def get_cell_ids_h3(lat:float, lng:float, angle: float) -> List:
p = shapely.geometry.Point([lng, lat])
# so to more accurately match projections maybe arc length of a sphere woulde be best?
arc_length = R_MEAN * angle # in km
n_points = 20
#arc_length should be the radius in kilometers so convert to diameter in meters
d = arc_length * 1000 # meters
angles = np.linspace(0, 360, n_points)
polygon = geog.propagate(p, angles, d)
try:
mapping = shapely.geometry.mapping(shapely.geometry.Polygon(polygon))
except ValueError as e:
print(f"lat:{lat}, lng:{lng}")
print(polygon)
cells = h3.polyfill(mapping, H3_RESOLUTION_LEVEL, True)
return cells
def polyfill():
import h3
# 1. Definit le polygon à remplir en geojson
#
# En geojson:
# -les polygons sont décris dans le sens inverse des aiguilles d'une montre
# - le premier point doit-être répété à la fin.
# - les points sont décris comme un tableau en 3 dimensions
# [[[latitude, longitude]]]
polygon_geojson = {
"type":
"Polygon",
"coordinates": [[
[2.25, 48.82], # point0: sud ouest de Paris
[2.40, 48.82], # point1: sud est de Paris
[2.40, 48.89], # point2: nord est de Paris
[2.25, 48.89], # point3: nord ouest de Paris
[2.25, 48.82], # point4: sud ouest de Paris
]],
}
# 2. Appelle polyfill
geocodes = h3.polyfill(geojson=polygon_geojson,
res=8,
geo_json_conformant=True)
# 3. Affiche les resultat
print(set(list(geocodes)[0:10]))
print("")
# returns
{
"881fb46755fffff",
"881fb475a7fffff",
"881fb46639fffff",
"881fb46739fffff",
"881fb46601fffff",
"881fb475abfffff",
"881fb46441fffff",
"881fb4660dfffff",
"881fb4674dfffff",
"881fb4666dfffff",
}
def json_to_h3(country, res):
"""
Remplit une surface avec des hexagones.
Parameters
----------
country : str
res : int
Résolution H3, entre 1 et 16
Returns
-------
Liste des identifiants des cellules H3 remplissant la surface.
"""
path = f"../GeoJSONs/{country}.geo.json"
fObj = open(path)
s = json.load(fObj)
s = s['features'][0]['geometry']
swap_lng_lat(s)
return h3.polyfill(s, res=res)
def test_compact_and_uncompact():
geo = {
'type': 'Polygon',
'coordinates': [
[
[37.813318999983238, -122.4089866999972145],
[37.7866302000007224, -122.3805436999997056],
[37.7198061999978478, -122.3544736999993603],
[37.7076131999975672, -122.5123436999983966],
[37.7835871999971715, -122.5247187000021967],
[37.8151571999998453, -122.4798767000009008],
]
]
}
hexes = h3.polyfill(geo, 9)
compact_hexes = h3.compact(hexes)
assert len(compact_hexes) == 209
uncompact_hexes = h3.uncompact(compact_hexes, 9)
assert len(uncompact_hexes) == 1253
def fill_hexagons(geom_geojson, res, flag_swap=False, flag_return_df=False):
"""Fill the input polygons with h3 hexagons. The result of each polygon is stored in a single dataframe item.
Note: Each time you execute this function, the set of hexagons is the same, but the sequence order is different."""
