"""Define the WeightedInterpolator class."""

import numpy as np

from openmdao.surrogate_models.nn_interpolators.nn_base import NNBase


class WeightedInterpolator(NNBase):
    """
    Weighted Neighbor Interpolation.
    """

    @staticmethod
    def _get_weights(ndist, dist_eff):
        """
        Compute the weights based on the distance and distance effect.

        Parameters
        ----------
        ndist : ndarray
            Distances from point to nearest neighbors.
        dist_eff : float
            Exponent used for the distance weighting.

        Returns
        -------
        ndarray
            Weights for each neighbor based on distance.
        """
        # Find the weighted neighbors per defined formula for distance effect
        dist = np.power(ndist, dist_eff)

        # Ignore Division by Zero Warnings
        with np.errstate(divide='ignore'):
            weights = 1.0 / dist

        # Replace any nan's to use the training data
        rows, cols = np.where(np.isinf(weights))

        weights[rows, :] = 0.
        weights[rows, cols] = 1.

        return weights

    def __call__(self, prediction_points, num_neighbors=5, dist_eff=0):
        """
        Do interpolation.

        Parameters
        ----------
        prediction_points : ndarray
            Points at which interpolation is done.
        num_neighbors :  int
            Number of neighbors to use.
        dist_eff : float
            Exponent used for the distance weighting. Using dist_eff=0 will default to
            `self._indep_dims + 1`.

        Returns
        -------
        ndarray
            Interpolated values at the prediction points.
        """
        if self._ntpts < num_neighbors:
            raise ValueError('WeightedInterpolant does not have sufficient '
                             'training data to use num_neighbors={0}, only {1} points'
                             ' available.'.format(num_neighbors, self._ntpts))

        if dist_eff == 0:
            # If default, use #dims + 1
            dist_eff = self._indep_dims + 1

        if len(prediction_points.shape) == 1:
            # Reshape vector to n x 1 array
            prediction_points.shape = (1, prediction_points.shape[0])

        normalized_pts = (prediction_points - self._tpm) / self._tpr

        # Find them neigbors
        # KData query takes (data, #ofneighbors) to determine closest
        # training points to predicted data
        ndist, nloc = self._KData.query(normalized_pts.real, num_neighbors)

        # Setup problem

        # Reshape ndist for 1D problems.
        if len(ndist.shape) == 1:
            ndist.shape = (1, ndist.shape[0])
            nloc.shape = (1, nloc.shape[0])

        weights = self._get_weights(ndist, dist_eff)

        weight_sum = np.sum(weights, axis=1)
        vals = self._tv[nloc]
        wt = np.einsum('ijk,ij->ik', vals, weights)
        predz = ((wt / weight_sum[:, np.newaxis]) * self._tvr) + self._tvm

        self._pt_cache = (normalized_pts, ndist, nloc)

        return predz

    def gradient(self, prediction_points, num_neighbors=5, dist_eff=0):
        """
        Find the gradient at each location of a set of supplied predicted points.

        Parameters
        ----------
        prediction_points : ndarray
            Points at which interpolation is done.
        num_neighbors :  int
            Number of neighbors to use.
        dist_eff : float
            Exponent used for the distance weighting. Using dist_eff=0 will default to
            `self._indep_dims + 1`.

        Returns
        -------
        ndarray
            Gradient values at the prediction points.
        """
        if self._ntpts < num_neighbors:
            raise ValueError('WeightedInterpolant does not have sufficient '
                             'training data to use num_neighbors={0}, only {1} points'
                             ' available.'.format(num_neighbors, self._ntpts))

        if dist_eff == 0:
            # If default, use #dims + 1
            dist_eff = self._indep_dims + 1

        if len(prediction_points.shape) == 1:
            # Reshape vector to num_neighbors x 1 array
            prediction_points.shape = (1, prediction_points.shape[0])

        normalized_pts = (prediction_points - self._tpm) / self._tpr

        if self._pt_cache is not None and \
                np.allclose(self._pt_cache[0], normalized_pts):
            ndist, nloc = self._pt_cache[1:]
        else:
            ndist, nloc = self._KData.query(normalized_pts, num_neighbors)

        # Reshape ndist for 1D problems.
        if len(ndist.shape) == 1:
            ndist.shape = (1, ndist.shape[0])
            nloc.shape = (1, nloc.shape[0])

        dimdiff = normalized_pts - self._tp[nloc]

        weights = np.power(ndist, -dist_eff)
        dweights = -dist_eff * \
            np.power(ndist[..., np.newaxis], -(dist_eff + 2)) * dimdiff

        weight_sum = np.sum(weights, axis=1)

        vals = self._tv[nloc]

        gradient = (weight_sum * np.einsum('ikj,ikl->ilj', dweights, vals)
                    - (np.einsum('ij,ijk->ik', weights, vals)[..., np.newaxis]
                       * np.sum(dweights, axis=1))) / np.power(weight_sum, 2)

        grad = gradient * (self._tvr[..., np.newaxis] / self._tpr)

        return grad
