# Requete sur PHOTON pour connaitre les coordonnees spatiales d'une adresse postale
add_MAME = "49 Boulevard Preuilly 37000 Tours France"
geocoder = Nominatim(user_agent="trainee")
geo_MAME = geocoder.geocode(add_MAME, True, 30)
# Chargement du fond de carte
carte = folium.Map(location=[geo_MAME.latitude, geo_MAME.longitude],
zoom_start=13)
print(carte)
# Ajout d'information sur la carte
folium.Marker(location=[geo_MAME.latitude, geo_MAME.longitude]).add_to(carte)
print(carte)
folium.CircleMarker(location=[geo_MAME.latitude, geo_MAME.longitude],
radius=10).add_to(carte)
print(carte)
# Personnalisation des informations restituées
msg = "DataLab 2019, c'est ici ! On s'éclate et nos formateurs sont géniaux !"
carte = folium.CircleMarker(location=[geo_MAME.latitude, geo_MAME.longitude],
radius=10,
popup=msg).add_to(carte)
print(carte)
# Sauvegarde de la carte au format 'html'
import os
import webbrowser
chemin = os.getcwd() + '/carte_py.html'
carte.save(chemin)
webbrowser.open('file://' + chemin)
elevs = list(volcanoes.ELEV)
def pick_color(val):
if val < 1000:
return 'green'
elif 1000 <= val < 3000:
return 'orange'
else:
return 'red'
for lat, lon, elev in zip(lats, lons, elevs):
#fgroup.add_child(folium.Marker(location=[lat, lon], popup=str(elev) + ' m', icon=folium.Icon(icon='circle-o', color=pick_color(elev))))
fgroup_v.add_child(folium.CircleMarker(location=[lat, lon],
radius=6,
popup=str(elev) + ' m',
fill_opacity=.8,
fill=True,
color=pick_color(elev)))
data = open('web_map/world.json', 'r', encoding='utf-8-sig')
fgroup_p.add_child(folium.GeoJson(data=data.read(),
style_function=lambda x: {'fillColor': 'green' if x['properties']['POP2005'] < 10000000
else 'orange' if 10000000 <= x['properties']['POP2005'] < 20000000
else 'red'}))
my_map.add_child(fgroup_v)
my_map.add_child(fgroup_p)
my_map.add_child(folium.LayerControl())
data.close()
# print(df.columns.values)
# print(df.head())
# print(df.info())
# print(df.describe())
# print('--')
mschool_map = fm.Map(location=[37.55, 126.98],
tiles='Stamen Terrain',
zoom_start=12)
for name, lat, lng in zip(df.학교명, df.위도, df.경도):
fm.CircleMarker([lat, lng],
radius=5,
color='brown',
fill=True,
fill_color='coral',
fill_opacity=0.7,
popup=name).add_to(mschool_map)
mschool_map.save('./Seoul_Middle_School_locations.html')
from sklearn import preprocessing
label_encoder = preprocessing.LabelEncoder()
onehot_encoder = preprocessing.OneHotEncoder()
onehot_location = label_encoder.fit_transform(df['지역'])
onehot_code = label_encoder.fit_transform(df['코드'])
onehot_type = label_encoder.fit_transform(df['유형'])
onehot_day = label_encoder.fit_transform(df['주야'])
print('The geograpical coordinate of Toronto are {}, {}.'.format(
latitude, longitude))
# In[18]:
map_toronto = folium.Map(location=[latitude, longitude], zoom_start=11)
# add markers to map
for lat, lng, borough, neighborhood in zip(toronto_geo_df['Latitude'],
toronto_geo_df['Longitude'],
toronto_geo_df['Borough'],
toronto_geo_df['Neighbourhood']):
label = '{}, {}'.format(neighborhood, borough)
label = folium.Popup(label, parse_html=True)
folium.CircleMarker([lat, lng],
radius=5,
popup=label,
color='blue',
fill=True,
fill_color='#3186cc',
fill_opacity=0.7,
parse_html=False).add_to(map_toronto)
map_toronto
# In[ ]:
# In[ ]:
# In[ ]:
stability = root.attrib['stability']
if (stability):
if "Poor" in stability:
fill_color = "red"
elif "Fair" in stability:
fill_color = "orange"
elif "Good" in stability:
fill_color = "lime"
#print(root.attrib)
if (slope):
if int(slope) >= 35:
color = "black"
elif int(slope) >= 25:
color = "blue"
else:
color = "green"
html = '<b>#' + str(
i) + '</b><img src="' + image + '" width=700px;>'
popup = folium.Popup(html, max_width=2650)
folium.CircleMarker([lat, lng],
weight=5,
radius=8,
color=color,
fill_color=fill_color,
fill_opacity=0.7,
popup=popup,
max_width=2600).add_to(m)
m.save('cluster.html')
print("saved cluster.html")
def plot_alert_system(self, postcode):
'''
Get map with corresponding risk level for different postcode area from a sequence of postcodes
Parameters
----------
postcode: sequence of str
Returns
-------
folium.Map
can produce picture of map with marks
'''
#plot real_time data alert system map
lat, long, level = self.get_Risk_Level(postcode, 0.1)
tem_DF = pd.DataFrame({'lat': lat, 'long': long})
tem_DF = tem_DF.dropna(how='any')
lat = np.array(tem_DF.lat)
long = np.array(tem_DF.long)
locationlist = np.array((lat, long)).transpose()
#the plot code must run in jupyter lab, or it cannot ouput a map
coldict = {
0: 'lightgreen',
1: 'green',
2: 'lightblue',
3: 'orange',
4: 'red'
}
#possible colours are [‘red’, ‘blue’, ‘green’, ‘purple’, ‘orange’, ‘darkred’,
#’lightred’, ‘beige’, ‘darkblue’, ‘darkgreen’, ‘cadetblue’, ‘darkpurple’, ‘white’, ‘pink’, ‘lightblue’, ‘lightgreen’, ‘gray’, ‘black’, ‘lightgray’]
#set the overview place at beginning to centre of England
map = folium.Map(location=[54, -1], zoom_start=6)
#plot the different risk level point with corresponding colorful mark
for point in range(0, len(locationlist)):
