import numpy as np
from scipy.spatial.distance import cdist

from ..base import Base
from ..utils.distance import euclidean, haversine


class SpatialAverage(Base):
    """
    Class to interpolate by fitting a XGBoost Regressor to given
    data.
    Note that radius you specify must be in kilometres if you are passing latitude and longitude as inputs
    """

    def __init__(
        self,
        radius=100,
        resolution="standard",
        coordinate_type="Euclidean",
        **kwargs
    ):
        super().__init__(resolution, coordinate_type)
        self.radius = radius
        if self.coordinate_type == "Geographic":
            self.distance = haversine
        elif self.coordinate_type == "Euclidean":
            self.distance = euclidean
        else:
            raise NotImplementedError(
                "Only Geographic and Euclidean Coordinates are available"
            )

    def _fit(self, X, y):
        """Function for fitting.
        This function is not supposed to be called directly.
        """
        self.X = X
        self.y = y
        return self

    def _predict_grid(self, x1lim, x2lim):
        """Function for grid interpolation.
        This function is not supposed to be called directly.
        """
        # getting the boundaries for interpolation
        x1min, x1max = x1lim
        x2min, x2max = x2lim

        # building the grid
        x1 = np.linspace(x1min, x1max, self.resolution)
        x2 = np.linspace(x2min, x2max, self.resolution)
        X1, X2 = np.meshgrid(x1, x2)
        return self._predict(np.asarray([X1.ravel(), X2.ravel()]).T)

    def _predict(self, X):
        """Function for interpolation on specific points.
        This function is not supposed to be called directly.
        """
        return self._average(X)

    def _average(self, X):
        dist = self.distance(X, self.X)
        mask = self.radius >= dist
        return (self.y * mask).sum(axis=1) / mask.sum(axis=1)