set_hexagons = h3.polyfill(geojson=geom_geojson,
res=res,
geo_json_conformant=flag_swap)
list_hexagons_filling = list(set_hexagons)
if flag_return_df is True:
df_fill_hex = pd.DataFrame({"hex_id": list_hexagons_filling})
df_fill_hex["geometry"] = df_fill_hex.hex_id.apply(
lambda x:
{"type": "Polygon",
"coordinates": [
h3.h3_to_geo_boundary(h=x,
geo_json=True)
]
})
assert (df_fill_hex.shape[0] == len(list_hexagons_filling))
return df_fill_hex
else:
return list_hexagons_filling
def test_polyfill_with_hole():
geo = {
'type': 'Polygon',
'coordinates': [
[
[37.813318999983238, -122.4089866999972145],
[37.7866302000007224, -122.3805436999997056],
[37.7198061999978478, -122.3544736999993603],
[37.7076131999975672, -122.5123436999983966],
[37.7835871999971715, -122.5247187000021967],
[37.8151571999998453, -122.4798767000009008],
],
[
[37.7869802, -122.4471197],
[37.7664102, -122.4590777],
[37.7710682, -122.4137097],
]
]
}
out = h3.polyfill(geo, 9)
assert len(out) > 1000
def fill_hex_grid(gdf: gpd.GeoDataFrame,
geom_column: str = "geometry") -> gpd.GeoDataFrame:
bbox = gdf.total_bounds
# Pandas somehow mangles Geopandas geometry column types so that the types
# become mixed after concatenation and may cause TypeErrors, i.e. some
# Shapely geometries may be cast as strings in the process. We have to
# concatenate regular dataframes instead and reconstruct a geodataframe
# from the hex indices afterwards. Utterly stupid.
df = gdf.drop(columns=['geometry'])
bbox_polygon = box(*bbox)
hex_column = next((col for col in df.columns if col.startswith("hex")),
False)
if not hex_column:
raise AssertionError(
"Cannot calculate clusters, hex column not found.")
resolution = int(hex_column.replace("hex", ""))
# H3 polyfill needs geojson-like stuff. geo_json_conformant switches coordinate order
hexes_in_bbox = h3.polyfill(mapping(bbox_polygon),
resolution,
geo_json_conformant=True)
# Add only missing hexes here
missing_hexes = set(hexes_in_bbox).difference(df[hex_column])
missing_df = pd.DataFrame(list(missing_hexes),
columns=[hex_column]).set_index(hex_column,
drop=False)
columns_to_add = df.columns.difference(missing_df.columns)
for column in columns_to_add:
# Just add zeroes for missing index values
missing_df.insert(0, column, 0)
combined_df = pd.concat((df, missing_df))
# Add centroid geometries and reconstruct the geodataframe
centroid_lat_lon = [h3.h3_to_geo(hex) for hex in combined_df[hex_column]]
centroids = [Point(geom[1], geom[0]) for geom in centroid_lat_lon]
combined_gdf = gpd.GeoDataFrame(combined_df)
combined_gdf = combined_gdf.set_geometry(centroids)
return combined_gdf
map_h3 = folium.Map(location=[40.7, -74],
tiles="cartodbpositron",
zoom_start=13)
folium.GeoJson(data=neighborhoods_pd["geometry"].iloc[0:10]).add_to(map_h3)
map_h3.save("source/output.html")
map_h3
# COMMAND ----------
# DBTITLE 1,Get H3 Indices that represent the Geometry
import shapely
import h3
polygon = neighborhoods_pd.iloc[11]["geometry"]
geo_json_geom = shapely.geometry.mapping(polygon)
indices = h3.polyfill(geo_json_geom, 10, True)
list(indices)[0]
# COMMAND ----------
# DBTITLE 1,Convert indices to Polygons
index_geoms = [h3.h3_to_geo_boundary(ind, True) for ind in indices]
index_geoms = [shapely.geometry.Polygon(ind) for ind in index_geoms]
# COMMAND ----------
# DBTITLE 1,Folium Style Functions
def pink_poly(feature):
return {
'fillColor': '#e6328c',
def h3fy(source, resolution=6, clip=False, return_geoms=True):
"""Generate a hexgrid geodataframe that covers the face of a source geodataframe.
Parameters
----------
source : geopandas.GeoDataFrame
GeoDataFrame to transform into a hexagonal grid
resolution : int, optional (default is 6)
resolution of output h3 hexgrid.