if level[point] == 'No Risk':
folium.CircleMarker(locationlist[point],
color='black',
fill_color=coldict[0],
fill=True,
fill_opacity=0.8,
radius=10,
icon=folium.Icon(color='red')).add_to(map)
elif level[point] == 'Low Risk':
folium.CircleMarker(locationlist[point],
color='black',
fill_color=coldict[1],
fill=True,
fill_opacity=0.8,
radius=10,
icon=folium.Icon(color='red')).add_to(map)
elif level[point] == 'Medium Risk':
folium.CircleMarker(locationlist[point],
color='black',
fill_color=coldict[2],
fill=True,
fill_opacity=0.8,
radius=10,
icon=folium.Icon(color='red')).add_to(map)
elif level[point] == 'High Risk':
folium.CircleMarker(locationlist[point],
color='black',
fill_color=coldict[3],
fill=True,
fill_opacity=0.8,
radius=10,
icon=folium.Icon(color='red')).add_to(map)
elif level[point] == 'Dangerous':
folium.CircleMarker(locationlist[point],
color='black',
fill_color=coldict[4],
fill=True,
fill_opacity=0.8,
radius=10,
icon=folium.Icon(color='red')).add_to(map)
return map
all_DESTINATIONS_df.LAT_DESTINATION)
]
crs = {'init': 'epsg:4326'}
all_DESTINATIONS_gdf = GeoDataFrame(all_DESTINATIONS_df,
crs=crs,
geometry=geometry)
# save first as geojson file
all_DESTINATIONS_gdf.geometry.to_file(filename='all_PATHS_gdf.geojson',
driver='GeoJSON')
# all_DESTINATIONS_gdf.plot()
for idx, row in all_ORIGINS_df.iterrows():
folium.CircleMarker(
location=[row["LAT_ORIGIN"], row["LON_ORIGIN"]],
# popup=row["deviceid"],
radius=0.5,
color="blue",
# fill=True,
# fill_color="black",
fill_opacity=0.1).add_to(my_map)
for idx, row in all_DESTINATIONS_df.iterrows():
folium.CircleMarker(
location=[row["LAT_DESTINATION"], row["LON_DESTINATION"]],
# popup=row["deviceid"],
radius=0.5,
color="red",
# fill=True,
# fill_color="blue",
fill_opacity=0.1).add_to(my_map)
my_map.save("clean_matched_route_OD.html")
Height: %s m """
#Volcano Feature Group
fgv = folium.FeatureGroup(name="Volcanoes")
for x, y, name, elev in zip(volcanoX, volcanoY, volcanoName, volcanoElevation):
iframe = folium.IFrame(html=html % (name, name, name, str(elev)),
width=200,
height=100)
#Option 1: CircleMarkers
fgv.add_child(
folium.CircleMarker(location=[x, y],
radius=5,
popup=folium.Popup(iframe),
weight=1,
opacity=10.0,
color="black",
fill=True,
fill_color=elev_color(elev),
fill_opacity=.7))
#Option 2: Markers
# map.add_child(folium.Marker(location=[x,y], popup=folium.Popup(iframe),
# icon=folium.Icon(color=elev_color(elev), icon_color='white', icon='flag')))
#Population Feature Group
fgp = folium.FeatureGroup(name="Population")
fgp.add_child(
folium.GeoJson(
data=(open("world.json", 'r', encoding='utf-8-sig').read()),
style_function=lambda x: {
return 'green'
elif elvation <= 3000:
return 'orange'
else:
return 'red'
volcanoes = folium.FeatureGroup(name="Volcanoes")
for lt, ln, el, nm in zip(lat, long, elev, name):
iframe = folium.IFrame(html=html % (nm, nm, el), width=240, height=64)
colour = colour_producer(el)
volcanoes.add_child(
folium.CircleMarker(location=[lt, ln],
radius=8,
popup=folium.Popup(iframe),
fill_color=colour,
color=colour,
fill_opacity=0.7))
population = folium.FeatureGroup(name="Population")
population.add_child(
folium.GeoJson(
data=open('population.json', 'r', encoding='utf-8-sig').read(),
style_function=lambda x: {
'fillColor':
'green' if x['properties']['POP2005'] < 15000000 else 'orange'
if 15000000 <= x['properties']['POP2005'] < 30000000 else 'red',
'color':
'blank'
}))
folium.Marker([44.796171, 20.437084],
popup="The Belgrade Fair. ",
tooltip=tooltip5,
icon=folium.Icon(color="blue")).add_to(m),
folium.Marker([44.786568, 20.427067],
popup="Hippodrome / Racecourse ",
tooltip=tooltip6,
icon=folium.Icon(color="blue")).add_to(m),
folium.Marker([44.814877, 20.455335],
popup="Best Trading Community in SouthEast Europe.",
tooltip=tooltip7,
icon=Logo1).add_to(m),
folium.CircleMarker(location=[44.770493, 20.469921],
radius=30,
popup="The Military Academy of the University of Defence.",
tooltip=tooltip8,
color='#fc5628',
fill=True,
fill_color='#fc5628').add_to(m),
# vis marker
folium.Marker(location=[44.829768, 20.433458],
tooltip=tooltip9,
popup=folium.Popup(max_width=550).add_child(
folium.Vega(json.load(open(visData)), width=550,
height=250))).add_to(m)
# geo marker
folium.GeoJson(geoOverlay, tooltip=tooltip10,
name='Kosutnjak Forest').add_to(m),
def plot_cvrp_solution_routes(
solution: CVRPSolution,
route_indices_to_plot: Optional[List[int]] = None,
config: Optional[OSRMConfig] = None,
) -> None:
"""Plot solution routes in a map along the streets
Parameters
----------
solution
A solution to any solver with the vehicles routes to plot
route_indices_to_plot
If specified, selects a smaller subset of routes to plot by their
indices. This can be useful to reduce the clutter in case of a
solution with too many vehicles
config
OSRM configuration
"""
config = config or OSRMConfig()
# Initialize map centered at the mean of the origins
origins_mean = np.mean(
[(vehicle.origin.lat, vehicle.origin.lng)
for vehicle in solution.vehicles],
axis=0,
)
m = folium.Map(
location=origins_mean,
