Interpolate#

class sionna.phy.utils.Interpolate[source]#

Bases: abc.ABC

Abstract class template for interpolating data defined on unstructured or rectangular grids.

Used in PHYAbstraction for BLER and SNR interpolation.

Methods

abstractmethod unstruct(z: numpy.ndarray, x: numpy.ndarray, y: numpy.ndarray, x_interp: numpy.ndarray, y_interp: numpy.ndarray, **kwargs) → numpy.ndarray[source]#

Interpolates unstructured data.

Parameters:

  • z (numpy.ndarray) – Co-domain sample values of shape [N]. Informally, z = f(x, y).

  • x (numpy.ndarray) – First coordinate of the domain sample values of shape [N].

  • y (numpy.ndarray) – Second coordinate of the domain sample values of shape [N].

  • x_interp (numpy.ndarray) – Interpolation grid for the first (x) coordinate of shape [L]. Typically, \(L \gg N\).

  • y_interp (numpy.ndarray) – Interpolation grid for the second (y) coordinate of shape [J]. Typically, \(J \gg N\).

Outputs:

z_interp – Interpolated data of shape [L, J].

abstractmethod struct(z: numpy.ndarray, x: numpy.ndarray, y: numpy.ndarray, x_interp: numpy.ndarray, y_interp: numpy.ndarray, **kwargs) → numpy.ndarray[source]#

Interpolates data structured in rectangular grids.

Parameters:

  • z (numpy.ndarray) – Co-domain sample values of shape [N, M]. Informally, z = f(x, y).

  • x (numpy.ndarray) – First coordinate of the domain sample values of shape [N].

  • y (numpy.ndarray) – Second coordinate of the domain sample values of shape [M].

  • x_interp (numpy.ndarray) – Interpolation grid for the first (x) coordinate of shape [L]. Typically, \(L \gg N\).

  • y_interp (numpy.ndarray) – Interpolation grid for the second (y) coordinate of shape [J]. Typically, \(J \gg M\).

Outputs:

z_interp – Interpolated data of shape [L, J].