def test_silly_geo_to_h3():
lat, lng = 37.3615593, -122.0553238
expected0 = '85283473fffffff'
out0 = h3.geo_to_h3(lat, lng, 5)
assert out0 == expected0
out1 = h3.geo_to_h3(lat + 180.0, lng + 360.0, 5)
expected1 = '85ca2d53fffffff'
assert out1 == expected1
def add_test_line_edges(self, test_lines):
og_lines = copy.copy(test_lines)
test_lines = test_lines.to_crs("epsg:3857")
max_node_full_graph = max(self.convert_ids.keys())
flip_node = {v: k for k, v in self.convert_ids.items()}
income_fn = f'{self.where}/{self.where.lower()}_income_pct.csv'
target_hex = []
if os.path.isfile(income_fn):
income_df = pd.read_csv(income_fn)
income_df = income_df[income_df['pct_above'] >= 0.5]
target_hex = income_df['hex'].to_list()
for i, line in enumerate(test_lines.geometry):
demand, node = line.coords[:]
s_coord_string = f'[{demand[0]:.0f}, {demand[1]:.0f}]'
e_coord_string = f'[{node[0]:.0f}, {node[1]:.0f}]'
end = self.look_up.get(e_coord_string, None)
start = self.look_up.get(s_coord_string, None)
if end is None:
continue
if start:
continue
if self.edges.get((start, end), False) or self.edges.get((end, start), False):
continue
if start is None:
start = self.index
max_node_full_graph += 1
self.convert_ids[max_node_full_graph] = start
self.look_up[s_coord_string] = start
self.index += 1
self.demand_nodes[start] = 1
self.demand_nodes[end] = 1
self.nodes_to_connect.add(start)
if target_hex:
ll_line_demand, ll_line_node = og_lines.geometry.iloc[i].coords[:]
demand_hex = h3.geo_to_h3(ll_line_demand[1], ll_line_demand[0], 10)
node_hex = h3.geo_to_h3(ll_line_node[1], ll_line_node[0], 10)
if demand_hex in target_hex or node_hex in target_hex:
self.demand_nodes[start] = 0
self.demand_nodes[end] = 0
self.nodes_to_connect.remove(start)
self.edge_to_geom[(max_node_full_graph, flip_node[end])] = line.wkt
self.edges[(start, end)] = line.length
self.flip_look_up = {v: k for k, v in self.look_up.items()}
def add_h3_index(gdf):
coords = gdf[["lat", "lng"]].values
index_7, index_8, index_9 = [], [], []
for coord in tqdm(coords):
index_7.append(h3.geo_to_h3(coord[0], coord[1], 7))
index_8.append(h3.geo_to_h3(coord[0], coord[1], 8))
index_9.append(h3.geo_to_h3(coord[0], coord[1], 9))
gdf["h3_7"] = index_7
gdf["h3_8"] = index_8
gdf["h3_9"] = index_9
return gdf
def test_validation_geo():
h = '8a28308280fffff' # invalid hex
with pytest.raises(H3CellError):
h3.h3_to_geo(h)
with pytest.raises(H3ResolutionError):
h3.geo_to_h3(0, 0, 17)
with pytest.raises(H3CellError):
h3.h3_to_geo_boundary(h)
with pytest.raises(H3CellError):
h3.h3_indexes_are_neighbors(h, h)
def test_areas_at_00():
areas_km2 = [
2.562182162955495529e+06,
4.476842018179409206e+05,
6.596162242711056024e+04,
9.228872919002589697e+03,
1.318694490797110348e+03,
1.879593512281297762e+02,
2.687164354763186225e+01,
3.840848847060638782e+00,
5.486939641329895423e-01,
7.838600808637447015e-02,
1.119834221989390345e-02,
1.599777169186613647e-03,
2.285390931423379875e-04,
3.264850232091780848e-05,
4.664070326136773890e-06,
6.662957615868890711e-07,
]
out = [h3.cell_area(h3.geo_to_h3(0, 0, r), unit='km^2') for r in range(16)]
assert approx2(out, areas_km2)
areas_rads2 = [
6.312389871006786335e-02,
1.102949377223657809e-02,
1.625081476657283096e-03,
2.273696413041990331e-04,
3.248837599063685022e-05,
4.630711750349743332e-06,
6.620305651949173071e-07,
9.462611873890716096e-08,
1.351804829317986891e-08,
1.931178237937334527e-09,
2.758910081529350229e-10,
3.941334595426616175e-11,
5.630465614578665530e-12,
8.043537197853909460e-13,
1.149076389260636790e-13,
1.641537700693487648e-14,
]
out = [
h3.cell_area(h3.geo_to_h3(0, 0, r), unit='rads^2') for r in range(16)
]
assert approx2(out, areas_rads2)
def add_hex(self, lat: float, lng: float, name=None):
"""Insert a hex into the HexDict.
