colour.Extrapolator#
- class colour.Extrapolator(interpolator: Optional[TypeInterpolator] = None, method: Union[Literal['Linear', 'Constant'], str] = 'Linear', left: Optional[Real] = None, right: Optional[Real] = None, dtype: Optional[Type[DTypeReal]] = None, *args: Any, **kwargs: Any)[source]#
Bases:
object
Extrapolate the 1-D function of given interpolator.
The
colour.Extrapolator
class acts as a wrapper around a given Colour or scipy interpolator class instance with compatible signature. Two extrapolation methods are available:Linear: Linearly extrapolates given points using the slope defined by the interpolator boundaries (xi[0], xi[1]) if x < xi[0] and (xi[-1], xi[-2]) if x > xi[-1].
Constant: Extrapolates given points by assigning the interpolator boundaries values xi[0] if x < xi[0] and xi[-1] if x > xi[-1].
Specifying the left and right arguments takes precedence on the chosen extrapolation method and will assign the respective left and right values to the given points.
- Parameters:
interpolator (Optional[TypeInterpolator]) – Interpolator object.
method (Union[Literal['Linear', 'Constant'], str]) – Extrapolation method.
left (Optional[Real]) – Value to return for x < xi[0].
right (Optional[Real]) – Value to return for x > xi[-1].
dtype (Optional[Type[DTypeReal]]) – Data type used for internal conversions.
args (Any) –
kwargs (Any) –
Methods
__class__()
Notes
The interpolator must define
x
andy
properties.
References
Examples
Extrapolating a single numeric variable:
>>> from colour.algebra import LinearInterpolator >>> x = np.array([3, 4, 5]) >>> y = np.array([1, 2, 3]) >>> interpolator = LinearInterpolator(x, y) >>> extrapolator = Extrapolator(interpolator) >>> extrapolator(1) -1.0
Extrapolating an ArrayLike variable:
>>> extrapolator(np.array([6, 7, 8])) array([ 4., 5., 6.])
Using the Constant extrapolation method:
>>> x = np.array([3, 4, 5]) >>> y = np.array([1, 2, 3]) >>> interpolator = LinearInterpolator(x, y) >>> extrapolator = Extrapolator(interpolator, method="Constant") >>> extrapolator(np.array([0.1, 0.2, 8, 9])) array([ 1., 1., 3., 3.])
Using defined left boundary and Constant extrapolation method:
>>> x = np.array([3, 4, 5]) >>> y = np.array([1, 2, 3]) >>> interpolator = LinearInterpolator(x, y) >>> extrapolator = Extrapolator(interpolator, method="Constant", left=0) >>> extrapolator(np.array([0.1, 0.2, 8, 9])) array([ 0., 0., 3., 3.])
- __init__(interpolator: Optional[TypeInterpolator] = None, method: Union[Literal['Linear', 'Constant'], str] = 'Linear', left: Optional[Real] = None, right: Optional[Real] = None, dtype: Optional[Type[DTypeReal]] = None, *args: Any, **kwargs: Any) None [source]#
- property interpolator: TypeInterpolator#
Getter and setter property for the Colour or scipy interpolator class instance.
- Parameters:
value – Value to set the Colour or scipy interpolator class instance with.
- Returns:
Colour or scipy interpolator class instance.
- Return type:
- __weakref__#
list of weak references to the object (if defined)
- property method: Union[Literal['Linear', 'Constant'], str]#
Getter and setter property for the extrapolation method.
- Parameters:
value – Value to set the extrapolation method. with.
- Returns:
Extrapolation method.
- Return type:
- property left: Optional[Real]#
Getter and setter property for left value to return for x < xi[0].
- Parameters:
value – Left value to return for x < xi[0].
- Returns:
Left value to return for x < xi[0].
- Return type:
None
or Real
- property right: Optional[Real]#
Getter and setter property for right value to return for x > xi[-1].
- Parameters:
value – Right value to return for x > xi[-1].
- Returns:
Right value to return for x > xi[-1].
- Return type:
None
or Real