordinates that define the bounding box.
This is not guaranteed to be sorted from bottom to top.
inverse_transformed
(transform)[source]¶Construct a Bbox
by statically transforming this one by the inverse
of transform.
is_affine
= True¶is_bbox
= True¶max
¶The topright corner of the bounding box.
min
¶The bottomleft corner of the bounding box.
overlaps
(other)[source]¶Return whether this bounding box overlaps with the other bounding box.
Parameters: 


p0
¶The first pair of (x, y) coordinates that define the bounding box.
This is not guaranteed to be the bottomleft corner (for that, use
min
).
p1
¶The second pair of (x, y) coordinates that define the bounding box.
This is not guaranteed to be the topright corner (for that, use
max
).
rotated
(radians)[source]¶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.
shrunk
(mx, my)[source]¶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.
shrunk_to_aspect
(box_aspect, container=None, fig_aspect=1.0)[source]¶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 relativethat
is, fractions of a larger box such as a figurethen 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.
size
¶The (signed) width and height of the bounding box.
splitx
(*args)[source]¶Return a list of new Bbox
objects formed by splitting the original
one with vertical lines at fractional positions given by args.
splity
(*args)[source]¶Return a list of new Bbox
objects formed by splitting the original
one with horizontal lines at fractional positions given by args.
width
¶The (signed) width of the bounding box.
x0
¶The first of the pair of x coordinates that define the bounding box.
This is not guaranteed to be less than x1
(for that, use
xmin
).
x1
¶The second of the pair of x coordinates that define the bounding box.
This is not guaranteed to be greater than x0
(for that, use
xmax
).
xmax
¶The right edge of the bounding box.
xmin
¶The left edge of the bounding box.
y0
¶The first of the pair of y coordinates that define the bounding box.
This is not guaranteed to be less than y1
(for that, use
ymin
).
y1
¶The second of the pair of y coordinates that define the bounding box.
This is not guaranteed to be greater than y0
(for that, use
ymax
).
ymax
¶The top edge of the bounding box.
ymin
¶The bottom edge of the bounding box.
matplotlib.transforms.
BboxTransform
(boxin, boxout, **kwargs)[source]¶Bases: matplotlib.transforms.Affine2DBase
BboxTransform
linearly transforms points from one Bbox
to another.
Create a new BboxTransform
that linearly transforms
points from boxin to boxout.
is_separable
= True¶matplotlib.transforms.
BboxTransformFrom
(boxin, **kwargs)[source]¶Bases: matplotlib.transforms.Affine2DBase
BboxTransformFrom
linearly transforms points from a given Bbox
to the
unit bounding box.
is_separable
= True¶matplotlib.transforms.
BboxTransformTo
(boxout, **kwargs)[source]¶Bases: matplotlib.transforms.Affine2DBase
BboxTransformTo
is a transformation that linearly transforms points from
the unit bounding box to a given Bbox
.
Create a new BboxTransformTo
that linearly transforms
points from the unit bounding box to boxout.
is_separable
= True¶matplotlib.transforms.
BboxTransformToMaxOnly
(boxout, **kwargs)[source]¶Bases: matplotlib.transforms.BboxTransformTo
BboxTransformTo
is a transformation that linearly transforms points from
the unit bounding box to a given Bbox
with a fixed upper left of (0, 0).
Create a new BboxTransformTo
that linearly transforms
points from the unit bounding box to boxout.
matplotlib.transforms.
BlendedAffine2D
(x_transform, y_transform, **kwargs)[source]¶Bases: matplotlib.transforms.Affine2DBase
A "blended" transform uses one transform for the xdirection, and another transform for the ydirection.
This version is an optimization for the case where both child
transforms are of type Affine2DBase
.
Create a new "blended" transform using x_transform to transform the xaxis and y_transform to transform the yaxis.
Both x_transform and y_transform must be 2D affine transforms.
You will generally not call this constructor directly but use the
blended_transform_factory
function instead, which can determine
automatically which kind of blended transform to create.
contains_branch_seperately
(transform)[source]¶Returns whether the given branch is a subtree of this transform on each separate dimension.
A common use for this method is to identify if a transform is a blended transform containing an axes' data transform. e.g.:
x_isdata, y_isdata = trans.contains_branch_seperately(ax.transData)
is_separable
= True¶matplotlib.transforms.
BlendedGenericTransform
(x_transform, y_transform, **kwargs)[source]¶Bases: matplotlib.transforms.Transform
A "blended" transform uses one transform for the xdirection, and another transform for the ydirection.
This "generic" version can handle any given child transform in the x and ydirections.
Create a new "blended" transform using x_transform to transform the xaxis and y_transform to transform the yaxis.
You will generally not call this constructor directly but use the
blended_transform_factory
function instead, which can determine
automatically which kind of blended transform to create.
contains_branch
(other)[source]¶Return whether the given transform is a subtree of this transform.
This routine uses transform equality to identify subtrees, therefore in many situations it is object id which will be used.
For the case where the given transform represents the whole of this transform, returns True.
