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.
class 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.
clear()
[source]
Reset the underlying matrix to the identity transform.
static from_values(a, b, c, d, e, f)
[source]
(staticmethod) 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
.
static identity()
[source]
(staticmethod) 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.
is_separable
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()
.
class 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
static matrix_from_values(a, b, c, d, e, f)
[source]
(staticmethod) 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
to_values()
[source]
Return the values of the matrix as a sequence (a,b,c,d,e,f)
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
.
class matplotlib.transforms.AffineBase(*args, **kwargs)
[source]
Bases: matplotlib.transforms.Transform
The base class of all affine transformations of any number of dimensions.
get_affine()
[source]
Get the affine part of this transform.
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.
class matplotlib.transforms.Bbox(points, **kwargs)
[source]
Bases: matplotlib.transforms.BboxBase
A mutable bounding box.
Parameters: 


If you need to create a Bbox
object from another form of data, consider the static methods unit()
, from_bounds()
and from_extents()
.
static from_bounds(x0, y0, width, height)
[source]
(staticmethod) Create a new Bbox
from x0, y0, width and height.
width and height may be negative.
static from_extents(*args)
[source]
(staticmethod) 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()
.
value : bool
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
intervalx
is the pair of x coordinates that define the bounding box. It is not guaranteed to be sorted from left to right.
intervaly
intervaly
is the pair of y coordinates that define the bounding box. It is not guaranteed to be sorted from bottom to top.
minpos
minposx
minposy
mutated()
[source]
Return whether the bbox has changed since init.
mutatedx()
[source]
Return whether the xlimits have changed since init.
mutatedy()
[source]
Return whether the ylimits have changed since init.
p0
p0
is the first pair of (x, y) coordinates that define the bounding box. It is not guaranteed to be the bottomleft corner. For that, use min
.
p1
p1
is the second pair of (x, y) coordinates that define the bounding box. It 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
x0
is the first of the pair of x coordinates that define the bounding box. x0
is not guaranteed to be less than x1
. If you require that, use xmin
.
x1
x1
is the second of the pair of x coordinates that define the bounding box. x1
is not guaranteed to be greater than x0
. If you require that, use xmax
.
class 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 a 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)}
contains(x, y)
[source]
Returns whether (x, y)
is in the bounding box or on its edge.
corners()
[source]
Return an array of points which are the four corners of this rectangle. For example, if this Bbox
is defined by the points (a, b) and (c, d), corners()
returns (a, b), (a, d), (c, b) and (c, d).
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]
Return a new Bbox
which is this Bbox
expanded around its center by the given 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_contains(x, y)
[source]
Returns whether x, y
is in the bounding box, but not on its edge.
fully_overlaps(other)
[source]
Returns whether this bounding box overlaps with the other bounding box, not including the edges.
Parameters: 


get_points()
[source]
static intersection(bbox1, bbox2)
[source]
Return the intersection of the two bboxes or None if they do not intersect.
intervalx
intervalx
is the pair of x coordinates that define the bounding box. It is not guaranteed to be sorted from left to right.
intervaly
intervaly
is the pair of y coordinates that define the bounding box. It is not guaranteed to be sorted from bottom to top.
inverse_transformed(transform)
[source]
Return a new Bbox
object, statically transformed by the inverse of the given transform.
is_affine = True
is_bbox = True
max
max
is the topright corner of the bounding box.
min
min
is the bottomleft corner of the bounding box.
overlaps(other)
[source]
Returns whether this bounding box overlaps with the other bounding box.
Parameters: 