zoom_start=12,
tiles="cartodbpositron",
)
num_vehicles = len(solution.vehicles)
route_indices_to_plot = route_indices_to_plot or range(num_vehicles)
vehicles_subset = [solution.vehicles[i] for i in route_indices_to_plot]
for i, vehicle in enumerate(vehicles_subset):
vehicle_color = MAP_COLORS[i % len(MAP_COLORS)]
# Plot origin
origin = (vehicle.origin.lat, vehicle.origin.lng)
folium.CircleMarker(origin, color="red", radius=3, weight=5).add_to(m)
# Plot street outlines
wiring = _route_wiring(vehicle.circuit, config)
folium.PolyLine(wiring,
color=vehicle_color,
weight=1.0,
popup=f"Vehicle {i}").add_to(m)
# Plot the deliveries as regular points
for delivery in vehicle.deliveries:
folium.Circle(
location=(delivery.point.lat, delivery.point.lng),
radius=10,
fill=True,
color=vehicle_color,
popup=(
f"Vehicle {i} ({delivery.point.lat}, {delivery.point.lng})"
),
).add_to(m)
return m
def create_seoul_crime_map(self):
#생성한 객체의 변수에 값 할당
self.dr.context = "./saved_data/"
self.dr.fname = "police_nomr.csv"
police = self.dr.csv_dframe()
print(police)
self.dr.context = "./data/"
self.dr.fname = 'geo_simple.json'
seoul_geo = self.dr.json_load()
self.dr.context = "./data/"
self.dr.fname = "crime_in_seoul.csv"
crime = self.dr.csv_dframe()
station_names = []
for name in crime["관서명"]:
station_names.append("서울" + str(name[:-1] + "경찰서"))
station_addrs = []
station_lats = [] # 위도
station_lngs = [] # 경도
gmaps = self.dr.create_gmaps()
for name in station_names:
t = gmaps.geocode(name, language="ko")
station_addrs.append(t[0].get("formatted_address"))
t_loc = t[0].get("geometry")
# 구글의 컬럼 명 그대로lat, lng -> api
station_lats.append(t_loc["location"]["lat"])
station_lngs.append(t_loc["location"]["lng"])
print(name + "--------------->" + t[0].get("formatted_address"))
# 기존 코드
self.dr.context = './data/'
self.dr.fname = 'police_position.csv'
police_pos = self.dr.csv_dframe()
police_pos['lat'] = station_lats
police_pos['lng'] = station_lngs
print(police_pos)
col = ['살인 검거', '강도 검거', '강간 검거', '절도 검거', ' 폭력 검거']
tmp = police_pos[col] / police_pos[col].max
police_pos['검거'] = np.sum(tmp, axis=1)
self.dr.fname = 'geo_simple.json'
m = folium.Map(location=[37.5902, 126.982],
zoom_start=12,
title='Stamen Toner')
# 속성값
m.choropleth(
geo_data=seoul_geo,
name="choropleth",
data=tuple(zip(police['구별'], police['범죄'])),
#columns=["State", "Unemployment"],
key_on="feature.id",
fill_color="PuRd",
fill_opacity=0.7,
line_opacity=0.2,
lengend_name="CRIME RATE (%)")
for i in police_pos.index:
#동그란 이미지 추가
folium.CircleMarker([police_pos['lat'][i], police_pos['lng'][i]],
radius=police_pos['검거'][i] * 10,
fill_color='#0a0a32').add_to(m)
#저장하기
m.save('./saved_data/Seoul_Crime_Map.html')
<br>Type: %s
"""
map = folium.Map([40, -100], zoom_start=5, tiles="Mapbox Bright")
fgv = folium.FeatureGroup(name="Volcanoes")
for lt, ln, nam, el, typ in zip(lat, lon, name, elev, type):
iframe = folium.IFrame(html=html % (nam, str(el), typ),
width=250,
height=120)
# fgv.add_child(folium.Marker(location=[lt, ln], popup=folium.Popup(iframe),
# icon=folium.Icon(color=color_producer(typ))))
fgv.add_child(
folium.CircleMarker(location=[lt, ln],
popup=folium.Popup(iframe),
fill_color=color_producer(typ),
color='grey',
fill_opacity=0.7))
fgp = folium.FeatureGroup(name="Population")
fgp.add_child(
folium.GeoJson(
data=open('world.json', 'r', encoding='utf-8-sig').read(),
style_function=lambda x: {
'fillColor':
'green' if x['properties']['POP2005'] < 10000000 else 'orange'
if 10000000 <= x['properties']['POP2005'] < 20000000 else 'red'
}))
map.add_child(fgv)
map.add_child(fgp)
else:
return 'red'
map = folium.Map(location=(38.58, -99.09),
zoom_start=6,
tiles="Stamen Terrain")
fgv = folium.FeatureGroup(name='Volcanoes')
#add multiple markers
for lt, ln, el in zip(lat, lon, elev):
#fg.add_child(folium.Marker(location=(lt,ln),popup=str(el)+'m',icon=folium.Icon(color=color_proceduer(el))))
fgv.add_child(
folium.CircleMarker(location=(lt, ln),
radius=6,
popup=str(el) + 'm',
fill_color=color_proceduer(el),
color='grey',
fill_opacity=0.7))
fgp = folium.FeatureGroup(name='Population')
fgp.add_child(
folium.GeoJson(
data=open('world.json', 'r', encoding='utf-8-sig').read(),
style_function=lambda x: {
'fillColor':
'green' if x['properties']['POP2005'] < 10000000 else 'orange'
if 10000000 <= x['properties']['POP2005'] < 40000000 else 'red'
}))
map.add_child(fgv)
map.add_child(fgp)
import folium
import webbrowser
svmap = folium.Map(location=[37, -122], titles="Stamen terrain", zoom_start=11)
folium.CircleMarker([37, -122], popup="cisco systems",
color="#0F8ABE").add_to(svmap)
filepath = 'C:\\temp\\map.html'
svmap.save(filepath)
webbrowser.open('file://' + filepath)
avriandlanf = folium.LayerControl()
layerControl = folium.LayerControl(position="topright")
volcanoes_layer = folium.FeatureGroup(name="volcanoes_layer")
population_layer = folium.FeatureGroup(name="population_layer")
html = """<h4>%s</h4> Height: %d meters"""
for lt, ln, el, nm in zip(lat, lon, elev, name):
i_frame = folium.IFrame(html=html % (nm, el), width=200, height=100)
