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matplotlib.transforms#Inheritance diagram of matplotlib.transforms
Matplotlib includes a framework for arbitrary geometric transformations that is used to determine the final position of all elements drawn on the canvas.
Transforms are composed into trees of TransformNode objects
whose actual value depends on their children. When the contents of
children change, their parents are automatically invalidated. The
next time an invalidated transform is accessed, it is recomputed to
reflect those changes. This invalidation/caching approach prevents
unnecessary recomputations of transforms, and contributes to better
interactive performance.
For example, here is a graph of the transform tree used to plot data to the figure:
Diagram of transform tree from data to figure coordinates.
The framework can be used for both affine and non-affine transformations. However, for speed, we want to use the backend renderers to perform affine transformations whenever possible. Therefore, it is possible to perform just the affine or non-affine part of a transformation on a set of data. The affine is always assumed to occur after the non-affine. For any transform:
full transform == non-affine part + affine part
The backends are not expected to handle non-affine transformations themselves.
See the tutorial Transformations Tutorial for examples of how to use transforms.
Bases: 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.
Create a new Affine2D instance from the given values:
a c e
b d f
0 0 1
.
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().
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().
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().
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().
Add a scale in place.
If sy is None, the same scale is applied in both the x- and y-directions.
Returns self, so this method can easily be chained with more
calls to rotate(), rotate_deg(), translate()
and scale().
Set this transformation from the frozen copy of another
Affine2DBase object.
Set the underlying transformation matrix from a 3x3 array:
a c e
b d f
0 0 1
.
Add a skew in place.
xShear and yShear are the shear angles along the x- and y-axes, respectively, in radians.
Returns self, so this method can easily be chained with more
calls to rotate(), rotate_deg(), translate()
and scale().
Add a skew in place.
xShear and yShear are the shear angles along the x- and y-axes, respectively, in degrees.
Returns self, so this method can easily be chained with more
calls to rotate(), rotate_deg(), translate()
and scale().
Add a translation in place.
Returns self, so this method can easily be chained with more
calls to rotate(), rotate_deg(), translate()
and scale().
Bases: 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 read-only 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.
A string representing the "name" of the transform. The name carries
no significance other than to improve the readability of
str(transform) when DEBUG=True.
Return a frozen copy of this transform node. The frozen copy will not
be updated when its children change. Useful for storing a previously
known state of a transform where copy.deepcopy() might normally be
used.
True if this transform has a corresponding inverse transform.
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
Return the corresponding inverse transformation.
It holds x == self.inverted().transform(self.transform(x)).
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.
Returns True when the argument 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.
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
Apply 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 non-affine transformations, this is generally a no-op. In
affine transformations, this is equivalent to
transform(values).
The input values as an array of length input_dims or
shape (N, input_dims).
The output values as an array of length output_dims or
shape (N, output_dims), depending on the input.
Bases: Transform
The base class of all affine transformations of any number of dimensions.
A string representing the "name" of the transform. The name carries
no significance other than to improve the readability of
str(transform) when DEBUG=True.
Apply this transformation on the given array of values.
The input values as an array of length input_dims or
shape (N, input_dims).
The output values as an array of length output_dims or
shape (N, output_dims), depending on the input.
Apply 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 non-affine transformations, this is generally a no-op. In
affine transformations, this is equivalent to
transform(values).
The input values as an array of length input_dims or
shape (N, input_dims).
The output values as an array of length output_dims or
shape (N, output_dims), depending on the input.
Apply only the non-affine part of this transformation.
transform(values) is always equivalent to
transform_affine(transform_non_affine(values)).
In non-affine transformations, this is generally equivalent to
transform(values). In affine transformations, this is
always a no-op.
The input values as an array of length
input_dims or
shape (N, input_dims).
The output values as an array of length
output_dims or shape
(N, output_dims),
depending on the input.
Apply the transform to Path path, returning a new Path.
In some cases, this transform may insert curves into the path that began as line segments.
Bases: Affine2DBase
A transform wrapper for transforming displacements between pairs of points.
