Interpolator

A general purpose that takes your data and just interpolates it without whining or making you do a pile of extra work

Properties and Methods

get_interpolator: method
get_extrapolator: method
morse_interpolator: method

 

__init__(self, grid, vals, interpolation_function=None, interpolation_order=None, extrapolator=None, extrapolation_order=1, **interpolation_opts):
  • grid: np.ndarray

    an unstructured grid of points or a structured grid of points or a 1D array

  • vals: np.ndarray

    the values at the grid points

  • interpolation_function: None | function

    the basic function to be used to handle the raw interpolation

  • interpolation_order: int | str | None

    the order of extrapolation to use (when applicable)

  • extrapolator: Extrapolator | None | str | function

    the extrapolator to use for data points not on the grid

  • extrapolation_order: int | str | None

    the order of extrapolation to use by default

  • interpolation_opts: Any

    the options to be fed into the interpolating_function

 

apply(self, grid_points, **opts):

Interpolates then extrapolates the function at the grid_points

  • grid_points: Any

    No description…

  • :returns: _

    No description…

 

__call__(self, *args, **kwargs):

 

regularize_mesh(self, interp_kind='cubic', interpolator=None, **kwargs):

Interpolates along the different slices in the grid, building a RegularMesh overall

  • grid: np.ndarray (x, y)

    a semistructured grid of points.

  • vals: np.ndarray (z)

    the values at the grid points.

  • interp_kind: str
    type of interpolation to do (‘cubic’ ‘linear’ ‘nearest’ …)
  • kwargs: Any

    No description…

  • :returns: _

    square_grid: a structured grid of points (np.ndarray) (x, y)

 

regular_grid(self, interp_kind='cubic', fillvalues=False, plot=False, **kwargs):

TODO: extend to also check y coordinates… maybe add param to do x, y, or both? creates a regular grid from a set of semistructured points. Only has 2D capabilities. :param grid: a semistructured grid of points. :type grid: np.ndarray (x, y) :param vals: the values at the grid points. :type vals: np.ndarray (z) :param interp_kind: type of interpolation to do (‘cubic’ | ‘linear’ | ‘nearest’ | …) :type interp_kind: str :param fillvalues: if true, outer edges are filled with last data point extending out. Otherwise extrapolates according to interp_kind (default)

  • fillvalues: bool

    No description…

  • plot: bool

    if true, plots the extrapolated cuts for visualization purposes.

  • kwargs: Any

    No description…

  • :returns: _

    square_grid: a structured grid of points (np.ndarray) (x, y)

Examples

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