p0
p0
is the first pair of (x, y) coordinates that define the bounding box. It is not guaranteed to be the bottomleft corner. For that, use min
.
p1
p1
is the second pair of (x, y) coordinates that define the bounding box. It 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 width and height of the bounding box. May be negative, in the same way as width
and height
.
splitx(*args)
[source]
e.g., bbox.splitx(f1, f2, ...)
Returns a list of new Bbox
objects formed by splitting the original one with vertical lines at fractional positions f1, f2, ...
splity(*args)
[source]
e.g., bbox.splitx(f1, f2, ...)
Returns a list of new Bbox
objects formed by splitting the original one with horizontal lines at fractional positions f1, f2, ...
transformed(transform)
[source]
Return a new Bbox
object, statically transformed by the given transform.
x0
x0
is the first of the pair of x coordinates that define the bounding box. x0
is not guaranteed to be less than x1
. If you require that, use xmin
.
x1
x1
is the second of the pair of x coordinates that define the bounding box. x1
is not guaranteed to be greater than x0
. If you require that, use xmax
.
xmax
xmax
is the right edge of the bounding box.
xmin
xmin
is the left edge of the bounding box.
y0
y0
is the first of the pair of y coordinates that define the bounding box. y0
is not guaranteed to be less than y1
. If you require that, use ymin
.
y1
y1
is the second of the pair of y coordinates that define the bounding box. y1
is not guaranteed to be greater than y0
. If you require that, use ymax
.
ymax
ymax
is the top edge of the bounding box.
ymin
ymin
is the bottom edge of the bounding box.
class matplotlib.transforms.BboxTransform(boxin, boxout, **kwargs)
[source]
Bases: matplotlib.transforms.Affine2DBase
BboxTransform
linearly transforms points from one Bbox
to another Bbox
.
Create a new BboxTransform
that linearly transforms points from boxin to boxout.
get_matrix()
[source]
Get the Affine transformation array for the affine part of this transform.
is_separable = True
class matplotlib.transforms.BboxTransformFrom(boxin, **kwargs)
[source]
Bases: matplotlib.transforms.Affine2DBase
BboxTransformFrom
linearly transforms points from a given Bbox
to the unit bounding box.
get_matrix()
[source]
Get the Affine transformation array for the affine part of this transform.
is_separable = True
class 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.
get_matrix()
[source]
Get the Affine transformation array for the affine part of this transform.
is_separable = True
class 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.
get_matrix()
[source]
Get the Affine transformation array for the affine part of this transform.
class 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)
get_matrix()
[source]
Get the Affine transformation array for the affine part of this transform.
is_separable = True
class 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.
get_affine()
[source]
Get the affine part of this transform.
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
.
class 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.
get_matrix()
[source]
Get the Affine transformation array for the affine part of this transform.
class 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
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.
get_affine()
[source]
Get the affine part of this transform.
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
.
class 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.
get_affine()
[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))
get_matrix()
[source]
Get the Affine transformation array for the affine part of this transform.
inverted()
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 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_affine(points)
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_non_affine(points)
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 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_path_affine(path)
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_path_non_affine(path)
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))
.
class 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: 


get_points()
[source]
Get the points of the bounding box directly as a numpy array of the form: [[x0, y0], [x1, y1]]
.
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.
class 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 transformad by the given transform scale_trans.
get_matrix()
[source]
Get the Affine transformation array for the affine part of this transform.
class 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()
(if has_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.
get_affine()
[source]
Get the affine part of this transform.
get_matrix()
[source]
Get the Affine transformation array for the affine part of this transform.
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]
Performs transformation on a set of angles anchored at specific locations.
The angles must be a column vector (i.e., numpy array).
The pts must be a twocolumn numpy array of x,y positions (angle transforms currently only work in 2D). This array must have the same number of rows as angles.
The transformed angles are returned in an array with the same size as angles.
The generic version of this method uses a very generic algorithm that transforms pts, as well as locations very close to pts, to find the angle in the transformed system.
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
.
class 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.
set_children(*children)
[source]
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.
class 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
set(child)
[source]
Replace the current child of this transform with another one.
The new child must have the same number of input and output dimensions as the current child.
class 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: 


get_points()
[source]
Get the points of the bounding box directly as a numpy array of the form: [[x0, y0], [x1, y1]]
.
class 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.
Create a new TransformedPatchPath
from the given Patch
.
class 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.
Create a new TransformedPath
from the given Path
and Transform
.
get_affine()
[source]
get_fully_transformed_path()
[source]
Return a fullytransformed copy of the child path.
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: 

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Licensed under the Matplotlib License Agreement.
https://matplotlib.org/3.0.0/api/transformations.html