REQUIRES:
lat: latitude of the coordinate.
lng: longitude of the coordinate.
EFFECTS:
-> If the RES_MAX hex does not exist, then the RES_MAX hex is added,
and the raw density is set to 1.
-> If the RES_MAX hex already exists, then the raw density of the hex
is just incremented by 1.
RETURNS: Hexagon
returns the RES_MAX hexagon of the given coordinates.
"""
hex_id = h3.geo_to_h3(lat, lng, RES_MAX)
if hex_id not in self.hex_dict[RES_MAX]:
self.hex_dict[RES_MAX][hex_id] = Hexagon(hex_id)
self.hex_dict[RES_MAX][hex_id].raw_density = 1
else:
self.hex_dict[RES_MAX][hex_id].raw_density += 1
self.hex_dict[RES_MAX][hex_id].residents.append(name)
return self.hex_dict[RES_MAX][hex_id]
def lat_lng_2_h3(lat, lng, res):
import h3
try:
result = h3.geo_to_h3(lat, lng, res)
return result
except:
return None # invalid coordinates will result in null index value.
def create(rs: Union[ResultSet, Query],
geom_column: str = 'geom',
name: Optional[str] = None,
resolution: Optional[int] = 0,
plot: str = "size",
column: Optional[str] = None,
group_by: Optional[str] = None):
gdf = to_gdf(rs, geom_column)
center = None
zoom = None
if len(gdf):
if gdf.crs is None:
gdf = gdf.set_crs(epsg=4326)
if gdf.crs.to_epsg() != 4326:
gdf = gdf.to_crs(epsg=4326)
center = get_center(gdf)
zoom = get_zoom(gdf)
if len(gdf) and resolution:
# Add H3 index
hex_col = 'hex' + str(resolution)
# H3 uses lat, lon
gdf[hex_col] = gdf[GEOM_COL].apply(
lambda geom: geo_to_h3(geom.y, geom.x, resolution), 1)
if group_by:
# Rows may be grouped by any field in a JSON
data_to_group = QueryResult.flatten_dataframe(gdf, group_by)
if "." in group_by:
_, group_by = group_by.rsplit(".", 1)
# Join rows with the same value in group_by field
data_to_group = data_to_group.groupby([hex_col, group_by],
sort=False,
as_index=False).size()
elif column and "." in column:
# Flatten the dataframe so we may calculate means etc. for the desired column
data_to_group = QueryResult.flatten_dataframe(gdf, column)
_, column = column.rsplit(".", 1)
else:
data_to_group = gdf
groupby = data_to_group.groupby(hex_col,
sort=False,
as_index=False)
if column:
groupby = groupby[column]
# plot = size (or mean, or median, or max, or min) of rows within the same hex
# Available functions: https://pandas.pydata.org/docs/reference/groupby.html
results = getattr(groupby, plot)()
# Add centroid geometry just in case. Of course, H3 has lat lon in the wrong order again
centroid_lat_lon = results[hex_col].map(lambda hex: h3_to_geo(hex))
# Convert to GeoSeries first (https://github.com/Toblerity/Shapely/issues/1096#issuecomment-962988370)
results[GEOM_COL] = gpd.GeoSeries(
[Point(geom[1], geom[0]) for geom in centroid_lat_lon])
gdf = gpd.GeoDataFrame(results, geometry=GEOM_COL, crs=gdf.crs)
gdf = gdf.set_index(hex_col)
# retain hex index as column too, so it can be plotted
gdf[hex_col] = gdf.index
return QueryResult(gdf, center, zoom, name)
def on_post(self, req, resp):
resp.status = falcon.HTTP_200
credentials = json.loads(req.stream.read())
body = json.loads(credentials["body"])
wind_power = body["windPower"];
offset = body["offset"];
resolution = body["resolution"];
trees = body["trees"];
tic = time.perf_counter()