This class is intended to be used to transform displacements ("position
deltas") between pairs of points (e.g., as the offset_transform
of Collections): given a transform t such that t =
AffineDeltaTransform(t) + offset, AffineDeltaTransform
satisfies AffineDeltaTransform(a - b) == AffineDeltaTransform(a) -
AffineDeltaTransform(b).
This is implemented by forcing the offset components of the transform matrix to zero.
This class is experimental as of 3.3, and the API may change.
A string representing the "name" of the transform. The name carries
no significance other than to improve the readability of
str(transform) when DEBUG=True.
If pass_through is True, all ancestors will always be invalidated, even if 'self' is already invalid.
Bases: BboxBase
A mutable bounding box.
Examples
Create from known bounds
The default constructor takes the boundary "points" [[xmin, ymin],
[xmax, ymax]].
>>> Bbox([[1, 1], [3, 7]])
Bbox([[1.0, 1.0], [3.0, 7.0]])
Alternatively, a Bbox can be created from the flattened points array, the
so-called "extents" (xmin, ymin, xmax, ymax)
>>> Bbox.from_extents(1, 1, 3, 7)
Bbox([[1.0, 1.0], [3.0, 7.0]])
or from the "bounds" (xmin, ymin, width, height).
>>> Bbox.from_bounds(1, 1, 2, 6)
Bbox([[1.0, 1.0], [3.0, 7.0]])
Create from collections of points
The "empty" object for accumulating Bboxs is the null bbox, which is a stand-in for the empty set.
>>> Bbox.null()
Bbox([[inf, inf], [-inf, -inf]])
Adding points to the null bbox will give you the bbox of those points.
>>> box = Bbox.null()
>>> box.update_from_data_xy([[1, 1]])
>>> box
Bbox([[1.0, 1.0], [1.0, 1.0]])
>>> box.update_from_data_xy([[2, 3], [3, 2]], ignore=False)
>>> box
Bbox([[1.0, 1.0], [3.0, 3.0]])
Setting ignore=True is equivalent to starting over from a null bbox.
>>> box.update_from_data_xy([[1, 1]], ignore=True)
>>> box
Bbox([[1.0, 1.0], [1.0, 1.0]])
Warning
It is recommended to always specify ignore explicitly. If not, the
default value of ignore can be changed at any time by code with
access to your Bbox, for example using the method ignore.
Properties of the ``null`` bbox
Note
The current behavior of Bbox.null() may be surprising as it does
not have all of the properties of the "empty set", and as such does
not behave like a "zero" object in the mathematical sense. We may
change that in the future (with a deprecation period).
The null bbox is the identity for intersections
>>> Bbox.intersection(Bbox([[1, 1], [3, 7]]), Bbox.null())
Bbox([[1.0, 1.0], [3.0, 7.0]])
except with itself, where it returns the full space.
>>> Bbox.intersection(Bbox.null(), Bbox.null())
Bbox([[-inf, -inf], [inf, inf]])
A union containing null will always return the full space (not the other set!)
>>> Bbox.union([Bbox([[0, 0], [0, 0]]), Bbox.null()])
Bbox([[-inf, -inf], [inf, inf]])
ndarrayA (2, 2) array of the form [[x0, y0], [x1, y1]].
Create a new Bbox from x0, y0, width and height.
width and height may be negative.
Create a new Bbox from left, bottom, right and top.
The y-axis increases upwards.
The four extents of the bounding box.
If this is supplied, the Bbox will have a minimum positive value set. This is useful when dealing with logarithmic scales and other scales where negative bounds result in floating point errors.
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.
Get the points of the bounding box as an array of the form
[[x0, y0], [x1, y1]].
Set whether the existing bounds of the box should be ignored
by subsequent calls to update_from_data_xy().
When True, subsequent calls to update_from_data_xy will
ignore the existing bounds of the Bbox.
When False, subsequent calls to update_from_data_xy will
include the existing bounds of the Bbox.
The pair of x coordinates that define the bounding box.
This is not guaranteed to be sorted from left to right.
