def _build(self, base_shape, meters, crs=constants.DEFAULT_CRS):
# translate input meters to appropriate h3 resolution
res = self._get_appropriate_res(base_shape, meters)
# cover the base_shape with h3 hexagonal polygons
hexs = self._handle_polyfill(base_shape, res)
# get actual geoms out of H3 hexagons
# from https://geographicdata.science/book/data/h3_grid/build_sd_h3_grid.html
polygonise = lambda hex_id: Polygon(
h3.h3_to_geo_boundary(hex_id, geo_json=True))
# prepare a geodf with all the H3 geoms
all_polys = gpd.GeoDataFrame(
{
'geometry': list(map(polygonise, hexs)),
'H3_INDEX': hexs
},
crs=constants.DEFAULT_CRS)
# add TileID
all_polys['TILE_ID'] = all_polys.index
# Convert TILE_ID to have str type
all_polys['TILE_ID'] = all_polys['TILE_ID'].astype('str')
return all_polys
def from_id(cls, id: int):
res = h3.h3_get_resolution(id)
coord_pairs = h3.h3_to_geo_boundary(id)
# ATTENTION: (lat, lng)! pairs
coords = to_numpy_polygon(coord_pairs, flipped=True)
x_coords, y_coords = coords[0], coords[1]
surr_n_pole = lies_in_h3_cell(id, lng=0.0, lat=MAX_LAT)
surr_s_pole = lies_in_h3_cell(id, lng=0.0, lat=-MAX_LAT)
bounds, x_overflow = get_corrected_hex_boundaries(
x_coords, y_coords, surr_n_pole, surr_s_pole)
return cls(id, res, coords, bounds, x_overflow, surr_n_pole,
surr_s_pole)
def _create_hexagon_polygons(self, hexagon_ids):
# from https://geographicdata.science/book/data/h3_grid/build_sd_h3_grid.html
return gpd.GeoDataFrame(
{
"geometry": [
Polygon(h3.h3_to_geo_boundary(hexagon_id, geo_json=True))
for hexagon_id in hexagon_ids
],
"H3_INDEX":
hexagon_ids,
},
crs=constants.DEFAULT_CRS,
)
def true_parents(self) -> HexIdSet:
"""
hexagons do not cleanly subdivide into seven finer hexagons.
the child hexagon cells protrude from the parent (cf. https://h3geo.org/docs/highlights/indexing)
and hence a cell does not have a single, but actually up to 2 "true" parents
returns: the hex ids of all parent cells which any of the cell points belong
"""
if self.res == 0:
raise ValueError("not defined for resolution 0")
lower_res = self.res - 1
# NOTE: (lat,lng) pairs!
coord_pairs = h3.h3_to_geo_boundary(self.id)
return {h3.geo_to_h3(pt[0], pt[1], lower_res) for pt in coord_pairs}
def array_to_dataframe(in_array,
transform,
h3_resolution,
nodata_value=None,
axis_order="yx",
compacted=True,
geo=False):
"""
convert a raster/array to a pandas dataframe containing H3 indexes
:param in_array: input 2-d array
:param transform: the affine transformation
:param nodata_value: the nodata value. For these cells of the array there will be no h3 indexes generated
:param axis_order: axis order of the 2d array. Either "xy" or "yx"
:param h3_resolution: target h3 resolution
:param compacted: return compacted h3 indexes (see H3 docs)
:param geo: return a geopandas geodataframe with geometries. increases the memory usage.
:return: pandas dataframe or geodataframe
"""
frames = []
for value, compacted_vec in array_to_h3(in_array,
transform,
h3_resolution,
nodata_value=nodata_value,
axis_order=axis_order).items():
indexes = None
if compacted:
indexes = compacted_vec.compacted_indexes()
else:
indexes = compacted_vec.uncompacted_indexes_at_resolution(
h3_resolution)
values = np.repeat(value, len(indexes))
frame = None
if geo:
frame = gp.GeoDataFrame({
"h3index":
indexes,
"value":
values,
"geometry": [
Polygon(h3.h3_to_geo_boundary(h, geo_json=True))
for h in np.nditer(indexes)
],
})
else:
frame = pd.DataFrame({"h3index": indexes, "value": values})
frames.append(frame)
return pd.concat(frames)