See <https://h3geo.org/docs/core-library/restable> for more information
clip : bool, optional (default is False)
if True, hexagons are clipped to the precise boundary of the source gdf. Otherwise,
heaxgons along the boundary will be left intact.
return_geoms: bool, optional (default is True)
whether to generate hexagon geometries as a geodataframe or simply return
hex ids as a pandas.Series
Returns
-------
pandas.Series or geopandas.GeoDataFrame
if `return_geoms` is True, a geopandas.GeoDataFrame whose rows comprise a hexagonal h3 grid (indexed on h3 hex id).
if `return_geoms` is False, a pandas.Series of h3 hexagon ids
"""
try:
import h3
except ImportError:
raise ImportError(
"This function requires the `h3` library. "
"You can install it with `conda install h3-py` or "
"`pip install h3`"
)
orig_crs = source.crs
if not source.crs.is_geographic:
source = source.to_crs(4326)
hexids = pandas.Series(
list(
h3.polyfill(
source.unary_union.__geo_interface__,
resolution,
geo_json_conformant=True,
)
),
name="hex_id",
)
if not return_geoms:
return hexids
polys = hexids.apply(
lambda hex_id: Polygon(h3.h3_to_geo_boundary(hex_id, geo_json=True)),
)
hexs = geopandas.GeoDataFrame(hexids, geometry=polys, crs=source.crs).set_index(
"hex_id"
)
if clip:
hexs = geopandas.clip(hexs, source)
if not hexs.crs.equals(orig_crs):
hexs = hexs.to_crs(orig_crs)
return hexs
def add_baseline_seismicity(folder_name: str,
folder_name_out: str,
fname_config: str,
fname_poly: str,
skip=[]):
"""
:param folder_name:
The name of the folder containing the files with GR parameters for the
points in each zone considered
:param folder_name_out:
The folder where to write the results
:param config_file:
A .toml file with the configuration parameters
:param shapefile:
The name of the shapefile containing the geometry of the polygons used
:param skip:
A list with the sources that should be skipped [NOT ACTIVE!!!]
:returns:
An updated set of .csv files
"""
# Create output folder
create_folder(folder_name_out)
# Parsing config
model = toml.load(fname_config)
h3_level = model['baseline']['h3_level']
basel_agr = model['baseline']['a_value']
basel_bgr = model['baseline']['b_value']
# Read polygons
polygons_gdf = gpd.read_file(fname_poly)
# Loop over the polygons
polygons_gdf.sort_values(by="id", ascending=True, inplace=True)
polygons_gdf.reset_index(drop=True, inplace=True)
for idx, poly in polygons_gdf.iterrows():
geojson_poly = eval(json.dumps(shapely.geometry.mapping(
poly.geometry)))
# Revert the positions of lons and lats
coo = [[c[1], c[0]] for c in geojson_poly['coordinates'][0]]
geojson_poly['coordinates'] = [coo]
# Discretizing the polygon i.e. find all the hexagons covering the
# polygon describing the current zone
hexagons = list(h3.polyfill(geojson_poly, h3_level))
# Read the file with the points obtained by the smoothing
print("Source ID", poly.id)
fname = os.path.join(folder_name, '{:s}.csv'.format(poly.id))
df = pd.read_csv(fname)
srcs_idxs = [
h3.geo_to_h3(la, lo, h3_level) for lo, la in zip(df.lon, df.lat)
]
hxg_idxs = [hxg for hxg in hexagons]
missing = list(set(hxg_idxs) - set(srcs_idxs))
tmp = np.nonzero([df.agr <= basel_agr])[0]
