# colour.SpragueInterpolator¶

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

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

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)  # doctest: +ELLIPSIS
7.2185025...


Interpolating an array_like variable:

>>> f([0.25, 0.75])  # doctest: +ELLIPSIS
array([ 6.7295161...,  7.8140625...])

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

Initialize self. See help(type(self)) for accurate signature.

Methods

 __init__(x, y[, dtype]) Initialize self.

Attributes

 SPRAGUE_C_COEFFICIENTS Defines the coefficients used to generate extra points for boundaries interpolation. x Getter and setter property for the independent $$x$$ variable. y Getter and setter property for the dependent and already known $$y$$ variable.