The pair of y coordinates that define the bounding box.
This is not guaranteed to be sorted from bottom to top.
The minimum positive value in both directions within the Bbox.
This is useful when dealing with logarithmic scales and other scales where negative bounds result in floating point errors, and will be used as the minimum extent instead of p0.
The minimum positive value in the x-direction within the Bbox.
This is useful when dealing with logarithmic scales and other scales where negative bounds result in floating point errors, and will be used as the minimum x-extent instead of x0.
The minimum positive value in the y-direction within the Bbox.
This is useful when dealing with logarithmic scales and other scales where negative bounds result in floating point errors, and will be used as the minimum y-extent instead of y0.
The first pair of (x, y) coordinates that define the bounding box.
This is not guaranteed to be the bottom-left corner (for that, use
min).
The second pair of (x, y) coordinates that define the bounding box.
This is not guaranteed to be the top-right corner (for that, use
max).
Set the points of the bounding box directly from an array of the form
[[x0, y0], [x1, y1]]. No error checking is performed, as this
method is mainly for internal use.
Update the x-bounds of the Bbox based on the passed in data. After
updating, the bounds will have positive width, and x0 will be the
minimal value.
Update the Bbox bounds based on the passed in xy coordinates.
After updating, the bounds will have positive width and height; x0 and y0 will be the minimal values.
Update the y-bounds of the Bbox based on the passed in data. After
updating, the bounds will have positive height, and y0 will be the
minimal value.
Update the bounds of the Bbox to contain the vertices of the
provided path. After updating, the bounds will have positive width
and height; x0 and y0 will be the minimal values.
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).
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).
Bases: TransformNode
The base class of all bounding boxes.
This class is immutable; Bbox is a mutable subclass.
The canonical representation is as two points, with no restrictions on their ordering. Convenience properties are provided to get the left, bottom, right and top edges and width and height, but these are not stored explicitly.
A string representing the "name" of the transform. The name carries
no significance other than to improve the readability of
str(transform) when DEBUG=True.
Return a copy of the Bbox anchored to c within container.
See also
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 the number of vertices contained in the Bbox.
Any vertices with a non-finite x or y value are ignored.
Count the number of bounding boxes that overlap this one.
BboxBaseConstruct a Bbox by expanding this one around its center by the
factors sw and sh.
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.
Return whether this bounding box overlaps with the other bounding box, not including the edges.
BboxBaseThe (signed) height of the bounding box.
Return the intersection of bbox1 and bbox2 if they intersect, or None if they don't.
The pair of x coordinates that define the bounding box.
This is not guaranteed to be sorted from left to right.
The pair of y coordinates that define the bounding box.
This is not guaranteed to be sorted from bottom to top.
The top-right corner of the bounding box.
The bottom-left corner of the bounding box.
Return whether this bounding box overlaps with the other bounding box.
BboxBaseThe first pair of (x, y) coordinates that define the bounding box.
This is not guaranteed to be the bottom-left corner (for that, use
min).
The second pair of (x, y) coordinates that define the bounding box.
This is not guaranteed to be the top-right corner (for that, use
max).
Construct a Bbox by padding this one on all four sides.
Width pad
Height pad. Defaults to w_pad.
Return the axes-aligned bounding box that bounds the result of rotating
this Bbox by an angle of radians.
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.
Return a copy of the Bbox, shrunk so that it is as
large as it can be while having the desired aspect ratio,
box_aspect. If the box coordinates are relative (i.e.
fractions of a larger box such as a figure) then the
physical aspect ratio of that figure is specified with
fig_aspect, so that box_aspect can also be given as a
ratio of the absolute dimensions, not the relative dimensions.
The (signed) width and height of the bounding box.
Return a list of new Bbox objects formed by splitting the original
one with vertical lines at fractional positions given by args.
Return a list of new Bbox objects formed by splitting the original
one with horizontal lines at fractional positions given by args.
The (signed) width of the bounding box.
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).
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).
The right edge of the bounding box.
The left edge of the bounding box.
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).
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).
The top edge of the bounding box.