wind = Wind();
trees_out = [];
for tree in trees:
longitude = tree["longitude"];
latitude = tree["latitude"];
color = tree["color"];
polygon = wind.getWindLayerCoordinates(longitude, latitude, wind_power, offset)
geoJson = {
'type': 'Polygon',
'coordinates': [polygon]
}
hexagons = h3.polyfill(geoJson, resolution)
h3_index = h3.geo_to_h3(latitude, longitude, resolution);
for hex in hexagons:
direction = h3.h3_distance(h3_index, hex);
opacity = 255 - direction * (80 / wind_power);
if (opacity > 0):
trees_out.append({ 'opacity': opacity, 'hex': hex, 'color': color });
toc = time.perf_counter()
print(f"Downloaded the tutorial in {(toc - tic)*1000:4f} ms")
content = {
'hex': json.dumps(trees_out, default=serialize_sets)
}
resp.body = json.dumps(content)
def counts_by_hexagon(plot_variable, df, resolution):
"""
Use h3.geo_to_h3 to index each data point into the spatial index of the specified resolution.
Use h3.h3_to_geo_boundary to obtain the geometries of these hexagons
Ex counts_by_hexagon(data, 9)
"""
df = df[["lat", "lng", plot_variable]]
df["hex_id"] = df.apply(
lambda row: h3.geo_to_h3(row["lat"], row["lng"], resolution), axis=1)
df_aggreg = df.groupby(by="hex_id").size().reset_index()
# print(df_aggreg.columns)
# import numpy as np
df_aggreg = df.groupby(['hex_id'], as_index=True).agg({
plot_variable: 'mean'
}).rename(columns={
plot_variable: 'value'
}).reset_index()
df_aggreg = df_aggreg[['hex_id', 'value']]
df_aggreg = df_aggreg[df_aggreg['value'] != np.inf]
df_aggreg["geometry"] = df_aggreg.hex_id.apply(
lambda x: {
"type": "Polygon",
"coordinates": [h3.h3_to_geo_boundary(h=x, geo_json=True)]
})
return df_aggreg
def lat_lng_to_h3():
import h3
h3_key = h3.geo_to_h3(lat=48.853, lng=2.348, resolution=8)
print(h3_key)
# returns
"881fb46625fffff"
def insertItem(data, h3index9):
latlon_ary = data["latlon"].split(",")
h3index8 = h3.geo_to_h3(float(latlon_ary[0]), float(latlon_ary[1]), 8)
h3index7 = h3.geo_to_h3(float(latlon_ary[0]), float(latlon_ary[1]), 7)
h3index6 = h3.geo_to_h3(float(latlon_ary[0]), float(latlon_ary[1]), 6)
dtnow = datetime.now()
time = dtnow.strftime("%Y%m%d-%H%M%S") + dtnow.strftime("%f")[0:3]
data["h3-9"] = h3index9 + "_" + time
data["h3-8"] = h3index8
data["h3-7"] = h3index7
data["h3-6"] = h3index6
# homepageがなければgoogleから探す
if "homepage" not in data or not data["homepage"]:
setSiteToData(data)
checkIFrameEnableItem(data)
insertItemD(data)
def h3_2set_polyfill(geometry, resolution):
def to_h3(geometry):
# buffering can result in MultiPolygon geometries
if 'MultiPolygon' in geometry.geom_type:
geometry = list(geometry.geoms)
else:
geometry = [geometry]
hex_set = set()
for p in geometry:
p_geojson = shapely.geometry.mapping(p)
hex_set = hex_set.union(h3.polyfill_geojson(p_geojson, resolution))
return hex_set
def get_result_struct(hex_i, polygon, dirty_set):
if hex_i in dirty_set:
hex_polygon = shapely.geometry.Polygon(
h3.h3_to_geo_boundary(hex_i, geo_json=True))
intersection = polygon.intersection(hex_polygon)
if intersection.is_empty: # does not represent any area of the original geometry
return None
elif intersection.equals(
hex_polygon): # fully contained by the original geometry
