colour.KernelInterpolator¶
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class
colour.
KernelInterpolator
(x, y, window=3, kernel=<function kernel_lanczos>, kernel_args=None, padding_args=None, dtype=<class 'numpy.float64'>)[source]¶ Kernel based interpolation of a 1-D function.
The reconstruction of a continuous signal can be described as a linear convolution operation. Interpolation can be expressed as a convolution of the given discrete function \(g(x)\) with some continuous interpolation kernel \(k(w)\):
\(\hat{g}(w_0) = [k * g](w_0) = \sum_{x=-\infty}^{\infty}k(w_0 - x)\cdot g(x)\)
Parameters: - x (array_like) – Independent \(x\) variable values corresponding with \(y\) variable.
- y (array_like) – Dependent and already known \(y\) variable values to interpolate.
- window (int, optional) – Width of the window in samples on each side.
- kernel (callable, optional) – Kernel to use for interpolation.
- kernel_args (dict, optional) – Arguments to use when calling the kernel.
- padding_args (dict, optional) – Arguments to use when padding \(y\) variable values with the
np.pad()
definition. - dtype (type) – Data type used for internal conversions.
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x
¶
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y
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window
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kernel
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kernel_args
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padding_args
¶
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 = KernelInterpolator(x, y) >>> f(0.5) # doctest: +ELLIPSIS 6.9411400...
Interpolating an array_like variable:
>>> f([0.25, 0.75]) # doctest: +ELLIPSIS array([ 6.1806208..., 8.0823848...])
Using a different lanczos kernel:
>>> f = KernelInterpolator(x, y, kernel=kernel_sinc) >>> f([0.25, 0.75]) # doctest: +ELLIPSIS array([ 6.5147317..., 8.3965466...])
Using a different window size:
>>> f = KernelInterpolator( ... x, ... y, ... window=16, ... kernel=kernel_lanczos, ... kernel_args={'a': 16}) >>> f([0.25, 0.75]) # doctest: +ELLIPSIS array([ 5.3961792..., 5.6521093...])
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__init__
(x, y, window=3, kernel=<function kernel_lanczos>, kernel_args=None, padding_args=None, dtype=<class 'numpy.float64'>)[source]¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
(x, y[, window, kernel, …])Initialize self. Attributes
kernel
Getter and setter property for the kernel callable. kernel_args
Getter and setter property for the kernel call time arguments. padding_args
Getter and setter property for the kernel call time arguments. window
Getter and setter property for the window. x
Getter and setter property for the independent \(x\) variable. y
Getter and setter property for the dependent and already known \(y\) variable.