colour.Extrapolator#

class colour.Extrapolator(interpolator: Optional[TypeInterpolator] = None, method: Union[Literal['Linear', 'Constant'], str] = 'Linear', left: Optional[Number] = None, right: Optional[Number] = None, dtype: Optional[Type[DTypeNumber]] = None)[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[Number]) – Value to return for x < xi[0].
right (Optional[Number]) – Value to return for x > xi[-1].
dtype (Optional[Type[DTypeNumber]]) – Data type used for internal conversions.

Methods

__init__()
__class__()

Notes

The interpolator must define x and y properties.

References

[sastanin], [WRC12b]

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[Number] = None, right: Optional[Number] = None, dtype: Optional[Type[DTypeNumber]] = None)[source]#

Parameters

interpolator (Optional[TypeInterpolator]) –
method (Union[Literal[('Linear', 'Constant')], str]) –
left (Optional[Number]) –
right (Optional[Number]) –
dtype (Optional[Type[DTypeNumber]]) –

property interpolator: colour.hints.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: TypeInterpolator

__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: str

property left: Optional[Number]#

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 Number

property right: Optional[Number]#

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 Number

__call__(x: FloatingOrArrayLike) → FloatingOrNDArray[source]#

Evaluate the Extrapolator at given point(s).

Parameters: x (FloatingOrArrayLike) – Point(s) to evaluate the Extrapolator at.
Returns: Extrapolated points value(s).
Return type: numpy.floating or numpy.ndarray

Algebra

colour.KernelInterpolator