# If we don't miss cells and rates are all above the threshold there
# is nothing else to do
fname = os.path.join(folder_name_out, "{:s}.csv".format(poly.id))
if len(missing) == 0 and len(tmp) == 0:
df.to_csv(fname, index=False)
continue
# Get the indexes of the point sources with low rates
idxs = np.nonzero(df.agr.to_numpy() <= basel_agr)[0]
low = [srcs_idxs[i] for i in idxs]
# Removing the sources with activity below the threshold
df.drop(df.index[idxs], inplace=True)
# Find the h3 indexes of the point sources either without seismicity
# or with a rate below the baseline
both = set(missing) | set(low)
# Adding baseline seismicity to the dataframe for the current source
if len(both) > 0:
tmp_df = create_missing(both, h3_level, basel_agr, basel_bgr)
df = df.append(tmp_df)
# Creating output file
assert len(hxg_idxs) == df.shape[0]
df.to_csv(fname, index=False)
def create_from_polygons(self, res: int, polygons: list) -> T:
hexes = h3.polyfill(polygons, res=9)
return SetH3Hex(res, hexes)
def get_h3_cells(geojson_in, resolution):
hexids = list(
h3.polyfill(geojson_in, res=resolution, geo_json_conformant=True))
return hexids
def poc_polar(hotspot, chals):
H = Hotspots()
haddr = hotspot['address']
hlat, hlng = hotspot['lat'], hotspot['lng']
hname = hotspot['name']
if os.path.exists(hname):
files = glob(hname + '\\*')
for file in files:
os.remove(file)
else:
os.mkdir(hname)
wl = {} #witnesslist
rl = {
} #received list of hotspots(hotspot of intereset has been witness to these or received from them)
c = 299792458
for chal in chals: # loop through challenges
for p in chal['path']: #path?
if p['challengee'] == haddr: # handles cases where hotspot of interest is transmitting
for w in p[
'witnesses']: #loop through witnesses so we can get rssi at each location challenge received
#print('witness',w)
lat = H.get_hotspot_by_addr(w['gateway'])['lat']
lng = H.get_hotspot_by_addr(w['gateway'])['lng']
name = H.get_hotspot_by_addr(w['gateway'])['name']
dist_km, heading = utils.haversine_km(hlat,
hlng,
lat,
lng,
return_heading=True)
fspl = 20 * log10((dist_km + 0.01) * 1000) + 20 * log10(
915000000) + 20 * log10(4 * pi / c) - 27
try:
wl[w['gateway']]['lat'] = lat
wl[w['gateway']]['lng'] = lng
wl[w['gateway']]['rssi'].append(w['signal'])
except KeyError:
wl[w['gateway']] = {
'rssi': [
w['signal'],
],
'dist_km': dist_km,
'heading': heading,
'fspl': fspl,
'lat': lat,
'lng': lng,
'name': name
}
else: # hotspot of interest is not transmitting but may be a witness
challengee = p['challengee']
name = H.get_hotspot_by_addr(challengee)['name']
for w in p['witnesses']:
if w['gateway'] != haddr:
continue
#print('transmitter ', name)
#print('witness ', H.get_hotspot_by_addr(w['gateway'])['name']) # hotspot of interest was a witness
lat = H.get_hotspot_by_addr(challengee)['lat']
lng = H.get_hotspot_by_addr(challengee)['lng']
#name=H.get_hotspot_by_addr(w['gateway'])['name']
dist_km, heading = utils.haversine_km(hlat,
hlng,
lat,
lng,
return_heading=True)
fspl = 20 * log10((dist_km + 0.01) * 1000) + 20 * log10(
915000000) + 20 * log10(4 * pi / c) - 27
try:
rl[challengee]['lat'] = lat
rl[challengee]['lng'] = lng
rl[challengee]['rssi'].append(w['signal'])
except KeyError:
rl[challengee] = {