return (hex_i, False, None)
else:
return (hex_i, True, intersection.wkb
) # partially contained by the original geometry
else:
return (
hex_i, False, None
) # fully contained by the original geometry (not in the dirty set)
polygon = geometry # placeholder for when we are loading wkt/wkb in udfs
# get centroid of the geometry
# get centroid index
# get centroid index geometry
# compute radius based on teh minimum enclosing rectangel (radius of pseudo minimal enclosing circle)
cent = polygon.centroid.xy
centroid_hex = h3.geo_to_h3(cent[1][0], cent[0][0], resolution)
centroid_geom = shapely.geometry.Polygon(
h3.h3_to_geo_boundary(centroid_hex, geo_json=True))
radius = math.sqrt(centroid_geom.minimum_rotated_rectangle.area) / 2
dirty = polygon.boundary.buffer(radius)
original_set = to_h3(polygon)
dirty_set = to_h3(dirty)
result = [
get_result_struct(hex_i, polygon, dirty_set)
for hex_i in list(original_set.union(dirty_set))
]
result = [c for c in result if c is not None]
return result
def test_h3_is_valid():
assert h3.h3_is_valid('85283473fffffff')
assert h3.h3_is_valid('850dab63fffffff')
assert not h3.h3_is_valid('lolwut')
# H3 0.x Addresses are not considered valid
assert not h3.h3_is_valid('5004295803a88')
for res in range(16):
assert h3.h3_is_valid(h3.geo_to_h3(37, -122, res))
def h3_2set_polyfill(geometry, resolution):
# we need to account for possibility of MultiPolygon shapes
def to_h3(geometry):
if 'MultiPolygon' in geometry.geom_type:
geometry = list(geometry.geoms)
else:
geometry = [geometry]
hex_set = set()
for p in geometry:
p_geojson = shapely.geometry.mapping(p)
hex_set = hex_set.union(h3.polyfill_geojson(p_geojson, resolution))
return hex_set
# convenience method for converting an index to a chip of a polygon
def get_result_struct(hex_i, polygon, dirty_set):
if hex_i in dirty_set:
hex_polygon = shapely.geometry.Polygon(
h3.h3_to_geo_boundary(hex_i, geo_json=True))
intersection = polygon.intersection(hex_polygon)
if intersection.is_empty:
return None
elif intersection.equals(hex_polygon):
return (hex_i, False, None)
else:
return (hex_i, True, intersection.wkb)
else:
return (hex_i, False, None)
polygon = shapely.wkb.loads(bytes(geometry))
# compute the buffer radius
# we cannot use the hexagon side lenght due to curvatrure
# we use minimum rotated rectange - we assume that the rectangle is near rectangle in shape
# the alternative would be to iterate through boundary vertices and take the longest side
cent = polygon.centroid.xy
centroid_hex = h3.geo_to_h3(cent[1][0], cent[0][0], resolution)
centroid_geom = shapely.geometry.Polygon(
h3.h3_to_geo_boundary(centroid_hex, geo_json=True))
radius = math.sqrt(centroid_geom.minimum_rotated_rectangle.area) / 2
# any index that may touch the boundary
dirty = polygon.boundary.buffer(radius)
original_set = to_h3(polygon)
dirty_set = to_h3(dirty)
result = [
get_result_struct(hex_i, polygon, dirty_set)
for hex_i in list(original_set.union(dirty_set))
]
return result
def _h3_district_corners(geojson):
idx = {}
for feature in geojson["features"]:
for c in feature["geometry"]["coordinates"][0][0]:
# resolution=9 is roughly 0.17km end to end
key = h3.geo_to_h3(c[1], c[0], 9)