contains_branch_seperately
(transform)[source]¶Returns whether the given branch is a subtree of this transform on each separate dimension.
A common use for this method is to identify if a transform is a blended transform containing an axes' data transform. e.g.:
x_isdata, y_isdata = trans.contains_branch_seperately(ax.transData)
depth
¶Returns the number of transforms which have been chained together to form this Transform instance.
Note
For the special case of a Composite transform, the maximum depth of the two is returned.
frozen
()[source]¶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.
has_inverse
¶input_dims
= 2¶inverted
()[source]¶Return the corresponding inverse transformation.
The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.
x === self.inverted().transform(self.transform(x))
is_affine
¶is_separable
= True¶output_dims
= 2¶pass_through
= True¶transform_non_affine
(points)[source]¶Performs only the nonaffine part of the transformation.
transform(values)
is always equivalent to
transform_affine(transform_non_affine(values))
.
In nonaffine transformations, this is generally equivalent to
transform(values)
. In affine transformations, this is
always a noop.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
matplotlib.transforms.
CompositeAffine2D
(a, b, **kwargs)[source]¶Bases: matplotlib.transforms.Affine2DBase
A composite transform formed by applying transform a then transform b.
This version is an optimization that handles the case where both a and b are 2D affines.
Create a new composite transform that is the result of applying transform a then transform b.
Both a and b must be instances of Affine2DBase
.
You will generally not call this constructor directly but use the
composite_transform_factory
function instead, which can automatically
choose the best kind of composite transform instance to create.
depth
¶Returns the number of transforms which have been chained together to form this Transform instance.
Note
For the special case of a Composite transform, the maximum depth of the two is returned.
matplotlib.transforms.
CompositeGenericTransform
(a, b, **kwargs)[source]¶Bases: matplotlib.transforms.Transform
A composite transform formed by applying transform a then transform b.
This "generic" version can handle any two arbitrary transformations.
Create a new composite transform that is the result of applying transform a then transform b.
You will generally not call this constructor directly but use the
composite_transform_factory
function instead, which can automatically
choose the best kind of composite transform instance to create.
depth
¶frozen
()[source]¶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.
has_inverse
¶inverted
()[source]¶Return the corresponding inverse transformation.
The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.
x === self.inverted().transform(self.transform(x))
is_affine
¶is_separable
¶pass_through
= True¶transform_affine
(points)[source]¶Performs only the affine part of this transformation on the given array of values.
transform(values)
is always equivalent to
transform_affine(transform_non_affine(values))
.
In nonaffine transformations, this is generally a noop. In
affine transformations, this is equivalent to
transform(values)
.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_non_affine
(points)[source]¶Performs only the nonaffine part of the transformation.
transform(values)
is always equivalent to
transform_affine(transform_non_affine(values))
.
In nonaffine transformations, this is generally equivalent to
transform(values)
. In affine transformations, this is
always a noop.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
matplotlib.transforms.
IdentityTransform
(*args, **kwargs)[source]¶Bases: matplotlib.transforms.Affine2DBase
A special class that does one thing, the identity transform, in a fast way.
frozen
()[source]¶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.
inverted
()[source]¶Return the corresponding inverse transformation.
The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.
x === self.inverted().transform(self.transform(x))
transform
(points)[source]¶Performs the transformation on the given array of values.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_affine
(points)[source]¶Performs only the affine part of this transformation on the given array of values.
transform(values)
is always equivalent to
transform_affine(transform_non_affine(values))
.
In nonaffine transformations, this is generally a noop. In
affine transformations, this is equivalent to
transform(values)
.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_non_affine
(points)[source]¶Performs only the nonaffine part of the transformation.
transform(values)
is always equivalent to
transform_affine(transform_non_affine(values))
.
In nonaffine transformations, this is generally equivalent to
transform(values)
. In affine transformations, this is
always a noop.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_path
(path)[source]¶Returns a transformed path.
path: a Path
instance.
In some cases, this transform may insert curves into the path that began as line segments.
matplotlib.transforms.
LockableBbox
(bbox, x0=None, y0=None, x1=None, y1=None, **kwargs)[source]¶Bases: matplotlib.transforms.BboxBase
A Bbox
where some elements may be locked at certain values.
When the child bounding box changes, the bounds of this bbox will update accordingly with the exception of the locked elements.
Parameters: 


