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transformed(transform)[source]
Construct a Bbox by statically transforming this one by transform. | matplotlib.transformations#matplotlib.transforms.BboxBase.transformed |
translated(tx, ty)[source]
Construct a Bbox by translating this one by tx and ty. | matplotlib.transformations#matplotlib.transforms.BboxBase.translated |
staticunion(bboxes)[source]
Return a Bbox that contains all of the given bboxes. | matplotlib.transformations#matplotlib.transforms.BboxBase.union |
classmatplotlib.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. __init__(boxin, boxout, **kwargs)[source]
Create a... | matplotlib.transformations#matplotlib.transforms.BboxTransform |
__init__(boxin, boxout, **kwargs)[source]
Create a new BboxTransform that linearly transforms points from boxin to boxout. | matplotlib.transformations#matplotlib.transforms.BboxTransform.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.BboxTransform.__module__ |
__str__()[source]
Return str(self). | matplotlib.transformations#matplotlib.transforms.BboxTransform.__str__ |
get_matrix()[source]
Get the matrix for the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.BboxTransform.get_matrix |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.transformations#matplotlib.transforms.BboxTransform.is_separable |
classmatplotlib.transforms.BboxTransformFrom(boxin, **kwargs)[source]
Bases: matplotlib.transforms.Affine2DBase BboxTransformFrom linearly transforms points from a given Bbox to the unit bounding box. Parameters
shorthand_namestr
A string representing the "name" of the transform. The name carries no significanc... | matplotlib.transformations#matplotlib.transforms.BboxTransformFrom |
__init__(boxin, **kwargs)[source]
Parameters
shorthand_namestr
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. | matplotlib.transformations#matplotlib.transforms.BboxTransformFrom.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.BboxTransformFrom.__module__ |
__str__()[source]
Return str(self). | matplotlib.transformations#matplotlib.transforms.BboxTransformFrom.__str__ |
get_matrix()[source]
Get the matrix for the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.BboxTransformFrom.get_matrix |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.transformations#matplotlib.transforms.BboxTransformFrom.is_separable |
classmatplotlib.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 boxo... | matplotlib.transformations#matplotlib.transforms.BboxTransformTo |
__init__(boxout, **kwargs)[source]
Create a new BboxTransformTo that linearly transforms points from the unit bounding box to boxout. | matplotlib.transformations#matplotlib.transforms.BboxTransformTo.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.BboxTransformTo.__module__ |
__str__()[source]
Return str(self). | matplotlib.transformations#matplotlib.transforms.BboxTransformTo.__str__ |
get_matrix()[source]
Get the matrix for the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.BboxTransformTo.get_matrix |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.transformations#matplotlib.transforms.BboxTransformTo.is_separable |
classmatplotlib.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 transfor... | matplotlib.transformations#matplotlib.transforms.BboxTransformToMaxOnly |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.BboxTransformToMaxOnly.__module__ |
get_matrix()[source]
Get the matrix for the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.BboxTransformToMaxOnly.get_matrix |
matplotlib.transforms.blended_transform_factory(x_transform, y_transform)[source]
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. | matplotlib.transformations#matplotlib.transforms.blended_transform_factory |
classmatplotlib.transforms.BlendedAffine2D(x_transform, y_transform, **kwargs)[source]
Bases: matplotlib.transforms._BlendedMixin, matplotlib.transforms.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 wh... | matplotlib.transformations#matplotlib.transforms.BlendedAffine2D |
__init__(x_transform, y_transform, **kwargs)[source]
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_fa... | matplotlib.transformations#matplotlib.transforms.BlendedAffine2D.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.BlendedAffine2D.__module__ |
get_matrix()[source]
Get the matrix for the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.BlendedAffine2D.get_matrix |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.transformations#matplotlib.transforms.BlendedAffine2D.is_separable |
classmatplotlib.transforms.BlendedGenericTransform(x_transform, y_transform, **kwargs)[source]
Bases: matplotlib.transforms._BlendedMixin, matplotlib.transforms.Transform A "blended" transform uses one transform for the x-direction, and another transform for the y-direction. This "generic" version can handle any give... | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform |
__init__(x_transform, y_transform, **kwargs)[source]
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... | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.__module__ |
contains_branch(other)[source]
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. | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.contains_branch |
frozen()[source]
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. | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.frozen |
get_affine()[source]
Get the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.get_affine |
input_dims=2
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.input_dims |
inverted()[source]
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. | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.inverted |
is_separable=True
