Module transformnd.transforms.simple
Simple transformations like rigid translation and scaling.
Expand source code
"""
Simple transformations like rigid translation and scaling.
"""
import numpy as np
from numpy.typing import ArrayLike
from ..base import SpaceTuple, Transform
from ..util import chain_or
class Identity(Transform):
def __init__(
self,
*,
spaces: SpaceTuple = (None, None),
):
"""
Transform which does nothing.
Parameters
----------
spaces : tuple[SpaceRef, SpaceRef]
Optional source and target spaces
Raises
------
ValueError
[description]
"""
src = chain_or(*spaces, default=None)
tgt = chain_or(*spaces[::-1], default=None)
if src != tgt:
raise ValueError("Source and target spaces are different")
super().__init__(spaces=(src, src))
def __invert__(self) -> Transform:
return self
def apply(self, coords: np.ndarray) -> np.ndarray:
return coords.copy()
class Translate(Transform):
def __init__(
self,
translation: ArrayLike,
*,
spaces: SpaceTuple = (None, None),
):
"""Simple translation.
Parameters
----------
translation : scalar or D-length array
Translation to apply in all dimensions, or each dimension.
spaces : tuple[SpaceRef, SpaceRef]
Optional source and target spaces
Raises
------
ValueError
If the translation is the wrong shape
"""
super().__init__(spaces=spaces)
self.translation = np.asarray(translation)
if self.translation.ndim > 1:
raise ValueError("Translation must be scalar or 1D")
if self.translation.shape not in [(), (1,)]:
self.ndim = {self.translation.shape[0]}
# otherwise, can be broadcast to anything
def apply(self, coords: np.ndarray) -> np.ndarray:
self._validate_coords(coords)
return coords + self.translation
def __invert__(self) -> Transform:
return type(self)(-self.translation, spaces=self.spaces[::-1])
class Scale(Transform):
def __init__(
self,
scale: ArrayLike,
*,
spaces: SpaceTuple = (None, None),
):
"""Simple scale transform.
All points are scaled, i.e. distance from the origin may also change.
Parameters
----------
scale : scalar or D-length array-like
Scaling to apply in all dimensions, or each dimension.
spaces : tuple[SpaceRef, SpaceRef]
Optional source and target spaces
Raises
------
ValueError
If scale is the wrong shape.
"""
super().__init__(spaces=spaces)
self.scale = np.asarray(scale)
if self.scale.ndim > 1:
raise ValueError("Scale must be scalar or 1D")
if self.scale.shape not in [(), (1,)]:
self.ndim = {self.scale.shape[0]}
# otherwise, can be broadcast to anything
def apply(self, coords: np.ndarray) -> np.ndarray:
coords = self._validate_coords(coords)
return coords * self.scale
def __invert__(self) -> Transform:
return type(self)(
1 / self.scale,
spaces=self.spaces[::-1],
)Classes
class Identity (*, spaces: Tuple[Optional[Hashable], Optional[Hashable]] = (None, None))-
Helper class that provides a standard way to create an ABC using inheritance.
Transform which does nothing.
Parameters
spaces:tuple[SpaceRef, SpaceRef]- Optional source and target spaces
Raises
ValueError- [description]
Expand source code
class Identity(Transform): def __init__( self, *, spaces: SpaceTuple = (None, None), ): """ Transform which does nothing. Parameters ---------- spaces : tuple[SpaceRef, SpaceRef] Optional source and target spaces Raises ------ ValueError [description] """ src = chain_or(*spaces, default=None) tgt = chain_or(*spaces[::-1], default=None) if src != tgt: raise ValueError("Source and target spaces are different") super().__init__(spaces=(src, src)) def __invert__(self) -> Transform: return self def apply(self, coords: np.ndarray) -> np.ndarray: return coords.copy()Ancestors
- Transform
- abc.ABC
Inherited members
class Scale (scale: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], *, spaces: Tuple[Optional[Hashable], Optional[Hashable]] = (None, None))-
Helper class that provides a standard way to create an ABC using inheritance.
Simple scale transform.
All points are scaled, i.e. distance from the origin may also change.
Parameters
scale:scalarorD-length array-like- Scaling to apply in all dimensions, or each dimension.
spaces:tuple[SpaceRef, SpaceRef]- Optional source and target spaces
Raises
ValueError- If scale is the wrong shape.
Expand source code
class Scale(Transform): def __init__( self, scale: ArrayLike, *, spaces: SpaceTuple = (None, None), ): """Simple scale transform. All points are scaled, i.e. distance from the origin may also change. Parameters ---------- scale : scalar or D-length array-like Scaling to apply in all dimensions, or each dimension. spaces : tuple[SpaceRef, SpaceRef] Optional source and target spaces Raises ------ ValueError If scale is the wrong shape. """ super().__init__(spaces=spaces) self.scale = np.asarray(scale) if self.scale.ndim > 1: raise ValueError("Scale must be scalar or 1D") if self.scale.shape not in [(), (1,)]: self.ndim = {self.scale.shape[0]} # otherwise, can be broadcast to anything def apply(self, coords: np.ndarray) -> np.ndarray: coords = self._validate_coords(coords) return coords * self.scale def __invert__(self) -> Transform: return type(self)( 1 / self.scale, spaces=self.spaces[::-1], )Ancestors
- Transform
- abc.ABC
Inherited members
class Translate (translation: Union[numpy.__array_like._SupportsArray[numpy.dtype], numpy.__nested_sequence._NestedSequence[numpy.__array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy.__nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]], *, spaces: Tuple[Optional[Hashable], Optional[Hashable]] = (None, None))-
Helper class that provides a standard way to create an ABC using inheritance.
Simple translation.
Parameters
translation:scalarorD-length array- Translation to apply in all dimensions, or each dimension.
spaces:tuple[SpaceRef, SpaceRef]- Optional source and target spaces
Raises
ValueError- If the translation is the wrong shape
Expand source code
class Translate(Transform): def __init__( self, translation: ArrayLike, *, spaces: SpaceTuple = (None, None), ): """Simple translation. Parameters ---------- translation : scalar or D-length array Translation to apply in all dimensions, or each dimension. spaces : tuple[SpaceRef, SpaceRef] Optional source and target spaces Raises ------ ValueError If the translation is the wrong shape """ super().__init__(spaces=spaces) self.translation = np.asarray(translation) if self.translation.ndim > 1: raise ValueError("Translation must be scalar or 1D") if self.translation.shape not in [(), (1,)]: self.ndim = {self.translation.shape[0]} # otherwise, can be broadcast to anything def apply(self, coords: np.ndarray) -> np.ndarray: self._validate_coords(coords) return coords + self.translation def __invert__(self) -> Transform: return type(self)(-self.translation, spaces=self.spaces[::-1])Ancestors
- Transform
- abc.ABC
Inherited members