locked_x0
¶float or None: The value used for the locked x0.
locked_x1
¶float or None: The value used for the locked x1.
locked_y0
¶float or None: The value used for the locked y0.
locked_y1
¶float or None: The value used for the locked y1.
matplotlib.transforms.
ScaledTranslation
(xt, yt, scale_trans, **kwargs)[source]¶Bases: matplotlib.transforms.Affine2DBase
A transformation that translates by xt and yt, after xt and yt have been transformed by scale_trans.
matplotlib.transforms.
Transform
(shorthand_name=None)[source]¶Bases: matplotlib.transforms.TransformNode
The base class of all TransformNode
instances that
actually perform a transformation.
All nonaffine transformations should be subclasses of this class.
New affine transformations should be subclasses of Affine2D
.
Subclasses of this class should override the following members (at minimum):
input_dims
output_dims
transform()
is_separable
has_inverse
inverted()
(ifhas_inverse
is True)
If the transform needs to do something nonstandard with
matplotlib.path.Path
objects, such as adding curves
where there were once line segments, it should override:
Creates a new TransformNode
.
Parameters: 


contains_branch
(other)[source]¶Return whether the given transform is a subtree of this transform.
This routine uses transform equality to identify subtrees, therefore in many situations it is object id which will be used.
For the case where the given transform represents the whole of this transform, returns True.
contains_branch_seperately
(other_transform)[source]¶Returns whether the given branch is a subtree of this transform on each separate dimension.
A common use for this method is to identify if a transform is a blended transform containing an axes' data transform. e.g.:
x_isdata, y_isdata = trans.contains_branch_seperately(ax.transData)
depth
¶Returns the number of transforms which have been chained together to form this Transform instance.
Note
For the special case of a Composite transform, the maximum depth of the two is returned.
has_inverse
= False¶True if this transform has a corresponding inverse transform.
input_dims
= None¶The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
inverted
()[source]¶Return the corresponding inverse transformation.
The return value of this method should be treated as temporary. An update to self does not cause a corresponding update to its inverted copy.
x === self.inverted().transform(self.transform(x))
is_separable
= False¶True if this transform is separable in the x and y dimensions.
output_dims
= None¶The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
transform
(values)[source]¶Performs the transformation on the given array of values.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_affine
(values)[source]¶Performs only the affine part of this transformation on the given array of values.
transform(values)
is always equivalent to
transform_affine(transform_non_affine(values))
.
In nonaffine transformations, this is generally a noop. In
affine transformations, this is equivalent to
transform(values)
.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_angles
(angles, pts, radians=False, pushoff=1e05)[source]¶Transforms a set of angles anchored at specific locations.
Parameters: 


Returns: 

transform_bbox
(bbox)[source]¶Transform the given bounding box.
Note, for smarter transforms including caching (a common
requirement for matplotlib figures), see TransformedBbox
.
transform_non_affine
(values)[source]¶Performs only the nonaffine part of the transformation.
transform(values)
is always equivalent to
transform_affine(transform_non_affine(values))
.
In nonaffine transformations, this is generally equivalent to
transform(values)
. In affine transformations, this is
always a noop.
Accepts a numpy array of shape (N x input_dims
) and
returns a numpy array of shape (N x output_dims
).
Alternatively, accepts a numpy array of length input_dims
and returns a numpy array of length output_dims
.
transform_path
(path)[source]¶Returns a transformed path.
path: a Path
instance.
In some cases, this transform may insert curves into the path that began as line segments.
transform_path_affine
(path)[source]¶Returns a path, transformed only by the affine part of this transform.
path: a Path
instance.
transform_path(path)
is equivalent to
transform_path_affine(transform_path_non_affine(values))
.
transform_path_non_affine
(path)[source]¶Returns a path, transformed only by the nonaffine part of this transform.
path: a Path
instance.
transform_path(path)
is equivalent to
transform_path_affine(transform_path_non_affine(values))
.
transform_point
(point)[source]¶A convenience function that returns the transformed copy of a single point.
The point is given as a sequence of length input_dims
.
The transformed point is returned as a sequence of length
output_dims
.
matplotlib.transforms.
TransformNode
(shorthand_name=None)[source]¶Bases: object
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.
Creates a new TransformNode
.
Parameters: 