The bottom edge of the bounding box.
Bases: Affine2DBase
BboxTransform linearly transforms points from one Bbox to another.
Create a new BboxTransform that linearly transforms
points from boxin to boxout.
True if this transform is separable in the x- and y- dimensions.
Bases: Affine2DBase
BboxTransformFrom linearly transforms points from a given Bbox to the
unit bounding box.
A string representing the "name" of the transform. The name carries
no significance other than to improve the readability of
str(transform) when DEBUG=True.
True if this transform is separable in the x- and y- dimensions.
Bases: 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.
True if this transform is separable in the x- and y- dimensions.
Bases: _BlendedMixin, Affine2DBase
A "blended" transform uses one transform for the x-direction, and another transform for the y-direction.
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 x-axis and y_transform to transform the y-axis.
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.
True if this transform is separable in the x- and y- dimensions.
Bases: _BlendedMixin, Transform
A "blended" transform uses one transform for the x-direction, and another transform for the y-direction.
This "generic" version can handle any given child transform in the x- and y-directions.
Create a new "blended" transform using x_transform to transform the x-axis and y_transform to transform the y-axis.
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.
Return whether the given transform is a sub-tree of this transform.
This routine uses transform equality to identify sub-trees, 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.
Return 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.
Return a frozen copy of this transform node. The frozen copy will not
be updated when its children change. Useful for storing a previously
known state of a transform where copy.deepcopy() might normally be
used.
Returns True when the argument 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.
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
Return the corresponding inverse transformation.
It holds x == self.inverted().transform(self.transform(x)).
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.
Returns True when the argument 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.
True if this transform is separable in the x- and y- dimensions.
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
If pass_through is True, all ancestors will always be invalidated, even if 'self' is already invalid.
Apply only the non-affine part of this transformation.
transform(values) is always equivalent to
transform_affine(transform_non_affine(values)).
In non-affine transformations, this is generally equivalent to
transform(values). In affine transformations, this is
always a no-op.
The input values as an array of length
input_dims or
shape (N, input_dims).
The output values as an array of length
output_dims or shape
(N, output_dims),
depending on the input.
Bases: 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 Affine2DBase a then Affine2DBase b.
You will generally not call this constructor directly but write a +
b instead, which will automatically choose the best kind of composite
transform instance to create.
Return 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.
Bases: 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 write a +
b instead, which will automatically choose the best kind of composite
transform instance to create.
Return whether the given branch is a sub-tree 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_separately(ax.transData)
Return 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.
Return a frozen copy of this transform node. The frozen copy will not
be updated when its children change. Useful for storing a previously
known state of a transform where copy.deepcopy() might normally be
used.
Returns True when the argument 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.
Return the corresponding inverse transformation.
It holds x == self.inverted().transform(self.transform(x)).
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.
Returns True when the argument 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.
Returns True when the argument 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.
If pass_through is True, all ancestors will always be invalidated, even if 'self' is already invalid.
Apply 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 non-affine transformations, this is generally a no-op. In
affine transformations, this is equivalent to
transform(values).
The input values as an array of length input_dims or
shape (N, input_dims).
The output values as an array of length output_dims or
shape (N, output_dims), depending on the input.
Apply only the non-affine part of this transformation.
transform(values) is always equivalent to
transform_affine(transform_non_affine(values)).
In non-affine transformations, this is generally equivalent to
transform(values). In affine transformations, this is
always a no-op.
The input values as an array of length
input_dims or
shape (N, input_dims).
The output values as an array of length
output_dims or shape
(N, output_dims),
depending on the input.
Bases: Affine2DBase
A special class that does one thing, the identity transform, in a fast way.
A string representing the "name" of the transform. The name carries
no significance other than to improve the readability of
str(transform) when DEBUG=True.
Return a frozen copy of this transform node. The frozen copy will not
be updated when its children change. Useful for storing a previously
known state of a transform where copy.deepcopy() might normally be
used.
Return the corresponding inverse transformation.
It holds x == self.inverted().transform(self.transform(x)).
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.