if key not in idx:
idx[key] = {feature['properties']['DISTRICT']}
else:
idx[key].add(feature['properties']['DISTRICT'])
# if there are more than 2 districts in a set then it's a corner of more than 2 districts
return {k for k, v in idx.items() if len(v) > 2}
def importLine(csvLine, fileName, forceTitle):
try:
data = convertCsv2Json(csvLine)
if data == None:
return False
# telに数字以外が入ってたり、11文字より長かったらクリアする
if "tel" in data and data["tel"]:
if data["tel"].isdecimal() == False:
data["tel"] = ""
elif len(data["tel"]) > 11:
data["tel"] = ""
# 電話番号がない場合は住所から取得してみる
if "tel" not in data or not data["tel"]:
setTelAndLatLonToData(data)
# 緯度経度をタイトルと住所から取得する
if "latlon" not in data or not data["latlon"]:
setLatLonToData(data)
# 既に登録済みか確認する
latlon_ary = data["latlon"].split(",")
h3index9 = h3.geo_to_h3(float(latlon_ary[0]), float(latlon_ary[1]), 9)
records = selectItem(data, h3index9)
if records is None or len(records) == 0:
insertItem(data, h3index9)
elif len(records) == 1:
updateItem(records[0], data, forceTitle)
elif not data["tel"]:
message = "CSV IMPORTER : {0}\n Alert : MULTIPLE NO TELEPHONE\n {1}".format(
fileName, data["title"])
notifyToSlack(SLACK_WEBHOOK_HAMAMATSU, message)
return False
else:
message = "CSV IMPORTER : {0}\n Alert : MULTIPLE RECORDS({1}) {2}\n {3}\n {4}".format(
fileName, len(records), data["title"], records[0], records[1])
notifyToSlack(SLACK_WEBHOOK_HAMAMATSU, message)
return False
return True
except Exception as e:
logger.exception(e)
message = "CSV IMPORTER : {0}\n Exception : {1}".format(
fileName, e.__class__.__name__)
notifyToSlack(SLACK_WEBHOOK_HAMAMATSU, message)
raise
def parse_result(r):
data = r['data']
lat = round(get_nested_value(data, 9, 2),
7) # 7 digits equals a precision of 1 cm
lng = round(get_nested_value(data, 9, 3),
7) # 7 digits equals a precision of 1 cm
# noinspection PyUnresolvedReferences
h3_index = h3.geo_to_h3(lat, lng, POI_RESOLUTION)
pb_id = get_nested_value(data, 10)
return dict(query=r['query'],
data=dict(location=dict(lat=lat, lng=lng),
h3Index=h3_index,
id=pb_id))
def test_to_local_ij_error():
h = h3.geo_to_h3(0, 0, 0)
# error if we cross a face
nb = h3.hex_ring(h, k=2)
with pytest.raises(H3ValueError):
[h3.experimental_h3_to_local_ij(h, p) for p in nb]
# should be fine if we do not cross a face
nb = h3.hex_ring(h, k=1)
out = {h3.experimental_h3_to_local_ij(h, p) for p in nb}
expected = {(-1, 0), (0, -1), (0, 1), (1, 0), (1, 1)}
assert out == expected
def calc_h3d(cls, lat, lon, res):
"""
Calculate h3 distilled index for given parameters
:param lat: Latitude
:type lat: float
:param lon: Longiture
:type lon: float
:param res: resolution (level, depth) of H3 index, 0-15
:type res: int
:returns: h3 distilled index
:rtype: int
"""
return h3d.h3s_to_h3d(h3.geo_to_h3(lat, lon, res))
def parse_result(r):
data = r['data'][6]
name = get_nested_value(data, 11)
place_id = get_nested_value(data, 78)
lat = round(get_nested_value(data, 9, 2), 7) # 7 digits equals a precision of 1 cm
lng = round(get_nested_value(data, 9, 3), 7) # 7 digits equals a precision of 1 cm
# noinspection PyUnresolvedReferences
h3_index = h3.geo_to_h3(lat, lng, POI_RESOLUTION)
address = get_nested_value(data, 2)
