interpolate

sherpa.utils.interpolate(xout: ~numpy.ndarray, xin: ~numpy.ndarray, yin: ~numpy.ndarray, function: ~collections.abc.Callable[[~numpy.ndarray, ~numpy.ndarray, ~numpy.ndarray], ~numpy.ndarray] = <function linear_interp>)

One-dimensional interpolation.

Parameters:

xoutarray_like

The positions at which to interpolate.

xinarray_like

The x values of the data to interpolate. This must be sorted so that it is monotonically increasing.

yinarray_like

The y values of the data to interpolate (must be the same size as xin).

functionfunc, optional

The function to perform the interpolation. It accepts the arguments (xout, xin, yin) and returns the interpolated values. The default is to use linear interpolation.

Returns:

youtarray_like

The interpolated y values (same size as xout).

See also

linear_interp, nearest_interp, neville

Examples

Use linear interpolation to calculate the Y values for the xgrid array:

>>> import numpy as np
>>> x = np.asarray([1.2, 3.4, 4.5, 5.2])
>>> y = np.asarray([12.2, 14.4, 16.8, 15.5])
>>> xgrid = np.linspace(2, 5, 5)
>>> ygrid = interpolate(xgrid, x, y)

Use Neville’s algorithm for the interpolation:

>>> ygrid = interpolate(xgrid, x, y, neville)