Apply this transformation on the given array of values.
The input values as an array of length input_dims or
shape (N, input_dims).
The output values as an array of length output_dims or
shape (N, output_dims), depending on the input.
Apply 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 non-affine transformations, this is generally a no-op. In
affine transformations, this is equivalent to
transform(values).
The input values as an array of length input_dims or
shape (N, input_dims).
The output values as an array of length output_dims or
shape (N, output_dims), depending on the input.
Apply only the non-affine part of this transformation.
transform(values) is always equivalent to
transform_affine(transform_non_affine(values)).
In non-affine transformations, this is generally equivalent to
transform(values). In affine transformations, this is
always a no-op.
The input values as an array of length
input_dims or
shape (N, input_dims).
The output values as an array of length
output_dims or shape
(N, output_dims),
depending on the input.
Apply the transform to Path path, returning a new Path.
In some cases, this transform may insert curves into the path that began as line segments.
Bases: 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.
BboxThe child bounding box to wrap.
The locked value for x0, or None to leave unlocked.
The locked value for y0, or None to leave unlocked.
The locked value for x1, or None to leave unlocked.
The locked value for y1, or None to leave unlocked.
float or None: The value used for the locked x0.
float or None: The value used for the locked x1.
float or None: The value used for the locked y0.
float or None: The value used for the locked y1.
Bases: Affine2DBase
A transformation that translates by xt and yt, after xt and yt have been transformed by scale_trans.
A string representing the "name" of the transform. The name carries
no significance other than to improve the readability of
str(transform) when DEBUG=True.
Bases: TransformNode
The base class of all TransformNode instances that
actually perform a transformation.
All non-affine 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):
inverted() (if an inverse exists)
The following attributes may be overridden if the default is unsuitable:
is_separable (defaults to True for 1D -> 1D transforms, False
otherwise)
has_inverse (defaults to True if inverted() is overridden,
False otherwise)
If the transform needs to do something non-standard with
matplotlib.path.Path objects, such as adding curves
where there were once line segments, it should override:
A string representing the "name" of the transform. The name carries
no significance other than to improve the readability of
str(transform) when DEBUG=True.
Compose two transforms together so that self is followed by other.
A + B returns a transform C so that
C.transform(x) == B.transform(A.transform(x)).
Compose self with the inverse of other, cancelling identical terms if any:
# In general:
A - B == A + B.inverted()
# (but see note regarding frozen transforms below).
# If A "ends with" B (i.e. A == A' + B for some A') we can cancel
# out B:
(A' + B) - B == A'
# Likewise, if B "starts with" A (B = A + B'), we can cancel out A:
A - (A + B') == B'.inverted() == B'^-1
Cancellation (rather than naively returning A + B.inverted()) is
important for multiple reasons:
It avoids floating-point inaccuracies when computing the inverse of
B: B - B is guaranteed to cancel out exactly (resulting in the
identity transform), whereas B + B.inverted() may differ by a
small epsilon.
B.inverted() always returns a frozen transform: if one computes
A + B + B.inverted() and later mutates B, then
B.inverted() won't be updated and the last two terms won't cancel
out anymore; on the other hand, A + B - B will always be equal to
A even if B is mutated.
Return whether the given transform is a sub-tree of this transform.
This routine uses transform equality to identify sub-trees, 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.
Return whether the given branch is a sub-tree 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_separately(ax.transData)
Return 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.
True if this transform has a corresponding inverse transform.
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass.
Return the corresponding inverse transformation.
It holds x == self.inverted().transform(self.transform(x)).
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.
True if this transform is separable in the x- and y- dimensions.
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass.
Apply this transformation on the given array of values.
The input values as an array of length input_dims or
shape (N, input_dims).
The output values as an array of length output_dims or
shape (N, output_dims), depending on the input.
Apply 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 non-affine transformations, this is generally a no-op. In
affine transformations, this is equivalent to
transform(values).
The input values as an array of length input_dims or
shape (N, input_dims).
The output values as an array of length output_dims or
shape (N, output_dims), depending on the input.
