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 to improve the readability of str(transform) when DEBUG=True.       __module__='matplotlib.transforms'

   __str__()[source]
 
Return str(self). 
   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. 
   get_affine()[source]
 
Get the affine part of this transform. 
   get_matrix()[source]
 
Get the matrix for the affine part of this transform. 
   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. 
   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.     
   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 transform(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.     
   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.  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.     
   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. 
   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)). 
   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)).