# You can add marker clusters as well to make it look much nicer.
# And make the map smaller on a website!
volcanoes_layer.add_child(folium.CircleMarker(location=[lt, ln],
popup=folium.Popup(i_frame),
radius=6,
fill_color=set_colour(el),
color=set_colour(el),
fill_opacity=1))
population_layer.add_child(folium.GeoJson(data=open("world.json", "r",
encoding="utf-8-sig").read(),
style_function=lambda x:{"fillColor":"green" if x["properties"]["POP2005"] <
10000000 else "orange" if x["properties"]["POP2005"] < 100000000 else "red"}))
world_map.add_child(population_layer)
world_map.add_child(volcanoes_layer)
world_map.add_child(layerControl)
world_map.save("Map1.html")
import folium
import pandas as pd
import numpy as np
dtypes = {'BusID': np.int32, 'LAT': np.float64, 'LON': np.float64, 'Angle': np.float32, 'Speed': np.float32, 'Timestamp': 'str'}
big_df = pd.read_csv('../dataset/small_tail.csv', dtype=dtypes, chunksize=1000000)
i = 0
mapit = folium.Map(location=[12.9716, 77.5946], zoom_start=12, prefer_canvas=True)
bus = []
for df in big_df:
for index, row in df.iterrows():
print(row['Timestamp'])
time = {"2016-07-01": "#eb4034", "2016-07-02":"#eb9934","2016-07-03":"#e2eb34","2016-07-04":"#68eb34","2016-07-04":"#34ebeb","2016-07-05":"#345ceb","2016-07-06":"#d934eb"}
folium.CircleMarker(location=[row['LAT'], row['LON']], color=time[row['Timestamp'].split(" ")[0]], radius=2, weight=2,label=index).add_child(folium.Popup(index)).add_to(mapit)
i += 1
mapit.save('small_tail.html')
def weatherAPI():
# create a coordinates variable to be used in the api
coordniates = post_code.get()
params = {
'postal_code': coordniates,
'country': 'JP',
'key': 'aa48ad88872540c2b629a07581fda25f',
}
# handle errors in connecting to api
try:
api_result = requests.get(
'https://api.weatherbit.io/v2.0/forecast/daily?', params)
api_output = api_result.json()
except Exception as e:
api_output = "Error connecting to API, please check your parameters"
# create api output variables
latitude = api_output['lat']
longitude = api_output['lon']
timezone = api_output['timezone']
city = api_output['city_name']
day_1 = api_output['data'][0]
day_2 = api_output['data'][1]
day_3 = api_output['data'][2]
# create LISTS of results
dates = [day_1['datetime'], day_2['datetime'], day_3['datetime']]
icons = [
day_1['weather']['icon'], day_2['weather']['icon'],
day_3['weather']['icon']
]
dscrpts = [
day_1['weather']['description'], day_2['weather']['description'],
day_3['weather']['description']
]
maxtemp = [day_1['max_temp'], day_2['max_temp'], day_3['max_temp']]
mintemp = [day_1['min_temp'], day_2['min_temp'], day_3['min_temp']]
days = []
# print(dates)
# print(days)
for dy in dates:
values = dy.split('-')
# print(values)
day, month, year = (int(values[2]), int(values[1]), int(values[0]))
new_values = datetime.date(year, month, day)
list.append(days, new_values.strftime('%a'))
# print(days)
# print(timezone)
new_maxtemp = [
str(int(maxtemp[0])) + " °",
str(int(maxtemp[1])) + " °",
str(int(maxtemp[2])) + " °"
]
new_mintemp = [
str(int(mintemp[0])) + " °",
str(int(mintemp[1])) + " °",
str(int(mintemp[2])) + " °"
]
#print address location
# print(city)
#print required values date, day, weather icon, weather description, max temp and min temp
t = Texttable()
t.add_rows([[
'Date', 'Day', 'Weather icon', 'Description', 'Max temp', 'Min temp'
], [
dates[0], days[0], icons[0], dscrpts[0], new_maxtemp[0], new_mintemp[0]
], [
dates[1], days[1], icons[1], dscrpts[1], new_maxtemp[1], new_mintemp[1]
], [
dates[2], days[2], icons[2], dscrpts[2], new_maxtemp[2], new_mintemp[2]
]])
address_label = Label(window, text=city + "\n" + "3-day forecast")
address_label.grid(row=1, column=0, columnspan=3, pady=(10, 0))
# print(type(icons[0]))
#SET FRAME 1 FOR DAY 1
# create frame label
frame_1 = LabelFrame(window)
#display frame on main window
frame_1.grid(row=2, column=0, pady=(10, 0), padx=(10, 0))
# GET THE WEATHER ICON FOR DAY 1
# open day 1 weather icon
url_1 = "https://www.weatherbit.io/static/img/icons/" + icons[0] + ".png"
u_1 = urlopen(url_1)
raw_data_1 = u_1.read()
u_1.close()
icon_1 = ImageTk.PhotoImage(Image.open(BytesIO(raw_data_1)))
#create day 1 weather icon label
icon_1_label = Label(frame_1, image=icon_1)
#display day 1 weather icon label
icon_1_label.grid(row=0, column=0, columnspan=2)
# SET DATE FOR DAY 1
# create date label
date_1_label = Label(frame_1, text=dates[0], anchor=W)
#display date on frame
date_1_label.grid(row=1, column=0)
# SET DAY FOR DAY 1
# create day label
day_1_label = Label(frame_1, text=days[0], anchor=E)
#display day on frame
day_1_label.grid(row=1, column=1)
# SET WEATHER DESCRIPTION FOR DAY 1
# create description label
descrption_1_label = Label(frame_1, text=dscrpts[0], anchor=W)
#display description on frame
descrption_1_label.grid(row=2, column=0, columnspan=2)