True if this transform is separable in the x- and y- dimensions. | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.is_separable |
output_dims=2
The number of output dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.output_dims |
pass_through=True
If pass_through is True, all ancestors will always be invalidated, even if 'self' is already invalid. | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.pass_through |
transform_non_affine(points)[source]
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. Pa... | matplotlib.transformations#matplotlib.transforms.BlendedGenericTransform.transform_non_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. Comp... | matplotlib.transformations#matplotlib.transforms.composite_transform_factory |
classmatplotlib.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 re... | matplotlib.transformations#matplotlib.transforms.CompositeAffine2D |
__init__(a, b, **kwargs)[source]
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. | matplotlib.transformations#matplotlib.transforms.CompositeAffine2D.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.CompositeAffine2D.__module__ |
__str__()[source]
Return str(self). | matplotlib.transformations#matplotlib.transforms.CompositeAffine2D.__str__ |
get_matrix()[source]
Get the matrix for the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.CompositeAffine2D.get_matrix |
classmatplotlib.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 applyin... | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform |
__eq__(other)[source]
Return self==value. | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.__eq__ |
__hash__=None | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.__hash__ |
__init__(a, b, **kwargs)[source]
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. | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.__module__ |
__str__()[source]
Return str(self). | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.__str__ |
frozen()[source]
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. | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.frozen |
get_affine()[source]
Get the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.get_affine |
inverted()[source]
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. | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.inverted |
pass_through=True
If pass_through is True, all ancestors will always be invalidated, even if 'self' is already invalid. | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.pass_through |
transform_affine(points)[source]
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 transfor... | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.transform_affine |
transform_non_affine(points)[source]
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. Pa... | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.transform_non_affine |
transform_path_non_affine(path)[source]
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)). | matplotlib.transformations#matplotlib.transforms.CompositeGenericTransform.transform_path_non_affine |
classmatplotlib.transforms.IdentityTransform(*args, **kwargs)[source]
Bases: matplotlib.transforms.Affine2DBase A special class that does one thing, the identity transform, in a fast way. Parameters
shorthand_namestr
A string representing the "name" of the transform. The name carries no significance other than ... | matplotlib.transformations#matplotlib.transforms.IdentityTransform |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.IdentityTransform.__module__ |
__str__()[source]
Return str(self). | matplotlib.transformations#matplotlib.transforms.IdentityTransform.__str__ |
frozen()[source]
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. | matplotlib.transformations#matplotlib.transforms.IdentityTransform.frozen |
get_affine()[source]
Get the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.IdentityTransform.get_affine |
get_matrix()[source]
Get the matrix for the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.IdentityTransform.get_matrix |
inverted()[source]
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. | matplotlib.transformations#matplotlib.transforms.IdentityTransform.inverted |
transform(points)[source]
Apply this transformation on the given array of values. Parameters
valuesarray
The input values as NumPy array of length input_dims or shape (N x input_dims). Returns
array
The output values as NumPy array of length input_dims or shape (N x output_dims), depending on the input. | matplotlib.transformations#matplotlib.transforms.IdentityTransform.transform |
transform_affine(points)[source]
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 transfor... | matplotlib.transformations#matplotlib.transforms.IdentityTransform.transform_affine |
transform_non_affine(points)[source]
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. Pa... | matplotlib.transformations#matplotlib.transforms.IdentityTransform.transform_non_affine |
transform_path(path)[source]
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. | matplotlib.transformations#matplotlib.transforms.IdentityTransform.transform_path |
transform_path_affine(path)[source]
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)). | matplotlib.transformations#matplotlib.transforms.IdentityTransform.transform_path_affine |
transform_path_non_affine(path)[source]
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)). | matplotlib.transformations#matplotlib.transforms.IdentityTransform.transform_path_non_affine |
matplotlib.transforms.interval_contains(interval, val)[source]
Check, inclusively, whether an interval includes a given value. Parameters
interval(float, float)
The endpoints of the interval.