INVALID
= 3¶INVALID_AFFINE
= 2¶INVALID_NON_AFFINE
= 1¶frozen
()[source]¶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.
invalidate
()[source]¶Invalidate this TransformNode
and triggers an invalidation of its
ancestors. Should be called any time the transform changes.
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.
matplotlib.transforms.
TransformWrapper
(child)[source]¶Bases: matplotlib.transforms.Transform
A helper class that holds a single child transform and acts equivalently to it.
This is useful if a node of the transform tree must be replaced at run time with a transform of a different type. This class allows that replacement to correctly trigger invalidation.
Note that TransformWrapper
instances must have the same
input and output dimensions during their entire lifetime, so the
child transform may only be replaced with another child transform
of the same dimensions.
child: A class:Transform
instance. This child may later
be replaced with set()
.
frozen
()[source]¶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.
has_inverse
¶is_affine
¶is_separable
¶pass_through
= True¶matplotlib.transforms.
TransformedBbox
(bbox, transform, **kwargs)[source]¶Bases: matplotlib.transforms.BboxBase
A Bbox
that is automatically transformed by a given
transform. When either the child bounding box or transform
changes, the bounds of this bbox will update accordingly.
Parameters: 

matplotlib.transforms.
TransformedPatchPath
(patch)[source]¶Bases: matplotlib.transforms.TransformedPath
A TransformedPatchPath
caches a nonaffine transformed copy of the
Patch
. This cached copy is automatically updated when the
nonaffine part of the transform or the patch changes.
Parameters: 


matplotlib.transforms.
TransformedPath
(path, transform)[source]¶Bases: matplotlib.transforms.TransformNode
A TransformedPath
caches a nonaffine transformed copy of the
Path
. This cached copy is automatically updated when the
nonaffine part of the transform changes.
Note
Paths are considered immutable by this class. Any update to the path's vertices/codes will not trigger a transform recomputation.
Parameters: 

get_transformed_path_and_affine
()[source]¶Return a copy of the child path, with the nonaffine part of the transform already applied, along with the affine part of the path necessary to complete the transformation.
get_transformed_points_and_affine
()[source]¶Return a copy of the child path, with the nonaffine part of
the transform already applied, along with the affine part of
the path necessary to complete the transformation. Unlike
get_transformed_path_and_affine()
, no interpolation will
be performed.
matplotlib.transforms.
blended_transform_factory
(x_transform, y_transform)[source]¶Create a new "blended" transform using x_transform to transform the xaxis and y_transform to transform the yaxis.
A faster version of the blended transform is returned for the case where both child transforms are affine.
matplotlib.transforms.
composite_transform_factory
(a, b)[source]¶Create a new composite transform that is the result of applying transform a then transform b.
Shortcut versions of the blended transform are provided for the case where both child transforms are affine, or one or the other is the identity transform.
Composite transforms may also be created using the '+' operator, e.g.:
c = a + b
matplotlib.transforms.
interval_contains
(interval, val)[source]¶Check, inclusively, whether an interval includes a given value.
Parameters: 


Returns: 

matplotlib.transforms.
interval_contains_open
(interval, val)[source]¶Check, excluding endpoints, whether an interval includes a given value.
Parameters: 


Returns: 

matplotlib.transforms.
nonsingular
(vmin, vmax, expander=0.001, tiny=1e15, increasing=True)[source]¶Modify the endpoints of a range as needed to avoid singularities.
Parameters: 


Returns: 

matplotlib.transforms.
offset_copy
(trans, fig=None, x=0.0, y=0.0, units='inches')[source]¶Return a new transform with an added offset.
Parameters: 


Returns: 