'rssi': [
w['signal'],
],
'dist_km': dist_km,
'heading': heading,
'fspl': fspl,
'lat': lat,
'lng': lng,
'name': name
}
#print('rl:',rl)
ratios = [1.0] * 16
rratios = [1.0] * 16
N = len(ratios) - 1
angles = []
rangles = []
#angles = [n / float(N) *2 *pi for n in range(N+1)]
angles = list(np.arange(0.0, 2 * np.pi + (2 * np.pi / N), 2 * np.pi / N))
rangles = list(np.arange(0.0, 2 * np.pi + (2 * np.pi / N), 2 * np.pi / N))
#print(angles,len(angles))
#print(ratios,len(ratios))
markers = []
encoded = {}
rencoded = {}
for w in wl: #for witness in witnesslist
#print(wl[w])
mean_rssi = sum(wl[w]['rssi']) / len(wl[w]['rssi'])
ratio = wl[w]['fspl'] / mean_rssi * (-1)
if ratio > 3.0:
ratio = 3.0
elif ratio < -3.0:
ratio = -3.0
ratios.append(ratio)
angles.append(wl[w]['heading'] * pi / 180)
#markers.append(folium.Marker([wl[w]['lat'],wl[w]['lng']],popup=wl[w]['name']))
markers.append([[wl[w]['lat'], wl[w]['lng']], wl[w]['name']])
# the histogram of the data
#unique=set(wl[w]['rssi'])
#num_unique=len(unique)
n, bins, patches = plt.hist(
wl[w]['rssi'], 10) #, density=True, facecolor='g', alpha=0.75,)
plt.xlabel('RSSI(dB)')
plt.ylabel('Count(Number of Packets)')
wit = str(wl[w]['name'])
plt.title('Packets from ' + hname + ' measured at ' + wit)
#plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
#plt.xlim(40, 160)
#plt.ylim(0, 0.03)
plt.grid(True)
#plt.show()
strFile = str(wl[w]['name']) + '.jpg'
strWitness = str(wl[w]['name'])
if os.path.isfile(strFile):
#print('remove')
os.remove(strFile) # Opt.: os.system("rm "+strFile)
plt.savefig(hname + '//' + strFile)
encoded[strWitness] = base64.b64encode(
open(hname + '//' + strFile, 'rb').read())
plt.close()
for w in rl: #for witness in witnesslist
#print(rl[w])
mean_rssi = sum(rl[w]['rssi']) / len(rl[w]['rssi'])
rratio = rl[w]['fspl'] / mean_rssi * (-1)
if rratio > 3.0:
rratio = 3.0
elif rratio < -3.0:
rratio = -3.0
rratios.append(rratio)
rangles.append(rl[w]['heading'] * pi / 180)
#markers.append([[wl[w]['lat'],wl[w]['lng']],wl[w]['name']])
n, bins, patches = plt.hist(
rl[w]['rssi'], 10) #, density=True, facecolor='g', alpha=0.75,)
plt.xlabel('RSSI(dB)')
plt.ylabel('Count(Number of Packets)')
wit = str(rl[w]['name'])
plt.title('Packets from ' + wit + ' measured at ' + hname)
plt.grid(True)
#plt.show()
strFile = 'rrr' + str(rl[w]['name']) + '.jpg'
strWitness = str(rl[w]['name'])
if os.path.isfile(strFile):
#print('remove')
os.remove(strFile) # Opt.: os.system("rm "+strFile)
plt.savefig(hname + '//' + strFile)
rencoded[strWitness] = base64.b64encode(
open(hname + '//' + strFile, 'rb').read())
plt.close()
# create polar chart
angles, ratios = zip(*sorted(zip(angles, ratios)))
rangles, rratios = zip(*sorted(zip(rangles, rratios)))
angles, ratios = (list(t) for t in zip(*sorted(zip(angles, ratios))))
rangles, rratios = (list(t) for t in zip(*sorted(zip(rangles, rratios))))
fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
#ax.set_rmax(3)
#ax.set_rmin(-3)
ax.set_ylim(-3, 3)
ax.plot(angles,
ratios,
marker='^',
linestyle='solid',
color='tomato',
linewidth=2,
markersize=5,
label='Transmitting') #markerfacecolor='m', markeredgecolor='k',
ax.plot(rangles,
rratios,
marker='v',
linestyle='solid',