# SET MAX TEMP OR DAY 1
# create max temp label
max_temp_1_label = Label(frame_1, text="max " + new_maxtemp[0], anchor=E)
#display max temp on frame
max_temp_1_label.grid(row=3, column=0, columnspan=2)
# SET MIN TEMP OR DAY 1
# create max temp label
min_temp_1_label = Label(frame_1, text="min " + new_mintemp[0], anchor=W)
#display max temp on frame
min_temp_1_label.grid(row=3, column=2, columnspan=2)
#SET FRAME 2 FOR DAY 2
frame_2 = LabelFrame(window)
frame_2.grid(row=2, column=1, pady=(10, 0), padx=(10, 0))
# GET THE WEATHER ICON FOR DAY 2
# open day 2 weather icon
url_2 = "https://www.weatherbit.io/static/img/icons/" + icons[1] + ".png"
u_2 = urlopen(url_2)
raw_data_2 = u_2.read()
u_2.close()
icon_2 = ImageTk.PhotoImage(Image.open(BytesIO(raw_data_2)))
# create day 1 weather icon label
icon_2_label = Label(frame_2, image=icon_2)
# display day 1 weather icon label
icon_2_label.grid(row=0, column=0, columnspan=2)
# SET DATE FOR DAY 2
# create date label
date_2_label = Label(frame_2, text=dates[1], anchor=W)
#display date on frame
date_2_label.grid(row=1, column=0)
# SET DAY FOR DAY 2
# create day label
day_2_label = Label(frame_2, text=days[1], anchor=E)
#display day on frame
day_2_label.grid(row=1, column=1)
# SET WEATHER DESCRIPTION FOR DAY 2
# create description label
descrption_2_label = Label(frame_2, text=dscrpts[1], anchor=W)
#display description on frame
descrption_2_label.grid(row=2, column=0, columnspan=2)
# SET MAX TEMP OR DAY 2
# create max temp label
max_temp_2_label = Label(frame_2, text="max " + new_maxtemp[1], anchor=E)
#display max temp on frame
max_temp_2_label.grid(row=3, column=0, columnspan=2)
# SET MIN TEMP OR DAY 2
# create max temp label
min_temp_2_label = Label(frame_2, text="min " + new_mintemp[1], anchor=W)
#display max temp on frame
min_temp_2_label.grid(row=3, column=2, columnspan=2)
#SET FRAME 3 FOR DAY 3
frame_3 = LabelFrame(window)
frame_3.grid(row=2, column=2, pady=(10, 0), padx=(10, 10))
# GET THE WEATHER ICON FOR DAY 3
# open day 2 weather icon
url_3 = "https://www.weatherbit.io/static/img/icons/" + icons[2] + ".png"
u_3 = urlopen(url_3)
raw_data_3 = u_3.read()
u_3.close()
icon_3 = ImageTk.PhotoImage(Image.open(BytesIO(raw_data_2)))
# create day 1 weather icon label
icon_3_label = Label(frame_3, image=icon_3)
# display day 1 weather icon label
icon_3_label.grid(row=0, column=0, columnspan=2)
# SET DATE FOR DAY 3
# create date label
date_3_label = Label(frame_3, text=dates[2], anchor=W)
#display date on frame
date_3_label.grid(row=1, column=0)
# SET DAY FOR DAY 3
# create day label
day_3_label = Label(frame_3, text=days[2], anchor=E)
#display day on frame
day_3_label.grid(row=1, column=1)
# SET WEATHER DESCRIPTION FOR DAY 3
# create description label
descrption_3_label = Label(frame_3, text=dscrpts[2], anchor=W)
#display description on frame
descrption_3_label.grid(row=2, column=0, columnspan=2)
# SET MAX TEMP OR DAY 3
# create max temp label
max_temp_3_label = Label(frame_3, text="max " + new_maxtemp[2], anchor=E)
#display max temp on frame
max_temp_3_label.grid(row=3, column=0, columnspan=2)
# SET MIN TEMP OR DAY 3
# create max temp label
min_temp_3_label = Label(frame_3, text="min " + new_mintemp[2], anchor=W)
#display max temp on frame
min_temp_3_label.grid(row=3, column=2, columnspan=2)
#SET FRAME 4 FOR MAP
frame_4 = LabelFrame(window)
frame_4.grid(row=3, column=0, columnspan=2, pady=(10, 10), padx=(10, 0))
# VISUALISE MAP
map1 = folium.Map(
location=[latitude, longitude],
tiles='cartodbpositron',
zoom_start=12,
)
lambda row: folium.CircleMarker(location=[latitude, longitude]).add_to(map1
)
# mapWidth, mapHeight = (400,500) # width and height of the displayed iFrame, in pixels
# srcdoc = map1._repr_html_()
# embed = HTML('<iframe srcdoc="{}" '
# 'style="width: {}px; height: {}px; display:block; width: 50%; margin: 0 auto; '
# 'border: none"></iframe>'.format(srcdoc, mapWidth, mapHeight))
map1.save('1.html')
map_label = Label(frame_4, text=webbrowser.open('1.html'))
# map_label = Label(frame_4, text=display(embed))
map_label.grid(row=0, column=0, columnspan=2, sticky=W + E)
#SET FRAME 5 FOR FREE SPACE
frame_5 = LabelFrame(window)
frame_5.grid(row=3, column=1, columnspan=2, pady=(10, 10), padx=(0, 10))
def plotGraph():
# Make a data frame
bars = [(str(days[0]) + " " + str(dates[0])),
(str(days[1]) + " " + str(dates[1])),
(str(days[2]) + " " + str(dates[2]))]
df = pd.DataFrame({
'x':
bars,
'max_temp': [int(maxtemp[0]),
int(maxtemp[0]),
int(maxtemp[0])],
'min_temp': [int(mintemp[0]),
int(mintemp[1]),
int(mintemp[2])]
})
# Initialize the figure
plt.style.use('seaborn-darkgrid')
# create a color palette
palette = plt.get_cmap('Set1')
# multiple line plot
num = 0
for column in df.drop('x', axis=1):
num += 1
# Find the right spot on the plot
plt.subplot(2, 1, num)
# Plot the lineplot
plt.plot(df['x'],
df[column],
marker='',
color=palette(num),
linewidth=1.9,
alpha=0.9,
label=column)