valfloat
Value to check is within interval. Returns
bool
Whether val is within the interval. | matplotlib.transformations#matplotlib.transforms.interval_contains |
matplotlib.transforms.interval_contains_open(interval, val)[source]
Check, excluding endpoints, whether an interval includes a given value. Parameters
interval(float, float)
The endpoints of the interval.
valfloat
Value to check is within interval. Returns
bool
Whether val is within the interval. | matplotlib.transformations#matplotlib.transforms.interval_contains_open |
classmatplotlib.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 el... | matplotlib.transformations#matplotlib.transforms.LockableBbox |
__init__(bbox, x0=None, y0=None, x1=None, y1=None, **kwargs)[source]
Parameters
bboxBbox
The child bounding box to wrap.
x0float or None
The locked value for x0, or None to leave unlocked.
y0float or None
The locked value for y0, or None to leave unlocked.
x1float or None
The locked value for x1, or... | matplotlib.transformations#matplotlib.transforms.LockableBbox.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.LockableBbox.__module__ |
__str__()[source]
Return str(self). | matplotlib.transformations#matplotlib.transforms.LockableBbox.__str__ |
get_points()[source] | matplotlib.transformations#matplotlib.transforms.LockableBbox.get_points |
matplotlib.transforms.nonsingular(vmin, vmax, expander=0.001, tiny=1e-15, increasing=True)[source]
Modify the endpoints of a range as needed to avoid singularities. Parameters
vmin, vmaxfloat
The initial endpoints.
expanderfloat, default: 0.001
Fractional amount by which vmin and vmax are expanded if the or... | matplotlib.transformations#matplotlib.transforms.nonsingular |
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
transTransform subclass
Any transform, to which offset will be applied.
figFigure, default: None
Current figure. It can be None if units are 'dots'.
x, yfloat, d... | matplotlib.transformations#matplotlib.transforms.offset_copy |
classmatplotlib.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. Parameters
shorthand_namestr
A string representing the "name" of the transform. The n... | matplotlib.transformations#matplotlib.transforms.ScaledTranslation |
__init__(xt, yt, scale_trans, **kwargs)[source]
Parameters
shorthand_namestr
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. | matplotlib.transformations#matplotlib.transforms.ScaledTranslation.__init__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.ScaledTranslation.__module__ |
__str__()[source]
Return str(self). | matplotlib.transformations#matplotlib.transforms.ScaledTranslation.__str__ |
get_matrix()[source]
Get the matrix for the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.ScaledTranslation.get_matrix |
classmatplotlib.transforms.Transform(shorthand_name=None)[source]
Bases: matplotlib.transforms.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 Affin... | matplotlib.transformations#matplotlib.transforms.Transform |
__add__(other)[source]
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)). | matplotlib.transformations#matplotlib.transforms.Transform.__add__ |
__array__(*args, **kwargs)[source]
Array interface to get at this Transform's affine matrix. | matplotlib.transformations#matplotlib.transforms.Transform.__array__ |
classmethod__init_subclass__()[source]
This method is called when a class is subclassed. The default implementation does nothing. It may be overridden to extend subclasses. | matplotlib.transformations#matplotlib.transforms.Transform.__init_subclass__ |
__module__='matplotlib.transforms' | matplotlib.transformations#matplotlib.transforms.Transform.__module__ |
__sub__(other)[source]
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... | matplotlib.transformations#matplotlib.transforms.Transform.__sub__ |
contains_branch(other)[source]
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. | matplotlib.transformations#matplotlib.transforms.Transform.contains_branch |
contains_branch_seperately(other_transform)[source]
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_seperat... | matplotlib.transformations#matplotlib.transforms.Transform.contains_branch_seperately |
get_affine()[source]
Get the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.Transform.get_affine |
get_matrix()[source]
Get the matrix for the affine part of this transform. | matplotlib.transformations#matplotlib.transforms.Transform.get_matrix |
has_inverse=False
True if this transform has a corresponding inverse transform. | matplotlib.transformations#matplotlib.transforms.Transform.has_inverse |
input_dims=None
The number of input dimensions of this transform. Must be overridden (with integers) in the subclass. | matplotlib.transformations#matplotlib.transforms.Transform.input_dims |
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