timezone = get_nested_value(data, 30)
categories = [t[0] for t in (get_nested_value(data, 76) or [])]
opening_hours = parse_opening_hours(get_nested_value(data, 34, 1))
permanently_closed = get_nested_value(data, 88, 0) == 'CLOSED'
temporarily_closed = get_nested_value(data, 96, 5, 0, 2) == 'Reopen this place' and not permanently_closed
inside_of = get_nested_value(data, 93, 0, 0, 0, 1)
phone = get_nested_value(data, 178, 0, 3)
website = get_nested_value(data, 7, 0)
rating_stars = get_nested_value(data, 4, 7)
rating_number_of_reviews = get_nested_value(data, 4, 8)
price_level = get_nested_value(data, 4, 2)
popularity_data = get_nested_value(data, 84, 0)
spending_time = parse_spending_time_data(get_nested_value(data, 117, 0))
popularity, waiting_time = None, None
if popularity_data:
popularity, waiting_time = parse_popularity_data(popularity_data, timezone)
return dict(
id=r['id'],
data=dict(
name=name,
placeID=place_id,
location=dict(lat=lat, lng=lng),
h3Index=h3_index,
address=address,
timezone=timezone,
categories=categories,
temporarilyClosed=temporarily_closed,
permanentlyClosed=permanently_closed,
insideOf=inside_of,
contact=dict(phone=phone, website=website),
openingHours=opening_hours,
rating=dict(stars=rating_stars, numberOfReviews=rating_number_of_reviews),
priceLevel=len(price_level) if price_level else None,
popularity=popularity,
waitingTime=waiting_time,
spendingTime=spending_time
)
)
def test_from_local_ij_error():
h = h3.geo_to_h3(0, 0, 0)
baddies = [(1, -1), (-1, 1), (-1, -1)]
for i, j in baddies:
with pytest.raises(H3ValueError):
h3.experimental_local_ij_to_h3(h, i, j)
# inverting output should give good data
nb = h3.hex_ring(h, k=1)
goodies = {h3.experimental_h3_to_local_ij(h, p) for p in nb}
out = {h3.experimental_local_ij_to_h3(h, i, j) for i, j in goodies}
assert out == nb
def __create_h3_from_lat_lon(self, lat, lon):
"""
Get h3index from latitude and longitude
:param lat:
:param lon:
:return:
"""
try:
return h3.geo_to_h3(float(lat), float(lon), RES)
except ValueError:
print("Wrong latitude or longitude values\n")
return None
except Exception as e:
print(f"Error: {e}")
return None
def h3_polyfill_extended(geometry, resolution):
(cx, cy) = polygon.centroid.coords.xy # get centroid location
centroid_ind = h3.geo_to_h3(
cx[0], cy[0], resolution) # get h3 index containing the centroid
centroid_geom = shapely.geometry.Polygon(
h3.h3_to_geo_boundary(centroid_ind)) # get centroid index geometry
radius = math.sqrt(
centroid_geom.minimum_rotated_rectangle.area
) / 2 # find the radius of (pseudo) minimal enclosing circle (via side of the rotated enclosing square)
geom_extended = geometry.buffer(
distance=radius, resolution=1
) # buffer the original geometry by the radius of minimum enclosing cicle
geo_json_geom = shapely.geometry.mapping(geom_extended)
indices = h3.polyfill_geojson(geo_json_geom, resolution) # get the indices
return indices
def compute_reward_scale(self, lat: float, lng: float):
"""Generate the reward scale for a given location."""
hex_id = h3.geo_to_h3(lat, lng, RES_MAX)
if hex_id not in self.hex_dict[RES_MAX]:
raise Exception(
"Cannot compute reward scale. Invalid starting hex.")