# Same limits for everybody!
# plt.xlim(0,100)
plt.ylim(-11, 40)
# Not ticks everywhere
plt.tick_params(axis='both',
which='major',
labelsize=9,
pad=10,
colors='black')
# plt.tick_params(labelleft='off')
# Add title
plt.title(column,
loc='left',
fontsize=10,
fontweight=0,
color=palette(num))
# general title
plt.suptitle(city + "\n3-day Temperature forecast",
fontsize=9,
fontweight=0,
color='black',
style='italic',
y=0.99)
# Axis title
plt.text(1, -25, 'Date', fontsize=10)
plt.text(-0.30, 40, 'Temperature', fontsize=10, rotation='vertical')
plt.show()
button_plot = Button(frame_5,
text="View Temperature graph",
command=plotGraph)
button_plot.grid(row=0, column=0, pady=(10, 0), padx=10)
def plot_his_map(self):
'''
Get map with corresponding rainfall level for different stations
Parameters
----------
None
Returns
-------
folium.Map
can produce picture of map with marks
'''
lat, long, level = self.get_spe_date()
tem_DF = pd.DataFrame({'lat': lat, 'long': long})
tem_DF = tem_DF.dropna(how='any')
lat = np.array(tem_DF.lat)
long = np.array(tem_DF.long)
locationlist = np.array((lat, long)).transpose()
#the plot code must run in jupyter lab, or it cannot ouput a map
coldict = {0: 'lightgreen', 1: 'blue', 2: 'orange', 3: 'red'}
#possible colours are [‘red’, ‘blue’, ‘green’, ‘purple’, ‘orange’, ‘darkred’,
#’lightred’, ‘beige’, ‘darkblue’, ‘darkgreen’, ‘cadetblue’, ‘darkpurple’, ‘white’, ‘pink’, ‘lightblue’, ‘lightgreen’, ‘gray’, ‘black’, ‘lightgray’]
#set the overview place at beginning to centre of England
map = folium.Map(location=[54, -1], zoom_start=6)
#plot the different risk level point with corresponding colorful mark
for point in range(0, len(locationlist)):
if level[point] == 'Low':
folium.CircleMarker(locationlist[point],
color='black',
fill_color=coldict[0],
fill=True,
fill_opacity=0.8,
radius=10,
icon=folium.Icon(color='red')).add_to(map)
elif level[point] == 'Medium':
folium.CircleMarker(locationlist[point],
color='black',
fill_color=coldict[1],
fill=True,
fill_opacity=0.8,
radius=10,
icon=folium.Icon(color='red')).add_to(map)
elif level[point] == 'High':
folium.CircleMarker(locationlist[point],
color='black',
fill_color=coldict[2],
fill=True,
fill_opacity=0.8,
radius=10,
icon=folium.Icon(color='red')).add_to(map)
elif level[point] == 'Very High':
folium.CircleMarker(locationlist[point],
color='black',
fill_color=coldict[3],
fill=True,
fill_opacity=0.8,
radius=10,
icon=folium.Icon(color='red')).add_to(map)
return map
def add_point_markers(mapobj, gdf, type):
"""
add point data onto folium map object
:param mapobj: folium map object
:param gdf: geopandas dataframe
:param type: dataframe type (Police Stations, Clinics, Courts and Shelters)
:return: updated folium map object
"""
colr = {'Clinic': '#ef6548', 'Police Station': '#9ecae1', 'Sexual Offence Court': "#fed976", 'Shelter': '#addd8e'}
pt_lyr1 = folium.FeatureGroup(name='Clinic')
pt_lyr2 = folium.FeatureGroup(name='Police Station')
pt_lyr3 = folium.FeatureGroup(name='Sexual Offence Court')
pt_lyr4 = folium.FeatureGroup(name='Shelter')
layer = {'Clinic': pt_lyr1, 'Police Station': pt_lyr2, 'Sexual Offence Court': pt_lyr3, 'Shelter': pt_lyr4}
icon = {'Clinic': pt_lyr1, 'Police Station': pt_lyr2, 'Sexual Offence Court': pt_lyr3, 'Shelter': pt_lyr4}
icon_types = {'Clinic': "hospital", 'Police Station': "shield-alt", 'Sexual Offence Court': "balance-scale", 'Shelter': "hands-helping"}
# Create a Folium feature group for this layer, since we will be displaying multiple layers
pt_lyr = layer[type]
for i, row in gdf.iterrows():
# Append lat and long coordinates to "coords" list
long = row.geometry.y
lat = row.geometry.x
name_tag = row.Name
try:
tel = str(row.Tel)
except:
tel = ''
if type == "Shelter":
if "Shelter" not in name_tag:
name_tag = "{} Shelter Tel: {}".format(name_tag, tel)
else:
name_tag = "{} Tel: {}".format(name_tag, tel)
size = 8
label = folium.Popup('{}'.format(name_tag), parse_html=True)
alpha=0.9
elif type == "Sexual Offence Court":
court_type = row.Type
name_tag = "{} {} Tel: {}".format(name_tag, court_type, tel)
size = 6
label = folium.Popup('{}'.format(name_tag), parse_html=True)
alpha = 1
elif type == "Clinic":
size = 6
name_tag = name_tag.replace("Clinic Clinic", "Clinic")
name_tag = "{} Tel: {}".format(name_tag, tel)
label = folium.Popup('{}'.format(name_tag), parse_html=True)
alpha = 1
else:
size = 6
name_tag = "{} {} Tel: {}".format(name_tag, type, tel)
label = folium.Popup('{}'.format(name_tag), parse_html=True)
alpha = 1
folium.CircleMarker(location=[long, lat],
popup=label,
radius=size,
fill=True,
fill_color=colr[type],
fill_opacity=alpha,
color=colr[type]).add_to(pt_lyr)
# Add this point layer to the map object
mapobj.add_child(pt_lyr)
return mapobj
return 'orange'
else:
return 'red'
# for lt, ln, el, name in zip(lat, lon, elev, name):
# iframe = folium.IFrame(html=html % (name, name, el), width=600, height=400)
# fgv.add_child(folium.CircleMarker(location=[lt, ln], radius = 6, popup=str(el)+" m",
# fill_color=color_producer(el), color = 'color_producer(el)', fill_opacity = 0.7))
for lt, ln, el in zip(lat, lon, elev):
fgv.add_child(
folium.CircleMarker(location=[lt, ln],
radius=20,
popup="Steez",
fill_color=color_producer(el),
fill=True,
color='grey',
fill_opacity=0.7))
#fgv.add_child(folium.Marker(location=[lt, ln], popup=folium.Popup(iframe, max_width = 600), icon=folium.Icon(color=color_producer(el))))
# Population
fgp = folium.FeatureGroup(name="Population")
fgp.add_child(
folium.GeoJson(
data=open('world.json', 'r', encoding='utf-8-sig').read(),
