"""Fill holes in raster dataset by interpolation from the edges."""

import rasterio
from rasterio._fill import _fillnodata
from rasterio.env import ensure_env


@ensure_env
def fillnodata(
        image,
        mask=None,
        max_search_distance=100.0,
        smoothing_iterations=0):
    """Fill holes in a raster dataset by interpolation from the edges.

    This algorithm will interpolate values for all designated nodata
    pixels (marked by zeros in `mask`). For each pixel a four direction
    conic search is done to find values to interpolate from (using
    inverse distance weighting). Once all values are interpolated, zero
    or more smoothing iterations (3x3 average filters on interpolated
    pixels) are applied to smooth out artifacts.

    This algorithm is generally suitable for interpolating missing
    regions of fairly continuously varying rasters (such as elevation
    models for instance). It is also suitable for filling small holes
    and cracks in more irregularly varying images (like aerial photos).
    It is generally not so great for interpolating a raster from sparse
    point data.

    Parameters
    ----------
    image : numpy ndarray
        The source containing nodata holes.
    mask : numpy ndarray or None
        A mask band indicating which pixels to interpolate. Pixels to
        interpolate into are indicated by the value 0. Values > 0
        indicate areas to use during interpolation. Must be same shape
        as image. If `None`, a mask will be diagnosed from the source
        data.
    max_search_distance : float, optional
        The maxmimum number of pixels to search in all directions to
        find values to interpolate from. The default is 100.
    smoothing_iterations : integer, optional
        The number of 3x3 smoothing filter passes to run. The default is
        0.

    Returns
    -------
    out : numpy ndarray
        The filled raster array.
    """
    max_search_distance = float(max_search_distance)
    smoothing_iterations = int(smoothing_iterations)
    return _fillnodata(
        image, mask, max_search_distance, smoothing_iterations)
