aerosandbox.numpy.interpolate#

Module Contents#

Functions#

interp(x, xp, fp[, left, right, period])

One-dimensional linear interpolation, analogous to numpy.interp().

is_data_structured(x_data_coordinates, y_data_structured)

Determines if the shapes of a given dataset are consistent with "structured" (i.e. gridded) data.

interpn(points, values, xi[, method, bounds_error, ...])

Performs multidimensional interpolation on regular grids. Analogue to scipy.interpolate.interpn().
aerosandbox.numpy.interpolate.interp(x, xp, fp, left=None, right=None, period=None)[source]#

One-dimensional linear interpolation, analogous to numpy.interp().

Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (xp, fp), evaluated at x.

See syntax here: https://numpy.org/doc/stable/reference/generated/numpy.interp.html

Specific notes: xp is assumed to be sorted.

aerosandbox.numpy.interpolate.is_data_structured(x_data_coordinates, y_data_structured)[source]#

Determines if the shapes of a given dataset are consistent with “structured” (i.e. gridded) data.

For this to evaluate True, the inputs should be:

x_data_coordinates: A tuple or list of 1D ndarrays that represent coordinates along each axis of a N-dimensional hypercube.

y_data_structured: The values of some scalar defined on that N-dimensional hypercube, expressed as an N-dimesional array. In other words, y_data_structured is evaluated at np.meshgrid(*x_data_coordinates, indexing=”ij”).

Returns: Boolean of whether the above description is true.

Parameters:

  • x_data_coordinates (Tuple[numpy.ndarray]) –

  • y_data_structured (numpy.ndarray) –

Return type:

bool

aerosandbox.numpy.interpolate.interpn(points, values, xi, method='linear', bounds_error=True, fill_value=_onp.nan)[source]#

Performs multidimensional interpolation on regular grids. Analogue to scipy.interpolate.interpn().

See syntax here: https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interpn.html

Parameters:

  • points (Tuple[numpy.ndarray]) – The points defining the regular grid in n dimensions. Tuple of coordinates of each axis. Shapes (m1,

  • ) (mn,) –

  • ... (mn,) –

  • values (numpy.ndarray) –

  • xi (numpy.ndarray) –

  • method (str) –

Return type:

numpy.ndarray

:param : :type : mn, :param values: The data on the regular grid in n dimensions. Shape (m1, …, mn) :param xi: The coordinates to sample the gridded data at. (…, ndim) :param method: The method of interpolation to perform. one of:

  • “bspline” (Note: differentiable and suitable for optimization - made of piecewise-cubics. For other

applications, other interpolators may be faster. Not monotonicity-preserving - may overshoot.)

  • “linear” (Note: differentiable, but not suitable for use in optimization w/o subgradient treatment due

to C1-discontinuity)

  • “nearest” (Note: NOT differentiable, don’t use in optimization. Fast.)

Parameters:

  • bounds_error – If True, when interpolated values are requested outside of the domain of the input data,

  • False (a ValueError is raised. If) –

  • used. (then fill_value is) –

  • fill_value – If provided, the value to use for points outside of the interpolation domain. If None,

  • extrapolated. (values outside the domain are) –

  • points (Tuple[numpy.ndarray]) –

  • values (numpy.ndarray) –

  • xi (numpy.ndarray) –

  • method (str) –

Return type:

numpy.ndarray

Returns: Interpolated values at input coordinates.