style_function=lambda x: {
'fillColor':
'green' if x['properties']['POP2005'] < 10000000 else 'orange'
if 10000000 <= x['properties']['POP2005'] < 20000000 else 'red'
if elevation < 1000:
return "green"
elif 1000 <= elevation < 3000:
return "orange"
else:
return "red"
map = folium.Map(location=[38, -101], zoom_start=5, tiles="Stamen Terrain")
fgVolcanos = folium.FeatureGroup(name="Volcanos")
for lt, ln, nam, el in zip(lat, lon, name, elev):
fgVolcanos.add_child(
folium.CircleMarker(location=[lt, ln],
radius=7,
popup=str(nam) + ": " + str(el) + "m",
fill_color=colorGrade(el),
color="black",
fill_opacity="0.7"))
fgPop = folium.FeatureGroup(name="Country Population")
fgPop.add_child(
folium.GeoJson(
data=open("world.json", "r", encoding="utf-8-sig").read(),
style_function=lambda x: {
"fillColor":
"green" if x['properties']['POP2005'] < 10000000 else 'orange'
if 10000000 <= x['properties']['POP2005'] < 100000000 else 'red'
}))
map.add_child(fgVolcanos)
map.add_child(fgPop)
# In[52]:
# Generate map to visualize hotel neighbourhood including shopping stores and Cafeteria
hotel_map = folium.Map(location=[latitude, longitude], zoom_start=14)
for lat, lng, name, categories, address in zip(university_neighbourhood_df['lat'], university_neighbourhood_df['lng'],
university_neighbourhood_df['name'], university_neighbourhood_df['categories'],\
university_neighbourhood_df['address']):
label = '{}, {}'.format(name, address)
label = folium.Popup(label, parse_html=True)
folium.CircleMarker(
[lat, lng],
radius=5,
popup=label,
color='blue',
fill=True,
fill_color='blue',
fill_opacity=0.7,
parse_html=False).add_to(hotel_map)
hotel_map
# Generate map to visualize Park, Restaurant and Cafeteria and how they cluster together
# In[54]:
# create dataframe of Park, Restaurant and Cafeteria
park_neighbourhood_df = pd.concat([df_park, df_Restaurant, df_Cafeteria,], ignore_index=True)
g3 = all_indian.drop(['postalCode', 'name', 'categories', 'id'], 1)
kclusters = 5
kmeans = KMeans(n_clusters=kclusters, random_state=0).fit(g3)
g2 = all_indian
g2.insert(0, 'Cluster Labels', kmeans.labels_)
# create map
map_clusters = folium.Map(location=[latitude, longitude], zoom_start=11)
# set color scheme for the clusters
x = np.arange(kclusters)
ys = [i + x + (i * x)**2 for i in range(kclusters)]
colors_array = cm.rainbow(np.linspace(0, 1, len(ys)))
rainbow = [colors.rgb2hex(i) for i in colors_array]
# add markers to the map
markers_colors = []
for lat, lon, cluster in zip(g2['lat'], g2['lng'], g2['Cluster Labels']):
label = folium.Popup(' Cluster ' + str(cluster), parse_html=True)
folium.CircleMarker([lat, lon],
radius=5,
popup=label,
color=rainbow[cluster - 1],
fill=True,
fill_color=rainbow[cluster - 1],
fill_opacity=0.7).add_to(map_clusters)
map_clusters
def rad(people):
if people > 500:
return 50
elif 300 < people <= 500:
return 40
elif 100 < people <= 300:
return 30
elif 50 < people <= 100:
return 20
elif 25 < people <= 50:
return 20
else:
return 10
map = folium.Map(location=[56.484680, 84.948197], zoom_start=8)
marker_cluster = MarkerCluster().add_to(map)
for city, lat, lon, people, mark in zip(city, lat, lon, people, mark):
# if color_change(mark) == 'green':
folium.CircleMarker(location=[lat, lon],
radius=rad(people),
popup=(city + ": " + str(people) +
" чел., ср. балл=" + str(mark)),
fill_color=fadeColor(c1, c2, mark / n),
color="gray",
fill_opacity=0.9).add_to(marker_cluster)
map.save("HtmlMaps\map1.html")
d[item.find_all("td")[1].text] = int(tcases.replace(",", ""))
cdata = pandas.read_csv("countries.csv")
lat = list(cdata["latitude"])
lon = list(cdata["longitude"])
cnt = list(cdata["name"])
map = folium.Map(location=[21.69, 31.09], zoom_start=3, tiles="Stamen Terrain")
fg = folium.FeatureGroup(name="Countries")
for lt, ln, ct in zip(lat, lon, cnt):
if ct in d.keys():
try:
fg.add_child(
folium.CircleMarker(location=[lt, ln],
popup=str(ct) + "\n" + str(d[ct]),
radius=radius_gen(d[ct]),
fill_color=color_gen(d[ct]),
color="#666666",
fill_opacity=0.7))
except:
pass
map.add_child(fg)
map.save("CoronaMap.html")
location_data = pd.read_excel('./files/광주나주위치정보.xlsx')
location_data.info()
location_data.head()
import folium
Mt_Naju_Station = [35.014250, 126.717045]
map_naju = folium.Map(location=Mt_Naju_Station, zoom_start=11)
for i in range(len(location_data)):
name = location_data['카카오맵위치명'][i]
count = location_data['place'][i]
size = int(count) * 2
long = float(location_data['위도'][i])
lat = float(location_data['경도'][i])
folium.CircleMarker((long, lat), radius=size, color='red',
popup=name).add_to(map_naju)
map_naju
from folium.plugins import MarkerCluster
locations = []
names = []
for i in range(len(location_data)):
data = location_data.iloc[i]
locations.append((float(data['위도']), float(data['경도'])))
names.append(data['카카오맵위치명'])
icon_create_function = """\
function(cluster) {
len(locationlist)
linear = cm.LinearColormap(['yellow', 'orange', 'red', 'red'],
index=[0, 0.1, 0.5, 1.0])
hmap = folium.Map(location=[40.7484, -73.9857], zoom_start=13)
fg = folium.FeatureGroup(name="NYC targets")
for point in range(0, len(locationlist)):
fg.add_child(
folium.CircleMarker(
location=locationlist[point],
radius=4,
fill=True,
fill_color=linear(for_map['y_pred'][point]),
popup=("{name}<br>"
"Category: {category}<br>"
"Score: {score}<br>"
"Note: {note}").format(
name=re.sub(r"[^a-zA-Z0-9_]+", ' ',
for_map['TARGETNAME'][point]),
score=str(round(for_map['y_pred'][point], 3)),
category=for_map['CATEGORYNAME'][point],
note=for_map['NOTES'][point]),
