aerosandbox.numpy.interpolate
=============================

.. py:module:: aerosandbox.numpy.interpolate


Functions
---------

.. autoapisummary::

   aerosandbox.numpy.interpolate.interp
   aerosandbox.numpy.interpolate.is_data_structured
   aerosandbox.numpy.interpolate.interpn


Module Contents
---------------

.. py:function:: interp(x, xp, fp, left=None, right=None, period=None)

   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.


.. py:function:: 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.

   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.


.. py:function:: interpn(points, values, xi, method = 'linear', bounds_error=True, fill_value=_onp.nan)

   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

   :param points: The points defining the regular grid in n dimensions. Tuple of coordinates of each axis. Shapes (m1,
   :param ):
   :type ): mn,
   :param ...:
   :type ...: mn,
   :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.)
   :param bounds_error: If True, when interpolated values are requested outside of the domain of the input data,
   :param a ValueError is raised. If False:
   :param then fill_value is used.:
   :param fill_value: If provided, the value to use for points outside of the interpolation domain. If None,
   :param values outside the domain are extrapolated.:

   Returns: Interpolated values at input coordinates.