color='dodgerblue',
linewidth=1,
markersize=5,
label='Receiving') #, markerfacecolor='m', markeredgecolor='k'
ax.legend(bbox_to_anchor=(0, 1),
fancybox=True,
framealpha=0,
loc="lower left",
facecolor='#000000')
plt.xlabel('FSPL/RSSI')
plt.savefig(hname + '//' + hname + '.png', transparent=True)
#plt.show()
# add polar chart as a custom icon in map
m = folium.Map([hlat, hlng],
tiles='stamentoner',
zoom_start=18,
control_scale=True,
max_zoom=20)
polargroup = folium.FeatureGroup(name='Polar Plot')
icon = folium.features.CustomIcon(icon_image=hname + '//' +
hotspot['name'] + '.png',
icon_size=(640, 480))
marker = folium.Marker([hlat, hlng], popup=hotspot['name'], icon=icon)
polargroup.add_child(marker)
# add witness markers
hsgroup = folium.FeatureGroup(name='Witnesses')
hsgroup.add_child(folium.Marker([hlat, hlng], popup=hotspot['name']))
# add the witness markers
for marker in markers:
#html = '<img src="data:image/jpg;base64,{}">'.format
html = '<p><img src="data:image/jpg;base64,{}" alt="" width=640 height=480 /></p> \
<p><img src="data:image/jpg;base64,{}" alt="" width=640 height=480 /></p>'.format
#print('marker',marker)
try:
iframe = IFrame(html(encoded[marker[1]].decode('UTF-8'),
rencoded[marker[1]].decode('UTF-8')),
width=640 + 25,
height=960 + 40)
popup = folium.Popup(iframe, max_width=2650)
mark = folium.Marker(marker[0], popup=popup)
hsgroup.add_child(mark)
except KeyError: # this means this witness never heard from us so there is no marker for it
pass # not sure where to put the receive packet histogram so just ignore for now
radius = 0.01
center = Point(hlat, hlng)
circle = center.buffer(radius) # Degrees Radius
gjcircle = shapely.geometry.mapping(circle)
circle = center.buffer(radius * 25) # Degrees Radius
gjcircle8 = shapely.geometry.mapping(circle)
dcgroup = folium.FeatureGroup(name='Distance Circles', show=False)
radius = 0.01
center = Point(hlat, hlng)
circle = center.buffer(radius) # Degrees Radius
gjcircle = shapely.geometry.mapping(circle)
circle = gjcircle['coordinates'][0]
my_Circle = folium.Circle(location=[hlat, hlng],
radius=300,
popup='300m',
tooltip='300m')
dcgroup.add_child(my_Circle)
my_Circle = folium.Circle(location=[hlat, hlng],
radius=1000,
popup='1km',
tooltip='1km')
dcgroup.add_child(my_Circle)
my_Circle = folium.Circle(location=[hlat, hlng],
radius=2000,
popup='2km',
tooltip='2km')
dcgroup.add_child(my_Circle)
my_Circle = folium.Circle(location=[hlat, hlng],
radius=3000,
name='circles',
popup='3km',
tooltip='3km')
dcgroup.add_child(my_Circle)
my_Circle = folium.Circle(location=[hlat, hlng],
radius=4000,
popup='4km',
tooltip='4km')
dcgroup.add_child(my_Circle)
my_Circle = folium.Circle(location=[hlat, hlng],
radius=5000,
popup='5km',
tooltip='5km')
dcgroup.add_child(my_Circle)
my_Circle = folium.Circle(location=[hlat, hlng],
radius=10000,
popup='10km',
tooltip='10km')
dcgroup.add_child(my_Circle)
h3colorgroup = folium.FeatureGroup(name='h3 Hexagon Grid Color Fill',
show=False)
style = {'fillColor': '#f5f5f5', 'lineColor': '#ffffbf'}
#polygon = folium.GeoJson(gjson, style_function = lambda x: style).add_to(m)
h3group = folium.FeatureGroup(name='h3 r11 Hex Grid', show=False)