current_hex = self.hex_dict[RES_MAX][hex_id]
reward_scale = 1
while current_hex.res >= RES_MIN:
parent = self[h3.h3_to_parent(current_hex.hex_id,
current_hex.res - 1)]
reward_scale = reward_scale * (parent.clipped_density /
parent.unclipped_density)
current_hex = parent
return reward_scale
def load_incidents(aperture_size=None, df_freq=None):
if df_freq is None:
df = pd.read_pickle('resources/tdot_testing_incidents.pk')
df_freq = df.groupby([
'unit_segment_id', 'GPS Coordinate Latitude',
'GPS Coordinate Longitude', 'timestamp'
]).size().reset_index()
df_freq.set_axis(['segment_id', 'lat', 'lng', 'timestamp', 'count'],
axis=1,
inplace=True)
df_freq.loc[:, 'segment_id'] = df_freq.segment_id.apply(int)
df_freq.loc[:, 'time'] = pd.to_datetime(df_freq['timestamp'], unit='s')
if aperture_size is not None:
aperture_size = int(aperture_size)
df_freq.loc[:, 'region'] = df_freq.apply(
lambda x: h3.geo_to_h3(x.lat, x.lng, aperture_size), 1)
return df_freq
def filter_h3d_around(cls, lat, lon, res, k_distance=1, queryset=None):
"""
Filter all instances with the same h3 cell, or in cells around it
:param lat: latitude
:param lon: longitude
:param res: resolution (level, depth) of the index where to find
:param k_distance: max distance in hexagon cells from the cell containing the start point
0 for the containing cell only,
1 for the containing cell and cells immediately around,
etc.
:param queryset: the queryset to search in, None means cls.objects.all()
"""
cells = h3.compact(h3.k_ring(h3.geo_to_h3(lat, lon, res), k_distance))
d_cells = [h3d.h3s_to_h3d(c) for c in cells]
filters = [models.Q(h3d__range=h3d.h3d_range(c)) for c in d_cells]
if not queryset:
queryset = cls.objects.all()
return queryset.filter(functools.reduce(operator.or_, filters))
def _lower_resolution(list_of_coords, h3_corners, reduction_factor):
if reduction_factor > 9:
raise Exception("max reduction factor is 9")
result = []
# The polygon's first and last coordinate must be the same in order to enclose the area so we always append the first coordinate here
result.append(list_of_coords[0])
for coord in list_of_coords:
if int(str(coord[0])[-1]) < reduction_factor and h3.geo_to_h3(
coord[1], coord[0], 9) not in h3_corners:
continue
result.append(coord)
# ensure that we always append the last coordinate to enclose the area
if int(str(list_of_coords[-1][0])[-1]) < reduction_factor:
result.append(list_of_coords[-1])
return result
def select_reliable_fire(year):
"""Load the fire data of a single year and reserve rows with confidence higher than 0.8 .
Calculate the h3 hexagon where the remaining fire occurred based on the latitude and longitude,
and return it after merging."""
raw_file = input_dir + "modis_" + year + "_Australia.csv"
df_raw = pd.read_csv(raw_file, sep=',')
df_sel = df_raw[df_raw["confidence"] >= 80].reset_index(drop=True)
df_sel = df_sel[["latitude", "longitude"]]
df_sel["hex_id"] = df_sel.apply(
lambda row: h3.geo_to_h3(
lat=row["latitude"],
lng=row["longitude"],
resolution=7),
axis=1)
print("A total of {} fire data are counted".format(df_sel.shape[0]))
df_count = pd.DataFrame(df_sel["hex_id"].value_counts())
df_count = df_count.reset_index()
fire_column = "fire_" + year
df_count.columns = ["hex_id", fire_column]
return df_count
def h3_tile_by_dim(lon, lat, bdim=BASE_DIM, asc=True, more=False):
osmproj = pyproj.Proj("epsg:3857")
resolutions = range(H3_MAX_ZOOM) if asc else reversed(range(H3_MAX_ZOOM))
for resolution in resolutions:
tile = h3.geo_to_h3(lat, lon, resolution)
polygon = h3.h3_to_geo_boundary(tile)
x1, y1 = osmproj(*polygon[0])
x2, y2 = osmproj(*polygon[1])
dist = Point(x1, y1).distance(Point(x2, y2))
if (asc and dist < bdim) or (not asc and dist > bdim):
break
return tile if not more else (
tile,
resolution,
)