color=linear(for_map['y_pred'][point]),
opacity=0.7,
fill_opacity=0.7))
hmap.add_child(fg)
hmap.save(
os.path.join(outfile1,
'nyc_target_from_Sashi_20180504_modelversion3.html'))
return 'red'
map = folium.Map(location=[38.58, -99.09], zoom_start=6, tiles="Mapbox Bright")
fg = folium.FeatureGroup(name="My Map")
for lt, ln, el in zip(lat, lon, elev):
#for coordinates in [[12.85, 77.80],[12.90, 77.58]]:
#fg.add_child(folium.Marker(location = [lt, ln], popup = "Hi I am a Marker", icon = folium.Icon(color= 'blue')))
#fg.add_child(folium.Marker(location = [lt, ln], popup = folium.Popup(str(el) + " m" , parse_html=True), icon = folium.Icon(color= color_producer())))
fg.add_child(
folium.CircleMarker(location=[lt, ln],
radius=6,
popup=str(el) + " m",
fill_color=color_producer(el),
color='grey',
fill=True,
fill_opacity=0.7))
#fg.add_child(folium.Marker(location = [12.90, 77.58], popup = "Hi I am a Marker", icon = folium.Icon(color= 'blue')))
fg.add_child(
folium.GeoJson(
data=(open('world.json', 'r', encoding='utf-8-sig').read())))
map.add_child(fg)
#map.add_child(folium.Marker(location = [12.85, 77.80], popup = "Hi I am a Marker", icon = folium.Icon(color= 'blue')))
#map.save("Mapbg.html")
def CreaMap(adresse,nom):
Courbevoie_map= f.Map(location=[48.8967,2.2567],zoom_start=15)
icon_market=f.Icon(icon='shopping-cart', prefix='fa',markerColor='orange')
icon_pharmacie=f.Icon(icon='',prefix='fa',marketColor='green')
icon_medecin=f.Icon(icon='medkit',prefix='fa')
icon_pharmacie=f.Icon(icon='ambulance',prefix='fa',markerColor='green')
icon=icon_maison=f.Icon(icon='home',markerColor='red')
icon1=f.Icon(marketColor='green')
f.Marker([48.8967, 2.2567], popup="Monoprix",tooltip="Monoprix",icon=icon_pharmacie).add_to(Courbevoie_map)
iti_sup=f.FeatureGroup(name='Itinéraire Supérette la plus proche')
iti=f.FeatureGroup(name='Itinéraire Pharmacie la plus proche')
zone_sport=f.FeatureGroup(name='Zone de sport')
Courbevoie_map = Market_adresse(adresse,Courbevoie_map)
Courbevoie_map = Affichage_client(adresse, Courbevoie_map,icon_maison)
#on crée la map
#on peut rajouter des marqueurs definis par leur longitude et latitude
#On cherche à créer des icon
#info-sign
#cloud
#home
#arrow-right
#file="communes-93-seine-saint-denis.geojson"
#f.GeoJson(file,name="Courbevoie").add_to(Courbevoie_map)
#Traiter les données d'un fichier csv pas aboutis car données eronnées
#lperi=[]
#lperifinale=[]
#g=open(r'C:\Users\u\Downloads\BAN_odbl_92026.csv', newline='')
#reader=csv.reader(g)
#for row in reader:
#l=[]
#if (row[14]!='lat' and row[15]!="long"):
#a=float(row[14])
#b=float(row[15])
#l.append(a)
#l.append(b)
#lperi.append(tuple(l))
#print(lperi)
#for i in range(100):
#lperifinale.append(lperi[i])
#f.Polygon(lperifinale, color="blue", weight=7.5, opacity=8).add_to(Courbevoie_map)
#Trouver les données de Courbevoie :
lcourbevoie=[( 48.906294552742324,2.266398166116926), (48.902283797738605,2.284270341799327), (48.898400020270955,2.271065300760704),(48.896657,2.264661), (48.88688989505178,2.253756950262125), (48.89567616526661,2.2337852355532384), (48.900653602651886,2.2316248960533365), (48.90607328944114,2.2582158432572785), (48.906294552742324,2.266398166116926)]
#f.Polygon(lcourbevoie, color="blue", weight=7.5, opacity=8).add_to(Courbevoie_map)
#Mettre un bouton
#a=f.LayerControl(position='bottomright', collapsed=True,autoZIndex=True).add_to(Courbevoie_map)
#a.add_child(name="blabla")
tooltip="Clique pour voir ce magasin"
#popup1=f.Popup(print("blabla"))
#f.Marker([48.90,2.26],popup=f.Popup(max_width=450).add_child(f.Marker([48.95,2.26]).add_to(Courbevoie_map)).add_to(Courbevoie_map))
#le popup ce qui apparait
#le tooltip ce qui nous fais cliquer
#Pour changer les graphiques de l'affichage
#f.raster_layers.TileLayer('Stamen Terrain').add_to(Courbevoie_map)
#f.raster_layers.TileLayer('Stamen Toner').add_to(Courbevoie_map)
#f.raster_layers.TileLayer('Stamen Watercolor').add_to(Courbevoie_map)
#f.raster_layers.TileLayer('CartoDB Positron').add_to(Courbevoie_map)
#f.raster_layers.TileLayer('CartoDB Dark_Matter',name="map sombre").add_to(Courbevoie_map)
#Le par defaut est le mieux dans notre cas
# using ipywidgets
# plot location with marker
Courbevoie_map = Affichage_supérette(Courbevoie_map)
Courbevoie_map = Affichage_pharmacie(Courbevoie_map)
iti = Itinéraire_Pharmacie_la_plus_proche(Convertisseur_adresse_gps(adresse),Convertisseur_adresse_gps(Adresse_Pharmacie_la_plus_proche(adresse)),iti)
iti_sup= Itinéraire_Supérette_la_plus_proche(Convertisseur_adresse_gps(adresse),Coordonnée_Superette_la_plus_proche(adresse),iti_sup)
Courbevoie_map.add_child(iti)
Courbevoie_map.add_child(iti_sup)
#Affichage Zone de Sport
loc=Convertisseur_adresse_gps(adresse)
f.CircleMarker(location=loc,tooltip="Zone de sport",radius=300,color='black').add_to(zone_sport)
Courbevoie_map.add_child(zone_sport)
#On fait afficher les featureGroup
all_subgroups = f.FeatureGroup(name='Contour Courbevoie')
f.Polygon(lcourbevoie, color="blue", weight=7.5, opacity=8).add_to(all_subgroups)
Courbevoie_map.add_child(all_subgroups)
# add layer control to map (allows layers to be turned on or off)
f.LayerControl(collapsed=False).add_to(Courbevoie_map)
#Mesure Control
# measure control
measure_control = plugins.MeasureControl(position='bottomright',
active_color='red',
completed_color='red',
primary_length_unit='meter')
# add measure control to map
Courbevoie_map.add_child(measure_control)
url = 'courbevoie/templates/courbevoie/maCarteCourbevoie.html'
Courbevoie_map.save(url)