h3namegroup = folium.FeatureGroup(name='h3 r11 Hex Grid Names', show=False)
h3fillgroup = folium.FeatureGroup(name='h3 r11 Hex Grid Color Fill',
show=True)
h3r8namegroup = folium.FeatureGroup(name='h3 r8 Hex Grid Names',
show=False)
h3r8group = folium.FeatureGroup(name='h3 r8 Hex Grid', show=False)
hexagons = list(h3.polyfill(gjcircle, 11))
hexagons8 = list(h3.polyfill(gjcircle8, 8))
polylines = []
lat = []
lng = []
i = 0
#print('hexagon',hexagons[0])
#print(dir(h3))
home_hex = h3.geo_to_h3(hlat, hlng, 11)
a = h3.k_ring(home_hex, 7)
for h in a:
gjhex = h3.h3_to_geo_boundary(h, geo_json=True)
gjhex = geometry.Polygon(gjhex)
mean_rsrp = -60
folium.GeoJson(
gjhex,
style_function=lambda x, mean_rsrp=mean_rsrp: {
'fillColor': map_color_rsrp(mean_rsrp),
'color': map_color_rsrp(mean_rsrp),
'weight': 1,
'fillOpacity': 0.5
},
#tooltip='tooltip'
).add_to(h3fillgroup)
for hex in hexagons:
p2 = h3.h3_to_geo(hex)
#p2 = [45.3311, -121.7113]
folium.Marker(
p2,
name='hex_names',
icon=DivIcon(
#icon_size=(150,36),
#icon_anchor=(35,-45),
icon_anchor=(35, 0),
html='<div style="font-size: 6pt; color : black">' + str(hex) +
'</div>',
)).add_to(h3namegroup)
#m.add_child(folium.CircleMarker(p2, radius=15))
polygons = h3.h3_set_to_multi_polygon([hex], geo_json=False)
# flatten polygons into loops.
outlines = [loop for polygon in polygons for loop in polygon]
polyline = [outline + [outline[0]] for outline in outlines][0]
lat.extend(map(lambda v: v[0], polyline))
lng.extend(map(lambda v: v[1], polyline))
polylines.append(polyline)
for polyline in polylines:
my_PolyLine = folium.PolyLine(locations=polyline,
weight=1,
color='blue')
h3group.add_child(my_PolyLine)
polylines = []
lat = []
lng = []
#polylines8 = []
for hex in hexagons8:
p2 = h3.h3_to_geo(hex)
folium.Marker(
p2,
name='hex_names',
icon=DivIcon(
#icon_size=(150,36),
#icon_anchor=(35,-45),
icon_anchor=(35, 0),
html='<div style="font-size: 8pt; color : black">' + str(hex) +
'</div>',
)).add_to(h3r8namegroup)
polygons = h3.h3_set_to_multi_polygon([hex], geo_json=False)
# flatten polygons into loops.
outlines = [loop for polygon in polygons for loop in polygon]
polyline = [outline + [outline[0]] for outline in outlines][0]
lat.extend(map(lambda v: v[0], polyline))
lng.extend(map(lambda v: v[1], polyline))
polylines.append(polyline)
for polyline in polylines:
my_PolyLine = folium.PolyLine(locations=polyline,
weight=1,
color='blue')
h3r8group.add_child(my_PolyLine)
# add possible tiles
folium.TileLayer('cartodbpositron').add_to(m)
folium.TileLayer('cartodbdark_matter').add_to(m)
folium.TileLayer('openstreetmap').add_to(m)
folium.TileLayer('Mapbox Bright').add_to(m)
#folium.TileLayer('stamentoner').add_to(m)
# add markers layer
#marker_cluster = MarkerCluster().add_to(m)
polargroup.add_to(m) #polar plot
hsgroup.add_to(m) #hotspots
dcgroup.add_to(m) #distance circles
h3group.add_to(m)
h3namegroup.add_to(m)
h3fillgroup.add_to(m)
m.keep_in_front(h3group)
h3r8group.add_to(m)
h3r8namegroup.add_to(m)
# add the layer control
folium.LayerControl(collapsed=False).add_to(m)
m.save(hname + '//' + hname + '_map.html')
def test_polyfill_bogus_geo_json():
with pytest.raises(ValueError):
bad_geo = {'type': 'whatwhat'}
h3.polyfill(bad_geo, 9)