interpolation

Interpolation.

Syntax

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interpolation(x, y, x_est)
interpolation(x, y, x_est, kind)

Description

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y_est = interpolation(x, y, x_est)

x and y are arrays of values used to approximate some function f: y = f(x). This function returns an array of the interpolated values at x_est by linear interpolation as default.

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y_est = interpolation(x, y, x_est, kind)

The interpolation method is specified by the parameter kind.

Examples

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>>> from OpenHA.processing.preprocess import interpolation
>>> import numpy as np

>>> x = np.array([0, 1, 2, 3])
>>> y = x + np.random.random((4,)) * 0.1
>>> x_est = np.array([1.5, 2.5])
>>> y_est = interpolation(x, y, x_est)
>>> print(x, y)

[0 1 2 3] [0.04362888 1.03800604 2.0397634  3.09172125]

>>> print(y_est)

[1.53888472 2.56574232]

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>>> from OpenHA.processing.preprocess import interpolation
>>> import numpy as np

>>> x = np.array([0, 1, 2, 3])
>>> y = x**2 + np.random.random((4,)) * 0.1
>>> x_est = np.array([1.5, 2.5])
>>> y_est = interpolation(x, y, x_est, method='cubic')
>>> print(x, y)

[0 1 2 3] [0.03392971 1.08425927 4.08819547 9.0204196 ]

>>> print(y_est)

[2.34360896 6.31485397]

Input Arguments

x —— $x$$x$-coordinates. A 1-D array of real value.

y —— A N-D array of real values. The length of y along the interpolation axis must be equal to the length of x.

x_est —— A 1-D array of values to evaluate the interpolant.

kind —— Kind of interpolation, specified as one of the following strings.

• zero, slinear, quadratic and cubic refer to a spline interpolation of zeroth, first, second or third order.
• previous and next simply return the previous or next value of the point.
• nearest-up and nearest differ when interpolating half-integers (e.g., 0.5, 1.5) in that nearest-up rounds up and nearest rounds down.

The default is linear.

Properties of Arguments