colour.SpragueInterpolator¶

class colour.SpragueInterpolator(x, y, dtype=<class 'numpy.float64'>)[source]¶

Bases: object

Constructs a fifth-order polynomial that passes through \(y\) dependent variable.

Sprague (1880) method is recommended by the CIE for interpolating functions having a uniformly spaced independent variable.

Parameters

x (array_like) – Independent \(x\) variable values corresponding with \(y\) variable.
y (array_like) – Dependent and already known \(y\) variable values to interpolate.
dtype (type) – Data type used for internal conversions.

x¶

y¶

__call__()[source]¶

Notes

The minimum number \(k\) of data points required along the interpolation axis is \(k=6\).

References

[CIET13805b], [WRC12e]

Examples

Interpolating a single numeric variable:

>>> y = np.array([5.9200, 9.3700, 10.8135, 4.5100,
...               69.5900, 27.8007, 86.0500])
>>> x = np.arange(len(y))
>>> f = SpragueInterpolator(x, y)
>>> f(0.5)  
7.2185025...

Interpolating an array_like variable:

>>> f([0.25, 0.75])  
array([ 6.7295161...,  7.8140625...])

SPRAGUE_C_COEFFICIENTS = array([[ 884, -1960, 3033, -2648, 1080, -180], [ 508, -540, 488, -367, 144, -24], [ -24, 144, -367, 488, -540, 508], [ -180, 1080, -2648, 3033, -1960, 884]])¶

Defines the coefficients used to generate extra points for boundaries interpolation.

SPRAGUE_C_COEFFICIENTS : array_like, (4, 6)

References

[CIET13805d]

property x

Getter and setter property for the independent \(x\) variable.

Parameters: value (array_like) – Value to set the independent \(x\) variable with.
Returns: Independent \(x\) variable.
Return type: array_like

property y

Getter and setter property for the dependent and already known \(y\) variable.

Parameters: value (array_like) – Value to set the dependent and already known \(y\) variable with.
Returns: Dependent and already known \(y\) variable.
Return type: array_like