/// <summary>
/// maxPolyfillSize returns the number of hexagons to allocate space for when
/// performing a polyfill on the given GeoJSON-like data structure.
///
/// Currently a laughably padded response, being a k-ring that wholly contains
/// a bounding box of the GeoJSON, but still less wasted memory than initializing
/// a Python application? ;)
/// </summary>
/// <param name="geoPolygon">A GeoJSON-like data structure indicating the poly to fill</param>
/// <param name="res">Hexagon resolution (0-15)</param>
/// <returns>number of hexagons to allocate for</returns>
/// <!-- Based off 3.1.1 -->
internal static int maxPolyfillSize(ref GeoPolygon geoPolygon, int res)
{
// Get the bounding box for the GeoJSON-like struct
BBox bbox = new BBox();
Polygon.bboxFromGeofence(ref geoPolygon.Geofence, ref bbox);
int minK = BBox.bboxHexRadius(bbox, res);
// The total number of hexagons to allocate can now be determined by
// the k-ring hex allocation helper function.
return(maxKringSize(minK));
}
///<summary>
/// polyfill takes a given GeoJSON-like data structure and preallocated,
/// zeroed memory, and fills it with the hexagons that are contained by
/// the GeoJSON-like data structure.
///
/// The current implementation is very primitive and slow, but correct,
/// performing a point-in-poly operation on every hexagon in a k-ring defined
/// around the given Geofence.
/// </summary>
/// <param name="geoPolygon">The Geofence and holes defining the relevant area</param>
/// <param name="res"> The Hexagon resolution (0-15)</param>
/// <param name="out_hex">The slab of zeroed memory to write to. Assumed to be big enough.</param>
/// <!-- Based off 3.1.1 -->
internal static void polyfill(GeoPolygon geoPolygon, int res, List <H3Index> out_hex)
{
// One of the goals of the polyfill algorithm is that two adjacent polygons
// with zero overlap have zero overlapping hexagons. That the hexagons are
// uniquely assigned. There are a few approaches to take here, such as
// deciding based on which polygon has the greatest overlapping area of the
// hexagon, or the most number of contained points on the hexagon (using the
// center point as a tiebreaker).
//
// But if the polygons are convex, both of these more complex algorithms can
// be reduced down to checking whether or not the center of the hexagon is
// contained in the polygon, and so this is the approach that this polyfill
// algorithm will follow, as it's simpler, faster, and the error for concave
// polygons is still minimal (only affecting concave shapes on the order of
// magnitude of the hexagon size or smaller, not impacting larger concave
// shapes)
//
// This first part is identical to the maxPolyfillSize above.
// Get the bounding boxes for the polygon and any holes
int cnt = geoPolygon.numHoles + 1;
List <BBox> bboxes = new List <BBox>();
for (int i = 0; i < cnt; i++)
{
bboxes.Add(new BBox());
}
Polygon.bboxesFromGeoPolygon(geoPolygon, ref bboxes);
int minK = BBox.bboxHexRadius(bboxes[0], res);
int numHexagons = maxKringSize(minK);
// Get the center hex
GeoCoord center = new GeoCoord();
BBox.bboxCenter(bboxes[0], ref center);
H3Index centerH3 = H3Index.geoToH3(ref center, res);
// From here on it works differently, first we get all potential
// hexagons inserted into the available memory
kRing(centerH3, minK, ref out_hex);
// Next we iterate through each hexagon, and test its center point to see if
// it's contained in the GeoJSON-like struct
for (int i = 0; i < numHexagons; i++)
{
// Skip records that are already zeroed
if (out_hex[i] == 0)
{
continue;
}
// Check if hexagon is inside of polygon
GeoCoord hexCenter = new GeoCoord();
H3Index.h3ToGeo(out_hex[i], ref hexCenter);
hexCenter.lat = GeoCoord.constrainLat(hexCenter.lat);
hexCenter.lon = GeoCoord.constrainLng(hexCenter.lon);
// And remove from list if not
if (!Polygon.pointInsidePolygon(geoPolygon, bboxes, hexCenter))
{
out_hex[i] = H3Index.H3_INVALID_INDEX;
}
}
}