Transform a set of angles anchored at specific locations.
The angles to transform.
The points where the angles are anchored.
Whether angles are radians or degrees.
For each point in pts and angle in angles, the transformed angle is computed by transforming a segment of length pushoff starting at that point and making that angle relative to the horizontal axis, and measuring the angle between the horizontal axis and the transformed segment.
Transform the given bounding box.
For smarter transforms including caching (a common requirement in
Matplotlib), see TransformedBbox.
Apply only the non-affine part of this transformation.
transform(values) is always equivalent to
transform_affine(transform_non_affine(values)).
In non-affine transformations, this is generally equivalent to
transform(values). In affine transformations, this is
always a no-op.
The input values as an array of length
input_dims or
shape (N, input_dims).
The output values as an array of length
output_dims or shape
(N, output_dims),
depending on the input.
Apply the transform to Path path, returning a new Path.
In some cases, this transform may insert curves into the path that began as line segments.
Apply the affine part of this transform to Path path, returning a
new Path.
transform_path(path) is equivalent to
transform_path_affine(transform_path_non_affine(values)).
Apply the non-affine part of this transform to Path path,
returning a new Path.
transform_path(path) is equivalent to
transform_path_affine(transform_path_non_affine(values)).
Return a transformed point.
This function is only kept for backcompatibility; the more general
transform method is capable of transforming both a list of points
and 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.
Bases: object
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.
A string representing the "name" of the transform. The name carries
no significance other than to improve the readability of
str(transform) when DEBUG=True.
Return a frozen copy of this transform node. The frozen copy will not
be updated when its children change. Useful for storing a previously
known state of a transform where copy.deepcopy() might normally be
used.
Invalidate this TransformNode and triggers an invalidation of its
ancestors. Should be called any time the transform changes.
If pass_through is True, all ancestors will always be invalidated, even if 'self' is already invalid.
Bases: 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.
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 Transform instance. This child may later
be replaced with set().
Return a frozen copy of this transform node. The frozen copy will not
be updated when its children change. Useful for storing a previously
known state of a transform where copy.deepcopy() might normally be
used.
Returns True when the argument 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.
The type of the None singleton.
Returns True when the argument 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.
Returns True when the argument 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.
The type of the None singleton.
If pass_through is True, all ancestors will always be invalidated, even if 'self' is already invalid.
Bases: 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.
Bases: TransformedPath
A TransformedPatchPath caches a non-affine transformed copy of the
Patch. This cached copy is automatically updated when the
non-affine part of the transform or the patch changes.
PatchBases: TransformNode
A TransformedPath caches a non-affine transformed copy of the
Path. This cached copy is automatically updated when the
non-affine 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.
Return a copy of the child path, with the non-affine part of the transform already applied, along with the affine part of the path necessary to complete the transformation.
Return a copy of the child path, with the non-affine 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.
Create a new "blended" transform using x_transform to transform the x-axis and y_transform to transform the y-axis.
A faster version of the blended transform is returned for the case where both child transforms are affine.
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
[Deprecated] Check, inclusively, whether an interval includes a given value.
The endpoints of the interval.
Value to check is within interval.
Whether val is within the interval.
Notes
Deprecated since version 3.11.
[Deprecated] Check, excluding endpoints, whether an interval includes a given value.
The endpoints of the interval.
Value to check is within interval.
Whether val is within the interval.
Notes
Deprecated since version 3.11.
[Deprecated] Modify the endpoints of a range as needed to avoid singularities.
The initial endpoints.
Fractional amount by which vmin and vmax are expanded if the original interval is too small, based on tiny.
Threshold for the ratio of the interval to the maximum absolute value of its endpoints. If the interval is smaller than this, it will be expanded. This value should be around 1e-15 or larger; otherwise the interval will be approaching the double precision resolution limit.
If True, swap vmin, vmax if vmin > vmax.
Endpoints, expanded and/or swapped if necessary. If either input is inf or NaN, or if both inputs are 0 or very close to zero, it returns -expander, expander.
Notes
Deprecated since version 3.11.
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